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VTBuilder: a tool for the assembly of multi isoform transcriptomes

Overview of attention for article published in BMC Bioinformatics, December 2014
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (84th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

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VTBuilder: a tool for the assembly of multi isoform transcriptomes
Published in
BMC Bioinformatics, December 2014
DOI 10.1186/s12859-014-0389-8
Pubmed ID

John Archer, Gareth Whiteley, Nicholas R Casewell, Robert A Harrison, Simon C Wagstaff


BackgroundWithin many research areas, such as transcriptomics, the millions of short DNA fragments (reads) produced by current sequencing platforms need to be assembled into transcript sequences before they can be utilized. Despite recent advances in assembly software, creating such transcripts from read data harboring isoform variation remains challenging. This is because current approaches fail to identify all variants present or they create chimeric transcripts within which relationships between co-evolving sites and other evolutionary factors are disrupted. We present VTBuilder, a tool for constructing non-chimeric transcripts from read data that has been sequenced from sources containing isoform complexity.ResultsWe validated VTBuilder using reads simulated from 54 Sanger sequenced transcripts (SSTs) expressed in the venom gland of the saw scaled viper, Echis ocellatus. The SSTs were selected to represent genes from major co-expressed toxin groups known to harbor isoform variants. From the simulated reads, VTBuilder constructed 55 transcripts, 50 of which had a greater than 99% sequence similarity to 48 of the SSTs. In contrast, using the popular assembler tool Trinity (r2013-02-25), only 14 transcripts were constructed with a similar level of sequence identity to just 11 SSTs. Furthermore VTBuilder produced transcripts with a similar length distribution to the SSTs while those produced by Trinity were considerably shorter. To demonstrate that our approach can be scaled to real world data we assembled the venom gland transcriptome of the African puff adder Bitis arietans using paired-end reads sequenced on Illumina¿s MiSeq platform. VTBuilder constructed 1481 transcripts from 5 million reads and, following annotation, all major toxin genes were recovered demonstrating reconstruction of complex underlying sequence and isoform diversity.ConclusionUnlike other approaches, VTBuilder strives to maintain the relationships between co-evolving sites within the constructed transcripts, and thus increases transcript utility for a wide range of research areas ranging from transcriptomics to phylogenetics and including the monitoring of drug resistant parasite populations. Additionally, improving the quality of transcripts assembled from read data will have an impact on future studies that query these data. VTBuilder has been implemented in java and is available, under the GPL GPU V0.3 license, from http:// http://www.lstmed.ac.uk/vtbuilder.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Netherlands 2 3%
Germany 1 1%
Switzerland 1 1%
Portugal 1 1%
Turkey 1 1%
Slovenia 1 1%
Spain 1 1%
United States 1 1%
Unknown 58 87%

Demographic breakdown

Readers by professional status Count As %
Researcher 20 30%
Student > Ph. D. Student 14 21%
Student > Master 9 13%
Student > Bachelor 3 4%
Other 3 4%
Other 8 12%
Unknown 10 15%
Readers by discipline Count As %
Agricultural and Biological Sciences 31 46%
Biochemistry, Genetics and Molecular Biology 13 19%
Computer Science 3 4%
Neuroscience 2 3%
Arts and Humanities 2 3%
Other 3 4%
Unknown 13 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 24 September 2015.
All research outputs
of 22,772,779 outputs
Outputs from BMC Bioinformatics
of 7,276 outputs
Outputs of similar age
of 360,895 outputs
Outputs of similar age from BMC Bioinformatics
of 147 outputs
Altmetric has tracked 22,772,779 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,276 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 done well, scoring higher than 79% 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 360,895 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 147 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.