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Missing Data and Technical Variability in Single-Cell RNA- Sequencing Experiments [article]

Stephanie C Hicks, F. William Townes, Mingxiang Teng, Rafael A Irizarry
<span title="2015-08-25">2015</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Until recently, high-throughput gene expression technology, such as RNA-Sequencing (RNA-seq) required hundreds of thousands of cells to produce reliable measurements. Recent technical advances permit genome-wide gene expression measurement at the single-cell level. Single-cell RNA-Seq (scRNA-seq) is the most widely used and numerous publications are based on data produced with this technology. However, RNA-Seq and scRNA-seq data are markedly different. In particular, unlike RNA-Seq, the
more &raquo; ... of reported expression levels in scRNA-seq are zeros, which could be either biologically-driven, genes not expressing RNA at the time of measurement, or technically-driven, gene expressing RNA, but not at a sufficient level to detected by sequencing technology. Another difference is that the proportion of genes reporting the expression level to be zero varies substantially across single cells compared to RNA-seq samples. However, it remains unclear to what extent this cell-to-cell variation is being driven by technical rather than biological variation. Furthermore, while systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies, these issues have received minimal attention in published studies based on scRNA-seq technology. Here, we use an assessment experiment to examine data from published studies and demonstrate that systematic errors can explain a substantial percentage of observed cell-to-cell expression variability. Specifically, we present evidence that some of these reported zeros are driven by technical variation by demonstrating that scRNA-seq produces more zeros than expected and that this bias is greater for lower expressed genes. In addition, this missing data problem is exacerbated by the fact that this technical variation varies cell-to-cell. Then, we show how this technical cell-to-cell variability can be confused with novel biological results. Finally, we demonstrate and discuss how batch-effects and confounded experiments can intensify the problem.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/025528">doi:10.1101/025528</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gfvgzdkj7vcwjj2od5tgoydvsm">fatcat:gfvgzdkj7vcwjj2od5tgoydvsm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170921213050/https://www.biorxiv.org/content/biorxiv/early/2015/12/27/025528.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] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/025528"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-Seq data [article]

Mingxiang Teng, Rafael A. Irizarry
<span title="2016-11-30">2016</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The main application of ChIP-seq technology is the detection of genomic regions that bind to a protein of interest. A large part of functional genomics public catalogs are based on ChIP-seq data. These catalogs rely on peak calling algorithms that infer protein-binding sites by detecting genomic regions associated with more mapped reads (coverage) than expected by chance as a result of the experimental protocol's lack of perfect specificity. We find that GC-content bias accounts for substantial
more &raquo; ... variability in the observed coverage for ChIP-Seq experiments and that this variability leads to false-positive peak calls. More concerning is that GC-effect varies across experiments, with the effect strong enough to result in a substantial number of peaks called differently when different laboratories perform experiments on the same cell-line. However, accounting for GC-content in ChIP-Seq is challenging because the binding sites of interest tend to be more common in high GC- content regions, which confounds real biological signal with the unwanted variability. To account for this challenge we introduce a statistical approach that accounts for GC-effects on both non-specific noise and signal induced by the binding site. The method can be used to account for this bias in binding quantification as well to improve existing peak calling algorithms. We use this approach to show a reduction in false positive peaks as well as improved consistency across laboratories.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/090704">doi:10.1101/090704</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hqbtuesw6fckpnpfwqj537rhum">fatcat:hqbtuesw6fckpnpfwqj537rhum</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190502022531/https://www.biorxiv.org/content/biorxiv/early/2017/01/15/090704.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/5a/9a/5a9af9061ad665037faa6794e2278f537b913bb0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/090704"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Missing data and technical variability in single-cell RNA-sequencing experiments

Stephanie C Hicks, F William Townes, Mingxiang Teng, Rafael A Irizarry
<span title="2017-11-06">2017</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/d2xjsctbnvayjlur7x2f46qpxy" style="color: black;">Biostatistics</a> </i> &nbsp;
Single-cell RNA-Sequencing (scRNA-Seq) has become the most widely used high-throughput method for transcription profiling of individual cells. Systematic errors, including batch effects, have been widely reported as a major challenge in high-throughput technologies. Surprisingly, these issues have received minimal attention in published studies based on scRNA-Seq technology. We examined data from all fifteen published studies including at least 200 samples and found that systematic errors can
more &raquo; ... plain a substantial percentage of observed cell-to-cell expression variability. Specifically, we found that the proportion of genes reported as expressed explains a substantial part of observed variability and that this quantity varies systematically across experimental batches. Furthermore, we found that experimental designs that confound outcomes of interest with batch effects are common. Finally, we propose a simple experimental design that can ameliorate the effect of theses systematic errors have on downstream results.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/biostatistics/kxx053">doi:10.1093/biostatistics/kxx053</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29121214">pmid:29121214</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lc57lgl6qzcmfnvtkkms5jqzdm">fatcat:lc57lgl6qzcmfnvtkkms5jqzdm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170921213050/https://www.biorxiv.org/content/biorxiv/early/2015/12/27/025528.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] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/biostatistics/kxx053"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a>

Chromatin structure characteristics of pre-miRNA genomic sequences

Shijia Zhu, Qinghua Jiang, Guohua Wang, Bo Liu, Mingxiang Teng, Yadong Wang
<span title="2011-06-25">2011</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4srzxifvfrdlhjhg3dimznkp7m" style="color: black;">BMC Genomics</a> </i> &nbsp;
MicroRNAs (miRNAs) are non-coding RNAs with important roles in regulating gene expression. Recent studies indicate that transcription and cleavage of miRNA are coupled, and that chromatin structure may influence miRNA transcription. However, little is known about the relationship between the chromatin structure and cleavage of pre-miRNA from pri-miRNA. Results: By analysis of genome-wide nucleosome positioning data sets from human and Caenorhabditis elegans (C. elegans), we found an enrichment
more &raquo; ... f positioned nucleosome on pre-miRNA genomic sequences, which is highly correlated with GC content within pre-miRNA. In addition, obvious enrichments of three histone modifications (H2BK5me1, H3K36me3 and H4K20me1) as well as RNA Polymerase II (RNAPII) were observed on pre-miRNA genomic sequences corresponding to the active-promoter miRNAs and expressed miRNAs. Conclusion: Our results revealed the chromatin structure characteristics of pre-miRNA genomic sequences, and implied potential mechanisms that can recognize these characteristics, thus improving pre-miRNA cleavage.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1471-2164-12-329">doi:10.1186/1471-2164-12-329</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/21702984">pmid:21702984</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3135579/">pmcid:PMC3135579</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p6c6qwhhirajbmxtel6fna5whm">fatcat:p6c6qwhhirajbmxtel6fna5whm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170830135415/https://bmcgenomics.biomedcentral.com/track/pdf/10.1186/1471-2164-12-329?site=http://bmcgenomics.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/e8/00/e8005e5efa1b9b8d9c662f6f901c62c0a34b0d96.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1471-2164-12-329"> <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/PMC3135579" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Editorial: System Biology Methods and Tools for Integrating Omics Data

Liang Cheng, Lei Deng, Mingxiang Teng
<span title="2020-11-12">2020</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/r7trx2kj6je5jhtaoy3rztibgy" style="color: black;">Frontiers in Genetics</a> </i> &nbsp;
Copyright © 2020 Cheng, Deng and Teng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2020.563108">doi:10.3389/fgene.2020.563108</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33281868">pmid:33281868</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC7689002/">pmcid:PMC7689002</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pn2budkix5hilaowdyx65f4hqy">fatcat:pn2budkix5hilaowdyx65f4hqy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201113042425/https://fjfsdata01prod.blob.core.windows.net/articles/files/563108/pubmed-zip/.versions/1/.package-entries/fgene-11-563108/fgene-11-563108.pdf?sv=2018-03-28&amp;sr=b&amp;sig=JDL%2B%2BfwN7usjC2KOwYQUl8pIPGdrVtdSpt8cB%2FF%2FIe8%3D&amp;se=2020-11-13T04%3A24%3A54Z&amp;sp=r&amp;rscd=attachment%3B%20filename%2A%3DUTF-8%27%27fgene-11-563108.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/92/ac/92ac95cbdc928ed1ba7ac7bb1f10af04b076f4e9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fgene.2020.563108"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689002" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A practical guide to methods controlling false discoveries in computational biology [article]

Keegan Korthauer, Patrick K Kimes, Claire Duvallet, Alejandro Reyes, Ayshwarya Subramanian, Mingxiang Teng, Chinmay Shukla, Eric J Alm, Stephanie C Hicks
<span title="2018-10-31">2018</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In high-throughput studies, hundreds to millions of hypotheses are typically tested. Statistical methods that control the false discovery rate (FDR) have emerged as popular and powerful tools for error rate control. While classic FDR methods use only p-values as input, more modern FDR methods have been shown to increase power by incorporating complementary information as "informative covariates" to prioritize, weight, and group hypotheses. However, there is currently no consensus on how the
more &raquo; ... rn methods compare to one another. We investigated the accuracy, applicability, and ease of use of two classic and six modern FDR-controlling methods by performing a systematic benchmark comparison using simulation studies as well as six case studies in computational biology. Methods that incorporate informative covariates were modestly more powerful than classic approaches, and did not underperform classic approaches, even when the covariate was completely uninformative. The majority of methods were successful at controlling the FDR, with the exception of two modern methods under certain settings. Furthermore, we found the improvement of the modern FDR methods over the classic methods increased with the informativeness of the covariate, total number of hypothesis tests, and proportion of truly non-null hypotheses. Modern FDR methods that use an informative covariate provide advantages over classic FDR-controlling procedures, with the relative gain dependent on the application and informativeness of available covariates. We present our findings as a practical guide and provide recommendations to aid researchers in their choice of methods to correct for false discoveries.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/458786">doi:10.1101/458786</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uaknu7waz5h3dd5ouf3zlnhefq">fatcat:uaknu7waz5h3dd5ouf3zlnhefq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190501144545/https://www.biorxiv.org/content/biorxiv/early/2018/10/31/458786.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/1b/19/1b1945e40609f3f04101b02bc362c0d54fda190b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/458786"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Accounting for GC-content bias reduces systematic errors and batch effects in ChIP-seq data

Mingxiang Teng, Rafael A. Irizarry
<span title="2017-10-12">2017</span> <i title="Cold Spring Harbor Laboratory"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nwro4atxwbec5pfkpfslaxbdae" style="color: black;">Genome Research</a> </i> &nbsp;
www.genome.orgCold Spring Harbor Laboratory Press on July 20, 2018 -Published by genome.cshlp.org Downloaded from © 2017 Teng and Irizarry; Published by Cold Spring Harbor Laboratory Press Cold Spring  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/gr.220673.117">doi:10.1101/gr.220673.117</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29025895">pmid:29025895</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5668949/">pmcid:PMC5668949</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sczfwxdxdjdalg3a2os3gh3qwy">fatcat:sczfwxdxdjdalg3a2os3gh3qwy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180721023237/https://genome.cshlp.org/content/27/11/1930.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/de/2f/de2f850ac1a9a8e751e6516a664c06f544156f0f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/gr.220673.117"> <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/PMC5668949" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Prioritizing single-nucleotide variations that potentially regulate alternative splicing

Mingxiang Teng, Yadong Wang, Guohua Wang, Jeesun Jung, Howard J Edenberg, Jeremy R Sanford, Yunlong Liu
<span title="">2011</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/noie53zo7fdytpet5j552fhwny" style="color: black;">BMC Proceedings</a> </i> &nbsp;
Loss binding Q1 C3S4860 BCHE HuR Loss binding Q2 C3S4874 BCHE SLM2 Gain binding Q2 C8S1799 PLAT RBM4/Vts1 Loss/loss binding Q2 C6S5449 VNN3 RBM4 Gain binding Q2 © 2011 Teng  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1753-6561-5-s9-s40">doi:10.1186/1753-6561-5-s9-s40</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/22373210">pmid:22373210</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3287877/">pmcid:PMC3287877</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oprpxy2a2zekvkfdked6ieor6e">fatcat:oprpxy2a2zekvkfdked6ieor6e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170923015922/https://bmcproc.biomedcentral.com/track/pdf/10.1186/1753-6561-5-S9-S40?site=bmcproc.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/ef/69/ef69a420162bee15f30bf66bdc739eaca0817839.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1753-6561-5-s9-s40"> <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/PMC3287877" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Histone loaders CAF1 and HIRA restrict Epstein-Barr virus B-cell lytic reactivation [article]

Yuchen Zhang, Chang Jiang, Stephen J. Trudeau, Yohei Narita, Bo Zhao, Mingxiang Teng, Rui Guo, Benjamin Gewurz
<span title="2020-04-30">2020</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Epstein-Barr virus (EBV) infects 95% of adults worldwide and causes infectious mononucleosis. EBV is associated with endemic Burkitt lymphoma, Hodgkin lymphoma, post-transplant lymphomas, nasopharyngeal and gastric carcinomas. In these cancers and in most infected B-cells, EBV maintains a state of latency, where nearly 80 lytic cycle antigens are epigenetically suppressed. To gain insights into host epigenetic factors necessary for EBV latency, we recently performed a human genome-wide CRISPR
more &raquo; ... reen that identified the chromatin assembly factor CAF1 as a putative Burkitt latency maintenance factor. CAF1 loads histones H3 and H4 onto newly synthesized host DNA, though its roles in EBV genome chromatin assembly are uncharacterized. Here, we identified that CAF1 depletion triggered lytic reactivation and transforming virion secretion from Burkitt cells, despite strongly also inducing interferon stimulated genes. CAF1 perturbation diminished occupancy of histones 3.1, 3.3 and repressive H3K9me3 and H3K27me3 marks at multiple viral genome lytic cycle regulatory elements. Suggestive of an early role in establishment of latency, EBV strongly upregulated CAF1 expression in newly infected primary human B-cells prior to the first mitosis, and histone 3.1 and 3.3 were loaded on the EBV genome by this timepoint. Knockout of CAF1 subunit CHAF1B impaired establishment of latency in newly EBV-infected Burkitt cells. A non-redundant latency maintenance role was also identified for the DNA synthesis-independent histone 3.3 loader HIRA. Since EBV latency also requires histone chaperones ATRX and DAXX, EBV coopts multiple host histone pathways to maintain latency, and these are potential targets for lytic induction therapeutic approaches.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.04.28.067371">doi:10.1101/2020.04.28.067371</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/76ss4ddpabghzgsl4lc2vc4lie">fatcat:76ss4ddpabghzgsl4lc2vc4lie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200711163032/https://www.biorxiv.org/content/biorxiv/early/2020/04/30/2020.04.28.067371.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/e1/42/e1421ef7bc3a803d1a89e740e9d5e330977db2a6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2020.04.28.067371"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Characterizing batch effects and binding site-specific variability in ChIP-seq data

Mingxiang Teng, Dongliang Du, Danfeng Chen, Rafael A Irizarry
<span title="2021-10-04">2021</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dhm4lwu4tvbf5dklpg7zonzosa" style="color: black;">NAR Genomics and Bioinformatics</a> </i> &nbsp;
Multiple sources of variability can bias ChIP-seq data toward inferring transcription factor (TF) binding profiles. As ChIP-seq datasets increase in public repositories, it is now possible and necessary to account for complex sources of variability in ChIP-seq data analysis. We find that two types of variability, the batch effects by sequencing laboratories and differences between biological replicates, not associated with changes in condition or state, vary across genomic sites. This implies
more &raquo; ... at observed differences between samples from different conditions or states, such as cell-type, must be assessed statistically, with an understanding of the distribution of obscuring noise. We present a statistical approach that characterizes both differences of interests and these source of variability through the parameters of a mixed effects model. We demonstrate the utility of our approach on a CTCF binding dataset composed of 211 samples representing 90 different cell-types measured across three different laboratories. The results revealed that sites exhibiting large variability were associated with sequence characteristics such as GC-content and low complexity. Finally, we identified TFs associated with high-variance CTCF sites using TF motifs documented in public databases, pointing the possibility of these being false positives if the sources of variability are not properly accounted for.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nargab/lqab098">doi:10.1093/nargab/lqab098</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/34661103">pmid:34661103</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC8515842/">pmcid:PMC8515842</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6ipv6kx7fvds3gi63frvpwgsym">fatcat:6ipv6kx7fvds3gi63frvpwgsym</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211020221531/https://watermark.silverchair.com/lqab098.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAsswggLHBgkqhkiG9w0BBwagggK4MIICtAIBADCCAq0GCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMFjnMJ19lhjIVVT7bAgEQgIICfroCtz4yM1b0D74lc3cKswqBG2GfVCjwYInqBPm1RDrT0BAAFNAAWOahmN9HPp4JOWOgu47fCQHo8heBTIY77Q-gupeeOMEEi0nsAV5oN_iuTfzN_TTzTCtkut47dKizFxkGO-ayDU7xcyezVgRxFTb4M0YA7HT8AXaekNUaOWLAPVpjJQqYTgAmFB-KLvQfEtPQdtMYgvNCq63M37HTS8-yk-qWcFhntT0CjataCPxtzQ1ViAk65bNcsWMcRyHHOUme2Ad6C7advxv5NOQ0L8UwFdLozPgDg-Gh7OksvAqF99uhZ2B3-8qhA6xsqUb_-rFlHsEqojbPXZ7Fkgb08bQTlGW9roQhhR0jKbEs-j8GcSlaq2dwla2Gd3V3vCr8ukSrCoGsJMbKgyafv4JNsNb-Euv7fAuNKCyH-Nc9mGpr0pWn_hzZLhLPOG0Sa--mQ69Zri6N5QaBwOVgAdOjZuCYH8i6yxyOfj5CRQqszLc9m-UEzdqhyGvVsS99VVUl74iylMCddSU6p5TOU-f3E2T06YPpFivqKVrxFiOn-WwH2GNZD8HShkLnaLgmii10S_FuA8HCebEvhfsBMXcGma_BXINKqf_PEZ419ofODHv-wcDe2l5SY8M2Hp8O2I1dZTZw7kBKj5G_iqDgvJtkUaNmnWLB2g1drmTMtXW93ZlN6Ozse-CaQqgLj3YhlYBFUBjt5jHRr-fN1rN-Fd2A6pdYEhiZEfRWKbE0ziOPrvR38X-rtXMRObAHS3NDsy0ij15aCQSqQr2QjZo6Cff3nylgHfpqa5QdaWHNB_aPD2srZmErSlImEU2fG7kPw1o7Xf2EKn3LSQl2J3GkPyxX" 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/6b/24/6b2478d40a52e825176d0554a84211788e7dbe8c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nargab/lqab098"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8515842" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Size-Controlled Graphene Nanodot Arrays/ZnO Hybrids for High-Performance UV Photodetectors

Ruidie Tang, Sancan Han, Feng Teng, Kai Hu, Zhiming Zhang, Mingxiang Hu, Xiaosheng Fang
<span title="2017-11-17">2017</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ifj4v2fyuvegzgqkhtt4dthbna" style="color: black;">Advanced Science</a> </i> &nbsp;
optoelectronic devices. [2] Many researches have been conducted to open the band gap by fabricating nanostructures such as graphene nanodots (GNDs), [3] graphene nanomeshes, [4] and graphene nanoribbons. [5] Compared with graphene, GNDs have excellent optical and electrical properties, which are tunable depending on size and shape, [6] thus holding promising application prospects in photoelectric devices. [7] The optoelectronic properties of GNDs are highly size dependable [8] and it has been
more &raquo; ... monstrated that patterning periodic structures will influence the optical fields in graphene, [9] so it is critical to control the size and alignment of GNDs for exploring the graphene nanodots-based electro-optical devices. Many methods have been tried to fabricate GNDs, such as Hummers method, [10] hydrothermal process, [11] and electrochemical method, [12] but it is difficult to accurately control the size and orderly alignment of GNDs using these methods. Here lithography based on reactive ion etching (RIE) with polystyrene (PS)-nanosphere (NS) as template is employed to fabricate uniformly sized graphene nanodots array (GNDA) directly from large-scale graphene as illustrated in Scheme 1. The size of GNDA could be controlled by O 2 -plasma etching time. UV photodetectors (PDs) have great value and a wide range of applications, including communications, flame detection, optical imaging, ozone hole monitoring, and air and water sterilization. [13] Among the variety of materials, ZnO as an environmentally friendly semiconductor has drawn great attention for UV detector because of its wide band gap (≈3.37 eV at room temperature), low cost, and easy fabrication. [14] It is reported that monolayer graphene film [15] and reduced graphene oxide [16] have been introduced to combine with ZnO nanostructures to enhance performance ZnO-based photodetectors. But as to our knowledge, there is no reported research that reveals the function of GNDA in photodetector. [16, 17] In order to explore the application potential of GNDA in photodetector, GNDA was combined with ZnO nanofilm to fabricate ZnO/GNDA hybrids UV photodetector, of which the performance were compared with ZnO photodetector (Scheme 2). The photodetection performance of ZnO/GNDA PD was highly GNDA size-dependent. ZnO/GNDA (20 nm) PD and ZnO/GNDA (30 nm) PD exhibited better performance than ZnO PD. Graphene nanodots (GNDs) are one of the most attractive graphene nanostructures due to their tunable optoelectronic properties. Fabricated by polystyrene-nanosphere lithography, uniformly sized graphene nanodots array (GNDA) is constructed as an ultraviolet photodetector (PD) with ZnO nanofilm spin coated on it. The size of GNDA can be well controlled from 45 to 20 nm varying the etching time. It is revealed in the study that the photoelectric properties of ZnO/GNDA PD are highly GNDA size-dependent. The highest responsivity (R) and external quantum efficiency of ZnO/GNDA (20 nm) PD are 22.55 mA W −1 and 9.32%, almost twofold of that of ZnO PD. Both ZnO/GNDA (20 nm) PD and ZnO/GNDA (30 nm) PD exhibit much faster response speed under on/off switching light and have shorter rise/ decay time compared with ZnO PD. However, as the size of GNDA increase to 45 nm, the PD appears poor performance. The size-dependent phenomenon can be explained by the energy band alignments in ZnO/GNDA hybrids. These efforts reveal the enhancement of GNDs on traditional photodetectors with tunable optoelectronic properties and hold great potential to pave a new way to explore the various remarkable photodetection performances by controlling the size of the nanostructures. Photodetectors
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/advs.201700334">doi:10.1002/advs.201700334</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/29375965">pmid:29375965</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC5770666/">pmcid:PMC5770666</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/o4sbw3dmyrhqbpr62pllkoje4e">fatcat:o4sbw3dmyrhqbpr62pllkoje4e</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200512180401/http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5770666&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/5b/23/5b236940c2e68e4ae090d06656cffc2099e5da72.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/advs.201700334"> <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/PMC5770666" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Prioritization of disease microRNAs through a human phenome-microRNAome network

Qinghua Jiang, Yangyang Hao, Guohua Wang, Liran Juan, Tianjiao Zhang, Mingxiang Teng, Yunlong Liu, Yadong Wang
<span title="">2010</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xua5vbjwszdirewaoqnikiu5zm" style="color: black;">BMC Systems Biology</a> </i> &nbsp;
The identification of disease-related microRNAs is vital for understanding the pathogenesis of diseases at the molecular level, and is critical for designing specific molecular tools for diagnosis, treatment and prevention. Experimental identification of disease-related microRNAs poses considerable difficulties. Computational analysis of microRNA-disease associations is an important complementary means for prioritizing microRNAs for further experimental examination. Results: Herein, we devised
more &raquo; ... computational model to infer potential microRNA-disease associations by prioritizing the entire human microRNAome for diseases of interest. We tested the model on 270 known experimentally verified microRNA-disease associations and achieved an area under the ROC curve of 75.80%. Moreover, we demonstrated that the model is applicable to diseases with which no known microRNAs are associated. The microRNAome-wide prioritization of microRNAs for 1,599 disease phenotypes is publicly released to facilitate future identification of disease-related microRNAs. Conclusions: We presented a network-based approach that can infer potential microRNA-disease associations and drive testable hypotheses for the experimental efforts to identify the roles of microRNAs in human diseases.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1752-0509-4-s1-s2">doi:10.1186/1752-0509-4-s1-s2</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/20522252">pmid:20522252</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC2880408/">pmcid:PMC2880408</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4ycz4tte6jadhap66gxx57cvzq">fatcat:4ycz4tte6jadhap66gxx57cvzq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170814145601/https://genomebiology.biomedcentral.com/track/pdf/10.1186/1752-0509-4-S1-S2?site=genomebiology.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/9b/67/9b67cd25e0d166401817de5b63f048a4f6b1be3f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1752-0509-4-s1-s2"> <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/PMC2880408" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Identification of regulatory regions of bidirectional genes in cervical cancer

Guohua Wang, Ke Qi, Yuming Zhao, Yu Li, Liran Juan, Mingxiang Teng, Lang Li, Yunlong Liu, Yadong Wang
<span title="">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/rbtzjpig4jcsfbjr5yo23sjnj4" style="color: black;">BMC Medical Genomics</a> </i> &nbsp;
Bidirectional promoters are shared promoter sequences between divergent gene pair (genes proximal to each other on opposite strands), and can regulate the genes in both directions. In the human genome, > 10% of protein-coding genes are arranged head-to-head on opposite strands, with transcription start sites that are separated by < 1,000 base pairs. Many transcription factor binding sites occur in the bidirectional promoters that influence the expression of 2 opposite genes. Recently, RNA
more &raquo; ... rase II (RPol II) ChIP-seq data are used to identify the promoters of coding genes and non-coding RNAs. However, a bidirectional promoter with RPol II ChIP-Seq data has not been found. Results: In some bidirectional promoter regions, the RPol II forms a bi-peak shape, which indicates that 2 promoters are located in the bidirectional region. We have developed a computational approach to identify the regulatory regions of all divergent gene pairs using genome-wide RPol II binding patterns derived from ChIP-seq data, based upon the assumption that the distribution of RPol II binding patterns around the bidirectional promoters are accumulated by RPol II binding of 2 promoters. In HeLa S3 cells, 249 promoter pairs and 1094 single promoters were identified, of which 76 promoters cover only positive genes, 86 promoters cover only negative genes, and 932 promoters cover 2 genes. Gene expression levels and STAT1 binding sites for different promoter categories were therefore examined. Conclusions: The regulatory region of bidirectional promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription. From gene expression and transcription factor binding site analysis, the promoters in bidirectional regions may regulate the closest gene, and STAT1 is involved in primary promoter.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1755-8794-6-s1-s5">doi:10.1186/1755-8794-6-s1-s5</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/23369456">pmid:23369456</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3552671/">pmcid:PMC3552671</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/str2v2unsrhwznfvulypqr4cme">fatcat:str2v2unsrhwznfvulypqr4cme</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170814091218/https://genomebiology.biomedcentral.com/track/pdf/10.1186/1755-8794-6-S1-S5?site=genomebiology.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/b6/93/b6939e40602506e6ba2dc29560268a3f09c0ad2f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1186/1755-8794-6-s1-s5"> <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/PMC3552671" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

The personal genome browser: visualizing functions of genetic variants

Liran Juan, Mingxiang Teng, Tianyi Zang, Yafeng Hao, Zhenxing Wang, Chengwu Yan, Yongzhuang Liu, Jie Li, Tianjiao Zhang, Yadong Wang
<span title="2014-05-05">2014</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hfp6p6inqbdexbsu4r7usndpte" style="color: black;">Nucleic Acids Research</a> </i> &nbsp;
Advances in high-throughput sequencing technologies have brought us into the individual genome era. Projects such as the 1000 Genomes Project have led the individual genome sequencing to become more and more popular. How to visualize, analyse and annotate individual genomes with knowledge bases to support genome studies and personalized healthcare is still a big challenge. The Personal Genome Browser (PGB) is developed to provide comprehensive functional annotation and visualization for
more &raquo; ... al genomes based on the genetic-molecular-phenotypic model. Investigators can easily view individual genetic variants, such as single nucleotide variants (SNVs), INDELs and structural variations (SVs), as well as genomic features and phenotypes associated to the individual genetic variants. The PGB especially highlights potential functional variants using the PGB built-in method or SIFT/PolyPhen2 scores. Moreover, the functional risks of genes could be evaluated by scanning individual genetic variants on the whole genome, a chromosome, or a cytoband based on functional implications of the variants. Investigators can then navigate to high risk genes on the scanned individual genome. The PGB accepts Variant Call Format (VCF) and Genetic Variation Format (GVF) files as the input. The functional annotation of input individual genome variants can be visualized in real time by well-defined symbols and shapes. The PGB is available at http://www.pgbrowser.org/.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nar/gku361">doi:10.1093/nar/gku361</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24799434">pmid:24799434</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4086072/">pmcid:PMC4086072</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dl5rp2tthfea7ldq67k7vgzh3y">fatcat:dl5rp2tthfea7ldq67k7vgzh3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190228171109/http://pdfs.semanticscholar.org/95bd/31556325fc200aece7047f83912a9f479769.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/95/bd/95bd31556325fc200aece7047f83912a9f479769.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/nar/gku361"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086072" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

The Influence of cis-Regulatory Elements on DNA Methylation Fidelity

Mingxiang Teng, Curt Balch, Yunlong Liu, Meng Li, Tim H. M. Huang, Yadong Wang, Kenneth P. Nephew, Lang Li, Michael Freitag
<span title="2012-03-06">2012</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;
It is now established that, as compared to normal cells, the cancer cell genome has an overall inverse distribution of DNA methylation ("methylome"), i.e., predominant hypomethylation and localized hypermethylation, within "CpG islands" (CGIs). Moreover, although cancer cells have reduced methylation "fidelity" and genomic instability, accurate maintenance of aberrant methylomes that underlie malignant phenotypes remains necessary. However, the mechanism(s) of cancer methylome maintenance
more &raquo; ... s largely unknown. Here, we assessed CGI methylation patterns propagated over 1, 3, and 5 divisions of A2780 ovarian cancer cells, concurrent with exposure to the DNA cross-linking chemotherapeutic cisplatin, and observed cell generation-successive increases in total hyper-and hypo-methylated CGIs. Empirical Bayesian modeling revealed five distinct modes of methylation propagation: (1) heritable (i.e., unchanged) high-methylation (1186 probe loci in CGI microarray); (2) heritable (i.e., unchanged) low-methylation (286 loci); (3) stochastic hypermethylation (i.e., progressively increased, 243 loci); (4) stochastic hypomethylation (i.e., progressively decreased, 247 loci); and (5) considerable "random" methylation (582 loci). These results support a "stochastic model" of DNA methylation equilibrium deriving from the efficiency of two distinct processes, methylation maintenance and de novo methylation. A role for cis-regulatory elements in methylation fidelity was also demonstrated by highly significant (p,2.2610 25 ) enrichment of transcription factor binding sites in CGI probe loci showing heritably high (118 elements) and low (47 elements) methylation, and also in loci demonstrating stochastic hyper-(30 elements) and hypo-(31 elements) methylation. Notably, loci having "random" methylation heritability displayed nearly no enrichment. These results demonstrate an influence of cis-regulatory elements on the nonrandom propagation of both strictly heritable and stochastically heritable CGIs.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1371/journal.pone.0032928">doi:10.1371/journal.pone.0032928</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/22412954">pmid:22412954</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3295790/">pmcid:PMC3295790</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bq3yefp6hjdepakdbpgdnb7jeu">fatcat:bq3yefp6hjdepakdbpgdnb7jeu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20171003080059/http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0032928&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/59/f3/59f33ccc069b5c9553684b2234176c73444b936e.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.0032928"> <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/PMC3295790" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>
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