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Attention Score in Context
| Title |
SeedsGraph: an efficient assembler for next-generation sequencing data
|
|---|---|
| Published in |
BMC Medical Genomics, May 2015
|
| DOI | 10.1186/1755-8794-8-s2-s13 |
| Pubmed ID | |
| Authors |
Chunyu Wang, Maozu Guo, Xiaoyan Liu, Yang Liu, Quan Zou |
| Abstract |
DNA sequencing technology has been rapidly evolving, and produces a large number of short reads with a fast rising tendency. This has led to a resurgence of research in whole genome shotgun assembly algorithms. We start the assembly algorithm by clustering the short reads in a cloud computing framework, and the clustering process groups fragments according to their original consensus long-sequence similarity. We condense each group of reads to a chain of seeds, which is a kind of substring with reads aligned, and then build a graph accordingly. Finally, we analyze the graph to find Euler paths, and assemble the reads related in the paths into contigs, and then lay out contigs with mate-pair information for scaffolds. The result shows that our algorithm is efficient and feasible for a large set of reads such as in next-generation sequencing technology. |
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 3 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 33% |
| Student > Doctoral Student | 1 | 33% |
| Unknown | 1 | 33% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 2 | 67% |
| Unknown | 1 | 33% |
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 06 June 2015.
All research outputs
#15,334,706
of 22,808,725 outputs
Outputs from BMC Medical Genomics
#676
of 1,223 outputs
Outputs of similar age
#156,208
of 265,921 outputs
Outputs of similar age from BMC Medical Genomics
#17
of 28 outputs
Altmetric has tracked 22,808,725 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 1,223 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 35th percentile – i.e., 35% of its peers scored the same or lower than it.
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We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.