gene prediction

Gene prediction programs used are described in Table 2.

But researchers using more reliable methods of gene prediction have started saying the number could be double that.

Hypothetical proteins are created by gene prediction software during genome analysis.

Number of genes is an estimate as it is in part based on gene predictions.

Gene prediction programs also tend to merge and split gene models.

It has been suggested that signals other than those directly detectable in sequences may improve gene prediction.

As new sequence and improved gene predictions arise, additional elements will be added to this evolving platform.

Such projects may also include gene prediction to find out where the genes are in a genome, and what those genes do.

One common use of open reading frames is as one piece of evidence to assist in gene prediction.

The patterns of protein-coding sequence evolution detected in our data have important implications for comparative gene prediction.

All the gene-sets of the WormBase species were initially generated by gene prediction programs.

An additional 3,154 units were identified on the basis of protein homology, with exons supported by at least one gene prediction program.

Some of these contain very short 'micro-exons' that are usually missed by ab initio gene prediction programs.

The large apparent false negative and positive rates imply that pure computational gene prediction is not yet a practical alternative to experimental evidence.

Gene prediction programs, which identify the DNA sequences that code for proteins, face a number of challenges.

It has been proposed that identification of processed pseudogenes can help improve the accuracy of gene prediction methods.

Many WGS3 heterochromatic gene models are based solely on gene predictions.

Sometimes the original predictions were spaced quite far apart in the genome, a probable reason that the gene prediction algorithm(s) separated the exons.

Gene predictions were manually verified and refined using the annotation platform Apollo [ 13].

Processed pseudogenes often pose a problem for gene prediction programs, often being misidentified as real genes or exons.

Scientific curators continue to appraise the set of known genes, such that new gene predictions continue to be added and incorrect ones modified or removed.

This is realised by supporting the parsing of the following popular standard file formats generated by open source gene prediction applications:

Gene prediction programs predict exon boundaries correctly only about 80% of the time, even for the most intensively studied organisms [ 7, 8, 9].

His current research interests include promotor analysis, non-coding RNA, gene prediction and protein structure prediction.

They build a discriminative model using hidden Markov support vector machines or conditional random fields to learn an accurate gene prediction scoring function.