目录
- DATAtraining update3年前
- DNAstill setting up8年前
- HMMstill setting up8年前
- Zoeall-exon3年前
- LICENSEstill setting up8年前
- Makefilestill setting up8年前
- README.mdup3年前
- dependall-exon3年前
- fathom.cstill setting up8年前
- forge.ctraining update3年前
- gff3_to_zff.pltraining update3年前
- hmm-assembler.plstill setting up8年前
- snap.cUpdate snap.c6年前
- zff2gff3.plstill setting up8年前
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SNAP Documentation
Introduction
SNAP is a general purpose gene finding program suitable for both eukaryotic and prokaryotic genomes. SNAP is an acroynm for Semi-HMM-based Nucleic Acid Parser.
Reference
Korf I. Gene finding in novel Genomes. BMC Bioinformatics 2004, 5:59
Contact
I appreciate bug reports, comments, and suggestions. My current contact information is:
License
This software is covered by the MIT License.
Files and Directories
Your favorite genome…
If you wish to train SNAP for a new genome, please contact me. Parameter estimation is not particularly difficult, but the procedure is not well documented and I have only included the minimum applications here. I’ve included the basic strategy at the end of this document.
INSTALLATION INSTRUCTIONS
The software is routinely compiled and tested on Mac and Linux on a variety of architectures. It should compile cleanly on any Linux/Unix type operating systems but new compilers sometimes complain, so please let me know if you have problems.
Enviroment
The ZOE environment variable can be used by SNAP to find the HMM files. Set this to the directory containing this file. For example, if you unpackaged the tar-ball in /usr/local, set the ZOE environment variable to /usr/local/Zoe
If you do not use the ZOE environment variable, you can still use SNAP but you must specify the explict path to the parameter file.
Compiling
Provided you have gcc and make, compiling should be as simple as:
Testing
PARAMETER ESTIMATION
Sequences must be in FASTA format. It’s a good idea if you don’t have genes that are too related to each other.
Gene structures must be in ZFF format. What is ZFF? It is a non-standard format (ie. nobody uses it but me) that bears resemblence to FASTA and GFF (both true standards). There are two styles of ZFF, the short format and the long format. In both cases, the sequence records are separated by a definition line, just like FASTA. In the short format, there are 4 fields: Label, Begin, End, Group. The 4th field is optional. Label is a controlled vocabulary (see zoeFeature.h for a complete list). All exons of a gene (or more appropriately a transcriptional unit) must share the same unique group name. The strand of the feature is implied in the coordinates, so if Begin > End, the feature is on the minus strand. Here’s and example of the short format with two sequences, each containing a single gene on the plus strand:
The long format adds 5 fields between the coordinates and the group: Strand, Score, 5’-overhang, 3’-overhang, and Frame. Strand is +/-. Score is any floating point value. 5’- and 3’-overhang are the number of bp of an incomplete codon at each end of an exon. Frame is the reading frame (0..2 and not 1..3). Here’s an example of the long format:
TUTORIAL
In this tutorial, we will create SNAP HMM files for 3 different genomes. In the
DATAdirectory, you will find fasta and gff3 files corresponding to 1 percent of the A. thaliana, C. elegans, and D. melanogaster genomes. Let’s start by creating a directory for training A. thaliana in the main SNAP directory. We’ll rungff3_to_zff.plto convert the annotation to ZFF.The next step is to check for errors in the annotation. The training procedure assumes that genes are canonical in various respects.
Running
fathom -validatewill tell you which genes look ok and which genes look suspicious. Let’s try one.This will produce a bunch of output to STDERR. You will see several WARNING lines saying the DNA and annotation don’t have the same definition lines. That’s okay. The ZFF contains only the sequence id from the FASTA file and not the whole definition line present in the original FASTA. The last line gives some overall stats.
If you examine the
at.validatefile, you will see warnings for some short genes, short exons, non-canonical introns, etc. We won’t be using these to train SNAP. To split genes into various categories use the-categorizefunction offathomand give it a value for how much intergenic sequence you want on each side of a gene. For examplefathom -categorize 1000attempts to put 1000 bp of genomic sequence on each side of a gene. However, if two genes are close to each other, say only 400 bp apart, they split the intergenic sequence and each get 200 bp of intergenic.This produces several new files:
alt.*contains genes with alternative splicingerr.*contains genes with errorsolp.*contains overlapping genesuni.*contains unique geneswrn.*contains genes with warningsfathomdoesn’t want to create training files with alternative splicing. It could create a case of overtraining for those specific genes. If you have a lot of alternative splicing, you may want to remove all of the isoforms except for the main one.fathomalso doesn’t know what to do with overlapping genes because it requires genes to have intergenic sequence on either side. These tend to be rare. Genes with unusual features or outright errors are separated also.The next step is to export all of the
unigenes into their plus-stranded versions.This creates 4 new files:
export.aacontains the amino acid sequencesexport.anncontains the annotation in ZFF formatexport.dnacontains the sequences in FASTA formatexport.txcontains the sequences of the coding sequencesIdeally, all of the proteins in
export.aastart withMand end with*. Similarly, theexport.txfiles should start withATGand end in a stop codon. All of the genes should validate without any reported warnings or errors.The next step is to run
forge, which will create a large number of model files.Finally, run
hmm-assembler.plto glue the various models together to form an hmm parameter file. There are several options, but we’ll just use the defaults.To verify this works, you can try it on the various fasta files we’ve used.
Next, we are going to try a slightly different training procedure that might be better if you have a lot of genes that are getting stuffed into the
wrn.*files. Let’s rewind a bit.Let’s see how many genes are in each category.
The standard procedure will have us training from the 236 genes in the
unicategory. To recover the genes in thewrncategory, we’ll just glue them to theuniand then export that for the training.What about all of the genes in the
altcategory? Those genes are reported to have multiple isoforms. Training from a gene with 10 isoforms would count that gene 10 times, so these are generally skipped. However, as more and more isoforms are found, this will become problematic. You will need some way to figure out which isoform is canonical and delete the rest. I don’t have an automated way to do that as each community has their own standards.Try the training procedures for the C. elegans and D. melanogaster genomes next. Note that these training sets represent just 1% of each chromosome and are just for demonstration purposes.