SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.
SeqGL Identifies Context-Dependent Binding Signals in Genome-Wide Regulatory Element Maps.
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DOI：
10.1371/journal.pcbi.1004271
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发表时间：
2015-05
影响因子：
4.3
通讯作者：
Leslie CS
中科院分区：
文献类型：
--
作者：
Setty M;Leslie CS
转录因子(Tf)占有率和开放染色质区域的全基因组图谱隐含着多种因子的DNA序列信号。我们提出了一种新的从头基序发现算法SeqGL，用于从ChIP-、DNase-和ATAC-SEQ谱中识别多个TF序列信号。SeqGL使用k-mer特征表示和组套索正则化一起训练判别模型，以提取区分峰序列和侧翼区域的序列信号的集合。以100多个芯片序列实验为基准，SeqGL在区分准确性方面优于传统的基序发现工具。此外，SeqGL可以自然地与多任务学习一起使用，以确定TF结合的基因组和细胞类型上下文决定因素。SeqGL成功地扩展到DNase-或ATAC-Seq图中的大量序列信号。特别是，SeqGL能够识别传统基序发现算法无法发现的大量芯片序列验证的序列信号。因此，与广泛使用的基序发现算法相比，SeqGL在检测调控元件图谱下的DNA序列信号方面表现出更高的判别准确性和更高的灵敏度。SeqGL可在http://cbio.mskcc.org/public/Leslie/SeqGL/.上获得转录调控是细胞控制基因表达的主要方式。转录因子是识别和结合特定DNA序列信号以调节靶基因表达的蛋白质。近年来，全基因组分析技术迅速发展，用来分析单个转铁蛋白的结合位置，或者更广泛地说，是被一组复杂的DNA结合因子占据的开放染色质区域。因此，需要新的方法来检测和表示这些全基因组调控元件图中的DNA序列信号。在这里，我们提出了一种名为SeqGL的新工具，用于从全基因组图谱中提取多个TF结合信号。SeqGL使用机器学习框架来识别最能区分峰的特征，我们预计DNA序列信号出现的地方，与不应该包含这些信号的侧翼区域。我们的工具在识别准确率上明显好于广泛使用的基序发现方法，并且在检测调控元件图谱背后的大量序列信号时获得了更高的灵敏度。
Genome-wide maps of transcription factor (TF) occupancy and regions of open chromatin implicitly contain DNA sequence signals for multiple factors. We present SeqGL, a novel de novo motif discovery algorithm to identify multiple TF sequence signals from ChIP-, DNase-, and ATAC-seq profiles. SeqGL trains a discriminative model using a k-mer feature representation together with group lasso regularization to extract a collection of sequence signals that distinguish peak sequences from flanking regions. Benchmarked on over 100 ChIP-seq experiments, SeqGL outperformed traditional motif discovery tools in discriminative accuracy. Furthermore, SeqGL can be naturally used with multitask learning to identify genomic and cell-type context determinants of TF binding. SeqGL successfully scales to the large multiplicity of sequence signals in DNase- or ATAC-seq maps. In particular, SeqGL was able to identify a number of ChIP-seq validated sequence signals that were not found by traditional motif discovery algorithms. Thus compared to widely used motif discovery algorithms, SeqGL demonstrates both greater discriminative accuracy and higher sensitivity for detecting the DNA sequence signals underlying regulatory element maps. SeqGL is available at http://cbio.mskcc.org/public/Leslie/SeqGL/. Transcriptional regulation is the cell’s primary mode of controlling gene expression. Transcription factors (TFs) are proteins that recognize and bind specific DNA sequence signals to regulate the expression of target genes. Recent years have seen the rapid development of genome-wide assays to profile the binding locations of a single TF or, more generally, regions of open chromatin that are occupied by a complex repertoire of DNA binding factors. New methods are therefore needed to detect and represent DNA sequence signals in these genome-wide regulatory element maps. Here we present a novel tool called SeqGL to extract multiple TF binding signals from genome-wide maps. SeqGL employs a machine learning framework to identify features that best discriminate the peaks, where we expect DNA sequence signals to occur, from the flank regions that should not contain these signals. Our tool performed significantly better than widely used motif discovery methods in discriminative accuracy and achieved higher sensitivity in detecting the numerous sequence signals underlying regulatory element maps.
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DOI：
10.1083/jcb.126.1.223
发表时间：
1994-07
期刊：
The Journal of cell biology
影响因子：
--
作者：
Nagy P;Bisgaard HC;Thorgeirsson SS
通讯作者：
Thorgeirsson SS
影响因子：
4.3
作者：
Agius, Phaedra;Arvey, Aaron;Leslie, Christina
通讯作者：
Leslie, Christina
影响因子：
16
作者：
Heinz S;Benner C;Spann N;Bertolino E;Lin YC;Laslo P;Cheng JX;Murre C;Singh H;Glass CK
通讯作者：
Glass CK
影响因子：
12.3
作者：
Georgiev S;Boyle AP;Jayasurya K;Ding X;Mukherjee S;Ohler U
通讯作者：
Ohler U
影响因子：
2.7
作者：
Bulger, Michael;Groudine, Mark
通讯作者：
Groudine, Mark