| Title |
Efficient digest of high-throughput sequencing data in a reproducible report
|
|---|---|
| Published in |
BMC Bioinformatics, September 2013
|
| DOI | 10.1186/1471-2105-14-s11-s3 |
| Pubmed ID | |
| Authors |
Zhe Zhang, Jeremy Leipzig, Ariella Sasson, Angela M Yu, Juan C Perin, Hongbo M Xie, Mahdi Sarmady, Patrick V Warren, Peter S White |
| Abstract |
High-throughput sequencing (HTS) technologies are spearheading the accelerated development of biomedical research. Processing and summarizing the large amount of data generated by HTS presents a non-trivial challenge to bioinformatics. A commonly adopted standard is to store sequencing reads aligned to a reference genome in SAM (Sequence Alignment/Map) or BAM (Binary Alignment/Map) files. Quality control of SAM/BAM files is a critical checkpoint before downstream analysis. The goal of the current project is to facilitate and standardize this process. |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 50% |
| Norway | 1 | 25% |
| India | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 24 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Sweden | 1 | 4% |
| Unknown | 22 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 33% |
| Student > Ph. D. Student | 6 | 25% |
| Student > Bachelor | 3 | 13% |
| Professor > Associate Professor | 2 | 8% |
| Student > Doctoral Student | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 2 | 8% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 46% |
| Engineering | 3 | 13% |
| Computer Science | 3 | 13% |
| Biochemistry, Genetics and Molecular Biology | 2 | 8% |
| Veterinary Science and Veterinary Medicine | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 2 | 8% |
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 08 May 2014.
All research outputs
#7,432,447
of 22,721,584 outputs
Outputs from BMC Bioinformatics
#3,026
of 7,261 outputs
Outputs of similar age
#66,286
of 197,514 outputs
Outputs of similar age from BMC Bioinformatics
#38
of 100 outputs
Altmetric has tracked 22,721,584 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 7,261 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 50% 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 197,514 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 100 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 57% of its contemporaries.