This track shows ab initio predictions from the program
AUGUSTUS (version 3.1).
for the 06 Feb 2024 Heterodontus francisci/GCF_036365525.1_sHetFra1.hap1 genome assembly.
The predictions are based on the genome sequence alone.
Gene count: 22,510; Bases covered: 2,270,969,508
Download GCF_036365525.1_sHetFra1.hap1.augustus.gtf.gz GTF file.
Statistical signal models were built for splice sites, branch-point patterns, translation start sites, and the poly-A signal. Furthermore, models were built for the sequence content of protein-coding and non-coding regions as well as for the length distributions of different exon and intron types. Detailed descriptions of most of these different models can be found in Mario Stanke's dissertation. This track shows the most likely gene structure according to a Semi-Markov Conditional Random Field model. Alternative splicing transcripts were obtained with a sampling algorithm (--alternatives-from-sampling=true --sample=100 --minexonintronprob=0.2 --minmeanexonintronprob=0.5 --maxtracks=3 --temperature=2).
The different models used by Augustus were trained on a number of different species-specific gene sets, which included 1000-2000 training gene structures. The --species option allows one to choose the species used for training the models. Different training species were used for the --species option when generating these predictions for different groups of assemblies.
| Assembly Group | Training Species |
| Fish | zebrafish |
| Birds | chicken |
| Human and all other vertebrates | human |
| Nematodes | caenorhabditis |
| Drosophila | fly |
| A. mellifera | honeybee1 |
| A. gambiae | culex |
| S. cerevisiae | saccharomyces |
This table describes which training species was used for a particular group of assemblies. When available, the closest related training species was used.
Stanke M, Diekhans M, Baertsch R, Haussler D. Using native and syntenically mapped cDNA alignments to improve de novo gene finding. Bioinformatics. 2008 Mar 1;24(5):637-44. PMID: 18218656
Stanke M, Waack S. Gene prediction with a hidden Markov model and a new intron submodel. Bioinformatics. 2003 Oct;19 Suppl 2:ii215-25. PMID: 14534192