Description
This track shows ab initio predictions from the program
AUGUSTUS (version 3.1).
The predictions are based on the genome sequence alone.
For more information on the different gene tracks, see our Genes FAQ.
Methods
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.
Credits
Thanks to the
Stanke lab
for providing the AUGUSTUS program. The training for the chicken version was
done by Stefanie König and the training for the
human and zebrafish versions was done by Mario Stanke.
References
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
|