GeneCluEDO
Hox gene clustering https://github.com/choener/GeneCluEDO
Latest on Hackage:  0.0.0.2 
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generalized Algebraic Dynamic Programming Homepage
GeneCluEDO: Gene Cluster Evolution Determined Order
The first paper describes the biological problem. The 2nd and 3rd paper provide algorithmic background.

Prohaska, Sonja J. and Berkemer, Sarah and Externbrink, Fabian and Gatter, Thomas
and Retzlaff, Nancy and The Students of the Graphs and Biological Networks Lab 2017
and Hoener zu Siederdissen, Christian and Stadler, Peter F.
Expansion of Gene Clusters and the Shortest Hamiltonian Path Problem
2017
preprint: http://www.bioinf.unileipzig.de/~choener/pdfs/prober2017.pdf 
Hoener zu Siederdissen, Christian and Prohaska, Sonja J. and Stadler, Peter F.
Algebraic Dynamic Programming over General Data Structures
2015, BMC Bioinformatics
oa: https://doi.org/10.1186/1471210516S19S2 
Hoener zu Siederdissen, Christian and Prohaska, Sonja J. and Stadler, Peter F.
Dynamic Programming for Set Data Types
2014, Lecture Notes in Bioinformatics, 8826,
preprint: http://www.bioinf.unileipzig.de/~choener/pdfs/hoepro2014.pdf
This program accepts a matrix with distances between nodes (see below for an example). It then proceeds to calculate the Hamiltonian path with the shortest distance between each pair of nodes, where the path has to travel from the start, then to all other nodes, finally stopping at the last node.
We further calculate all neighbour probabilities via Inside/Outside. This means
that for any two nodes we calculate the weight of the edge between these two
nodes. The weight is between [0, ... ,1]
where 0
denotes the the nodes
are almost surely not direct neighbours on a weightedrandomly drawn path,
while 1
denotes that they almost surely are.
Finally, we calculate the probability that a node is one of the terminal nodes in the Hamiltonian path, i.e. either the first or the last node.
Installation / Precompiled Binaries
 Binaries are available from github for Linux x8664. They can be downloaded here: https://github.com/choener/GeneCluEDO/releases
 Installation from sources is possible using the Haskell stack tool, as described at the bottom of this page: http://www.bioinf.unileipzig.de/~choener/software/GeneCluEDO.html
 Another installation option is via
cabal newinstall
(preferred for development, but more involved to setup)
Input data used for the Expansion of Gene Clusters paper
The data sets are available together with the sources or the binary release.
Check the data
folder. The runall.sh
script runs the four examples.
The Biological Problem We Solve
Hox clusters are a set of genes that are linearly ordered. The genes are (assumed) to have a single originating gene, and repeated duplication has led to the cluster with unknown duplication tree.
The long time scales involved make it hard to produce a tree that can be trusted. This program therefore produces summary information in the form of edge path probabilities.
Example matrix:
In this artificial distance matrix, we have prime numbers as distances between
nodes. Store the matrix in a file, say mat.dat
.
# A B C D E
A 0 2 3 5 7
B 2 0 11 13 17
C 3 11 0 19 23
D 5 13 19 0 27
E 7 17 23 27 0
Now, run the algorithm ./GeneCluEDO o output.run ./mat.dat
. After the
program has run, output.run
contains the a wealth of information about the
input. The maximum likelihood path, the edge weights, end probabilities, and
maximum expected accuracy path are calculated. Two additional files, here
output.boundary.svg
, and output.edge.svg
are produced. The boundary
plot provides graphical output of the probability that a node (or gene) is the
start or end node. The edge probability plot provides probabilities for each
edge (i,j) between nodes. This shows the most likely neighbors, and therefore
genetic relationship, over all possible gene orders.
Contact
Christian Hoener zu Siederdissen
Leipzig University, Leipzig, Germany
choener@bioinf.unileipzig.de
http://www.bioinf.unileipzig.de/~choener/
Changes
0.0.0.1
 originally named â€˜HoxClusterâ€™ for the earlier papers, now renamed to GeneCluEDO during the work on the expansion of gene clusters problem (see README)
 initial checkin
 travisci integration