Abstract
BackgroundThe 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.MethodologyThe 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.ConclusionIt 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.
Highlights
With generation sequencing (NGS), the amount of transcriptome data is increasing rapidly
Typical analyses performed on transcripts are GC-content, open reading frames (ORF), single-nucleotide polymorphisms (SNP), comparisons with protein databases, gene ontology (GO) [1], differential expression (DE), and homology clustering
Transcriptome Computational Workbench (TCW) is composed of five graphical interfaces: runSingleTCW for building the singleTCW database, runDE for adding DE results to the database, viewSingleTCW for query and display of the results, runMultiTCW for building a comparison database, and viewMultiTCW for query and display of the results
Summary
With generation sequencing (NGS), the amount of transcriptome data is increasing rapidly. Most publications do not state what software they used for merging the results, which indicates either that they did not properly reference the software, or they wrote their own scripts and/or used Excel spreadsheets This causes an ‘ad hoc’ style of analysis that can lead to human error, loss of data and results, inefficient use of time, and lack of verifiability, repeatability and extensibility. This approach does not make the data and results available on the web in a queryable form for the community. 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
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