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  • 1
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    Transcriptome and genome sequencing uncovers functional variation in humans (2013)
    Nature Publishing Group (NPG)
    Details
    Publication Date: 2013-09-17
    Description: Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project--the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences. We discover extremely widespread genetic variation affecting the regulation of most genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on the cellular mechanisms of regulatory and loss-of-function variation, and allows us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.〈br /〉〈br /〉〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918453/" target="_blank"〉〈img src="https://static.pubmed.gov/portal/portal3rc.fcgi/4089621/img/3977009" border="0"〉〈/a〉   〈a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918453/" target="_blank"〉This paper as free author manuscript - peer-reviewed and accepted for publication〈/a〉〈br /〉〈br /〉〈span class="detail_caption"〉Notes: 〈/span〉Lappalainen, Tuuli -- Sammeth, Michael -- Friedlander, Marc R -- 't Hoen, Peter A C -- Monlong, Jean -- Rivas, Manuel A -- Gonzalez-Porta, Mar -- Kurbatova, Natalja -- Griebel, Thasso -- Ferreira, Pedro G -- Barann, Matthias -- Wieland, Thomas -- Greger, Liliana -- van Iterson, Maarten -- Almlof, Jonas -- Ribeca, Paolo -- Pulyakhina, Irina -- Esser, Daniela -- Giger, Thomas -- Tikhonov, Andrew -- Sultan, Marc -- Bertier, Gabrielle -- MacArthur, Daniel G -- Lek, Monkol -- Lizano, Esther -- Buermans, Henk P J -- Padioleau, Ismael -- Schwarzmayr, Thomas -- Karlberg, Olof -- Ongen, Halit -- Kilpinen, Helena -- Beltran, Sergi -- Gut, Marta -- Kahlem, Katja -- Amstislavskiy, Vyacheslav -- Stegle, Oliver -- Pirinen, Matti -- Montgomery, Stephen B -- Donnelly, Peter -- McCarthy, Mark I -- Flicek, Paul -- Strom, Tim M -- Geuvadis Consortium -- Lehrach, Hans -- Schreiber, Stefan -- Sudbrak, Ralf -- Carracedo, Angel -- Antonarakis, Stylianos E -- Hasler, Robert -- Syvanen, Ann-Christine -- van Ommen, Gert-Jan -- Brazma, Alvis -- Meitinger, Thomas -- Rosenstiel, Philip -- Guigo, Roderic -- Gut, Ivo G -- Estivill, Xavier -- Dermitzakis, Emmanouil T -- 075491/Z/04/B/Wellcome Trust/United Kingdom -- 076113/Wellcome Trust/United Kingdom -- 081917/Wellcome Trust/United Kingdom -- 083270/Wellcome Trust/United Kingdom -- 085475/B/08/Z/Wellcome Trust/United Kingdom -- 085475/Z/08/Z/Wellcome Trust/United Kingdom -- 085532/Wellcome Trust/United Kingdom -- 090367/Wellcome Trust/United Kingdom -- 090532/Wellcome Trust/United Kingdom -- 090532/Z/09/Z/Wellcome Trust/United Kingdom -- 095552/Wellcome Trust/United Kingdom -- 095552/Z/11/Z/Wellcome Trust/United Kingdom -- 098381/Wellcome Trust/United Kingdom -- G0601261/Medical Research Council/United Kingdom -- MH090941/MH/NIMH NIH HHS/ -- R01 GM104371/GM/NIGMS NIH HHS/ -- R01 MH090941/MH/NIMH NIH HHS/ -- WT085532/Wellcome Trust/United Kingdom -- England -- Nature. 2013 Sep 26;501(7468):506-11. doi: 10.1038/nature12531. Epub 2013 Sep 15.〈br /〉〈span class="detail_caption"〉Author address: 〈/span〉Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland. tuuli.e.lappalainen@gmail.com〈br /〉〈span class="detail_caption"〉Record origin:〈/span〉 〈a href="http://www.ncbi.nlm.nih.gov/pubmed/24037378" target="_blank"〉PubMed〈/a〉
    Keywords: Alleles ; Cell Line, Transformed ; Exons/genetics ; Gene Expression Profiling ; Genetic Variation/*genetics ; Genome, Human/*genetics ; *High-Throughput Nucleotide Sequencing ; Humans ; Polymorphism, Single Nucleotide/genetics ; Quantitative Trait Loci/genetics ; RNA, Messenger/analysis/genetics ; *Sequence Analysis, RNA ; Transcriptome/*genetics
    Print ISSN: 0028-0836
    Electronic ISSN: 1476-4687
    Topics: Biology , Chemistry and Pharmacology , Medicine , Natural Sciences in General , Physics
    Published by Nature Publishing Group (NPG)
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  • 2
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    Janus--a comprehensive tool investigating the two faces of transcription (2013)
    Oxford University Press
    Details
    Publication Date: 2013-06-24
    Description: Motivation: Protocols to generate strand-specific transcriptomes with next-generation sequencing platforms have been used by the scientific community roughly since 2008. Strand-specific reads allow for detection of antisense events and a higher resolution of expression profiles enabling extension of current transcript annotations. However, applications making use of this strandedness information are still scarce. Results: Here we present a tool ( Janus ), which focuses on the identification of transcriptional active regions in antisense orientation to known and novel transcribed elements of the genome. Janus can compare the antisense events of multiple samples and assigns scores to identify mutual expression of either transcript in a sense/antisense pair, which could hint to regulatory mechanisms. Janus is able to make use of single-nucleotide variant (SNV) and methylation data, if available, and reports the sense to antisense ratio of regions in the vicinity of the identified genetic and epigenetic variation. Janus interrogates positions of heterozygous SNVs to identify strand-specific allelic imbalance. Availability: Janus is written in C/C++ and freely available at http://www.ikmb.uni-kiel.de/janus/janus.html under terms of GNU General Public License, for both, Linux and Windows 64 x . Although the binaries will work without additional downloads, the software depends on bamtools ( https://github.com/pezmaster31/bamtools ) for compilation. A detailed tutorial section is included in the first section of the supplemental material and included as brief readme.txt in the tutorial archive. Contact: m.barann@mucosa.de or p.rosenstiel@mucosa.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Print ISSN: 1367-4803
    Electronic ISSN: 1460-2059
    Topics: Biology , Computer Science , Medicine
    Published by Oxford University Press
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  • 3
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    Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction (2017)
    Oxford University Press
    Details
    Publication Date: 2017-01-10
    Description: The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
    Keywords: Protein-nucleic acid interaction, Computational Methods, Genomics
    Print ISSN: 0305-1048
    Electronic ISSN: 1362-4962
    Topics: Biology
    Published by Oxford University Press
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  • 4
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    Which agonist properties are important for the activation of 5-HT3A receptors? (2013)
    Elsevier
    Details
    Publication Date: 2013-11-01
    Print ISSN: 0005-2736
    Electronic ISSN: 1879-2642
    Topics: Biology , Chemistry and Pharmacology , Medicine , Physics
    Published by Elsevier
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  • 5
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    Fast and slow interactions of n-alkanols with human 5-HT3A receptors: Implications for anesthetic mechanisms (2015)
    Elsevier
    Details
    Publication Date: 2015-07-01
    Print ISSN: 0005-2736
    Electronic ISSN: 1879-2642
    Topics: Biology , Chemistry and Pharmacology , Medicine , Physics
    Published by Elsevier
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