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  • 1.
    Dabrowski, Michal
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Pilot, M.
    Kruczyk, M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    Zmihorski, M.
    Umer, Husen Muhammad
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Gliwicz, J.
    Reliability assessment of null allele detection: inconsistencies between and within different methods2014In: Molecular Ecology Resources, ISSN 1755-098X, E-ISSN 1755-0998, Vol. 14, no 2, p. 361-373Article in journal (Refereed)
    Abstract [en]

    Microsatellite loci are widely used in population genetic studies, but the presence of null alleles may lead to biased results. Here, we assessed five methods that indirectly detect null alleles and found large inconsistencies among them. Our analysis was based on 20 microsatellite loci genotyped in a natural population of Microtus oeconomus sampled during 8years, together with 1200 simulated populations without null alleles, but experiencing bottlenecks of varying duration and intensity, and 120 simulated populations with known null alleles. In the natural population, 29% of positive results were consistent between the methods in pairwise comparisons, and in the simulated data set, this proportion was 14%. The positive results were also inconsistent between different years in the natural population. In the null-allele-free simulated data set, the number of false positives increased with increased bottleneck intensity and duration. We also found a low concordance in null allele detection between the original simulated populations and their 20% random subsets. In the populations simulated to include null alleles, between 22% and 42% of true null alleles remained undetected, which highlighted that detection errors are not restricted to false positives. None of the evaluated methods clearly outperformed the others when both false-positive and false-negative rates were considered. Accepting only the positive results consistent between at least two methods should considerably reduce the false-positive rate, but this approach may increase the false-negative rate. Our study demonstrates the need for novel null allele detection methods that could be reliably applied to natural populations.

  • 2.
    Diamanti, Klev
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
    Umer, Husen M.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
    Kruczyk, Marcin
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
    Dabrowski, Michal J.
    Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland..
    Cavalli, Marco
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Medicinsk genetik och genomik. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Komorowski, Jan
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Polish Acad Sci, Inst Comp Sci, PL-01248 Warsaw, Poland..
    Maps of context-dependent putative regulatory regions and genomic signal interactions2016In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 44, no 19, p. 9110-9120Article in journal (Refereed)
    Abstract [en]

    Gene transcription is regulated mainly by transcription factors (TFs). ENCODE and Roadmap Epigenomics provide global binding profiles of TFs, which can be used to identify regulatory regions. To this end we implemented a method to systematically construct cell-type and species-specific maps of regulatory regions and TF-TF interactions. We illustrated the approach by developing maps for five human cell-lines and two other species. We detected similar to 144k putative regulatory regions among the human cell-lines, with the majority of them being similar to 300 bp. We found similar to 20k putative regulatory elements in the ENCODE heterochromatic domains suggesting a large regulatory potential in the regions presumed transcriptionally silent. Among the most significant TF interactions identified in the heterochromatic regions were CTCF and the cohesin complex, which is in agreement with previous reports. Finally, we investigated the enrichment of the obtained putative regulatory regions in the 3D chromatin domains. More than 90% of the regions were discovered in the 3D contacting domains. We found a significant enrichment of GWAS SNPs in the putative regulatory regions. These significant enrichments provide evidence that the regulatory regions play a crucial role in the genomic structural stability. Additionally, we generated maps of putative regulatory regions for prostate and colorectal cancer human cell-lines.

  • 3.
    Kruczyk, Marcin
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Umer, Husen Muhammad
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Enroth, Stefan
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Genomics.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Peak Finder Metaserver - a novel application for finding peaks in ChIP-seq data2013In: BMC Bioinformatics, ISSN 1471-2105, E-ISSN 1471-2105, Vol. 14, p. 280-Article in journal (Refereed)
    Abstract [en]

    Background: Finding peaks in ChIP-seq is an important process in biological inference. In some cases, such as positioning nucleosomes with specific histone modifications or finding transcription factor binding specificities, the precision of the detected peak plays a significant role. There are several applications for finding peaks (called peak finders) based on different algorithms (e.g. MACS, Erange and HPeak). Benchmark studies have shown that the existing peak finders identify different peaks for the same dataset and it is not known which one is the most accurate. We present the first meta-server called Peak Finder MetaServer (PFMS) that collects results from several peak finders and produces consensus peaks. Our application accepts three standard ChIP-seq data formats: BED, BAM, and SAM. Results: Sensitivity and specificity of seven widely used peak finders were examined. For the experiments we used three previously studied Transcription Factors (TF) ChIP-seq datasets and identified three of the selected peak finders that returned results with high specificity and very good sensitivity compared to the remaining four. We also ran PFMS using the three selected peak finders on the same TF datasets and achieved higher specificity and sensitivity than the peak finders individually. Conclusions: We show that combining outputs from up to seven peak finders yields better results than individual peak finders. In addition, three of the seven peak finders outperform the remaining four, and running PFMS with these three returns even more accurate results. Another added value of PFMS is a separate report of the peaks returned by each of the included peak finders.

  • 4.
    Umer, Husen M.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Khaliq, Zeeshan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Marzouka, Nour-al-dain
    Department of Clinical Sciences, Faculty of Medicine, Lund University.
    Smolinska, Karolina
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Wadelius, Claes
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    Komorowski, Jan
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology. nstitute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.
    funMotifs: Tissue-specific transcription factor motifsManuscript (preprint) (Other academic)
  • 5.
    Umer, Husen M.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Komorowski, Jan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Uppsala University, Science for Life Laboratory, SciLifeLab. Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland.
    Wadelius, Claes
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    the PCAWG Drivers and Functional Interpretation Group, the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Network
    Functional annotation of noncoding mutations identifies candidate regulatory aberrations in cancerManuscript (preprint) (Other academic)
  • 6.
    Umer, Husen Muhammad
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics.
    Computational Modelling of Gene Regulation in Cancer: Coding the noncoding genome2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Technological advancements have enabled quantification of processes within and around us. The information stored within our body converts into petabytes of data. Processing and learning from such data requires comprehensive computational programs and software systems. We developed software programs to systematically investigate the process of gene regulation in the human genome. Gene regulation is a complex process where several genomic elements control expression of a gene through recruiting many transcription factor (TF) proteins. The TFs recognize specific DNA sequences known as motifs. DNA mutations in regulatory elements and particularly in TF motifs may cause gene deregulation. Therefore, defining the landscape of regulatory elements and their roles in cancer and complex diseases is of major importance.

    We developed an algorithm (tfNet) to identify regulatory elements based on transcription factor binding sites. tfNet identified nearly 144,000 regulatory elements in five human cell lines. Investigating the elements we identified TF interaction networks and enrichment of many GWAS SNPs. We also defined the regulatory landscape for other conditions and species. Next, we investigated the role of regulatory elements in cancer. Cancer is initiated and developed by genetic aberrations in the genome. Genetic changes that are present in a cancer genome are obtained through whole genome sequencing technologies. We analyzed somatic mutations that had been detected in 326 whole genomes of liver cancer patients. Our results indicated 907 candidate mutations affecting TF motifs. Genome wide alignment of the mutated motifs revealed a significant enrichment of mutations in a highly conserved position of the CTCF motif. Gene expression analysis exhibited disruption of topologically associated domains in the mutated samples. We also confirmed the mutational pattern in pancreatic, gastric and esophagus cancers. Finally, enrichment of cancer associated gene sets and pathways suggested great role of noncoding mutations in cancer.

    To systematically analyze DNA mutations in TF motifs, we developed an online database system (funMotifs). Publicly available datasets were collected for thousands experiments. The datasets were integrated using a logistic regression model. Functionality annotations and scores for motifs of 519 TFs were derived. The database allows for identification of variants affecting functional motifs in a selected tissue type. Finally, a comprehensive analysis was performed to identify mutations overlapping functional TF motifs in 37 cancer types. Somatic mutations from a pan-cancer cohort of 2,515 cancer whole genomes were investigated. A significant enrichment of mutations in the CpG site of the CEBPB motif was identified. Overall, 10,806 mutated regulatory elements were identified including 406 highly recurrent ones. Genes associated to the mutated elements were highly enriched for cancer-related pathways. Our analyses provide further insights onto the role of regulatory elements and their impacts on cancer development.

    List of papers
    1. Maps of context-dependent putative regulatory regions and genomic signal interactions
    Open this publication in new window or tab >>Maps of context-dependent putative regulatory regions and genomic signal interactions
    Show others...
    2016 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 44, no 19, p. 9110-9120Article in journal (Refereed) Published
    Abstract [en]

    Gene transcription is regulated mainly by transcription factors (TFs). ENCODE and Roadmap Epigenomics provide global binding profiles of TFs, which can be used to identify regulatory regions. To this end we implemented a method to systematically construct cell-type and species-specific maps of regulatory regions and TF-TF interactions. We illustrated the approach by developing maps for five human cell-lines and two other species. We detected similar to 144k putative regulatory regions among the human cell-lines, with the majority of them being similar to 300 bp. We found similar to 20k putative regulatory elements in the ENCODE heterochromatic domains suggesting a large regulatory potential in the regions presumed transcriptionally silent. Among the most significant TF interactions identified in the heterochromatic regions were CTCF and the cohesin complex, which is in agreement with previous reports. Finally, we investigated the enrichment of the obtained putative regulatory regions in the 3D chromatin domains. More than 90% of the regions were discovered in the 3D contacting domains. We found a significant enrichment of GWAS SNPs in the putative regulatory regions. These significant enrichments provide evidence that the regulatory regions play a crucial role in the genomic structural stability. Additionally, we generated maps of putative regulatory regions for prostate and colorectal cancer human cell-lines.

    National Category
    Biochemistry and Molecular Biology
    Identifiers
    urn:nbn:se:uu:diva-310761 (URN)10.1093/nar/gkw800 (DOI)000388016900012 ()27625394 (PubMedID)
    Funder
    AstraZenecaSwedish Research CouncilSwedish Diabetes AssociationeSSENCE - An eScience Collaboration
    Note

    De två första författarna delar förstaförfattarskapet.

    Available from: 2016-12-19 Created: 2016-12-19 Last updated: 2018-01-25Bibliographically approved
    2. A Significant Regulatory Mutation Burden at a High-Affinity Position of the CTCF Motif in Gastrointestinal Cancers
    Open this publication in new window or tab >>A Significant Regulatory Mutation Burden at a High-Affinity Position of the CTCF Motif in Gastrointestinal Cancers
    Show others...
    2016 (English)In: Human Mutation, ISSN 1059-7794, E-ISSN 1098-1004, Vol. 37, no 9, p. 904-913Article in journal (Refereed) Published
    Abstract [en]

    Somatic mutations drive cancer and there are established ways to study those in coding sequences. It has been shown that some regulatory mutations are over-represented in cancer. We develop a new strategy to find putative regulatory mutations based on experimentally established motifs for transcription factors (TFs). In total, we find 1,552 candidate regulatory mutations predicted to significantly reduce binding affinity of many TFs in hepatocellular carcinoma and affecting binding of CTCF also in esophagus, gastric, and pancreatic cancers. Near mutated motifs, there is a significant enrichment of (1) genes mutated in cancer, (2) tumor-suppressor genes, (3) genes in KEGG cancer pathways, and (4) sets of genes previously associated to cancer. Experimental and functional validations support the findings. The strategy can be applied to identify regulatory mutations in any cell type with established TF motifs and will aid identifications of genes contributing to cancer.

    Keyword
    mutated binding sites, motifs, noncoding regulatory regions, CTCF, driver mutations, whole-genome sequencing, WGS
    National Category
    Medical and Health Sciences
    Identifiers
    urn:nbn:se:uu:diva-305547 (URN)10.1002/humu.23014 (DOI)000382777100009 ()27174533 (PubMedID)
    Funder
    Swedish Cancer Society, 15 0878Swedish Research CouncileSSENCE - An eScience Collaboration, DEC 2015/16/W/NZ2/00314
    Available from: 2016-10-20 Created: 2016-10-19 Last updated: 2018-01-25Bibliographically approved
    3. funMotifs: Tissue-specific transcription factor motifs
    Open this publication in new window or tab >>funMotifs: Tissue-specific transcription factor motifs
    Show others...
    (English)Manuscript (preprint) (Other academic)
    Keyword
    noncoding genome, transcription factor motifs, database, annotation, genetic variants
    National Category
    Bioinformatics (Computational Biology)
    Research subject
    Bioinformatics
    Identifiers
    urn:nbn:se:uu:diva-339915 (URN)
    Available from: 2018-01-24 Created: 2018-01-24 Last updated: 2018-01-25
    4. Functional annotation of noncoding mutations identifies candidate regulatory aberrations in cancer
    Open this publication in new window or tab >>Functional annotation of noncoding mutations identifies candidate regulatory aberrations in cancer
    (English)Manuscript (preprint) (Other academic)
    Keyword
    noncoding genome, transcription factor motifs, regulatory elements, cancer
    National Category
    Bioinformatics and Systems Biology Medical Genetics
    Research subject
    Bioinformatics; Medical Genetics
    Identifiers
    urn:nbn:se:uu:diva-339913 (URN)
    Available from: 2018-01-24 Created: 2018-01-24 Last updated: 2018-01-25
1 - 6 of 6
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