Sole-Search: an integrated analysis program for peak detection and functional annotation using ChIP-seq data

Abstract

Next-generation sequencing is revolutionizing the identification of transcription factor binding sites throughout the human genome. However, the bioinformatics analysis of large datasets collected using chromatin immunoprecipitation and high-throughput sequencing is often a roadblock that impedes researchers in their attempts to gain biological insights from their experiments. We have developed integrated peak-calling and analysis software (Sole-Search) which is available through a user-friendly interface and (i) converts raw data into a format for visualization on a genome browser, (ii) outputs ranked peak locations using a statistically based method that overcomes the significant problem of false positives, (iii) identifies the gene nearest to each peak, (iv) classifies the location of each peak relative to gene structure, (v) provides information such as the number of binding sites per chromosome and per gene and (vi) allows the user to determine overlap between two different experiments. In addition, the program performs an analysis of amplified and deleted regions of the input genome. This software is web-based and automated, allowing easy and immediate access to all investigators. We demonstrate the utility of our software by collecting, analyzing and comparing ChIP-seq data for six different human transcription factors/cell line combinations.

Global transcriptome and chromatin occupancy analysis reveal the short isoform of GATA1 is deficient for erythroid specification and gene expression

“…35 To identify GATA1s-deficient sites, the threshold score for enrichment on QuEST was reduced from 30 to 10, and the resulting peak set was overlapped with the original GATA1 set using the ChIP-Seq Tool Kit. 36 GATA1 peaks without a peak in the expanded GATA1s peak set were identified as GATA1s-deficient. For analysis of genomic location and overlap with gene expression data, each peak was assigned to the gene with the nearest TSS using the ChIP-Seq Tool Set.…”

Global loss of DNA methylation uncovers intronic enhancers in genes showing expression changes

“…The ChIP-seq libraries were sequenced on a HiSeq2000; the H3K4me3 datasets from HCT116 were available via ENCODE and were downloaded from the UCSC browser, accession number [UCSC: wgEncodeEH000949]. All ChIPseq data were mapped to hg19 using BWA (default parameters) and peaks were called using Sole-Search [37,38] with the following parameters: Permutation:5; Fragment:250; AlphaValue: 0.00010 = 1.0E-4; FDR: 0.00010 = 1.0E-4; PeakMergeDistance:0; HistoneBlurLength:1200 for H3K4me3 and H3K27ac. Each replicate ChIP-seq dataset was analyzed separately and only peaks present in both replicates were used for the subsequent analyses; see Additional file 1 for ChIP-seq reproducibility measures.…”

“…ChIP-seq reads were mapped using BWA and peaks were called using Sole-Search [37,38]. All data was collected as part of this study except for H3K4me3 from HCT116, which was from ENCODE.…”

The human TTAGGG repeat factors 1 and 2 bind to a subset of interstitial telomeric sequences and satellite repeats

“…We retrieved the coordinates and the alignment features of the ITSs, sat2/3 and alpha satellite repeats from the repeat masker file from UCSC. We identified the positions of the 68 TRF peaks relative to the genes and to the REST peaks (after coordinates conversion to hg18) using SoleSearch software [40]. Fifty-seven peaks fell within the coding regions or putative regulatory regions (within 100 kb of the CDS), in a total of 43 genes.…”