Comput Biol Chem - piClust: a density based piRNA clustering algorithm.

Tópicos

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Resumo

Piwi-interacting RNAs (piRNAs) are recently discovered, endogenous small non-coding RNAs. piRNAs protect the genome from invasive transposable elements (TE) and sustain integrity of the genome in germ cell lineages. Small RNA-sequencing data can be used to detect piRNA activations in a cell under a specific condition. However, identification of cell specific piRNA activations requires sophisticated computational methods. As of now, there is only one computational method, proTRAC, to locate activated piRNAs from the sequencing data. proTRAC detects piRNA clusters based on a probabilistic analysis with assumption of a uniform distribution. Unfortunately, we were not able to locate activated piRNAs from our proprietary sequencing data in chicken germ cells using proTRAC. With a careful investigation on data sets, we found that a uniform or any statistical distribution for detecting piRNA clusters may not be assumed. Furthermore, small RNA-seq data contains many different types of RNAs which was not carefully taken into account in previous studies. To improve piRNA cluster identification, we developed piClust that uses a density based clustering approach without assumption of any parametric distribution. In previous studies, it is known that piRNAs exhibit a strong tendency of forming piRNA clusters in syntenic regions of the genome. Thus, the density based clustering approach is effective and robust to the existence of non-piRNAs or noise in the data. In experiments with piRNA data from human, mouse, rat and chicken, piClust was able to detect piRNA clusters from total small RNA-seq data from germ cell lines, while proTRAC was not successful. piClust outperformed proTRAC in terms of sensitivity and running time (up to 200 folds). piClust is currently available as a web service at http://epigenomics.snu.ac.kr/piclustweb.

Resumo Limpo

piwiinteract rnas pirna recent discov endogen small noncod rnas pirna protect genom invas transpos element te sustain integr genom germ cell lineag small rnasequenc data can use detect pirna activ cell specif condit howev identif cell specif pirna activ requir sophist comput method now one comput method protrac locat activ pirna sequenc data protrac detect pirna cluster base probabilist analysi assumpt uniform distribut unfortun abl locat activ pirna proprietari sequenc data chicken germ cell use protrac care investig data set found uniform statist distribut detect pirna cluster may assum furthermor small rnaseq data contain mani differ type rnas care taken account previous studi improv pirna cluster identif develop piclust use densiti base cluster approach without assumpt parametr distribut previous studi known pirna exhibit strong tendenc form pirna cluster synten region genom thus densiti base cluster approach effect robust exist nonpirna nois data experi pirna data human mous rat chicken piclust abl detect pirna cluster total small rnaseq data germ cell line protrac success piclust outperform protrac term sensit run time fold piclust current avail web servic httpepigenomicssnuackrpiclustweb

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