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This article is part of the supplement: Proceedings of the Sixth Annual MCBIOS Conference. Transformational Bioinformatics: Delivering Value from Genomes

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Novel software package for cross-platform transcriptome analysis (CPTRA)

Xin Zhou1, Zhen Su2, R Douglas Sammons3, Yanhui Peng4, Patrick J Tranel5, C Neal Stewart4 and Joshua S Yuan16*

Author Affiliations

1 Institute of Plant Genomics and Biotechnology, Texas A&M University, College Station, TX, USA

2 State Key Laboratory of Plant Physiology and Biochemistry, College of Biological Sciences, China Agricultural University, Beijing, PR China

3 Monsanto Co., Saint Louis, MO, USA

4 Department of Plant Sciences, University of Tennessee, Knoxville, TN, USA

5 Department of Crop Sciences, University of Illinois, Urbana Champion, IL, USA

6 Department of Plant Pathology and Microbiology, Texas A&M University, College Station, TX, USA

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BMC Bioinformatics 2009, 10(Suppl 11):S16  doi:10.1186/1471-2105-10-S11-S16

Published: 8 October 2009

Abstract

Background

Next-generation sequencing techniques enable several novel transcriptome profiling approaches. Recent studies indicated that digital gene expression profiling based on short sequence tags has superior performance as compared to other transcriptome analysis platforms including microarrays. However, the transcriptomic analysis with tag-based methods often depends on available genome sequence. The use of tag-based methods in species without genome sequence should be complemented by other methods such as cDNA library sequencing. The combination of different next generation sequencing techniques like 454 pyrosequencing and Illumina Genome Analyzer (Solexa) will enable high-throughput and accurate global gene expression profiling in species with limited genome information. The combination of transcriptome data acquisition methods requires cross-platform transcriptome data analysis platforms, including a new software package for data processing.

Results

Here we presented a software package, CPTRA: Cross-Platform TRanscriptome Analysis, to analyze transcriptome profiling data from separate methods. The software package is available at http://people.tamu.edu/~syuan/cptra/cptra.html webcite. It was applied to the case study of non-target site glyphosate resistance in horseweed; and the data was mined to discover resistance target gene(s). For the software, the input data included a long-read sequence dataset with proper annotation, and a short-read sequence tag dataset for the quantification of transcripts. By combining the two datasets, the software carries out the unique sequence tag identification, tag counting for transcript quantification, and cross-platform sequence matching functions, whereby the short sequence tags can be annotated with a function, level of expression, and Gene Ontology (GO) classification. Multiple sequence search algorithms were implemented and compared. The analysis highlighted the importance of transport genes in glyphosate resistance and identified several candidate genes for down-stream analysis.

Conclusion

CPTRA is a powerful software package for next generation sequencing-based transcriptome profiling in species with limited genome information. According to our case study, the strategy can greatly broaden the application of the next generation sequencing for transcriptome analysis in species without reference genome sequence.