The methods used are described in the paper:
The program predicts whole genes, so the predicted exons always splice correctly. It can predict several whole or partial genes in one sequence, so it can be used on whole cosmids or even longer sequences. HMMgene can also be used to predict splice sites and start/stop codons. If some features of a sequence are known, such as hits to ESTs, proteins, or repeat elements, these regions can be locked as coding or non-coding and then the program will find the best gene structure under these constraints.
The program is based on a hidden Markov model, which is a probabilistic model of the gene structure. This means that all predictions have associated probabilities that reflect how confident it is in the predictions. Apart from reporting the best prediction, HMMgene can also report the N best gene predictions for a sequence. This is useful if the there are several equally likely gene structures and may even indicate alternative splicing.
HMMgene takes an input file with one or more DNA sequences in FASTA format. It also has a few options for changing the default behavior of the program.
The output is a prediction of partial or complete genes in the sequences. The output is in a standardized format that is easily read by other programs, which specifies the location of all the predicted genes and their coding regions and scores for whole genes as well as exon scores.
There are currently models for vertebrate and C. elegans. The vertebrate model is trained entirely on human genes, but it should work reasonably well for other vertebrates.
>SEQ1 Any text following the identifier is ignored TCATTGTATCAGAAAGATAAAGAAAAAATAATCGTATTTCAGTACTTCTATACATCCTAAAAGGGAAGAC GGAACACTTAAGTGGTTGATAAATTTGAAAAGCTGATTAAACATAATAATCACCATGTTGGGGGAAGACA TAAAAGTCATAAAACAGATTTTTTATAATATTAAAAAAGTGACATGAAAATTATACAATTTTAGAAAGGA ATATAAAAAGGCAGGAGTTAAAAAATAGTGGGACTAATATCATAGAAAACTATCCATGAGGAAGGTCAAA TTTATTTTCAACATGTAAAAAGGATAAAGAGTAGAGGTATTTTAAAAATTCACAGATTCTTAATGAGGCA AATGTTAAAATATGGAACCCAATCTCAGACAAATACATAGAAAGGAGTAAGGGCCAACTCTCATGCATAA GGTATCCCATCCTATAGCAAATCAGATATATAGGTACGCTTGA >HS1433PR H. sapiens gene for 14-3-3 protein. GAATTCGCGGCGCCGAGAGGGCGCGAGCGGCGGCGCTGCCTGCAGCCTGCAGCCTGCAGCCTCCGGCCGG CCGGCGAGCCAGTGCGCGTGCGCGGCGGCGGCCTCCGCAGCGACCGGGGAGCGGACTGACCGGCGGGAGG GCTAGCGAGCCAGCGGTGTGAGGCGCGAGGCGAGGCCGAGCCGCGAGCGACATGGGGGACCGGGAGCAGC TGLetters can be upper or lower case. Spaces and other non-letter characters in the sequence are ignored. Letter U is translated to T. All letters not equal to A, C, G, T or U are treated as unknown (N). The sequences can be of any length.
All lines starting with `#' are treated as comment lines, lines starting with `%%' may contain annotation (see below). The execution time of the program is roughly proportional to the sequence length.GFF format, which is a sequence annotation format developed with gene finding in mind. It is very simple and therefore it is easy to develop programs in perl or awk to post-process the output. The following is an example of the form it takes with hmmgene.
Note that hmmgene only predicts coding regions. That is, the first exon (`firstex' below) is only the coding part of the first coding exon and similarly for the last exon (`lastex' below). Below a `gene' therefore means the region of the gene from start to stop codon.
SEQ1 HMMgene1.1 firstex 692 702 0.347 + 2 bestparse:cds_1 SEQ1 HMMgene1.1 exon_1 2473 2711 0.421 + 1 bestparse:cds_1 SEQ1 HMMgene1.1 exon_2 2897 3081 0.544 + 0 bestparse:cds_1 SEQ1 HMMgene1.1 exon_3 10376 10563 0.861 + 2 bestparse:cds_1 SEQ1 HMMgene1.1 exon_4 11841 11891 0.857 + 2 bestparse:cds_1 SEQ1 HMMgene1.1 exon_5 12387 12483 0.993 + 0 bestparse:cds_1 SEQ1 HMMgene1.1 exon_6 13076 13211 0.970 + 1 bestparse:cds_1 SEQ1 HMMgene1.1 exon_7 13332 13415 0.926 + 1 bestparse:cds_1 SEQ1 HMMgene1.1 exon_8 13515 13603 1.000 + 0 bestparse:cds_1 SEQ1 HMMgene1.1 exon_9 14180 14235 1.000 + 2 bestparse:cds_1 SEQ1 HMMgene1.1 exon_10 14321 14408 0.999 + 0 bestparse:cds_1 SEQ1 HMMgene1.1 exon_11 14483 14579 0.877 + 1 bestparse:cds_1 SEQ1 HMMgene1.1 exon_12 14697 14764 0.639 + 0 bestparse:cds_1 SEQ1 HMMgene1.1 exon_13 14901 15030 0.835 + 1 bestparse:cds_1 SEQ1 HMMgene1.1 lastex 15643 15704 0.987 + 0 bestparse:cds_1 SEQ1 HMMgene1.1 CDS 692 15704 0.132 + . bestparse:cds_1(the real list is tab separated)
The signal prediction is different from most other predictors of splice sites and start/stop, in that only signals that fit well into a whole gene structure is predicted, i.e., the signals are not predicted from the local sequence alone. This yields fewer predictions and usually better, however, if there is an error that frameshifts an actual gene or something like that, the splice sites might be missed as well as the gene.
Because of the slow-down of the program and the large amount of information produced, it is best to use this option on a region, where it is likely that there is only one gene. Then it will be possible to see alternative ways of splicing it together. Although it is quite possible that real alternative splicing can be predicted in this way, this has not yet been investigated. Whether a gene is alternatively spliced or not, it will often be usefull to see the alternative possibilities that might score almost as well as the best prediction.
SEQ2 non-coding 105 443
The same can be specified in the sequence file by preceeding each line with `%%',
%% SEQ2 non-coding 105 443 This has to come before the actual sequence in the file, e.g., all annotation lines can come in the very beginning.
This is very useful if there are database hits to a sequence or if repeats are mapped by some other program. Assume for instance that there is a database hit to base 1503-1594 and alu repeats are found at position 10731-10890 and 13205-13356 in SEQ2. Then one might want to enter the lines
SEQ2 coding 1503 1594 + SEQ2 non-coding 1503 1594 - SEQ2 non-coding 10731 10890 SEQ2 non-coding 13205 13356Here we indicated that the sequence is coding on the direct strand from 1503 to 1594 and non-coding in this region on the complementary strand. The two last lines means that the regions are non-coding on BOTH STRANDS.
Regions specified in the file are not allowed to overlap except on opposite strands. If the annotation you give does not conform to the model, the program will die. This happens for instance if the annotaion you give forces
If no start codon or stop codon is predicted for a gene (e.g. begins and ends with an intron) the frame information and scores might be wrong.
HMMgene can in principle predict a gene with a stop codon in frame,
if splicing happens in the middle of it. I have not yet seen
any examples though.
Scientific problems: Technical problems: