Please use this identifier to cite or link to this item: https://doi.org/10.1186/1471-2164-12-S3-S14
Title: In silico secretome analysis approach for next generation sequencing transcriptomic data
Authors: Garg, G.
Ranganathan, S. 
Issue Date: 2011
Citation: Garg, G., Ranganathan, S. (2011). In silico secretome analysis approach for next generation sequencing transcriptomic data. 10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet) 12 (SUPPL. 3) : -. ScholarBank@NUS Repository. https://doi.org/10.1186/1471-2164-12-S3-S14
Abstract: Background: Excretory/secretory proteins (ESPs) play a major role in parasitic infection as they are present at the host-parasite interface and regulate host immune system. In case of parasitic helminths, transcriptomics has been used extensively to understand the molecular basis of parasitism and for developing novel therapeutic strategies against parasitic infections. However, none of transcriptomic studies have extensively covered ES protein prediction for identifying novel therapeutic targets, especially as parasites adopt non-classical secretion pathways. Results: We developed a semi-automated computational approach for prediction and annotation of ES proteins using transcriptomic data from next generation sequencing platforms. For the prediction of non-classically secreted proteins, we have used an improved computational strategy, together with homology matching to a dataset of experimentally determined parasitic helminth ES proteins. We applied this protocol to analyse 454 short reads of parasitic nematode, Strongyloides ratti. From 296231 reads, we derived 28901 contigs, which were translated into 20877 proteins. Based on our improved ES protein prediction pipeline, we identified 2572 ES proteins, of which 407 (1.9%) proteins have classical N-terminal signal peptides, 923 (4.4%) were computationally identified as nonclassically secreted while 1516 (7.26%) were identified by homology to experimentally identified parasitic helminth ES proteins. Out of 2572 ES proteins, 2310 (89.8%) ES proteins had homologues in the free-living nematode Caenorhabditis elegans and 2220 (86.3%) in parasitic nematodes. We could functionally annotate 1591 (61.8%) ES proteins with protein families and domains and establish pathway associations for 691 (26.8%) proteins. In addition, we have identified 19 representative ES proteins, which have no homologues in the host organism but homologous to lethal RNAi phenotypes in C. elegans, as potential therapeutic targets. Conclusion: We report a comprehensive approach using freely available computational tools for the secretome analysis of NGS data. This approach has been applied to S. ratti 454 transcriptomic data for in silico excretory/ secretory proteins prediction and analysis, providing a foundation for developing new therapeutic solutions for parasitic infections. © 2011 licensee BioMed Central Ltd.
Source Title: 10th Int. Conference on Bioinformatics - 1st ISCB Asia Joint Conference 2011, InCoB 2011/ISCB-Asia 2011: Computational Biology - Proceedings from Asia Pacific Bioinformatics Network (APBioNet)
URI: http://scholarbank.nus.edu.sg/handle/10635/109753
DOI: 10.1186/1471-2164-12-S3-S14
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

20
checked on Sep 18, 2018

WEB OF SCIENCETM
Citations

18
checked on Sep 18, 2018

Page view(s)

42
checked on Jul 6, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.