↓ Skip to main content

Orthograph: a versatile tool for mapping coding nucleotide sequences to clusters of orthologous genes

Overview of attention for article published in BMC Bioinformatics, February 2017
Altmetric Badge

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 (74th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
15 tweeters

Citations

dimensions_citation
83 Dimensions

Readers on

mendeley
148 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
Orthograph: a versatile tool for mapping coding nucleotide sequences to clusters of orthologous genes
Published in
BMC Bioinformatics, February 2017
DOI 10.1186/s12859-017-1529-8
Pubmed ID
Authors

Malte Petersen, Karen Meusemann, Alexander Donath, Daniel Dowling, Shanlin Liu, Ralph S. Peters, Lars Podsiadlowski, Alexandros Vasilikopoulos, Xin Zhou, Bernhard Misof, Oliver Niehuis

Abstract

Orthology characterizes genes of different organisms that arose from a single ancestral gene via speciation, in contrast to paralogy, which is assigned to genes that arose via gene duplication. An accurate orthology assignment is a crucial step for comparative genomic studies. Orthologous genes in two organisms can be identified by applying a so-called reciprocal search strategy, given that complete information of the organisms' gene repertoire is available. In many investigations, however, only a fraction of the gene content of the organisms under study is examined (e.g., RNA sequencing). Here, identification of orthologous nucleotide or amino acid sequences can be achieved using a graph-based approach that maps nucleotide sequences to genes of known orthology. Existing implementations of this approach, however, suffer from algorithmic issues that may cause problems in downstream analyses. We present a new software pipeline, Orthograph, that addresses and solves the above problems and implements useful features for a wide range of comparative genomic and transcriptomic analyses. Orthograph applies a best reciprocal hit search strategy using profile hidden Markov models and maps nucleotide sequences to the globally best matching cluster of orthologous genes, thus enabling researchers to conveniently and reliably delineate orthologs and paralogs from transcriptomic and genomic sequence data. We demonstrate the performance of our approach on de novo-sequenced and assembled transcript libraries of 24 species of apoid wasps (Hymenoptera: Aculeata) as well as on published genomic datasets. With Orthograph, we implemented a best reciprocal hit approach to reference-based orthology prediction for coding nucleotide sequences such as RNAseq data. Orthograph is flexible, easy to use, open source and freely available at https://mptrsen.github.io/Orthograph . Additionally, we release 24 de novo-sequenced and assembled transcript libraries of apoid wasp species.

Twitter Demographics

The data shown below were collected from the profiles of 15 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 148 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 148 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 36 24%
Student > Master 28 19%
Researcher 18 12%
Student > Bachelor 10 7%
Student > Postgraduate 9 6%
Other 27 18%
Unknown 20 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 62 42%
Biochemistry, Genetics and Molecular Biology 39 26%
Immunology and Microbiology 7 5%
Computer Science 7 5%
Engineering 4 3%
Other 5 3%
Unknown 24 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 7. 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 17 November 2017.
All research outputs
#3,314,099
of 18,455,809 outputs
Outputs from BMC Bioinformatics
#1,388
of 6,393 outputs
Outputs of similar age
#66,126
of 271,507 outputs
Outputs of similar age from BMC Bioinformatics
#5
of 23 outputs
Altmetric has tracked 18,455,809 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,393 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 77% 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 271,507 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 23 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.