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MAC: identifying and correcting annotation for multi-nucleotide variations

Overview of attention for article published in BMC Genomics, August 2015
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (73rd percentile)

Mentioned by

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4 tweeters
patent
1 patent

Citations

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24 Dimensions

Readers on

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45 Mendeley
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2 CiteULike
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Title
MAC: identifying and correcting annotation for multi-nucleotide variations
Published in
BMC Genomics, August 2015
DOI 10.1186/s12864-015-1779-7
Pubmed ID
Authors

Lei Wei, Lu T. Liu, Jacob R. Conroy, Qiang Hu, Jeffrey M. Conroy, Carl D. Morrison, Candace S. Johnson, Jianmin Wang, Song Liu

Abstract

Next-Generation Sequencing (NGS) technologies have rapidly advanced our understanding of human variation in cancer. To accurately translate the raw sequencing data into practical knowledge, annotation tools, algorithms and pipelines must be developed that keep pace with the rapidly evolving technology. Currently, a challenge exists in accurately annotating multi-nucleotide variants (MNVs). These tandem substitutions, when affecting multiple nucleotides within a single protein codon of a gene, result in a translated amino acid involving all nucleotides in that codon. Most existing variant callers report a MNV as individual single-nucleotide variants (SNVs), often resulting in multiple triplet codon sequences and incorrect amino acid predictions. To correct potentially misannotated MNVs among reported SNVs, a primary challenge resides in haplotype phasing which is to determine whether the neighboring SNVs are co-located on the same chromosome. Here we describe MAC (Multi-Nucleotide Variant Annotation Corrector), an integrative pipeline developed to correct potentially mis-annotated MNVs. MAC was designed as an application that only requires a SNV file and the matching BAM file as data inputs. Using an example data set containing 3024 SNVs and the corresponding whole-genome sequencing BAM files, we show that MAC identified eight potentially mis-annotated SNVs, and accurately updated the amino acid predictions for seven of the variant calls. MAC can identify and correct amino acid predictions that result from MNVs affecting multiple nucleotides within a single protein codon, which cannot be handled by most existing SNV-based variant pipelines. The MAC software is freely available and represents a useful tool for the accurate translation of genomic sequence to protein function.

Twitter Demographics

The data shown below were collected from the profiles of 4 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 7%
United Kingdom 1 2%
Sweden 1 2%
Canada 1 2%
France 1 2%
Unknown 38 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 29%
Student > Ph. D. Student 7 16%
Other 5 11%
Professor 4 9%
Professor > Associate Professor 3 7%
Other 8 18%
Unknown 5 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 17 38%
Agricultural and Biological Sciences 8 18%
Engineering 2 4%
Mathematics 2 4%
Computer Science 2 4%
Other 9 20%
Unknown 5 11%

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 26 July 2018.
All research outputs
#5,278,163
of 20,091,699 outputs
Outputs from BMC Genomics
#2,303
of 9,938 outputs
Outputs of similar age
#63,810
of 246,476 outputs
Outputs of similar age from BMC Genomics
#1
of 1 outputs
Altmetric has tracked 20,091,699 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 9,938 research outputs from this source. They receive a mean Attention Score of 4.5. This one has done well, scoring higher than 76% 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 246,476 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
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