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Life
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Life: Computational Genomics, Volume II
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Life: Computational Genomics, Volume II

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Genetics and Genomics".

Deadline for manuscript submissions: closed (27 January 2023) | Viewed by 5286

Special Issue Editors

Prof. Dr. Yuriy L. Orlov

E-Mail Website
Guest Editor
The Digital Health Institute, I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
Interests: computer genomics; bioinformatics; digital medicine (e-Health); gene expression regulation; ChIP-seq
Special Issues, Collections and Topics in MDPI journals
Dr. Anastasia A. Anashkina

E-Mail Website
Guest Editor
Engelhardt Institute of Molecular Biology RAS, Moscow, Russia
Interests: structural bioinformatics; biophysics; bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is a continuation of a previous issue on systems biology applications, computational genomics and bioinformatics methods in life sciences titled “Life: Computational Genomics” (https://www.mdpi.com/journal/life/special_issues/computational_genomics_life).

Based on the readers’ interest in computational genomics and systems biology, we have decided to launch a second edition focusing on this topical issue, highlighting novel technological approaches in sequencing, gene networks reconstruction, and proteomics. Here, we focus on bioinformatics and systems biology approaches in model organisms and the basics of gene expression regulation.

The topics of the Special Issue include:

  • Bioinformatics approaches for life sciences;
  • Computational genomics in model organisms for biotechnology;
  • Applications of genomics research in agrobiology;
  • Systems biology;
  • Interdisciplinary research in computational genomics.

The current collection continues the series of post-conference Special Issues, presenting the highlights from the meetings on genetics and systems biology that were held in Russia at the BGRS\SB-2022 (Bioinformatics of Genome Regulation and Structure \ Systems Biology)-2022 https://bgrssb.icgbio.ru/2022/ and the anniversary conference of the Yu. P. Altukhov Laboratory of Population Genetics of the N. I. Vavilov Institute of General Genetics of the Russian Academy of Sciences: https://confpopgen.confreg.org/

We welcome novel materials beyond those discussed at the conference.

Prof. Dr. Yuriy L. Orlov
Dr. Anastasia A. Anashkina
Guest Editors

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. Life 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.

Published Papers (3 papers)

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Research

16 pages, 4674 KiB  
Article
Identification of Specific Biomarkers and Pathways in the Treatment Response of Infliximab for Inflammatory Bowel Disease: In-Silico Analysis
by Rachid Kaddoura, Hardik Ghelani, Fatma Alqutami, Hala Altaher, Mahmood Hachim and Reem Kais Jan
Life 2023, 13(3), 680; https://doi.org/10.3390/life13030680 - 02 Mar 2023
Cited by 5 | Viewed by 1581
Abstract
Background: Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract. In biological therapy, infliximab became the first anti-tumor necrosis factor (TNF) agent approved for IBD. Despite this success, infliximab is expensive, often ineffective, and associated with adverse events. Prediction [...] Read more.
Background: Inflammatory bowel disease (IBD) is characterized by chronic inflammation of the gastrointestinal tract. In biological therapy, infliximab became the first anti-tumor necrosis factor (TNF) agent approved for IBD. Despite this success, infliximab is expensive, often ineffective, and associated with adverse events. Prediction of infliximab resistance would improve overall potential outcomes. Therefore, there is a pressing need to widen the scope of investigating the role of genetics in IBD to their association with therapy response. Methods: In the current study, an in-silico analysis of publicly available IBD patient transcriptomics datasets from Gene Expression Omnibus (GEO) are used to identify subsets of differentially expressed genes (DEGs) involved in the pathogenesis of IBD and may serve as potential biomarkers for Infliximab response. Five datasets were found that met the inclusion criteria. The DEGs for datasets were identified using limma R packages through the GEOR2 tool. The probes’ annotated genes in each dataset intersected with DGEs from all other datasets. Enriched gene Ontology Clustering for the identified genes was performed using Metascape to explore the possible connections or interactions between the genes. Results: 174 DEGs between IBD and healthy controls were found from analyzing two datasets (GSE14580 and GSE73661), indicating a possible role in the pathogenesis of IBD. Of the 174 DEGs, five genes (SELE, TREM1, AQP9, FPR2, and HCAR3) were shared between all five datasets. Moreover, these five genes were identified as downregulated in the infliximab responder group compared to the non-responder group. Conclusions: We hypothesize that alteration in the expression of these genes leads to an impaired response to infliximab in IBD patients. Thus, these genes can serve as potential biomarkers for the early detection of compromised infliximab response in IBD patients. Full article
(This article belongs to the Special Issue Life: Computational Genomics, Volume II)
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15 pages, 760 KiB  
Article
COOBoostR: An Extreme Gradient Boosting-Based Tool for Robust Tissue or Cell-of-Origin Prediction of Tumors
by Sungmin Yang, Kyungsik Ha, Woojeung Song, Masashi Fujita, Kirsten Kübler, Paz Polak, Eiso Hiyama, Hidewaki Nakagawa, Hong-Gee Kim and Hwajin Lee
Life 2023, 13(1), 71; https://doi.org/10.3390/life13010071 - 27 Dec 2022
Cited by 1 | Viewed by 1675
Abstract
We present here COOBoostR, a computational method designed for the putative prediction of the tissue- or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR [...] Read more.
We present here COOBoostR, a computational method designed for the putative prediction of the tissue- or cell-of-origin of various cancer types. COOBoostR leverages regional somatic mutation density information and chromatin mark features to be applied to an extreme gradient boosting-based machine-learning algorithm. COOBoostR ranks chromatin marks from various tissue and cell types, which best explain the somatic mutation density landscape of any sample of interest. A specific tissue or cell type matching the chromatin mark feature with highest explanatory power is designated as a potential tissue- or cell-of-origin. Through integrating either ChIP-seq based chromatin data, along with regional somatic mutation density data derived from normal cells/tissue, precancerous lesions, and cancer types, we show that COOBoostR outperforms existing random forest-based methods in prediction speed, with comparable or better tissue or cell-of-origin prediction performance (prediction accuracy—normal cells/tissue: 76.99%, precancerous lesions: 95.65%, cancer cells: 89.39%). In addition, our results suggest a dynamic somatic mutation accumulation at the normal tissue or cell stage which could be intertwined with the changes in open chromatin marks and enhancer sites. These results further represent chromatin marks shaping the somatic mutation landscape at the early stage of mutation accumulation, possibly even before the initiation of precancerous lesions or neoplasia. Full article
(This article belongs to the Special Issue Life: Computational Genomics, Volume II)
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13 pages, 4340 KiB  
Article
Role of ZNF143 and Its Association with Gene Expression Patterns, Noncoding Mutations, and the Immune System in Human Breast Cancer
by Salma Saddeek, Rehab Almassabi and Mohammad Mobashir
Life 2023, 13(1), 27; https://doi.org/10.3390/life13010027 - 22 Dec 2022
Cited by 1 | Viewed by 1504
Abstract
The function of noncoding sequence variations at ZNF143 binding sites in breast cancer cells is currently not well understood. Distal elements and promoters, also known as cis-regulatory elements, control the expression of genes. They may be identified by functional genomic techniques and sequence [...] Read more.
The function of noncoding sequence variations at ZNF143 binding sites in breast cancer cells is currently not well understood. Distal elements and promoters, also known as cis-regulatory elements, control the expression of genes. They may be identified by functional genomic techniques and sequence conservation, and they frequently show cell- and tissue-type specificity. The creation, destruction, or modulation of TF binding and function may be influenced by genetic modifications at TF binding sites that affect the binding affinity. Therefore, noncoding mutations that affect the ZNF143 binding site may be able to alter the expression of some genes in breast cancer. In order to understand the relationship among ZNF143, gene expression patterns, and noncoding mutations, we adopted an integrative strategy in this study and paid close attention to putative immunological signaling pathways. The immune system-related pathways ErbB, HIF1a, NF-kB, FoxO, JAK-STAT, Wnt, Notch, cell cycle, PI3K–AKT, RAP1, calcium signaling, cell junctions and adhesion, actin cytoskeleton regulation, and cancer pathways are among those that may be significant, according to the overall analysis. Full article
(This article belongs to the Special Issue Life: Computational Genomics, Volume II)
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