Dataset Information

Identifying novel genes in C. elegans using SAGE tags.

ABSTRACT: Despite extensive efforts devoted to predicting protein-coding genes in genome sequences, many bona fide genes have not been found and many existing gene models are not accurate in all sequenced eukaryote genomes. This situation is partly explained by the fact that gene prediction programs have been developed based on our incomplete understanding of gene feature information such as splicing and promoter characteristics. Additionally, full-length cDNAs of many genes and their isoforms are hard to obtain due to their low level or rare expression. In order to obtain full-length sequences of all protein-coding genes, alternative approaches are required.In this project, we have developed a method of reconstructing full-length cDNA sequences based on short expressed sequence tags which is called sequence tag-based amplification of cDNA ends (STACE). Expressed tags are used as anchors for retrieving full-length transcripts in two rounds of PCR amplification. We have demonstrated the application of STACE in reconstructing full-length cDNA sequences using expressed tags mined in an array of serial analysis of gene expression (SAGE) of C. elegans cDNA libraries. We have successfully applied STACE to recover sequence information for 12 genes, for two of which we found isoforms. STACE was used to successfully recover full-length cDNA sequences for seven of these genes.The STACE method can be used to effectively reconstruct full-length cDNA sequences of genes that are under-represented in cDNA sequencing projects and have been missed by existing gene prediction methods, but their existence has been suggested by short sequence tags such as SAGE tags.

SUBMITTER: Nesbitt MJ

PROVIDER: S-EPMC3017025 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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Identifying novel genes in C. elegans using SAGE tags.

Nesbitt Matthew J MJ Moerman Donald G DG Chen Nansheng N

BMC molecular biology 20101210

<h4>Background</h4>Despite extensive efforts devoted to predicting protein-coding genes in genome sequences, many bona fide genes have not been found and many existing gene models are not accurate in all sequenced eukaryote genomes. This situation is partly explained by the fact that gene prediction programs have been developed based on our incomplete understanding of gene feature information such as splicing and promoter characteristics. Additionally, full-length cDNAs of many genes and their i ...[more]

PMID: 21143975

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Project description:BACKGROUND: We have applied a high-throughput pyrosequencing technology for transcriptome profiling of Caenorhabditis elegans in its first larval stage. Using this approach, we have generated a large amount of data for expressed sequence tags, which provides an opportunity for the discovery of putative novel transcripts and alternative splice variants that could be developmentally specific to the first larval stage. This work also demonstrates the successful and efficient application of a next generation sequencing methodology. RESULTS: We have generated over 30 million bases of novel expressed sequence tags from first larval stage worms utilizing high-throughput sequencing technology. We have shown that approximately 14% of the newly sequenced expressed sequence tags map completely or partially to genomic regions where there are no annotated genes or splice variants and therefore, imply that these are novel genetic structures. Expressed sequence tags, which map to intergenic (around 1000) and intronic regions (around 580), may represent novel transcribed regions, such as unannotated or unrecognized small protein-coding or non-protein-coding genes or splice variants. Expressed sequence tags, which map across intron-exon boundaries (around 300), indicate possible alternative splice sites, while expressed sequence tags, which map near the ends of known transcripts (around 600), suggest extension of the coding or untranslated regions. We have also discovered that intergenic and intronic expressed sequence tags, which are well conserved across different nematode species, are likely to represent non-coding RNAs. Lastly, we have incorporated available serial analysis of gene expression data generated from first larval stage worms, in order to predict novel transcripts that might be specifically or predominantly expressed in the first larval stage. CONCLUSION: We have demonstrated the use of a high-throughput sequencing methodology to efficiently produce a snap-shot of transcriptional activities occurring in the first larval stage of C. elegans development. Such application of this new sequencing technique allows for high-throughput, genome-wide experimental verification of known and novel transcripts. This study provides a more complete C. elegans transcriptome profile and, furthermore, gives insight into the evolutionary and biological complexity of this organism.

Project description:BackgroundSerial Analysis of Gene Expression (SAGE) is a powerful tool for genome-wide transcription studies. Unlike microarrays, it has the ability to detect novel forms of RNA such as alternatively spliced and antisense transcripts, without the need for prior knowledge of their existence. One limitation of using SAGE on an organism with a complex genome and lacking detailed sequence information, such as the hexaploid bread wheat Triticum aestivum, is accurate annotation of the tags generated. Without accurate annotation it is impossible to fully understand the dynamic processes involved in such complex polyploid organisms. Hence we have developed and utilised novel procedures to characterise, in detail, SAGE tags generated from the whole grain transcriptome of hexaploid wheat.ResultsExamination of 71,930 Long SAGE tags generated from six libraries derived from two wheat genotypes grown under two different conditions suggested that SAGE is a reliable and reproducible technique for use in studying the hexaploid wheat transcriptome. However, our results also showed that in poorly annotated and/or poorly sequenced genomes, such as hexaploid wheat, considerably more information can be extracted from SAGE data by carrying out a systematic analysis of both perfect and "fuzzy" (partially matched) tags. This detailed analysis of the SAGE data shows first that while there is evidence of alternative polyadenylation this appears to occur exclusively within the 3' untranslated regions. Secondly, we found no strong evidence for widespread alternative splicing in the developing wheat grain transcriptome. However, analysis of our SAGE data shows that antisense transcripts are probably widespread within the transcriptome and appear to be derived from numerous locations within the genome. Examination of antisense transcripts showing sequence similarity to the Puroindoline a and Puroindoline b genes suggests that such antisense transcripts might have a role in the regulation of gene expression.ConclusionOur results indicate that the detailed analysis of transcriptome data, such as SAGE tags, is essential to understand fully the factors that regulate gene expression and that such analysis of the wheat grain transcriptome reveals that antisense transcripts maybe widespread and hence probably play a significant role in the regulation of gene expression during grain development.

Dataset Information

Identifying novel genes in C. elegans using SAGE tags.

Publications

Identifying novel genes in C. elegans using SAGE tags.

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