Research output: Contribution to journal › Article › peer-review
Argo_CUDA : Exhaustive GPU based approach for motif discovery in large DNA datasets. / Vishnevsky, Oleg V.; Bocharnikov, Andrey V.; Kolchanov, Nikolay A.
In: Journal of Bioinformatics and Computational Biology, Vol. 16, No. 1, 1740012, 01.02.2018.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Argo_CUDA
T2 - Exhaustive GPU based approach for motif discovery in large DNA datasets
AU - Vishnevsky, Oleg V.
AU - Bocharnikov, Andrey V.
AU - Kolchanov, Nikolay A.
N1 - Publisher Copyright: © 2018 World Scientific Publishing Europe Ltd.
PY - 2018/2/1
Y1 - 2018/2/1
N2 - The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches. Unfortunately, the efficiency of the motif discovery programs dramatically declines with the query set size increase. This leads to the fact that only a fraction of top “peak” ChIP-Seq segments can be analyzed or the area of analysis should be narrowed. Thus, the motif discovery in massive datasets remains a challenging issue. Argo_Compute Unified Device Architecture (CUDA) web service is designed to process the massive DNA data. It is a program for the detection of degenerate oligonucleotide motifs of fixed length written in 15-letter IUPAC code. Argo_CUDA is a full-exhaustive approach based on the high-performance GPU technologies. Compared with the existing motif discovery web services, Argo_CUDA shows good prediction quality on simulated sets. The analysis of ChIP-Seq sequences revealed the motifs which correspond to known transcription factor binding sites.
AB - The development of chromatin immunoprecipitation sequencing (ChIP-seq) technology has revolutionized the genetic analysis of the basic mechanisms underlying transcription regulation and led to accumulation of information about a huge amount of DNA sequences. There are a lot of web services which are currently available for de novo motif discovery in datasets containing information about DNA/protein binding. An enormous motif diversity makes their finding challenging. In order to avoid the difficulties, researchers use different stochastic approaches. Unfortunately, the efficiency of the motif discovery programs dramatically declines with the query set size increase. This leads to the fact that only a fraction of top “peak” ChIP-Seq segments can be analyzed or the area of analysis should be narrowed. Thus, the motif discovery in massive datasets remains a challenging issue. Argo_Compute Unified Device Architecture (CUDA) web service is designed to process the massive DNA data. It is a program for the detection of degenerate oligonucleotide motifs of fixed length written in 15-letter IUPAC code. Argo_CUDA is a full-exhaustive approach based on the high-performance GPU technologies. Compared with the existing motif discovery web services, Argo_CUDA shows good prediction quality on simulated sets. The analysis of ChIP-Seq sequences revealed the motifs which correspond to known transcription factor binding sites.
KW - ChIP-Seq
KW - Motif discovery
KW - oligonucleotide motif
KW - transcription regulation
KW - SEQUENCE MOTIFS
KW - INFORMATION-CONTENT
KW - FACTOR-BINDING PROFILES
KW - IDENTIFICATION
KW - NUCLEOTIDE
KW - CHIP-SEQ DATA
KW - ELEMENTS
KW - OPEN-ACCESS DATABASE
KW - SITES
KW - TOOL
KW - Chromatin Immunoprecipitation/methods
KW - Databases, Genetic
KW - Computational Biology/methods
KW - Algorithms
KW - Animals
KW - Nucleotide Motifs
KW - Transcription Factors/metabolism
KW - Hepatocyte Nuclear Factor 3-beta/genetics
KW - Mice
KW - Binding Sites
UR - http://www.scopus.com/inward/record.url?scp=85039543928&partnerID=8YFLogxK
U2 - 10.1142/S0219720017400121
DO - 10.1142/S0219720017400121
M3 - Article
C2 - 29281953
AN - SCOPUS:85039543928
VL - 16
JO - Journal of Bioinformatics and Computational Biology
JF - Journal of Bioinformatics and Computational Biology
SN - 0219-7200
IS - 1
M1 - 1740012
ER -
ID: 9399223