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Towards reproducible computational biology [article]

Sebastian Schmeier
2018 Figshare  
Slides from my seminar and hands-on session at the Research Bazaar (ResBaz) conference June 2018 about how to achieve reproducibility in computational biology / bioinformatics analyses. I discussed life sciences package managers, isolated software environments with conda, workflow management with Snakemake, and containerization with Singularity and how to integrate everything into a consistent analysis workflow. The tutorial is available at: https://reproducible.sschmeier.com
doi:10.6084/m9.figshare.6726416.v1 fatcat:bxyvhguspvegllvrath3mnffwu

Finding Kinetic Parameters Using Text Mining

Jörg Hakenberg, Sebastian Schmeier, Axel Kowald, Edda Klipp, Ulf Leser
2004 Omics  
Thus, the overall fraction of positive publications was 19.5% with a 95%-CI of 16.8% to 22.4% (Schmeier et al., 2003) .  ... 
doi:10.1089/1536231041388366 pmid:15268772 fatcat:6jbj7gg655a5llcw56jpeef4qu

Genome-wide profiling of transcribed enhancers during macrophage activation [article]

Elena Denisenko, Reto Guler, Musa Mhlanga, Harukazu Suzuki, Frank Brombacher, Sebastian Schmeier
2017 bioRxiv   pre-print
Macrophages are sentinel cells essential for tissue homeostasis and host defence. Owing to their plasticity, macrophages acquire a range of functional phenotypes in response to microenvironmental stimuli, of which M(IFN-γ) and M(IL-4/IL-13) are well-known for their opposing pro- and anti-inflammatory roles. Enhancers have emerged as regulatory DNA elements crucial for transcriptional activation of gene expression. Using cap analysis of gene expression and epigenetic data, we identify on
more » ... ale transcribed enhancers in mouse macrophages, their time kinetics and target protein-coding genes. We observe an increase in target gene expression, concomitant with increasing numbers of associated enhancers and find that genes associated to many enhancers show a shift towards stronger enrichment for macrophage-specific biological processes. We infer enhancers that drive transcriptional responses of genes upon M(IFN-γ) and M(IL-4/IL-13) macrophage activation and demonstrate stimuli-specificity of regulatory associations. Finally, we show that enhancer regions are enriched for binding sites of inflammation-related transcription factors, suggesting a link between stimuli response and enhancer transcriptional control. Our study provides new insights into genome-wide enhancer-mediated transcriptional control of macrophage genes, including those implicated in macrophage activation, and offers a detailed genome-wide catalogue to further elucidate enhancer regulation in macrophages.
doi:10.1101/163519 fatcat:gosepzpefngv5edt5gjmckzbye

Molecular subtyping improves prognostication of Stage 2 colorectal cancer [article]

Rachel V Purcell, Sebastian Schmeier, Yee Chen Lau, John F Pearson, Frank A Frizelle
2019 bioRxiv   pre-print
Post-surgical staging is the mainstay of prognostic stratification for colorectal cancer (CRC). Here, we compare TNM staging to consensus molecular subtyping (CMS) and assess the value of subtyping in addition to stratification by TNM. Three hundred and eight treatment-naive colorectal tumours were accessed from our institutional tissue bank. CMS was carried out using tumour gene-expression data. Staging and CMS were analysed with respect to clinicopathologic variables and patient outcome. CMS
more » ... lone was not associated with survival, while TNM stage significantly explained mortality. Addition of CMS to TNM-stratified tumours showed a prognostic effect in stage 2 tumours; CMS3 tumours had a significantly lower overall survival (P = 0.006). Stage 2 patients with a good prognosis showed immune activation and up-regulation of tumour suppressor genes. Although stratification using CMS does not outperform TNM staging as a prognostic indicator, gene-expression based subtyping shows promise for improved prognostication in stage 2 CRC.
doi:10.1101/674614 fatcat:zdnrqlwa4bf5fdy3ptrpnkdapy

IRNdb: The database of immunologically relevant non-coding RNAs [article]

Elena Denisenko, Daniel Ho, Ousman Tamgue, Mumin Ozturk, Harukazu Suzuki, Frank Brombacher, Reto Guler, Sebastian Schmeier
2016 bioRxiv   pre-print
MicroRNAs (miRNAs), long non-coding RNAs (lncRNAs) and other functional non-coding RNAs (ncRNAs) have emerged as pivotal regulators involved in multiple biological processes. Recently, ncRNA control of gene expression has been identified as a critical regulatory mechanism in the immune system. Despite the great efforts made to discover and characterize ncRNAs, the functional role for most remains unknown. To facilitate discoveries in ncRNA regulation of immune system-related processes we
more » ... ed the database of immunologically relevant ncRNAs and target genes (IRNdb). We integrated mouse data on predicted and experimentally supported ncRNA-target interactions, ncRNA and gene annotations, biological pathways and processes, and experimental data in a uniform format with a user-friendly web interface. The current version of IRNdb documents 12,930 experimentally supported miRNA-target interactions between 724 miRNAs and 2,427 immune-related murine targets. In addition, we recorded 22,453 lncRNA-immune target and 377 PIWI-interacting RNA-immune target interactions. IRNdb is a comprehensive searchable data repository which will be of help in studying the role of ncRNAs in the immune system. Database URL: http://irndb.org
doi:10.1101/037911 fatcat:euk7kipbwfahrhwdymwppoflsm

dPORE-miRNA: Polymorphic Regulation of MicroRNA Genes

Sebastian Schmeier, Ulf Schaefer, Cameron R. MacPherson, Vladimir B. Bajic, Christos Ouzounis
2011 PLoS ONE  
MicroRNAs (miRNAs) are short non-coding RNA molecules that act as post-transcriptional regulators and affect the regulation of protein-coding genes. Mostly transcribed by PolII, miRNA genes are regulated at the transcriptional level similarly to protein-coding genes. In this study we focus on human miRNAs. These miRNAs are involved in a variety of pathways and can affect many diseases. Our interest is on possible deregulation of the transcription initiation of the miRNA encoding genes, which is
more » ... facilitated by variations in the genomic sequence of transcriptional control regions (promoters). Methodology: Our aim is to provide an online resource to facilitate the investigation of the potential effects of single nucleotide polymorphisms (SNPs) on miRNA gene regulation. We analyzed SNPs overlapped with predicted transcription factor binding sites (TFBSs) in promoters of miRNA genes. We also accounted for the creation of novel TFBSs due to polymorphisms not present in the reference genome. The resulting changes in the original TFBSs and potential creation of new TFBSs were incorporated into the Dragon Database of Polymorphic Regulation of miRNA genes (dPORE-miRNA). Conclusions: The dPORE-miRNA database enables researchers to explore potential effects of SNPs on the regulation of miRNAs. dPORE-miRNA can be interrogated with regards to: a/miRNAs (their targets, or involvement in diseases, or biological pathways), b/SNPs, or c/transcription factors. dPORE-miRNA can be accessed at http://cbrc.kaust.edu.sa/ dpore and http://apps.sanbi.ac.za/dpore/. Its use is free for academic and non-profit users.
doi:10.1371/journal.pone.0016657 pmid:21326606 pmcid:PMC3033892 fatcat:liyqzxhgrvbeth7lm7yyh3u5fi

MOESM3 of Genome-wide profiling of transcribed enhancers during macrophage activation

Elena Denisenko, Reto Guler, Musa Mhlanga, Harukazu Suzuki, Frank Brombacher, Sebastian Schmeier
2017 Figshare  
Additional file 3: Figure S1. Comparison of 222,870 TAD-based E–P pairs to a subset of 64,891 correlation-based E–P pairs. Figure S2. 1844 macrophage-specific and 8923 non-macrophage-specific genes. Figure S3. Expression of macrophage-specific and non-macrophage-specific genes associated with different number of enhancers. Figure S4. KEGG pathway maps significantly enriched for G1 and G2 genes. Figure S5. Overlaps of M(IFN-γ)- and M(IL-4/IL-13)-responsive and macrophage-specific genes and
more » ... ers. Figure S6. M(IFN-γ) marker enhancer associated with Cxcl9, Cxcl10, and Cxcl11 M(IFN-γ) marker genes. Figure S7. Time-course expression of Arg1 and associated M(IL-4/IL-13)-specific enhancer. Figure S8. Igf1 marker gene. Figure S9. M(IL-4/IL-13) marker enhancer associated with Igf1 M(IL-4/IL-13) marker gene. Figure S10. Macrophage-specific enhancer, associated with Spi1 gene.
doi:10.6084/m9.figshare.c.3911671_d3.v1 fatcat:5eoi2lxaj5frhd44eonhwtkxom

MetaFunc: Taxonomic and Functional Analyses of High Throughput Sequencing for Microbiomes [article]

Arielle Kae Lacerona Sulit, Tyler Kolisnik, Frank A Frizelle, Rachel Purcell, Sebastian Schmeier
2020 bioRxiv   pre-print
The identification of functional processes taking place in microbiome communities augment traditional microbiome taxonomic studies, giving a more complete picture of interactions taking place within the community. While there are applications that perform functional annotation on metagenome or metatranscriptomes, very few of these are able to link taxonomic identity to function and are limited by their input types or databases used. Results: Here we present MetaFunc, a workflow which takes
more » ... reads, and from these 1) identifies species present in the microbiome sample and 2) provides gene ontology (GO) annotations associated with the species identified. MetaFunc can also provide a differential abundance analysis step comparing species between sample conditions. In addition, MetaFunc allows mapping of reads to a host genome, and separates these reads, before proceeding with the microbiome analyses. From the host reads, MetaFunc is able to identify host genes, perform differential gene expression analysis, and gene-set enrichment analysis. A final correlation analysis between microbial species and host genes can also be performed. Finally, MetaFunc builds an R shiny application that allows users to view and interact with the microbiome results. In this paper we show how MetaFunc can be applied to metatranscriptomic datasets of colorectal cancer. Conclusion: MetaFunc is a one-stop shop microbiome analysis pipeline that can identify taxonomies and their respective functional contributions in a microbiome sample through GO annotations. It can also analyse host reads in a microbiome sample, providing information on host gene expression, and allowing for correlations between the microbiome and host genes. MetaFunc comes with a user-friendly R shiny application that allows for easier visualisation and exploration of its results. MetaFunc is freely available through https://gitlab.com/schmeierlab/workflows/metafunc.git.
doi:10.1101/2020.09.02.271098 fatcat:wrvqidhbf5h5lhskgv2kiyys2i

Transcribed enhancers in the macrophage immune response to Mycobacterium tuberculosis infection [article]

Elena Denisenko, Reto Guler, Musa M Mhlanga, Harukazu Suzuki, Frank Brombacher, Sebastian Schmeier
2018 bioRxiv   pre-print
Tuberculosis (TB) remains a major public health threat and cause of death worldwide. Macrophages are immune cells that compose the first line of an organism's defence against Mycobacterium tuberculosis (M.tb), the causative agent of TB. Interactions between macrophages and M.tb define the infection outcome. Enhancers are cis-regulatory DNA elements that activate transcription of target genes and mediate various responses in macrophages. To what extent the host's genetic response to infection is
more » ... controlled by enhancers remains unexplored. We analysed the regulation by transcribed enhancers in M.tb-infected mouse bone marrow-derived macrophages. We found that transcribed enhancers have a strong influence in the M.tb infection response and mediate up-regulation of many important immune genes. We characterise highly transcriptionally induced enhancers and show that many genes acquire de novo transcribed enhancers upon M.tb infection. We report enhancers targeting known immune genes crucial for the host's genetic response to M.tb, such as Tnf, Tnfrsf1b, Irg1, Hilpda, Ccl3, and Ccl4, and highlight transcription factors that are likely regulating these enhancers including AP-1, NF-kB, Irf1, and Rbpj. Finally, we highlight particular chromosomal domains carrying groups of highly transcriptionally induced enhancers and genes with previously unappreciated roles in M.tb infection, such as Fbxl3, Tapt1, Edn1, and Hivep1. Our study links M.tb-responsive transcription factors to activation of transcribed enhancers, which, in turn, target protein-coding immune genes upon infection. We find that many genes who respond with increased expression to M.tb are under the control of transcribed enhancers. Our findings extend current knowledge of M.tb-response regulation in macrophages and provide a basis for future functional studies on enhancer-gene interactions in this process.
doi:10.1101/303552 fatcat:jujlvfubp5htteletgkki53b6u

Distinct gut microbiome patterns associate with consensus molecular subtypes of colorectal cancer [article]

Rachel Violet Purcell, Martina Visnovska, Patrick J. Biggs, Sebastian Schmeier, Frank A. Frizelle
2017 bioRxiv   pre-print
We ran differential analysis of each CMS subtype against all the other classified samples (more information can be found at https://gitlab.com/s-schmeier/crc-study-2017).  ...  All P-values were adjusted for multiple testing using the false-discovery adjustment method from Benjamini & Hochberg, using R-method "p.adjust" (more information can be found at https://gitlab.com/s-schmeier  ... 
doi:10.1101/153809 fatcat:5kjsbr6wkzeorlwc34pw4lpzqy

Deep Learning in LncRNAome: Contribution, Challenges, and Perspectives

Tanvir Alam, Hamada R. H. Al-Absi, Sebastian Schmeier
2020 Non-Coding RNA  
Long non-coding RNAs (lncRNA), the pervasively transcribed part of the mammalian genome, have played a significant role in changing our protein-centric view of genomes. The abundance of lncRNAs and their diverse roles across cell types have opened numerous avenues for the research community regarding lncRNAome. To discover and understand lncRNAome, many sophisticated computational techniques have been leveraged. Recently, deep learning (DL)-based modeling techniques have been successfully used
more » ... n genomics due to their capacity to handle large amounts of data and produce relatively better results than traditional machine learning (ML) models. DL-based modeling techniques have now become a choice for many modeling tasks in the field of lncRNAome as well. In this review article, we summarized the contribution of DL-based methods in nine different lncRNAome research areas. We also outlined DL-based techniques leveraged in lncRNAome, highlighting the challenges computational scientists face while developing DL-based models for lncRNAome. To the best of our knowledge, this is the first review article that summarizes the role of DL-based techniques in multiple areas of lncRNAome.
doi:10.3390/ncrna6040047 pmid:33266128 fatcat:n7c7kn5dh5gpjhfmlkylrscmmu

SynsetRank: Degree-adjusted Random Walk for Relation Identification [article]

Shinichi Nakajima, Sebastian Krause, Dirk Weissenborn, Sven Schmeier, Nico Goernitz, Feiyu Xu
2016 arXiv   pre-print
Sven Schmeier holds a Diploma in Computer Science and a PhD in Computational Linguistics from the University of Saarland.  ...  Sven Schmeier is a senior consultant and project leader at the Language Technology Lab at the German Research Center for Artificial Intelligence (DFKI) in Berlin.  ... 
arXiv:1609.00626v2 fatcat:lulerd5qrnbqfp3pj2p6eib36u

Metagenomics and transcriptomics data from human colorectal cancer

Tina Visnovska, Patrick J. Biggs, Sebastian Schmeier, Frank A. Frizelle, Rachel V. Purcell
2019 Scientific Data  
Colorectal cancer is a heterogenous and mostly sporadic disease, the development of which is associated with microbial dysbiosis. Recent advances in subtype classification have successfully stratified the disease using molecular profiling. To understand potential relationships between molecular mechanisms differentiating the subtypes of colorectal cancer and composition of gut microbial community, we classified a set of 34 tumour samples into molecular subtypes using RNA-sequencing gene
more » ... on profiles and determined relative abundances of bacterial taxonomic groups. To identify bacterial community composition, 16S rRNA amplicon metabarcoding was used as well as whole genome metagenomics of the non-human part of RNA-sequencing data. The generated data expands the collection of the data sources related to the disease and connects molecular aspects of the cancer with environmental impact of microbial community.
doi:10.1038/s41597-019-0117-3 pmid:31278253 pmcid:PMC6611873 fatcat:cesqndgcanfmfbtl4gar7umeqy

Genome-wide profiling of transcribed enhancers during macrophage activation

Elena Denisenko, Reto Guler, Musa M. Mhlanga, Harukazu Suzuki, Frank Brombacher, Sebastian Schmeier
2017 Epigenetics & Chromatin  
Macrophages are sentinel cells essential for tissue homeostasis and host defence. Owing to their plasticity, macrophages acquire a range of functional phenotypes in response to microenvironmental stimuli, of which M(IFN-γ) and M(IL-4/IL-13) are well-known for their opposing pro-and anti-inflammatory roles. Enhancers have emerged as regulatory DNA elements crucial for transcriptional activation of gene expression. Using cap analysis of gene expression and epigenetic data, we identify on
more » ... le transcribed enhancers in mouse macrophages, their time kinetics and target protein-coding genes. We observe an increase in target gene expression, concomitant with increasing numbers of associated enhancers and find that genes associated to many enhancers show a shift towards stronger enrichment for macrophage-specific biological processes. We infer enhancers that drive transcriptional responses of genes upon M(IFN-γ) and M(IL-4/IL-13) macrophage activation and demonstrate stimuli-specificity of regulatory associations. Finally, we show that enhancer regions are enriched for binding sites of inflammation-related transcription factors, suggesting a link between stimuli response and enhancer transcriptional control. Our study provides new insights into genome-wide enhancer-mediated transcriptional control of macrophage genes, including those implicated in macrophage activation, and offers a detailed genome-wide catalogue to further elucidate enhancer regulation in macrophages. Imbalance in populations of macrophages with opposing pro-and anti-inflammatory roles has been implicated in disease progression 1 . Intracellular pathogen Mycobacterium tuberculosis, the causative agent of tuberculosis, interferes with classical activation of macrophages to avoid its anti-bacterial action, and promotes alternative activation state 11,12 . Tumour microenvironments promote phenotypic switches from pro-to anti-inflammatory macrophages, which might contribute to the tumour progression by inhibiting immune responses to tumour antigens 1,2 . Conversely, the phenotypic switch from anti-to proinflammatory population of macrophages might contribute to obesity and metabolic syndrome 1,2,13 . Therefore, the development of techniques for manipulation and specific targeting of macrophage populations could ultimately improve diagnosis and treatment of inflammatory diseases 2 . To advance this area of research, the cellular mechanisms responsible for macrophage activation need to be further deciphered. Gene expression in eukaryotic cells is a complex process guided by a multitude of mechanisms 14 . Precise regulation is required to ensure dynamic control of tissue-specific gene expression and to fine tune the responses to external stimuli 15 . One such level of control is facilitated via regulation of RNA transcription. This process is mediated by a complex transcriptional machinery with its components recognising specific regulatory regions of DNA. Promoters represent a better-characterized class of such regions from which RNA transcription is initiated 16, 17 . They act in concert with other cis-regulatory DNA elements, including enhancers, which are believed to play key roles in transcriptional regulation 18 . Enhancers are defined as regulatory DNA regions that activate transcription of target genes in a distance-and orientation-independent manner 18 . According to the dominant model, transcriptional regulation by enhancers is exerted via direct physical interaction between enhancer and target gene promoter mediated by DNA looping 18, 19 . Recent identification of distinct properties of enhancer regions enabled novel approaches to enhancer profiling 18 . Enhancer regions are often distinguished by a specific combination of chromatin marks present at these locations, such as H3K4me1 and H3K27ac 20,21 . Enhancer sequences contain transcription factor binding sites (TFBS) that recruit transcription factors (TFs) to regulate target genes 22,23 . In addition, enhancers are frequently bound by proteins such as histone acetyltransferase p300 and insulator-binding protein CTCF 21,24-26 . Large-scale profiling of these enhancerassociated signatures by chromatin immunoprecipitation followed by sequencing (ChIP-seq) 26,27 has greatly advanced enhancer identification and enabled systematic and genome-wide enhancer mapping 28,29 . Another group of methods such as chromosome conformation capture (3C) 30 and its variant Hi-C 31 has been employed to profile physical DNA contacts, including those between promoters and enhancers 32,33 . However, none of these methods has become a gold standard of enhancer detection, and the field is still actively developing. Recent studies have led to the unexpected finding that most active enhancers recruit RNA polymerase II and are bi-directionally and divergently transcribed to produce RNA transcripts, referred to as eRNAs 34, 35 . While the functionality of eRNA remains controversial, a recent study by Hon et al. showed that many enhancers are transcribed into potentially functional long-noncoding RNAs (lncRNAs) playing a role in inflammation and immunity 36, 37 . Recently, quantification of eRNA transcription laid the foundation peer-reviewed)
doi:10.1186/s13072-017-0158-9 pmid:29061167 pmcid:PMC5654053 fatcat:4fladwer3ferteqmwekcvibhgm

Molecular subtyping improves prognostication of Stage 2 colorectal cancer

Rachel V. Purcell, Sebastian Schmeier, Yee Chen Lau, John F. Pearson, Francis A. Frizelle
2019 BMC Cancer  
Post-surgical staging is the mainstay of prognostic stratification for colorectal cancer (CRC). Here, we compare TNM staging to consensus molecular subtyping (CMS) and assess the value of subtyping in addition to stratification by TNM. Three hundred and eight treatment-naïve colorectal tumours were accessed from our institutional tissue bank. CMS typing was carried out using tumour gene-expression data. Post-surgical TNM-staging and CMS were analysed with respect to clinicopathologic variables
more » ... nd patient outcome. CMS alone was not associated with survival, while TNM stage significantly explained mortality. Addition of CMS to TNM-stratified tumours showed a prognostic effect in stage 2 tumours; CMS3 tumours had a significantly lower overall survival (P = 0.006). Stage 2 patients with a good prognosis showed immune activation and up-regulation of tumour suppressor genes. Although stratification using CMS does not outperform TNM staging as a prognostic indicator, gene-expression based subtyping shows promise for improved prognostication in stage 2 CRC.
doi:10.1186/s12885-019-6327-4 pmid:31775679 pmcid:PMC6882162 fatcat:2c3c2mgubnd6fmwgyhve42doea
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