Biological databases and their application

Sharma, Parva Kumar; Yadav, Inderjit Singh

doi:10.1016/b978-0-323-89775-4.00021-3

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…As the identification process progresses, each successfully matched database name and pertinent information are returned as a row in the result table, displayed beneath the search box and can be saved and outputted in various file formats. The original names retrieved from the databases are added with modifiers and shown in the same column as ‘name’ to distinguish between the identical entities within databases, enabling an ID query to identify all matched biological entities such as a gene, protein, or transcript ( 44 , 45 ).…”

Section: Methodsmentioning

confidence: 99%

MantaID: a machine learning–based tool to automate the identification of biological database IDs

Zeng

Cao

et al. 2023

Database

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The number of biological databases is growing rapidly, but different databases use different identifiers (IDs) to refer to the same biological entity. The inconsistency in IDs impedes the integration of various types of biological data. To resolve the problem, we developed MantaID, a data-driven, machine learning–based approach that automates identifying IDs on a large scale. The MantaID model’s prediction accuracy was proven to be 99%, and it correctly and effectively predicted 100,000 ID entries within 2 min. MantaID supports the discovery and exploitation of ID from large quantities of databases (e.g. up to 542 biological databases). An easy-to-use freely available open-source software R package, a user-friendly web application and application programming interfaces were also developed for MantaID to improve applicability. To our knowledge, MantaID is the first tool that enables an automatic, quick, accurate and comprehensive identification of large quantities of IDs and can therefore be used as a starting point to facilitate the complex assimilation and aggregation of biological data across diverse databases.

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Section: Methodsmentioning

confidence: 99%

MantaID: a machine learning–based tool to automate the identification of biological database IDs

Zeng

Cao

et al. 2023

Database

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“…As a consortium, the databases that make up InterPro use predictive models (called signatures) contributed by other databases to properly categorize proteins [47]. The advantage of InterPro is that it combines the protein fingerprints of its member databases into a single searchable resource, leveraging the best features of each database to create a comprehensive diagnostic and research tool [48,49]. The pleurocidin IPR012515 (Pfam08108) motif was present on 360 proteins and 2 domain architectures.…”

Section: Uniprot-reviewed Piscidinsmentioning

confidence: 99%

Teleost Piscidins—In Silico Perspective of Natural Peptide Antibiotics from Marine Sources

et al. 2023

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Fish, like all other animals, are exposed to constant contact with microbes, both on their skin and on the surfaces of their respiratory and digestive systems. Fish have a system of non-specific immune responses that provides them with initial protection against infection and allows them to survive under normal conditions despite the presence of these potential invaders. However, fish are less protected against invading diseases than other marine vertebrates because their epidermal surface, composed primarily of living cells, lacks the keratinized skin that serves as an efficient natural barrier in other marine vertebrates. Antimicrobial peptides (AMPs) are one type of innate immune protection present in all life forms. AMPs have been shown to have a broader range of biological effects than conventional antibiotics, including antibacterial, antiviral, antiprotozoal, and antifungal effects. Although other AMPs, such as defensins and hepcidins, are found in all vertebrates and are relatively well conserved, piscidins are found exclusively in Teleost fish and are not found in any other animal. Therefore, there is less information on the expression and bioactivity of piscidins than on other AMPs. Piscidins are highly effective against Gram-positive and Gram-negative bacteria that cause disease in fish and humans and have the potential to be used as pharmacological anti-infectives in biomedicine and aquaculture. To better understand the potential benefits and limitations of using these peptides as therapeutic agents, we are conducting a comprehensive study of the Teleost piscidins included in the “reviewed” category of the UniProt database using bioinformatics tools. They all have amphipathic alpha-helical structures. The amphipathic architecture of piscidin peptides and positively charged residues influence their antibacterial activity. These alpha-helices are intriguing antimicrobial drugs due to their stability in high-salt and metal environments. New treatments for multidrug-resistant bacteria, cancer, and inflammation may be inspired by piscidin peptides.

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“…Recently, there has been enormous progress in unraveling the complexities of naturally-occurring biological systems, which has provided abundant scientific information in the field of nucleic acid sequences and protein databases for agriculture, biomedical research, synthetic biology (biological and chemical engineering), and metabolic engineering including production of pharmaceuticals and nutraceuticals (Fig. 1) [2][3][4][5]. Such interdisciplinary approaches utilize computational and bioinformatics tools to manipulate microorganisms for getting a deeper understanding of the complex biological systems and have linked the multiple networks of fundamental biological discoveries [6,7].…”

Section: Introductionmentioning

confidence: 99%

Biological Databases and Resources: Their Engineering and Applications in Synthetic Biology

Sindhu¹,

Sindhu²

2023

IJASE

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Biological sciences pose a unique set of engineering challenges due to incomplete understanding of natural biological systems. Currently, sequencing of macromolecules i.e., DNA (deoxyribose nucleic acid) and proteins obtained from living cells has provided significant information, which is available in different database repositories. These databases comprising of genomic sequences and amino acid sequences (proteins) are utilized in genetic engineering of biological systems to increase the production of chemicals and pharmaceuticals for improving plant and animal health. Recently, synthetic biology approaches are being employed in rational and highthroughput biological engineering to enhance the production of beneficial chemicals. Recent molecular and bioinformatics tools have enabled to redesign the entire biological cycle, including construction of synthetic DNA inside the cell or replacement of entire genome to create synthetic organisms by utilizing gene libraries, computational tools and interfaces. This review describes the genomic, proteomic and phylogenetic databases, which may be utilized for designing and manipulation of synthetic gene circuits to perform novel functions and desired phenotypes in different ecosystems. In addition, synthetic biology approaches were discussed for designing biological systems for production and release of specific metabolic products. The progress and challenges faced in computational methodology and synthetic biology approaches are discussed for their potential applications in synthetic biology.

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Biological databases and their application

Cited by 4 publications

References 43 publications

MantaID: a machine learning–based tool to automate the identification of biological database IDs

MantaID: a machine learning–based tool to automate the identification of biological database IDs

Teleost Piscidins—In Silico Perspective of Natural Peptide Antibiotics from Marine Sources

Biological Databases and Resources: Their Engineering and Applications in Synthetic Biology

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