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BAMQL: a query language for extracting reads from BAM files

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

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

Mentioned by

twitter
12 tweeters

Citations

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11 Dimensions

Readers on

mendeley
46 Mendeley
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Title
BAMQL: a query language for extracting reads from BAM files
Published in
BMC Bioinformatics, August 2016
DOI 10.1186/s12859-016-1162-y
Pubmed ID
Authors

Andre P. Masella, Christopher M. Lalansingh, Pragash Sivasundaram, Michael Fraser, Robert G. Bristow, Paul C. Boutros

Abstract

It is extremely common to need to select a subset of reads from a BAM file based on their specific properties. Typically, a user unpacks the BAM file to a text stream using SAMtools, parses and filters the lines using AWK, then repacks them using SAMtools. This process is tedious and error-prone. In particular, when working with many columns of data, mix-ups are common and the bit field containing the flags is unintuitive. There are several libraries for reading BAM files, such as Bio-SamTools for Perl and pysam for Python. Both allow access to the BAM's read information and can filter reads, but require substantial boilerplate code; this is high overhead for mostly ad hoc filtering. We have created a query language that gathers reads using a collection of predicates and common logical connectives. Queries run faster than equivalents and can be compiled to native code for embedding in larger programs. BAMQL provides a user-friendly, powerful and performant way to extract subsets of BAM files for ad hoc analyses or integration into applications. The query language provides a collection of predicates beyond those in SAMtools, and more flexible connectives.

Twitter Demographics

The data shown below were collected from the profiles of 12 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 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 4%
United Kingdom 1 2%
Switzerland 1 2%
Unknown 42 91%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 26%
Student > Master 11 24%
Student > Bachelor 6 13%
Student > Ph. D. Student 5 11%
Student > Doctoral Student 2 4%
Other 4 9%
Unknown 6 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 15 33%
Agricultural and Biological Sciences 10 22%
Computer Science 7 15%
Medicine and Dentistry 4 9%
Mathematics 1 2%
Other 2 4%
Unknown 7 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 15 December 2016.
All research outputs
#3,559,500
of 17,800,904 outputs
Outputs from BMC Bioinformatics
#1,513
of 6,267 outputs
Outputs of similar age
#65,575
of 273,100 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 27 outputs
Altmetric has tracked 17,800,904 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,267 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 75% 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 273,100 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 27 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.