Title |
Software for pre-processing Illumina next-generation sequencing short read sequences
|
---|---|
Published in |
Source Code for Biology and Medicine, May 2014
|
DOI | 10.1186/1751-0473-9-8 |
Pubmed ID | |
Authors |
Chuming Chen, Sari S Khaleel, Hongzhan Huang, Cathy H Wu |
Abstract |
When compared to Sanger sequencing technology, next-generation sequencing (NGS) technologies are hindered by shorter sequence read length, higher base-call error rate, non-uniform coverage, and platform-specific sequencing artifacts. These characteristics lower the quality of their downstream analyses, e.g. de novo and reference-based assembly, by introducing sequencing artifacts and errors that may contribute to incorrect interpretation of data. Although many tools have been developed for quality control and pre-processing of NGS data, none of them provide flexible and comprehensive trimming options in conjunction with parallel processing to expedite pre-processing of large NGS datasets. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
Canada | 2 | 29% |
France | 1 | 14% |
Spain | 1 | 14% |
Cameroon | 1 | 14% |
Unknown | 2 | 29% |
Demographic breakdown
Type | Count | As % |
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Scientists | 4 | 57% |
Members of the public | 3 | 43% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | <1% |
Spain | 2 | <1% |
Italy | 2 | <1% |
Brazil | 2 | <1% |
France | 1 | <1% |
Norway | 1 | <1% |
Netherlands | 1 | <1% |
Canada | 1 | <1% |
Chile | 1 | <1% |
Other | 4 | 1% |
Unknown | 286 | 94% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 69 | 23% |
Researcher | 61 | 20% |
Student > Master | 47 | 15% |
Student > Bachelor | 30 | 10% |
Student > Doctoral Student | 15 | 5% |
Other | 45 | 15% |
Unknown | 37 | 12% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 129 | 42% |
Biochemistry, Genetics and Molecular Biology | 60 | 20% |
Computer Science | 19 | 6% |
Immunology and Microbiology | 8 | 3% |
Medicine and Dentistry | 6 | 2% |
Other | 30 | 10% |
Unknown | 52 | 17% |