- Research article
- Open Access
Importing statistical measures into Artemis enhances gene identification in the Leishmania genome project
© Aggarwal et al; licensee BioMed Central Ltd. 2003
- Received: 19 February 2003
- Accepted: 7 June 2003
- Published: 7 June 2003
Seattle Biomedical Research Institute (SBRI) as part of the Leishmania Genome Network (LGN) is sequencing chromosomes of the trypanosomatid protozoan species Leishmania major. At SBRI, chromosomal sequence is annotated using a combination of trained and untrained non-consensus gene-prediction algorithms with ARTEMIS, an annotation platform with rich and user-friendly interfaces.
Here we describe a methodology used to import results from three different protein-coding gene-prediction algorithms (GLIMMER, TESTCODE and GENESCAN) into the ARTEMIS sequence viewer and annotation tool. Comparison of these methods, along with the CODON USAGE algorithm built into ARTEMIS, shows the importance of combining methods to more accurately annotate the L. major genomic sequence.
An improvised and powerful tool for gene prediction has been developed by importing data from widely-used algorithms into an existing annotation platform. This approach is especially fruitful in the Leishmania genome project where there is large proportion of novel genes requiring manual annotation.
- Codon Usage
- Gene Prediction
- Codon Usage Bias
- Manual Annotation
- Slide Window Method
At Seattle Biomedical Research Institute (SBRI), we are involved, as part of the Leishmania Genome Network (LGN), in the sequencing and annotation of the trypanosomatid protozoan species L. major Friedlin (LmjF). Following DNA sequence determination, putative protein-coding regions within the sequence are predicted and functionally classified. Although trypanosomatids are eukaryotes, their gene structure is more similar to that of prokaryotes; they have essentially no introns and small intergenic regions. Two small LmjF chromosomes (chr1 and chr3) have been completely sequenced and annotated. The 79 protein-coding genes predicted from chr1 are organized in two large divergent polycistronic gene clusters of 29 and 50 genes, on the "bottom" and "top" DNA strains, respectively ; while chr3 contains two convergent polycistronic clusters of 65 and 29 genes, with a single divergent gene at one telomere and a single tRNA between the two large clusters .
Presently, a large number of methods exist for in silico prediction of coding regions [3–7]. These computational methods use a range of underlying statistical properties of the coding regions and can be generally classified as consensus (signal sensors) and non-consensus (content sensors) [8, 9]. The non-consensus methods can be further classified as trained, which require unbiased sets of coding regions, and untrained, which use statistical properties to discriminate between coding and non-coding regions. Although non-consensus methods have been very successful in identifying genes in most of the sequencing projects, currently none have 100% specificity and sensitivity. In the absence of such a method, the use of a combination of methods is next best option [10–13]. Since LmjF genes do not contain introns, and the signal sequences for trans-splicing and polyadenylation are poorly defined, consensus methods have little utility for Leishmania gene prediction. In addition, ~70% of the genes have no significant homology to existing genes in sequence databases, so extrinsic content sensing methods are of limited use; leaving only intrinsic content sensing methods for possible use in gene prediction. Given that the number of experimentally confirmed gene prediction in Leishmania is currently small, and many methods use similar statistical approaches , the choice of two trained methods (GLIMMER and CODON USAGE) and two untrained methods (TESTCODE, and GENESCAN) which rely on unrelated statistical measures should provide substantial power for gene prediction in LmjF.
The freely available JAVA-based software package ARTEMIS was designed specifically as an annotation platform and has a user-friendly graphical interface. It simplifies time-consuming processes such as inter-file format conversion, BLAST analysis , and provides a convenient environment for viewing the gene structure and organization of large DNA segments. Here we describe a method for importing data from GLIMMER, TESTCODE, and into GENESCAN into ARTEMIS, to enhance gene prediction and annotation.
Automated gene predictiona in Leishmania major
Automated gene prediction by combination of different methods.
The semi-automated comparative analysis clear shows that some degree of manual annotation is still necessary in projects where there is large proportion of novel genes. The manual annotation is time consuming and labor intensive. The ARTEMIS desktop environment, with importation of trained and non-trained non-consensus gene-prediction algorithms, facilitates easy comparison of the results and allows the user to make more-informed decisions for calling protein-coding genes. Thus, this improvised and powerful software, developed using already existing gene identification methods and annotation platform, is extremely helpful for whole genome sequencing projects.
GLIMMER 2.0 http://www.tigr.org/software/glimmer/ was trained using predicted protein-coding genes from LmjF chr1  (manual annotations based on TESTCODE and CODON USAGE) and chr4 (manual annotations using HEXAMER and CODON USAGE: A. Ivens, personal communication) using the default settings. The trained GLIMMER was run on LmjF sequence using the default setting with a minimum gene length of 75 amino acids and output was parsed into an EMBL-formatted feature table file. This data were imported into ARTEMIS 4.0 (installed on Intel-based Linux or Windows 2000 machines) using the "Read Features Into" option of the "File" menu. This allows the GLIMMER-predicted genes to be displayed as CDS Features. The TESTCODE, GENESCANhttp://18.104.22.168/public_htmlnew/gs.htm and CODON USAGE algorithms were re-coded in C++ and the statistical results collected in text files with single value for each sliding window (100 nt windows, sliding by onent increments). These TESTCODE and GENESCAN data were imported into ARTEMIShttp://www.sanger.ac.uk/Software/Artemis/ using the "Add User Plot" option of the "Display" menu, and displayed graphically. This procedure can be used to import other sliding window methods. The CODON USAGE bias statistics, which has been coded as part of ARTEMIS, is calculated for the three reading frames of each DNA strand and displayed in different colors using the "Add Usage Plot" option of the "Display" menu to import Leishmania CODON USAGE tables. Figure 1 shows a panel containing results from each of the four gene-prediction methods for a typical LmjF sequence.
For automated GENESCAN, TESTCODE and CODON USAGE predictions, genes were called only for those ORFs larger than 100 amino acids with mean scores (over the entire ORF) above thresholds of 4.0, 9.7, and 0, respectively. For overlapping ORFs (on the same or opposite strands), the one with the highest signal was used.
The authors thank Kim Rutherford (Wellcome Trust Sanger Institute) for the help and useful discussion. This work was supported by NIH grant AI40599.
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