Finding intron/exon splice junctions using INFO, INterruption Finder and Organizer

Gene identification in novel eukaryotic genomes by self-training algorithm

Finding new protein-coding genes is one of the most important goals of eukaryotic genome sequencing projects. However, genomic organization of novel eukaryotic genomes is diverse and ab initio gene finding tools tuned up for previously studied species are rarely suitable for efficacious gene hunting in DNA sequences of a new genome. Gene identification methods based on cDNA and expressed sequence tag (EST) mapping to genomic DNA or those using alignments to closely related genomes rely either on existence of abundant cDNA and EST data and/or availability on reference genomes. Conventional statistical ab initio methods require large training sets of validated genes for estimating gene model parameters. In practice, neither one of these types of data may be available in sufficient amount until rather late stages of the novel genome sequencing. Nevertheless, we have shown that gene finding in eukaryotic genomes could be carried out in parallel with statistical models estimation directly from yet anonymous genomic DNA. The suggested method of parallelization of gene prediction with the model parameters estimation follows the path of the iterative Viterbi training. Rounds of genomic sequence labeling into coding and non-coding regions are followed by the rounds of model parameters estimation. Several dynamically changing restrictions on the possible range of model parameters are added to filter out fluctuations in the initial steps of the algorithm that could redirect the iteration process away from the biologically relevant point in parameter space. Tests on well-studied eukaryotic genomes have shown that the new method performs comparably or better than conventional methods where the supervised model training precedes the gene prediction step. Several novel genomes have been analyzed and biologically interesting findings are discussed. Thus, a self-training algorithm that had been assumed feasible only for prokaryotic genomes has now been developed for ab initio eukaryotic gene identification.

Quantifying optimal accuracy of local primary sequence bioinformatics methods

MOTIVATION

Traditional bioinformatics methods scan primary sequences for local patterns. It is important to assess how accurate local primary sequence methods can be.

RESULTS

We study the problem of donor pre-mRNA splice site recognition, where the sequence overlaps between real and decoy datasets can be quantified, exposing the intrinsic limitations of the performance of local primary sequence methods. We assess the accuracy of primary sequence methods generally by studying how they scale with dataset size and demonstrate that our new primary sequence ranking methods have superior performance.

Common carp insulin-like growth factor-I gene: complete nucleotide sequence and functional characterization of the 5′-flanking region

Insulin-like growth factor-I (IGF-I) plays an important role in the growth and development of fish. To understand the molecular mechanism which controls the transcription of the IGF-I gene in common carp, we have cloned and completely sequenced the IGF-I gene and the 5'-flanking region from a local tropical fish, the common carp (Cyprinus carpio), and characterized its promoter activity by transfection into human embryonic kidney (293GHR) cells which express the human growth hormone (GH) receptor. The common carp gene is the smallest IGF-I gene known so far, spanning approximately 13 kb, and is consisted of five exons and four introns. The sequence of the gene is consistent with the single type of IGF-I cDNA that we have isolated previously from a common carp liver cDNA library. The expression pattern of IGF-I is similar between juvenile carp and adult carp. While liver was found to be the major site of IGF-I gene expression in common carp, the expression levels in other tissues are relatively low. Like many other IGF-I gene promoters, there are no apparent TATA box and CCAAT box upstream of the transcription initiation site. However, sequence analysis of the common carp IGF-I promoter region identified several consensus liver-enriched transcription factor binding sites, including HNF-1alpha, HNF-3beta, C/EBP, and one STAT5. We have analyzed the promoter activity of the 5'-flanking region of the common carp IGF-I gene by performing luciferase reporter assays in transfected 293GHR cells. Addition of human GH to the transfected cells led to an increased expression of the reporter gene, indicating that the cloned genomic fragment possessed promoter activity. This was confirmed by the lack of promoter activity of a construct in which the putative promoter was cloned in a reverse orientation upstream of the reporter gene. The liver-specific transcription factor, hepatic nuclear factor (HNF)-1alpha, was also found to be involved in the regulation of the common carp IGF-I transcription. Transfection results from a set of deletion mutants helped to map the locations of the responsive elements on the promoter responsible for the GH effect and for the interaction with HNF-1alpha. These observations provide information for delineating the transcriptional regulation of IGF-I gene expression in common carp.

Seabream growth hormone receptor: molecular cloning and functional studies of the full-length cDNA, and tissue expression of two alternatively spliced forms

A full-length clone of the growth hormone receptor (GHR) was isolated from a cDNA library constructed from the liver of black seabream (Acanthopagrus schlegeli). The seabream GHR (sbGHR) cDNA sequence encodes a transmembrane protein of 640 amino acids (aa) possessing the characteristic motifs and architectural design of GHRs of other species. When compared to the other fish GHRs, it is most homologous to another marine fish species, the turbot, where the aa identity is 79.3%. But the sbGHR sequence is more remotely related to the goldfish GHR (51.6% aa identity) and the salmonid GHRs (approximately 46-48% aa identities). Phylogenetic comparison with other known GHRs indicates that the fish GHRs constitute a distinct group among the different vertebrate classes. The aa identities between sbGHR and other GHRs are low, being around 40% with mammalian GHRs, around 45% with avian and reptilian GHRs, and less than 35% with Xenopus GHR. CHO cells transfected with the sbGHR cDNA can be stimulated to proliferate by recombinant seabream growth hormone (sbGH). In addition, the transfected cells can transactivate a co-expressed mammalian serine protease inhibitor (Spi) 2.1 promoter upon stimulation by sbGH. These functional assays indicated that the fish receptor can interact with its homologous ligand to evoke the downstream post-receptor events. Reverse transcription-polymerase chain reaction (RT-PCR) and genomic PCR using a pair of gene-specific primers revealed the expression of two alternatively spliced forms of sbGHR in various tissues of the fish. A 93-bp intron, unique to the sbGHR gene and not found in any other known GHR genes, is alternatively spliced to give rise to two forms of receptor mRNA transcripts. The two forms of the receptor are differentially expressed among the different tissues of the fish.

Current methods of gene prediction, their strengths and weaknesses

While the genomes of many organisms have been sequenced over the last few years, transforming such raw sequence data into knowledge remains a hard task. A great number of prediction programs have been developed that try to address one part of this problem, which consists of locating the genes along a genome. This paper reviews the existing approaches to predicting genes in eukaryotic genomes and underlines their intrinsic advantages and limitations. The main mathematical models and computational algorithms adopted are also briefly described and the resulting software classified according to both the method and the type of evidence used. Finally, the several difficulties and pitfalls encountered by the programs are detailed, showing that improvements are needed and that new directions must be considered.

Detection of protein coding sequences using a mixture model for local protein amino acid sequence

Locating protein coding regions in genomic DNA is a critical step in accessing the information generated by large scale sequencing projects. Current methods for gene detection depend on statistical measures of content differences between coding and noncoding DNA in addition to the recognition of promoters, splice sites, and other regulatory sites. Here we explore the potential value of recurrent amino acid sequence patterns 3-19 amino acids in length as a content statistic for use in gene finding approaches. A finite mixture model incorporating these patterns can partially discriminate protein sequences which have no (detectable) known homologs from randomized versions of these sequences, and from short (< or = 50 amino acids) non-coding segments extracted from the S. cerevisiea genome. The mixture model derived scores for a collection of human exons were not correlated with the GENSCAN scores, suggesting that the addition of our protein pattern recognition module to current gene recognition programs may improve their performance.

A dictionary-based approach for gene annotation

This paper describes a fast and fully automated dictionary-based approach to gene annotation and exon prediction. Two dictionaries are constructed, one from the nonredundant protein OWL database and the other from the dbEST database. These dictionaries are used to obtain O (1) time lookups of tuples in the dictionaries (4 tuples for the OWL database and 11 tuples for the dbEST database). These tuples can be used to rapidly find the longest matches at every position in an input sequence to the database sequences. Such matches provide very useful information pertaining to locating common segments between exons, alternative splice sites, and frequency data of long tuples for statistical purposes. These dictionaries also provide the basis for both homology determination, and statistical approaches to exon prediction.