---
name: "tigr-build-icm"
section: "1"
platform: "linux"
package: "tigr-glimmer"
version: "3.02b-6+b1"
release: "Debian 13 (Trixie)"
platforms: ["linux"]
url: "https://new.carta.tech/tigr-build-icm"
---

# tigr-build-icm(1)

Creates and outputs an interpolated Markov model(IMM)

## NAME

tigr-glimmer — Creates 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 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.

## 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.