Practical guidelines for the comprehensive analysis of ChIP-seq data.

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections

An integrative view of the regulatory and transcriptional landscapes in mouse hematopoiesis.

Thousands of epigenomic data sets have been generated in the past decade, but it is difficult for researchers to effectively use all the data relevant to their projects. Systematic integrative analysis can help meet this need, and the VISION project was established for validated systematic integration of epigenomic data in hematopoiesis. Here, we systematically integrated extensive data recording epigenetic features and transcriptomes from many sources, including individual laboratories and consortia, to produce a comprehensive view of the regulatory landscape of differentiating hematopoietic cell types in mouse. By using IDEAS as our integrative and discriminative epigenome annotation system, we identified and assigned epigenetic states simultaneously along chromosomes and across cell types, precisely and comprehensively. Combining nuclease accessibility and epigenetic states produced a set of more than 200,000 candidate cis-regulatory elements (cCREs) that efficiently capture enhancers and promoters. The transitions in epigenetic states of these cCREs across cell types provided insights into mechanisms of regulation, including decreases in numbers of active cCREs during differentiation of most lineages, transitions from poised to active or inactive states, and shifts in nuclease accessibility of CTCF-bound elements. Regression modeling of epigenetic states at cCREs and gene expression produced a versatile resource to improve selection of cCREs potentially regulating target genes. These resources are available from our VISION website to aid research in genomics and hematopoiesis.National Institute of Diabetes and Digestive and Kidney Diseases (grant number R24DK106766-01A1), the National Human Genome Research Institute (grant number U54HG006998

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

Condition-adaptive fused graphical lasso (CFGL): An adaptive procedure for inferring condition-specific gene co-expression network.

Co-expression network analysis provides useful information for studying gene regulation in biological processes. Examining condition-specific patterns of co-expression can provide insights into the underlying cellular processes activated in a particular condition. One challenge in this type of analysis is that the sample sizes in each condition are usually small, making the statistical inference of co-expression patterns highly underpowered. A joint network construction that borrows information from related structures across conditions has the potential to improve the power of the analysis. One possible approach to constructing the co-expression network is to use the Gaussian graphical model. Though several methods are available for joint estimation of multiple graphical models, they do not fully account for the heterogeneity between samples and between co-expression patterns introduced by condition specificity. Here we develop the condition-adaptive fused graphical lasso (CFGL), a data-driven approach to incorporate condition specificity in the estimation of co-expression networks. We show that this method improves the accuracy with which networks are learned. The application of this method on a rat multi-tissue dataset and The Cancer Genome Atlas (TCGA) breast cancer dataset provides interesting biological insights. In both analyses, we identify numerous modules enriched for Gene Ontology functions and observe that the modules that are upregulated in a particular condition are often involved in condition-specific activities. Interestingly, we observe that the genes strongly associated with survival time in the TCGA dataset are less likely to be network hubs, suggesting that genes associated with cancer progression are likely to govern specific functions or execute final biological functions in pathways, rather than regulating a large number of biological processes. Additionally, we observed that the tumor-specific hub genes tend to have few shared edges with normal tissue, revealing tumor-specific regulatory mechanism

OnTAD: hierarchical domain structure reveals the divergence of activity among TADs and boundaries

The spatial organization of chromatin in the nucleus has been implicated in regulating gene expression. Maps of high-frequency interactions between different segments of chromatin have revealed topologically associating domains (TADs), within which most of the regulatory interactions are thought to occur. TADs are not homogeneous structural units but appear to be organized into a hierarchy. We present OnTAD, an optimized nested TAD caller from Hi-C data, to identify hierarchical TADs. OnTAD reveals new biological insights into the role of different TAD levels, boundary usage in gene regulation, the loop extrusion model, and compartmental domains. OnTAD is available at https://github.com/anlin00007/OnTAD.NIH (Grant R01 GM121613, R01 GM109453, R24 DK106766)NIH (Training Grant T32 GM102057

Strontium ranelate inhibits wear particle-induced aseptic loosening in mice

The imbalance between bone formation and osteolysis plays a key role in the pathogenesis of aseptic loosening. Strontium ranelate (SR) can promote bone formation and inhibit osteolysis. The aim of this study was to explore the role and mechanism of SR in aseptic loosening induced by wear particles. Twenty wild-type (WT) female C57BL/6j mice and 20 sclerostin-/- female C57BL/6j mice were used in this study. Mice were randomly divided into four groups: WT control group, WT SR group, knockout (KO) control group, and KO SR group. We found that SR enhanced the secretion of osteocalcin (0.72±0.007 in WT control group, 0.98±0.010 in WT SR group, P=0.000), Runx2 (0.34±0.005 in WT control group, 0.47±0.010 in WT SR group, P=0.000), β-catenin (1.04±0.05 in WT control group, 1.22±0.02 in WT SR group, P=0.000), and osteoprotegerin (OPG) (0.59±0.03 in WT control group, 0.90±0.02 in WT SR group, P=0.000). SR significantly decreased the level of receptor activator for nuclear factor-κB ligand (RANKL) (1.78±0.08 in WT control group, 1.37±0.06 in WT SR group, P=0.000) and improved the protein ratio of OPG/RANKL, but these effects were not observed in sclerostin-/- mice. Our findings demonstrated that SR enhanced bone formation and inhibited bone resorption in a wear particle-mediated osteolysis model in wild-type mice, and this effect relied mainly on the down-regulation of sclerostin levels to ameliorate the inhibition of the canonical Wnt pathway

Effect of population density on reproduction in Microtus fortis under laboratory conditions

Between December 2011 and March 2012, the reproductive characteristics of Microtus fortis reared in the laboratory at different population densities were assessed. In all, 258 male and female voles were randomly divided into 4 groups and reared at densities of 2, 4, 6, and 8 animals per cage (sex ratio: 1:1). The results showed that the pregnancy rate (χ2 = 21.671, df = 3, P 0.05). In particular, the reproductive index and sex hormone levels showed a significant difference among the different density groups studied