| Title |
MGC: a metagenomic gene caller
|
|---|---|
| Published in |
BMC Bioinformatics, June 2013
|
| DOI | 10.1186/1471-2105-14-s9-s6 |
| Pubmed ID | |
| Authors |
Achraf El Allali, John R Rose |
| Abstract |
Computational gene finding algorithms have proven their robustness in identifying genes in complete genomes. However, metagenomic sequencing has presented new challenges due to the incomplete and fragmented nature of the data. During the last few years, attempts have been made to extract complete and incomplete open reading frames (ORFs) directly from short reads and identify the coding ORFs, bypassing other challenging tasks such as the assembly of the metagenome. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 3 | 5% |
| United States | 2 | 4% |
| Unknown | 52 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 14 | 25% |
| Student > Master | 12 | 21% |
| Student > Bachelor | 7 | 12% |
| Researcher | 6 | 11% |
| Student > Postgraduate | 3 | 5% |
| Other | 9 | 16% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 22 | 39% |
| Computer Science | 10 | 18% |
| Biochemistry, Genetics and Molecular Biology | 9 | 16% |
| Medicine and Dentistry | 2 | 4% |
| Engineering | 2 | 4% |
| Other | 4 | 7% |
| Unknown | 8 | 14% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 August 2013.
All research outputs
#15,708,425
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#5,490
of 7,387 outputs
Outputs of similar age
#122,707
of 196,917 outputs
Outputs of similar age from BMC Bioinformatics
#66
of 90 outputs
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