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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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  • Good Attention Score compared to outputs of the same age (71st percentile)
  • Good Attention Score compared to outputs of the same age and source (66th percentile)

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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.

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The data shown below were collected from the profiles of 10 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 54 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 50 93%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 24%
Student > Master 11 20%
Student > Bachelor 7 13%
Student > Ph. D. Student 5 9%
Student > Doctoral Student 3 6%
Other 4 7%
Unknown 11 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 33%
Agricultural and Biological Sciences 10 19%
Computer Science 7 13%
Medicine and Dentistry 4 7%
Mathematics 1 2%
Other 2 4%
Unknown 12 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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
#6,831,300
of 25,381,384 outputs
Outputs from BMC Bioinformatics
#2,380
of 7,683 outputs
Outputs of similar age
#105,464
of 366,575 outputs
Outputs of similar age from BMC Bioinformatics
#40
of 115 outputs
Altmetric has tracked 25,381,384 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,683 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 gotten more attention than average, scoring higher than 68% 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 366,575 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 71% of its contemporaries.
We're also able to compare this research output to 115 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 66% of its contemporaries.