↓ Skip to main content

GAML: genome assembly by maximum likelihood

Overview of attention for article published in Algorithms for Molecular Biology, June 2015
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#3 of 190)
  • High Attention Score compared to outputs of the same age (91st percentile)

Mentioned by

blogs
1 blog
twitter
23 tweeters

Citations

dimensions_citation
2 Dimensions

Readers on

mendeley
17 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
GAML: genome assembly by maximum likelihood
Published in
Algorithms for Molecular Biology, June 2015
DOI 10.1186/s13015-015-0052-6
Pubmed ID
Authors

Vladimír Boža, Broňa Brejová, Tomáš Vinař

Abstract

Resolution of repeats and scaffolding of shorter contigs are critical parts of genome assembly. Modern assemblers usually perform such steps by heuristics, often tailored to a particular technology for producing paired or long reads. We propose a new framework that allows systematic combination of diverse sequencing datasets into a single assembly. We achieve this by searching for an assembly with the maximum likelihood in a probabilistic model capturing error rate, insert lengths, and other characteristics of the sequencing technology used to produce each dataset. We have implemented a prototype genome assembler GAML that can use any combination of insert sizes with Illumina or 454 reads, as well as PacBio reads. Our experiments show that we can assemble short genomes with N50 sizes and error rates comparable to ALLPATHS-LG or Cerulean. While ALLPATHS-LG and Cerulean require each a specific combination of datasets, GAML works on any combination. We have introduced a new probabilistic approach to genome assembly and demonstrated that this approach can lead to superior results when used to combine diverse set of datasets from different sequencing technologies. Data and software is available at http://compbio.fmph.uniba.sk/gaml.

Twitter Demographics

The data shown below were collected from the profiles of 23 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 1 6%
France 1 6%
Germany 1 6%
Brazil 1 6%
Unknown 13 76%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 47%
Researcher 7 41%
Student > Master 4 24%
Student > Postgraduate 3 18%
Student > Doctoral Student 2 12%
Other 4 24%
Readers by discipline Count As %
Computer Science 13 76%
Agricultural and Biological Sciences 11 65%
Biochemistry, Genetics and Molecular Biology 2 12%
Mathematics 1 6%

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 18 November 2016.
All research outputs
#785,283
of 12,680,068 outputs
Outputs from Algorithms for Molecular Biology
#3
of 190 outputs
Outputs of similar age
#19,312
of 233,961 outputs
Outputs of similar age from Algorithms for Molecular Biology
#1
of 4 outputs
Altmetric has tracked 12,680,068 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 190 research outputs from this source. They receive a mean Attention Score of 2.8. This one has done particularly well, scoring higher than 98% 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 233,961 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them