**What it does**

This tool predicts protein-coding genes on the complete sequence given
in the FASTA file. The output will be given as a GFF3 file. For some
organisms we have already trained a model. To use one of those, choose
"Pretrained predictor" as *Predictor source* and check the list of
organisms. If your organism of interest is not included in that list,
you can use mGeneTrain to train a model yourself. However,
you will need some annotated genes in a GFF3 file for this task.


**Input**

* To run an example, go to the "Examples and Instructions" tool
  and upload one of the example data sets provided,
  e.g., "Caenorhabditis elegans (small part of chromosome II)". A FASTA
  file and a GFF3 file will appear in your History. Now choose
  the FASTA file as input for this tool. Then choose "Pretrained
  predictor" as *Predictor source* and select "Caenorhabditis elegans".

* To run the tool on your own data, upload a sequence file (in FASTA
  format) from your computer with the "Upload file". It will then
  appear in the History. Alternatively, you can also get FASTA files from one
  of the connected databases like UCSC by choosing the "Get Data" tool. 

* If you have previously run mGeneTrain, this has generated an output
  file in TmGP format. You can use this model by choosing "Predictor
  from current history" and picking the TmGP file. Otherwise, choose
  "Pretrained predictor" and pick the model from the list of pre-trained models. 

**Common Errors**


**Output**


* The tool will generate a GFF3 file containing all predicted
  genes. To assess the quality of the prediction, use
  mGeneEval and compare it to another annotation on the same sequence.

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**About formats**


**FASTA format** A sequence in FASTA format begins with a single-line
description, followed by lines of sequence data. The description line
is distinguished from the sequence data by a "greater than" (>) symbol
in the first column. The token until the first space or the end of the 
line is used as an identifier for the sequence. The remainder of the 
description line is ignored.

An example sequence in FASTA format is:

>CHR1

CCCTAAACCCTAAACCCTAAACCCTAAACCTCTGAATCCTTAATCCCTAAATCCCTAAAT
CTTTAAATCCTACATCCATGAATCCCTAAATACCTAATTCCCTAAACCCGAAACCGGTTT
CTCTGGTTGAAAATCATTGTGTATATAATGATAATTTTATCGTTTTTATGTAATTGCTTA
TTGTTGTGTGTAGATTTTTTAAAAATATCATTTGAGGTCAATACAAATCCTATTTCTTGT
GGTTTTCTTTCCTTCACTTAGCTATGGATGGTTTATCTTCATTTGTTATATTGGATACAA
GCTTTGCTACGATCTACATTTGGGAATGTGAGTCTCTTATTGTAACCTTAGGGTTGGTTT
ATCTCAAGAATCTTATTAATTGTTTGGACTGTTTATGTTTGGACATTTATTGTCATTCTT
ACTCCTTTGTGGAAATGTTTGTTCTATCAATTTATCTTTTGTGGGAAAATTATTTAGTTG
TAGGGATGAAGTCTTTCTTCGTTGTTGTTACGCTTGTCATCTCATCTCTCAATGATATGG
GATGGTCCTTTAGCATTTATTCTGAAGTTCTTCTGCTTGATGATTTTATCCTTAGCCAAA
AGGATTGGTGGTTTGAAGACACATCATATCAAAAAAGCTATCGCCTCGACGATGCTCTAT
TTCTATCCTTGTAGCACACATTTTGGCACTCAAAAAAGTATTTTTAGATGTTTGTTTTGC
...

**GFF3 format** General Feature Format is a format for describing genes
and other features associated with DNA, RNA, and protein sequences. GFF
lines have nine tab-separated fields:

    1. seqname - The name of the chromosome or scaffold (e.g., chr1, chrY_random).
    2. source - The program that generated this feature.
    3. feature - The name of this type of feature. Some examples of standard feature types are "CDS", "start_codon", "stop_codon", and "exon".
    4. start - The starting position of the feature in the sequence. (The first base on a chromosome is numbered 1.)
    5. end - The ending position of the feature (inclusive).
    6. score - A score between 0 and 1000. If there is no score value, enter ".".
    7. strand - Defines the strand - either '+' or '-'.
    8. frame - If the feature is a coding exon, *frame* should be a number between 0 and 2 that represents the reading frame of the first base. If the feature is not a coding exon, the value should be '.'.
    9. group - All lines with the same group are linked together into a single item.

For more information see http://www.sequenceontology.org/gff3.shtml


**TmGP format** Trained mGene Predictor is an internal structure that
contains the complete trained model including all signal and content
predictors as well as the gene predictor to predict genes from DNA
sequences.

