TY - GEN
T1 - Power spectrum-based genomic feature extraction from high-throughput ChIP-seq sequences
AU - Tang, Binhua
AU - Zhou, Yufan
AU - Jin, Victor X.
N1 - Funding Information:
This work was supported by the Fundamental Research Funds for China Central Universities [grant number 2016B08914 to BHT] and Changzhou Science & Technology Program [grant number CE20155050 to BHT]. This work made use of the resources supported by the NSFC-Guangdong Mutual Funds for Super Computing Program (China), and the Open Cloud Consortium (OCC)-sponsored project resource, which supported in part by grants from Gordon and Betty Moore Foundation and the National Science Foundation (USA) and major contributions from OCC members.
Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - Due to its enhanced accuracy and high-throughput capability in capturing genetic activities, recently Next Generation Sequencing technology is being applied prevalently in biomedical study for tackling diverse topics. Within the work, we propose a computational method for answering such questions as deciding optimal argument pairs (peak number, p-value threshold, selected bin size and false discovery rate) from estrogen receptor α ChIP-seq data, and detecting corresponding transcription factor binding sites. We employ a signal processing-based approach to extract inherent genomic features from the identified transcription factor binding sites, which illuminates novel evidence for further analysis and experimental validation. Thus eventually we attempt to exploit the potentiality of ChIP-seq for deep comprehension of inherent biological meanings from the high-throughput genomic sequences.
AB - Due to its enhanced accuracy and high-throughput capability in capturing genetic activities, recently Next Generation Sequencing technology is being applied prevalently in biomedical study for tackling diverse topics. Within the work, we propose a computational method for answering such questions as deciding optimal argument pairs (peak number, p-value threshold, selected bin size and false discovery rate) from estrogen receptor α ChIP-seq data, and detecting corresponding transcription factor binding sites. We employ a signal processing-based approach to extract inherent genomic features from the identified transcription factor binding sites, which illuminates novel evidence for further analysis and experimental validation. Thus eventually we attempt to exploit the potentiality of ChIP-seq for deep comprehension of inherent biological meanings from the high-throughput genomic sequences.
KW - ChIP-seq
KW - Comprehensive analysis
KW - Genomic feature
KW - Optimal argument pair
KW - Transcription factor binding site
UR - http://www.scopus.com/inward/record.url?scp=84978811452&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84978811452&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42291-6_44
DO - 10.1007/978-3-319-42291-6_44
M3 - Conference contribution
AN - SCOPUS:84978811452
SN - 9783319422909
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 439
EP - 447
BT - Intelligent Computing Theories and Application - 12th International Conference, ICIC 2016, Proceedings
A2 - Premaratne, Prashan
A2 - Huang, De-Shuang
A2 - Bevilacqua, Vitoantonio
PB - Springer Verlag
T2 - 12th International Conference on Intelligent Computing Theories and Application, ICIC 2016
Y2 - 2 August 2016 through 5 August 2016
ER -