Title |
Quality control of microbiota metagenomics by k-mer analysis
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Published in |
BMC Genomics, March 2015
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DOI | 10.1186/s12864-015-1406-7 |
Pubmed ID | |
Authors |
Florian Plaza Onate, Jean-Michel Batto, Catherine Juste, Jehane Fadlallah, Cyrielle Fougeroux, Doriane Gouas, Nicolas Pons, Sean Kennedy, Florence Levenez, Joel Dore, S Dusko Ehrlich, Guy Gorochov, Martin Larsen |
Abstract |
The biological and clinical consequences of the tight interactions between host and microbiota are rapidly being unraveled by next generation sequencing technologies and sophisticated bioinformatics, also referred to as microbiota metagenomics. The recent success of metagenomics has created a demand to rapidly apply the technology to large case-control cohort studies and to studies of microbiota from various habitats, including habitats relatively poor in microbes. It is therefore of foremost importance to enable a robust and rapid quality assessment of metagenomic data from samples that challenge present technological limits (sample numbers and size). Here we demonstrate that the distribution of overlapping k-mers of metagenome sequence data predicts sequence quality as defined by gene distribution and efficiency of sequence mapping to a reference gene catalogue. |
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Geographical breakdown
Country | Count | As % |
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United States | 9 | 30% |
France | 2 | 7% |
Finland | 2 | 7% |
Spain | 1 | 3% |
Germany | 1 | 3% |
India | 1 | 3% |
Mexico | 1 | 3% |
China | 1 | 3% |
Austria | 1 | 3% |
Other | 1 | 3% |
Unknown | 10 | 33% |
Demographic breakdown
Type | Count | As % |
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Scientists | 18 | 60% |
Members of the public | 12 | 40% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 4 | 2% |
Sweden | 3 | 2% |
United States | 2 | 1% |
Estonia | 2 | 1% |
Canada | 1 | <1% |
France | 1 | <1% |
Germany | 1 | <1% |
Egypt | 1 | <1% |
Unknown | 146 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 41 | 25% |
Student > Ph. D. Student | 32 | 20% |
Student > Bachelor | 19 | 12% |
Student > Master | 17 | 11% |
Student > Doctoral Student | 13 | 8% |
Other | 22 | 14% |
Unknown | 17 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 67 | 42% |
Biochemistry, Genetics and Molecular Biology | 38 | 24% |
Computer Science | 10 | 6% |
Medicine and Dentistry | 8 | 5% |
Immunology and Microbiology | 7 | 4% |
Other | 9 | 6% |
Unknown | 22 | 14% |