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Logo of bmcgenoBioMed Centralsearchsubmit a manuscriptregisterthis articleBMC Genomics
 
BMC Genomics. 2009; 10: 618.
Published online Dec 18, 2009. doi:  10.1186/1471-2164-10-618
PMCID: PMC2804666
A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments
Teemu D Laajala,1 Sunil Raghav,1 Soile Tuomela,1,2 Riitta Lahesmaa,#1,3 Tero Aittokallio,#1,4 and Laura L Elocorresponding author1,4
1Turku Centre for Biotechnology, FI-20521 Turku, Finland
2Turku Graduate School of Biomedical Sciences, FI-20520 Turku, Finland
3Immune Disease Institute, Harvard Medical School, Boston, USA
4Department of Mathematics, University of Turku, FI-20014 Turku, Finland
corresponding authorCorresponding author.
#Contributed equally.
Teemu D Laajala: tlaajala/at/cc.hut.fi; Sunil Raghav: sunil.raghav/at/btk.fi; Soile Tuomela: soile.tuomela/at/btk.fi; Riitta Lahesmaa: riitta.lahesmaa/at/btk.fi; Tero Aittokallio: tero.aittokallio/at/utu.fi; Laura L Elo: laura.elo/at/utu.fi
Received June 5, 2009; Accepted December 18, 2009.
Abstract
Background
Chromatin immunoprecipitation coupled with massively parallel sequencing (ChIP-seq) is increasingly being applied to study transcriptional regulation on a genome-wide scale. While numerous algorithms have recently been proposed for analysing the large ChIP-seq datasets, their relative merits and potential limitations remain unclear in practical applications.
Results
The present study compares the state-of-the-art algorithms for detecting transcription factor binding sites in four diverse ChIP-seq datasets under a variety of practical research settings. First, we demonstrate how the biological conclusions may change dramatically when the different algorithms are applied. The reproducibility across biological replicates is then investigated as an internal validation of the detections. Finally, the predicted binding sites with each method are compared to high-scoring binding motifs as well as binding regions confirmed in independent qPCR experiments.
Conclusions
In general, our results indicate that the optimal choice of the computational approach depends heavily on the dataset under analysis. In addition to revealing valuable information to the users of this technology about the characteristics of the binding site detection approaches, the systematic evaluation framework provides also a useful reference to the developers of improved algorithms for ChIP-seq data.
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