Inherent Signals in Sequencing-Based Chromatin-ImmunoPrecipitation Control Libraries

The growth of sequencing-based Chromatin Immuno-Precipitation studies call for a more in-depth understanding of the nature of the technology and of the resultant data to reduce false positives and false negatives. Control libraries are typically constructed to complement such studies in order to mitigate the effect of systematic biases that might be present in the data. In this study, we explored multiple control libraries to obtain better understanding of what they truly represent.First, we analyzed the genome-wide profiles of various sequencing-based libraries at a low resolution of 1 Mbp, and compared them with each other as well as against aCGH data. We found that copy number plays a major influence in both ChIP-enriched as well as control libraries. Following that, we inspected the repeat regions to assess the extent of mapping bias. Next, significantly tag-rich 5 kbp regions were identified and they were associated with various genomic landmarks. For instance, we discovered that gene boundaries were surprisingly enriched with sequenced tags. Further, profiles between different cell types were noticeably distinct although the cell types were somewhat related and similar.We found that control libraries bear traces of systematic biases. The biases can be attributed to genomic copy number, inherent sequencing bias, plausible mapping ambiguity, and cell-type specific chromatin structure. Our results suggest careful analysis of control libraries can reveal promising biological insights

SICTIN: Rapid footprinting of massively parallel sequencing data

BACKGROUND: Massively parallel sequencing allows for genome-wide hypothesis-free investigation of for instance transcription factor binding sites or histone modifications. Although nucleotide resolution detailed information can easily be generated, biological insight often requires a more general view of patterns (footprints) over distinct genomic features such as transcription start sites, exons or repetitive regions. The construction of these footprints is however a time consuming task. METHODS: The presented software generates a binary representation of the signals enabling fast and scalable lookup. This representation allows for footprint generation in mere minutes on a desktop computer. Several different input formats are accepted, e.g. the SAM format, bed-files and the UCSC wiggle track. CONCLUSIONS: Hypothesis-free investigation of genome wide interactions allows for biological data mining at a scale never before seen. Until recently, the main focus of analysis of sequencing data has been targeted on signal patterns around transcriptional start sites which are in manageable numbers. Today, focus is shifting to a wider perspective and numerous genomic features are being studied. To this end, we provide a system allowing for fast querying in the order of hundreds of thousands of features

Applying refinement to the use of mice and rats in rheumatoid arthritis research

Rheumatoid arthritis (RA) is a painful, chronic disorder and there is currently an unmet need for effective therapies that will benefit a wide range of patients. The research and development process for therapies and treatments currently involves in vivo studies, which have the potential to cause discomfort, pain or distress. This Working Group report focuses on identifying causes of suffering within commonly used mouse and rat ‘models’ of RA, describing practical refinements to help reduce suffering and improve welfare without compromising the scientific objectives. The report also discusses other, relevant topics including identifying and minimising sources of variation within in vivo RA studies, the potential to provide pain relief including analgesia, welfare assessment, humane endpoints, reporting standards and the potential to replace animals in RA research

Tiling Histone H3 Lysine 4 and 27 Methylation in Zebrafish Using High-Density Microarrays

BACKGROUND: Uncovering epigenetic states by chromatin immunoprecipitation and microarray hybridization (ChIP-chip) has significantly contributed to the understanding of gene regulation at the genome-scale level. Many studies have been carried out in mice and humans; however limited high-resolution information exists to date for non-mammalian vertebrate species. PRINCIPAL FINDINGS: We report a 2.1-million feature high-resolution Nimblegen tiling microarray for ChIP-chip interrogations of epigenetic states in zebrafish (Danio rerio). The array covers 251 megabases of the genome at 92 base-pair resolution. It includes ∼15 kb of upstream regulatory sequences encompassing all RefSeq promoters, and over 5 kb in the 5' end of coding regions. We identify with high reproducibility, in a fibroblast cell line, promoters enriched in H3K4me3, H3K27me3 or co-enriched in both modifications. ChIP-qPCR and sequential ChIP experiments validate the ChIP-chip data and support the co-enrichment of trimethylated H3K4 and H3K27 on a subset of genes. H3K4me3- and/or H3K27me3-enriched genes are associated with distinct transcriptional status and are linked to distinct functional categories. CONCLUSIONS: We have designed and validated for the scientific community a comprehensive high-resolution tiling microarray for investigations of epigenetic states in zebrafish, a widely used developmental and disease model organism

ChIPseqR: analysis of ChIP-seq experiments

<p>Abstract</p> <p>Background</p> <p>The use of high-throughput sequencing in combination with chromatin immunoprecipitation (ChIP-seq) has enabled the study of genome-wide protein binding at high resolution. While the amount of data generated from such experiments is steadily increasing, the methods available for their analysis remain limited. Although several algorithms for the analysis of ChIP-seq data have been published they focus almost exclusively on transcription factor studies and are usually not well suited for the analysis of other types of experiments.</p> <p>Results</p> <p>Here we present ChIPseqR, an algorithm for the analysis of nucleosome positioning and histone modification ChIP-seq experiments. The performance of this novel method is studied on short read sequencing data of <it>Arabidopsis thaliana </it>mononucleosomes as well as on simulated data.</p> <p>Conclusions</p> <p>ChIPseqR is shown to improve sensitivity and spatial resolution over existing methods while maintaining high specificity. Further analysis of predicted nucleosomes reveals characteristic patterns in nucleosome sequences and placement.</p

Genome-Scale Validation of Deep-Sequencing Libraries

Chromatin immunoprecipitation followed by high-throughput (HTP) sequencing (ChIP-seq) is a powerful tool to establish protein-DNA interactions genome-wide. The primary limitation of its broad application at present is the often-limited access to sequencers. Here we report a protocol, Mab-seq, that generates genome-scale quality evaluations for nucleic acid libraries intended for deep-sequencing. We show how commercially available genomic microarrays can be used to maximize the efficiency of library creation and quickly generate reliable preliminary data on a chromosomal scale in advance of deep sequencing. We also exploit this technique to compare enriched regions identified using microarrays with those identified by sequencing, demonstrating that they agree on a core set of clearly identified enriched regions, while characterizing the additional enriched regions identifiable using HTP sequencing

Systematic Evaluation of Factors Influencing ChIP-Seq Fidelity

We performed a systematic evaluation of how variations in sequencing depth and other parameters influence interpretation of Chromatin immunoprecipitation (ChIP) followed by sequencing (ChIP-seq) experiments. Using Drosophila S2 cells, we generated ChIP-seq datasets for a site-specific transcription factor (Suppressor of Hairy-wing) and a histone modification (H3K36me3). We detected a chromatin state bias, open chromatin regions yielded higher coverage, which led to false positives if not corrected and had a greater effect on detection specificity than any base-composition bias. Paired-end sequencing revealed that single-end data underestimated ChIP library complexity at high coverage. The removal of reads originating at the same base reduced false-positives while having little effect on detection sensitivity. Even at a depth of ~1 read/bp coverage of mappable genome, ~1% of the narrow peaks detected on a tiling array were missed by ChIP-seq. Evaluation of widely-used ChIP-seq analysis tools suggests that adjustments or algorithm improvements are required to handle datasets with deep coverage