Predicting gene expression in massively parallel reporter assays: A comparative study.
Predicting gene expression in massively parallel reporter assays: A comparative study.
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DOI：
10.1002/humu.23197
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发表时间：
2017-09
期刊：
影响因子：
3.9
通讯作者：
Yosef N
中科院分区：
文献类型：
--
作者：
Kreimer A;Zeng H;Edwards MD;Guo Y;Tian K;Shin S;Welch R;Wainberg M;Mohan R;Sinnott-Armstrong NA;Li Y;Eraslan G;Amin TB;Tewhey R;Sabeti PC;Goke J;Mueller NS;Kellis M;Kundaje A;Beer MA;Keles S;Gifford DK;Yosef N
在许多人类疾病中，相关的遗传变化往往发生在非编码区，其影响可能与转录控制有关。人类遗传学的一个中心目标是了解这些非编码区的功能：给定一个与基因表达变化（表达数量性状基因座; eQTL）统计相关的区域，它实际上是否起着调节作用？如果是这样，这个角色是如何在其序列中“编码”的？这些问题是基因组解释eQTL挑战的关键评估的主题。参与者被给予一组位于人类eQTL侧翼的序列，并被要求预测这些序列是否能够调节转录（通过大规模平行报告基因测定进行评估），以及这种能力是否在替代等位基因之间发生变化。在这里，我们报告从这一社区努力中吸取的教训。通过孤立地检查预测特性，并对竞争方法进行荟萃分析，我们发现使用染色质可及性和转录因子结合作为分类器或回归模型集合中的特征会导致最准确的结果。然后，我们描述了难以预测的位点，将重点放在薄弱领域，我们希望这将成为未来研究的主题。
In many human diseases, associated genetic changes tend to occur within non-coding regions, whose effect might be related to transcriptional control. A central goal in human genetics is to understand the function of such non-coding regions: Given a region that is statistically associated with changes in gene expression (expression Quantitative Trait Locus; eQTL), does it in fact play a regulatory role? And if so, how is this role “coded” in its sequence? These questions were the subject of the Critical Assessment of Genome Interpretation eQTL challenge. Participants were given a set of sequences that flank eQTLs in humans and were asked to predict whether these are capable of regulating transcription (as evaluated by massively parallel reporter assays), and whether this capability changes between alternative alleles. Here, we report lessons learned from this community effort. By inspecting predictive properties in isolation, and conducting meta-analysis over the competing methods, we find that using chromatin accessibility and transcription factor binding as features in an ensemble of classifiers or regression models leads to the most accurate results. We then characterize the loci that are harder to predict, putting the spotlight on areas of weakness, which we expect to be the subject of future studies.
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DOI：
10.1093/bioinformatics/btr064
发表时间：
2011-04-01
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Grant CE;Bailey TL;Noble WS
通讯作者：
Noble WS
来自1,092个人基因组的遗传变异的综合图。
影响因子：
64.8
作者：
通讯作者：
--
影响因子：
14.9
作者：
Kheradpour P;Kellis M
通讯作者：
Kellis M
影响因子：
64.8
作者：
Gertz, Jason;Siggia, Eric D.;Cohen, Barak A.
通讯作者：
Cohen, Barak A.
影响因子：
7
作者：
Inoue, Fumitaka;Kircher, Martin;Shendure, Jay
通讯作者：
Shendure, Jay