Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.1
3.5
Journals
Genes
Special Issues
Bioinformatics Analysis for Diseases
share announcement

Bioinformatics Analysis for Diseases

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

Deadline for manuscript submissions: closed (20 March 2022) | Viewed by 18143

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 Issues, Collections and Topics in MDPI journals

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 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. 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 2600 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.

Keywords

  • 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 (7 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

16 pages, 6910 KiB  
Article
Identification and Functional Analysis of Individual-Specific Subpathways in Lung Adenocarcinoma
by Jingya Fang, Zutan Li, Mingmin Xu, Jinwen Ji, Yanru Li, Liangyun Zhang and Yuanyuan Chen
Genes 2022, 13(7), 1122; https://doi.org/10.3390/genes13071122 - 23 Jun 2022
Cited by 1 | Viewed by 1424
Abstract
Small molecular networks within complex pathways are defined as subpathways. The identification of patient-specific subpathways can reveal the etiology of cancer and guide the development of personalized therapeutic strategies. The dysfunction of subpathways has been associated with the occurrence and development of cancer. [...] Read more.
Small molecular networks within complex pathways are defined as subpathways. The identification of patient-specific subpathways can reveal the etiology of cancer and guide the development of personalized therapeutic strategies. The dysfunction of subpathways has been associated with the occurrence and development of cancer. Here, we propose a strategy to identify aberrant subpathways at the individual level by calculating the edge score and using the Gene Set Enrichment Analysis (GSEA) method. This provides a novel approach to subpathway analysis. We applied this method to the expression data of a lung adenocarcinoma (LUAD) dataset from The Cancer Genome Atlas (TCGA) database. We validated the effectiveness of this method in identifying LUAD-relevant subpathways and demonstrated its reliability using an independent Gene Expression Omnibus dataset (GEO). Additionally, survival analysis was applied to illustrate the clinical application value of the genes and edges in subpathways that were associated with the prognosis of patients and cancer immunity, which could be potential biomarkers. With these analyses, we show that our method could help uncover subpathways underlying lung adenocarcinoma. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
►▼ Show Figures

Figure 1

18 pages, 7979 KiB  
Article
Correlation of NTRK1 Downregulation with Low Levels of Tumor-Infiltrating Immune Cells and Poor Prognosis of Prostate Cancer Revealed by Gene Network Analysis
by Arash Bagherabadi, Amirreza Hooshmand, Nooshin Shekari, Prithvi Singh, Samaneh Zolghadri, Agata Stanek and Ravins Dohare
Genes 2022, 13(5), 840; https://doi.org/10.3390/genes13050840 - 08 May 2022
Cited by 4 | Viewed by 2876
Abstract
Prostate cancer (PCa) is a life-threatening heterogeneous malignancy of the urinary tract. Due to the incidence of prostate cancer and the crucial need to elucidate its molecular mechanisms, we searched for possible prognosis impactful genes in PCa using bioinformatics analysis. A script in [...] Read more.
Prostate cancer (PCa) is a life-threatening heterogeneous malignancy of the urinary tract. Due to the incidence of prostate cancer and the crucial need to elucidate its molecular mechanisms, we searched for possible prognosis impactful genes in PCa using bioinformatics analysis. A script in R language was used for the identification of Differentially Expressed Genes (DEGs) from the GSE69223 dataset. The gene ontology (GO) of the DEGs and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed. A protein–protein interaction (PPI) network was constructed using the STRING online database to identify hub genes. GEPIA and UALCAN databases were utilized for survival analysis and expression validation, and 990 DEGs (316 upregulated and 674 downregulated) were identified. The GO analysis was enriched mainly in the “collagen-containing extracellular matrix”, and the KEGG pathway analysis was enriched mainly in “focal adhesion”. The downregulation of neurotrophic receptor tyrosine kinase 1 (NTRK1) was associated with a poor prognosis of PCa and had a significant positive correlation with infiltrating levels of immune cells. We acquired a collection of pathways related to primary PCa, and our findings invite the further exploration of NTRK1 as a biomarker for early diagnosis and prognosis, and as a future potential molecular therapeutic target for PCa. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
►▼ Show Figures

Figure 1

11 pages, 3052 KiB  
Article
In Silico Analysis of the L-2-Hydroxyglutarate Dehydrogenase Gene Mutations and Their Biological Impact on Disease Etiology
by Muhammad Muzammal, Alessandro Di Cerbo, Eman M. Almusalami, Arshad Farid, Muzammil Ahmad Khan, Shakira Ghazanfar, Mohammed Al Mohaini, Abdulkhaliq J. Alsalman, Yousef N. Alhashem, Maitham A. Al Hawaj and Abdulmonem A. Alsaleh
Genes 2022, 13(4), 698; https://doi.org/10.3390/genes13040698 - 15 Apr 2022
Cited by 6 | Viewed by 2029
Abstract
The L-2-hydroxyglutarate dehydrogenase (L2HGDH) gene encodes an important mitochondrial enzyme. However, its altered activity results in excessive levels of L-2-hydroxyglutarate, which results in diverse psychiatric features of intellectual disability. In the current study, we executed an in-silico analysis of all reported L2HGDH missense [...] Read more.
The L-2-hydroxyglutarate dehydrogenase (L2HGDH) gene encodes an important mitochondrial enzyme. However, its altered activity results in excessive levels of L-2-hydroxyglutarate, which results in diverse psychiatric features of intellectual disability. In the current study, we executed an in-silico analysis of all reported L2HGDH missense and nonsense variants in order to investigate their biological significance. Among the superimposed 3D models, the highest similarity index for a wild-type structure was shown by the mutant Glu336Lys (87.26%), while the lowest similarity index value was shown by Arg70* (10.00%). Three large active site pockets were determined using protein active site prediction, in which the 2nd largest pocket was shown to encompass the substrate L-2-hydroxyglutarate (L2HG) binding residues, i.e., 89Gln, 195Tyr, 402Ala, 403Gly and 404Val. Moreover, interactions of wild-type and mutant L2HGDH variants with the close functional interactor D2HGDH protein resulted in alterations in the position, number and nature of networking residues. We observed that the binding of L2HG with the L2HGDH enzyme is affected by the nature of the amino acid substitution, as well as the number and nature of bonds between the substrate and protein molecule, which are able to affect its biological activity. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
►▼ Show Figures

Figure 1

15 pages, 2003 KiB  
Article
Robust Mutation Profiling of SARS-CoV-2 Variants from Multiple Raw Illumina Sequencing Data with Cloud Workflow
by Hendrick Gao-Min Lim, Shih-Hsin Hsiao, Yang C. Fann and Yuan-Chii Gladys Lee
Genes 2022, 13(4), 686; https://doi.org/10.3390/genes13040686 - 13 Apr 2022
Cited by 6 | Viewed by 2482
Abstract
Several variants of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are emerging all over the world. Variant surveillance from genome sequencing has become crucial to determine if mutations in these variants are rendering the virus more infectious, potent, or resistant to [...] Read more.
Several variants of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are emerging all over the world. Variant surveillance from genome sequencing has become crucial to determine if mutations in these variants are rendering the virus more infectious, potent, or resistant to existing vaccines and therapeutics. Meanwhile, analyzing many raw sequencing data repeatedly with currently available code-based bioinformatics tools is tremendously challenging to be implemented in this unprecedented pandemic time due to the fact of limited experts and computational resources. Therefore, in order to hasten variant surveillance efforts, we developed an installation-free cloud workflow for robust mutation profiling of SARS-CoV-2 variants from multiple Illumina sequencing data. Herein, 55 raw sequencing data representing four early SARS-CoV-2 variants of concern (Alpha, Beta, Gamma, and Delta) from an open-access database were used to test our workflow performance. As a result, our workflow could automatically identify mutated sites of the variants along with reliable annotation of the protein-coding genes at cost-effective and timely manner for all by harnessing parallel cloud computing in one execution under resource-limitation settings. In addition, our workflow can also generate a consensus genome sequence which can be shared with others in public data repositories to support global variant surveillance efforts. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
►▼ Show Figures

Graphical abstract

18 pages, 3209 KiB  
Article
Transcriptional Profiling and Deriving a Seven-Gene Signature That Discriminates Active and Latent Tuberculosis: An Integrative Bioinformatics Approach
by Sudhakar Natarajan, Mohan Ranganathan, Luke Elizabeth Hanna and Srikanth Tripathy
Genes 2022, 13(4), 616; https://doi.org/10.3390/genes13040616 - 29 Mar 2022
Cited by 12 | Viewed by 2840
Abstract
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the [...] Read more.
Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (M.tb.). Our integrative analysis aims to identify the transcriptional profiling and gene expression signature that distinguish individuals with active TB (ATB) disease, and those with latent tuberculosis infection (LTBI). In the present study, we reanalyzed a microarray dataset (GSE37250) from GEO database and explored the data for differential gene expression analysis between those with ATB and LTBI derived from Malawi and South African cohorts. We used BRB array tool to distinguish DEGs (differentially expressed genes) between ATB and LTBI. Pathway enrichment analysis of DEGs was performed using DAVID bioinformatics tool. The protein–protein interaction (PPI) network of most upregulated genes was constructed using STRING analysis. We have identified 375 upregulated genes and 152 downregulated genes differentially expressed between ATB and LTBI samples commonly shared among Malawi and South African cohorts. The constructed PPI network was significantly enriched with 76 nodes connected to 151 edges. The enriched GO term/pathways were mainly related to expression of IFN stimulated genes, interleukin-1 production, and NOD-like receptor signaling pathway. Downregulated genes were significantly enriched in the Wnt signaling, B cell development, and B cell receptor signaling pathways. The short-listed DEGs were validated in a microarray data from an independent cohort (GSE19491). ROC curve analysis was done to assess the diagnostic accuracy of the gene signature in discrimination of active and latent tuberculosis. Thus, we have derived a seven-gene signature, which included five upregulated genes FCGR1B, ANKRD22, CARD17, IFITM3, TNFAIP6 and two downregulated genes FCGBP and KLF12, as a biomarker for discrimination of active and latent tuberculosis. The identified genes have a sensitivity of 80–100% and specificity of 80–95%. Area under the curve (AUC) value of the genes ranged from 0.84 to 1. This seven-gene signature has a high diagnostic accuracy in discrimination of active and latent tuberculosis. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
►▼ Show Figures

Figure 1

19 pages, 4440 KiB  
Article
Identification of a Novel Epithelial–Mesenchymal Transition-Related Gene Signature for Endometrial Carcinoma Prognosis
by Tianyuan Ruan, Jing Wan, Qian Song, Peigen Chen and Xiaomao Li
Genes 2022, 13(2), 216; https://doi.org/10.3390/genes13020216 - 25 Jan 2022
Cited by 9 | Viewed by 2897
Abstract
(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database [...] Read more.
(1) Background: Endometrial cancer is the most prevalent cause of gynecological malignant tumor worldwide. The prognosis of endometrial carcinoma patients with distant metastasis is poor. (2) Method: The RNA-Seq expression profile and corresponding clinical data were downloaded from the Cancer Genome Atlas database and the Gene Expression Omnibus databases. To predict patients’ overall survival, a 9 EMT-related genes prognosis risk model was built by machine learning algorithm and multivariate Cox regression. Expressions of nine genes were verified by RT-qPCR. Responses to immune checkpoint blockades therapy and drug sensitivity were separately evaluated in different group of patients with the risk model. (3) Endometrial carcinoma patients were assigned to the high- and low-risk groups according to the signature, and poorer overall survival and disease-free survival were showed in the high-risk group. This EMT-related gene signature was also significantly correlated with tumor purity and immune cell infiltration. In addition, eight chemical compounds, which may benefit the high-risk group, were screened out. (4) Conclusions: We identified a novel EMT-related gene signature for predicting the prognosis of EC patients. Our findings provide potential therapeutic targets and compounds for personalized treatment. This may facilitate decision making during endometrial carcinoma treatment. Full article
(This article belongs to the Special Issue Bioinformatics Analysis for Diseases)
►▼ Show Figures

Figure 1

16 pages, 15523 KiB  
Article
MOSES: A New Approach to Integrate Interactome Topology and Functional Features for Disease Gene Prediction
by Manuela Petti, Lorenzo Farina, Federico Francone, Stefano Lucidi, Amalia Macali, Laura Palagi and Marianna De Santis
Genes 2021, 12(11), 1713; https://doi.org/10.3390/genes12111713 - 27 Oct 2021
Cited by 5 | Viewed by 2064
Abstract
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-7
Back to TopTop