In silico secretome analysis approach for next generation sequencing transcriptomic data

Gagan Garg, Shoba Ranganathan*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

26 Citations (Scopus)

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.

Original languageEnglish
Title of host publication10th International Conference on Bioinformatics. First ISCB Asia Joint Conference 2011 (InCoB/ISCB-Asia 2011)
Subtitle of host publicationComputational Biology
EditorsChristian Schoenbach, Sheila Nathan, Tin Wee Tan, Shoba Ranganathan
Place of PublicationPiscataway, N.J.
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages1-10
Number of pages10
Volume12, Suppl. 3
ISBN (Print)9781457712944
DOIs
Publication statusPublished - 2011
Event10th International Conference on Bioinformatics and 1st ISCB Asia Joint Conference 2011: Computational Biology, InCoB 2011/ISCB-Asia 2011 - Kuala Lumpur, Malaysia
Duration: 30 Nov 20112 Dec 2011

Other

Other10th International Conference on Bioinformatics and 1st ISCB Asia Joint Conference 2011: Computational Biology, InCoB 2011/ISCB-Asia 2011
CountryMalaysia
CityKuala Lumpur
Period30/11/112/12/11

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