BACKGROUND: The transcriptome of an organism can be studied with the analysis of expressed sequence tag (EST) data sets that offers a rapid and cost effective approach with several new and updated bioinformatics approaches and tools for assembly and annotation. The comprehensive analyses comprehend an organism along with the genome and proteome analysis. With the advent of large-scale sequencing projects and generation of sequence data at protein and cDNA levels, automated analysis pipeline is necessary to store, organize and annotate ESTs. RESULTS: TranSeqAnnotator is a workflow for large-scale analysis of transcriptomic data with the most appropriate bioinformatics tools for data management and analysis. The pipeline automatically cleans, clusters, assembles and generates consensus sequences, conceptually translates these into possible protein products and assigns putative function based on various DNA and protein similarity searches. Excretory/secretory (ES) proteins inferred from ESTs/short reads are also identified. The TranSeqAnnotator accepts FASTA format raw and quality ESTs along with protein and short read sequences and are analysed with user selected programs. After pre-processing and assembly, the dataset is annotated at the nucleotide, protein and ES protein levels. CONCLUSION: TranSeqAnnotator has been developed in a Linux cluster, to perform an exhaustive and reliable analysis and provide detailed annotation. TranSeqAnnotator outputs gene ontologies, protein functional identifications in terms of mapping to protein domains and metabolic pathways. The pipeline is applied to annotate large EST datasets to identify several novel and known genes with therapeutic experimental validations and could serve as potential targets for parasite intervention. TransSeqAnnotator is freely available for the scientific community at http://estexplorer.biolinfo.org/TranSeqAnnotator/.
BACKGROUND: The transcriptome of an organism can be studied with the analysis of expressed sequence tag (EST) data sets that offers a rapid and cost effective approach with several new and updated bioinformatics approaches and tools for assembly and annotation. The comprehensive analyses comprehend an organism along with the genome and proteome analysis. With the advent of large-scale sequencing projects and generation of sequence data at protein and cDNA levels, automated analysis pipeline is necessary to store, organize and annotate ESTs. RESULTS: TranSeqAnnotator is a workflow for large-scale analysis of transcriptomic data with the most appropriate bioinformatics tools for data management and analysis. The pipeline automatically cleans, clusters, assembles and generates consensus sequences, conceptually translates these into possible protein products and assigns putative function based on various DNA and protein similarity searches. Excretory/secretory (ES) proteins inferred from ESTs/short reads are also identified. The TranSeqAnnotator accepts FASTA format raw and quality ESTs along with protein and short read sequences and are analysed with user selected programs. After pre-processing and assembly, the dataset is annotated at the nucleotide, protein and ES protein levels. CONCLUSION: TranSeqAnnotator has been developed in a Linux cluster, to perform an exhaustive and reliable analysis and provide detailed annotation. TranSeqAnnotator outputs gene ontologies, protein functional identifications in terms of mapping to protein domains and metabolic pathways. The pipeline is applied to annotate large EST datasets to identify several novel and known genes with therapeutic experimental validations and could serve as potential targets for parasite intervention. TransSeqAnnotator is freely available for the scientific community at http://estexplorer.biolinfo.org/TranSeqAnnotator/.
Authors: Tamberlyn Bieri; Darin Blasiar; Philip Ozersky; Igor Antoshechkin; Carol Bastiani; Payan Canaran; Juancarlos Chan; Nansheng Chen; Wen J Chen; Paul Davis; Tristan J Fiedler; Lisa Girard; Michael Han; Todd W Harris; Ranjana Kishore; Raymond Lee; Sheldon McKay; Hans-Michael Müller; Cecilia Nakamura; Andrei Petcherski; Arun Rangarajan; Anthony Rogers; Gary Schindelman; Erich M Schwarz; Will Spooner; Mary Ann Tuli; Kimberly Van Auken; Daniel Wang; Xiaodong Wang; Gary Williams; Richard Durbin; Lincoln D Stein; Paul W Sternberg; John Spieth Journal: Nucleic Acids Res Date: 2006-11-11 Impact factor: 16.971
Authors: Mariano Latorre; Herman Silva; Juan Saba; Carito Guziolowski; Paula Vizoso; Veronica Martinez; Jonathan Maldonado; Andrea Morales; Rodrigo Caroca; Veronica Cambiazo; Reinaldo Campos-Vargas; Mauricio Gonzalez; Ariel Orellana; Julio Retamales; Lee A Meisel Journal: BMC Bioinformatics Date: 2006-11-23 Impact factor: 3.169