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Computational Genomics in Disease and Wellness Genetics
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Computational Genomics in Disease and Wellness Genetics

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Omics/Informatics".

Deadline for manuscript submissions: closed (5 January 2022) | Viewed by 9709

Special Issue Editor

Dr. Gwênlyn Glusman

E-Mail Website
Guest Editor
Institute for Systems Biology, Seattle, WA 98109, USA
Interests: computational genomics; disease and wellness genetics; genome analysis; bioinformatics; big data analytics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the current abundance of massive biological datasets, computational genomics has become one of the most important means to study human diseases and wellness. This special issue will encapsulate the latest applications of computational genomics in human disease and health genetics in the era of personalized medicine. We are soliciting papers focusing on, but not limited to, novel algorithms and data structures for genome analysis, analysis and integration of genetics, genomics, transcriptomics and networks in human health, disease and personalized medicine.

Dr. Gwênlyn Glusman
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. Journal of Personalized Medicine 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

  • computational genomics
  • family genomics
  • bioinformatics
  • algorithm development
  • human genetics
  • genome variation
  • transcriptomic analysis
  • comparative genomics
  • genome sequencing

Published Papers (3 papers)

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Research

25 pages, 3750 KiB  
Article
Evaluating, Filtering and Clustering Genetic Disease Cohorts Based on Human Phenotype Ontology Data with Cohort Analyzer
by Elena Rojano, José Córdoba-Caballero, Fernando M. Jabato, Diana Gallego, Mercedes Serrano, Belén Pérez, Álvaro Parés-Aguilar, James R. Perkins, Juan A. G. Ranea and Pedro Seoane-Zonjic
J. Pers. Med. 2021, 11(8), 730; https://doi.org/10.3390/jpm11080730 - 27 Jul 2021
Cited by 3 | Viewed by 2737
Abstract
Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts [...] Read more.
Exhaustive and comprehensive analysis of pathological traits is essential to understanding genetic diseases, performing precise diagnosis and prescribing personalized treatments. It is particularly important for disease cohorts, as thoroughly detailed phenotypic profiles allow patients to be compared and contrasted. However, many disease cohorts contain patients that have been ascribed low numbers of very general and relatively uninformative phenotypes. We present Cohort Analyzer, a tool that measures the phenotyping quality of patient cohorts. It calculates multiple statistics to give a general overview of the cohort status in terms of the depth and breadth of phenotyping, allowing us to detect less well-phenotyped patients for re-examining or excluding from further analyses. In addition, it performs clustering analysis to find subgroups of patients that share similar phenotypic profiles. We used it to analyse three cohorts of genetic diseases patients with very different properties. We found that cohorts with the most specific and complete phenotypic characterization give more potential insights into the disease than those that were less deeply characterised by forming more informative clusters. For two of the cohorts, we also analysed genomic data related to the patients, and linked the genomic data to the patient-subgroups by mapping shared variants to genes and functions. The work highlights the need for improved phenotyping in this era of personalized medicine. The tool itself is freely available alongside a workflow to allow the analyses shown in this work to be applied to other datasets. Full article
(This article belongs to the Special Issue Computational Genomics in Disease and Wellness Genetics)
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23 pages, 4987 KiB  
Article
RNA-seq Characterization of Sex-Differences in Adipose Tissue of Obesity Affected Patients: Computational Analysis of Differentially Expressed Coding and Non-Coding RNAs
by Federica Rey, Letizia Messa, Cecilia Pandini, Erika Maghraby, Bianca Barzaghini, Maria Garofalo, Giancarlo Micheletto, Manuela Teresa Raimondi, Simona Bertoli, Cristina Cereda, Gian Vincenzo Zuccotti, Raffaella Cancello and Stephana Carelli
J. Pers. Med. 2021, 11(5), 352; https://doi.org/10.3390/jpm11050352 - 28 Apr 2021
Cited by 9 | Viewed by 3152
Abstract
Obesity is a multifactorial disease presenting sex-related differences including adipocyte functions, sex hormone effects, genetics, and metabolic inflammation. These can influence individuals’ risk for metabolic dysfunctions, with an urgent need to perform sex-based analysis to improve prevention, treatment, and rehabilitation programs. This research [...] Read more.
Obesity is a multifactorial disease presenting sex-related differences including adipocyte functions, sex hormone effects, genetics, and metabolic inflammation. These can influence individuals’ risk for metabolic dysfunctions, with an urgent need to perform sex-based analysis to improve prevention, treatment, and rehabilitation programs. This research work is aimed at characterizing the transcriptional differences present in subcutaneous adipose tissue (SAT) of five obesity affected men versus five obesity affected women, with an additional focus on the role of long non-coding RNAs. Through RNA-sequencing, we highlighted the presence of both coding and non-coding differentially expressed RNAs, and with numerous computational analyses we identified the processes in which these genes are implicated, along with their role in co-morbidities development. We report 51 differentially expressed transcripts, 32 of which were coding genes and 19 were non-coding. Using the WGCNA R package (Weighted Correlation Network Analysis, version 1.70-3), we describe the interactions between coding and non-coding RNAs, and the non-coding RNAs association with the insurgence of specific diseases, such as cancer development, neurodegenerative diseases, and schizophrenia. In conclusion, our work highlights a specific gender sex-related transcriptional signature in the SAT of obesity affected patients. Full article
(This article belongs to the Special Issue Computational Genomics in Disease and Wellness Genetics)
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13 pages, 19445 KiB  
Article
ScanBious: Survey for Obesity Genes Using PubMed Abstracts and DisGeNET
by Svetlana Tarbeeva, Ekaterina Lyamtseva, Andrey Lisitsa, Anna Kozlova, Elena Ponomarenko and Ekaterina Ilgisonis
J. Pers. Med. 2021, 11(4), 246; https://doi.org/10.3390/jpm11040246 - 29 Mar 2021
Cited by 4 | Viewed by 2877
Abstract
We used automatic text-mining of PubMed abstracts of papers related to obesity, with the aim of revealing that the information used in abstracts reflects the current understanding and key concepts of this widely explored problem. We compared expert data from DisGeNET to the [...] Read more.
We used automatic text-mining of PubMed abstracts of papers related to obesity, with the aim of revealing that the information used in abstracts reflects the current understanding and key concepts of this widely explored problem. We compared expert data from DisGeNET to the results of an automated MeSH (Medical Subject Heading) search, which was performed by the ScanBious web tool. The analysis provided an overview of the obesity field, highlighting major trends such as physiological conditions, age, and diet, as well as key well-studied genes, such as adiponectin and its receptor. By intersecting the DisGeNET knowledge with the ScanBious results, we deciphered four clusters of obesity-related genes. An initial set of 100+ thousand abstracts and 622 genes was reduced to 19 genes, distributed among just a few groups: heredity, inflammation, intercellular signaling, and cancer. Rapid profiling of articles could drive personalized medicine: if the disease signs of a particular person were superimposed on a general network, then it would be possible to understand which are non-specific (observed in cohorts and, therefore, most likely have known treatment solutions) and which are less investigated, and probably represent a personalized case. Full article
(This article belongs to the Special Issue Computational Genomics in Disease and Wellness Genetics)
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