A framework for group-wise summarization and comparison of chromatin state annotations.
A framework for group-wise summarization and comparison of chromatin state annotations.
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DOI：
10.1093/bioinformatics/btac722
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发表时间：
2023-01-01
期刊：
影响因子：
5.8
通讯作者：
Ernst, Jason
中科院分区：
文献类型：
--
作者：
Vu, Ha;Koch, Zane;Fiziev, Petko;Ernst, Jason
表观遗传修饰的基因组图是多种表观遗传学标记的非编码基因组注释的强大资源。生物学上相似样品的地图，有必要有效地总结样本组中染色质状态注释的信息并识别在高分辨率的样品组之间的差异。
我们开发了CSREP，作为一组样品的输入染色质态注释，可能会估计每个基因组位置的状态，并为CSREP提供代表性的染色质状态图。预测每个样品的染色质状态分配，从所有样品中的状态图。组可用于使用染色质状态分配差异的基因组位置。高分辨率。
CSREP源代码和生成的数据可在http://github.com/ernstlab/csrep上获得。
补充数据可在线生物信息学上获得。
Genome-wide maps of epigenetic modifications are powerful resources for non-coding genome annotation. Maps of multiple epigenetics marks have been integrated into cell or tissue type-specific chromatin state annotations for many cell or tissue types. With the increasing availability of multiple chromatin state maps for biologically similar samples, there is a need for methods that can effectively summarize the information about chromatin state annotations within groups of samples and identify differences across groups of samples at a high resolution. We developed CSREP, which takes as input chromatin state annotations for a group of samples. CSREP then probabilistically estimates the state at each genomic position and derives a representative chromatin state map for the group. CSREP uses an ensemble of multi-class logistic regression classifiers that predict the chromatin state assignment of each sample given the state maps from all other samples. The difference in CSREP’s probability assignments for the two groups can be used to identify genomic locations with differential chromatin state assignments. Using groups of chromatin state maps of a diverse set of cell and tissue types, we demonstrate the advantages of using CSREP to summarize chromatin state maps and identify biologically relevant differences between groups at a high resolution. The CSREP source code and generated data are available at http://github.com/ernstlab/csrep. Supplementary data are available at Bioinformatics online.
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DOI：
10.1073/pnas.1204398110
发表时间：
2013-04-23
影响因子：
11.1
作者：
Ji, Hongkai;Li, Xia;Ning, Yang
通讯作者：
Ning, Yang
DOI：
10.1093/bioinformatics/btq033
发表时间：
2010-03-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Quinlan AR;Hall IM
通讯作者：
Hall IM
影响因子：
64.8
作者：
Ernst, Jason;Kheradpour, Pouya;Mikkelsen, Tarjei S.;Shoresh, Noam;Ward, Lucas D.;Epstein, Charles B.;Zhang, Xiaolan;Wang, Li;Issner, Robbyn;Coyne, Michael;Ku, Manching;Durham, Timothy;Kellis, Manolis;Bernstein, Bradley E.
通讯作者：
Bernstein, Bradley E.
影响因子：
64.8
作者：
Boix CA;James BT;Park YP;Meuleman W;Kellis M
通讯作者：
Kellis M
影响因子：
64.5
作者：
Xie W;Schultz MD;Lister R;Hou Z;Rajagopal N;Ray P;Whitaker JW;Tian S;Hawkins RD;Leung D;Yang H;Wang T;Lee AY;Swanson SA;Zhang J;Zhu Y;Kim A;Nery JR;Urich MA;Kuan S;Yen CA;Klugman S;Yu P;Suknuntha K;Propson NE;Chen H;Edsall LE;Wagner U;Li Y;Ye Z;Kulkarni A;Xuan Z;Chung WY;Chi NC;Antosiewicz-Bourget JE;Slukvin I;Stewart R;Zhang MQ;Wang W;Thomson JA;Ecker JR;Ren B
通讯作者：
Ren B