ALIEN_HUNTER(1) User Commands ALIEN_HUNTER(1)NAME
alien_hunter - Interpolated Variable Order Motifs for identification of horizontally acquired DNA
SYNOPSIS
alien_hunter <sequence.file> <output.file> [Options]
DESCRIPTION
Alien_hunter is an application for the prediction of putative Horizontal Gene Transfer (HGT) events with the implementation of Interpolated
Variable Order Motifs (IVOMs). An IVOM approach exploits compositional biases using variable order motif distributions and captures more
reliably the local composition of a sequence compared to fixed-order methods. Optionally the predictions can be parsed into a 2-state 2nd
order Hidden Markov Model (HMM), in a change-point detection framework, to optimize the localization of the boundaries of the predicted
regions. The predictions (embl format) can be automatically loaded into Artemis genome viewer freely available at:
http://www.sanger.ac.uk/Software/Artemis/.
INPUT: raw genomic sequence PREDICTION: HGT regions based on Interpolated Variable Order Motifs (IVOMs) arguments:
<sequence.file>
raw genomic sequence
<output.file>
filename for output data
Optional arguments:
-a to load the prediction file into Artemis
-c optimize predicted boundaries with a change-point detection 2 state 2nd order HMM
Output files:
<output.file>
predictions (tab file) in embl format
<output.file>.opt
optimized (HMM) predictions (tab file) in embl format
<output.file>.plot
predictions in Artemis User Plot format to be loaded manually using Graph -> Add User Plot...
<output.file>.opt.plot
optimized (HMM) predictions in Artemis User Plot format to be loaded manually using Graph -> Add User Plot...
<output.file>.sco
the scores over all the sliding windows - for score distribution check
Note: Predictions that overlap with rRNA operon are mentioned in the note qualifier
SEE ALSO
The manuscript describing the alien_hunter algorithm is available from Bioinformatics: Interpolated variable order motifs for identifica-
tion of horizontally acquired DNA: revisiting the Salmonella pathogenicity islands. Vernikos GS, Parkhill J Bioinformatics. 2006;. PMID:
16837528
AUTHOR
This manual page was written by Andreas Tille <tille@debian.com> for the Debian(TM) system (but may be used by others). This man page is
released under the same conditions as the software, that is GPL.
alien_hunter 1.7 October 2009 ALIEN_HUNTER(1)
Check Out this Related Man Page
DISULFINDER(1) User Commands DISULFINDER(1)NAME
disulfinder - cysteines disulfide bonding state and connectivity predictor
SYNOPSIS
disulfinder [OPTIONS]
DESCRIPTION
'disulfinder' is for predicting the disulfide bonding state of cysteines and their disulfide connectivity starting from sequence alone.
Disulfide bridges play a major role in the stabilization of the folding process for several proteins. Prediction of disulfide bridges from
sequence alone is therefore useful for the study of structural and functional properties of specific proteins. In addition, knowledge about
the disulfide bonding state of cysteines may help the experimental structure determination process and may be useful in other genomic
annotation tasks. 'disulfinder' predicts disulfide patterns in two computational stages: (1) the disulfide bonding state of each cysteine
is predicted by a BRNN-SVM binary classifier; (2) cysteines that are known to participate in the formation of bridges are paired by a
Recursive Neural Network to obtain a connectivity pattern.
REFERENCES
A. Ceroni, A. Passerini, A. Vullo and P. Frasconi. DISULFIND: a Disulfide Bonding State and Cysteine Connectivity Prediction Server,
Nucleic Acids Research, 34(Web Server issue):W177-W181, 2006.
For the disulphide connectivity predictor see:
A. Vullo and P. Frasconi. Disulfide Connectivity Prediction using Recursive Neural Networks and Evolutionary Information, Bioinformatics,
20, 653-659, 2004.
For the cystein bonding state predictor see:
P. Frasconi, A. Passerini, and A. Vullo. A Two-Stage SVM Architecture for Predicting the Disulfide Bonding State of Cysteines, Proc. IEEE
Workshop on Neural Networks for Signal Processing, pp.25-34, 2002.
A.Ceroni, P.Frasconi, A.Passerini and A.Vullo. Predicting the Disulfide Bonding State of Cysteines with Combinations of Kernel Machines,
Journal of VLSI Signal Processing, 35, 287-295, 2003.
OPTIONS -a, --alternatives=NUMBER
alternative connectivity patterns (default=3)
-o, --output=DIR
output dir where predictions will be saved (default=$PWD)
-p, --psi2=FILE|DIR
input in psi2 format (PSI-BLAST Matrix in ASCII), either a single file or a directory(?). Generate this with "blastpgp -j <N> -Q FILE"
where N >= 2.
-r, --rootdir=DIR
work directory (default=~/disulfinder)
-k, --pkgdatadir=DIR
package data directory containing Models (default=/usr/share/disulfinder)
-F, --format={html|ascii}
output format type (default=ascii)
-d --blastdb=DIR
blastpgp -d option (default=/data/sp+trembl)
-c, --cleanpred
cleanup intermediate prediction files (default=false)
-P, --usepssm
use pssm instead of counts for profiles (default=false)
-C, --knownbondingstate
assume bonding state is known (one file for each chain in directory <rootdir>/Predictions/Bondstate/Viterbi) (default=false)
-v, --version
disulfinder version
-?, --help
help screen
EXAMPLES
"disulfinder -a 1 -p /usr/share/doc/disulfinder/examples/res_id_41483.blastPsiMatTmb -o ./disulfinder_results_dir"
FILES
/usr/share/disulfinder
default package data directory
~/disulfinder
default work directory
RESTRICTIONS
The work directory is not cleaned up automatically.
AUTHOR
Ceroni A, Passerini A, Vullo A, Frasconi P.
Packaging by Laszlo Kajan <lkajan@rostlab.org>
COPYRIGHT AND LICENSE
GPL
SEE ALSO
See official web site for help:
<http://disulfind.dsi.unifi.it/>
DISULFIND: a disulfide bonding state and cysteine connectivity prediction server:
<http://www.ncbi.nlm.nih.gov/pubmed/?term=16844986[uid]>
1.2.11 2012-04-07 DISULFINDER(1)