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Light-weight reference-based compression of FASTQ data

Overview of attention for article published in BMC Bioinformatics, June 2015
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  • Above-average Attention Score compared to outputs of the same age (60th percentile)

Mentioned by

5 tweeters


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Readers on

41 Mendeley
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Light-weight reference-based compression of FASTQ data
Published in
BMC Bioinformatics, June 2015
DOI 10.1186/s12859-015-0628-7
Pubmed ID

Yongpeng Zhang, Linsen Li, Yanli Yang, Xiao Yang, Shan He, Zexuan Zhu


The exponential growth of next generation sequencing (NGS) data has posed big challenges to data storage, management and archive. Data compression is one of the effective solutions, where reference-based compression strategies can typically achieve superior compression ratios compared to the ones not relying on any reference. This paper presents a lossless light-weight reference-based compression algorithm namely LW-FQZip to compress FASTQ data. The three components of any given input, i.e., metadata, short reads and quality score strings, are first parsed into three data streams in which the redundancy information are identified and eliminated independently. Particularly, well-designed incremental and run-length-limited encoding schemes are utilized to compress the metadata and quality score streams, respectively. To handle the short reads, LW-FQZip uses a novel light-weight mapping model to fast map them against external reference sequence(s) and produce concise alignment results for storage. The three processed data streams are then packed together with some general purpose compression algorithms like LZMA. LW-FQZip was evaluated on eight real-world NGS data sets and achieved compression ratios in the range of 0.111-0.201. This is comparable or superior to other state-of-the-art lossless NGS data compression algorithms. LW-FQZip is a program that enables efficient lossless FASTQ data compression. It contributes to the state of art applications for NGS data storage and transmission. LW-FQZip is freely available online at: http://csse.szu.edu.cn/staff/zhuzx/LWFQZip .

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 2 5%
Sweden 1 2%
France 1 2%
Switzerland 1 2%
Japan 1 2%
United States 1 2%
Unknown 34 83%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 29%
Student > Ph. D. Student 8 20%
Student > Master 7 17%
Professor 3 7%
Professor > Associate Professor 2 5%
Other 5 12%
Unknown 4 10%
Readers by discipline Count As %
Computer Science 12 29%
Engineering 10 24%
Biochemistry, Genetics and Molecular Biology 6 15%
Agricultural and Biological Sciences 5 12%
Social Sciences 1 2%
Other 2 5%
Unknown 5 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 20 October 2015.
All research outputs
of 16,106,857 outputs
Outputs from BMC Bioinformatics
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Outputs of similar age
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Outputs of similar age from BMC Bioinformatics
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Altmetric has tracked 16,106,857 research outputs across all sources so far. This one is in the 46th percentile – i.e., 46% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,837 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 47th percentile – i.e., 47% of its peers scored the same or lower than it.
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 238,223 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them