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  • 1.
    Abaurre, María del Carmen
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre. Department of Medical Biochemistry and Biophysics, Karolinska Institutet.
    Transcriptional states of human oligodendroglia during development2021Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Differentiation of oligodendroglia lineage cells in humans still remains largely unclear. Oligodendrocyte progenitor cells (OPCs) are known to participate in remyelination processes by proliferating, migrating to the area of the lesion and then differentiating into oligodendrocytes (OLs), which can myelinate the affected axons again. This has sparked an interest in OPCs, since cell transplant could be a potential form of therapy for demyelinating diseases such as multiple sclerosis. However, that is not the only relevant aspect about them. OPCs have been shown to present heterogeneous populations with different functions, such as participating in immunological processes or responses to injury.

    Single cell technologies have become a powerful tool for the identification of unknown functions in OPCs and the characterization of the evolution of the oligodendroglia lineage. In this project, we analysed single-nuclei data of human foetal brain samples. For most of the steps of this pipeline, we used the Scanpy toolbox. In order to mitigate batch effects in our data, the Harmony algorithm was used for the correction. The Harmony-corrected principal components still retained part of the bias by batch. Leiden graph-based clustering resulted in a total of 19 clusters, 14 of which we were able to successfully annotate. Annotation was performed in combination of differential expression analysis and literature markers from public datasets. We obtained a single OPC cluster in our data, but marker genes expression suggests not all cells within this cluster are equally mature. Instead, some of them seem to be closer to commitment to an OL fate. This hypothesis would have to be confirmed by lineage inference analysis, which we could not include in this study. Finally, validation of our annotation with label transfer gave mixed results depending on the dataset used. This step was performed in Seurat. A possible explanation of these results could be sensitivity to differences between plate-based and droplet-based technologies for library preparation before sequencing. OPCs were successfully transferred regardless of the dataset used, so we can be certain of their identity.

  • 2.
    Abbasi, Ahtisham Fazeel
    et al.
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; University of Kaiserslautern-Landau, Kaiserslautern (RPTU), Germany.
    Asim, Muhammad Nabeel
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Trygg, Johan
    Umeå University, Faculty of Science and Technology, Department of Chemistry. Sartorius Corporate Research, Sartorius Stedim Data Analytics, Umeå, Sweden.
    Dengel, Andreas
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany; University of Kaiserslautern-Landau, Kaiserslautern (RPTU), Germany.
    Ahmed, Sheraz
    German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany.
    Deep learning architectures for the prediction of YY1-mediated chromatin loops2023In: Bioinformatics research and applications: 19th international symposium, ISBRA 2023, Wrocław, Poland, October 9–12, 2023, proceedings / [ed] Xuan Guo; Serghei Mangul; Murray Patterson; Alexander Zelikovsky, Springer, 2023, p. 72-84Conference paper (Refereed)
    Abstract [en]

    YY1-mediated chromatin loops play substantial roles in basic biological processes like gene regulation, cell differentiation, and DNA replication. YY1-mediated chromatin loop prediction is important to understand diverse types of biological processes which may lead to the development of new therapeutics for neurological disorders and cancers. Existing deep learning predictors are capable to predict YY1-mediated chromatin loops in two different cell lines however, they showed limited performance for the prediction of YY1-mediated loops in the same cell lines and suffer significant performance deterioration in cross cell line setting. To provide computational predictors capable of performing large-scale analyses of YY1-mediated loop prediction across multiple cell lines, this paper presents two novel deep learning predictors. The two proposed predictors make use of Word2vec, one hot encoding for sequence representation and long short-term memory, and a convolution neural network along with a gradient flow strategy similar to DenseNet architectures. Both of the predictors are evaluated on two different benchmark datasets of two cell lines HCT116 and K562. Overall the proposed predictors outperform existing DEEPYY1 predictor with an average maximum margin of 4.65%, 7.45% in terms of AUROC, and accuracy, across both of the datases over the independent test sets and 5.1%, 3.2% over 5-fold validation. In terms of cross-cell evaluation, the proposed predictors boast maximum performance enhancements of up to 9.5% and 27.1% in terms of AUROC over HCT116 and K562 datasets.

  • 3.
    Abbaszadeh Shahri, Abbas
    KTH, School of Architecture and the Built Environment (ABE), Civil and Architectural Engineering. Islamic Azad University.
    An Optimized Artificial Neural Network Structure to Predict Clay Sensitivity in a High Landslide Prone Area Using Piezocone Penetration Test (CPTu) Data: A Case Study in Southwest of Sweden2016In: Geotechnical and Geological Engineering, ISSN 0960-3182, E-ISSN 1573-1529, p. 1-14Article in journal (Refereed)
    Abstract [en]

    Application of artificial neural networks (ANN) in various aspects of geotechnical engineering problems such as site characterization due to have difficulty to solve or interrupt through conventional approaches has demonstrated some degree of success. In the current paper a developed and optimized five layer feed-forward back-propagation neural network with 4-4-4-3-1 topology, network error of 0.00201 and R2 = 0.941 under the conjugate gradient descent ANN training algorithm was introduce to predict the clay sensitivity parameter in a specified area in southwest of Sweden. The close relation of this parameter to occurred landslides in Sweden was the main reason why this study is focused on. For this purpose, the information of 70 piezocone penetration test (CPTu) points was used to model the variations of clay sensitivity and the influences of direct or indirect related parameters to CPTu has been taken into account and discussed in detail. Applied operation process to find the optimized ANN model using various training algorithms as well as different activation functions was the main advantage of this paper. The performance and feasibility of proposed optimized model has been examined and evaluated using various statistical and analytical criteria as well as regression analyses and then compared to in situ field tests and laboratory investigation results. The sensitivity analysis of this study showed that the depth and pore pressure are the two most and cone tip resistance is the least effective factor on prediction of clay sensitivity.

  • 4. Aberer, André
    et al.
    Stamatakis, Alexis
    Ronquist, Fredrik
    Swedish Museum of Natural History, Department of Bioinformatics and Genetics.
    An efficient independence sampler for updating branches in Bayesian Markov chain Monte Carlo sampling of phylogenetic trees2016In: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 65, no 1, p. 161-176Article in journal (Refereed)
  • 5. Abrams, M. B.
    et al.
    Bjaalie, J. G.
    Das, S.
    Egan, G. F.
    Ghosh, S. S.
    Goscinski, W. J.
    Grethe, J. S.
    Hellgren Kotaleski, Jeanette
    KTH, School of Electrical Engineering and Computer Science (EECS), Computer Science, Computational Science and Technology (CST).
    Ho, E. T. W.
    Kennedy, D. N.
    Lanyon, L. J.
    Leergaard, T. B.
    Mayberg, H. S.
    Milanesi, L.
    Mouček, R.
    Poline, J. B.
    Roy, P. K.
    Strother, S. C.
    Tang, T. B.
    Tiesinga, P.
    Wachtler, T.
    Wójcik, D. K.
    Martone, M. E.
    A Standards Organization for Open and FAIR Neuroscience: the International Neuroinformatics Coordinating Facility2021In: Neuroinformatics, ISSN 1539-2791, E-ISSN 1559-0089Article in journal (Refereed)
    Abstract [en]

    There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body.

  • 6.
    Aftab, Obaid
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Fryknäs, Mårten
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Hammerling, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Larsson, Rolf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Gustafsson, Mats
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Detection of cell aggregation and altered cell viability by automated label-free video microscopy: A promising alternative to endpoint viability assays in high throughput screening2015In: Journal of Biomolecular Screening, ISSN 1087-0571, E-ISSN 1552-454X, Vol. 20, no 3, p. 372-381Article in journal (Refereed)
    Abstract [en]

    Automated phase-contrast video microscopy now makes it feasible to monitor a high-throughput (HT) screening experiment in a 384-well microtiter plate format by collecting one time-lapse video per well. Being a very cost-effective and label-free monitoring method, its potential as an alternative to cell viability assays was evaluated. Three simple morphology feature extraction and comparison algorithms were developed and implemented for analysis of differentially time-evolving morphologies (DTEMs) monitored in phase-contrast microscopy videos. The most promising layout, pixel histogram hierarchy comparison (PHHC), was able to detect several compounds that did not induce any significant change in cell viability, but made the cell population appear as spheroidal cell aggregates. According to recent reports, all these compounds seem to be involved in inhibition of platelet-derived growth factor receptor (PDGFR) signaling. Thus, automated quantification of DTEM (AQDTEM) holds strong promise as an alternative or complement to viability assays in HT in vitro screening of chemical compounds.

  • 7.
    Agarwal, Prasoon
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Hematology and Immunology.
    Regulation of Gene Expression in Multiple Myeloma Cells and Normal Fibroblasts: Integrative Bioinformatic and Experimental Approaches2014Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The work presented in this thesis applies integrative genomic and experimental approaches to investigate mechanisms involved in regulation of gene expression in the context of disease and normal cell biology.

    In papers I and II, we have explored the role of epigenetic regulation of gene expression in multiple myeloma (MM). By using a bioinformatic approach we identified the Polycomb repressive complex 2 (PRC2) to be a common denominator for the underexpressed gene signature in MM. By using inhibitors of the PRC2 we showed an activation of the genes silenced by H3K27me3 and a reduction in the tumor load and increased overall survival in the in vivo 5TMM model. Using ChIP-sequencing we defined the distribution of H3K27me3 and H3K4me3 marks in MM patients cells. In an integrated bioinformatic approach, the H3K27me3-associated genes significantly correlated to under-expression in patients with less favorable survival. Thus, our data indicates the presence of a common under-expressed gene profile and provides a rationale for implementing new therapies focusing on epigenetic alterations in MM.

    In paper III we address the existence of a small cell population in MM presenting with differential tumorigenic properties in the 5T33MM murine model. We report that the predominant population of CD138+ cells had higher engraftment potential, higher clonogenic growth, whereas the CD138- MM cells presented with less mature phenotype and higher drug resistance. Our findings suggest that while designing treatment regimes for MM, both the cellpopulations must be targeted.

    In paper IV we have studied the general mechanism of differential gene expression regulation by CGGBP1 in response to growth signals in normal human fibroblasts. We found that CGGBP1 binding affects global gene expression by RNA Polymerase II. This is mediated by Alu RNAdependentinhibition of RNA Polymerase II. In presence of growth signals CGGBP1 is retained in the nuclei and exhibits enhanced Alu binding thus inhibiting RNA Polymerase III binding on Alus. Hence we suggest a mechanism by which CGGBP1 orchestrates Alu RNA-mediated regulation of RNA Polymerase II. This thesis provides new insights for using integrative bioinformatic approaches to decipher gene expression regulation mechanisms in MM and in normal cells.

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  • 8. Agosti, Edoardo
    et al.
    Saraceno, Giorgio
    Rampinelli, Vittorio
    Raffetti, Elena
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. Department of Global Public Health Sciences, Karolinska Institute, Stockholm, Sweden.
    Veiceschi, Pierlorenzo
    Buffoli, Barbara
    Rezzani, Rita
    Giorgianni, Andrea
    Hirtler, Lena
    Alexander, Alex Yohan
    Deganello, Alberto
    Piazza, Cesare
    Nicolai, Piero
    Castelnuovo, Paolo
    Locatelli, Davide
    Peris-Celda, Maria
    Fontanella, Marco Maria
    Doglietto, Francesco
    Quantitative Anatomic Comparison of Endoscopic Transnasal and Microsurgical Transcranial Approaches to the Anterior Cranial Fossa2022In: Operative Neurosurgery, ISSN 2332-4252, E-ISSN 2332-4260, Vol. 23, no 4, p. e256-e266Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: 

    Several microsurgical transcranial approaches (MTAs) and endoscopic transnasal approaches (EEAs) to the anterior cranial fossa (ACF) have been described.

    OBJECTIVE: 

    To provide a preclinical, quantitative, anatomic, comparative analysis of surgical approaches to the ACF.

    METHODS: 

    Five alcohol-fixed specimens underwent high-resolution computed tomography. The following approaches were performed on each specimen: EEAs (transcribriform, transtuberculum, and transplanum), anterior MTAs (transfrontal sinus interhemispheric, frontobasal interhemispheric, and subfrontal with unilateral and bilateral frontal craniotomy), and anterolateral MTAs (supraorbital, minipterional, pterional, and frontotemporal orbitozygomatic approach). An optic neuronavigation system and dedicated software (ApproachViewer, part of GTx-Eyes II—UHN) were used to quantify the working volume of each approach and extrapolate the exposure of different ACF regions. Mixed linear models with random intercepts were used for statistical analyses.

    RESULTS: 

    EEAs offer a large and direct route to the midline region of ACF, whose most anterior structures (ie, crista galli, cribriform plate, and ethmoidal roof) are also well exposed by anterior MTAs, whereas deeper ones (ie, planum sphenoidale and tuberculum sellae) are also well exposed by anterolateral MTAs. The orbital roof region is exposed by both anterolateral and lateral MTAs. The posterolateral region (ie, sphenoid wing and optic canal) is well exposed by anterolateral MTAs.

    CONCLUSION: 

    Anterior and anterolateral MTAs play a pivotal role in the exposure of most anterior and posterolateral ACF regions, respectively, whereas midline regions are well exposed by EEAs. Furthermore, certain anterolateral approaches may be most useful when involvement of the optic canal and nerves involvement are suspected.

  • 9.
    Ahmed, Laeeq
    et al.
    Royal Inst Technol KTH, Dept Elect Engn & Computat Sci, Lindstedtsvagen 5, S-10044 Stockholm, Sweden..
    Alogheli, Hiba
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Arvidsson Mc Shane, Staffan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Alvarsson, Jonathan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Berg, Arvid
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Larsson, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Laure, Erwin
    Royal Inst Technol KTH, Dept Elect Engn & Computat Sci, Lindstedtsvagen 5, S-10044 Stockholm, Sweden..
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Predicting target profiles with confidence as a service using docking scores2020In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 12, article id 62Article in journal (Refereed)
    Abstract [en]

    Background: Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues.

    Contributions: We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis.

    Results: The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.

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  • 10. Ahmed, Laeeq
    et al.
    Georgiev, Valentin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Capuccini, Marco
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Toor, Salman
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computational Science.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Laure, Erwin
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Efficient iterative virtual screening with Apache Spark and conformal prediction2018In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 10, article id 8Article in journal (Refereed)
  • 11.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
    Brickman, Staffan
    Mälardalen University.
    Dengg, Alexander
    Mälardalen University.
    Fasth, Niklas
    Mälardalen University.
    Mihajlovic, Marko
    Mälardalen University.
    Norman, Jacob
    Mälardalen University.
    A machine learning approach to classify pedestrians’ events based on IMU and GPS2019In: International Journal of Artificial Intelligence, E-ISSN 0974-0635, Vol. 17, no 2, p. 154-167Article in journal (Refereed)
    Abstract [en]

    This paper investigates and implements six Machine Learning (ML) algorithms, i.e. Artificial Neural Network (ANN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gradient Boosted Trees (GBT) to classify different Pedestrians’ events based on Inertial Measurement Unit (IMU) and Global Positioning System (GPS) signals. Pedestrians’ events are pedestrian movements as the first step of H2020 project called SimuSafe1 with a goal to reduce traffic fatalities by doing risk assessments of the pedestrians. The movements the MLs’ models are attempting to classify are standing, walking, and running. Data, i.e. IMU, GPS sensor signals and other contextual information are collected by a smartphone through a controlled procedure. The smartphone is placed in five different positions onto the body of participants, i.e. arm, chest, ear, hand and pocket. The recordings are filtered, trimmed, and labeled. Next, samples are generated from small overlapping sections from which time and frequency domain features are extracted. Three different experiments are conducted to evaluate the performances in term of accuracy of the MLs’ models in different circumstances. The best performing MLs’ models determined by the average accuracy across all experiments is Extra Tree (ET) with a classification accuracy of 91%. 

  • 12. Aidas, Kestutis
    et al.
    Angeli, Celestino
    Bak, Keld L.
    Bakken, Vebjorn
    Bast, Radovan
    KTH, School of Biotechnology (BIO), Theoretical Chemistry and Biology.
    Boman, Linus
    Christiansen, Ove
    Cimiraglia, Renzo
    Coriani, Sonia
    Dahle, Pal
    Dalskov, Erik K.
    Ekstrom, Ulf
    Enevoldsen, Thomas
    Eriksen, Janus J.
    Ettenhuber, Patrick
    Fernandez, Berta
    Ferrighi, Lara
    Fliegl, Heike
    Frediani, Luca
    Hald, Kasper
    Halkier, Asger
    Hattig, Christof
    Heiberg, Hanne
    Helgaker, Trygve
    Hennum, Alf Christian
    Hettema, Hinne
    Hjertenaes, Eirik
    Host, Stinne
    Hoyvik, Ida-Marie
    Iozzi, Maria Francesca
    Jansik, Branislav
    Jensen, Hans Jorgen Aa.
    Jonsson, Dan
    Jorgensen, Poul
    Kauczor, Joanna
    Kirpekar, Sheela
    Kjrgaard, Thomas
    Klopper, Wim
    Knecht, Stefan
    Kobayashi, Rika
    Koch, Henrik
    Kongsted, Jacob
    Krapp, Andreas
    Kristensen, Kasper
    Ligabue, Andrea
    Lutnaes, Ola B.
    Melo, Juan I.
    Mikkelsen, Kurt V.
    Myhre, Rolf H.
    Neiss, Christian
    Nielsen, Christian B.
    Norman, Patrick
    Olsen, Jeppe
    Olsen, Jogvan Magnus H.
    Osted, Anders
    Packer, Martin J.
    Pawlowski, Filip
    Pedersen, Thomas B.
    Provasi, Patricio F.
    Reine, Simen
    Rinkevicius, Zilvinas
    KTH, School of Biotechnology (BIO), Theoretical Chemistry and Biology. KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Ruden, Torgeir A.
    Ruud, Kenneth
    Rybkin, Vladimir V.
    Salek, Pawel
    Samson, Claire C. M.
    de Meras, Alfredo Sanchez
    Saue, Trond
    Sauer, Stephan P. A.
    Schimmelpfennig, Bernd
    Sneskov, Kristian
    Steindal, Arnfinn H.
    Sylvester-Hvid, Kristian O.
    Taylor, Peter R.
    Teale, Andrew M.
    Tellgren, Erik I.
    Tew, David P.
    Thorvaldsen, Andreas J.
    Thogersen, Lea
    Vahtras, Olav
    KTH, School of Biotechnology (BIO), Theoretical Chemistry and Biology.
    Watson, Mark A.
    Wilson, David J. D.
    Ziolkowski, Marcin
    Ågren, Hans
    KTH, School of Biotechnology (BIO), Theoretical Chemistry and Biology.
    The Dalton quantum chemistry program system2014In: WIREs Computational Molecular Science, ISSN 1759-0876, E-ISSN 1759-0884, Vol. 4, no 3, p. 269-284Article in journal (Refereed)
    Abstract [en]

    Dalton is a powerful general-purpose program system for the study of molecular electronic structure at the Hartree-Fock, Kohn-Sham, multiconfigurational self-consistent-field, MOller-Plesset, configuration-interaction, and coupled-cluster levels of theory. Apart from the total energy, a wide variety of molecular properties may be calculated using these electronic-structure models. Molecular gradients and Hessians are available for geometry optimizations, molecular dynamics, and vibrational studies, whereas magnetic resonance and optical activity can be studied in a gauge-origin-invariant manner. Frequency-dependent molecular properties can be calculated using linear, quadratic, and cubic response theory. A large number of singlet and triplet perturbation operators are available for the study of one-, two-, and three-photon processes. Environmental effects may be included using various dielectric-medium and quantum-mechanics/molecular-mechanics models. Large molecules may be studied using linear-scaling and massively parallel algorithms. Dalton is distributed at no cost from for a number of UNIX platforms.

  • 13.
    Ajawatanawong, Pravech
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    Atkinson, Gemma C.
    Watson-Haigh, Nathan S.
    MacKenzie, Bryony
    Baldauf, Sandra L.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Systematic Biology.
    SeqFIRE: a web application for automated extraction of indel regions and conserved blocks from protein multiple sequence alignments2012In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 40, no W1, p. W340-W347Article in journal (Refereed)
    Abstract [en]

    Analyses of multiple sequence alignments generally focus on well-defined conserved sequence blocks, while the rest of the alignment is largely ignored or discarded. This is especially true in phylogenomics, where large multigene datasets are produced through automated pipelines. However, some of the most powerful phylogenetic markers have been found in the variable length regions of multiple alignments, particularly insertions/deletions (indels) in protein sequences. We have developed Sequence Feature and Indel Region Extractor (SeqFIRE) to enable the automated identification and extraction of indels from protein sequence alignments. The program can also extract conserved blocks and identify fast evolving sites using a combination of conservation and entropy. All major variables can be adjusted by the user, allowing them to identify the sets of variables most suited to a particular analysis or dataset. Thus, all major tasks in preparing an alignment for further analysis are combined in a single flexible and user-friendly program. The output includes a numbered list of indels, alignments in NEXUS format with indels annotated or removed and indel-only matrices. SeqFIRE is a user-friendly web application, freely available online at www.seqfire.org/.

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  • 14. Aktürk, Şevva
    et al.
    Mapelli, Igor
    Güler, Merve N.
    Gürün, Kanat
    Katırcıoğlu, Büşra
    Vural, Kıvılcım Başak
    Sağlıcan, Ekin
    Çetin, Mehmet
    Yaka, Reyhan
    Stockholm University, Faculty of Humanities, Department of Archaeology and Classical Studies. Middle East Technical University, Turkey; Centre for Palaeogenetics, Sweden.
    Sürer, Elif
    Atağ, Gözde
    Çokoğlu, Sevim Seda
    Sevkar, Arda
    Altınışık, N. Ezgi
    Koptekin, Dilek
    Somel, Mehmet
    Benchmarking kinship estimation tools for ancient genomes using pedigree simulations2024In: Molecular Ecology Resources, ISSN 1755-098X, E-ISSN 1755-0998Article in journal (Refereed)
    Abstract [en]

    There is growing interest in uncovering genetic kinship patterns in past societies using low-coverage palaeogenomes. Here, we benchmark four tools for kinship estimation with such data: lcMLkin, NgsRelate, KIN, and READ, which differ in their input, IBD estimation methods, and statistical approaches. We used pedigree and ancient genome sequence simulations to evaluate these tools when only a limited number (1 to 50 K, with minor allele frequency ≥0.01) of shared SNPs are available. The performance of all four tools was comparable using ≥20 K SNPs. We found that first-degree related pairs can be accurately classified even with 1 K SNPs, with 85% F1 scores using READ and 96% using NgsRelate or lcMLkin. Distinguishing third-degree relatives from unrelated pairs or second-degree relatives was also possible with high accuracy (F1 > 90%) with 5 K SNPs using NgsRelate and lcMLkin, while READ and KIN showed lower success (69 and 79% respectively). Meanwhile, noise in population allele frequencies and inbreeding (first-cousin mating) led to deviations in kinship coefficients, with different sensitivities across tools. We conclude that using multiple tools in parallel might be an effective approach to achieve robust estimates on ultra-low-coverage genomes. 

  • 15. Alger, Ingela
    et al.
    Weibull, Jörgen W.
    KTH, School of Engineering Sciences (SCI), Mathematics (Dept.).
    A generalization of Hamilton's rule-Love others how much?2012In: Journal of Theoretical Biology, ISSN 0022-5193, E-ISSN 1095-8541, Vol. 299, p. 42-54Article in journal (Refereed)
    Abstract [en]

    According to Hamilton's (1964a, b) rule, a costly action will be undertaken if its fitness cost to the actor falls short of the discounted benefit to the recipient, where the discount factor is Wright's index of relatedness between the two. We propose a generalization of this rule, and show that if evolution operates at the level of behavior rules, rather than directly at the level of actions, evolution will select behavior rules that induce a degree of cooperation that may differ from that predicted by Hamilton's rule as applied to actions. In social dilemmas there will be less (more) cooperation than under Hamilton's rule if the actions are strategic substitutes (complements). Our approach is based on natural selection, defined in terms of personal (direct) fitness, and applies to a wide range of pairwise interactions.

  • 16.
    Ali, Raja Hashim
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    From genomes to post-processing of Bayesian inference of phylogeny2016Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Life is extremely complex and amazingly diverse; it has taken billions of years of evolution to attain the level of complexity we observe in nature now and ranges from single-celled prokaryotes to multi-cellular human beings. With availability of molecular sequence data, algorithms inferring homology and gene families have emerged and similarity in gene content between two genes has been the major signal utilized for homology inference. Recently there has been a significant rise in number of species with fully sequenced genome, which provides an opportunity to investigate and infer homologs with greater accuracy and in a more informed way. Phylogeny analysis explains the relationship between member genes of a gene family in a simple, graphical and plausible way using a tree representation. Bayesian phylogenetic inference is a probabilistic method used to infer gene phylogenies and posteriors of other evolutionary parameters. Markov chain Monte Carlo (MCMC) algorithm, in particular using Metropolis-Hastings sampling scheme, is the most commonly employed algorithm to determine evolutionary history of genes. There are many softwares available that process results from each MCMC run, and explore the parameter posterior but there is a need for interactive software that can analyse both discrete and real-valued parameters, and which has convergence assessment and burnin estimation diagnostics specifically designed for Bayesian phylogenetic inference.

    In this thesis, a synteny-aware approach for gene homology inference, called GenFamClust (GFC), is proposed that uses gene content and gene order conservation to infer homology. The feature which distinguishes GFC from earlier homology inference methods is that local synteny has been combined with gene similarity to infer homologs, without inferring homologous regions. GFC was validated for accuracy on a simulated dataset. Gene families were computed by applying clustering algorithms on homologs inferred from GFC, and compared for accuracy, dependence and similarity with gene families inferred from other popular gene family inference methods on a eukaryotic dataset. Gene families in fungi obtained from GFC were evaluated against pillars from Yeast Gene Order Browser. Genome-wide gene families for some eukaryotic species are computed using this approach.

    Another topic focused in this thesis is the processing of MCMC traces for Bayesian phylogenetics inference. We introduce a new software VMCMC which simplifies post-processing of MCMC traces. VMCMC can be used both as a GUI-based application and as a convenient command-line tool. VMCMC supports interactive exploration, is suitable for automated pipelines and can handle both real-valued and discrete parameters observed in a MCMC trace. We propose and implement joint burnin estimators that are specifically applicable to Bayesian phylogenetics inference. These methods have been compared for similarity with some other popular convergence diagnostics. We show that Bayesian phylogenetic inference and VMCMC can be applied to infer valuable evolutionary information for a biological case – the evolutionary history of FERM domain.

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    Doctoral Thesis Hashim
  • 17.
    Ali, Raja Hashim
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Bark, Mikael
    KTH, School of Information and Communication Technology (ICT).
    Miró, Jorge
    KTH, School of Information and Communication Technology (ICT).
    Muhammad, Sayyed Auwn
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Sjöstrand, J.
    Zubair, Syed M.
    KTH, School of Electrical Engineering (EES), Communication Networks. University of Balochistan, Pakistan.
    Abbas, R. M.
    Arvestad, L.
    VMCMC: A graphical and statistical analysis tool for Markov chain Monte Carlo traces2017In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 18, no 1, article id 97Article in journal (Refereed)
    Abstract [en]

    Background: MCMC-based methods are important for Bayesian inference of phylogeny and related parameters. Although being computationally expensive, MCMC yields estimates of posterior distributions that are useful for estimating parameter values and are easy to use in subsequent analysis. There are, however, sometimes practical difficulties with MCMC, relating to convergence assessment and determining burn-in, especially in large-scale analyses. Currently, multiple software are required to perform, e.g., convergence, mixing and interactive exploration of both continuous and tree parameters. Results: We have written a software called VMCMC to simplify post-processing of MCMC traces with, for example, automatic burn-in estimation. VMCMC can also be used both as a GUI-based application, supporting interactive exploration, and as a command-line tool suitable for automated pipelines. Conclusions: VMCMC is a free software available under the New BSD License. Executable jar files, tutorial manual and source code can be downloaded from https://bitbucket.org/rhali/visualmcmc/.

  • 18.
    Ali, Raja Hashim
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Muhammad, Sayyed Auwn
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, Centres, SeRC - Swedish e-Science Research Centre.
    Khan, Mehmodd Alam
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Arvestad, Lars
    Stockholms universitet.
    Quantitative synteny scoring improves homology inference and partitioning of gene families2013In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 14, p. S12-Article in journal (Refereed)
    Abstract [en]

    Background: Clustering sequences into families has long been an important step in characterization of genes and proteins. There are many algorithms developed for this purpose, most of which are based on either direct similarity between gene pairs or some sort of network structure, where weights on edges of constructed graphs are based on similarity. However, conserved synteny is an important signal that can help distinguish homology and it has not been utilized to its fullest potential. Results: Here, we present GenFamClust, a pipeline that combines the network properties of sequence similarity and synteny to assess homology relationship and merge known homologs into groups of gene families. GenFamClust identifies homologs in a more informed and accurate manner as compared to similarity based approaches. We tested our method against the Neighborhood Correlation method on two diverse datasets consisting of fully sequenced genomes of eukaryotes and synthetic data. Conclusions: The results obtained from both datasets confirm that synteny helps determine homology and GenFamClust improves on Neighborhood Correlation method. The accuracy as well as the definition of synteny scores is the most valuable contribution of GenFamClust.

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  • 19. Ali, Raja Hashim
    et al.
    Muhammad, Sayyed Auwn
    Khan, Mehmood Alam
    Arvestad, Lars
    Stockholm University, Faculty of Science, Numerical Analysis and Computer Science (NADA). Stockholm University, Science for Life Laboratory (SciLifeLab). Swedish e-Science Research Center, Sweden .
    Quantitative synteny scoring improves homology inference and partitioning of gene families2013In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 14, no Suppl,15, p. S12-Article in journal (Refereed)
    Abstract [en]

    Background

    Clustering sequences into families has long been an important step in characterization of genes and proteins. There are many algorithms developed for this purpose, most of which are based on either direct similarity between gene pairs or some sort of network structure, where weights on edges of constructed graphs are based on similarity. However, conserved synteny is an important signal that can help distinguish homology and it has not been utilized to its fullest potential.

    Results

    Here, we present GenFamClust, a pipeline that combines the network properties of sequence similarity and synteny to assess homology relationship and merge known homologs into groups of gene families. GenFamClust identifies homologs in a more informed and accurate manner as compared to similarity based approaches. We tested our method against the Neighborhood Correlation method on two diverse datasets consisting of fully sequenced genomes of eukaryotes and synthetic data.

    Conclusions

    The results obtained from both datasets confirm that synteny helps determine homology and GenFamClust improves on Neighborhood Correlation method. The accuracy as well as the definition of synteny scores is the most valuable contribution of GenFamClust.

  • 20.
    Aliakbari, Massume
    et al.
    Department of Crop Production and Plant Breeding, Shiraz University, Shiraz, Iran.
    Cohen, Stephen P.
    Department of Plant Pathology, The Ohio State University, USA.
    Lindlöf, Angelica
    University of Skövde, School of Bioscience. University of Skövde, Systems Biology Research Environment.
    Shamloo-Dashtpagerdi, Roohollah
    Department of Agriculture and Natural Resources, Higher Education Center of Eghlid, Iran.
    Rubisco activase A (RcaA) is a central node in overlapping gene network of drought and salinity in Barley (Hordeum vulgare L.) and may contribute to combined stress tolerance2021In: Plant physiology and biochemistry (Paris), ISSN 0981-9428, E-ISSN 1873-2690, Vol. 161, p. 248-258Article in journal (Refereed)
    Abstract [en]

    Co-occurrence of abiotic stresses, especially drought and salinity, is a natural phenomenon in field conditions and is worse for crop production than any single stress. Nowadays, rigorous methods of meta-analysis and systems biology have made it possible to perform cross-study comparisons of single stress experiments, which can uncover main overlapping mechanisms underlying tolerance to combined stress. In this study, a meta-analysis of RNA-Seq data was conducted to obtain the overlapping gene network of drought and salinity stresses in barley (Hordeum vulgare L.), which identified Rubisco activase A (RcaA) as a hub gene in the dual-stress response. Thereafter, a greenhouse experiment was carried out using two barley genotypes with different abiotic stress tolerance and evaluated several physiochemical properties as well as the expression profile and protein activity of RcaA. Finally, machine learning analysis was applied to uncover relationships among combined stress tolerance and evaluated properties. We identified 441 genes which were differentially expressed under both drought and salinity stress. Results revealed that the photosynthesis pathway and, in particular, the RcaA gene are major components of the dual-stress responsive transcriptome. Comparative physiochemical and molecular evaluations further confirmed that enhanced photosynthesis capability, mainly through regulation of RcaA expression and activity as well as accumulation of proline content, have a significant association with combined drought and salinity stress tolerance in barley. Overall, our results clarify the importance of RcaA in combined stress tolerance and may provide new insights for future investigations. 

  • 21.
    Al-Jaff, Mohammad
    Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Civil and Industrial Engineering, Industrial Engineering and Management.
    Messing With The Gap: On The Modality Gap Phenomenon In Multimodal Contrastive Representation Learning2023Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In machine learning, a sub-field of computer science, a two-tower architecture model is a specialised type of neural network model that encodes paired data from different modalities (like text and images, sound and video, or proteomics and gene expression profiles) into a shared latent representation space. However, when training these models using a specific contrastive loss function, known as the multimodalinfoNCE loss, seems to often lead to a unique geometric phenomenon known as the modality gap. This gap is a clear geometric separation of the embeddings of the modalities in the joint contrastive latent space. This thesis investigates the modality gap in multimodal machine learning, specifically in two-tower neural networks trained with multimodal-infoNCE loss. We examine the adequacy of the current definition of the modality gap, the conditions under which the modality gap phenomenon manifests, and its impact on representation quality and downstream task performance.

    The approach to address these questions consists of a two-phase experimental strategy. Phase I involves a series of experiments, ranging from toy synthetic simulations to true multimodal machine learning with complex datasets, to explore and characterise the modality gap under varying conditions. Phase II focuses on modifying the modality gap and analysing representation quality, evaluating different loss functions and their impact on the modality gap. This methodical exploration allows us to systematically dissect the emergence and implications of the modality gap phenomenon, providing insights into its impact on downstream tasks, measured with proxy metrics based on semantic clustering in the shared latent representation space and modality-specific linear probe evaluation.

    Our findings reveal that the modality gap definition proposed by W. Liang et al. 2022, is insufficient. We demonstrate that similar modality gap magnitudes can exhibit varying linear separability between modality embeddings in the contrastive latent space and varying embedding topologies, indicating the need for additional metrics to capture the true essence of the gap.

    Furthermore, our experiments show that the temperature hyperparameter in the multimodal infoNCE loss function plays a crucial role in the emergence of the modality gap, and this effect varies with different data sets. This suggests that individual dataset characteristics significantly influence the modality gap's manifestation. A key finding is the consistent emergence of modality gaps with small temperature settings in the fixed temperature mode of the loss function and almost invariably under learned temperature mode settings, regardless of the initial temperature value. Additionally, we observe that the magnitude of the modality gap is influenced by distribution shifts, with the gap magnitude increasing progressively from the training set to the validation set, then to the test set, and finally to more distributionally shifted datasets.

    We discover that the choice of contrastive learning method, temperature settings, and temperature values is crucial in influencing the modality gap. However, reducing the gap does not consistently improve downstream task performance, suggesting its role may be more nuanced than previously understood. This insight indicates that the modality gap might be a geometric by-product of the learning methods rather than a critical determinant of representation quality. Our results encourage the need to reevaluate the modality gap's significance in multimodal contrastive learning, emphasising the importance of dataset characteristics and contrastive learning methodology.

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  • 22.
    Al-Jaff, Mohammed
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Sandström, Eric
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Grabherr, Manfred
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala Univ, Bioinformat Infrastruct Life Sci, S-75123 Uppsala, Sweden..
    microTaboo: a general and practical solution to the k-disjoint problem2017In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 18, article id 228Article in journal (Refereed)
    Abstract [en]

    Background: A common challenge in bioinformatics is to identify short sub-sequences that are unique in a set of genomes or reference sequences, which can efficiently be achieved by k-mer (k consecutive nucleotides) counting. However, there are several areas that would benefit from a more stringent definition of "unique", requiring that these sub-sequences of length W differ by more than k mismatches (i.e. a Hamming distance greater than k) from any other sub-sequence, which we term the k-disjoint problem. Examples include finding sequences unique to a pathogen for probe-based infection diagnostics; reducing off-target hits for re-sequencing or genome editing; detecting sequence (e.g. phage or viral) insertions; and multiple substitution mutations. Since both sensitivity and specificity are critical, an exhaustive, yet efficient solution is desirable.

    Results: We present microTaboo, a method that allows for efficient and extensive sequence mining of unique (k-disjoint) sequences of up to 100 nucleotides in length. On a number of simulated and real data sets ranging from microbe-to mammalian-size genomes, we show that microTaboo is able to efficiently find all sub-sequences of a specified length W that do not occur within a threshold of k mismatches in any other sub-sequence. We exemplify that microTaboo has many practical applications, including point substitution detection, sequence insertion detection, padlock probe target search, and candidate CRISPR target mining.

    Conclusions: microTaboo implements a solution to the k-disjoint problem in an alignment-and assembly free manner. microTaboo is available for Windows, Mac OS X, and Linux, running Java 7 and higher, under the GNU GPLv3 license, at:https://MohammedAlJaff.github.io/microTaboo

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  • 23. Allison, Timothy M.
    et al.
    Degiacomi, Matteo T.
    Marklund, Erik G.
    Jovine, Luca
    Elofsson, Arne
    Stockholm University, Science for Life Laboratory (SciLifeLab). Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Benesch, Justin L. P.
    Landreh, Michael
    Complementing machine learning-based structure predictions with native mass spectrometry2022In: Protein Science, ISSN 0961-8368, E-ISSN 1469-896X, Vol. 31, no 6, article id e4333Article in journal (Refereed)
    Abstract [en]

    The advent of machine learning-based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user-provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time-effective tool that provides information on post-translational modifications, ligand interactions, conformational changes, and higher-order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.

  • 24. Almagro Armenteros, Jose Juan
    et al.
    Salvatore, Marco
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Emanuelsson, Olof
    Winther, Ole
    von Heijne, Gunnar
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Elofsson, Arne
    Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
    Nielsen, Henrik
    Detecting Novel Sequence Signals in Targeting Peptides Using Deep LearningManuscript (preprint) (Other academic)
  • 25.
    Almstedt, Elin
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Elgendy, Ramy
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Hekmati, Neda
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Rosén, Emil
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Wärn, Caroline
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Olsen, Thale Kristin
    Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden..
    Dyberg, Cecilia
    Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden..
    Doroszko, Milena
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Larsson, Ida
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Sundström, Anders
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Arsenian Henriksson, Marie
    Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, Stockholm, Sweden.
    Påhlman, Sven
    Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden..
    Bexell, Daniel
    Division of Translational Cancer Research, Department of Laboratory Medicine, Lund University, Lund, Sweden..
    Vanlandewijck, Michael
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology. Department of Medicine, Integrated Cardio-Metabolic Centre Single Cell Facility, Karolinska Institutet, Stockholm, Sweden..
    Kogner, Per
    Childhood Cancer Research Unit, Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden.
    Jörnsten, Rebecka
    Mathematical Sciences, Chalmers University of Technology, Gothenburg, Sweden..
    Krona, Cecilia
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Nelander, Sven
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Neuro-Oncology.
    Integrative discovery of treatments for high-risk neuroblastoma2020In: Nature Communications, E-ISSN 2041-1723, Vol. 11, no 1, article id 71Article in journal (Refereed)
    Abstract [en]

    Despite advances in the molecular exploration of paediatric cancers, approximately 50% of children with high-risk neuroblastoma lack effective treatment. To identify therapeutic options for this group of high-risk patients, we combine predictive data mining with experimental evaluation in patient-derived xenograft cells. Our proposed algorithm, TargetTranslator, integrates data from tumour biobanks, pharmacological databases, and cellular networks to predict how targeted interventions affect mRNA signatures associated with high patient risk or disease processes. We find more than 80 targets to be associated with neuroblastoma risk and differentiation signatures. Selected targets are evaluated in cell lines derived from high-risk patients to demonstrate reversal of risk signatures and malignant phenotypes. Using neuroblastoma xenograft models, we establish CNR2 and MAPK8 as promising candidates for the treatment of high-risk neuroblastoma. We expect that our method, available as a public tool (targettranslator.org), will enhance and expedite the discovery of risk-associated targets for paediatric and adult cancers.

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  • 26.
    Alneberg, Johannes
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Bioinformatic Methods in Metagenomics2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Microbial organisms are a vital part of our global ecosystem. Yet, our knowledge of them is still lacking. Direct sequencing of microbial communities, i.e. metagenomics, have enabled detailed studies of these microscopic organisms by inspection of their DNA sequences without the need to culture them. Furthermore, the development of modern high- throughput sequencing technologies have made this approach more powerful and cost-effective. Taken together, this has shifted the field of microbiology from previously being centered around microscopy and culturing studies, to largely consist of computational analyses of DNA sequences. One such computational analysis which is the main focus of this thesis, aims at reconstruction of the complete DNA sequence of an organism, i.e. its genome, directly from short metagenomic sequences.

    This thesis consists of an introduction to the subject followed by five papers. Paper I describes a large metagenomic data resource spanning the Baltic Sea microbial communities. This dataset is complemented with a web-interface allowing researchers to easily extract and visualize detailed information. Paper II introduces a bioinformatic method which is able to reconstruct genomes from metagenomic data. This method, which is termed CONCOCT, is applied on Baltic Sea metagenomics data in Paper III and Paper V. This enabled the reconstruction of a large number of genomes. Analysis of these genomes in Paper III led to the proposal of, and evidence for, a global brackish microbiome. Paper IV presents a comparison between genomes reconstructed from metagenomes with single-cell sequenced genomes. This further validated the technique presented in Paper II as it was found to produce larger and more complete genomes than single-cell sequencing.

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  • 27.
    Alneberg, Johannes
    et al.
    Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    Bjarnason, Brynjar Smári
    Science for Life Laboratory, School of Biotechnology, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    de Bruijn, Ino
    Bioinformatics Infrastructure for Life Sciences (BILS), Stockholm, Sweden.
    Schirmer, Melanie
    School of Engineering, University of Glasgow, Glasgow, UK.
    Quick, Joshua
    Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK; National Institute for Health Research (NIHR), Surgical Reconstruction and Microbiology Research Centre, University of Birmingham, Birmingham, UK.
    Ijaz, Umer Z.
    School of Engineering, University of Glasgow, Glasgow, UK.
    Lahti, Leo
    Department of Veterinary Biosciences, University of Helsinki, Helsinki, Finland; Laboratory of Microbiology, Wageningen University, Wageningen, the Netherlands.
    Loman, Nicholas J
    Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK.
    Andersson, Anders F
    Science for Life Laboratory, School of Biotechnoloy, Division of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden.
    Quince, Christopher
    School of Engineering, University of Glasgow, Glasgow, UK.
    Binning metagenomic contigs by coverage and composition2014In: Nature Methods, ISSN 1548-7091, E-ISSN 1548-7105, Vol. 11, no 11, p. 1144-6Article in journal (Refereed)
    Abstract [en]

    Shotgun sequencing enables the reconstruction of genomes from complex microbial communities, but because assembly does not reconstruct entire genomes, it is necessary to bin genome fragments. Here we present CONCOCT, a new algorithm that combines sequence composition and coverage across multiple samples, to automatically cluster contigs into genomes. We demonstrate high recall and precision on artificial as well as real human gut metagenome data sets.

  • 28.
    Alneberg, Johannes
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Sundh, John
    Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden.
    Bennke, Christin
    Leibniz Institute for Baltic Sea Research, Warnemünde, Germany.
    Beier, Sara
    Leibniz Institute for Baltic Sea Research, Warnemünde, Germany.
    Lundin, Daniel
    Centre for Ecology and Evolution in Microbial Model Systems, Linnaeus University, Kalmar, Sweden.
    Hugerth, Luisa
    KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO).
    Pinhassi, Jarone
    Centre for Ecology and Evolution in Microbial Model Systems, Linnaeus University, Kalmar, Sweden.
    Kisand, Veljo
    University of Tartu, Institute of Technology, Tartu, Estonia.
    Riemann, Lasse
    Section for Marine Biological Section, Department of Biology, University of Copenhagen, Helsingør, Denmark.
    Jürgens, Klaus
    Leibniz Institute for Baltic Sea Research, Warnemünde, Germany.
    Labrenz, Matthias
    Leibniz Institute for Baltic Sea Research, Warnemünde, Germany.
    Andersson, Anders F.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    BARM and BalticMicrobeDB, a reference metagenome and interface to meta-omic data for the Baltic SeaManuscript (preprint) (Other academic)
    Abstract [en]

    The Baltic Sea is one of the world’s largest brackish water bodies and is characterised by pronounced physicochemical gradients where microbes are the main biogeochemical catalysts. Meta-omic methods provide rich information on the composition of, and activities within microbial ecosystems, but are computationally heavy to perform. We here present the BAltic Sea Reference Metagenome (BARM), complete with annotated genes to facilitate further studies with much less computational effort. The assembly is constructed using 2.6 billion metagenomic reads from 81 water samples, spanning both spatial and temporal dimensions, and contains 6.8 million genes that have been annotated for function and taxonomy. The assembly is useful as a reference, facilitating taxonomic and functional annotation of additional samples by simply mapping their reads against the assembly. This capability is demonstrated by the successful mapping and annotation of 24 external samples. In addition, we present a public web interface, BalticMicrobeDB, for interactive exploratory analysis of the dataset.

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  • 29.
    Alshourbaji, Ibrahim
    et al.
    Univ Hertfordshire, England; Jazan Univ, Saudi Arabia.
    Helian, Na
    Univ Hertfordshire, England.
    Sun, Yi
    Univ Hertfordshire, England.
    Hussien, Abdelazim
    Linköping University, Department of Computer and Information Science, Software and Systems. Linköping University, Faculty of Science & Engineering. Fayoum Univ, Egypt.
    Abualigah, Laith
    Al Al Bayt Univ, Jordan; Lebanese Amer Univ, Lebanon; Al Ahliyya Amman Univ, Jordan; Middle East Univ, Jordan; Appl Sci Private Univ, Jordan; Univ Sains Malaysia, Malaysia; Sunway Univ, Malaysia.
    Elnaim, Bushra
    Prince Sattam Bin Abdulaziz Univ, Saudi Arabia.
    An efficient churn prediction model using gradient boosting machine and metaheuristic optimization2023In: Scientific Reports, E-ISSN 2045-2322, Vol. 13, no 1, article id 14441Article in journal (Refereed)
    Abstract [en]

    Customer churn remains a critical challenge in telecommunications, necessitating effective churn prediction (CP) methodologies. This paper introduces the Enhanced Gradient Boosting Model (EGBM), which uses a Support Vector Machine with a Radial Basis Function kernel (SVMRBF) as a base learner and exponential loss function to enhance the learning process of the GBM. The novel base learner significantly improves the initial classification performance of the traditional GBM and achieves enhanced performance in CP-EGBM after multiple boosting stages by utilizing state-of-the-art decision tree learners. Further, a modified version of Particle Swarm Optimization (PSO) using the consumption operator of the Artificial Ecosystem Optimization (AEO) method to prevent premature convergence of the PSO in the local optima is developed to tune the hyper-parameters of the CP-EGBM effectively. Seven open-source CP datasets are used to evaluate the performance of the developed CP-EGBM model using several quantitative evaluation metrics. The results showed that the CP-EGBM is significantly better than GBM and SVM models. Results are statistically validated using the Friedman ranking test. The proposed CP-EGBM is also compared with recently reported models in the literature. Comparative analysis with state-of-the-art models showcases CP-EGBMs promising improvements, making it a robust and effective solution for churn prediction in the telecommunications industry.

  • 30.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Arvidsson McShane, Staffan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Norinder, Ulf
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. Department of Computer and Systems Sciences, Stockholm University; MTM Research Centre, School of Science and Technology, Örebro University.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Predicting With Confidence: Using Conformal Prediction in Drug Discovery2021In: Journal of Pharmaceutical Sciences, ISSN 0022-3549, E-ISSN 1520-6017, Vol. 110, no 1, p. 42-49Article, review/survey (Refereed)
    Abstract [en]

    One of the challenges with predictive modeling is how to quantify the reliability of the models' predictions on new objects. In this work we give an introduction to conformal prediction, a framework that sits on top of traditional machine learning algorithms and which outputs valid confidence estimates to predictions from QSAR models in the form of prediction intervals that are specific to each predicted object. For regression, a prediction interval consists of an upper and a lower bound. For classification, a prediction interval is a set that contains none, one, or many of the potential classes. The size of the prediction interval is affected by a user-specified confidence/significance level, and by the nonconformity of the predicted object; i.e., the strangeness as defined by a nonconformity function. Conformal prediction provides a rigorous and mathematically proven framework for in silico modeling with guarantees on error rates as well as a consistent handling of the models' applicability domain intrinsically linked to the underlying machine learning model. Apart from introducing the concepts and types of conformal prediction, we also provide an example application for modeling ABC transporters using conformal prediction, as well as a discussion on general implications for drug discovery.

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  • 31.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Eklund, Martin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Engkvist, Ola
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Carlsson, Lars
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Noeske, Tobias
    Ligand-Based Target Prediction with Signature Fingerprints2014In: Journal of Chemical Information and Modeling, ISSN 1549-9596, Vol. 54, no 10, p. 2647-2653Article in journal (Refereed)
    Abstract [en]

    When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.

  • 32.
    Alvarsson, Jonathan
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Lampa, Samuel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Schaal, Wesley
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Andersson, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Sciences, Cancer Pharmacology and Computational Medicine.
    Wikberg, Jarl E. S.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Spjuth, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Large-scale ligand-based predictive modelling using support vector machines2016In: Journal of Cheminformatics, E-ISSN 1758-2946, Vol. 8, article id 39Article in journal (Refereed)
    Abstract [en]

    The increasing size of datasets in drug discovery makes it challenging to build robust and accurate predictive models within a reasonable amount of time. In order to investigate the effect of dataset sizes on predictive performance and modelling time, ligand-based regression models were trained on open datasets of varying sizes of up to 1.2 million chemical structures. For modelling, two implementations of support vector machines (SVM) were used. Chemical structures were described by the signatures molecular descriptor. Results showed that for the larger datasets, the LIBLINEAR SVM implementation performed on par with the well-established libsvm with a radial basis function kernel, but with dramatically less time for model building even on modest computer resources. Using a non-linear kernel proved to be infeasible for large data sizes, even with substantial computational resources on a computer cluster. To deploy the resulting models, we extended the Bioclipse decision support framework to support models from LIBLINEAR and made our models of logD and solubility available from within Bioclipse.

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  • 33.
    Amanzadi, Amirhossein
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Predicting safe drug combinations with Graph Neural Networks (GNN)2021Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
    Abstract [en]

    Many people - especially during their elderly - consume multiple drugs for the treatment of complex or co-existing diseases. Identifying side effects caused by polypharmacy is crucial for reducing mortality and morbidity of the patients which will lead to improvement in their quality of life. Since there is immense space for possible drug combinations, it is infeasible to examine them entirely in the lab. In silico models can offer a convenient solution, however, due to the lack of a sufficient amount of homogenous data it is difficult to develop both reliable and scalable models in its ability to accurately predict Polypharmacy Side Effect. Recent advancement in the field of representational learning has utilized the power of graph networks to harmonize information from the heterogeneous biological databases and interactomes. This thesis takes advantage of those techniques and incorporates them with the state-of-the-art Graph Neural Network algorithms to implement a Deep learning pipeline capable of predicting the Adverse Drug Reaction of any given paired drug combinations.

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    Amanzadi
  • 34.
    Ameur, Adam
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, The Linnaeus Centre for Bioinformatics.
    A Bioinformatics Study of Human Transcriptional Regulation2008Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Regulation of transcription is a central mechanism in all living cells that now can be investigated with high-throughput technologies. Data produced from such experiments give new insights to how transcription factors (TFs) coordinate the gene transcription and thereby regulate the amounts of proteins produced. These studies are also important from a medical perspective since TF proteins are often involved in disease. To learn more about transcriptional regulation, we have developed strategies for analysis of data from microarray and massively parallel sequencing (MPS) experiments.

    Our computational results consist of methods to handle the steadily increasing amount of data from high-throughput technologies. Microarray data analysis tools have been assembled in the LCB-Data Warehouse (LCB-DWH) (paper I), and other analysis strategies have been developed for MPS data (paper V). We have also developed a de novo motif search algorithm called BCRANK (paper IV).

    The analysis has lead to interesting biological findings in human liver cells (papers II-V). The investigated TFs appeared to bind at several thousand sites in the genome, that we have identified at base pair resolution. The investigated histone modifications are mainly found downstream of transcription start sites, and correlated to transcriptional activity. These histone marks are frequently found for pairs of genes in a bidirectional conformation. Our results suggest that a TF can bind in the shared promoter of two genes and regulate both of them.

    From a medical perspective, the genes bound by the investigated TFs are candidates to be involved in metabolic disorders. Moreover, we have developed a new strategy to detect single nucleotide polymorphisms (SNPs) that disrupt the binding of a TF (paper IV). We further demonstrated that SNPs can affect transcription in the immediate vicinity. Ultimately, our method may prove helpful to find disease-causing regulatory SNPs.

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  • 35.
    Amrein, Beat Anton
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology.
    Steffen-Munsberg, Fabian
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Szeler, Ireneusz
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology.
    Purg, Miha
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kulkarni, Yashraj
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Kamerlin, Shina Caroline Lynn
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Structure and Molecular Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    CADEE: Computer-Aided Directed Evolution of Enzymes2017In: IUCrJ, E-ISSN 2052-2525, Vol. 4, no 1, p. 50-64Article in journal (Refereed)
    Abstract [en]

    The tremendous interest in enzymes as biocatalysts has led to extensive work in enzyme engineering, as well as associated methodology development. Here, a new framework for computer-aided directed evolution of enzymes (CADEE) is presented which allows a drastic reduction in the time necessary to prepare and analyze in silico semi-automated directed evolution of enzymes. A pedagogical example of the application of CADEE to a real biological system is also presented in order to illustrate the CADEE workflow.

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  • 36. Ancker, Julia
    et al.
    Berg, Elin
    Björkman, Therese
    Malmvall, Hanna
    Abdullahi, Hanad
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre.
    Wong, Victor
    Verktyg för optimerat val av testpanelerför antibiotikasensitivitetstester2023Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    Denna rapport beskriver ett projekt vars syfte är att underlätta valet av bakteri- estammar till testpaneler som används av Q-lineas instrument ASTar®. ASTar® är ett automatiserat instrument för snabb antibiotikasensitivitetstestning (AST). Med testpanel menas en uppsättning av bakteriestammar som används för träning av den algoritm som används av ASTar®. De huvudsakliga målen med detta projekt är att ta fram indikatorer som kan användas för att utvärdera en testpanel samt att skapa verktyg för visualisering av en testpanel. Indikatorerna återspeglar en pa- nels spridning, täckning och redundans. Spridning är hur många olika MIC-värden en testpanel innefattar för varje antibiotikum och hur utspridda de är, täckning är antalet MIC-värden som varje antibiotikum har i en testpanel och redundans är kopplat till hur unikt varje MIC-värde på panelen är. Med MIC-värden menas den minsta koncentration av antibiotika som hämmar en bakteries tillväxt. I detta projekt har indikatorer tagits fram för att kunna kvantifiera en panels spridning, täckning och redundans, och enkelt kunna jämföra olika testpaneler utifrån dessa aspekter. Ett skript compare.py har skrivits i programmeringsspråket Python för att skapa en visualisering som jämför de kvantitativa indikatorvärdena för olika paneler i relation till de högsta möjliga värdena. Ytterligare ett skript, master_vis.py har skrivits för att generera olika visualiseringar av en panel och dess täckning, sprid- ning och redundans. Sex olika grafer och två tabeller kan genereras med detta skript. Dessa visualiseringar och tabeller visar bland annat hur utspridda MIC-värdena är på en panel, hur många känsliga, intermediära och resistenta MIC-värden som finns för varje antibiotikum på en panel och hur många unika MIC-värden som finns för varje stam på panelen. Slutligen har även ett tredje skript skrivits, kallat isola- te_selection.py. Detta skript utgår från de framtagna kvantitativa indikatorerna för att välja ett specificerat antal stammar till en panel och utvecklades för att under- söka hur indikatorerna skulle kunna användas för att påverka stamvalet. Möjligen skulle en liknande implementering kunna göras i Q-lineas nuvarande stamvalsskript. Samtliga skript, visualiseringar och beräkningsmetoder som har arbetats fram i det- ta projekt är tänkta att kunna användas av Q-linea för att underlätta deras fram- tagning och utvärdering av testpaneler.

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  • 37.
    Andeer, Robin
    et al.
    Science for Life Laboratory, Department of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholms, Sweden.
    Magnusson, Måns
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab. Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden; Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.
    Wedell, Anna
    Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden; Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.
    Stranneheim, Henrik
    Department of Molecular Medicine and Surgery, Karolinska Institute, Stockholm, Sweden; Centre for Inherited Metabolic Diseases, Karolinska University Hospital, Stockholm, Sweden.
    Chanjo: Clincal grade sequence coverage analysis2020In: F1000 Research, E-ISSN 2046-1402, Vol. 9, article id 615Article in journal (Refereed)
    Abstract [en]

    Coverage analysis is essential when analysing massive parallel sequencing (MPS) data. The analysis indicates existence of false negatives or positives in a region of interest or poorly covered genomic regions. There are several tools that have excellent performance when doing coverage analysis on a few samples with predefined regions. However, there is no current tool for collecting samples over a longer period of time for aggregated coverage analysis of multiple samples or sequencing methods. Furthermore, current coverage analysis tools do not generate customized coverage reports or enable exploratory coverage analysis without extensive bioinformatic skill and access to the original alignment files. We present Chanjo, a user friendly coverage analysis tool for persistent storage of coverage data, that, accompanied with Chanjo Report, produces coverage reports that summarize coverage data for predefined regions in an elegant manner. Chanjo Report can produce both structured coverage reports and dynamic reports tailored to a subset of genomic regions, coverage cut-offs or samples. Chanjo stores data in an SQL database where thousands of samples can be added over time, which allows for aggregate queries to discover problematic regions. Chanjo is well tested, supports whole exome and genome sequencing, and follows common UNIX standards, allowing for easy integration into existing pipelines. Chanjo is easy to install and operate, and provides a solution for persistent coverage analysis and clinical-grade reporting. It makes it easy to set up a local database and automate the addition of multiple samples and report generation. To our knowledge there is no other tool with matching capabilities. Chanjo handles the common file formats in genetics, such as BED and BAM, and makes it easy to produce PDF coverage reports that are highly valuable for individuals with limited bioinformatic expertise. We believe Chanjo to be a vital tool for clinicians and researchers performing MPS analysis.

  • 38.
    Anders, Patrizia
    University of Skövde, School of Humanities and Informatics.
    A bioinformaticians view on the evolution of smell perception2006Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Background:

    The origin of vertebrate sensory systems still contains many mysteries and thus challenges to bioinformatics. Especially the evolution of the sense of smell maintains important puzzles, namely the question whether or not the vomeronasal system is older than the main olfactory system. Here I compare receptor sequences of the two distinct systems in a phylogenetic study, to determine their relationships among several different species of the vertebrates.

    Results:

    Receptors of the two olfactory systems share little sequence similarity and prove to be a challenge in multiple sequence alignment. However, recent dramatical improvements in the area of alignment tools allow for better results and high confidence. Different strategies and tools were employed and compared to derive a

    high quality alignment that holds information about the evolutionary relationships between the different receptor types. The resulting Maximum-Likelihood tree supports the theory that the vomeronasal system is rather an ancestor of the main olfactory system instead of being an evolutionary novelty of tetrapods.

    Conclusions:

    The connections between the two systems of smell perception might be much more fundamental than the common architecture of receptors. A better understanding of these parallels is desirable, not only with respect to our view on evolution, but also in the context of the further exploration of the functionality and complexity of odor perception. Along the way, this work offers a practical protocol through the jungle of programs concerned with sequence data and phylogenetic reconstruction.

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  • 39.
    Andersson, Alfred
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre.
    Neural networks for imputation of missing genotype data: An alternative to the classical statistical methods in bioinformatics2020Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    In this project, two different machine learning models were tested in an attempt at imputing missing genotype data from patients on two different panels. As the integrity of the patients had to be protected, initial training was done on data simulated from the 1000 Genomes Project. The first model consisted of two convolutional variational autoencoders and the latent representations of the networks were shuffled to force the networks to find the same patterns in the two datasets. This model was unfortunately unsuccessful at imputing the missing data. The second model was based on a UNet structure and was more successful at the task of imputation. This model had one encoder for each dataset, making each encoder specialized at finding patterns in its own data. Further improvements are required in order for the model to be fully capable at imputing the missing data.

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  • 40.
    Andersson, Axel
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    An Analytical Neighborhood Enrichment Score for Spatial OmicsManuscript (preprint) (Other academic)
    Abstract [en]

    The neighborhood enrichment test is commonly used to quantify spatial enrichment or depletion between spatial points with categorical labels — a data type frequently occurring in spatial omics. Traditionally, it is performed via permutation-based Monte Carlo methods, which can be computationally expensive. This study presents an analytical solution to the neighborhood enrichment problem. This direct calculation strongly correlated with traditional tests, offering substantially faster processing times across eight spatial omics datasets. Further validation on an extensive Xenium dataset highlighted the method’s ability to rapidly analyze large-scale data, making it a valuable tool for advancing spatial omics research. The implementation is publicly available.

  • 41.
    Andersson, Axel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Behanova, Andrea
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Windhager, Jonas
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics dataManuscript (preprint) (Other academic)
    Abstract [en]

    Imaging-based spatial transcriptomics techniques generate image data that, once processed, results in a set of spatial points with categorical labels for different mRNA species. A crucial part of analyzing downstream data involves the analysis of these point patterns. Here, biologically interesting patterns can be explored at different spatial scales. Molecular patterns on a cellular level would correspond to cell types, whereas patterns on a millimeter scale would correspond to tissue-level structures. Often, clustering methods are employed to identify and segment regions with distinct point-patterns. Traditional clustering techniques for such data are constrained by reliance on complementary data or extensive machine learning, limiting their applicability to tasks on a particular scale. This paper introduces 'Points2Regions', a practical tool for clustering spatial points with categorical labels. Its flexible and computationally efficient clustering approach enables pattern discovery across multiple scales, making it a powerful tool for exploratory analysis. Points2Regions has demonstrated efficient performance in various datasets, adeptly defining biologically relevant regions similar to those found by scale-specific methods. As a Python package integrated into TissUUmaps and a Napari plugin, it offers interactive clustering and visualization, significantly enhancing user experience in data exploration. In essence, Points2Regions presents a user-friendly and simple tool for exploratory analysis of spatial points with categorical labels. 

  • 42.
    Andersson, Axel
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Behanova, Andrea
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Malmberg, Filip
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Cell Segmentation of in situ Transcriptomics Data using Signed Graph Partitioning2023In: Graph-Based Representations in Pattern Recognition / [ed] Mario Vento; Pasquale Foggia; Donatello Conte; Vincenzo Carletti, Cham: Springer, 2023, p. 139-148Conference paper (Refereed)
    Abstract [en]

    The locations of different mRNA molecules can be revealed by multiplexed in situ RNA detection. By assigning detected mRNA molecules to individual cells, it is possible to identify many different cell types in parallel. This in turn enables investigation of the spatial cellular architecture in tissue, which is crucial for furthering our understanding of biological processes and diseases. However, cell typing typically depends on the segmentation of cell nuclei, which is often done based on images of a DNA stain, such as DAPI. Limiting cell definition to a nuclear stain makes it fundamentally difficult to determine accurate cell borders, and thereby also difficult to assign mRNA molecules to the correct cell. As such, we have developed a computational tool that segments cells solely based on the local composition of mRNA molecules. First, a small neural network is trained to compute attractive and repulsive edges between pairs of mRNA molecules. The signed graph is then partitioned by a mutex watershed into components corresponding to different cells. We evaluated our method on two publicly available datasets and compared it against the current state-of-the-art and older baselines. We conclude that combining neural networks with combinatorial optimization is a promising approach for cell segmentation of in situ transcriptomics data. The tool is open-source and publicly available for use at https://github.com/wahlby-lab/IS3G.

  • 43.
    Andersson, Axel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Diego, Ferran
    HCI/IWR and Department of Physics and Astronomy, Heidelberg University, Heidelberg.
    Hamprecht, Fred A.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. HCI/IWR and Department of Physics and Astronomy, Heidelberg University, Heidelberg.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    ISTDECO: In Situ Transcriptomics Decoding by DeconvolutionManuscript (preprint) (Other academic)
    Abstract [en]

    In Situ Transcriptomics (IST) is a set of image-based transcriptomics approaches that enables localisation of gene expression directly in tissue samples. IST techniques produce multiplexed image series in which fluorescent spots are either present or absent across imaging rounds and colour channels. A spot’spresence and absence form a type of barcoded pattern that labels a particular type of mRNA. Therefore, the expression of agene can be determined by localising the fluorescent spots and decode the barcode that they form. Existing IST algorithms usually do this in two separate steps: spot localisation and barcode decoding. Although these algorithms are efficient, they are limited by strictly separating the localisation and decoding steps. This limitation becomes apparent in regions with low signal-to-noise ratio or high spot densities. We argue that an improved gene expression decoding can be obtained by combining these two steps into a single algorithm. This allows for an efficient decoding that is less sensitive to noise and optical crowding. We present IST Decoding by Deconvolution (ISTDECO), a principled decoding approach combining spectral and spatial deconvolution into a single algorithm. We evaluate ISTDECOon simulated data, as well as on two real IST datasets, and compare with state-of-the-art. ISTDECO achieves state-of-the-art performance despite high spot densities and low signal-to-noise ratios. It is easily implemented and runs efficiently using a GPU.ISTDECO implementation, datasets and demos are available online at: github.com/axanderssonuu/istdeco

  • 44.
    Andersson, Axel
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Escriva Conde, Maria
    Stockholm University.
    Surova, Olga
    Stockholm University.
    Vermeulen, Peter
    GZA Hospital Sint-Augustinus.
    Wählby, Carolina
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division Vi3.
    Nilsson, Mats
    Stockholm University.
    Nyström, Hanna
    Umeå University.
    Spatial transcriptome mapping of the desmoplastic growth pattern of colorectal liver metastases by in situ sequencing reveals a biologically relevant zonation of the desmoplastic rimManuscript (preprint) (Other academic)
  • 45.
    Andersson, Hilda
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Biology Education Centre.
    A machine learning pipeline for predicting success rates in PrEST production2019Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Protein epitope signature tags (PrESTs) are antigens produced in Escherichia coli at Atlas Antibodies and immunized into rabbits for antibody production. This project uses machine learning models to predict success rates for production and immunization and to find features important for success. The features are generated based on the PrEST sequences using web servers, downloadable software and Pyhton scripts. An additional analysis of the effect of rabbit- and environmental features on immunization success is performed. Many different models, model architectures and a few thousand features were tried. The models reached a maximum F1 scores of about 0.55 for a target outcome divided into two classes for both production and immunization analysis. No important features could be identified with significance.

    The rabbit- and environmental analysis showed that this type of features is more important for PrEST immunization success than the PrEST-related features. F1 score rose to abut 0.6 and the environmental features ranked higher based on information gain. More data is needed to draw definitive conclusions, but this indicates that Atlas Antibodies should in the future focus on recording environmental features during production for better chances of predicting success rates.

  • 46.
    Andersson, Malin
    University of Skövde, Department of Computer Science.
    A method for identification of putatively co-regulated genes2002Independent thesis Advanced level (degree of Master (One Year))Student thesis
    Abstract [en]

    The genomes of several organisms have been sequenced and the need for methods to analyse the data is growing. In this project a method is described that tries to identify co-regulated genes. The method identifies transcription factor binding sites, documented in TRANSFAC, in the non-coding regions of genes. The algorithm counts the number of common binding sites and the number of unique binding sites for each pair of genes and decides if the genes are co-regulated. The result of the method is compared with the correlation between the gene expression patterns of the genes. The method is tested on 21 gene pairs from the genome of Saccharomyces cerevisiae. The algorithm first identified binding sites from all organisms. The accuracy of the program was very low in this case. When the algorithm was modified to only identify binding sites found in plants the accuracy was much improved, from 52% to 76% correct predictions.

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  • 47.
    Andersson, Samuel A.
    et al.
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Lagergren, Jens
    KTH, School of Computer Science and Communication (CSC), Numerical Analysis and Computer Science, NADA.
    Motif Yggdrasil: Sampling from a tree mixture model2006In: Research In Computational Molecular Biology, Proceedings / [ed] Apostolico, A; Guerra, C; Istrail, S; Pevzner, P; Waterman, M, 2006, Vol. 3909, p. 458-472Conference paper (Refereed)
    Abstract [en]

    In phylogenetic foot-printing, putative regulatory elements are found in upstream regions of orthologous genes by searching for common motifs. Motifs in different upstream sequences are subject to mutations along the edges of the corresponding phylogenetic tree, consequently taking advantage of the tree in the motif search is an appealing idea. We describe the Motif Yggdrasil sampler; the first Gibbs sampler based on a general tree that uses unaligned sequences. Previous tree-based Gibbs samplers have assumed a star-shaped tree or partially aligned upstream regions. We give a probabilistic model describing upstream sequences with regulatory elements and build a Gibbs sampler with respect to this model. We apply the collapsing technique to eliminate the need to sample nuisance parameters, and give a derivation of the predictive update formula. The use of the tree achieves a substantial increase in nucleotide level correlation coefficient both for synthetic data and 37 bacterial lexA genes.

  • 48.
    Andersson, Vendela
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Voxel-wise Longitudinal Analysis of Weight Gain from Different Dietary Fats using Image Registration-Based "Imiomics" Analysis2022Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    There is an emerging global epidemic of obesity and related complications, such as type 2diabetes (T2D). Alterations in body composition (adipose tissue, muscle volume and fatcontents) are known to be associated with an increased metabolic risk. Understanding of theunderlying mechanisms is key for development of novel intervention strategies. One study investigating the effect on body composition by different diets is Lipogain1. In this study, it was found that a small weight gain induced by polyunsaturated fats (PUFA, n=19) or saturated fats (SFA, n=20) had very different effects on body fat, liver fat and lean tissue mass respectively. The SFA group gained more liver fat and fat mass in general, while the PUFA group gained more muscle mass. These results were determined by magnetic resonance imaging. 

    The goal of this project was to visualize the results from Lipogain1 by utilizing the noveltechnique Imiomics. Imiomics is a method for statistical analysis of whole-body medical images. By utilizing image registration, all images are transformed to a common reference space. This enables point-wise comparisons between all images included in the analysis.

    In this project, mean images of the alterations in fat content and local volume change of the two groups were created. These were used to visualize the alterations in body composition from the study. Additionally, statistical tests were used to visualize statistically significant differences between the groups. 

    Differences between the groups could be seen in the mean images. Mainly a higher fat content increase was seen in SFA in comparison to PUFA. There was also a larger volume expansion in fat tissue in SFA than in PUFA, while PUFA instead had a larger volume expansion in muscles. An unexpected result was also found; the liver had expanded in PUFA but not in SFA. Unfortunately, few significant differences could be visualized between the groups when the statistical test was performed.

    The conclusion was that this method is promising for visualization of these kinds of studies, especially due to the potential of finding new, unexpected results. However, a somewhat larger cohort and possibly larger alterations in body composition might be needed to be able to visualize and quantify statistically significant differences between the groups on a voxel-wise level.

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  • 49.
    Andrade, Jorge
    KTH, School of Biotechnology (BIO), Gene Technology.
    Grid and High-Performance Computing for Applied Bioinformatics2007Doctoral thesis, comprehensive summary (Other scientific)
    Abstract [en]

    The beginning of the twenty-first century has been characterized by an explosion of biological information. The avalanche of data grows daily and arises as a consequence of advances in the fields of molecular biology and genomics and proteomics. The challenge for nowadays biologist lies in the de-codification of this huge and complex data, in order to achieve a better understanding of how our genes shape who we are, how our genome evolved, and how we function.

    Without the annotation and data mining, the information provided by for example high throughput genomic sequencing projects is not very useful. Bioinformatics is the application of computer science and technology to the management and analysis of biological data, in an effort to address biological questions. The work presented in this thesis has focused on the use of Grid and High Performance Computing for solving computationally expensive bioinformatics tasks, where, due to the very large amount of available data and the complexity of the tasks, new solutions are required for efficient data analysis and interpretation.

    Three major research topics are addressed; First, the use of grids for distributing the execution of sequence based proteomic analysis, its application in optimal epitope selection and in a proteome-wide effort to map the linear epitopes in the human proteome. Second, the application of grid technology in genetic association studies, which enabled the analysis of thousand of simulated genotypes, and finally the development and application of a economic based model for grid-job scheduling and resource administration.

    The applications of the grid based technology developed in the present investigation, results in successfully tagging and linking chromosomes regions in Alzheimer disease, proteome-wide mapping of the linear epitopes, and the development of a Market-Based Resource Allocation in Grid for Scientific Applications.

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  • 50.
    Andrade, Jorge
    et al.
    KTH, School of Biotechnology (BIO), Gene Technology.
    Andersen, Malin
    KTH, School of Biotechnology (BIO), Gene Technology.
    Berglund, Lisa
    KTH, School of Biotechnology (BIO), Proteomics.
    Odeberg, Jacob
    KTH, School of Biotechnology (BIO), Gene Technology.
    Applications of grid computing in genetics and proteomics2007In: Applied Parallel Computing: State Of The Art In Scientific Computing / [ed] Kagstrom, B; Elmroth, E; Dongarra, J; Wasniewski, J, 2007, Vol. 4699, p. 791-798Conference paper (Refereed)
    Abstract [en]

    The potential for Grid technologies in applied bioinformatics is largely unexplored. We have developed a model for solving computationally demanding bioinformatics tasks in distributed Grid environments, designed to ease the usability for scientists unfamiliar with Grid computing. With a script-based implementation that uses a strategy of temporary installations of databases and existing executables on remote nodes at submission, we propose a generic solution that do not rely on predefined Grid runtime environments and that can easily be adapted to other bioinformatics tasks suitable for parallelization. This implementation has been successfully applied to whole proteome sequence similarity analyses and to genome-wide genotype simulations, where computation time was reduced from years to weeks. We conclude that computational Grid technology is a useful resource for solving high compute tasks in genetics and proteomics using existing algorithms.

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