| Chapter title |
From Gene Annotation to Function Prediction for Metagenomics
|
|---|---|
| Chapter number | 3 |
| Book title |
Protein Function Prediction
|
| Published in |
Methods in molecular biology, April 2017
|
| DOI | 10.1007/978-1-4939-7015-5_3 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7013-1, 978-1-4939-7015-5, 978-1-4939-7013-1, 978-1-4939-7015-5
|
| Authors |
Fatemeh Sharifi, Yuzhen Ye |
| Editors |
Daisuke Kihara |
| Abstract |
Microbes play important roles in almost every aspect of life, including human health and diseases. Facilitated by the rapid development of sequencing technologies, metagenomics research has accelerated the accumulation of genomic sequences of microbial species that had been inaccessible before. Analysis of the metagenomic sequencing data can reveal not only the species but also the functional composition of microbial communities. Here, we report a pipeline for functional annotation of metagenomic datasets. The pipeline is built from several programs that we have developed for metagenomic sequence analysis including a protein-coding gene predictor for short reads (or contigs) and a fast similarity search tool. Given a metagenomic dataset, the pipeline reports putative protein-coding genes (or gene fragments) and functional annotations of the genes in Gene Ontology (GO) terms and Enzyme Commission (EC) numbers, and potential metabolic pathways that are likely encoded by the metagenome. Fun4Me is available for download at https://sourceforge.net/projects/fun4me . |
X Demographics
Geographical breakdown
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|---|---|---|
| China | 1 | 11% |
| United Kingdom | 1 | 11% |
| Netherlands | 1 | 11% |
| Unknown | 6 | 67% |
Demographic breakdown
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|---|---|---|
| Members of the public | 7 | 78% |
| Scientists | 2 | 22% |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Estonia | 1 | 3% |
| Unknown | 35 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 7 | 19% |
| Student > Bachelor | 6 | 17% |
| Student > Master | 4 | 11% |
| Student > Doctoral Student | 3 | 8% |
| Other | 2 | 6% |
| Other | 3 | 8% |
| Unknown | 11 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 9 | 25% |
| Biochemistry, Genetics and Molecular Biology | 8 | 22% |
| Computer Science | 2 | 6% |
| Immunology and Microbiology | 1 | 3% |
| Social Sciences | 1 | 3% |
| Other | 1 | 3% |
| Unknown | 14 | 39% |