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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Approaches for in silico finishing of microbial genome sequences
|
|---|---|
| Published in |
Genetics and Molecular Biology, January 2017
|
| DOI | 10.1590/1678-4685-gmb-2016-0230 |
| Pubmed ID | |
| Authors |
Frederico Schmitt Kremer, Alan John Alexander McBride, Luciano da Silva Pinto |
| Abstract |
The introduction of next-generation sequencing (NGS) had a significant effect on the availability of genomic information, leading to an increase in the number of sequenced genomes from a large spectrum of organisms. Unfortunately, due to the limitations implied by the short-read sequencing platforms, most of these newly sequenced genomes remained as "drafts", incomplete representations of the whole genetic content. The previous genome sequencing studies indicated that finishing a genome sequenced by NGS, even bacteria, may require additional sequencing to fill the gaps, making the entire process very expensive. As such, several in silico approaches have been developed to optimize the genome assemblies and facilitate the finishing process. The present review aims to explore some free (open source, in many cases) tools that are available to facilitate genome finishing. |
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 % |
|---|---|---|
| Netherlands | 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 111 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 111 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 26 | 23% |
| Student > Master | 17 | 15% |
| Student > Ph. D. Student | 14 | 13% |
| Student > Bachelor | 12 | 11% |
| Student > Doctoral Student | 6 | 5% |
| Other | 12 | 11% |
| Unknown | 24 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 32 | 29% |
| Biochemistry, Genetics and Molecular Biology | 28 | 25% |
| Immunology and Microbiology | 7 | 6% |
| Computer Science | 6 | 5% |
| Medicine and Dentistry | 3 | 3% |
| Other | 9 | 8% |
| Unknown | 26 | 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 11 September 2017.
All research outputs
#17,292,294
of 25,382,440 outputs
Outputs from Genetics and Molecular Biology
#408
of 772 outputs
Outputs of similar age
#267,895
of 421,709 outputs
Outputs of similar age from Genetics and Molecular Biology
#5
of 6 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 772 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 32nd percentile – i.e., 32% 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 421,709 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one.