International Journal of Molecular Sciences

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Dr. Farbod Sedaghat-Hamedani

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Department of Internal Medicine III, Heidelberg University Hospital, Heidelberg University, 69120 Heidelberg, Germany
Interests: genomics and miRNA; molecular cardiology; translational research and personalized medicine; genotype and phenotype of cardiomyopathies; new diagnostic methods and therapy of cardiomyopathies; sports and cardiovascular effects
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Dear Colleagues,

Incorporating high-throughput DNA and RNA sequencing in medical settings is only possible because of novel, extremely efficient methods developed in the years following the arrival of second- and third-generation sequencing machines. A major focus of “computational genomics” is applying statistical, mathematical, algorithmic, machine learning, artificial intelligence, and further innovative technological approaches to deal with the challenges that big data bring. With these, we hope to develop methods capable of extracting biological understanding from genomics data. For our Special Issue on “Computational Genomics”, we invite you to submit your scientific articles on innovative methods in interdisciplinary genomic research with a special focus on molecular levels. By publishing in an open access journal, your research will be freely and permanently available to the scientific community.

Dr. Farbod Sedaghat-Hamedani
Guest Editor

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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 submissions that pass pre-check are 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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. 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.

Keywords

computational genomics
interdisciplinary genomic research
molecular research
big data
high-throughput sequencing
long-read sequencing
single-molecule sequencing

Published Papers (1 paper)

Research

21 pages, 4118 KiB

Open AccessArticle

Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks

by Lorena de la Fuente, Marta Del Pozo-Valero, Irene Perea-Romero, Fiona Blanco-Kelly, Lidia Fernández-Caballero, Marta Cortón, Carmen Ayuso and Pablo Mínguez

Int. J. Mol. Sci. 2023, 24(2), 1661; https://doi.org/10.3390/ijms24021661 - 14 Jan 2023

Cited by 1 | Viewed by 2001

Abstract

Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene–disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene–gene functional networks to accelerate the discovery of gene–disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs’ ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene–disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies. Full article

(This article belongs to the Special Issue Current Topics in Computational Genomics)

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