Roderic Guigó, IMIM and UB, Barcelona


NOTE: click on the images through this document to download higher quality postscript images.

Accuracy of Gene Identification Programs

An issue that naturally arises is that of the reliability of the predictions obtained by Gene Identification and Gene Structure prediction programs. The issue concerns both users and developers. It is particularly important for the users confronted with the need to identify those functional domains potentially encoded in large uncharacterized genomic regions. Indeed, experiments are often planned on the basis of such predictions, and these may involve a considerable amount of effort and resources. It is also important for the developers in order to assess the actual status of the problem. Moreover, as we are entering the large-scale sequencing phase of the Genome Projects, with an expected rate of production of over two megabases daily during the next few years, annotation of genomic sequences will need to be partially automated. Automatic annotation, however, is only possible if highly reliable gene prediciton programs exist.

Burset and Guigó (Genomics 34, 353-367, 1996) attempted to develop data set and performance metrics standards for consistently testing of Gene Identification software. They tested a number of programs on a large set of vertebrate sequences with known gene structure, and several measures of predictive accuracy were computed at the nucleotide, exon, and protein product levels. The data set used by Burset and Guigó was highly biased including only relatively short DNA sequences with simple gene structure and high coding density. Thus, the performance of the programs is likely to provide an overoptimistic estimation of the actual accuracy of the progrmas when confronted with large DNA sequences from randomly sequenced genomic regions of less standard composition and more complex structure.

Recently, a web site ( Banbury Benchmark site) has been established to serve as repository of long (100kb +) finished genomic sequences for which it is intended to provide a thorough experimental validation of all gene structures in the region (using e.g. cDNA selection/sequencing, exon trapping, promoter mapping and other methods of transcript mapping). These sequences will consitute a more realistic benchmark for computational gene identification programs in the large scale sequencing phase of the genome projects.

The Burset and Guigó evaluation

For a postscript version of the paper click here

The evaluation by Burset and Guigó consisted of three steps:

  1. Extraction of a reliable set of vertebrate sequences of know genic structure (Test Set)
  2. Definition of a number of measures of predictive accuracy
  3. Evaluation of a number of programs on the Test Set using the measures of predictive accuracy

Generation of the Test Set

The details of the protocol used to generate the Test data set (ALLSEQ) are schematized in the figure below.

The ALLSEQ data set is characterized by two features.
First, it includes only short sequences encoding a single complete gene with simple structure, with high coding density, and free of sequencing errors.
Second, ALLSEQ was built attempting to minimize the overlap with the training sets used during the development of the programs analyzed. Thus, only sequences entered into the public databases after January 1993 were selected into ALLSEQ. In addition, a subset of ALLSEQ was considered separately (NEWSEQ), comprising only those sequences that did not show a significant similarity to the vertebrate sequences entered into the database prior to January 1993. The ALLSEQ data set has 570 vertebrate sequences, 196 of them included in the NEWSEQ subset.

Files containing the DNA sequences in ALLSEQ, the locations of the exons in the sequences, and the corresponding encoded amino acid sequences can be found at /databases/genomics96/index.html

Extraction of Test Sequences

Measures of Prediction Accuracy

The accuracy of the predictions were measured at three different levels
  1. coding nucleotide (base level)
  2. exonic structure (exon level)
  3. protein product (protein level)
The figure below illustrates graphically the measures at this three different levels.

Base Level

Prediction accuracy per base coding/non-coding.

Sn: Sensitivity = TP/(TP+FN)
Sp: Specificity = TN/(TN+FP)
AC: Approximate Coefficient = 0.5x((TP/(TP+FN)) + (TP/(TP+FP)) + (TN/(TN+FP)) + (TN/(TN+FN))) - 1

TP:true positives
TN:true negatives
FP:false positives
FN:false negatives

Exon Level

Prediction accuracy with respect to exact prediction of exon start and end points

Sn: Sensitivity = fraction of correct exons over actual exons
Sp: Specificity = fraction of correct exons over predicted exons
ME: Missing Exons: fraction of true exons that are not identified at all
WE: Wrong Exons: fraction of predicted exons that do not overlap any true exon

Protein Level

Predcition accuracy with respect to the protein product encoded by the predicted gene.

% Sim: percentage similarity between the amino acid sequence encoded by the predicted gene and the amino acid sequence encoded by the actual gene

Evaluation of the programs

The table below (adpated from Genie web page) summarizes the results obtained when the performance of a number of programs was analyzed on the ALLSEQ data set. A postscript version of a more complete table can be obtained here . The exhaustive results per sequence on each program can be found at

Base-level Exon-level
MethodSn Sp AC Sn Sp (Sn+Sp)/2 ME WE
FGENEH 0.77 0.85 0.78 0.61 0.61 0.61 0.15 0.11
GeneID 0.63 0.81 0.67 0.44 0.45 0.45 0.28 0.24
GeneParser2 0.66 0.79 0.66 0.35 0.39 0.37 0.29 0.17
GenLang 0.72 0.75 0.69 0.50 0.49 0.50 0.21 0.21
GRAILII 0.72 0.84 0.75 0.36 0.41 0.38 0.25 0.10
SORFIND 0.71 0.85 0.73 0.42 0.47 0.45 0.24 0.14
Xpound 0.61 0.82 0.68 0.15 0.17 0.16 0.32 0.13
GeneID+ 0.91 0.90 0.88 0.73 0.70 0.71 0.07 0.13
GeneParser3 0.86 0.91 0.86 0.56 0.58 0.57 0.14 0.09

GeneID+ and GeneParser3 incorporate results from amino acid database searches to make the gene predictions.

The Banbury Site

The Banbury Benchmark site is intended to be a forum for scientists working in the field of gene identification and anonymous genomic sequence annotation, with the goal of improving current methods in the context of very large (in particular) vertebrate genomic sequences.

Different methods are based on different approaches (ab initio, training set dependent, statistical bias, motif/database similarity, etc ..), or often, combinations of approaches. For this reason, direct competition between them is not meaningful. The aim instead is to provide new sets of experimentally verified data for blind testing of the programs, so that others as well as the authors can see how they perform with respect to their own specific goals.

Roderic Guigo (i Serra), IMIM and UB.