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Related Experiment Video

Updated: May 24, 2026

Cost-Efficient Transcriptomic-Based Drug Screening

Cost-Efficient Transcriptomic-Based Drug Screening

Published on: February 23, 2024

In silico secretome analysis approach for next generation sequencing transcriptomic data.

Gagan Garg¹, Shoba Ranganathan

¹Dept. of Chemistry and Biomolecular Sciences, Macquarie University, Sydney NSW 2109, Australia.

|February 29, 2012

Summary

We developed a computational method to identify excretory/secretory proteins (ESPs) in parasitic nematodes, revealing potential new drug targets. This approach aids in developing novel therapies for parasitic infections by analyzing transcriptomic data.

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Real-time Analysis of Transcription Factor Binding, Transcription, Translation, and Turnover to Display Global Events During Cellular Activation

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Published on: March 7, 2018

Area of Science:

Parasitology
Computational Biology
Genomics

Background:

Excretory/secretory proteins (ESPs) are crucial at the host-parasite interface and modulate host immunity.
Transcriptomics aids understanding of parasitic helminths and development of therapeutics.
Previous transcriptomic studies have not fully addressed ESP prediction, especially for non-classical secretion pathways.

Purpose of the Study:

To develop and apply a computational approach for predicting and annotating ESPs from next-generation sequencing transcriptomic data.
To identify novel therapeutic targets for parasitic infections by analyzing ESPs, including those secreted via non-classical pathways.

Main Methods:

A semi-automated computational pipeline was created for ESP prediction and annotation.
The pipeline incorporates an improved strategy for non-classical protein secretion prediction and homology matching.
The approach was applied to transcriptomic data from the parasitic nematode *Strongyloides ratti*.

Main Results:

Identified 2572 ESPs from *S. ratti* transcriptomic data.
Classically secreted ESPs accounted for 407 (1.9%), non-classically secreted for 923 (4.4%), and homology-identified ESPs for 1516 (7.26%).
Discovered 19 ESPs with no host homologues but homology to lethal phenotypes in *C. elegans*, indicating potential therapeutic targets.

Conclusions:

A comprehensive, freely available computational approach for secretome analysis of NGS data was reported.
The method was successfully applied to *S. ratti* transcriptomic data for *in silico* ESP prediction.
This work provides a foundation for developing new therapeutic strategies against parasitic infections.