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4Pipe4 – A 454 data analysis pipeline for SNP detection in datasets with no reference sequence or strain information

Overview of attention for article published in BMC Bioinformatics, January 2016
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Title
4Pipe4 – A 454 data analysis pipeline for SNP detection in datasets with no reference sequence or strain information
Published in
BMC Bioinformatics, January 2016
DOI 10.1186/s12859-016-0892-1
Pubmed ID
Authors

Francisco Pina-Martins, Bruno M. Vieira, Sofia G. Seabra, Dora Batista, Octávio S. Paulo

Abstract

Next-generation sequencing datasets are becoming more frequent, and their use in population studies is becoming widespread. For non-model species, without a reference genome, it is possible from a panel of individuals to identify a set of SNPs that can be used for further population genotyping. However the lack of a reference genome to which the sequenced data could be compared makes the finding of SNPs more troublesome. Additionally when the data sources (strains) are not identified (e.g. in datasets of pooled individuals), the problem of finding reliable variation in these datasets can become much more difficult due to the lack of specialized software for this specific task. Here we describe 4Pipe4, a 454 data analysis pipeline particularly focused on SNP detection when no reference or strain information is available. It uses a command line interface to automatically call other programs, parse their outputs and summarize the results. The variation detection routine is built-in in the program itself. Despite being optimized for SNP mining in 454 EST data, it is flexible enough to automate the analysis of genomic data or even data from other NGS technologies. 4Pipe4 will output several HTML formatted reports with metrics on many of the most common assembly values, as well as on all the variation found. There is also a module available for finding putative SSRs in the analysed datasets. This program can be especially useful for researchers that have 454 datasets of a panel of pooled individuals and want to discover and characterize SNPs for subsequent individual genotyping with customized genotyping arrays. In comparison with other SNP detection approaches, 4Pipe4 showed the best validation ratio, retrieving a smaller number of SNPs but with a considerably lower false positive rate than other methods. 4Pipe4's source code is available at https://github.com/StuntsPT/4Pipe4 .

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Unknown 29 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 30%
Student > Ph. D. Student 5 17%
Professor 3 10%
Student > Bachelor 2 7%
Student > Master 2 7%
Other 3 10%
Unknown 6 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 9 30%
Biochemistry, Genetics and Molecular Biology 4 13%
Computer Science 3 10%
Mathematics 1 3%
Environmental Science 1 3%
Other 5 17%
Unknown 7 23%
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 27 November 2017.
All research outputs
#6,213,112
of 23,511,526 outputs
Outputs from BMC Bioinformatics
#2,280
of 7,405 outputs
Outputs of similar age
#98,300
of 397,591 outputs
Outputs of similar age from BMC Bioinformatics
#53
of 144 outputs
Altmetric has tracked 23,511,526 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 7,405 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 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 397,591 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 144 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 63% of its contemporaries.