Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

ANNOgesic: A pipeline to translate bacterial/archaeal RNA-Seq data into high-resolution genome annotations

View ORCID ProfileSung-Huan Yu, View ORCID ProfileJörg Vogel, View ORCID ProfileKonrad U. Förstner
doi: https://doi.org/10.1101/143081
Sung-Huan Yu
1Institute of Molecular Infection Biology (IMIB), University of Würzburg, 97080 Würzburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Sung-Huan Yu
Jörg Vogel
1Institute of Molecular Infection Biology (IMIB), University of Würzburg, 97080 Würzburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Jörg Vogel
Konrad U. Förstner
1Institute of Molecular Infection Biology (IMIB), University of Würzburg, 97080 Würzburg, Germany
2Core Unit Systems Medicine, University of Würzburg, 97080 Würzburg, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Konrad U. Förstner
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

To understand the gene regulation of an organism of interest, a comprehensive genome annotation is essential. While some features, such as coding sequences, can be computationally predicted with high accuracy based purely on the genomic sequence, others, such as promoter elements or noncoding RNAs are harder to detect. RNA-Seq has proven to be an efficient method to identify these genomic features and to improve genome annotations. However, processing and integrating RNA-Seq data in order to generate high-resolution annotations is challenging, time consuming and requires numerous different steps. We have constructed a powerful and modular pipeline called ANNOgesic that provides the required analyses and simplifies RNA-Seq-based bacterial and archaeal genome annotation. It predicts and annotates numerous features, including small non-coding RNAs, with high precision. The software is available under an open source license (ISCL) at https://pythonhosted.org/ANNOgesic/.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
Back to top
PreviousNext
Posted May 29, 2017.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
ANNOgesic: A pipeline to translate bacterial/archaeal RNA-Seq data into high-resolution genome annotations
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
ANNOgesic: A pipeline to translate bacterial/archaeal RNA-Seq data into high-resolution genome annotations
Sung-Huan Yu, Jörg Vogel, Konrad U. Förstner
bioRxiv 143081; doi: https://doi.org/10.1101/143081
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
ANNOgesic: A pipeline to translate bacterial/archaeal RNA-Seq data into high-resolution genome annotations
Sung-Huan Yu, Jörg Vogel, Konrad U. Förstner
bioRxiv 143081; doi: https://doi.org/10.1101/143081

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (3701)
  • Biochemistry (7820)
  • Bioengineering (5695)
  • Bioinformatics (21343)
  • Biophysics (10603)
  • Cancer Biology (8206)
  • Cell Biology (11974)
  • Clinical Trials (138)
  • Developmental Biology (6786)
  • Ecology (10425)
  • Epidemiology (2065)
  • Evolutionary Biology (13908)
  • Genetics (9731)
  • Genomics (13109)
  • Immunology (8171)
  • Microbiology (20064)
  • Molecular Biology (7875)
  • Neuroscience (43171)
  • Paleontology (321)
  • Pathology (1282)
  • Pharmacology and Toxicology (2267)
  • Physiology (3363)
  • Plant Biology (7254)
  • Scientific Communication and Education (1316)
  • Synthetic Biology (2012)
  • Systems Biology (5550)
  • Zoology (1133)