Title |
Assembly and annotation of a non-model gastropod (Nerita melanotragus) transcriptome: a comparison of De novo assemblers
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Published in |
BMC Research Notes, August 2014
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DOI | 10.1186/1756-0500-7-488 |
Pubmed ID | |
Authors |
Shorash Amin, Peter J Prentis, Edward K Gilding, Ana Pavasovic |
Abstract |
The sequencing, de novo assembly and annotation of transcriptome datasets generated with next generation sequencing (NGS) has enabled biologists to answer genomic questions in non-model species with unprecedented ease. Reliable and accurate de novo assembly and annotation of transcriptomes, however, is a critically important step for transcriptome assemblies generated from short read sequences. Typical benchmarks for assembly and annotation reliability have been performed with model species. To address the reliability and accuracy of de novo transcriptome assembly in non-model species, we generated an RNAseq dataset for an intertidal gastropod mollusc species, Nerita melanotragus, and compared the assembly produced by four different de novo transcriptome assemblers; Velvet, Oases, Geneious and Trinity, for a number of quality metrics and redundancy. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Scientists | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Brazil | 3 | 3% |
Norway | 2 | 2% |
Spain | 2 | 2% |
United States | 2 | 2% |
Japan | 2 | 2% |
Australia | 1 | <1% |
Czechia | 1 | <1% |
United Kingdom | 1 | <1% |
France | 1 | <1% |
Other | 4 | 4% |
Unknown | 95 | 83% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 28 | 25% |
Student > Ph. D. Student | 23 | 20% |
Student > Master | 19 | 17% |
Student > Bachelor | 9 | 8% |
Student > Postgraduate | 7 | 6% |
Other | 19 | 17% |
Unknown | 9 | 8% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 74 | 65% |
Biochemistry, Genetics and Molecular Biology | 14 | 12% |
Environmental Science | 6 | 5% |
Immunology and Microbiology | 2 | 2% |
Computer Science | 2 | 2% |
Other | 3 | 3% |
Unknown | 13 | 11% |