Related Experiment Video
Updated: Aug 12, 2026
13:47
Chromatin Immunoprecipitation (ChIP) using Drosophila tissue
Published on: March 23, 2012
Ab initio gene finding in Drosophila genomic DNA
1The Sanger Centre, Hinxton, Cambridge CB10 1SA, UK.
Genome Research
|April 26, 2000
Summary
Computational gene prediction accurately identifies ~90% of coding nucleotides in genomic sequences. This study optimized gene identification schemes for Drosophila melanogaster, improving accuracy for reliable exon and gene prediction, crucial for new gene discovery.
Area of Science:
- Genomics and Bioinformatics
- Computational Biology
- Gene Prediction
Background:
- Accurate gene identification is essential for annotating newly sequenced genomes.
- Existing gene prediction tools often require cDNA or EST data, which may be incomplete.
- Developing ab initio methods that do not rely on such data is crucial for discovering novel genes.
Purpose of the Study:
- To evaluate and optimize ab initio gene identification accuracy using Fgenesh and human gene predictor programs.
- To develop strategies for predicting unambiguous gene sets, reliable exons, and complete exon sets.
- To assess the utility of computational gene prediction for annotating genomic sequences, including human chromosome 22.
Main Methods:
- Utilized Fgenesh and human gene predictor programs with organism-specific parameters for Drosophila melanogaster.
- Focused on ab initio prediction, minimizing reliance on cDNA/EST data to simulate real-world gene discovery scenarios.
- Developed and tested multiple annotation schemes (CGG1, CGG2, CGG3) to define gene sets, reliable exons, and complete exon sets.
Main Results:
- Achieved identification of approximately 90% of coding nucleotides with 20% false positives at the nucleotide level.
- Predicted 65% of exons accurately, and 89% including overlapping exons, with 49% false positives at the exon level.
- Optimized Fgenesh scheme demonstrated high sensitivity (Sn=98%) and specificity (Sp=86%) at the base level, and significant performance at exon and gene levels.
Conclusions:
- Computational gene prediction is a reliable tool for annotating genomic sequences, providing accurate information on coding sequences.
- The developed gene identification schemes offer improved accuracy for defining gene sets and identifying reliable exons for experimental validation.
- Ab initio prediction shows promise, with 88% of manually annotated exons on human chromosome 22 being identified, though gene-level prediction requires further refinement.
Related Concept Videos
02:32Exon Recombination
The evolution of new genes is critical for speciation. Exon recombination, also known as exon shuffling or domain shuffling, is an important means of new gene formation. It is observed across vertebrates, invertebrates, and in some plants such as potatoes and sunflowers. During exon recombination, exons from the same or different genes recombine and produce new exon-intron combinations, which might evolve into new genes.
Exon shuffling follows “splice frame rules.” Each exon has three reading...
Exon shuffling follows “splice frame rules.” Each exon has three reading...
02:46Genetic Screens
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which result in visible changes...