Title |
INC-Seq: accurate single molecule reads using nanopore sequencing
|
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Published in |
Giga Science, August 2016
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DOI | 10.1186/s13742-016-0140-7 |
Pubmed ID | |
Authors |
Chenhao Li, Kern Rei Chng, Esther Jia Hui Boey, Amanda Hui Qi Ng, Andreas Wilm, Niranjan Nagarajan |
Abstract |
Nanopore sequencing provides a rapid, cheap and portable real-time sequencing platform with the potential to revolutionize genomics. However, several applications are limited by relatively high single-read error rates (>10 %), including RNA-seq, haplotype sequencing and 16S sequencing. We developed the Intramolecular-ligated Nanopore Consensus Sequencing (INC-Seq) as a strategy for obtaining long and accurate nanopore reads, starting with low input DNA. Applying INC-Seq for 16S rRNA-based bacterial profiling generated full-length amplicon sequences with a median accuracy >97 %. INC-Seq reads enabled accurate species-level classification, identification of species at 0.1 % abundance and robust quantification of relative abundances, providing a cheap and effective approach for pathogen detection and microbiome profiling on the MinION system. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 13 | 22% |
United Kingdom | 8 | 14% |
Japan | 4 | 7% |
Singapore | 3 | 5% |
India | 2 | 3% |
Spain | 2 | 3% |
Belgium | 1 | 2% |
China | 1 | 2% |
Ireland | 1 | 2% |
Other | 4 | 7% |
Unknown | 20 | 34% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 27 | 46% |
Scientists | 27 | 46% |
Science communicators (journalists, bloggers, editors) | 5 | 8% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 1% |
Sweden | 2 | <1% |
Germany | 1 | <1% |
Uruguay | 1 | <1% |
Netherlands | 1 | <1% |
Switzerland | 1 | <1% |
United Kingdom | 1 | <1% |
India | 1 | <1% |
Denmark | 1 | <1% |
Other | 1 | <1% |
Unknown | 345 | 96% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 95 | 26% |
Student > Ph. D. Student | 63 | 18% |
Student > Bachelor | 32 | 9% |
Student > Master | 29 | 8% |
Other | 26 | 7% |
Other | 50 | 14% |
Unknown | 65 | 18% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 107 | 30% |
Biochemistry, Genetics and Molecular Biology | 84 | 23% |
Medicine and Dentistry | 17 | 5% |
Engineering | 16 | 4% |
Computer Science | 15 | 4% |
Other | 44 | 12% |
Unknown | 77 | 21% |