**What it does**

This tool compares two gene annotation files, where one is considered
to be the "true annotation", i.e., the label file, while the other is
considered to be a potentially incorrect annotation, i.e., the prediction file.
The performance metrices employed are similar to those used in the
EGASP competition [1]: Sensitivity and specifcity are 
determined on the level of nucleotides, exons, transcripts, and genes
while considering coding regions only. (See [2] for more details.)

**References** 

[1] Guigo R., J.F. Flicek, P. Abril, A. Reymond, J. Lagarde, F. Denoeud, S. Antonarakis,
M. Ashburner, V.B. Bajic, E. Birney, R. Castelo, E. Eyras, C. Ucla, T.R. Gingeras,
J. Harrow, T. Hubbard, S.E. Lewis, and M.G. Reese. EGASP: The human ENCODE
genome annotation assessment project. Genome Biology, 7(S2), 2006.

[2] Schweikert, G., Zien, A., Zeller, G., Behr, J.,
Dieterich, C., Ong, C., Philips, P., Bona, F. D., Hartmann, L.,
Bohlen, A., Kroeger, N., Sonnenburg, S., and Raetsch, G. (2009)
mGene: Accurate computational gene finding with application to
nematode genomes. Genome Research, under review.


**Input**

* Use two GFF3 files. 

* You also need a FASTA file containing the genomic sequence under consideration. 

* Select whether you wish to evaluate the gene predictions with
  respect to the complete sequence contained in the FASTA file or only
  in the vicinity of the annotated genes from the label file.

**Common Mistakes**

* Note: If your annotation was incomplete for the sequence under
  consideration while you computed predictions for the complete
  sequence, your specificity will naturally be low when comparing for the 
  the whole sequence, since any predicted gene that does not overlap with
  an annotated one is counted as a wrong prediction. In this case, you
  should choose the option "Only consider regions around annoted
  genes".

* If you upload a GFF3 file, you need to specify the file format as
  GFF3. If you choose "Auto-detect", the file will be uploaded as a GFF
  file and you can't select it as input for the mGeneEval tool.

* Both annotation files, the label file, as well as the prediction file
  need to correspond to the sequence specified in the FASTA file.


**Output**

This tool generates an extensive accuracy report, stored as a text
file for subsequent inspection. For an ideal prediction that exactly
reproduces the annotation, the output should look like the following
(where sensitivity is the proportion of annotated features
(nucleotide, exon, transcript, gene) that have been predicted; and specificity 
the proportion of predicted features that are annotated):

Evaluation
  on coding nucleotide level:
    sensitivity: 100.00%

    specificity: 100.00%

  on coding exon level:
    sensitivity: 100.00% (missing exons: 0.00%)

    specificity: 100.00% (wrong exons:   0.00%)

  on coding transcript level:
    sensitivity: 100.00% 

    specificity: 100.00% 

  on gene level:
    sensitivity: 100.00%  (missing genes: 0.00%)

    specificity: 100.00%  (wrong genes: 0.00%)
    

  TIS:
    sensitivity: 100.00%
    
    specificity: 100.00%

  STOP:
    sensitivity: 100.00%
    
    specificity: 100.00%



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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
