Title |
Improving prokaryotic transposable elements identification using a combination of de novo and profile HMM methods
|
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Published in |
BMC Genomics, October 2013
|
DOI | 10.1186/1471-2164-14-700 |
Pubmed ID | |
Authors |
Choumouss Kamoun, Thibaut Payen, Aurélie Hua-Van, Jonathan Filée |
Abstract |
Insertion Sequences (ISs) and their non-autonomous derivatives (MITEs) are important components of prokaryotic genomes inducing duplication, deletion, rearrangement or lateral gene transfers. Although ISs and MITEs are relatively simple and basic genetic elements, their detection remains a difficult task due to their remarkable sequence diversity. With the advent of high-throughput genome and metagenome sequencing technologies, the development of fast, reliable and sensitive methods of ISs and MITEs detection become an important challenge. So far, almost all studies dealing with prokaryotic transposons have used classical BLAST-based detection methods against reference libraries. Here we introduce alternative methods of detection either taking advantages of the structural properties of the elements (de novo methods) or using an additional library-based method using profile HMM searches. |
X Demographics
Geographical breakdown
Country | Count | As % |
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United States | 2 | 50% |
Canada | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 50% |
Scientists | 2 | 50% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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United States | 4 | 5% |
Brazil | 3 | 4% |
Canada | 1 | 1% |
Portugal | 1 | 1% |
Spain | 1 | 1% |
Russia | 1 | 1% |
Unknown | 73 | 87% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 30 | 36% |
Student > Ph. D. Student | 17 | 20% |
Student > Master | 11 | 13% |
Student > Bachelor | 9 | 11% |
Other | 3 | 4% |
Other | 7 | 8% |
Unknown | 7 | 8% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 38 | 45% |
Biochemistry, Genetics and Molecular Biology | 20 | 24% |
Immunology and Microbiology | 3 | 4% |
Computer Science | 3 | 4% |
Medicine and Dentistry | 2 | 2% |
Other | 7 | 8% |
Unknown | 11 | 13% |