tigr-build-icm (1)

tigr-build-icm

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 reference 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 combination 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 SAMPLE_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.

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.

This manual page was quickly copied from the glimmer web site and readme file by Steffen Moeller [email protected] for the Debian system.