Helminth secretome database (HSD): a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs)

Gagan Garg, Shoba Ranganathan

Research output: Contribution to journalConference paperResearchpeer-review

Abstract

Background: Helminths are important socio-economic organisms, responsible for causing major parasitic infections in humans, other animals and plants. These infections impose a significant public health and economic burden globally. Exceptionally, some helminth organisms like Caenorhabditis elegans are free-living in nature and serve as model organisms for studying parasitic infections. Excretory/secretory proteins play an important role in parasitic helminth infections which make these proteins attractive targets for therapeutic use. In the case of helminths, large volume of expressed sequence tags (ESTs) has been generated to understand parasitism at molecular level and for predicting excretory/secretory proteins for developing novel strategies to tackle parasitic infections. However, mostly predicted ES proteins are not available for further analysis and there is no repository available for such predicted ES proteins. Furthermore, predictions have, in the main, focussed on classical secretory pathways while it is well established that helminth parasites also utilise non-classical secretory pathways. Results: We developed a free Helminth Secretome Database (HSD), which serves as a repository for ES proteins predicted using classical and non-classical secretory pathways, from EST data for 78 helminth species (64 nematodes, 7 trematodes and 7 cestodes) ranging from parasitic to free-living organisms. Approximately 0.9 million ESTs compiled from the largest EST database, dbEST were cleaned, assembled and analysed by different computational tools in our bioinformatics pipeline and predicted ES proteins were submitted to HSD. Conclusion: We report the large-scale prediction and analysis of classically and non-classically secreted ES proteins from diverse helminth organisms. All the Unigenes (contigs and singletons) and excretory/secretory protein datasets generated from this analysis are freely available. A BLAST server is available at http://estexplorer.biolinfo. org/hsd, for checking the sequence similarity of new protein sequences against predicted helminth ES proteins.

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Helminths
Expressed Sequence Tags
Databases
Parasitic Diseases
Helminth Proteins
Proteins
Secretory Pathway
Economics
Cestoda
Caenorhabditis elegans
Therapeutic Uses
Computational Biology
Parasites
Public Health

Bibliographical note

This version is archived for private and non-commercial use under the terms of this BioMed Central open access license ("license") (see http://www.biomedcentral.com/info/about/license). The work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. For further rights please check the terms of the license, or contact the publisher.

Cite this

@article{588c8704c2fc497eac0de39a3ba79d49,
title = "Helminth secretome database (HSD): a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs)",
abstract = "Background: Helminths are important socio-economic organisms, responsible for causing major parasitic infections in humans, other animals and plants. These infections impose a significant public health and economic burden globally. Exceptionally, some helminth organisms like Caenorhabditis elegans are free-living in nature and serve as model organisms for studying parasitic infections. Excretory/secretory proteins play an important role in parasitic helminth infections which make these proteins attractive targets for therapeutic use. In the case of helminths, large volume of expressed sequence tags (ESTs) has been generated to understand parasitism at molecular level and for predicting excretory/secretory proteins for developing novel strategies to tackle parasitic infections. However, mostly predicted ES proteins are not available for further analysis and there is no repository available for such predicted ES proteins. Furthermore, predictions have, in the main, focussed on classical secretory pathways while it is well established that helminth parasites also utilise non-classical secretory pathways. Results: We developed a free Helminth Secretome Database (HSD), which serves as a repository for ES proteins predicted using classical and non-classical secretory pathways, from EST data for 78 helminth species (64 nematodes, 7 trematodes and 7 cestodes) ranging from parasitic to free-living organisms. Approximately 0.9 million ESTs compiled from the largest EST database, dbEST were cleaned, assembled and analysed by different computational tools in our bioinformatics pipeline and predicted ES proteins were submitted to HSD. Conclusion: We report the large-scale prediction and analysis of classically and non-classically secreted ES proteins from diverse helminth organisms. All the Unigenes (contigs and singletons) and excretory/secretory protein datasets generated from this analysis are freely available. A BLAST server is available at http://estexplorer.biolinfo. org/hsd, for checking the sequence similarity of new protein sequences against predicted helminth ES proteins.",
author = "Gagan Garg and Shoba Ranganathan",
note = "This version is archived for private and non-commercial use under the terms of this BioMed Central open access license ({"}license{"}) (see http://www.biomedcentral.com/info/about/license). The work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. For further rights please check the terms of the license, or contact the publisher.",
year = "2012",
doi = "10.1186/1471-2164-13-S7-S8",
language = "English",
volume = "13",
pages = "1--11",
journal = "BMC Genomics",
issn = "1471-2164",
publisher = "Springer, Springer Nature",

}

Helminth secretome database (HSD) : a collection of helminth excretory/secretory proteins predicted from expressed sequence tags (ESTs). / Garg, Gagan; Ranganathan, Shoba.

In: BMC Genomics, Vol. 13, S8, 2012, p. 1-11.

Research output: Contribution to journalConference paperResearchpeer-review

TY - JOUR

T1 - Helminth secretome database (HSD)

T2 - BMC Genomics

AU - Garg, Gagan

AU - Ranganathan, Shoba

N1 - This version is archived for private and non-commercial use under the terms of this BioMed Central open access license ("license") (see http://www.biomedcentral.com/info/about/license). The work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. For further rights please check the terms of the license, or contact the publisher.

PY - 2012

Y1 - 2012

N2 - Background: Helminths are important socio-economic organisms, responsible for causing major parasitic infections in humans, other animals and plants. These infections impose a significant public health and economic burden globally. Exceptionally, some helminth organisms like Caenorhabditis elegans are free-living in nature and serve as model organisms for studying parasitic infections. Excretory/secretory proteins play an important role in parasitic helminth infections which make these proteins attractive targets for therapeutic use. In the case of helminths, large volume of expressed sequence tags (ESTs) has been generated to understand parasitism at molecular level and for predicting excretory/secretory proteins for developing novel strategies to tackle parasitic infections. However, mostly predicted ES proteins are not available for further analysis and there is no repository available for such predicted ES proteins. Furthermore, predictions have, in the main, focussed on classical secretory pathways while it is well established that helminth parasites also utilise non-classical secretory pathways. Results: We developed a free Helminth Secretome Database (HSD), which serves as a repository for ES proteins predicted using classical and non-classical secretory pathways, from EST data for 78 helminth species (64 nematodes, 7 trematodes and 7 cestodes) ranging from parasitic to free-living organisms. Approximately 0.9 million ESTs compiled from the largest EST database, dbEST were cleaned, assembled and analysed by different computational tools in our bioinformatics pipeline and predicted ES proteins were submitted to HSD. Conclusion: We report the large-scale prediction and analysis of classically and non-classically secreted ES proteins from diverse helminth organisms. All the Unigenes (contigs and singletons) and excretory/secretory protein datasets generated from this analysis are freely available. A BLAST server is available at http://estexplorer.biolinfo. org/hsd, for checking the sequence similarity of new protein sequences against predicted helminth ES proteins.

AB - Background: Helminths are important socio-economic organisms, responsible for causing major parasitic infections in humans, other animals and plants. These infections impose a significant public health and economic burden globally. Exceptionally, some helminth organisms like Caenorhabditis elegans are free-living in nature and serve as model organisms for studying parasitic infections. Excretory/secretory proteins play an important role in parasitic helminth infections which make these proteins attractive targets for therapeutic use. In the case of helminths, large volume of expressed sequence tags (ESTs) has been generated to understand parasitism at molecular level and for predicting excretory/secretory proteins for developing novel strategies to tackle parasitic infections. However, mostly predicted ES proteins are not available for further analysis and there is no repository available for such predicted ES proteins. Furthermore, predictions have, in the main, focussed on classical secretory pathways while it is well established that helminth parasites also utilise non-classical secretory pathways. Results: We developed a free Helminth Secretome Database (HSD), which serves as a repository for ES proteins predicted using classical and non-classical secretory pathways, from EST data for 78 helminth species (64 nematodes, 7 trematodes and 7 cestodes) ranging from parasitic to free-living organisms. Approximately 0.9 million ESTs compiled from the largest EST database, dbEST were cleaned, assembled and analysed by different computational tools in our bioinformatics pipeline and predicted ES proteins were submitted to HSD. Conclusion: We report the large-scale prediction and analysis of classically and non-classically secreted ES proteins from diverse helminth organisms. All the Unigenes (contigs and singletons) and excretory/secretory protein datasets generated from this analysis are freely available. A BLAST server is available at http://estexplorer.biolinfo. org/hsd, for checking the sequence similarity of new protein sequences against predicted helminth ES proteins.

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U2 - 10.1186/1471-2164-13-S7-S8

DO - 10.1186/1471-2164-13-S7-S8

M3 - Conference paper

VL - 13

SP - 1

EP - 11

JO - BMC Genomics

JF - BMC Genomics

SN - 1471-2164

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