Filters








37 Hits in 0.67 sec

Misleading Metadata Detection on YouTube [article]

Priyank Palod, Ayush Patwari, Sudhanshu Bahety, Saurabh Bagchi and Pawan Goyal
<span title="2019-01-25">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Acknowledgement This material is based in part upon work supported by a Google Faculty Award to Saurabh.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.08759v1">arXiv:1901.08759v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5dhzkimx5ndmjdyluz6652dzfy">fatcat:5dhzkimx5ndmjdyluz6652dzfy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200909002917/https://arxiv.org/pdf/1901.08759v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/be/a1/bea13a809228b57d7ebed9707b79dc3dde3c5c90.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.08759v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

From Days to Hours: Reporting Clinically Actionable Variants from Whole Genome Sequencing

Sumit Middha, Saurabh Baheti, Steven N. Hart, Jean-Pierre A. Kocher, Charles Y. Chiu
<span title="2014-02-05">2014</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
As the cost of whole genome sequencing (WGS) decreases, clinical laboratories will be looking at broadly adopting this technology to screen for variants of clinical significance. To fully leverage this technology in a clinical setting, results need to be reported quickly, as the turnaround rate could potentially impact patient care. The latest sequencers can sequence a whole human genome in about 24 hours. However, depending on the computing infrastructure available, the processing of data can
more &raquo; ... ake several days, with the majority of computing time devoted to aligning reads to genomics regions that are to date not clinically interpretable. In an attempt to accelerate the reporting of clinically actionable variants, we have investigated the utility of a multi-step alignment algorithm focused on aligning reads and calling variants in genomic regions of clinical relevance prior to processing the remaining reads on the whole genome. This iterative workflow significantly accelerates the reporting of clinically actionable variants with no loss of accuracy when compared to genotypes obtained with the OMNI SNP platform or to variants detected with a standard workflow that combines Novoalign and GATK.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0086803">doi:10.1371/journal.pone.0086803</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24505267">pmid:24505267</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3914798/">pmcid:PMC3914798</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/btiqdjvggvhqrjrdmtn7tuhgcy">fatcat:btiqdjvggvhqrjrdmtn7tuhgcy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171009200203/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0086803&amp;type=printable" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/56/41/5641c8625ff7fb83cbd3daf998ebdc606dfcf3e1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0086803"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3914798" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Performance benchmarking of GATK3.8 and GATK4 [article]

Jacob R Heldenbrand, Saurabh Baheti, Matthew A Bockol, Travis M Drucker, Steven N Hart, Matthew E Hudson, Ravishankar K Iyer, Michael T Kalmbach, Eric W Klee, Eric D Wieben, Mathieu Wiepert, Derek E Wildman (+1 others)
<span title="2018-06-18">2018</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed significant rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving
more &raquo; ... et for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. We re-evaluated the options previously identified as advantageous, such as threading, parallel garbage collection, I/O options and data-level parallelization. Based on our results, we consider the performance and cost trade-offs of using GATK3.8 and GATK4 for different types of analyses.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/348565">doi:10.1101/348565</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ec44s3e425cnrhgnyfeijbigte">fatcat:ec44s3e425cnrhgnyfeijbigte</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190504150344/https://www.biorxiv.org/content/biorxiv/early/2018/06/18/348565.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/20/68/206801afc93b5bbf0700e3fae007d63579bdc726.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/348565"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

SoftSearch: Integration of Multiple Sequence Features to Identify Breakpoints of Structural Variations

Steven N. Hart, Vivekananda Sarangi, Raymond Moore, Saurabh Baheti, Jaysheel D. Bhavsar, Fergus J. Couch, Jean-Pierre A. Kocher, Haixu Tang
<span title="2013-12-16">2013</span> <i title="Public Library of Science (PLoS)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s3gm7274mfe6fcs7e3jterqlri" style="color: black;">PLoS ONE</a> </i> &nbsp;
Structural variation (SV) represents a significant, yet poorly understood contribution to an individual's genetic makeup. Advanced next-generation sequencing technologies are widely used to discover such variations, but there is no single detection tool that is considered a community standard. In an attempt to fulfil this need, we developed an algorithm, SoftSearch, for discovering structural variant breakpoints in Illumina paired-end nextgeneration sequencing data. SoftSearch combines multiple
more &raquo; ... strategies for detecting SV including split-read, discordant read-pair, and unmated pairs. Co-localized split-reads and discordant read pairs are used to refine the breakpoints. Results: We developed and validated SoftSearch using real and synthetic datasets. SoftSearch's key features are 1) not requiring secondary (or exhaustive primary) alignment, 2) portability into established sequencing workflows, and 3) is applicable to any DNA-sequencing experiment (e.g. whole genome, exome, custom capture, etc.). SoftSearch identifies breakpoints from a small number of soft-clipped bases from split reads and a few discordant read-pairs which on their own would not be sufficient to make an SV call. Conclusions: We show that SoftSearch can identify more true SVs by combining multiple sequence features. SoftSearch was able to call clinically relevant SVs in the BRCA2 gene not reported by other tools while offering significantly improved overall performance.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0083356">doi:10.1371/journal.pone.0083356</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24358278">pmid:24358278</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3865185/">pmcid:PMC3865185</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ifhtezbu25fvvac22zmmdrzzpq">fatcat:ifhtezbu25fvvac22zmmdrzzpq</a> </span>
<a target="_blank" rel="noopener" href="https://archive.org/download/pubmed-PMC3865185/PMC3865185-journal.pone.0083356.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> File Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2a/fc/2afcf73b01f220fc51857ef8c4bea150c71069c3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0083356"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> plos.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3865185" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Targeted alignment and end repair elimination increase alignment and methylation measure accuracy for reduced representation bisulfite sequencing data

Saurabh Baheti, Rahul Kanwar, Meike Goelzenleuchter, Jean-Pierre A. Kocher, Andreas S. Beutler, Zhifu Sun
<span title="2016-02-27">2016</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4srzxifvfrdlhjhg3dimznkp7m" style="color: black;">BMC Genomics</a> </i> &nbsp;
DNA methylation is an important epigenetic modification involved in many biological processes. Reduced representation bisulfite sequencing (RRBS) is a cost-effective method for studying DNA methylation at single base resolution. Although several tools are available for RRBS data processing and analysis, it is not clear which strategy performs the best and there has not been much attention to the contamination issue from artificial cytosines incorporated during the end repair step of library
more &raquo; ... aration. To address these issues, we describe a new method, Targeted Alignment and Artificial Cytosine Elimination for RRBS (TRACE-RRBS), which aligns bisulfite sequence reads to MSP1 digitally digested reference and specifically removes the end repair cytosines. We compared this approach on a simulated and a real dataset with 7 other RRBS analysis tools and Illumina 450 K microarray platform. Results: TRACE-RRBS aligns sequence reads to a small fraction of the genome where RRBS protocol targets on and was demonstrated as the fastest, most sensitive and specific tool for the simulated dataset. For the real dataset, TRACE-RRBS took about the same time as RRBSMAP, a third to a sixth of time needed for BISMARK and NOVOALIGN. TRACE-RRBS aligned more reads uniquely than other tools and achieved the highest correlation with 450 k microarray data. The end repair artificial cytosine removal increased correlation between nearby CpGs and accuracy of methylation quantification. Conclusions: TRACE-RRBS is fast and more accurate tool for RRBS data analysis. It is freely available for academic use at http://bioinformaticstools.mayo.edu/.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12864-016-2494-8">doi:10.1186/s12864-016-2494-8</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/26922377">pmid:26922377</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4769831/">pmcid:PMC4769831</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cro7j2u2vzhr7pwkqka2nqu62y">fatcat:cro7j2u2vzhr7pwkqka2nqu62y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190426090648/https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/s12864-016-2494-8" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f7/d6/f7d6d16d9ff0c828d81866bfcd26fba0fb1a7190.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12864-016-2494-8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4769831" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Aberrant signature methylome by DNMT1 hot spot mutation in hereditary sensory and autonomic neuropathy 1E

Zhifu Sun, Yanhong Wu, Tamas Ordog, Saurabh Baheti, Jinfu Nie, Xiaohui Duan, Kaori Hojo, Jean-Pierre Kocher, Peter J Dyck, Christopher J Klein
<span title="2014-07-07">2014</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/lkovtdud7bactlc5fb4ffvecii" style="color: black;">Epigenetics</a> </i> &nbsp;
These authors contributed equally to this work.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4161/epi.29676">doi:10.4161/epi.29676</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25033457">pmid:25033457</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4164503/">pmcid:PMC4164503</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3yqy25cu2rfp3nqin7zjcph56q">fatcat:3yqy25cu2rfp3nqin7zjcph56q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200504220515/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4164503&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/41/32/4132c836b7aa284de8ab3ec078285bfa6042d9c3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4161/epi.29676"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4164503" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Perinatal Nutritional Reprogramming of the Epigenome Promotes Subsequent Development of Nonalcoholic Steatohepatitis

Luz Helena Gutierrez Sanchez, Kyoko Tomita, Qianqian Guo, Kunimaro Furuta, Husam Alhuwaish, Petra Hirsova, Saurabh Baheti, Bonnie Alver, Ryan Hlady, Keith D. Robertson, Samar H. Ibrahim
<span title="2018-10-01">2018</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7o6egjnp5jh6hhm34ejavr7eym" style="color: black;">Hepatology Communications</a> </i> &nbsp;
With the epidemic of obesity, nonalcoholic fatty liver disease (NAFLD) has become the most common pediatric liver disease. The influence of a perinatal obesity-inducing diet (OID) on the development and progression of NAFLD in offspring is important but incompletely studied. Hence, we fed breeding pairs of C57BL/6J mice during gestation and lactation (perinatally) either chow or an OID rich in fat, fructose, and cholesterol (FFC). The offspring were weaned to either chow or an FFC diet,
more &raquo; ... ng four groups: perinatal (p)Chow-Chow, pChow-FFC, pFFC-Chow, and pFFC-FFC. Mice were sacrificed at 10 weeks of age. We examined the whole-liver transcriptome by RNA sequencing (RNA-seq) and whole-liver genome methylation by reduced representation bisulfite sequencing (RRBS). Our results indicated that the pFFC-FFC mice had a significant increase in hepatic steatosis, injury, inflammation, and fibrosis, as assessed histologically and biochemically. We identified 189 genes that were differentially expressed and methylated in the pFFC-FFC mice versus the pChow-FFC mice. Gene set enrichment analysis identified hepatic fibrosis/hepatic stellate cell activation as the top canonical pathway, suggesting that the differential DNA methylation events in the mice exposed to the FFC diet perinatally were associated with a profibrogenic transcriptome. To verify that this finding was consistent with perinatal nutritional reprogramming of the methylome, we exposed pFFC-Chow mice to an FFC diet in adulthood. These mice developed significant hepatic steatosis, injury, inflammation, and more importantly fibrosis when compared to the appropriate controls. Conclusion: Perinatal exposure to an OID primes the immature liver for an accentuated fibrosing nonalcoholic steatohepatitis (NASH) phenotype, likely through nutritional reprogramming of the offspring methylome. These data have potential clinical implications for monitoring children of obese mothers and risk stratification of children with NAFLD.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/hep4.1265">doi:10.1002/hep4.1265</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30556038">pmid:30556038</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6287484/">pmcid:PMC6287484</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jg55wxg3qvbo5eyv3qi2rsklx4">fatcat:jg55wxg3qvbo5eyv3qi2rsklx4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191129025306/https://aasldpubs.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/hep4.1265" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/31/4f/314fbe6e5d16812a8828d819b3b2d9c30d1f6d31.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/hep4.1265"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> wiley.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287484" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Computational performance and accuracy of Sentieon DNASeq variant calling workflow [article]

Katherine Kendig, Saurabh Baheti, Matthew A Bockol, Travis M Drucker, Steven N Hart, Jacob R Heldenbrand, Mikel Hernaez, Matthew E Hudson, Michael T Kalmbach, Eric W Klee, Nathan R Mattson, Christian A Ross (+5 others)
<span title="2018-08-20">2018</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
As reliable, efficient genome sequencing becomes more ubiquitous, the need for similarly reliable and efficient variant calling becomes increasingly important. The Genome Analysis Toolkit (GATK), maintained by the Broad Institute, is currently the widely accepted standard for variant calling software. However, alternative solutions may provide faster variant calling without sacrificing accuracy. One such alternative is Sentieon DNASeq, a toolkit analogous to GATK but built on a highly optimized
more &raquo; ... backend. We evaluated the DNASeq single-sample variant calling pipeline in comparison to that of GATK. Our results confirm the near-identical accuracy of the two software packages, showcase perfect scalability and great speed from Sentieon, and describe computational performance considerations for the deployment of Sentieon DNASeq.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/396325">doi:10.1101/396325</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qtp3srje2be45fii3azybkeecy">fatcat:qtp3srje2be45fii3azybkeecy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190504185446/https://www.biorxiv.org/content/biorxiv/early/2018/08/20/396325.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/82/31/82313fc01857eac6fb612e99bfaf4dcc4a1e3f6f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/396325"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Comprehensive Assessment of Genetic Variants WithinTCF4in Fuchs' Endothelial Corneal Dystrophy

Eric D. Wieben, Ross A. Aleff, Bruce W. Eckloff, Elizabeth J. Atkinson, Saurabh Baheti, Sumit Middha, William L. Brown, Sanjay V. Patel, Jean-Pierre A. Kocher, Keith H. Baratz
<span title="2014-09-29">2014</span> <i title="Association for Research in Vision and Ophthalmology (ARVO)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/bktgmrvaorcfle3nbaxcwpsb44" style="color: black;">Investigative Ophthalmology and Visual Science</a> </i> &nbsp;
Citation: Wieben ED, Aleff RA, Eckloff BW, et al. Comprehensive assessment of genetic variants within TCF4 in Fuchs' endothelial corneal dystrophy. Invest Ophthalmol Vis Sci. PURPOSE. The single nucleotide variant (SNV), rs613872, in the transcription factor 4 (TCF4) gene was previously found to be strongly associated (P ¼ 6 3 10 À26 ) with Fuchs' endothelial corneal dystrophy (FECD). Subsequently, an intronic expansion of the repeating trinucleotides, TGC, was found to be even more predictive
more &raquo; ... f disease. We performed comprehensive sequencing of the TCF4 gene region in order to identify the best marker for FECD within TCF4 and to identify other novel variants that may be associated with FECD. METHODS. Leukocyte DNA was isolated from 68 subjects with FECD and 16 unaffected individuals. A custom capture panel was used to isolate the region surrounding the two previously validated markers of FECD. Sequencing of the TCF4 coding region, introns and flanking sequence, spanning 465 kb was performed at >10003 average coverage using the Illumina HiSequation 2000. RESULTS. TGC expansion (>50 repeats) was present in 46 (68%) FECD-affected subjects and one (6%) normal subject. A total of 1866 variants, including 1540 SNVs, were identified. Only two previously reported SNVs resided in the TCF4 coding region, neither of which segregated with disease. No variant, including TGC expansion, correlated perfectly with disease status. Trinucleotide repeat expansion was a better predictor of disease than any other variant. CONCLUSIONS. Complete sequencing of the TCF4 genomic region revealed no single causative variant for FECD. The intronic trinucleotide repeat expansion within TCF4 continues to be more strongly associated with FECD than any other genetic variant. FIGURE 5. Identification of a three-base deletion at the beginning of the TGC repeat in one control sample with an expanded repeat. Both the NGS results (A) and Sanger sequencing results (B) for this sample are shown. This sample from an unaffected individual contains 74 TGC repeats. The heterozygous three-base deletion is confirmed by Sanger sequencing.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1167/iovs.14-14958">doi:10.1167/iovs.14-14958</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25168903">pmid:25168903</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4179444/">pmcid:PMC4179444</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pctwzbug4jfjnm7dt6h5lrnyyq">fatcat:pctwzbug4jfjnm7dt6h5lrnyyq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170813180426/http://iovs.arvojournals.org/pdfaccess.ashx?url=/data/journals/iovs/933257/i1552-5783-55-9-6101.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c3/3f/c33f29f441319f314a430d86aae22e69c1aff3bc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1167/iovs.14-14958"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4179444" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

MAP-RSeq: Mayo Analysis Pipeline for RNA sequencing

Krishna R Kalari, Asha A Nair, Jaysheel D Bhavsar, Daniel R O'Brien, Jaime I Davila, Matthew A Bockol, Jinfu Nie, Xiaojia Tang, Saurabh Baheti, Jay B Doughty, Sumit Middha, Hugues Sicotte (+3 others)
<span title="">2014</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
Although the costs of next generation sequencing technology have decreased over the past years, there is still a lack of simple-to-use applications, for a comprehensive analysis of RNA sequencing data. There is no one-stop shop for transcriptomic genomics. We have developed MAP-RSeq, a comprehensive computational workflow that can be used for obtaining genomic features from transcriptomic sequencing data, for any genome. Results: For optimization of tools and parameters, MAP-RSeq was validated
more &raquo; ... sing both simulated and real datasets. MAP-RSeq workflow consists of six major modules such as alignment of reads, quality assessment of reads, gene expression assessment and exon read counting, identification of expressed single nucleotide variants (SNVs), detection of fusion transcripts, summarization of transcriptomics data and final report. This workflow is available for Human transcriptome analysis and can be easily adapted and used for other genomes. Several clinical and research projects at the Mayo Clinic have applied the MAP-RSeq workflow for RNA-Seq studies. The results from MAP-RSeq have thus far enabled clinicians and researchers to understand the transcriptomic landscape of diseases for better diagnosis and treatment of patients. Conclusions: Our software provides gene counts, exon counts, fusion candidates, expressed single nucleotide variants, mapping statistics, visualizations, and a detailed research data report for RNA-Seq. The workflow can be executed on a standalone virtual machine or on a parallel Sun Grid Engine cluster. The software can be downloaded from
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1471-2105-15-224">doi:10.1186/1471-2105-15-224</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24972667">pmid:24972667</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4228501/">pmcid:PMC4228501</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q5u5p7x5u5eztpwippd7bqyeom">fatcat:q5u5p7x5u5eztpwippd7bqyeom</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170830110245/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/1471-2105-15-224?site=http://bmcbioinformatics.biomedcentral.com" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c8/ec/c8ec7f59ad713f83bf0e20087a210ab9477964b0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1471-2105-15-224"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4228501" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Recommendations for performance optimizations when using GATK3.8 and GATK4

Jacob R Heldenbrand, Saurabh Baheti, Matthew A Bockol, Travis M Drucker, Steven N Hart, Matthew E Hudson, Ravishankar K Iyer, Michael T Kalmbach, Katherine I Kendig, Eric W Klee, Nathan R Mattson, Eric D Wieben (+3 others)
<span title="2019-11-08">2019</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for
more &raquo; ... al deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-3169-7">doi:10.1186/s12859-019-3169-7</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/31703611">pmid:31703611</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6842142/">pmcid:PMC6842142</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6ztcuz7iavcyragi7d2aaijqua">fatcat:6ztcuz7iavcyragi7d2aaijqua</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200216012620/https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-3169-7" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/57/2c/572c07a13f0f31863c37038766de1b8e09076107.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-019-3169-7"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6842142" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

SAAP-RRBS: streamlined analysis and annotation pipeline for reduced representation bisulfite sequencing

Zhifu Sun, Saurabh Baheti, Sumit Middha, Rahul Kanwar, Yuji Zhang, Xing Li, Andreas S. Beutler, Eric Klee, Yan W. Asmann, E. Aubrey Thompson, Jean-Pierre A. Kocher
<span title="2012-06-10">2012</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4r72gbmtcrde5no3fwwogjs3cu" style="color: black;">Computer applications in the biosciences : CABIOS</a> </i> &nbsp;
Reduced representation bisulfite sequencing (RRBS) is a cost-effective approach for genome-wide methylation pattern profiling. Analyzing RRBS sequencing data is challenging and specialized alignment/mapping programs are needed. Although such programs have been developed, a comprehensive solution that provides researchers with good quality and analyzable data is still lacking. To address this need, we have developed a Streamlined Analysis and Annotation Pipeline for RRBS data (SAAP-RRBS) that
more &raquo; ... egrates read quality assessment/clean-up, alignment, methylation data extraction, annotation, reporting and visualization. This package facilitates a rapid transition from sequencing reads to a fully annotated CpG methylation report to biological interpretation. Availability and implementation: SAAP-RRBS is freely available to non-commercial users at the web site http://ndc.mayo.edu/mayo/ research/biostat/stand-alone-packages.cfm. Contact:
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/bts337">doi:10.1093/bioinformatics/bts337</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/22689387">pmid:22689387</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3413387/">pmcid:PMC3413387</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jkfkosk3bnbddnabmvnpy2j62e">fatcat:jkfkosk3bnbddnabmvnpy2j62e</a> </span>
<a target="_blank" rel="noopener" href="https://archive.org/download/pubmed-PMC3413387/PMC3413387-bts337.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> File Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/bts337"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3413387" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

RVboost: RNA-seq variants prioritization using a boosting method

Chen Wang, Jaime I. Davila, Saurabh Baheti, Aditya V. Bhagwate, Xue Wang, Jean-Pierre A. Kocher, Susan L. Slager, Andrew L. Feldman, Anne J. Novak, James R. Cerhan, E. Aubrey Thompson, Yan W. Asmann
<span title="2014-08-27">2014</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4r72gbmtcrde5no3fwwogjs3cu" style="color: black;">Computer applications in the biosciences : CABIOS</a> </i> &nbsp;
Motivation: RNA-Seq has become the method of choice to quantify genes and exons, discover novel transcripts, and detect fusion genes. However, reliable variant identification from RNA-Seq data remains challenging due to the complexities of the transcriptome, the challenges of accurately mapping exon boundary spanning reads, and the bias introduced during the sequencing library preparation. Method: We developed RVboost, a novel method specific for RNA variant prioritization. RVboost utilizes
more &raquo; ... ral attributes unique in the process of RNA library preparation, sequencing, and RNA-Seq data analyses. It employs a boosting method to train a model of "good quality" variants using common variants from HapMap, and prioritizes and calls the RNA variants based on the trained model. We packaged RVboost in a comprehensive workflow which integrates tools of variant calling, annotation, and filtering. Results: RVboost consistently outperforms Variant Quality Score Recalibration (VQSR) from the Genome Analysis Tool Kit (GATK) and the RNA-Seq variant calling pipeline SNPiR in 12 RNA-Seq samples using groundtruth variants from paired exome sequencing data. Several RNA-Seq specific attributes were identified as critical to differentiate true and false variants, including the distance of the variant positions to exon boundaries, and the percent of the reads supporting the variant in the first 6 base pairs. The latter identifies false variants introduced by the random hexamer priming during the library construction.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/btu577">doi:10.1093/bioinformatics/btu577</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25170027">pmid:25170027</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4296157/">pmcid:PMC4296157</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/67zylmrsy5guno5m3cc6dte5ai">fatcat:67zylmrsy5guno5m3cc6dte5ai</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20160528162906/http://bioinformatics.oxfordjournals.org:80/content/early/2014/08/27/bioinformatics.btu577.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/7c/3b/7c3b0be464d5ee26b6e0287f1862f9bfc8c50b0e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/btu577"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4296157" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

HGT-ID: an efficient and sensitive workflow to detect human-viral insertion sites using next-generation sequencing data

Saurabh Baheti, Xiaojia Tang, Daniel R. O'Brien, Nicholas Chia, Lewis R. Roberts, Heidi Nelson, Judy C. Boughey, Liewei Wang, Matthew P. Goetz, Jean-Pierre A. Kocher, Krishna R. Kalari
<span title="2018-07-17">2018</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/n5zrklrhlzhtdorf4rk4rmeo3i" style="color: black;">BMC Bioinformatics</a> </i> &nbsp;
Transfer of genetic material from microbes or viruses into the host genome is known as horizontal gene transfer (HGT). The integration of viruses into the human genome is associated with multiple cancers, and these can now be detected using next-generation sequencing methods such as whole genome sequencing and RNA-sequencing. Results: We designed a novel computational workflow, HGT-ID, to identify the integration of viruses into the human genome using the sequencing data. The HGT-ID workflow
more &raquo; ... marily follows a four-step procedure: i) pre-processing of unaligned reads, ii) virus detection using subtraction approach, iii) identification of virus integration site using discordant and soft-clipped reads and iv) HGT candidates prioritization through a scoring function. Annotation and visualization of the events, as well as primer design for experimental validation, are also provided in the final report. We evaluated the tool performance with the well-understood cervical cancer samples. The HGT-ID workflow accurately detected known human papillomavirus (HPV) integration sites with high sensitivity and specificity compared to previous HGT methods. We applied HGT-ID to The Cancer Genome Atlas (TCGA) whole-genome sequencing data (WGS) from liver tumor-normal pairs. Multiple hepatitis B virus (HBV) integration sites were identified in TCGA liver samples and confirmed by HGT-ID using the RNA-Seq data from the matched liver pairs. This shows the applicability of the method in both the data types and crossvalidation of the HGT events in liver samples. We also processed 220 breast tumor WGS data through the workflow; however, there were no HGT events detected in those samples. Conclusions: HGT-ID is a novel computational workflow to detect the integration of viruses in the human genome using the sequencing data. It is fast and accurate with functions such as prioritization, annotation, visualization and primer design for future validation of HGTs. The HGT-ID workflow is released under the MIT License and available at http://kalarikrlab.org/Software/HGT-ID.html.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-018-2260-9">doi:10.1186/s12859-018-2260-9</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30016933">pmid:30016933</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6050683/">pmcid:PMC6050683</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o6aty7lqdjfvdnejfzdy7fx3te">fatcat:o6aty7lqdjfvdnejfzdy7fx3te</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190216042410/http://pdfs.semanticscholar.org/016a/1cad2e7712961a8b4ad83f648c7c879183c0.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/01/6a/016a1cad2e7712961a8b4ad83f648c7c879183c0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/s12859-018-2260-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> springer.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6050683" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Comprehensively Evaluating cis -Regulatory Variation in the Human Prostate Transcriptome by Using Gene-Level Allele-Specific Expression

Nicholas B. Larson, Shannon McDonnell, Amy J. French, Zach Fogarty, John Cheville, Sumit Middha, Shaun Riska, Saurabh Baheti, Asha A. Nair, Liang Wang, Daniel J. Schaid, Stephen N. Thibodeau
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ho5llzhlhzhmrlusxzz4j2bery" style="color: black;">American Journal of Human Genetics</a> </i> &nbsp;
The identification of cis-acting regulatory variation in primary tissues has the potential to elucidate the genetic basis of complex traits and further our understanding of transcriptomic diversity across cell types. Expression quantitative trait locus (eQTL) association analysis using RNA sequencing (RNA-seq) data can improve upon the detection of cis-acting regulatory variation by leveraging allele-specific expression (ASE) patterns in association analysis. Here, we present a comprehensive
more &raquo; ... luation of cis-acting eQTLs by analyzing RNA-seq gene-expression data and genome-wide high-density genotypes from 471 samples of normal primary prostate tissue. Using statistical models that integrate ASE information, we identified extensive cis-eQTLs across the prostate transcriptome and found that approximately 70% of expressed genes corresponded to a significant eQTL at a gene-level false-discovery rate of 0.05. Overall, cis-eQTLs were heavily concentrated near the transcription start and stop sites of affected genes, and effects were negatively correlated with distance. We identified multiple instances of cis-acting co-regulation by using phased genotype data and discovered 233 SNPs as the most strongly associated eQTLs for more than one gene. We also noted significant enrichment (25/50, p ¼ 2EÀ5) of previously reported prostate cancer risk SNPs in prostate eQTLs. Our results illustrate the benefit of assessing ASE data in cis-eQTL analyses by showing better reproducibility of prior eQTL findings than of eQTL mapping based on total expression alone. Altogether, our analysis provides extensive functional context of thousands of SNPs in prostate tissue, and these results will be of critical value in guiding studies examining disease of the human prostate.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ajhg.2015.04.015">doi:10.1016/j.ajhg.2015.04.015</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25983244">pmid:25983244</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4457953/">pmcid:PMC4457953</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mcwrrljoizdufefqd5a2ktgjhi">fatcat:mcwrrljoizdufefqd5a2ktgjhi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200206071848/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC4457953&amp;blobtype=pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/99/3a99cf07a889114cae44ee81900ee9c7f33e6855.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ajhg.2015.04.015"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457953" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
&laquo; Previous Showing results 1 &mdash; 15 out of 37 results