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Title: Discovering Transcription Factor Binding Sites in Highly Repetitive Regions of Genomes with Multi-Read Analysis of ChIP-Seq Data}
Article:
Abstract:
Chromatin immunoprecipitation followed by high-throughput sequencing( ChIP-seq) is rapidly replacing chromatin immunoprecipitation combined with genome-wide tiling array analysis( ChIP-chip) as the preferred approach for mapping transcription-factor binding sites and chromatin modifications. The state of the art for analyzing ChIP-seq data relies on using only reads that map uniquely to a relevant reference genome( uni-reads). This can lead to the omission of up to 30% of alignable reads. We describe a general approach for utilizing reads that map to multiple locations on the reference genome( multi-reads). Our approach is based on allocating multi-reads as fractional counts using a weighted alignment scheme. Using human STAT1 and mouse GATA1 ChIP-seq datasets, we illustrate that incorporation of multi-reads significantly increases sequencing depths, leads to detection of novel peaks that are not otherwise identifiable with uni-reads, and improves detection of peaks in mappable regions. We investigate various genome-wide characteristics of peaks detected only by utilization of multi-reads via computational experiments. Overall, peaks from multi-read analysis have similar characteristics to peaks that are identified by uni-reads except that the majority of them reside in segmental duplications. We further validate a number of GATA1 multi-read only peaks by independent quantitative real-time ChIP analysis and identify novel target genes of GATA1. These computational and experimental results establish that multi-reads can be of critical importance for studying transcription factor binding in highly repetitive regions of genomes with ChIP-seq experiments.
Introduction:
The introduction of next generation sequencing has enabled a myriad of creative ways to answer genome-wide questions. Chromatin immunoprecipitation coupled with high-throughput next generation sequencing( ChIP-seq) has become a powerful technique for large scale profiling of transcription factor binding and chromatin modifications[1]–[5] and is offering a powerful alternative to ChIP on microarrays( ChIP-chip)[6]–[9]. In ChIP-seq, DNA fragments are sequenced directly instead of being hybridized on an array. Although there are multiple platforms for high-throughput sequencing, the Illumina sequencer, which works by sequencing a small region( 25 to 100 bp) from one or both ends of each fragment, is commonly used for ChIP-seq experiments. A ChIP-seq experiment generates millions of short reads/tags. The first step of data analysis is to map reads to the reference genome and retain reads that map to unique locations( uni-reads)[10]–[12]. Although constraining the analysis to uni-reads by discarding reads that map to multiple locations( multi-reads) leads to reduced coverage and sequencing depth, this may not render a serious problem in most cases. This is because many uni-reads might be adjacent to discarded multi-reads and can lead to identification of underlying peaks. However, discarding multi-reads poses a significant challenge for identifying binding locations residing in genomic regions that have been duplicated over evolutionary time since these regions will not have many uni-reads. Shortcomings of discarding multi-reads have been recognized in transcriptome sequencing( RNA-Seq)[13]–[18]. These studies demonstrated that discarding multi-reads leads to inaccurate estimation of expression of genes that reside in repetitive regions. Gene repetitiveness may be due to either low complexity segments or recent gene duplications. Numerous studies have highlighted the biological importance of segmental duplications[19],[20]. Duplicated genes could retain their original functions or acquire new functions by changes in coding sequences and regulatory regions[21],[22].[23],[24] showed that segmental duplications in human genomes are selectively enriched for genes associated with disease susceptibility, immunity, and defense. Overall, these studies highlighted that annotating segmental duplications in terms of transcription factor binding might aid in understanding functions of genes within these regions. In addition, retrotransposon elements, a major class of transposable elements which duplicate through RNA intermediates that are reverse transcribed and are inserted at new genomic locations, also carry transcription factor binding sites that regulate gene expression[25],[26]. For example,[27] showed with in vivo ChIP experiments that transcription factor p53 binds to human endogenous retrovirus( ERV) long terminal repeats( LTR), that are 100 bp to 5 kb long, with a p53 binding site. There has been little work in the literature that investigates the effects of multi-reads in ChIP-seq data analysis.[12] provided an example of how discarding multi-reads could result in potential false negatives.[10] briefly discussed that by randomly assigning multi-reads to one of their mapping locations, one can increase the number of detected binding sites.[28] recently studied enrichment of the known repetitive elements as described by the Repbase database[29] and the RepeatMasker[30] scans in ChIP-seq data of histone modifications. Similarly,[31] developed an algorithm for genomic mapping of ambiguous tags which increased read coverage for highly repetitive sequences. However, neither of these thoroughly investigated the effects of multi-reads on overall peak( binding location) detection. Currently, none of the popular ChIP-seq data analysis software[10]–[12],[32]–[42] takes multi-reads into account. We investigate the effects of discarding multi-reads on two different ChIP-seq datasets: STAT1 binding in interferon--stimulated HeLa S3 cells[10] and GATA1 binding in mouse G1E-ER4 cells[43]. We develop a method for utilizing multi-reads and illustrate that incorporation of multi-reads can lead to an increase of up to 25% in the sequencing depth and identify high quality novel peaks. Location analysis of these peaks reveals that a large fraction of them reside in close proximity to promoters and in genic regions within segmental duplications of the genomes. Furthermore, true peaks are highly enriched for retrotransposon elements such as LINE( long interspersed repetitive elements) and LTR( long terminal repeats). Therefore, they are likely to be critical for constructing comprehensible genetic networks with members in repetitive regions of the genomes. Our computational experiments demonstrate that multi-reads can not only lead to detection of novel peaks in low mappable regions but also improve peak identification in moderate to highly mappable regions. We support our computational experiments by experimental validation of a subset of GATA1 peaks that were only identifiable when multi-reads were incorporated. This leads to identification of novel GATA1 target genes.
Discussion:
We investigated the shortcomings of discarding multi-reads in ChIP-seq analysis and illustrated how incorporating multi-reads can improve detection of binding sites in highly repetitive regions of genomes. Multi-reads lead to identification of novel binding sites that are located in highly repetitive and low mappability regions and are not identifiable with uni-reads alone. They further contribute to effective utilization of uni-reads so that more peaks can be detected with lower sequencing depths. Utilizing location analysis and biological annotation, we further showed that a substantial fraction of peaks specific to multi-read analysis are located in segmental duplications of the human and mouse genomes, and attributed to genes that are well associated with immunity and defense. Since multi-reads arise automatically in ChIP-seq experiments, our pipeline does not require any additional experiments for utilizing these reads. Once multi-reads are appropriately converted into counts by an application of the multi-read allocation algorithm, peak calling might be performed with any method that can handle bin or nucleotide level count data; however, since many of existing software start the analysis with aligned or raw tag files, these would need to be modified. To accommodate some of the existing software that rely on aligned read files( or alignment results in the bed format), we developed a script that rounds the multi-read weights to the nearest integer and adds the ones that round up to 1 to the original alignment files as pseudo reads so that they can be utilized. This procedure is equivalent to( 1) allocating each multi-read to the location that it maps to with the largest weight;( 2) filtering out multi-reads with since they round to 0; and( 3) ignoring weight information( degree of confidence for multi-reads allocation). Although this implementation decreases the number of utilized multi-reads by about a half( for GATA1), it still leads to a significant increase in the sequencing depth compared to using uni-reads alone. An application of this strategy with the MACS algorithm[32] was able to identify 37% of the MR-only peaks identified by the MOSAiCS MR analysis. This set included the 3 true positive and 2 false positive peaks that we validated with the quantitative real time ChIP analysis. We showed that the overall conclusions of utilizing multi-reads agree well when peak calling is performed either with MOSAiCS[44] or CisGenome's conditional binomial model[11]. Almost all of the the published ChIP-seq studies in GEO( http://www. ncbi. nlm. nih. gov/geo/) utilize short reads( 25–36 mers) and we have observed that multi-reads can lead to a substantial increase in the sequencing depths of such datasets. A thorough investigation of peaks that were detectable only with multi-reads highlighted that a significant fraction of these peaks still have low mappability even when 75 mer SETs are used. Therefore, utilization of multi-reads is also likely to improve the analysis of data from longer and/or paired-end reads. The two factors we studied are not particularly known to bind to repetitive regions. However, there are many examples of DNA binding proteins, e. g., MECP2, KAP1, that selectively bind to repetitive regions. Our pipeline should even have a higher impact in the analysis of such datasets. We have initially implemented our methodology for use with ChIP-seq data from the Illumina Genome Analyzer platform; however it is straightforward to adapt it for use with other high-throughput sequencing platforms. A related question is whether utilizing a more flexible definition of uni-reads by relaxing the alignment criterion can provide a similar utility to that of multi-reads in terms of sequencing depth. Our computational experiments( data not shown) indicate that more flexible definitions of uni-reads( e. g. single best alignment of each read with at most 3 mismatches) can increase the sequencing depth and lead to identification of more peaks. However, such applications fail to identify high signal MR-only peaks that a multi-read analysis can identify. For example, defining uni-reads by considering the single best alignment of each read with at most 3 mismatches increases the sequencing depth by 12% for GATA1 but can only identify 2. 6% of the GATA1 MR-only peaks. Significant fractions of eukaryotic genomes are composed of repetitive regions, e. g., more than half of the human genome. Therefore, functional properties of the repetitive regions of genomes are of significant biological interest. In particular, genomic repeats play important roles in functioning and evolution of transcriptional regulatory networks[55],[56].[56] illustrated that binding sites embedded in genomic repeats are associated with significant regulatory expansions throughout the mammalian phylogeny. Therefore, analysis and/or re-analysis of available or future ChIP-seq datasets with our multi-read approach is expected to reveal fundamental insights into functional properties of highly repetitive regions of the genomes.
Annotating repetitive regions of genomes experimentally is a challenging task . Chromatin immunoprecipitation followed by high-throughput sequencing ( ChIP-seq ) provides valuable data for characterizing repetitive regions of genomes in terms of transcription factor binding . Although ChIP-seq technology has been maturing , available ChIP-seq analysis methods and software rely on discarding sequence reads that map to multiple locations on the reference genome ( multi-reads ) , thereby generating a missed opportunity for assessing transcription factor binding to highly repetitive regions of genomes . We develop a computational algorithm that takes multi-reads into account in ChIP-seq analysis . We show with computational experiments that multi-reads lead to significant increase in sequencing depths and identification of binding regions that are otherwise not identifiable when only reads that uniquely map to the reference genome ( uni-reads ) are used . In particular , we show that the number of binding regions identified can increase up to 36% . We support our computational predictions with independent quantitative real-time ChIP validation of binding regions identified only when multi-reads are incorporated in the analysis of a mouse GATA1 ChIP-seq experiment .