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Meta-aligner: long-read alignment based on genome statistics

Overview of attention for article published in BMC Bioinformatics, February 2017
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Title
Meta-aligner: long-read alignment based on genome statistics
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1518-y
Pubmed ID
Authors

Damoon Nashta-ali, Ali Aliyari, Ahmad Ahmadian Moghadam, Mohammad Amin Edrisi, Seyed Abolfazl Motahari, Babak Hossein Khalaj

Abstract

Current development of sequencing technologies is towards generating longer and noisier reads. Evidently, accurate alignment of these reads play an important role in any downstream analysis. Similarly, reducing the overall cost of sequencing is related to the time consumption of the aligner. The tradeoff between accuracy and speed is the main challenge in designing long read aligners. We propose Meta-aligner which aligns long and very long reads to the reference genome very efficiently and accurately. Meta-aligner incorporates available short/long aligners as subcomponents and uses statistics from the reference genome to increase the performance. Meta-aligner estimates statistics from reads and the reference genome automatically. Meta-aligner is implemented in C++ and runs in popular POSIX-like operating systems such as Linux. Meta-aligner achieves high recall rates and precisions especially for long reads and high error rates. Also, it improves performance of alignment in the case of PacBio long-reads in comparison with traditional schemes.

X Demographics

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The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 3%
Sweden 1 3%
Norway 1 3%
Unknown 28 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 39%
Researcher 7 23%
Student > Bachelor 4 13%
Other 1 3%
Student > Doctoral Student 1 3%
Other 2 6%
Unknown 4 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 8 26%
Agricultural and Biological Sciences 6 19%
Computer Science 5 16%
Engineering 5 16%
Mathematics 1 3%
Other 2 6%
Unknown 4 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 25 February 2017.
All research outputs
#14,052,256
of 22,955,959 outputs
Outputs from BMC Bioinformatics
#4,495
of 7,309 outputs
Outputs of similar age
#170,645
of 311,210 outputs
Outputs of similar age from BMC Bioinformatics
#71
of 139 outputs
Altmetric has tracked 22,955,959 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,309 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 35th percentile – i.e., 35% 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 311,210 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 139 others from the same source and published within six weeks on either side of this one. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.