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FGENESH-2

Reference: Solovyev V.V. (2007) Statistical approaches in Eukaryotic gene prediction. In Handbook of Statistical genetics (eds. Balding D., Cannings C., Bishop M.), Wiley-Interscience; 3d edition, 1616 p.

HMM gene prediction using two genomic sequences of close organisms (as Human and Mouse)

Example

Paste nucleotide sequence here:

Alternatively, load a local file with sequence in Fasta format:

Local file name:

Paste second sequence here:

Alternatively, load a local file with sequence in Fasta format:

Local file name:

Select organism specific gene-finding parameters :

Total 506 genome-specific parameters are available for genefinders of FGENESH suite

Write % of protein similarity you expect:

Advanced options [see example of value in (..)]:

get sequence from this position, i.e., consider the sequence starting from this position.
get sequence to this position, i.e., consider the sequence to and through this position.
Use condensed sequence.
print mRNA sequences for predicted genes.
print exons sequences for predicted genes.
if the weight of exon is less than the weight specified by this option, it is rejected from prediction process.
if a “bad” promoter is found while predicting, two variants of prediction are compared: the prediction made with the promoter in question and without it. Of these variants of prediction, the prediction displaying a better weight is chosen.
If a “bad” terminator is found during prediction process, two variants of prediction are compared: the variant when the terminator is taken into account and the variant without the terminator. Of all the possible variants of prediction, the variant with the best weight is chosen.
Set minimal first exon this length.
Set minimal internal exon this length.
Set minimal terminal exon this length.
Set minimal single exon this length.
Use translation table (1 - Standard (Default); 2 - Vertebrate Mitochondrial; 3 - Yeast Mitochondrial; 4 - Mold Mitochondrial, Protozoan Mitochondrial, Colenterate Mitochondrial, Mycoplasma, and Spiroplasma; 5 - Invertebrate Mitochondrial; 6 - Ciliate Nuclear, Dasycladacean Nuclear, and Hexamita Nuclear; 9 - Echinoderm Nuclear; 10 - Euplotid Nuclear; 11 - Bacterial; 12 - Alternative Yeast Nuclear; 13 - Ascidian Mitochondrial; 14 - Flatworm Mitochondrial; 15 - Blepharisma Macronuclear).
Exon is considered as completely lacking similarity, if its similarity with the homolog is less than the value specified.
if a potential exon displays a similarity to homolog (see the previous option), the exon is supplemented with a certain additional weight (score): We=We0+Z*G, where We0 - the original weight of the exon; G - a certain bonus given due to the similarity to homolog; and Z - coefficient (with a default value of Z = 1). This allows the contribution of homology to the structure of predicted gene to be regulated as well as the probability of inclusion of the exons displaying similarity to the homolog in the structure of predicted genes to be increased or decreased.
if this option is set, the program does not tend to complete the gene predicted precisely at the end of homolog.
it is assumed that homolog has a correctly determined start. For example, proteins beginning with methionine and complete cDNAs have a correctly determined start. In particular, complete mRNAs, proteins processed from the N-end, and EST sequences have erroneously determined start.
it is assumed that homolog has a correctly determined end. For example, proteins ending directly with the stop codon and cDNAs have a correctly determined end. In particular, complete mRNAs, proteins processed from the C-end, and EST sequences have erroneously determined end.
add a certain penalty to the internal predicted exons that display no similarity to homolog and lie between the exons displaying homology:.
do not include in prediction the exons that display no similarity with homolog and have a length less than the length specified by this parameter.
Put here other options, if any.

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Most gene finding parameters presented here were trained by Softberry for its own use and distribution, using proprietary and publicly available data. Some of the parameters were created for our academic customers, including Broad Institute/MIT, Washington University, University of Minnesota and The Institute for Genomic Research (TIGR).

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Last modification date: 12 Dec 2013