In this paper we propose a new implementation of EasyCluster, a robust software developed to generate reliable clusters by expressed sequence tags (EST). Such clusters can be used to infer and improve gene structures as well as discover potential alternative splicing events. The new version of EasyCluster software is able to manage genome scale transcriptome data produced by massive sequencing using Roche 454 sequencers. Moreover it can speed up the creation of gene-oriented clusters and facilitate downstream analyses as the assembly of full-length transcripts. Finally available annotations can now be employed to improve the overall clustering procedure. The new EasyCluster implementation embeds also a graphical browser to provide an overview of results at genome level, simplifying the interpretation of findings to researchers with no specific skills in bioinformatics.
|Titolo:||A Novel Approach to Clustering and Assembly of Large-Scale Roche 454 Transcriptome Data for Gene Validation and Alternative Splicing Analysis|
|Titolo del libro:||Bio-Inspired Computing and Applications : 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou,China, August 11-14. 2011, Revised Selected Papers|
|Data di pubblicazione:||2011|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.1007/978-3-642-24553-4_85|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|