Applications of Bioinformatics in Plant and Agriculture

Iquebal, Mir Asif; Jaiswal, Sarika; Mukhopadhyay, Chandra Sekhar; Sarkar, Chiranjib; Rai, Anil; Kumar, Dinesh

doi:10.1007/978-81-322-2172-2_27

Cited by 9 publications

(4 citation statements)

References 33 publications

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“…Techniques such as Support Vector Machines (SVMs), random forest, hidden Markov models (HMM), neural networks, and graphical models can be successfully applied to biological data because of their capabilities in handling randomness and the uncertainty of data noise, as well as their skill in generalization [215].…”

Section: How Can Machine Learning and Deep Learning Techniques Impmentioning

confidence: 99%

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Orozco-Arias

Isaza

Guyot

2019

IJMS

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Transposable elements (TEs) are genomic units able to move within the genome of virtually all organisms. Due to their natural repetitive numbers and their high structural diversity, the identification and classification of TEs remain a challenge in sequenced genomes. Although TEs were initially regarded as “junk DNA”, it has been demonstrated that they play key roles in chromosome structures, gene expression, and regulation, as well as adaptation and evolution. A highly reliable annotation of these elements is, therefore, crucial to better understand genome functions and their evolution. To date, much bioinformatics software has been developed to address TE detection and classification processes, but many problematic aspects remain, such as the reliability, precision, and speed of the analyses. Machine learning and deep learning are algorithms that can make automatic predictions and decisions in a wide variety of scientific applications. They have been tested in bioinformatics and, more specifically for TEs, classification with encouraging results. In this review, we will discuss important aspects of TEs, such as their structure, importance in the evolution and architecture of the host, and their current classifications and nomenclatures. We will also address current methods and their limitations in identifying and classifying TEs.

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Section: How Can Machine Learning and Deep Learning Techniques Impmentioning

confidence: 99%

Retrotransposons in Plant Genomes: Structure, Identification, and Classification through Bioinformatics and Machine Learning

Orozco-Arias

Isaza

Guyot

2019

IJMS

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“…However, although SE is a tissue culture tool widely used and studied for several decades, there are still many questions regarding its regulation, which would greatly help to manipulate and optimize the process and even to understand zygotic embryogenesis. New technologies allow the carrying out of studies at the genomic, transcriptomic, proteomic, and metabolomic levels and thus deepen research topics aimed at improving agriculture, the environment, human health, and biotechnology, among others [24][25][26]. Nowadays, many studies use transcriptomics to answer various biological questions.…”

Section: Discussionmentioning

confidence: 99%

Proteomic Approach during the Induction of Somatic Embryogenesis in Coffea canephora

Quintana-Escobar,

Bojórquez-Velázquez,

Ruiz-May

et al. 2023

Plants

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Plant growth regulators (PGR) are essential for somatic embryogenesis (SE) in different species, and Coffea canephora is no exception. In our study model, previously, we have been able to elucidate the participation of various genes involved in SE by using different strategies; however, until now, we have not used a proteomic approach. This research seeks to contribute to understanding the primary cellular pathways involved in developing SE in C. canephora. The process of our model consists of two stages: (1) preconditioning in MS medium with auxin (NAA) and cytokinin (KIN), and (2) induction in Yasuda liquid medium added with cytokinin (BA). Therefore, in this study, we analyzed different days of the SE induction process using shotgun label-free proteomics. An amount of 1630 proteins was found among different sampling days of the process, of which the majority were accumulated during the induction stage. We found that some of the most enriched pathways during this process were the biosynthesis of amino acids and secondary metabolites. Eighteen proteins were found related to auxin homeostasis and two to cytokinin metabolism, such as ABC, BIG, ILR, LOG, and ARR. Ten proteins and transcription factors related to SE were also identified, like SERK1, SKP1, nuclear transcription factor Y, MADS-box, and calreticulin, and 19 related to other processes of plant development, among which the 14-3-3 and PP2A proteins stand out. This is the first report on the proteomic approach to elucidate the mechanisms that operate during the induction of SE in C. canephora. So, our findings provide the groundwork for future, more in-depth research. Data are available via ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD047172.

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“…Definitely, given its limitations in term of time and costs, conventional agriculture is unlikely to handle this situation giving thereby a way to modern alternatives. With the adoption of advanced DNA-technologies and the onset of omic-based approaches (such as genomics, transcriptomics, proteomics and metabolomics), exponential influx of diverse biological data has required a new discipline for data management and interpretation (Iquebal et al 2015). Bioinformatics has emerged as a powerful computer-assisted science for digital data storage, modeling and analysis (Esposito et al 2016).…”

Section: Bioinformatics Tools and Resources For Peachmentioning

confidence: 99%