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SamQL: a structured query language and filtering tool for the SAM/BAM file format

Overview of attention for article published in BMC Bioinformatics, October 2021
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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 (85th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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1 blog
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Title
SamQL: a structured query language and filtering tool for the SAM/BAM file format
Published in
BMC Bioinformatics, October 2021
DOI 10.1186/s12859-021-04390-3
Pubmed ID
Authors

Christopher T. Lee, Manolis Maragkakis

Abstract

The Sequence Alignment/Map Format Specification (SAM) is one of the most widely adopted file formats in bioinformatics and many researchers use it daily. Several tools, including most high-throughput sequencing read aligners, use it as their primary output and many more tools have been developed to process it. However, despite its flexibility, SAM encoded files can often be difficult to query and understand even for experienced bioinformaticians. As genomic data are rapidly growing, structured, and efficient queries on data that are encoded in SAM/BAM files are becoming increasingly important. Existing tools are very limited in their query capabilities or are not efficient. Critically, new tools that address these shortcomings, should not be able to support existing large datasets but should also do so without requiring massive data transformations and file infrastructure reorganizations. Here we introduce SamQL, an SQL-like query language for the SAM format with intuitive syntax that supports complex and efficient queries on top of SAM/BAM files and that can replace commonly used Bash one-liners employed by many bioinformaticians. SamQL has high expressive power with no upper limit on query size and when parallelized, outperforms other substantially less expressive software. SamQL is a complete query language that we envision as a step to a structured database engine for genomics. SamQL is written in Go, and is freely available as standalone program and as an open-source library under an MIT license, https://github.com/maragkakislab/samql/ .

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X Demographics

The data shown below were collected from the profiles of 11 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 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 18 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 22%
Other 2 11%
Student > Bachelor 2 11%
Student > Ph. D. Student 1 6%
Researcher 1 6%
Other 1 6%
Unknown 7 39%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 17%
Agricultural and Biological Sciences 2 11%
Veterinary Science and Veterinary Medicine 1 6%
Computer Science 1 6%
Economics, Econometrics and Finance 1 6%
Other 2 11%
Unknown 8 44%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 13 October 2021.
All research outputs
#2,762,391
of 23,577,761 outputs
Outputs from BMC Bioinformatics
#876
of 7,418 outputs
Outputs of similar age
#62,773
of 434,729 outputs
Outputs of similar age from BMC Bioinformatics
#22
of 163 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,418 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 88% 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 434,729 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 85% of its contemporaries.
We're also able to compare this research output to 163 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.