Special Issue "Bioinformatics Analysis for Diseases"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 20 March 2022.

Special Issue Editor

Prof. Dr. Sven Rahmann
E-Mail Website
Guest Editor
Algorithmic Bioinformatics, Center for Bioinformatics, Saarland University, Saarbrücken, Germany
Interests: algorithms; hashing; alignment-free; sequence analysis; k-mer; tumor genetics

Special Issue Information

Dear Colleagues, 

This Special Issue of the open access journal Genes (MDPI) will be devoted to bioinformatics analysis methods for diseases. I invite all of you to contribute methodological articles with short proof-of-concept applications or re-analyses of previous datasets. As you all know, in a typical medical journal, there is very little space for methodological details, and frequently, the amount and complexity of the bioinformatics work is underappreciated in these articles. This Special Issue provides the opportunity to provide more details on bioinformatics analysis methods for disease identification, classification, diagnosis, and prognosis. It is expected that submissions focus on the methods and provide either proofs-of-concept of potential applications or short summaries of existing analysis results (with reference to published work) or a re-analysis with some new findings of a previously published dataset. Article topics may include but are not limited to the following:

  • Alignment-free methods for disease gene identification;
  • Pan-genomic approaches to better distinguish between non-disease and disease variants;
  • Methodology and tools for variant calling, evaluation, filtering, and visualization;
  • New approaches to gene expression analysis, in particular probabilistic methods, for both single-cell and bulk expression analysis;
  • Methods for identifying regulatory regions involved in diseases;
  • Identification of disease-related non-coding RNA molecules;
  • Re-analysis of published datasets with new or significantly improved bioinformatics methods that lead to new insights;
  • Best-practice workflows for any workflow management system (e.g., Snakemake, nextflow, Galaxy, Watchdog, and others).

Other article topics are very welcome, provided that they have a focus on bioinformatics methods for diseases and fit the general scope of the Genes journal.

I hope that many of you take this opportunity to showcase your state-of-the-art work in bioinformatics analysis methods for disease.

Prof. Dr. Sven Rahmann
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Genes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • disease genes
  • bioinformatics methods
  • pan-genomics
  • alignment-free methods
  • best-practice workflows
  • gene expression analysis
  • single-cell analysis
  • gene regulation
  • non-coding RNA analysis

Published Papers (1 paper)

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MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
Genes 2021, 12(11), 1713; https://0-doi-org.brum.beds.ac.uk/10.3390/genes12111713 - 27 Oct 2021
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Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly [...] Read more.
Disease gene prediction is to date one of the main computational challenges of precision medicine. It is still uncertain if disease genes have unique functional properties that distinguish them from other non-disease genes or, from a network perspective, if they are located randomly in the interactome or show specific patterns in the network topology. In this study, we propose a new method for disease gene prediction based on the use of biological knowledge-bases (gene-disease associations, genes functional annotations, etc.) and interactome network topology. The proposed algorithm called MOSES is based on the definition of two somewhat opposing sets of genes both disease-specific from different perspectives: warm seeds (i.e., disease genes obtained from databases) and cold seeds (genes far from the disease genes on the interactome and not involved in their biological functions). The application of MOSES to a set of 40 diseases showed that the suggested putative disease genes are significantly enriched in their reference disease. Reassuringly, known and predicted disease genes together, tend to form a connected network module on the human interactome, mitigating the scattered distribution of disease genes which is probably due to both the paucity of disease-gene associations and the incompleteness of the interactome. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
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