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tigr-build-icm(1) [debian man page]

TIGR-GLIMMER	 (1)   (1)				      General Commands Manual					TIGR-GLIMMER	 (1)   (1)

NAME
tigr-glimmer -- Ceates and outputs an interpolated Markov model(IMM) SYNOPSIS
tigr-build-icm DESCRIPTION
Program build-icm.c creates and outputs an interpolated Markov model (IMM) as described in the paper A.L. Delcher, D. Harmon, S. Kasif, O. White, and S.L. Salzberg. Improved Microbial Gene Identification with Glimmer. Nucleic Acids Research, 1999, in press. Please refer- ence this paper if you use the system as part of any published research. Input comes from the file named on the command-line. Format should be one string per line. Each line has an ID string followed by white space followed by the sequence itself. The script run-glimmer3 generates an input file in the correct format using the 'extract' program. The IMM is constructed as follows: For a given context, say acgtta, we want to estimate the probability distribution of the next character. We shall do this as a linear combination of the observed probability distributions for this context and all of its suffixes, i.e., cgtta, gtta, tta, ta, a and empty. By observed distributions I mean the counts of the number of occurrences of these strings in the training set. The linear combination is determined by a set of probabilities, lambda, one for each context string. For context acgtta the linear combi- nation coefficients are: lambda (acgtta) (1 - lambda (acgtta)) x lambda (cgtta) (1 - lambda (acgtta)) x (1 - lambda (cgtta)) x lambda (gtta) (1 - lambda (acgtta)) x (1 - lambda (cgtta)) x (1 - lambda (gtta)) x lambda (tta) (1 - lambda (acgtta)) x (1 - lambda (cgtta)) x (1 - lambda (gtta)) x (1 - lambda (tta)) x (1 - lambda (ta)) x (1 - lambda (a)) We compute the lambda values for each context as follows: - If the number of observations in the training set is >= the constant SAM- PLE_SIZE_BOUND, the lambda for that context is 1.0 - Otherwise, do a chi-square test on the observations for this context compared to the distribution predicted for the one-character shorter suffix context. If the chi-square significance < 0.5, set the lambda for this context to 0.0 Otherwise set the lambda for this context to: (chi-square significance) x (# observations) / SAMPLE_WEIGHT To run the program: build-icm <train.seq > train.model This will use the training data in train.seq to produce the file train.model, containing your IMM. SEE ALSO
tigr-glimmer3 (1), tigr-long-orfs (1), tigr-adjust (1), tigr-anomaly (1), tigr-extract (1), tigr-check (1), tigr-codon-usage (1), tigr- compare-lists (1), tigr-extract (1), tigr-generate (1), tigr-get-len (1), tigr-get-putative (1), http://www.tigr.org/software/glimmer/ Please see the readme in /usr/share/doc/tigr-glimmer for a description on how to use Glimmer3. AUTHOR
This manual page was quickly copied from the glimmer web site and readme file by Steffen Moeller moeller@debian.org for the Debian system. TIGR-GLIMMER (1) (1)

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LONG-ORFS(1)						      General Commands Manual						      LONG-ORFS(1)

NAME
long-orfs -- Find/Score potential genes in genome-file using the probability model in icm-file SYNOPSIS
tigr-long-orgs [genome-file options] DESCRIPTION
Program long-orfs takes a sequence file (in FASTA format) and outputs a list of all long "potential genes" in it that do not overlap by too much. By "potential gene" I mean the portion of an orf from the first start codon to the stop codon at the end. The first few lines of output specify the settings of various parameters in the program: Minimum gene length is the length of the smallest fragment considered to be a gene. The length is measured from the first base of the start codon to the last base *before* the stop codon. This value can be specified when running the program with the -g option. By default, the program now (April 2003) will compute an optimal length for this parameter, where "optimal" is the value that produces the greatest number of long ORFs, thereby increasing the amount of data used for training. Minimum overlap length is a lower bound on the number of bases overlap between 2 genes that is considered a problem. Overlaps shorter than this are ignored. Minimum overlap percent is another lower bound on the number of bases overlap that is considered a problem. Overlaps shorter than this percentage of *both* genes are ignored. The next portion of the output is a list of potential genes: Column 1 is an ID number for reference purposes. It is assigned sequentially starting with 1 to all long potential genes. If overlap- ping genes are eliminated, gaps in the numbers will occur. The ID prefix is specified in the constant ID_PREFIX . Column 2 is the position of the first base of the first start codon in the orf. Currently I use atg, and gtg as start codons. This is easily changed in the function Is_Start () . Column 3 is the position of the last base *before* the stop codon. Stop codons are taa, tag, and tga. Note that for orfs in the reverse reading frames have their start position higher than the end position. The order in which orfs are listed is in increasing order by Max {OrfStart, End}, i.e., the highest numbered position in the orf, except for orfs that "wrap around" the end of the sequence. When two genes with ID numbers overlap by at least a sufficient amount (as determined by Min_Olap and Min_Olap_Percent ), they are elimi- nated and do not appear in the output. The final output of the program (sent to the standard error file so it does not show up when output is redirected to a file) is the length of the longest orf found. Specifying Different Start and Stop Codons: To specify different sets of start and stop codons, modify the file gene.h . Specifically, the functions: Is_Forward_Start Is_Reverse_Start Is_Start Is_Forward_Stop Is_Reverse_Stop Is_Stop are used to determine what is used for start and stop codons. Is_Start and Is_Stop do simple string comparisons to specify which patterns are used. To add a new pattern, just add the comparison for it. To remove a pattern, comment out or delete the comparison for it. The other four functions use a bit comparison to determine start and stop patterns. They represent a codon as a 12-bit pattern, with 4 bits for each base, one bit for each possible value of the bases, T, G, C or A. Thus the bit pattern 0010 0101 1100 represents the base pattern [C] [A or G] [G or T]. By doing bit operations (& | ~) and comparisons, more complicated patterns involving ambiguous reads can be tested efficiently. Simple patterns can be tested as in the current code. For example, to insert an additional start codon of CAT requires 3 changes: 1. The line || (Codon & 0x218) == Codon should be inserted into Is_Forward_Start , since 0x218 = 0010 0001 1000 represents CAT. 2. The line || (Codon & 0x184) == Codon should be inserted into Is_Reverse_Start , since 0x184 = 0001 1000 0100 represents ATG, which is the reverse-complement of CAT. Alternately, the #define constant ATG_MASK could be used. 3. The line || strncmp (S, "cat", 3) == 0 should be inserted into Is_Start . OPTIONS
-g n Set minimum gene length to n. Default is to compute an optimal value automatically. Don't change this unless you know what you're doing. -l Regard the genome as linear (not circular), i.e., do not allow genes to "wrap around" the end of the genome. This option works on both glimmer and long-orfs . The default behavior is to regard the genome as circular. -o n Set maximum overlap length to n. Overlaps shorter than this are permitted. (Default is 0 bp.) -p n Set maximum overlap percentage to n%. Overlaps shorter than this percentage of *both* strings are ignored. (Default is 10%.) SEE ALSO
tigr-glimmer3 (1), tigr-adjust (1), tigr-anomaly (1), tigr-build-icm (1), tigr-check (1), tigr-codon-usage (1), tigr-compare-lists (1), tigr-extract (1), tigr-generate (1), tigr-get-len (1), tigr-get-putative (1), http://www.tigr.org/software/glimmer/ Please see the readme in /usr/share/doc/tigr-glimmer for a description on how to use Glimmer3. AUTHOR
This manual page was quickly copied from the glimmer web site by Steffen Moeller moeller@debian.org for the Debian system. LONG-ORFS(1)
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