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Related Concept Videos

Quality Control01:05

Quality Control

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Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
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Quality of Water01:19

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In concrete preparation, the quality of water is paramount as it affects the strength and durability of the concrete. Potable water is usually preferred; however, it must not have excessive sodium or potassium to prevent compromising the concrete's integrity. Water quality is typically evaluated based on impurities such as dissolved solids, chlorides, and sulfates, and its pH value is ideally between 6 and 8. Even slightly acidic natural water may be acceptable unless it contains harmful...
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Quality Assurance01:19

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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Related Experiment Video

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

Divy Kangeyan1,2, Andrew Dunford2, Sowmya Iyer3

  • 1Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA.

BMC Bioinformatics
|March 30, 2019
PubMed
Summary
This summary is machine-generated.

New preprocessing pipelines and an R/Bioconductor package, scmeth, standardize DNA methylation analysis from bisulfite sequencing data. These tools ensure reproducibility, scalability, and integration for large-scale and single-cell studies.

Keywords:
Bioinformatics workflowsCloud computingDNA methylationQuality control analysis

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Area of Science:

  • Genomics
  • Epigenetics
  • Bioinformatics

Background:

  • Bisulfite sequencing provides base-pair resolution DNA methylation profiling.
  • Analyzing single-cell bisulfite sequencing data is challenging due to data scale and preprocessing variations.

Purpose of the Study:

  • To present standardized preprocessing pipelines for bisulfite sequencing DNA methylation data.
  • To introduce scmeth, an R/Bioconductor package for efficient quality control of large datasets.

Main Methods:

  • Developed preprocessing pipelines for whole genome bisulfite sequencing (WGBS), reduced representation bisulfite sequencing (RRBS), and hybrid selection bisulfite sequencing (HSBS).
  • Pipelines are executable on single computers, HPC clusters, or Google Cloud Compute resources.
  • The scmeth package offers efficient quality control for large-scale datasets.

Main Results:

  • Pipelines generate CpG-level methylation estimates from raw data.
  • Achieved scalability for large whole genome datasets (100GB+ per sample) and single-cell datasets.
  • Enabled integration and comparison of user-provided and public data through standardized processing.

Conclusions:

  • Workflows yield data quality metrics, visualization tracks, and aggregated output for downstream analysis.
  • Cloud computing options facilitate large dataset analysis and integration with existing methylome profiles.
  • The workflow design principles are adaptable to other genomic data types.