Software for rapid time dependent ChIP-sequencing analysis (TDCA)

BMC Bioinformatics

Software

Marco Farren-Dai¹  Tien-Jui Chuang²  Mike Myschyshyn²  David Vocadlo³ 

[1] Chemistry, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada;Department of Molecular Biology and Biochemistry, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada;Department of Molecular Biology and Biochemistry, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada;Chemistry, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada;

关键词: ChIP-seq;    Time course experiment;    Bioinformatics;    Protein-DNA binding kinetics;    Data modeling;    Curve fitting;    Statistical analysis;    Genomic feature correlations;   

DOI  :  10.1186/s12859-017-1936-x

 received in 2017-06-28, accepted in 2017-11-14,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundChromatin immunoprecipitation followed by DNA sequencing (ChIP-seq) and associated methods are widely used to define the genome wide distribution of chromatin associated proteins, post-translational epigenetic marks, and modifications found on DNA bases. An area of emerging interest is to study time dependent changes in the distribution of such proteins and marks by using serial ChIP-seq experiments performed in a time resolved manner. Despite such time resolved studies becoming increasingly common, software to facilitate analysis of such data in a robust automated manner is limited.ResultsWe have designed software called Time-Dependent ChIP-Sequencing Analyser (TDCA), which is the first program to automate analysis of time-dependent ChIP-seq data by fitting to sigmoidal curves. We provide users with guidance for experimental design of TDCA for modeling of time course (TC) ChIP-seq data using two simulated data sets. Furthermore, we demonstrate that this fitting strategy is widely applicable by showing that automated analysis of three previously published TC data sets accurately recapitulates key findings reported in these studies. Using each of these data sets, we highlight how biologically relevant findings can be readily obtained by exploiting TDCA to yield intuitive parameters that describe behavior at either a single locus or sets of loci. TDCA enables customizable analysis of user input aligned DNA sequencing data, coupled with graphical outputs in the form of publication-ready figures that describe behavior at either individual loci or sets of loci sharing common traits defined by the user. TDCA accepts sequencing data as standard binary alignment map (BAM) files and loci of interest in browser extensible data (BED) file format.ConclusionsTDCA accurately models the number of sequencing reads, or coverage, at loci from TC ChIP-seq studies or conceptually related TC sequencing experiments. TC experiments are reduced to intuitive parametric values that facilitate biologically relevant data analysis, and the uncovering of variations in the time-dependent behavior of chromatin. TDCA automates the analysis of TC ChIP-seq experiments, permitting researchers to easily obtain raw and modeled data for specific loci or groups of loci with similar behavior while also enhancing consistency of data analysis of TC data within the genomics field.

【 授权许可】

CC BY   
© The Author(s). 2017