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Re-alignment of the unmapped reads with base quality score

Overview of attention for article published in BMC Bioinformatics, March 2015
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
Re-alignment of the unmapped reads with base quality score
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/1471-2105-16-s5-s8
Pubmed ID
Authors

Xiaoqing Peng, Jianxin Wang, Zhen Zhang, Qianghua Xiao, Min Li, Yi Pan

Abstract

Based on the next generation genome sequencing technologies, a variety of biological applications are developed, while alignment is the first step once the sequencing reads are obtained. In recent years, many software tools have been developed to efficiently and accurately align short reads to the reference genome. However, there are still many reads that can't be mapped to the reference genome, due to the exceeding of allowable mismatches. Moreover, besides the unmapped reads, the reads with low mapping qualities are also excluded from the downstream analysis, such as variance calling. If we can take advantages of the confident segments of these reads, not only can the alignment rates be improved, but also more information will be provided for the downstream analysis. This paper proposes a method, called RAUR (Re-align the Unmapped Reads), to re-align the reads that can not be mapped by alignment tools. Firstly, it takes advantages of the base quality scores (reported by the sequencer) to figure out the most confident and informative segments of the unmapped reads by controlling the number of possible mismatches in the alignment. Then, combined with an alignment tool, RAUR re-align these segments of the reads. We run RAUR on both simulated data and real data with different read lengths. The results show that many reads which fail to be aligned by the most popular alignment tools (BWA and Bowtie2) can be correctly re-aligned by RAUR, with a similar Precision. Even compared with the BWA-MEM and the local mode of Bowtie2, which perform local alignment for long reads to improve the alignment rate, RAUR also shows advantages on the Alignment rate and Precision in some cases. Therefore, the trimming strategy used in RAUR is useful to improve the Alignment rate of alignment tools for the next-generation genome sequencing. All source code are available at http://netlab.csu.edu.cn/bioinformatics/RAUR.html.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 2 3%
Luxembourg 1 2%
Unknown 60 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 24 38%
Researcher 8 13%
Student > Master 6 10%
Student > Bachelor 4 6%
Student > Doctoral Student 4 6%
Other 6 10%
Unknown 11 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 38%
Biochemistry, Genetics and Molecular Biology 12 19%
Computer Science 5 8%
Engineering 5 8%
Psychology 1 2%
Other 3 5%
Unknown 13 21%
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 17 June 2020.
All research outputs
#6,039,584
of 22,796,179 outputs
Outputs from BMC Bioinformatics
#2,248
of 7,281 outputs
Outputs of similar age
#76,127
of 286,004 outputs
Outputs of similar age from BMC Bioinformatics
#49
of 141 outputs
Altmetric has tracked 22,796,179 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 7,281 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 286,004 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 141 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 64% of its contemporaries.