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The coding and noncoding transcriptome of Neurospora crassa

Overview of attention for article published in BMC Genomics, December 2017
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Title
The coding and noncoding transcriptome of Neurospora crassa
Published in
BMC Genomics, December 2017
DOI 10.1186/s12864-017-4360-8
Pubmed ID
Authors

Ibrahim Avi Cemel, Nati Ha, Geza Schermann, Shusuke Yonekawa, Michael Brunner

Abstract

Long non protein coding RNAs (lncRNAs) have been identified in many different organisms and cell types. Emerging examples emphasize the biological importance of these RNA species but their regulation and functions remain poorly understood. In the filamentous fungus Neurospora crassa, the annotation and characterization of lncRNAs is incomplete. We have performed a comprehensive transcriptome analysis of Neurospora crassa by using ChIP-seq, RNA-seq and polysome fractionation datasets. We have annotated and characterized 1478 long intergenic noncoding RNAs (lincRNAs) and 1056 natural antisense transcripts, indicating that 20% of the RNA Polymerase II transcripts of Neurospora are not coding for protein. Both classes of lncRNAs accumulate at lower levels than protein-coding mRNAs and they are considerably shorter. Our analysis showed that the vast majority of lincRNAs and antisense transcripts do not contain introns and carry less H3K4me2 modifications than similarly expressed protein coding genes. In contrast, H3K27me3 modifications inversely correlate with transcription of protein coding and lincRNA genes. We show furthermore most lincRNA sequences evolve rapidly, even between phylogenetically close species. Our transcriptome analyses revealed distinct features of Neurospora lincRNAs and antisense transcripts in comparison to mRNAs and showed that the prevalence of noncoding transcripts in this organism is higher than previously anticipated. The study provides a broad repertoire and a resource for further studies of lncRNAs.

Twitter Demographics

The data shown below were collected from the profiles of 3 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 45 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 8 18%
Other 7 16%
Student > Ph. D. Student 7 16%
Researcher 4 9%
Student > Postgraduate 2 4%
Other 8 18%
Unknown 9 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 18 40%
Agricultural and Biological Sciences 11 24%
Immunology and Microbiology 3 7%
Unspecified 1 2%
Computer Science 1 2%
Other 4 9%
Unknown 7 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 28 December 2017.
All research outputs
#12,697,949
of 16,639,069 outputs
Outputs from BMC Genomics
#6,316
of 9,096 outputs
Outputs of similar age
#276,228
of 413,678 outputs
Outputs of similar age from BMC Genomics
#551
of 821 outputs
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