Dataset Information

A computational analysis of sequence features involved in recognition of short introns.

ABSTRACT: Splicing of short introns by the nuclear pre-mRNA splicing machinery is thought to proceed via an "intron definition" mechanism, in which the 5' and 3' splice sites (5'ss, 3'ss, respectively) are initially recognized and paired across the intron. Here, we describe a computational analysis of sequence features involved in recognition of short introns by using available transcript data from five eukaryotes with complete or nearly complete genomic sequences. The information content of five different transcript features was measured by using methods from information theory, and Monte Carlo simulations were used to determine the amount of information required for accurate recognition of short introns in each organism. We conclude: (i) that short introns in Drosophila melanogaster and Caenorhabditis elegans contain essentially all of the information for their recognition by the splicing machinery, and computer programs that simulate splicing specificity can predict the exact boundaries of approximately 95% of short introns in both organisms; (ii) that in yeast, the 5'ss, branch signal, and 3'ss can accurately identify intron locations but do not precisely determine the location of 3' cleavage in every intron; and (iii) that the 5'ss, branch signal, and 3'ss are not sufficient to accurately identify short introns in plant and human transcripts, but that specific subsets of candidate intronic enhancer motifs can be identified in both human and Arabidopsis that contribute dramatically to the accuracy of splicing simulators.

SUBMITTER: Lim LP

PROVIDER: S-EPMC58706 | biostudies-literature | 2001 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A computational analysis of sequence features involved in recognition of short introns.

Lim L P LP Burge C B CB

Proceedings of the National Academy of Sciences of the United States of America 20010901 20

Splicing of short introns by the nuclear pre-mRNA splicing machinery is thought to proceed via an "intron definition" mechanism, in which the 5' and 3' splice sites (5'ss, 3'ss, respectively) are initially recognized and paired across the intron. Here, we describe a computational analysis of sequence features involved in recognition of short introns by using available transcript data from five eukaryotes with complete or nearly complete genomic sequences. The information content of five differen ...[more]

PMID: 11572975

Similar Datasets

Project description:BACKGROUND: The splicing of pre-mRNAs is conspicuously often variable and produces multiple alternatively spliced (AS) isoforms that encode different messages from one gene locus. Computational studies uncovered a class of highly similar isoforms, which were related to tandem 5'-splice sites (5'ss) and 3'-splice sites (3'ss), yet with very sparse anecdotal evidence in experimental studies. To compare the types and levels of alternative tandem splice site exons occurring in different human organ systems and cell types, and to study known sequence features involved in the recognition and distinction of neighboring splice sites, we performed large-scale, stringent alignments of cDNA sequences and ESTs to the human and mouse genomes, followed by experimental validation. RESULTS: We analyzed alternative 5'ss exons (A5Es) and alternative 3'ss exons (A3Es), derived from transcript sequences that were aligned to assembled genome sequences to infer patterns of AS occurring in several thousands of genes. Comparing the levels of overlapping (tandem) and non-overlapping (competitive) A5Es and A3Es, a clear preference of isoforms was seen for tandem acceptors and donors, with four nucleotides and three to six nucleotides long exon extensions, respectively. A subset of inferred A5E tandem exons was selected and experimentally validated. With the focus on A5Es, we investigated their transcript coverage, sequence conservation and base-paring to U1 snRNA, proximal and distal splice site classification, candidate motifs for cis-regulatory activity, and compared A5Es with A3Es, constitutive and pseudo-exons, in H. sapiens and M. musculus. The results reveal a small but authentic enriched set of tandem splice site preference, with specific distances between proximal and distal 5'ss (3'ss), which showed a marked dichotomy between the levels of in- and out-of-frame splicing for A5Es and A3Es, respectively, identified a number of candidate NMD targets, and allowed a rough estimation of a number of undetected tandem donors based on splice site information. CONCLUSION: This comparative study distinguishes tandem 5'ss and 3'ss, with three to six nucleotides long extensions, as having unusually high proportions of AS, experimentally validates tandem donors in a panel of different human tissues, highlights the dichotomy in the types of AS occurring at tandem splice sites, and elucidates that human alternative exons spliced at overlapping 5'ss posses features of typical splice variants that could well be beneficial for the cell.

Project description:BACKGROUND: The basic process of RNA splicing is conserved among eukaryotic species. Three signals (5' and 3' splice sites and branch site) are commonly used to directly conduct splicing, while other features are also related to the recognition of an intron. Although there is experimental evidence pointing to the significant species specificities in the features of intron recognition, a quantitative evaluation of the divergence of these features among a wide variety of eukaryotes has yet to be conducted. RESULTS: To better understand the splicing process from the viewpoints of evolution and information theory, we collected introns from 61 diverse species of eukaryotes and analyzed the properties of the nucleotide sequences relevant to splicing. We found that trees individually constructed from the five features (the three signals, intron length, and nucleotide composition within an intron) roughly reflect the phylogenetic relationships among the species but sometimes extensively deviate from the species classification. The degree of topological deviation of each feature tree from the reference trees indicates the lowest discordance for the 5' splicing signal, followed by that for the 3' splicing signal, and a considerably greater discordance for the other three features. We also estimated the relative contributions of the five features to short intron recognition in each species. Again, moderate correlation was observed between the similarities in pattern of short intron recognition and the genealogical relationships among the species. When mammalian introns were categorized into three subtypes according to their terminal dinucleotide sequences, each subtype segregated into a nearly monophyletic group, regardless of the host species, with respect to the 5' and 3' splicing signals. It was also found that GC-AG introns are extraordinarily abundant in some species with high genomic G + C contents, and that the U12-type spliceosome might make a greater contribution than currently estimated in most species. CONCLUSIONS: Overall, the present study indicates that both splicing signals themselves and their relative contributions to short intron recognition are rather susceptible to evolutionary changes, while some poorly characterized properties seem to be preserved within the mammalian intron subtypes. Our findings may afford additional clues to understanding of evolution of splicing mechanisms.

Project description:Identification of splice sites is an important aspect with regard to the prediction of gene structure. In most of the existing splice site prediction studies, machine learning algorithms coupled with sequence-derived features have been successfully employed for splice site recognition. However, the splice site identification by incorporating the secondary structure information is lacking, particularly in plant species. Thus, we made an attempt in this study to evaluate the performance of structural features on the splice site prediction accuracy in Arabidopsis thaliana. Prediction accuracies were evaluated with the sequence-derived features alone as well as by incorporating the structural features into the sequence-derived features, where support vector machine (SVM) was employed as prediction algorithm. Both short (40 base pairs) and long (105 base pairs) sequence datasets were considered for evaluation. After incorporating the secondary structure features, improvements in accuracies were observed only for the longer sequence dataset and the improvement was found to be higher with the sequence-derived features that accounted nucleotide dependencies. On the other hand, either a little or no improvement in accuracies was found for the short sequence dataset. The performance of SVM was further compared with that of LogitBoost, Random Forest (RF), AdaBoost and XGBoost machine learning methods. The prediction accuracies of SVM, AdaBoost and XGBoost were observed to be at par and higher than that of RF and LogitBoost algorithms. While prediction was performed by taking all the sequence-derived features along with the structural features, a little improvement in accuracies was found as compared to the combination of individual sequence-based features and structural features. To the best of our knowledge, this is the first attempt concerning the computational prediction of splice sites using machine learning methods by incorporating the secondary structure information into the sequence-derived features. All the source codes are available at https://github.com/meher861982/SSFeature.Supplementary informationThe online version contains supplementary material available at 10.1007/s13205-021-03036-8.

Dataset Information

A computational analysis of sequence features involved in recognition of short introns.

Publications

A computational analysis of sequence features involved in recognition of short introns.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets