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Genomic characterization of mutant laboratory mouse strains by exome sequencing and annotation lift-over

Overview of attention for article published in BMC Genomics, May 2015
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Title
Genomic characterization of mutant laboratory mouse strains by exome sequencing and annotation lift-over
Published in
BMC Genomics, May 2015
DOI 10.1186/s12864-015-1548-7
Pubmed ID
Authors

Sophia Derdak, Sibylle Sabrautzki, Martin Hrabě de Angelis, Marta Gut, Ivo G Gut, Sergi Beltran

Abstract

Exome sequencing has become a popular method to evaluate undirected mutagenesis experiments in mice. However, the most suitable mouse strain for the biological model may be relatively distant from the standard mouse reference genome. For pinpointing causative variants, a matching reference with gene annotations is essential, but not always readily available. We present an approach that allows to use murine Ensembl annotations on alternative mouse strain assemblies. We resolved ENU-induced mutation screening for 8 phenotypic mutant lines generated on C3HeB/FeJ background aligning the sequences against the closely related, but not annotated reference of C3H/HeJ. Variants occurring in all strains were filtered out as specific for the C3HeB/FeJ strain but unrelated to mutagenesis. Variants occurring exclusively in all individuals of one mutant line and matching the inheritance model were selected as mutagenesis-related. These variants were annotated with gene and exon names lifted over from the standard murine reference mm9 to C3H/HeJ using megablast. For each mutant line, we could restrict the results to exonic variants in between 1 and 23 genes. The presented method of exonic annotation lift-over proved to be a valuable tool in the search for mutagenesis-derived coding genomic variants and the assessment of genotype-phenotype relationships.

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 12 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 8%
Unknown 11 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 42%
Student > Ph. D. Student 2 17%
Student > Bachelor 1 8%
Student > Master 1 8%
Unknown 3 25%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 50%
Biochemistry, Genetics and Molecular Biology 1 8%
Computer Science 1 8%
Mathematics 1 8%
Unknown 3 25%

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 14 May 2015.
All research outputs
#12,619,368
of 16,533,785 outputs
Outputs from BMC Genomics
#6,360
of 9,071 outputs
Outputs of similar age
#150,894
of 234,333 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 16,533,785 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,071 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 23rd percentile – i.e., 23% of its peers scored the same or lower than it.
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