Prediction Of Novel Pirna Rat Clusters Based On Mouse Pirna Clusters Using Downstream and Upstream Analysis

10 pages•Published: March 18, 2019

Tamer Aldwairi, Federico Hoffmann and Andy Perkins

Abstract

PiRNAs are a particular type of small non-coding RNA. They are distinct from miRNA in size as well as other characteristics, such as the lack of sequence conservation and increased complexity when compared to their miRNA counterparts. PiRNA is considered the largest class of sRNA that is expressed especially in the animal cells. piRNAs are derived from long single-stranded RNAs, which are transcribed from genomic clusters, in contrast to other small silencing RNAs. It has been speculated that one locus could generate more than one piRNA. PiRNA corresponding to repetitive elements is fewer in mammals than in other species like Drosophila and Danio rerio, which signifies that piRNA might have possessed or gained some additional functionality in mammals. While the functionality of piRNAs may not be fully understood, they are believed to be involved in gene silencing. In this paper, we will examine a novel approach to identify potential piRNA clusters based on genes downstream and upstream location and order.

Keyphrases: downstream and upstream analysis, pirna clusters, transposable element

In: Oliver Eulenstein, Hisham Al-Mubaid and Qin Ding (editors). Proceedings of 11th International Conference on Bioinformatics and Computational Biology, vol 60, pages 190-199.

Links:	https://easychair.org/publications/paper/h3mb
	https://doi.org/10.29007/zwxg

BibTeX entry

@inproceedings{BiCOB2019:Prediction_Novel_Pirna_Rat,
  author    = {Tamer Aldwairi and Federico Hoffmann and Andy Perkins},
  title     = {Prediction Of Novel Pirna Rat Clusters Based On Mouse Pirna Clusters Using Downstream and Upstream Analysis},
  booktitle = {Proceedings of 11th International Conference on Bioinformatics and Computational Biology},
  editor    = {Oliver Eulenstein and Hisham Al-Mubaid and Qin Ding},
  series    = {EPiC Series in Computing},
  volume    = {60},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/h3mb},
  doi       = {10.29007/zwxg},
  pages     = {190-199},
  year      = {2019}}

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