Hybrid Distance-score Rank Aggregation Software for Gene Prioritization

What is HyDRA?

HyDRA (Hybrid Distance-score Rank Aggregation) is a rank aggregation software tool for gene prioritization.

Why use HyDRA?

It is known that humans have 20,000-25,000 genes and one or more of these genes may be implicated in a particular disease.  Verification of disease genes involves knock-out experiments which are both time-consuming and costly.  Thus, researchers have developed computational tools to identify a set of genes likely to be involved in a disease, based on a set of criteria (linkage, sequence similarity, etc.) and named it gene prioritization.  One of the challenges in gene prioritization is rank aggregation: combining a set of rankings into one meaningful list, ordered from the most significant to the least significant genes.  HyDRA uses tools from combinatorics and information theory, rather than relying solely on statistical methods, and demonstrates that it often outperforms other state-of-the-art methods.

Gene Prioritization: which genes to test first? We pick the ones "most similar" to the known "Breast Cancer genes".

Gene Prioritization: which genes to test first? We pick the ones “most similar” to the known “Breast Cancer genes”, where we capture similarity based on sequence, function, etc.


In gene prioritization problem, each criteria has its own opinion. Rank aggregation methods combine rankings into one representative list.


We find the aggregate list by minimizing the distance between the aggregate and all other rankings.


How do I download HyDRA?

You may access HyDRA’s source code and README file here.

System Requirement

We tested HyDRA on a windows machine with Intel Core i7-2600 CPU at 3.4 GHz, with a 8 GB RAM.  HyDRA also requires MATLAB.


After following the README file, use the following commands to run HyDRA.

1.  Load the file into MATLAB using “uiopen” or “xlsread” command.  Type “help uiopen” or “help xlsread” on MATLAB for more information.

2.  Trim the data using either “EndeavourTrim.m” or “ToppGeneTrim.m”, according to the data type.

3.  Perform LBNorm, WeightedHB, WeightedHK on dataTrimmed.


“HyDRA: Gene Prioritization via Hybrid Distance-Score Rank Aggregation”.
M. Kim, F. Farnoud, O. Milenkovic.
Bioinformatics31(7), 1034–1043, (2015). [link]


Minji Kim (mkim158@illinois.edu) and Olgica Milenkovic (milenkov@illinois.edu)

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