ArticleBMC bioinformatics2016
MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Step-by-Step Metagenomics for Food Microbiome Analysis: A Detailed Review.Foods (Basel, Switzerland) · 2024Review
- Genome-resolved metagenomics using environmental and clinical samples.Briefings in bioinformatics · 2021Review
- CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data.NAR genomics and bioinformatics · 2021Article
- Literature on Applied Machine Learning in Metagenomic Classification: A Scoping Review.Biology · 2020Article
- New approaches for metagenome assembly with short reads.Briefings in bioinformatics · 2020Review
- Cram-JS: reference-based decompression in node and the browser.Bioinformatics (Oxford, England) · 2019Article
- Metagenomic Composition Analysis of an Ancient Sequenced Polar Bear Jawbone from Svalbard.Genes · 2018Article
- fastQ_brew: module for analysis, preprocessing, and reformatting of FASTQ sequence data.BMC research notes · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundMetagenomics is a genomics research discipline devoted to the study of microbial communities in environmental samples and human and animal organs and tissues. Sequenced metagenomic samples usually comprise reads from a large number of different bacterial communities and hence tend to result in large file sizes, typically ranging between 1-10 GB. This leads to challenges in analyzing, transferring and storing metagenomic data. In order to overcome these data processing issues, we introduce MetaCRAM, the first de novo, parallelized software suite specialized for FASTA and FASTQ format metagenomic read processing and lossless compression.
resultsMetaCRAM integrates algorithms for taxonomy identification and assembly, and introduces parallel execution methods; furthermore, it enables genome reference selection and CRAM based compression. MetaCRAM also uses novel reference-based compression methods designed through extensive studies of integer compression techniques and through fitting of empirical distributions of metagenomic read-reference positions. MetaCRAM is a lossless method compatible with standard CRAM formats, and it allows for fast selection of relevant files in the compressed domain via maintenance of taxonomy information. The performance of MetaCRAM as a stand-alone compression platform was evaluated on various metagenomic samples from the NCBI Sequence Read Archive, suggesting 2- to 4-fold compression ratio improvements compared to gzip. On average, the compressed file sizes were 2-13 percent of the original raw metagenomic file sizes.
conclusionsWe described the first architecture for reference-based, lossless compression of metagenomic data. The compression scheme proposed offers significantly improved compression ratios as compared to off-the-shelf methods such as zip programs. Furthermore, it enables running different components in parallel and it provides the user with taxonomic and assembly information generated during execution of the compression pipeline. AVAILABILITY: The MetaCRAM software is freely available at http://web.engr.illinois.edu/~mkim158/metacram.html. The website also contains a README file and other relevant instructions for running the code. Note that to run the code one needs a minimum of 16 GB of RAM. In addition, virtual box is set up on a 4GB RAM machine for users to run a simple demonstration.
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Registered trials
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