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TCW: Transcriptome Computational Workbench

BACKGROUND: The analysis of transcriptome data involves many steps and various programs, along with organization of large amounts of data and results. Without a methodical approach for storage, analysis and query, the resulting ad hoc analysis can lead to human error, loss of data and results, ineff...

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Autores principales: Soderlund, Carol, Nelson, William, Willer, Mark, Gang, David R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3714256/
https://www.ncbi.nlm.nih.gov/pubmed/23874959
http://dx.doi.org/10.1371/journal.pone.0069401
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author Soderlund, Carol
Nelson, William
Willer, Mark
Gang, David R.
author_facet Soderlund, Carol
Nelson, William
Willer, Mark
Gang, David R.
author_sort Soderlund, Carol
collection PubMed
description BACKGROUND: The analysis of transcriptome data involves many steps and various programs, along with organization of large amounts of data and results. Without a methodical approach for storage, analysis and query, the resulting ad hoc analysis can lead to human error, loss of data and results, inefficient use of time, and lack of verifiability, repeatability, and extensibility. METHODOLOGY: The Transcriptome Computational Workbench (TCW) provides Java graphical interfaces for methodical analysis for both single and comparative transcriptome data without the use of a reference genome (e.g. for non-model organisms). The singleTCW interface steps the user through importing transcript sequences (e.g. Illumina) or assembling long sequences (e.g. Sanger, 454, transcripts), annotating the sequences, and performing differential expression analysis using published statistical programs in R. The data, metadata, and results are stored in a MySQL database. The multiTCW interface builds a comparison database by importing sequence and annotation from one or more single TCW databases, executes the ESTscan program to translate the sequences into proteins, and then incorporates one or more clusterings, where the clustering options are to execute the orthoMCL program, compute transitive closure, or import clusters. Both singleTCW and multiTCW allow extensive query and display of the results, where singleTCW displays the alignment of annotation hits to transcript sequences, and multiTCW displays multiple transcript alignments with MUSCLE or pairwise alignments. The query programs can be executed on the desktop for fastest analysis, or from the web for sharing the results. CONCLUSION: It is now affordable to buy a multi-processor machine, and easy to install Java and MySQL. By simply downloading the TCW, the user can interactively analyze, query and view their data. The TCW allows in-depth data mining of the results, which can lead to a better understanding of the transcriptome. TCW is freely available from www.agcol.arizona.edu/software/tcw.
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spelling pubmed-37142562013-07-19 TCW: Transcriptome Computational Workbench Soderlund, Carol Nelson, William Willer, Mark Gang, David R. PLoS One Research Article BACKGROUND: The analysis of transcriptome data involves many steps and various programs, along with organization of large amounts of data and results. Without a methodical approach for storage, analysis and query, the resulting ad hoc analysis can lead to human error, loss of data and results, inefficient use of time, and lack of verifiability, repeatability, and extensibility. METHODOLOGY: The Transcriptome Computational Workbench (TCW) provides Java graphical interfaces for methodical analysis for both single and comparative transcriptome data without the use of a reference genome (e.g. for non-model organisms). The singleTCW interface steps the user through importing transcript sequences (e.g. Illumina) or assembling long sequences (e.g. Sanger, 454, transcripts), annotating the sequences, and performing differential expression analysis using published statistical programs in R. The data, metadata, and results are stored in a MySQL database. The multiTCW interface builds a comparison database by importing sequence and annotation from one or more single TCW databases, executes the ESTscan program to translate the sequences into proteins, and then incorporates one or more clusterings, where the clustering options are to execute the orthoMCL program, compute transitive closure, or import clusters. Both singleTCW and multiTCW allow extensive query and display of the results, where singleTCW displays the alignment of annotation hits to transcript sequences, and multiTCW displays multiple transcript alignments with MUSCLE or pairwise alignments. The query programs can be executed on the desktop for fastest analysis, or from the web for sharing the results. CONCLUSION: It is now affordable to buy a multi-processor machine, and easy to install Java and MySQL. By simply downloading the TCW, the user can interactively analyze, query and view their data. The TCW allows in-depth data mining of the results, which can lead to a better understanding of the transcriptome. TCW is freely available from www.agcol.arizona.edu/software/tcw. Public Library of Science 2013-07-17 /pmc/articles/PMC3714256/ /pubmed/23874959 http://dx.doi.org/10.1371/journal.pone.0069401 Text en © 2013 Soderlund et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Soderlund, Carol
Nelson, William
Willer, Mark
Gang, David R.
TCW: Transcriptome Computational Workbench
title TCW: Transcriptome Computational Workbench
title_full TCW: Transcriptome Computational Workbench
title_fullStr TCW: Transcriptome Computational Workbench
title_full_unstemmed TCW: Transcriptome Computational Workbench
title_short TCW: Transcriptome Computational Workbench
title_sort tcw: transcriptome computational workbench
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3714256/
https://www.ncbi.nlm.nih.gov/pubmed/23874959
http://dx.doi.org/10.1371/journal.pone.0069401
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