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SlopMap: A Software Application Tool for Quick and Flexible Identification of Similar Sequences Using Exact K-Mer Matching

Overview of attention for article published in Journal of data mining in genomics proteomics, January 2013
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Article details
Title
SlopMap: A Software Application Tool for Quick and Flexible Identification of Similar Sequences Using Exact K-Mer Matching
Published in
Journal of data mining in genomics proteomics, January 2013
DOI 10.4172/2153-0602.1000133
Pubmed ID
Authors
Abstract

With the advent of Next-Generation (NG) sequencing, it has become possible to sequence a entire genomes quickly and inexpensively. However, in some experiments one only needs to extract and assembly a portion of the sequence reads, for example when performing transcriptome studies, sequencing mitochondrial genomes, or characterizing exomes. With the raw DNA-library of a complete genome it would appear to be a trivial problem to identify reads of interest. But it is not always easy to incorporate well-known tools such as BLAST, BLAT, Bowtie, and SOAP directly into a bioinformatics pipelines before the assembly stage, either due to incompatibility with the assembler's file inputs, or because it is desirable to incorporate information that must be extracted separately. For example, in order to incorporate flowgrams from a Roche 454 sequencer into the Newbler assembler it is necessary to first extract them from the original SFF files. We present SlopMap, a bioinformatics software utility that allows quickly identification similar to the provided reference reads from either Roche 454 or Illumnia DNA library. With simple and intuitive command-line interface along with file output formats compatible to assembly programs, SlopMap can be directly embedded to biological data processing pipeline without any additional programming work. In addition, SlopMap preserves flowgram information needed for Roche 454 assembler.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 18 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 6%
Sweden 1 6%
Norway 1 6%
Canada 1 6%
Unknown 14 78%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 6 33%
Student > Ph. D. Student 4 22%
Student > Bachelor 2 11%
Student > Master 2 11%
Professor 1 6%
Other 0 0%
Unknown 3 17%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 10 56%
Biochemistry, Genetics and Molecular Biology 2 11%
Computer Science 1 6%
Chemistry 1 6%
Unknown 4 22%
Attention Score in Context

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 11 January 2014.
All research outputs
#28,811,852
of 34,601,305 outputs
Outputs from Journal of data mining in genomics proteomics
#43
of 53 outputs
Outputs of similar age
#290,103
of 347,055 outputs
Outputs of similar age from Journal of data mining in genomics proteomics
#8
of 8 outputs
Altmetric has tracked 34,601,305 research outputs across all sources so far. This one is in the 9th percentile – i.e., 9% of other outputs scored the same or lower than it.
So far Altmetric has tracked 53 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one.