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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Clustering of reads with alignment-free measures and quality values
|
|---|---|
| Published in |
Algorithms for Molecular Biology, January 2015
|
| DOI | 10.1186/s13015-014-0029-x |
| Pubmed ID | |
| Authors |
Matteo Comin, Andrea Leoni, Michele Schimd |
| Abstract |
The data volume generated by Next-Generation Sequencing (NGS) technologies is growing at a pace that is now challenging the storage and data processing capacities of modern computer systems. In this context an important aspect is the reduction of data complexity by collapsing redundant reads in a single cluster to improve the run time, memory requirements, and quality of post-processing steps like assembly and error correction. Several alignment-free measures, based on k-mers counts, have been used to cluster reads. Quality scores produced by NGS platforms are fundamental for various analysis of NGS data like reads mapping and error detection. Moreover future-generation sequencing platforms will produce long reads but with a large number of erroneous bases (up to 15 %). |
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 % |
|---|---|---|
| Unknown | 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 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 2 | 4% |
| Czechia | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 52 | 93% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 30% |
| Student > Ph. D. Student | 13 | 23% |
| Student > Master | 8 | 14% |
| Professor > Associate Professor | 3 | 5% |
| Professor | 3 | 5% |
| Other | 6 | 11% |
| Unknown | 6 | 11% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 23 | 41% |
| Agricultural and Biological Sciences | 15 | 27% |
| Biochemistry, Genetics and Molecular Biology | 5 | 9% |
| Immunology and Microbiology | 1 | 2% |
| Social Sciences | 1 | 2% |
| Other | 2 | 4% |
| Unknown | 9 | 16% |
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 29 August 2017.
All research outputs
#15,821,622
of 23,498,099 outputs
Outputs from Algorithms for Molecular Biology
#140
of 251 outputs
Outputs of similar age
#213,920
of 356,459 outputs
Outputs of similar age from Algorithms for Molecular Biology
#4
of 13 outputs
Altmetric has tracked 23,498,099 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 251 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 33rd percentile – i.e., 33% 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 356,459 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 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 61% of its contemporaries.