English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

LSTrAP: efficiently combining RNA sequencing data into co-expression networks

MPS-Authors
/persons/resource/persons134185

Proost,  Sebastian
Regulatory Networks, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

/persons/resource/persons97312

Mutwil,  M.
Regulatory Networks, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

External Resource

Link
(Any fulltext)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Proost, S., Krawczyk, A., & Mutwil, M. (2017). LSTrAP: efficiently combining RNA sequencing data into co-expression networks. BMC Bioinformatics, 18(1): 444. doi:10.1186/s12859-017-1861-z.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002E-089E-5
Abstract
Since experimental elucidation of gene function is often laborious, various in silico methods have been developed to predict gene function of uncharacterized genes. Since functionally related genes are often expressed in the same tissues, conditions and developmental stages (co-expressed), functional annotation of characterized genes can be transferred to co-expressed genes lacking annotation. With genome-wide expression data available, the construction of co-expression networks, where genes are nodes and edges connect significantly co-expressed genes, provides unprecedented opportunities to predict gene function. However, the construction of such networks requires large volumes of high-quality data, multiple processing steps and a considerable amount of computation power. While efficient tools exist to process RNA-Seq data, pipelines which combine them to construct co-expression networks efficiently are currently lacking.