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BioTech
Special Issues
Bioinformatics: Present and Future Biotechnology
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Bioinformatics: Present and Future Biotechnology

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

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 14329

Special Issue Editors

Dr. Pietro Pinoli

E-Mail Website
Guest Editor
1. Department of Information, Electronic and Bioengineering, Politecnico di Milano, Milan, Italy
2. Stanford University School of Medicine, Stanford, CA, USA
Interests: bioinformatics; computational biology; applied machine learning; data science; biomedicine
Special Issues, Collections and Topics in MDPI journals
Dr. Anna Bernasconi

E-Mail Website
Guest Editor
Department of Information, Electronic and Bioengineering, Politecnico di Milano, Milan, Italy
Interests: bioinformatics; data modeling; data integration; knowledge management; vi-ral genomics

Special Issue Information

Dear Colleagues,

Bioinformatics, and in broader terms, computer science, has provided a significant speedup in many fields of life sciences thanks to its intrinsic capability of gleaning insights from almost any kind of structured, semi-structured, and unstructured data. As wet-lab technologies evolve, new challenges arise for the bioinformatics community. These include processing and analyzing NGS and single-cell data, drug discovery and repurposing, multi-omics data management and querying, and genomic data integration with clinical information (paving the way for personalized medicine). Consequently, bioinformatics outputs are becoming increasingly crucial for biologists, physicians, and biomedical researchers. So far, every significant advance in genomics has been paired with the development of groundbreaking statistical and computational approaches. We expect this trend to continue in the coming years.

In this Special Issue, we welcome contributions in terms of original research articles, reviews, and opinions, concerning a large spectrum of present and future challenges for bioinformatics. Relevant topics include, but are not limited to:

  • Processing and integration of single-cell data, single-cell omics, single-cell imaging;
  • Machine learning and deep learning approaches for patient stratification and personalized medicine;
  • Big-data approaches to bioinformatics;
  • High-performance computing for bioinformatics;
  • Drug discovery and repurposig methods;
  • Pathway and network analysis;
  • Analysis of mutational signatures in cancer patients;
  • Application of explainable AI approaches to life science problems;
  • Biobanking: modeling, integration, and querying of multi-omics data;
  • Ontology and controlled vocabularies supporting bioinformatics workflows;
  • Data science workflows for bioinformatics datasets;
  • Algorithms and statistical testing for bioinformatics problems;
  • HCI advances to facilitate the adoption of data science and bioinformatics by clinicians and biologists;
  • Computational methods for monitoring and mitigating epidemics (pathogen bioinformatics);
  • Methods for bio-data visualization.

Dr. Pietro Pinoli
Dr. Anna Bernasconi
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. BioTech is an international peer-reviewed open access quarterly 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 1600 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

  • bionformatics
  • computational biology
  • biobanks
  • machine learning
  • artificial intelligence

Published Papers (5 papers)

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Research

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14 pages, 1653 KiB  
Article
Novel CaLB-like Lipase Found Using ProspectBIO, a Software for Genome-Based Bioprospection
by Gabriela C. Brêda, Priscila E. Faria, Yuri S. Rodrigues, Priscila B. Pinheiro, Maria Clara R. Nucci, Pau Ferrer, Denise M. G. Freire, Rodrigo V. Almeida and Rafael D. Mesquita
BioTech 2023, 12(1), 6; https://doi.org/10.3390/biotech12010006 - 06 Jan 2023
Viewed by 2286
Abstract
Enzymes have been highly demanded in diverse applications such as in the food, pharmaceutical, and industrial fuel sectors. Thus, in silico bioprospecting emerges as an efficient strategy for discovering new enzyme candidates. A new program called ProspectBIO was developed for this purpose as [...] Read more.
Enzymes have been highly demanded in diverse applications such as in the food, pharmaceutical, and industrial fuel sectors. Thus, in silico bioprospecting emerges as an efficient strategy for discovering new enzyme candidates. A new program called ProspectBIO was developed for this purpose as it can find non-annotated sequences by searching for homologs of a model enzyme directly in genomes. Here we describe the ProspectBIO software methodology and the experimental validation by prospecting for novel lipases by sequence homology to Candida antarctica lipase B (CaLB) and conserved motifs. As expected, we observed that the new bioprospecting software could find more sequences (1672) than a conventional similarity-based search in a protein database (733). Additionally, the absence of patent protection was introduced as a criterion resulting in the final selection of a putative lipase-encoding gene from Ustilago hordei (UhL). Expression of UhL in Pichia pastoris resulted in the production of an enzyme with activity towards a tributyrin substrate. The recombinant enzyme activity levels were 4-fold improved when lowering the temperature and increasing methanol concentrations during the induction phase in shake-flask cultures. Protein sequence alignment and structural modeling showed that the recombinant enzyme has high similarity and capability of adjustment to the structure of CaLB. However, amino acid substitutions identified in the active pocket entrance may be responsible for the differences in the substrate specificities of the two enzymes. Thus, the ProspectBIO software allowed the finding of a new promising lipase for biotechnological application without the need for laborious and expensive conventional bioprospecting experimental steps. Full article
(This article belongs to the Special Issue Bioinformatics: Present and Future Biotechnology)
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25 pages, 1186 KiB  
Article
Bio-Strings: A Relational Database Data-Type for Dealing with Large Biosequences
by Sergio Lifschitz, Edward H. Haeusler, Marcos Catanho, Antonio B. de Miranda, Elvismary Molina de Armas, Alexandre Heine, Sergio G. M. P. Moreira and Cristian Tristão
BioTech 2022, 11(3), 31; https://doi.org/10.3390/biotech11030031 - 30 Jul 2022
Cited by 7 | Viewed by 2394
Abstract
DNA sequencers output a large set of very long biological data strings that we should persist in databases rather than basic text file systems. Many different data models and database management systems (DBMS) may deal with both storage and efficiency issues regarding genomic [...] Read more.
DNA sequencers output a large set of very long biological data strings that we should persist in databases rather than basic text file systems. Many different data models and database management systems (DBMS) may deal with both storage and efficiency issues regarding genomic datasets. Specifically, there is a need for handling strings with variable sizes while keeping their biological meaning. Relational database management systems (RDBMS) provide several data types that could be further explored for the genomics context. Besides, they enforce integrity, consistency, and enable good abstractions for more conventional data. We propose the relational text data type to represent and manipulate biological sequences and their derivatives. We present a logical schema for representing the core biological information, which may be inferred from a given biological conceptual data schema and the corresponding function manipulations. We implement and evaluate these stored functions into an actual RDBMS for both efficacy and efficiency. We show that it is possible to enforce basic and complex requirements for the genomic domain. We claim that the well-established relational text data type in RDBMS may appropriately handle the representation and persistency of biological sequences. We base our approach on the idea of domain-specific abstract data types that can store data with semantically defined functions while hiding those details from non-technical end-users. Full article
(This article belongs to the Special Issue Bioinformatics: Present and Future Biotechnology)
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21 pages, 356 KiB  
Article
Challenges and Limitations of Biological Network Analysis
by Marianna Milano, Giuseppe Agapito and Mario Cannataro
BioTech 2022, 11(3), 24; https://doi.org/10.3390/biotech11030024 - 07 Jul 2022
Cited by 9 | Viewed by 2967
Abstract
High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other [...] Read more.
High-Throughput technologies are producing an increasing volume of data that needs large amounts of data storage, effective data models and efficient, possibly parallel analysis algorithms. Pathway and interactomics data are represented as graphs and add a new dimension of analysis, allowing, among other features, graph-based comparison of organisms’ properties. For instance, in biological pathway representation, the nodes can represent proteins, RNA and fat molecules, while the edges represent the interaction between molecules. Otherwise, biological networks such as Protein–Protein Interaction (PPI) Networks, represent the biochemical interactions among proteins by using nodes that model the proteins from a given organism, and edges that model the protein–protein interactions, whereas pathway networks enable the representation of biochemical-reaction cascades that happen within the cells or tissues. In this paper, we discuss the main models for standard representation of pathways and PPI networks, the data models for the representation and exchange of pathway and protein interaction data, the main databases in which they are stored and the alignment algorithms for the comparison of pathways and PPI networks of different organisms. Finally, we discuss the challenges and the limitations of pathways and PPI network representation and analysis. We have identified that network alignment presents a lot of open problems worthy of further investigation, especially concerning pathway alignment. Full article
(This article belongs to the Special Issue Bioinformatics: Present and Future Biotechnology)

Review

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14 pages, 1463 KiB  
Review
Cardiovascular Diseases in the Digital Health Era: A Translational Approach from the Lab to the Clinic
by Ana María Sánchez de la Nava, Lidia Gómez-Cid, Gonzalo Ricardo Ríos-Muñoz, María Eugenia Fernández-Santos, Ana I. Fernández, Ángel Arenal, Ricardo Sanz-Ruiz, Lilian Grigorian-Shamagian, Felipe Atienza and Francisco Fernández-Avilés
BioTech 2022, 11(3), 23; https://doi.org/10.3390/biotech11030023 - 30 Jun 2022
Viewed by 2674
Abstract
Translational science has been introduced as the nexus among the scientific and the clinical field, which allows researchers to provide and demonstrate that the evidence-based research can connect the gaps present between basic and clinical levels. This type of research has played a [...] Read more.
Translational science has been introduced as the nexus among the scientific and the clinical field, which allows researchers to provide and demonstrate that the evidence-based research can connect the gaps present between basic and clinical levels. This type of research has played a major role in the field of cardiovascular diseases, where the main objective has been to identify and transfer potential treatments identified at preclinical stages into clinical practice. This transfer has been enhanced by the intromission of digital health solutions into both basic research and clinical scenarios. This review aimed to identify and summarize the most important translational advances in the last years in the cardiovascular field together with the potential challenges that still remain in basic research, clinical scenarios, and regulatory agencies. Full article
(This article belongs to the Special Issue Bioinformatics: Present and Future Biotechnology)
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Other

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10 pages, 285 KiB  
Commentary
Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups
by Ricardo J. Pais
BioTech 2022, 11(3), 35; https://doi.org/10.3390/biotech11030035 - 17 Aug 2022
Cited by 3 | Viewed by 2700
Abstract
Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and [...] Read more.
Clinical bioinformatics is a newly emerging field that applies bioinformatics techniques for facilitating the identification of diseases, discovery of biomarkers, and therapy decision. Mathematical modelling is part of bioinformatics analysis pipelines and a fundamental step to extract clinical insights from genomes, transcriptomes and proteomes of patients. Often, the chosen modelling techniques relies on either statistical, machine learning or deterministic approaches. Research that combines bioinformatics with modelling techniques have been generating innovative biomedical technology, algorithms and models with biotech applications, attracting private investment to develop new business; however, startups that emerge from these technologies have been facing difficulties to implement clinical bioinformatics pipelines, protect their technology and generate profit. In this commentary, we discuss the main concepts that startups should know for enabling a successful application of predictive modelling in clinical bioinformatics. Here we will focus on key modelling concepts, provide some successful examples and briefly discuss the modelling framework choice. We also highlight some aspects to be taken into account for a successful implementation of cost-effective bioinformatics from a business perspective. Full article
(This article belongs to the Special Issue Bioinformatics: Present and Future Biotechnology)
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