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看图说话系列-ChIP-seq analysis

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发表于 2017-5-19 14:40:04 | 显示全部楼层 |阅读模式
我以前写过一个看图说话:生物信息数据分析文章就是看图说话(http://www.biotrainee.com/thread-601-1-1.html)
最近有看到了一个更经典的:


是不是很清晰呀!!!从测序到分析,到结果的可视化,全部在里面了:


(a) Assessment of ChIP-seq sample quality, represented here as the sequence quality score versus base pair position using FastQC, is a critical first step in ChIP-seq data analysis and is required for appropriate interpretations of subsequent downstream analyses. The example shows relatively poor quality data, given the high error rate and variability toward the 3′ end of the reads.

(b) Example pie charts describing the genome-wide distribution patterns of differential histone modification sites for H3K4me3, H3K4me1 and H3K27me3 (as determined using diffReps) in nucleus accumbens (NAc) of saline- versus chronic cocaine–treated mice16. 1K, 0.25–1 kb upstream of a TSS; 3K, 1–3 kb upstream of a TSS. Reprinted with permission from ref. 17.

(c) Example gene body plots for H3K36me3 in NAc of control mice derived by ngs.plot. Lines represent average profiles for four gene groups ordered by mRNA expression levels by RNA-seq, defined as “high,” “med2,” “med1” and “low,” and illustrates increasing levels of this histone mark with increasing gene expression. ngs.plot easily generates these kinds of figures for accessible representations of protein enrichment throughout different functional genomic regions.

(d) Representative log2 enrichment heat maps generated in ngs.plot for several histone marks versus DNA input in mouse NAc at TSS ± 5 kb genomic regions. Gene expression levels analyzed by RNA-seq are illustrated by enrichment in the same gene order as ChIP-seq enrichment patterns. Red is high, green is low.

(e) Integrated genomics viewer (IGV) screenshots for ChIP-seq data of various histone marks in NAc of cocaine-treated mice, as well as RNA-seq data from poly(A)-selected RNA. The genomic region displayed represents the TSS and ~20 kb downstream of the Egr1 gene.

(f) Chromatin signatures, such as those shown here, can be defined to characterize groups of transcripts displaying similar patterns of chromatin modifications at specific genomic regions following environmental stimuli (for example, at splicing-related regions following cocaine treatment)16. Motif finding can then be used to identify potential transcriptional and splicing-associated factors deduced to regulate these signatures.

(g) IGV genome browser screenshot for ChIP-seq coverage of H3K4me3 from NAc of control mice. Three biological replicates are shown as separate tracks, with significant peaks identified by MACS depicted as solid bars beneath the tracks. The genomic region depicted is ~chr11:116,974,000–116,990,000. These data highlight the difficulty of using peak calling–based approaches to identify differential enrichment patterns across samples. Although the three biological replicates appear to be generally similar in size and distribution, MACS identified discordant peaks among the samples owing to intrinsic variations in peak location. Unlike MACS, diffReps uses a sliding window approach that allows one to focus on a region of a fixed size across all samples (for example, 1 kb), thus allowing more unified comparisons across samples.

(h) ChIP-seq differential analysis using diffReps to compare two groups of samples representing two distinct biological conditions and test for significance. Here, diffReps was used to compare differential H3K4me3 enrichment in NAc between saline- versus cocaine-treated mice at the TSS of the Enah gene.

文章来源:
Analytical tools and current challenges in the modern era of neuroepigenomics
http://www.nature.com/neuro/journal/v17/n11/full/nn.3816.html



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