Title |
Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation
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Published in |
Microbiome, October 2014
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DOI | 10.1186/2049-2618-2-39 |
Pubmed ID | |
Authors |
Albi Celaj, Janet Markle, Jayne Danska, John Parkinson |
Abstract |
Microbiome-wide gene expression profiling through high-throughput RNA sequencing ('metatranscriptomics') offers a powerful means to functionally interrogate complex microbial communities. Key to successful exploitation of these datasets is the ability to confidently match relatively short sequence reads to known bacterial transcripts. In the absence of reference genomes, such annotation efforts may be enhanced by assembling reads into longer contiguous sequences ('contigs'), prior to database search strategies. Since reads from homologous transcripts may derive from several species, represented at different abundance levels, it is not clear how well current assembly pipelines perform for metatranscriptomic datasets. Here we evaluate the performance of four currently employed assemblers including de novo transcriptome assemblers - Trinity and Oases; the metagenomic assembler - Metavelvet; and the recently developed metatranscriptomic assembler IDBA-MT. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 23% |
France | 2 | 9% |
India | 1 | 5% |
Germany | 1 | 5% |
Norway | 1 | 5% |
United Kingdom | 1 | 5% |
Spain | 1 | 5% |
Brazil | 1 | 5% |
Unknown | 9 | 41% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 14 | 64% |
Members of the public | 8 | 36% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 6 | 3% |
Germany | 2 | <1% |
Portugal | 1 | <1% |
Chile | 1 | <1% |
Australia | 1 | <1% |
Brazil | 1 | <1% |
Netherlands | 1 | <1% |
Canada | 1 | <1% |
Czechia | 1 | <1% |
Other | 2 | <1% |
Unknown | 222 | 93% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 64 | 27% |
Student > Ph. D. Student | 62 | 26% |
Student > Master | 30 | 13% |
Student > Bachelor | 13 | 5% |
Other | 11 | 5% |
Other | 35 | 15% |
Unknown | 24 | 10% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 111 | 46% |
Biochemistry, Genetics and Molecular Biology | 41 | 17% |
Environmental Science | 20 | 8% |
Computer Science | 11 | 5% |
Immunology and Microbiology | 7 | 3% |
Other | 17 | 7% |
Unknown | 32 | 13% |