A (fire)cloud-based DNA methylation data preprocessing and quality control platform

Background Bisulfite sequencing allows base-pair resolution profiling of DNA methylation and has recently been adapted for use in single-cells. Analyzing these data, including making comparisons with existing data, remains challenging due to the scale of the data and differences in preprocessing met...

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Vydáno v:BMC bioinformatics Ročník 20; číslo 1; s. 160 - 5
Hlavní autoři: Kangeyan, Divy, Dunford, Andrew, Iyer, Sowmya, Stewart, Chip, Hanna, Megan, Getz, Gad, Aryee, Martin J.
Médium: Journal Article
Jazyk:angličtina
Vydáno: London BioMed Central 29.03.2019
BioMed Central Ltd
Springer Nature B.V
BMC
Témata:
Algorithms
Analysis and modelling of complex systems
Base sequence
Bioinformatics
Bioinformatics workflows
Biomedical and Life Sciences
Bisulfite
Cancer
Cloud computing
Computational biology
Computational Biology/Bioinformatics
Computer Appl. in Life Sciences
Containers
CpG islands
Criminal investigation
Data processing
Datasets
Deoxyribonucleic acid
DNA
DNA fingerprinting
DNA methylation
DNA sequencing
Epigenetics
Gene expression
Genetic research
Genomes
Genomics
Integration
Life Sciences
Methods
Methylation
Microarrays
Pipelines
Pipelining (computers)
Preprocessing
Quality control
Quality control analysis
Reproducibility
Software
Sulfites
Visualization (Computer)
Workflow
Workflow software
United States
DNA methylation
Cloud computing
Bioinformatics workflows
Quality control analysis
ISSN:1471-2105, 1471-2105
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Shrnutí:Background Bisulfite sequencing allows base-pair resolution profiling of DNA methylation and has recently been adapted for use in single-cells. Analyzing these data, including making comparisons with existing data, remains challenging due to the scale of the data and differences in preprocessing methods between published datasets. Results We present a set of preprocessing pipelines for bisulfite sequencing DNA methylation data that include a new R/Bioconductor package, scmeth , for a series of efficient QC analyses of large datasets. The pipelines go from raw data to CpG-level methylation estimates and can be run, with identical results, either on a single computer, in an HPC cluster or on Google Cloud Compute resources. These pipelines are designed to allow users to 1) ensure reproducibility of analyses, 2) achieve scalability to large whole genome datasets with 100 GB+ of raw data per sample and to single-cell datasets with thousands of cells, 3) enable integration and comparison between user-provided data and publicly available data, as all samples can be processed through the same pipeline, and 4) access to best-practice analysis pipelines. Pipelines are provided for whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS) and hybrid selection (capture) bisulfite sequencing (HSBS). Conclusions The workflows produce data quality metrics, visualization tracks, and aggregated output for further downstream analysis. Optional use of cloud computing resources facilitates analysis of large datasets, and integration with existing methylome profiles. The workflow design principles are applicable to other genomic data types.
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ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-019-2750-4