↓ Skip to main content

The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants

Overview of attention for article published in Nucleic Acids Research, December 2009
Altmetric Badge

About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

Mentioned by

policy
2 policy sources
twitter
42 tweeters
patent
28 patents
facebook
1 Facebook page
wikipedia
9 Wikipedia pages
q&a
4 Q&A threads

Citations

dimensions_citation
893 Dimensions

Readers on

mendeley
1965 Mendeley
citeulike
47 CiteULike
connotea
5 Connotea
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants
Published in
Nucleic Acids Research, December 2009
DOI 10.1093/nar/gkp1137
Pubmed ID
Authors

Peter J. A. Cock, Christopher J. Fields, Naohisa Goto, Michael L. Heuer, Peter M. Rice

Abstract

FASTQ has emerged as a common file format for sharing sequencing read data combining both the sequence and an associated per base quality score, despite lacking any formal definition to date, and existing in at least three incompatible variants. This article defines the FASTQ format, covering the original Sanger standard, the Solexa/Illumina variants and conversion between them, based on publicly available information such as the MAQ documentation and conventions recently agreed by the Open Bioinformatics Foundation projects Biopython, BioPerl, BioRuby, BioJava and EMBOSS. Being an open access publication, it is hoped that this description, with the example files provided as Supplementary Data, will serve in future as a reference for this important file format.

Twitter Demographics

The data shown below were collected from the profiles of 42 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 1,965 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 52 3%
United Kingdom 26 1%
Germany 25 1%
France 14 <1%
Italy 13 <1%
Brazil 13 <1%
China 8 <1%
Sweden 6 <1%
Netherlands 6 <1%
Other 60 3%
Unknown 1742 89%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 462 24%
Researcher 416 21%
Student > Master 336 17%
Student > Bachelor 234 12%
Student > Doctoral Student 86 4%
Other 262 13%
Unknown 169 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 935 48%
Biochemistry, Genetics and Molecular Biology 396 20%
Computer Science 169 9%
Medicine and Dentistry 72 4%
Engineering 34 2%
Other 144 7%
Unknown 215 11%

Attention Score in Context

This research output has an Altmetric Attention Score of 51. 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 18 September 2021.
All research outputs
#564,034
of 19,164,538 outputs
Outputs from Nucleic Acids Research
#200
of 24,527 outputs
Outputs of similar age
#2,210
of 101,948 outputs
Outputs of similar age from Nucleic Acids Research
#1
of 234 outputs
Altmetric has tracked 19,164,538 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 24,527 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one has done particularly well, scoring higher than 99% 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 101,948 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 97% of its contemporaries.
We're also able to compare this research output to 234 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 99% of its contemporaries.