You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
Mendeley readers
Attention Score in Context
| Title |
INDUS - a composition-based approach for rapid and accurate taxonomic classification of metagenomic sequences
|
|---|---|
| Published in |
BMC Genomics, November 2011
|
| DOI | 10.1186/1471-2164-12-s3-s4 |
| Pubmed ID | |
| Authors |
Monzoorul Haque Mohammed, Tarini Shankar Ghosh, Rachamalla Maheedhar Reddy, Chennareddy Venkata Siva Kumar Reddy, Nitin Kumar Singh, Sharmila S Mande |
| Abstract |
Taxonomic classification of metagenomic sequences is the first step in metagenomic analysis. Existing taxonomic classification approaches are of two types, similarity-based and composition-based. Similarity-based approaches, though accurate and specific, are extremely slow. Since, metagenomic projects generate millions of sequences, adopting similarity-based approaches becomes virtually infeasible for research groups having modest computational resources. In this study, we present INDUS - a composition-based approach that incorporates the following novel features. First, INDUS discards the 'one genome-one composition' model adopted by existing compositional approaches. Second, INDUS uses 'compositional distance' information for identifying appropriate assignment levels. Third, INDUS incorporates steps that attempt to reduce biases due to database representation. |
Mendeley readers
The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 3% |
| Portugal | 1 | 1% |
| United Kingdom | 1 | 1% |
| New Zealand | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 63 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 19 | 28% |
| Researcher | 14 | 20% |
| Student > Master | 11 | 16% |
| Student > Bachelor | 6 | 9% |
| Other | 4 | 6% |
| Other | 8 | 12% |
| Unknown | 7 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 35 | 51% |
| Biochemistry, Genetics and Molecular Biology | 9 | 13% |
| Computer Science | 8 | 12% |
| Engineering | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Other | 2 | 3% |
| Unknown | 11 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 18 June 2014.
All research outputs
#7,455,523
of 22,792,160 outputs
Outputs from BMC Genomics
#3,597
of 10,647 outputs
Outputs of similar age
#69,523
of 240,106 outputs
Outputs of similar age from BMC Genomics
#97
of 302 outputs
Altmetric has tracked 22,792,160 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,647 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 240,106 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 302 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 59% of its contemporaries.