Genomic feature extraction and comparison based on global alignment of ChIP-sequencing data.
Enhanced accuracy and high-throughput capability in capturing genetic activities lead ChIP-sequencing technology to be applied prevalently in diverse study for tackling DNA-protein interaction problems. Till now, such questions as deciding suitable ChIP-seq arguments and comparing sample quality still haunt biologists. We propose the methods for answering such questions as deciding optimal argument pairs in global alignment of ChIP sequencing data; then we employ a modern signal processing approach to extract inherent genomic features from the global alignments of transcriptional binding activities; together with pairwise comparison from intra- and inter-sample perspectives; thus we can further determine alignment quality and decide the optimal candidate for multi-source heterogeneous high-throughput sequences. The work provides a practical approach to quantitatively compare the alignment quality for heterogeneous sequencing data, especially in determining the efficiency of transcriptional binding from replicate samples, thus it helps to exploit the potentiality of ChIP-seq for deep comprehension of inherent biological meanings from the high-throughput genomic sequences.
a Epigenetics & Function Group , College of the Internet of Things, Hohai University , Jiangsu , China.|b School of Public Health , Shanghai Jiao Tong University , Shanghai , China.
Binhua
B
Tang
D000465:Algorithms; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D002874:Chromosome Mapping / Q000379:methods*; D004247:DNA / Q000235:genetics*; D004268:DNA-Binding Proteins / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
D004268:DNA-Binding Proteins; D004247:DNA
ChIP-seq; comprehensive analysis; genomic feature; optimal argument
Proteins. 2010 May 15;78(7):1789-97
20196081
Mol Cell. 2006 Feb 3;21(3):393-404
16455494
Sci Rep. 2012;2:875
23166858
Nat Methods. 2007 Aug;4(8):613-4
17664943
IEEE/ACM Trans Comput Biol Bioinform. 2016 Jan-Feb;13(1):55-63
26886732
Science. 2012 Nov 16;338(6109):891-2
23161983
Nat Genet. 2011 Jan;43(1):11-2
21217639
Bioinformatics. 2012 Mar 1;28(5):607-13
22262674
Trends Genet. 2013 Jan;29(1):23-30
23103023
IEEE/ACM Trans Comput Biol Bioinform. 2012;9(2):580-91
22025761
Genome Biol. 2008;9(9):R137
18798982
Nat Rev Genet. 2012 Dec;13(12):840-52
23090257
Bioinformatics. 2008 Aug 1;24(15):1729-30
18599518
EMBO J. 2009 May 20;28(10):1418-28
19339991
Bioinformatics. 2011 Feb 1;27(3):428-30
21138948
BMC Bioinformatics. 2009 Sep 21;10:299
19772557
Nat Genet. 2011 Jan;43(1):27-33
21151129