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Mendeley readers
Attention Score in Context
| Title |
EasyCluster2: an improved tool for clustering and assembling long transcriptome reads
|
|---|---|
| Published in |
BMC Bioinformatics, December 2014
|
| DOI | 10.1186/1471-2105-15-s15-s7 |
| Pubmed ID | |
| Authors |
Vitoantonio Bevilacqua, Nicola Pietroleonardo, Ely Ignazio Giannino, Fabio Stroppa, Domenico Simone, Graziano Pesole, Ernesto Picardi |
| Abstract |
Expressed sequences (e.g. ESTs) are a strong source of evidence to improve gene structures and predict reliable alternative splicing events. When a genome assembly is available, ESTs are suitable to generate gene-oriented clusters through the well-established EasyCluster software. Nowadays, EST-like sequences can be massively produced using Next Generation Sequencing (NGS) technologies. In order to handle genome-scale transcriptome data, we present here EasyCluster2, a reimplementation of EasyCluster able to speed up the creation of gene-oriented clusters and facilitate downstream analyses as the assembly of full-length transcripts and the detection of splicing isoforms. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Sweden | 1 | 4% |
| Brazil | 1 | 4% |
| Unknown | 24 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 31% |
| Student > Doctoral Student | 3 | 12% |
| Professor | 3 | 12% |
| Student > Bachelor | 2 | 8% |
| Student > Ph. D. Student | 2 | 8% |
| Other | 4 | 15% |
| Unknown | 4 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 6 | 23% |
| Agricultural and Biological Sciences | 6 | 23% |
| Engineering | 4 | 15% |
| Biochemistry, Genetics and Molecular Biology | 3 | 12% |
| Medicine and Dentistry | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 6 | 23% |
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 05 December 2014.
All research outputs
#20,245,139
of 22,772,779 outputs
Outputs from BMC Bioinformatics
#6,848
of 7,276 outputs
Outputs of similar age
#302,275
of 360,895 outputs
Outputs of similar age from BMC Bioinformatics
#141
of 147 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,276 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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 360,895 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.