Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing

Coarfa, Cristian; Yu, Fuli; Miller, Christopher A.; Chen, Zuozhou; Harris, R. Alan; Milosavljevic, Aleksandar

doi:10.1186/1471-2105-11-572

Cited by 49 publications

(38 citation statements)

References 36 publications

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“…This not only causes problems in library construction and cluster formation on a sequencing plate, but also more profoundly affects alignment of bisulfite reads to the genome, i.e., mapping. Several algorithms have been developed to improve mapping of WGBS data (Xi and Li 2009;Coarfa et al 2010;Krueger and Andrews 2011;Frith et al 2012;Otto et al 2012), but the problem remains not entirely solved. The confidence of mapping WGBS reads is generally lower than mapping standard, non-bisulfite-converted reads.…”

Section: Discussionmentioning

confidence: 99%

Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods

et al. 2013

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Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to map complete DNA methylomes. These include whole-genome bisulfite sequencing (WGBS, MethylC-seq, or BS-seq), reduced-representation bisulfite sequencing (RRBS), and enrichment-based methods such as MeDIP-seq, MBD-seq, and MRE-seq. These methods yield largely comparable results but differ significantly in extent of genomic CpG coverage, resolution, quantitative accuracy, and cost, at least while using current algorithms to interrogate the data. None of these existing methods provides single-CpG resolution, comprehensive genome-wide coverage, and cost feasibility for a typical laboratory. We introduce methylCRF, a novel conditional random fields-based algorithm that integrates methylated DNA immunoprecipitation (MeDIP-seq) and methylation-sensitive restriction enzyme (MRE-seq) sequencing data to predict DNA methylation levels at single-CpG resolution. Our method is a combined computational and experimental strategy to produce DNA methylomes of all 28 million CpGs in the human genome for a fraction (<10%) of the cost of whole-genome bisulfite sequencing methods. methylCRF was benchmarked for accuracy against Infinium arrays, RRBS, WGBS sequencing, and locus-specific bisulfite sequencing performed on the same human embryonic stem cell line. methylCRF transformation of MeDIP-seq/MRE-seq was equivalent to a biological replicate of WGBS in quantification, coverage, and resolution. We used conventional bisulfite conversion, PCR, cloning, and sequencing to validate loci where our predictions do not agree with whole-genome bisulfite data, and in 11 out of 12 cases, methylCRF predictions of methylation level agree better with validated results than does whole-genome bisulfite sequencing. Therefore, methylCRF transformation of MeDIPseq/MRE-seq data provides an accurate, inexpensive, and widely accessible strategy to create full DNA methylomes. [Supplemental material is available for this article.]The haploid human genome contains ;28 million CpGs that exist in methylated, hydroxymethylated, or unmethylated states. The methylation status of cytosines in CpGs influences protein-DNA interactions, gene expression, and chromatin structure and stability; and plays a vital role in the regulation of cellular processes including host defense against endogenous parasitic sequences, embryonic development, transcription, X-chromosome inactivation, and genomic imprinting, as well as possibly playing a role in learning and memory (Robertson 2005;Suzuki and Bird 2008;Laird 2010;Jones 2012). Understanding the role of DNA methylation in development and disease requires accurate assessment of the genomic distribution of these modifications (Laird 2010). Recent advancements in sequencing-based DNA methylation profiling methods provide an unprecedented opportunity to map complete DNA methylomes. Techniques for high-throughput detection of cytosine methylation include bisulfite conversion of unmethylated cytosines to uracil, immunop...

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Section: Discussionmentioning

confidence: 99%

Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods

et al. 2013

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“…The first and most resource-intensive step is aligning the sequence reads to the genome. Most sequencing platforms come with alignment pipelines, however third party aligners are commonly used, such as MAQ[105], Bowtie[106], BWA[107], SOAP[108,109] and PASH[110]. These packages differ by alignment algorithm, as well as how multi-aligning reads and gapped vs. un-gapped alignments are handled, resulting in differences in sensitivity and specificity.…”

Section: Chromatin Immunoprecipitation-sequencing (Chip-seq)mentioning

confidence: 99%

Methods for Cancer Epigenome Analysis

Nagarajan

Fouse

Bell

et al. 2012

Advances in Experimental Medicine and Biology

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Accurate detection of epimutations in tumor cells is crucial for understanding the molecular pathogenesis of cancer. Alterations in DNA methylation in cancer are functionally important and clinically relevant, but even this well-studied area is continually re-evaluated in light of unanticipated results, including a strong connection between aberrant DNA methylation in adult tumors and polycomb group profiles in embryonic stem cells, cancer-associated genetic mutations in epigenetic regulators such as DNMT3A and TET family genes, and the discovery of abundant 5-hydroxymethylcytosine, a product of TET proteins acting on 5-methylcytosine, in human tissues. The abundance and distribution of covalent histone modifications in primary cancer tissues relative to normal cells is a largely uncharted area, although there is good evidence for a mechanistic role of cancer-specific alterations in epigenetic marks in tumor etiology, drug response and tumor progression. Meanwhile, the discovery of new epigenetic marks continues, and there are many useful methods for epigenome analysis applicable to primary tumor samples, in addition to cancer cell lines. For DNA methylation and hydroxymethylation, next-generation sequencing allows increasingly inexpensive and quantitative whole-genome profiling. Similarly, the refinement and maturation of chromatin immunoprecipitation with next-generation sequencing (ChIP-seq) has made possible genome-wide mapping of histone modifications, open chromatin and transcription factor binding sites. Computational tools have been developed apace with these epigenome methods to better enable the accuracy and interpretation of the data from the profiling methods.

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“…Batman for the analysis of MeDIP enrichment data is available as a suite of Java scripts (Down et al 2008). Software for sequencing methods, not reviewed here, include PASH 3.0 (Coarfa et al 2010) and edgeR, another Bioconductor package (Robinson et al 2010a). …”

Section: Discussionmentioning

confidence: 99%

Statistical approaches for the analysis of DNA methylation microarray data

Siegmund

2011

Hum Genet

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Following the rapid development and adoption in DNA methylation microarray assays, we are now experiencing a growth in the number of statistical tools to analyze the resulting large-scale data sets. As is the case for other microarray applications, biases caused by technical issues are of concern. Some of these issues are old (e.g. two-color dye bias and probe- and array-specific effects), while others are new (e.g. fragment length bias and bisulfite conversion efficiency). Here, I highlight characteristics of DNA methylation that suggest standard statistical tools developed for other data types may not be directly suitable. I then describe the microarray technologies most commonly in use, along with the methods used for pre-processing and obtaining a summary measure. I finish with a section describing down-stream analyses of the data, focusing on methods that model percentage DNA methylation as the outcome, and methods for integrating DNA methylation with gene expression or genotype data.

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Pash 3.0: A versatile software package for read mapping and integrative analysis of genomic and epigenomic variation using massively parallel DNA sequencing

Cited by 49 publications

References 36 publications

Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods

Estimating absolute methylation levels at single-CpG resolution from methylation enrichment and restriction enzyme sequencing methods

Methods for Cancer Epigenome Analysis

Statistical approaches for the analysis of DNA methylation microarray data

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