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MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression

Overview of attention for article published in BMC Bioinformatics, February 2016
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (91st percentile)

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2 blogs
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19 X users

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Title
MetaCRAM: an integrated pipeline for metagenomic taxonomy identification and compression
Published in
BMC Bioinformatics, February 2016
DOI 10.1186/s12859-016-0932-x
Pubmed ID
Authors

Minji Kim, Xiejia Zhang, Jonathan G. Ligo, Farzad Farnoud, Venugopal V. Veeravalli, Olgica Milenkovic

Abstract

Metagenomics 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. MetaCRAM 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. We 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. 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 84 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 5%
Brazil 2 2%
India 1 1%
Sweden 1 1%
Japan 1 1%
Estonia 1 1%
Unknown 74 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 26%
Student > Ph. D. Student 21 25%
Student > Master 11 13%
Student > Bachelor 6 7%
Other 4 5%
Other 14 17%
Unknown 6 7%
Readers by discipline Count As %
Agricultural and Biological Sciences 35 42%
Biochemistry, Genetics and Molecular Biology 14 17%
Computer Science 12 14%
Environmental Science 4 5%
Engineering 4 5%
Other 6 7%
Unknown 9 11%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 March 2016.
All research outputs
#1,694,330
of 25,364,603 outputs
Outputs from BMC Bioinformatics
#295
of 7,692 outputs
Outputs of similar age
#27,203
of 312,126 outputs
Outputs of similar age from BMC Bioinformatics
#13
of 145 outputs
Altmetric has tracked 25,364,603 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,692 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,126 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 145 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 91% of its contemporaries.