Dataset Information

ASmodeler: gene modeling of alternative splicing from genomic alignment of mRNA, EST and protein sequences.

ABSTRACT: Alternative splicing is in important mechanism of modulating gene function and expression which greatly expands transcriptome diversity. ASmodeler is a novel web-based utility that finds gene models including alternative splicing events from genomic alignment of mRNA, EST and protein sequences. User-supplied sequences are aligned against the genome map using the BLAT and SIM4 programs. Resulting exon connectivity is analyzed by applying graph-theoretic methods to build all possible gene models including splice variants. The algorithm essentially combines the genome-based sequence clustering and transcript assembly procedures in a coherent fashion. In addition to the user-supplied sequences, UniGene clusters and many well-known gene predictions such as Genscan, Ensembl and Acembly may be included in gene modeling. The current implementation supports human, mouse and rat genomes. ASmodeler is available at http://genome.ewha.ac.kr/ECgene/ASmodeler/.

SUBMITTER: Kim N

PROVIDER: S-EPMC441542 | biostudies-literature | 2004 Jul

REPOSITORIES: biostudies-literature

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ASmodeler: gene modeling of alternative splicing from genomic alignment of mRNA, EST and protein sequences.

Kim Namshin N Shin Seokmin S Lee Sanghyuk S

Nucleic acids research 20040701 Web Server issue

Alternative splicing is in important mechanism of modulating gene function and expression which greatly expands transcriptome diversity. ASmodeler is a novel web-based utility that finds gene models including alternative splicing events from genomic alignment of mRNA, EST and protein sequences. User-supplied sequences are aligned against the genome map using the BLAT and SIM4 programs. Resulting exon connectivity is analyzed by applying graph-theoretic methods to build all possible gene models i ...[more]

PMID: 15215376

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Project description:Splice isoform structure and abundance can be affected by either noncoding or masquerading coding variants that alter the structure or abundance of transcripts. When these variants are common in the population, these nonconstitutive transcripts are sufficiently frequent so as to resemble naturally occurring, alternative mRNA splicing. Prediction of the effects of such variants has been shown to be accurate using information theory-based methods. Single nucleotide polymorphisms (SNPs) predicted to significantly alter natural and/or cryptic splice site strength were shown to affect gene expression. Splicing changes for known SNP genotypes were confirmed in HapMap lymphoblastoid cell lines with gene expression microarrays and custom designed q-RT-PCR or TaqMan assays. The majority of these SNPs (15 of 22) as well as an independent set of 24 variants were then subjected to RNAseq analysis using the ValidSpliceMut web beacon (http://validsplicemut.cytognomix.com), which is based on data from the Cancer Genome Atlas and International Cancer Genome Consortium. SNPs from different genes analyzed with gene expression microarray and q-RT-PCR exhibited significant changes in affected splice site use. Thirteen SNPs directly affected exon inclusion and 10 altered cryptic site use. Homozygous SNP genotypes resulting in stronger splice sites exhibited higher levels of processed mRNA than alleles associated with weaker sites. Four SNPs exhibited variable expression among individuals with the same genotypes, masking statistically significant expression differences between alleles. Genome-wide information theory and expression analyses (RNAseq) in tumor exomes and genomes confirmed splicing effects for 7 of the HapMap SNP and 14 SNPs identified from tumor genomes. q-RT-PCR resolved rare splice isoforms with read abundance too low for statistical significance in ValidSpliceMut. Nevertheless, the web-beacon provides evidence of unanticipated splicing outcomes, for example, intron retention due to compromised recognition of constitutive splice sites. Thus, ValidSpliceMut and q-RT-PCR represent complementary resources for identification of allele-specific, alternative splicing.

Project description:BackgroundAlternative splicing contributes significantly to the complexity of the human transcriptome and proteome. Computational prediction of alternative splice isoforms are usually based on EST sequences that also allow to approximate the expression pattern of the related transcripts. However, the limited number of tissues represented in the EST data as well as the different cDNA construction protocols may influence the predictive capacity of ESTs to unravel tissue-specifically expressed transcripts.MethodsWe predict tissue and tumor specific splice isoforms based on the genomic mapping (SpliceNest) of the EST consensus sequences and library annotation provided in the GeneNest database. We further ascertain the potentially rare tissue specific transcripts as the ones represented only by ESTs derived from normalized libraries. A subset of the predicted tissue and tumor specific isoforms are then validated via RT-PCR experiments over a spectrum of 40 tissue types.ResultsOur strategy revealed 427 genes with at least one tissue specific transcript as well as 1120 genes showing tumor specific isoforms. While our experimental evaluation of computationally predicted tissue-specific isoforms revealed a high success rate in confirming the expression of these isoforms in the respective tissue, the strategy frequently failed to detect the expected restricted expression pattern. The analysis of putative lowly expressed transcripts using normalized cDNA libraries suggests that our ability to detect tissue-specific isoforms strongly depends on the expression level of the respective transcript as well as on the sensitivity of the experimental methods. Especially splice isoforms predicted to be disease-specific tend to represent transcripts that are expressed in a set of healthy tissues rather than novel isoforms.ConclusionsWe propose to combine the computational prediction of alternative splice isoforms with experimental validation for efficient delineation of an accurate set of tissue-specific transcripts.

Dataset Information

ASmodeler: gene modeling of alternative splicing from genomic alignment of mRNA, EST and protein sequences.

Publications

ASmodeler: gene modeling of alternative splicing from genomic alignment of mRNA, EST and protein sequences.

Similar Datasets

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