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Fully automated pipeline for detection of sex linked genes using RNA-Seq data

Overview of attention for article published in BMC Bioinformatics, March 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

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11 tweeters
facebook
1 Facebook page
googleplus
1 Google+ user

Citations

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15 Dimensions

Readers on

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77 Mendeley
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2 CiteULike
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Title
Fully automated pipeline for detection of sex linked genes using RNA-Seq data
Published in
BMC Bioinformatics, March 2015
DOI 10.1186/s12859-015-0509-0
Pubmed ID
Authors

Monika Michalovova, Zdenek Kubat, Roman Hobza, Boris Vyskot, Eduard Kejnovsky

Abstract

Sex chromosomes present a genomic region which to some extent, differs between the genders of a single species. Reliable high-throughput methods for detection of sex chromosomes specific markers are needed, especially in species where genome information is limited. Next generation sequencing (NGS) opens the door for identification of unique sequences or searching for nucleotide polymorphisms between datasets. A combination of classical genetic segregation analysis along with RNA-Seq data can present an ideal tool to map and identify sex chromosome-specific expressed markers. To address this challenge, we established genetic cross of dioecious plant Rumex acetosa and generated RNA-Seq data from both parental generation and male and female offspring. We present a pipeline for detection of sex linked genes based on nucleotide polymorphism analysis. In our approach, tracking of nucleotide polymorphisms is carried out using a cross of preferably distant populations. For this reason, only 4 datasets are needed - reads from high-throughput sequencing platforms for parent generation (mother and father) and F1 generation (male and female progeny). Our pipeline uses custom scripts together with external assembly, mapping and variant calling software. Given the resource-intensive nature of the computation, servers with high capacity are a requirement. Therefore, in order to keep this pipeline easily accessible and reproducible, we implemented it in Galaxy - an open, web-based platform for data-intensive biomedical research. Our tools are present in the Galaxy Tool Shed, from which they can be installed to any local Galaxy instance. As an output of the pipeline, user gets a FASTA file with candidate transcriptionally active sex-linked genes, sorted by their relevance. At the same time, a BAM file with identified genes and alignment of reads is also provided. Thus, polymorphisms following segregation pattern can be easily visualized, which significantly enhances primer design and subsequent steps of wet-lab verification. Our pipeline presents a simple and freely accessible software tool for identification of sex chromosome linked genes in species without an existing reference genome. Based on combination of genetic crosses and RNA-Seq data, we have designed a high-throughput, cost-effective approach for a broad community of scientists focused on sex chromosome structure and evolution.

Twitter Demographics

The data shown below were collected from the profiles of 11 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 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 1%
Netherlands 1 1%
Sweden 1 1%
Czechia 1 1%
Taiwan 1 1%
Unknown 72 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 21 27%
Student > Ph. D. Student 19 25%
Student > Bachelor 9 12%
Student > Master 9 12%
Student > Doctoral Student 5 6%
Other 10 13%
Unknown 4 5%
Readers by discipline Count As %
Agricultural and Biological Sciences 40 52%
Biochemistry, Genetics and Molecular Biology 13 17%
Computer Science 8 10%
Medicine and Dentistry 2 3%
Environmental Science 2 3%
Other 8 10%
Unknown 4 5%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 13 May 2015.
All research outputs
#4,374,739
of 21,545,543 outputs
Outputs from BMC Bioinformatics
#1,745
of 6,975 outputs
Outputs of similar age
#52,363
of 232,304 outputs
Outputs of similar age from BMC Bioinformatics
#1
of 1 outputs
Altmetric has tracked 21,545,543 research outputs across all sources so far. Compared to these this one has done well and is in the 76th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,975 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 73% 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 232,304 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 76% of its contemporaries.
We're also able to compare this research output to 1 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