title: Systematic evaluation of the impact of ChIP-seq read designs on genome coverage, peak identification, and allele-specific binding detection.
abstract: BACKGROUND
  Chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiments revolutionized genome-wide profiling of transcription factors and histone modifications. Although maturing sequencing technologies allow these experiments to be carried out with short (36-50 bps), long (75-100 bps), single-end, or paired-end reads, the impact of these read parameters on the downstream data analysis are not well understood. In this paper, we evaluate the effects of different read parameters on genome sequence alignment, coverage of different classes of genomic features, peak identification, and allele-specific binding detection.
  
  
  RESULTS
  We generated 101 bps paired-end ChIP-seq data for many transcription factors from human GM12878 and MCF7 cell lines. Systematic evaluations using in silico variations of these data as well as fully simulated data, revealed complex interplay between the sequencing parameters and analysis tools, and indicated clear advantages of paired-end designs in several aspects such as alignment accuracy, peak resolution, and most notably, allele-specific binding detection.
  
  
  CONCLUSIONS
  Our work elucidates the effect of design on the downstream analysis and provides insights to investigators in deciding sequencing parameters in ChIP-seq experiments. We present the first systematic evaluation of the impact of ChIP-seq designs on allele-specific binding detection and highlights the power of pair-end designs in such studies.
authors.affiliation: Department of Statistics, University of Nebraska Lincoln, Lincoln, Nebraska, USA. qi.zhang@unl.edu.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhang
authors.affiliation: Department of Statistics, University of Wisconsin Madison, Madison, Wisconsin, USA.
authors.forename: Xin
authors.initials: X
authors.lastname: Zeng
authors.affiliation: Department of Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, Wisconsin, USA.
authors.forename: Sam
authors.initials: S
authors.lastname: Younkin
authors.affiliation: Department of Genetics, Stanford University School of Medicine, Palo Alto, California, USA. trupti@stanford.edu.
authors.forename: Trupti
authors.initials: T
authors.lastname: Kawli
authors.affiliation: Department of Genetics, Stanford University School of Medicine, Palo Alto, California, USA. mpsnyder@stanford.edu.|Stanford Center for Genomics and Personalized Medicine, Palo Alto, California, USA. mpsnyder@stanford.edu.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Snyder
authors.affiliation: Department of Statistics, University of Wisconsin Madison, Madison, Wisconsin, USA. keles@stat.wisc.edu.|Department of Biostatistics and Medical Informatics, University of Wisconsin Madison, Madison, Wisconsin, USA. keles@stat.wisc.edu.
authors.forename: Sündüz
authors.initials: S
authors.lastname: Keleş
mesh_terms: D000483:Alleles; D047369:Chromatin Immunoprecipitation / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D016415:Sequence Alignment; D014157:Transcription Factors / Q000378:metabolism
chemical_list: D014157:Transcription Factors