Perm-seq: Mapping Protein-DNA Interactions in Segmental Duplication and Highly Repetitive Regions of Genomes with Prior-Enhanced Read Mapping.
Segmental duplications and other highly repetitive regions of genomes contribute significantly to cells' regulatory programs. Advancements in next generation sequencing enabled genome-wide profiling of protein-DNA interactions by chromatin immunoprecipitation followed by high throughput sequencing (ChIP-seq). However, interactions in highly repetitive regions of genomes have proven difficult to map since short reads of 50-100 base pairs (bps) from these regions map to multiple locations in reference genomes. Standard analytical methods discard such multi-mapping reads and the few that can accommodate them are prone to large false positive and negative rates. We developed Perm-seq, a prior-enhanced read allocation method for ChIP-seq experiments, that can allocate multi-mapping reads in highly repetitive regions of the genomes with high accuracy. We comprehensively evaluated Perm-seq, and found that our prior-enhanced approach significantly improves multi-read allocation accuracy over approaches that do not utilize additional data types. The statistical formalism underlying our approach facilitates supervising of multi-read allocation with a variety of data sources including histone ChIP-seq. We applied Perm-seq to 64 ENCODE ChIP-seq datasets from GM12878 and K562 cells and identified many novel protein-DNA interactions in segmental duplication regions. Our analysis reveals that although the protein-DNA interactions sites are evolutionarily less conserved in repetitive regions, they share the overall sequence characteristics of the protein-DNA interactions in non-repetitive regions.
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America.
Xin
X
Zeng
California Institute for Quantitative Biosciences, University of California, Berkeley, California, United States of America.
Bo
B
Li
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America.
Rene
R
Welch
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America.
Constanza
C
Rojo
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America.
Ye
Y
Zheng
Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America.
Colin N
CN
Dewey
Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America.
Sündüz
S
Keleş
D000465:Algorithms; D001483:Base Sequence; D047369:Chromatin Immunoprecipitation / Q000379:methods; D002874:Chromosome Mapping / Q000379:methods*; D004247:DNA / Q000737:chemistry / Q000235:genetics*; D004268:DNA-Binding Proteins / Q000737:chemistry / Q000235:genetics*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D020014:K562 Cells; D008969:Molecular Sequence Data; D025941:Protein Interaction Mapping / Q000379:methods*; D012091:Repetitive Sequences, Nucleic Acid / Q000235:genetics*; D056916:Segmental Duplications, Genomic / Q000235:genetics*
D004268:DNA-Binding Proteins; D004247:DNA