Title |
SInC: an accurate and fast error-model based simulator for SNPs, Indels and CNVs coupled with a read generator for short-read sequence data
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Published in |
BMC Bioinformatics, February 2014
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DOI | 10.1186/1471-2105-15-40 |
Pubmed ID | |
Authors |
Swetansu Pattnaik, Saurabh Gupta, Arjun A Rao, Binay Panda |
Abstract |
The rapid advancements in the field of genome sequencing are aiding our understanding on many biological systems. In the last five years, computational biologists and bioinformatics specialists have come up with newer, better and more efficient tools towards the discovery, analysis and interpretation of different genomic variants from high-throughput sequencing data. Availability of reliable simulated dataset is essential and is the first step towards testing any newly developed analytical tools for variant discovery. Although there are tools currently available that can simulate variants, none present the possibility of simulating all the three major types of variations (Single Nucleotide Polymorphisms, Insertions and Deletions and Copy Number Variations) and can generate reads taking a realistic error-model into consideration. Therefore, an efficient simulator and read generator is needed that can simulate variants taking the error rates of true biological samples into consideration. |
X Demographics
Geographical breakdown
Country | Count | As % |
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France | 4 | 57% |
United States | 1 | 14% |
Norway | 1 | 14% |
Sweden | 1 | 14% |
Demographic breakdown
Type | Count | As % |
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Scientists | 5 | 71% |
Members of the public | 2 | 29% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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France | 2 | 2% |
United States | 2 | 2% |
Spain | 2 | 2% |
Sweden | 1 | 1% |
Taiwan | 1 | 1% |
Unknown | 82 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 27 | 30% |
Student > Ph. D. Student | 20 | 22% |
Student > Bachelor | 10 | 11% |
Student > Doctoral Student | 7 | 8% |
Student > Master | 5 | 6% |
Other | 14 | 16% |
Unknown | 7 | 8% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 30 | 33% |
Computer Science | 17 | 19% |
Biochemistry, Genetics and Molecular Biology | 16 | 18% |
Engineering | 4 | 4% |
Medicine and Dentistry | 3 | 3% |
Other | 6 | 7% |
Unknown | 14 | 16% |