Inferring genes from open reading frames

The gene identification problem: an overview for developers

The gene identification problem is the problem of interpreting nucleotide sequences by computer, in order to provide tentative annotation on the location, structure, and functional class of protein-coding genes. This problem is of self-evident importance, and is far from being fully solved, particularly for higher eukaryotes. Thus it is not surprising that the number of algorithm and software developers working in the area is rapidly increasing. The present paper is an overview of the field, with an emphasis on eukaryotes, for such developers.

Gene identification in bacterial and organellar genomes using GeneScan

The performance of the GeneScan algorithm for gene identification has been improved by incorporation of a directed iterative scanning procedure. Application is made here to the cases of bacterial and organnellar genomes. The sensitivity of gene identification was 100% in Plasmodium falciparum plastid-like genome (35 kb) and in 98% in the Mycoplasma genitalium genome (approximately 580 kb) and the Haemophilus influenzae Rd genome (approximately 1.8 Mb). Sensitivity was found to improve in both the Open Reading Frames (ORFs) which have been identified as genes (by homology or by other methods) and those that are classified as hypothetical. False positive assignments (at the nucleotide level) were 0.25% in H. influenzae genome and 0.3% in M. genitalium. There were no false positive assignments in the plastid-like genome. The agreement between the GeneScan predictions and GeneMark predictions of putative ORFs was 97% in M. genitalium genome and 86% in H. influenzae genome. In terms of an exact match between predicted genes/ORFs and the annotation in the databank, GeneScan performance was evaluated to be between 72% and 90% in different genomes. We predict five putative ORFs that were not annotated earlier in the GenBank files for both M. genitalium and H. influenzae genomes. Our preliminary analysis of the newly sequenced G + C rich genome of Mycobacterium tuberculosis H37Rv also shows comparable sensitivity (99%).

Sequencing and analysis of genes involved in the biosynthesis of a vancomycin group antibiotic

BACKGROUND

The emergence of resistance to vancomycin, the drug of choice against methicillin-resistant Staphylococcus aureus, in enterococci has increased the need for new antibiotics. As chemical modification of the antibiotic structure is not trivial, we have initiated studies towards enzymatic modification by sequencing the DNA coding for the biosynthesis of chloroeremomycin (also known as A82846B and LY264826).

RESULTS

Analysis of 72 kilobases of genomic DNA from Amycolatopsis orientalis, the organism that produces chloroeremomycin, revealed the presence of 39 putative genes, including those coding for the biosynthesis of the antibiotic. Translation and subsequent comparison with known proteins in public databases identified enzymes responsible for the biosynthesis of the heptapeptide backbone and 4-epi-vancosamine, as well as those for chlorination and oxidation reactions involved in the biosynthesis of chloroeremomycin.

CONCLUSIONS

The genes responsible for the biosynthesis of chloroeremomycin have been identified, and selective expression of these genes could lead to the synthesis of new potent glycopeptide antibiotics.

The difficulty of identifying genes in anonymous vertebrate sequences

The identification of genes in newly determined vertebrate genomic sequences can range from a trivial to an impossible task. In a statistical preamble, we show how "insignificant" are the individual features on which gene identification can be rigorously based: promoter signals, splice sites, open reading frames, etc. The practical identification of genes is thus ultimately a tributary of their resemblance to those already present in sequence databases, or incorporated into training sets. The inherent conservatism of the currently popular methods (database similarity search, GRAIL) will greatly limit our capacity for making unexpected biological discoveries from increasingly abundant genomic data. Beyond a very limited subset of trivial cases, the automated interpretation (i.e. without experimental validation) of genomic data, is still a myth. On the other hand, characterizing the 60,000 to 100,000 genes thought to be hidden in the human genome by the mean of individual experiments is not feasible. Thus, it appears that our only hope of turning genome data into genome information must rely on drastic progresses in the way we identify and analyse genes in silico.

Discrimination between fortuitous and biologically constrained open reading frames in DNA sequences of Saccharomyces cerevisiae

The systematic sequencing of the yeast genome has raised the problem of the biological significance of the open reading frames (ORFs) revealed: it is possible that some of these are fortuitous. To avoid the analysis of such fortuitous ORFs, a minimum length of 100 sense codons was adopted. Nevertheless, the presence of fortuitous ORFs of more than 100 codons cannot be excluded. Thus, in the context of functional analysis, a method for discrimination between fortuitous and biologically active ORFs may be useful. The discrimination method described here is based on multiple criteria: ORF length, codon bias, and both amino-acid and dipeptide composition of the corresponding polypeptide. The thresholds for each criterion are based on the comparison between two learning sets: one drawn from random DNA sequences and the second from known genes. The method was validated by two test sets (one random and one biological) and then applied to the ORFs of chromosomes I, II, III, V, VIII, IX and XI. This method predicts 123 fortuitous ORFs among the 1773 identified on these chromosomes.

Prediction of function in DNA sequence analysis

Recognition of function of newly sequenced DNA fragments is an important area of computational molecular biology. Here we present an extensive review of methods for prediction of functional sites, tRNA, and protein-coding genes and discuss possible further directions of research in this area.