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BioTech
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The Emerging Role of Bioinformatics in Biotechnology
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The Emerging Role of Bioinformatics in Biotechnology

A special issue of BioTech (ISSN 2673-6284). This special issue belongs to the section "Computational Biology".

Deadline for manuscript submissions: 31 March 2027 | Viewed by 3093

Editor

Dr. Shirin Jamshidi

E-Mail Website
Guest Editor
Faculty of Life Sciences & Medicine, King's College London, London, UK
Interests: machine learning; data analysis; molecular dynamics simulations; computational drug design and development; molecular docking and virtual screening; protein structure; function and dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics has become a central driver of innovation in modern biotechnology, transforming how we discover, design and develop new therapies, diagnostics and biomaterials. This Special Issue, “The Emerging Role of Bioinformatics in Biotechnology,” aims to showcase cutting-edge computational approaches that bridge data and experiment, accelerating translation from molecules to mechanisms to products.

We invite contributions that highlight how bioinformatics unlocks value from complex biological data, including genomics, transcriptomics, proteomics, structural and imaging datasets. Relevant topics include, but are not limited to, the following: machine learning and AI for biomarker discovery and target identification; computational drug design and virtual screening; molecular dynamics simulations and protein structure–function analysis; integrative pipelines for multi-omics data; bioinformatics for gene and cell therapies; synthetic biology and rational design of biological systems; and data-driven bioprocess optimization.

Both methodological papers and application-focused studies are welcome, as well as reviews that synthesize emerging trends at the interface of computation and biotechnology. The overarching aim of this Special Issue is to illustrate how robust, transparent and scalable bioinformatics solutions can de-risk R&D, enable precision biotechnology, and open new frontiers in health, industry and beyond.

Dr. Shirin Jamshidi
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. BioTech is an international peer-reviewed open access semimonthly 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 1800 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

  • bioinformatics
  • biotechnology
  • multi-omics data analysis
  • machine learning
  • synthetic biology

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Published Papers (3 papers)

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Research

14 pages, 1292 KB  
Article
RNA-seq Co-Expression Analysis Reveals a Midgut-Associated Digestive Gene Module in Helicoverpa armigera
by Bairon J. Matabanchoy Pejendino, Vicente E. Mallama Cadena, María C. Díaz Rodríguez, Claudia Salazar Gonzalez and Pedro A. Velasquez-Vasconez
BioTech 2026, 15(3), 53; https://doi.org/10.3390/biotech15030053 - 13 Jul 2026
Viewed by 389
Abstract
Helicoverpa armigera is one of the most destructive polyphagous pests, yet the transcriptional organization underlying its digestive capacity remains poorly resolved. Here, we compiled 579 publicly available RNA-seq libraries representing 54 independent experiments and quantified transcript abundance across tissues and developmental stages. This [...] Read more.
Helicoverpa armigera is one of the most destructive polyphagous pests, yet the transcriptional organization underlying its digestive capacity remains poorly resolved. Here, we compiled 579 publicly available RNA-seq libraries representing 54 independent experiments and quantified transcript abundance across tissues and developmental stages. This complete dataset was used to support broader tissue-level expression profiling. After metadata harmonization and quality filtering, a subset of 130 biologically comparable libraries from five tissue/developmental categories was retained for weighted gene co-expression network analysis. WGCNA identified four biologically informative modules, among which the turquoise module was positively associated with fourth- and fifth-instar larval midgut samples. Independent expression profiling revealed strong midgut-biased expression of several trypsin- and chymotrypsin-like serine proteases, although only a subset of these genes was assigned to the turquoise module. Descriptive functional annotation of this module identified 202 co-expressed loci, including digestive enzymes, nutrient transporters, detoxification-related proteins, epithelial components and putative transcriptional or signaling-associated genes. Phylogenetic analyses and manual inspection of genomic locations further showed that several digestive protease genes occur in local clusters and have closely related counterparts in H. zea, suggesting partial conservation of local genomic organization. Collectively, these results describe a midgut-associated co-expression module containing genes associated with digestive, absorptive and protective functions and provide candidate genes for future functional studies. Full article
(This article belongs to the Special Issue The Emerging Role of Bioinformatics in Biotechnology)
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25 pages, 1130 KB  
Article
Bioinformatics Strategy for 16s and 23s rRNA Metabarcoding Data
by Rita Domingues and José C. M. Pires
BioTech 2026, 15(2), 42; https://doi.org/10.3390/biotech15020042 - 8 Jun 2026
Viewed by 893
Abstract
Understanding biological communities is essential for elucidating ecosystem structure and function. Metabarcoding based on ribosomal RNA (rRNA) genes, particularly 16S and 23S, is widely used to characterise bacterial and microalgal communities. However, analysing high-throughput sequencing data generated by platforms such as the Illumina [...] Read more.
Understanding biological communities is essential for elucidating ecosystem structure and function. Metabarcoding based on ribosomal RNA (rRNA) genes, particularly 16S and 23S, is widely used to characterise bacterial and microalgal communities. However, analysing high-throughput sequencing data generated by platforms such as the Illumina MiSeq remains challenging due to fragmented bioinformatics tools, complex parameterisation, and limited accessibility for non-specialist users. In this study, a comprehensive and user-friendly bioinformatics pipeline is proposed for the analysis of 16S and 23S paired-end metabarcoding data. The workflow integrates all critical processing steps, including read merging, primer and adapter trimming, quality filtering, dereplication, chimaera removal, and clustering into Operational Taxonomic Units (OTUs). Taxonomic assignment is performed using curated reference databases, namely EZBioCloud for bacterial communities and µgreen for microalgae. The pipeline was developed in Python 3.11 and incorporates validated tools such as VSEARCH and Cutadapt, ensuring robustness and computational efficiency. Additionally, modules for alpha and beta diversity analysis are included to support comprehensive ecological interpretation. The main novelty of this work lies in providing a unified, GUI-based framework that enables the standardised processing of dual-marker (16S/23S) metabarcoding data within a single environment. In its current implementation, SOMBA supports the analysis of each marker through separate but harmonised workflows, ensuring consistency in parameterisation, processing steps, and output structure. This approach provides an accessible and standardised solution that bridges the gap between raw sequencing data and reliable biological insights, supporting applications in environmental microbiology and biotechnology. Full article
(This article belongs to the Special Issue The Emerging Role of Bioinformatics in Biotechnology)
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24 pages, 3457 KB  
Article
Hypoxia and DNA-Repair Radiosensitivity Signatures Are Associated with Radiotherapy-Modified Survival in TCGA Breast Cancer, with External Prognostic Validation of the Hypoxia Score in METABRIC
by Jimmy Carter Osei, Mei-Han Chen and Tim A. D. Smith
BioTech 2026, 15(2), 28; https://doi.org/10.3390/biotech15020028 - 31 Mar 2026
Viewed by 1057
Abstract
Radiotherapy (RT) is one of the main treatments for breast cancer, but response varies between patients. Tumour hypoxia and intrinsic radiosensitivity are major determinants of response to RT. Using TCGA-BRCA, a 563-gene hypoxia meta-signature was built by combining curated hypoxia gene sets from [...] Read more.
Radiotherapy (RT) is one of the main treatments for breast cancer, but response varies between patients. Tumour hypoxia and intrinsic radiosensitivity are major determinants of response to RT. Using TCGA-BRCA, a 563-gene hypoxia meta-signature was built by combining curated hypoxia gene sets from MSigDB with published hypoxia metagenes (Buffa, Winter, Elvidge, Fardin, and related sets). After Cox screening and penalised regression, a simple three-gene hypoxia score (CP, GPC3, STC1) was derived. In parallel, based on DSB-repair factors highlighted by Mladenov et al. as key regulators of intrinsic radiosensitivity, a four-gene radiosensitivity (RS) signature (ATR, RPA2, BLM, MRE11A) was trained using only RT-treated patients. In TCGA, both signatures were prognostic and showed significant interaction with RT status in Cox models. The hypoxia score was strongly associated with worse outcomes in RT-untreated patients, but this effect was much weaker in RT-treated patients (Hypoxia × RT HR = 0.009, p = 0.044). The RS score showed a similarly strong interaction with RT (RS × RT HR = 0.011, p = 0.003). When we combined both signatures into one interaction model, it gave the best performance (C-index = 0.785), and both interaction terms stayed independently significant. The hypoxia score was then validated externally in METABRIC (N = 1979; 1143 events), where it remained associated with overall survival, although more weakly than in TCGA (HR = 1.34, 95% CI: 1.10–1.63; p = 0.0042). Overall, these results suggest that hypoxia and DSB-repair capacity capture two complementary sides of radiosensitivity and RT-modified survival patterns, and they support further prospective testing and validation in independent datasets with strong RT annotation. Full article
(This article belongs to the Special Issue The Emerging Role of Bioinformatics in Biotechnology)
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