NAME
tigr-glimmer — Find/Score potential genes in genome-file using the
probability model in icm-file
SYNOPSIS
tigr-glimmer3 [genome-file] [icm-file] [[options]]
DESCRIPTION
tigr-glimmer is a system for finding genes in microbial DNA, especially
the genomes of bacteria and archaea. tigr-glimmer (Gene Locator and
Interpolated Markov Modeler) uses interpolated Markov models (IMMs) to
identify the coding regions and distinguish them from noncoding DNA.
The IMM approach, described in our Nucleic Acids Research paper on
tigr-glimmer 1.0 and in our subsequent paper on tigr-glimmer 2.0, uses
a combination of Markov models from 1st through 8th-order, weighting
each model according to its predictive power. tigr-glimmer 1.0 and 2.0
use 3-periodic nonhomogenous Markov models in their IMMs.
tigr-glimmer is the primary microbial gene finder at TIGR, and has been
used to annotate the complete genomes of B. burgdorferi (Fraser et al.,
Nature, Dec. 1997), T. pallidum (Fraser et al., Science, July 1998), T.
maritima, D. radiodurans, M. tuberculosis, and non-TIGR projects
including C. trachomatis, C. pneumoniae, and others. Its analyses of
some of these genomes and others is available at the TIGR microbial
database site.
A special version of tigr-glimmer designed for small eukaryotes,
GlimmerM, was used to find the genes in chromosome 2 of the malaria
parasite, P. falciparum.. GlimmerM is described in S.L. Salzberg, M.
Pertea, A.L. Delcher, M.J. Gardner, and H. Tettelin, "Interpolated
Markov models for eukaryotic gene finding," Genomics 59 (1999), 24-31.
Click here (http://www.tigr.org/software/glimmerm/) to visit the
GlimmerM site, which includes information on how to download the
GlimmerM system.
The tigr-glimmer system consists of two main programs. The first of
these is the training program, build-imm. This program takes an input
set of sequences and builds and outputs the IMM for them. These
sequences can be complete genes or just partial orfs. For a new genome,
this training data can consist of those genes with strong database hits
as well as very long open reading frames that are statistically almost
certain to be genes. The second program is glimmer, which uses this IMM
to identify putative genes in an entire genome. tigr-glimmer
automatically resolves conflicts between most overlapping genes by
choosing one of them. It also identifies genes that are suspected to
truly overlap, and flags these for closer inspection by the user. These
‘‘suspect’’ gene candidates have been a very small percentage of the
total for all the genomes analyzed thus far. tigr-glimmer is a program
that...
OPTIONS
-C n Use n as GC percentage of independent model
Note: n should be a percentage, e.g., -C 45.2
-f Use ribosome-binding energy to choose start codon
+f Use first codon in orf as start codon
-g n Set minimum gene length to n
-i filename
Use filename to select regions of bases that are off
limits, so that no bases within that area will be examined
-l Assume linear rather than circular genome, i.e., no
wraparound
-L filename
Use filename to specify a list of orfs that should be scored
separately, with no overlap rules
-M Input is a multifasta file of separate genes to be scored
separately, with no overlap rules
-o n Set minimum overlap length to n. Overlaps shorter than this
are ignored.
-p n Set minimum overlap percentage to n%. Overlaps shorter than
this percentage of *both* strings are ignored.
-q n Set the maximum length orf that can be rejected because of
the independent probability score column to (n - 1)
-r Don’t use independent probability score column
+r Use independent probability score column
-r Don’t use independent probability score column
-s s Use string s as the ribosome binding pattern to find start
codons.
+S Do use stricter independent intergenic model that doesn’t
give probabilities to in-frame stop codons. (Option is
obsolete since this is now the only behaviour
-t n Set threshold score for calling as gene to n. If the in-
frame score >= n, then the region is given a number and
considered a potential gene.
-w n Use "weak" scores on tentative genes n or longer. Weak
scores ignore the independent probability score.
SEE ALSO
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), tigr-
glimmer3 (1), tigr-long-orfs (1)
http://www.tigr.org/software/glimmer/
Please see the readme in /usr/share/doc/glimmer for a description on
how to use Glimmer.
AUTHOR
This manual page was quickly copied from the glimmer web site by
Steffen Moeller moeller@debian.org for the Debian system.
TIGR-GLIMMER(1)