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Application of Bioinformatics in Plants and Animals
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Application of Bioinformatics in Plants and Animals

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 5572

Special Issue Editors

Prof. Dr. Atmakuri Ramakrishna Rao

E-Mail Website
Guest Editor
Indian Council of Agricultural Research, New Delhi 110001, India
Interests: bioinformatics; statistical genetics; plant and animal genomics; computational algorithms; multivariate analysis
Dr. Tanmaya Kumar Sahu

E-Mail Website
Guest Editor
Division of Crop Improvement, ICAR—Indian Grassland and Fodder Research Institute, Jhansi 284003, India
Interests: bioinformatics; plant genomics; animal genomics; computational algorithms; protein modeling and dynamics; bioprogramming

Special Issue Information

Dear Colleagues,

With the advent of high-throughput sequencing and phenotyping technologies, voluminous genomic and phenotypic data have been generated from plant, animal, insect, fish, and microbial experiments. Knowledge mining from such huge amounts of data and its further utilization in crop and animal improvement programs has remained a major challenge. Bioinformatics applications play an important role in bridging the gap between data generation and data analysis and, to a certain extent, mining knowledge with the help of statistics, computer science, and data analytics. During the last two decades, bioinformatics applications have contributed significantly to analyzing plant and animal -omics data to generate and utilize meaningful information for trait improvement in crops and animals. Thus, this Special Issue on “Application of Bioinformatics in Plants and Animals” aims to (i) provide potential research findings from the application of bioinformatics in plant and animal -omics for trait improvement, (ii) establish a link between computational and biological researchers for sharing their pool of knowledge, and (iii) enlist novel tools and techniques available in public domain for genomic and phenotypic data analyses. This Special Issue will cover a wide range of issues from interdisciplinary domains and provide insights into cutting-edge research topics such as application of artificial intelligence, high-dimensional genome data analysis, computational algorithms, and big data analytics for trait improvement. Solicited in this issue are original research papers on novel methods and algorithms for analysis of sequencing and phenotypic data, novel application of tools and techniques for unraveling complex trait phenomena, and prediction servers and genomic databases of crop, animal, insect, fish, and microbial species. The Special Issue also invites critical reviews on methods and techniques for phenomics data analysis. The main thematic areas for this issue are plant and animal genomics; insect, fish. and microbial genomics; transcriptomics; metagenomics; genome selection; molecular modeling and dynamics; in silico aspects of animal disease therapeutics; learning algorithms for sequencing data analysis; and genomic databases pertaining to important crop, animal, insect, fish, and microbial species.

Prof. Dr. Atmakuri Ramakrishna Rao
Dr. Tanmaya Kumar Sahu
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. Genes 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

  • plant genomics
  • animal genomics
  • computational algorithms
  • genomic databases
  • candidate gene identification
  • computer-aided vaccine designing
  • metagenomics
  • big data analytics and AI applications

Published Papers (4 papers)

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Research

17 pages, 5534 KiB  
Article
Genome Assembly and Microsatellite Marker Development Using Illumina and PacBio Sequencing in the Carex pumila (Cyperaceae) from Korea
by Kang-Rae Kim, Jeong-Nam Yu, Jeong Min Hong, Sun-Yu Kim and So Young Park
Genes 2023, 14(11), 2063; https://doi.org/10.3390/genes14112063 - 10 Nov 2023
Cited by 1 | Viewed by 1007
Abstract
This study is the first to report the characterization of Carex pumila genomic information. Assembly of the genome generated a draft of C. pumila based on PacBio Sequel II and Illumina paired-end sequencing, which was assembled from 2941 contigs with an estimated genome [...] Read more.
This study is the first to report the characterization of Carex pumila genomic information. Assembly of the genome generated a draft of C. pumila based on PacBio Sequel II and Illumina paired-end sequencing, which was assembled from 2941 contigs with an estimated genome size of 0.346 Gb. The estimate of repeats in the genome was 31.0%, and heterozygosity ranged from 0.426 to 0.441%. The integrity evaluation of the assembly revealed 1481 complete benchmarked universal single-copy orthologs (BUSCO) (91.76%), indicating the high quality of the draft assembly. A total of 23,402 protein-coding genes were successfully predicted and annotated in the protein database. UpsetR plots showed that 7481 orthogroups were shared by all species. The phylogenetic tree showed that C. pumila is a close but distant relative of Ananas comosus. C. pumila had greater contraction (3154) than expansion (392). Among the extended gene families, aquaporins have been found to be enriched. Primers for microsatellite markers determined 30 polymorphic markers out of 100. The average number of alleles amplified by these 30 polymorphic markers was 4 to 12, with an average polymorphism information content (PIC) value of 0.660. In conclusion, our study provides a useful resource for comparative genomics, phylogeny, and future population studies of C. pumila. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Plants and Animals)
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15 pages, 2855 KiB  
Article
SAMBA: Structure-Learning of Aquaculture Microbiomes Using a Bayesian Approach
by Beatriz Soriano, Ahmed Ibrahem Hafez, Fernando Naya-Català, Federico Moroni, Roxana Andreea Moldovan, Socorro Toxqui-Rodríguez, María Carla Piazzon, Vicente Arnau, Carlos Llorens and Jaume Pérez-Sánchez
Genes 2023, 14(8), 1650; https://doi.org/10.3390/genes14081650 - 19 Aug 2023
Viewed by 1561
Abstract
Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these [...] Read more.
Gut microbiomes of fish species consist of thousands of bacterial taxa that interact among each other, their environment, and the host. These complex networks of interactions are regulated by a diverse range of factors, yet little is known about the hierarchy of these interactions. Here, we introduce SAMBA (Structure-Learning of Aquaculture Microbiomes using a Bayesian Approach), a computational tool that uses a unified Bayesian network approach to model the network structure of fish gut microbiomes and their interactions with biotic and abiotic variables associated with typical aquaculture systems. SAMBA accepts input data on microbial abundance from 16S rRNA amplicons as well as continuous and categorical information from distinct farming conditions. From this, SAMBA can create and train a network model scenario that can be used to (i) infer information of how specific farming conditions influence the diversity of the gut microbiome or pan-microbiome, and (ii) predict how the diversity and functional profile of that microbiome would change under other variable conditions. SAMBA also allows the user to visualize, manage, edit, and export the acyclic graph of the modelled network. Our study presents examples and test results of Bayesian network scenarios created by SAMBA using data from a microbial synthetic community, and the pan-microbiome of gilthead sea bream (Sparus aurata) in different feeding trials. It is worth noting that the usage of SAMBA is not limited to aquaculture systems as it can be used for modelling microbiome–host network relationships of any vertebrate organism, including humans, in any system and/or ecosystem. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Plants and Animals)
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12 pages, 5545 KiB  
Article
Cloning, Identification, and Functional Analysis of the Chalcone Isomerase Gene from Astragalus sinicus
by Xian Zhang, Jing Xu, Linlin Si, Kai Cao, Yuge Wang, Hua Li and Jianhong Wang
Genes 2023, 14(7), 1400; https://doi.org/10.3390/genes14071400 - 05 Jul 2023
Viewed by 978
Abstract
Astragalus sinicus is an important winter-growing cover crop. It is widely utilized, not only as a cover crop for its benefits in fertilizing the soil but also as a landscape ground cover plant. Anthocyanins are involved in the pigmentation of plants in leaves [...] Read more.
Astragalus sinicus is an important winter-growing cover crop. It is widely utilized, not only as a cover crop for its benefits in fertilizing the soil but also as a landscape ground cover plant. Anthocyanins are involved in the pigmentation of plants in leaves and flowers, which is a crucial characteristic trait for A. sinicus. The formation of anthocyanins depends significantly on the enzyme chalcone isomerase (CHI). However, research on the CHI gene of A. sinicus remains unexplored. The rapid amplification of cDNA ends (RACE) approach was used in this research to clone the CHI sequence from A. sinicus (AsiCHI). The expression profiles of the AsiCHI gene in multiple tissues of A. sinicus were subsequently examined by qRT-PCR (Quantitative Real-Time PCR). Furthermore, the function of the AsiCHI was identified by the performance of ectopic expression in Arabidopsis (Arabidopsis thaliana). The outcomes revealed that the full-length cDNA of the AsiCHI gene (GeneBank: OQ870547) measured 972 bp in length and included an open reading frame of 660 bp. The encoded protein contains 219 amino acids with a molecular weight of 24.14 kDa and a theoretical isoelectric point of 5.11. In addition, the remarkable similarity between the AsiCHI protein and the CHI proteins of other Astragalus species was demonstrated by the sequence alignment and phylogenetic analysis. Moreover, the highest expression level of AsiCHI was observed in leaves and showed a positive correlation with anthocyanin content. The functional analysis further revealed that the overexpression of AsiCHI enhanced the anthocyanidin accumulation in the transgenic lines. This study provided a better understanding of AsiCHI and elucidated its role in anthocyanin production. Full article
(This article belongs to the Special Issue Application of Bioinformatics in Plants and Animals)
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20 pages, 3229 KiB  
Article
An Improved Machine Learning-Based Approach to Assess the Microbial Diversity in Major North Indian River Ecosystems
by Nalinikanta Choudhury, Tanmaya Kumar Sahu, Atmakuri Ramakrishna Rao, Ajaya Kumar Rout and Bijay Kumar Behera
Genes 2023, 14(5), 1082; https://doi.org/10.3390/genes14051082 - 14 May 2023
Cited by 4 | Viewed by 1450
Abstract
The rapidly evolving high-throughput sequencing (HTS) technologies generate voluminous genomic and metagenomic sequences, which can help classify the microbial communities with high accuracy in many ecosystems. Conventionally, the rule-based binning techniques are used to classify the contigs or scaffolds based on either sequence [...] Read more.
The rapidly evolving high-throughput sequencing (HTS) technologies generate voluminous genomic and metagenomic sequences, which can help classify the microbial communities with high accuracy in many ecosystems. Conventionally, the rule-based binning techniques are used to classify the contigs or scaffolds based on either sequence composition or sequence similarity. However, the accurate classification of the microbial communities remains a major challenge due to massive data volumes at hand as well as a requirement of efficient binning methods and classification algorithms. Therefore, we attempted here to implement iterative K-Means clustering for the initial binning of metagenomics sequences and applied various machine learning algorithms (MLAs) to classify the newly identified unknown microbes. The cluster annotation was achieved through the BLAST program of NCBI, which resulted in the grouping of assembled scaffolds into five classes, i.e., bacteria, archaea, eukaryota, viruses and others. The annotated cluster sequences were used to train machine learning algorithms (MLAs) to develop prediction models to classify unknown metagenomic sequences. In this study, we used metagenomic datasets of samples collected from the Ganga (Kanpur and Farakka) and the Yamuna (Delhi) rivers in India for clustering and training the MLA models. Further, the performance of MLAs was evaluated by 10-fold cross validation. The results revealed that the developed model based on the Random Forest had a superior performance compared to the other considered learning algorithms. The proposed method can be used for annotating the metagenomic scaffolds/contigs being complementary to existing methods of metagenomic data analysis. An offline predictor source code with the best prediction model is available at (https://github.com/Nalinikanta7/metagenomics). Full article
(This article belongs to the Special Issue Application of Bioinformatics in Plants and Animals)
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