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MinorityReport, software for generalized analysis of causal genetic variants

Overview of attention for article published in Malaria Journal, February 2017
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2 tweeters

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2 Dimensions

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26 Mendeley
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Title
MinorityReport, software for generalized analysis of causal genetic variants
Published in
Malaria Journal, February 2017
DOI 10.1186/s12936-017-1730-2
Pubmed ID
Authors

Jeremy A. Horst, Wesley Wu, Joseph L. DeRisi

Abstract

The widespread availability of next generation genome sequencing technologies has enabled a wide range of variant detection applications, especially in cancer and inborn genetic disorders. For model systems and microorganisms, the same technology may be used to discover the causative mutations for any phenotype, including those generated in response to chemical perturbation. In the case of pathogenic organisms, these approaches have allowed the determination of drug targets by means of resistance selection followed by genome sequencing. MinorityReport is open source software written in python that facilitates the comparison of any two sets of genome alignments for the purpose of rapidly identifying the spectrum of nonsynonymous changes, insertions or deletions, and copy number variations in a presumed mutant relative to its parent. Specifically, MinorityReport relates mapped sequence reads in SAM format output from any alignment tool for both the mutant and parent genome, relative to a reference genome, and produces the set of variants that distinguishes the mutant from the parent, all presented in an intuitive, straightforward report format. MinorityReport features tunable parameters for evaluating evidence and a scoring system that prioritizes reported variants based on relative proportions of read counts supporting the variant in the mutant versus parent data sets. The utility of MinorityReport is demonstrated using previously published publicly available data sets to find the determinants of resistance for novel anti-malarial drugs. MinorityReport is readily available (github: JeremyHorst/MinorityReport) to identify the genetic mechanisms of drug resistance in Plasmodium, genotype-phenotype relationships in human diads, or genomic variations between any two related organisms.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Denmark 1 4%
Unknown 25 96%

Demographic breakdown

Readers by professional status Count As %
Student > Master 7 27%
Student > Ph. D. Student 6 23%
Researcher 4 15%
Student > Bachelor 3 12%
Professor > Associate Professor 2 8%
Other 3 12%
Unknown 1 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 31%
Biochemistry, Genetics and Molecular Biology 7 27%
Chemistry 2 8%
Social Sciences 2 8%
Nursing and Health Professions 1 4%
Other 2 8%
Unknown 4 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 23 February 2017.
All research outputs
#11,117,150
of 17,177,772 outputs
Outputs from Malaria Journal
#3,712
of 4,757 outputs
Outputs of similar age
#161,154
of 267,481 outputs
Outputs of similar age from Malaria Journal
#1
of 1 outputs
Altmetric has tracked 17,177,772 research outputs across all sources so far. This one is in the 23rd percentile – i.e., 23% of other outputs scored the same or lower than it.
So far Altmetric has tracked 4,757 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.1. This one is in the 15th percentile – i.e., 15% of its peers scored the same or lower than it.
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 267,481 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them