A graph-based framework to prioritize regulatory players involved in transcriptional responses within the regulatory network of an organism, whereby every regulatory path containing genes of interest is explored and incorporated into the analysis. This tool was integrated in both YEASTRACT and regulatory snapshots prototype. Check our paper for algorithmic details and experimental results.
A prototype and yeast related files are available for evaluation purposes. Source code is available upon request.
Assuming that you have a Java environment available and that all files in the archive above are in your current directory, you can run our prototype as follows:
[aplf@darkstar ~]$ java -cp yrank.jar com.yeastract.rank.core.RankIt2 \ orf2name.txt \ gene_association.sgd \ weighted_network.txt \ 0 0 0 1 \ 0.25 \ 100 \ none \ false \ < sample_input.txt
Parameters are as follows:
null
as parameter.null
as parameter.
TF_Gene Gene_Target Wtab separated. The weight
W
may be omitted, taking the default
value 1.0. Note that TFs should be identified by their encoding gene.out
),
1 + in-weight (option in
),
1 + weight sum (option sum
),
log(1 + out-weight) (option logout
),
log(1 + in-weight) (option login
),
or log(1 + weight sum) (option logsum
).
For no normalization we should use option none
as in the
example. When applied to the original network graph, these will mitigate
the effect of TFs regulating many genes, genes regulated by many TFs or
both, respectively. true
as parameter to view
the subnetwork where ranked TFs and genes are highlighted.We provide also two input examples in the archive above,
sample_input.txt
and sample_weighted_input.txt
,
where the latter includes expression weights for each gene in the
input.
Please, let us know if you need help with anything.