Title |
Consensus Rules in Variant Detection from Next-Generation Sequencing Data
|
---|---|
Published in |
PLOS ONE, June 2012
|
DOI | 10.1371/journal.pone.0038470 |
Pubmed ID | |
Authors |
Peilin Jia, Fei Li, Jufeng Xia, Haiquan Chen, Hongbin Ji, William Pao, Zhongming Zhao |
Abstract |
A critical step in detecting variants from next-generation sequencing data is post hoc filtering of putative variants called or predicted by computational tools. Here, we highlight four critical parameters that could enhance the accuracy of called single nucleotide variants and insertions/deletions: quality and deepness, refinement and improvement of initial mapping, allele/strand balance, and examination of spurious genes. Use of these sequence features appropriately in variant filtering could greatly improve validation rates, thereby saving time and costs in next-generation sequencing projects. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Germany | 2 | 33% |
United States | 1 | 17% |
Canada | 1 | 17% |
Unknown | 2 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 4 | 67% |
Members of the public | 2 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 188 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Netherlands | 3 | 2% |
United States | 3 | 2% |
Switzerland | 2 | 1% |
Canada | 2 | 1% |
Spain | 2 | 1% |
Brazil | 2 | 1% |
United Kingdom | 2 | 1% |
Sweden | 1 | <1% |
Chile | 1 | <1% |
Other | 5 | 3% |
Unknown | 165 | 88% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 63 | 34% |
Student > Ph. D. Student | 38 | 20% |
Student > Master | 18 | 10% |
Other | 14 | 7% |
Student > Bachelor | 11 | 6% |
Other | 29 | 15% |
Unknown | 15 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 94 | 50% |
Biochemistry, Genetics and Molecular Biology | 29 | 15% |
Medicine and Dentistry | 20 | 11% |
Computer Science | 13 | 7% |
Engineering | 4 | 2% |
Other | 8 | 4% |
Unknown | 20 | 11% |