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Optimizing and benchmarking de novo transcriptome sequencing: from library preparation to assembly evaluation

Overview of attention for article published in BMC Genomics, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (92nd percentile)
  • High Attention Score compared to outputs of the same age and source (96th percentile)

Mentioned by

blogs
1 blog
twitter
22 tweeters
googleplus
1 Google+ user

Citations

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35 Dimensions

Readers on

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103 Mendeley
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Title
Optimizing and benchmarking de novo transcriptome sequencing: from library preparation to assembly evaluation
Published in
BMC Genomics, November 2015
DOI 10.1186/s12864-015-2007-1
Pubmed ID
Authors

Yuichiro Hara, Kaori Tatsumi, Michio Yoshida, Eriko Kajikawa, Hiroshi Kiyonari, Shigehiro Kuraku

Abstract

RNA-seq enables gene expression profiling in selected spatiotemporal windows and yields massive sequence information with relatively low cost and time investment, even for non-model species. However, there remains a large room for optimizing its workflow, in order to take full advantage of continuously developing sequencing capacity. Transcriptome sequencing for three embryonic stages of Madagascar ground gecko (Paroedura picta) was performed with the Illumina platform. The output reads were assembled de novo for reconstructing transcript sequences. In order to evaluate the completeness of transcriptome assemblies, we prepared a reference gene set consisting of vertebrate one-to-one orthologs. To take advantage of increased read length of >150 nt, we demonstrated shortened RNA fragmentation time, which resulted in a dramatic shift of insert size distribution. To evaluate products of multiple de novo assembly runs incorporating reads with different RNA sources, read lengths, and insert sizes, we introduce a new reference gene set, core vertebrate genes (CVG), consisting of 233 genes that are shared as one-to-one orthologs by all vertebrate genomes examined (29 species)., The completeness assessment performed by the computational pipelines CEGMA and BUSCO referring to CVG, demonstrated higher accuracy and resolution than with the gene set previously established for this purpose. As a result of the assessment with CVG, we have derived the most comprehensive transcript sequence set of the Madagascar ground gecko by means of assembling individual libraries followed by clustering the assembled sequences based on their overall similarities. Our results provide several insights into optimizing de novo RNA-seq workflow, including the coordination between library insert size and read length, which manifested in improved connectivity of assemblies. The approach and assembly assessment with CVG demonstrated here would be applicable to transcriptome analysis of other species as well as whole genome analyses.

Twitter Demographics

The data shown below were collected from the profiles of 22 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 103 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Ukraine 1 <1%
Switzerland 1 <1%
United Kingdom 1 <1%
Brazil 1 <1%
Portugal 1 <1%
Japan 1 <1%
United States 1 <1%
Norway 1 <1%
Other 2 2%
Unknown 91 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 30 29%
Student > Ph. D. Student 25 24%
Student > Master 18 17%
Student > Bachelor 10 10%
Student > Doctoral Student 7 7%
Other 13 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 61 59%
Biochemistry, Genetics and Molecular Biology 25 24%
Computer Science 6 6%
Unspecified 4 4%
Engineering 3 3%
Other 4 4%

Attention Score in Context

This research output has an Altmetric Attention Score of 19. 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 23 February 2017.
All research outputs
#757,726
of 12,819,488 outputs
Outputs from BMC Genomics
#258
of 7,537 outputs
Outputs of similar age
#26,038
of 349,935 outputs
Outputs of similar age from BMC Genomics
#34
of 999 outputs
Altmetric has tracked 12,819,488 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,537 research outputs from this source. They receive a mean Attention Score of 4.3. This one has done particularly well, scoring higher than 96% 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 349,935 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 92% of its contemporaries.
We're also able to compare this research output to 999 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 96% of its contemporaries.