Computation for ChIP-seq and RNA-seq studies

By Shirley Pepke, Barbara J. Wold, Ali Mortazavi. Nature Methods, 2009

624 citations recorded by the source corpus.

Genome-wide measurements of protein-DNA interactions and transcriptomes are increasingly done by deep DNA sequencing methods (ChIP-seq and RNA-seq). The power and richness of these counting-based measurements comes at the cost of routinely handling tens to hundreds of millions of reads. Whereas early adopters necessarily developed their own custom computer code to analyze the first ChIP-seq and RNA-seq datasets, a new generation of more sophisticated algorithms and software tools are emerging to assist in the analysis phase of these projects. Here we describe the multilayered analyses of ChIP-seq and RNA-seq datasets, discuss the software packages currently available to perform tasks at each layer and describe some upcoming challenges and features for future analysis tools. We also discuss how software choices and uses are affected by specific aspects of the underlying biology and data structure, including genome size, positional clustering of transcription factor binding sites, transcript discovery and expression quantification.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1e2ac9587c9a57a49583990602142c84b3f19625

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Citations in the corpus

Genome-wide analysis of ETS-family DNA-binding in vitro and in vivo (2010, neutral): “…and complexity estimation (Supplementary Table S4), peak calling (Audic and Claverie, 1997; Li et al, 2008; Nix et al, 2008; Laajala et al, 2009; Pepke et al, 2009), motif analysis by MEME (Bailey and Elkan, 1994) (Figure 6D; Supplementary Figures S8 and S11), motif enrichment in peaks and peak…”
Applications of next generation sequencing in molecular ecology of non-model organisms (2011, methodology): “By using this approach, detailed methylation maps of the genome will become available (Pomraning et al., 2009), and by comparing these for different samples we will be able to address the importance of methylation for a large range of ecologically and evolutionary important questions, like the…”
JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles (2023, methodology): “In order to have credible profiles within the UNVALIDATED collection, we kept the notion of high quality by putting the profiles under rigorous curation, where we look at the enrichment for TFBSs close to ChIP-seq peak summits ( 14 ), among other criteria.”
Computational analysis of bacterial RNA-Seq data (2013, methodology): “There are numerous tools available [reviewed in (14)] that support various aspects of RNA-seq data processing, management and analysis, but most of these tools are designed primarily for eukaryotic RNA-seq data.”
From RNA-seq reads to differential expression results (2010, neutral): “A detailed explanation of these techniques is beyond the scope of this review, but can be found in [23,26-30].”
Next-generation sequencing data interpretation: enhancing reproducibility and accessibility (2012, methodology): “…novel functional assays, including quantification of protein–DNA binding or histone modifications (using chromatin immunoprecipitation followed by high-throughput sequencing (ChIP–seq) 4 ), transcript levels (using RNA sequencing (RNA-seq) 5 ), spatial interactions (using Hi-C 6 ) and others.”
Gene Mapping via Bulked Segregant RNA-Seq (BSR-Seq) (2012, methodology): “This relative quantification is reproducible and highly accurate [2,25– 28].”
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool (2013, methodology): “Previous studies [22] have indicated that the use of control sample substantially reduces DNA shearing biases and sequencing artifacts; therefore, for each experiment, an input control sample was matched according to the description in GEO.”
Galaxy: a comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences (2010, background): “This is exemplified in genomics, where first microarrays and now massively parallel DNA sequencing have enabled a variety of genome-wide functional assays, such as ChIP-seq [1] and RNA-seq [2] (and many others), that require increasingly complex analysis tools [3].”
ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data (2012, positive): “Ultra-high-throughput next-generation sequencing technology has recently been developed for mapping TFBSs (10,11).”
Cloud-scale RNA-sequencing differential expression analysis with Myrna (2010, background): “As cost and throughput continue to improve, second generation sequencing [1], in conjunction with RNA-Seq [2,3], is becoming an increasingly efficient and popular tool for studying gene expression.”
Evaluation of Algorithm Performance in ChIP-Seq Peak Detection (2010, background): “Though there is no single nucleotide at which cross-linking occurs, this estimate is meant to facilitate the precise discovery of cis-regulatory elements [11].”
A survey of sequence alignment algorithms for next-generation sequencing (2010, background): “For example, the suffix array interval of AG is [1, 2].”
Epigenetics and the environment: emerging patterns and implications (2012, neutral): “Combined with appropriate statistical and bioinformatic tool”
Sequence-specific error profile of Illumina sequencers (2011, methodology): “The data also can be used for quantitative analysis based on the number of sequence segments (9,10).”
MEME-ChIP: motif analysis of large DNA datasets (2011, positive): “In the case of MEME and AME, the most significant motif found is a composite motif believed to represent binding of a protein complex involving SCL and GATA-1, another transcription factor that plays a central role in erythropoeisis (Fig.”
High‐throughput DNA sequencing – concepts and limitations (2010, background): “Given the immense diversity, we have not discussed these applications in depth here; other reviews with a stronger focus on specific applications and data analysis are available [24, 82–88].”