Transposable elements (TEs) are indispensable components of eukaryotic genomes that play diverse roles in gene regulation, recombination, and environmental adaptation. Their ability to mobilize within the genome leads to gene expression and DNA structure changes. TEs serve as valuable markers for genetic and evolutionary studies and facilitate genetic mapping and phylogenetic analysis. They also provide insight into how organisms adapt to a changing environment by promoting gene rearrangements that lead to new gene combinations. These repetitive sequences significantly impact genome structure, function and evolution. This review takes a comprehensive look at TEs and their applications in biotechnology, particularly in the context of plant biology, where they are now considered “genomic gold” due to their extensive functionalities. The article addresses various aspects of TEs in plant development, including their structure, epigenetic regulation, evolutionary patterns, and their use in gene editing and plant molecular markers. The goal is to systematically understand TEs and shed light on their diverse roles in plant biology.

Accurate gene prediction in metagenomics fragments is a computationally challenging task due to the short-read length, incomplete, and fragmented nature of the data. Most gene-prediction programs are based on extracting a large number of features and then applying statistical approaches or supervised classification approaches to predict genes. In our study, we introduce a convolutional neural network for metagenomics gene prediction (CNN-MGP) program that predicts genes in metagenomics fragments directly from raw DNA sequences, without the need for manual feature extraction and feature selection stages. CNN-MGP is able to learn the characteristics of coding and non-coding regions and distinguish coding and non-coding open reading frames (ORFs). We train 10 CNN models on 10 mutually exclusive datasets based on pre-defined GC content ranges. We extract ORFs from each fragment; then, the ORFs are encoded numerically and inputted into an appropriate CNN model based on the fragment-GC content. The output from the CNN is the probability that an ORF will encode a gene. Finally, a greedy algorithm is used to select the final gene list. Overall, CNN-MGP is effective and achieves a 91% accuracy on testing dataset. CNN-MGP shows the ability of deep learning to predict genes in metagenomics fragments, and it achieves an accuracy higher than or comparable to state-of-the-art gene-prediction programs that use pre-defined features.

Soil salinity is significant abiotic stress that severely limits global crop production. Chickpea (Cicer arietinum L.) is an important grain legume that plays a substantial role in nutritional food security, especially in the developing world. This study used a chickpea population collected from the International Center for Agricultural Research in the Dry Area (ICARDA) genebank using the focused identification of germplasm strategy. The germplasm included 186 genotypes with broad Asian and African origins and genotyped with 1856 DArTseq markers. We conducted phenotyping for salinity in the field (Arish, Sinai, Egypt) and greenhouse hydroponic experiments at 100 mM NaCl concentration. Based on the performance in both hydroponic and field experiments, we identified seven genotypes from Azerbaijan and Pakistan (IGs: 70782, 70430, 70764, 117703, 6057, 8447, and 70249) as potential sources for high salinity tolerance. Multi-trait genome-wide association analysis (mtGWAS) detected one locus on chromosome Ca4 at 10618070 bp associated with salinity tolerance under hydroponic and field conditions. In addition, we located another locus specific to the hydroponic system on chromosome Ca2 at 30537619 bp. Gene annotation analysis revealed the location of rs5825813 within the Embryogenesis-associated protein (EMB8-like), while the location of rs5825939 is within the Ribosomal Protein Large P0 (RPLP0). Utilizing such markers in practical breeding programs can effectively improve the adaptability of current chickpea cultivars in saline soil. Moreover, researchers can use our markers to facilitate the incorporation of new genes into commercial cultivars.

Ribonucleic acid (RNA) modifications are post-transcriptional chemical composition changes that have a fundamental role in regulating the main aspect of RNA function. Recently, large datasets have become available thanks to the recent development in deep sequencing and large-scale profiling. This availability of transcriptomic datasets has led to increased use of machine learning based approaches in epitranscriptomics, particularly in identifying RNA modifications. In this review, we comprehensively explore machine learning based approaches used for the prediction of 11 RNA modification types, namely, $m^{1}A$, $m^{6}A$, $m^{5}C$, $5\mathit{hmC}$, $\psi$, $2\prime -O-\mathit{Me}$, $\mathit{ac}4C$, $m^{7}G$, $A-\mathit{to}-I$, $m^{2}G$, and $D$. This review covers the life cycle of machine learning methods to predict RNA modification sites including available benchmark datasets, feature extraction, and classification algorithms. We compare available methods in terms of datasets, target species, approach, and accuracy for each RNA modification type. Finally, we discuss the advantages and limitations of the reviewed approaches and suggest future perspectives.

Recent advances in genome sequencing have led to an increase in the number of sequenced genomes. However, the presence of repetitive sequences complicates the assembly of plant genomes. The LTR assembly index (LAI) has recently been widely used to assess the quality of genome assembly, as a higher LAI is associated with a higher quality of assembly. Here, we assessed the quality of assembled genomes of 1664 plant and algal genomes using LAI and reported the results as data repository called PlantLAI (https://bioinformatics.um6p.ma/PlantLAI). A number of 55 117 586 pseudomolecules/scaffolds with a total length of 988.11 gigabase-pairs were examined using the LAI workflow. A total of 46 583 551 accurate LTR-RTs were discovered, including 2 263 188 Copia, 2 933 052 Gypsy, and 1 387 311 unknown superfamilies. Consequently, only 1136 plant genomes are suitable for LAI calculation, with values ranging from 0 to 31.59. Based on the quality classification system, 476 diploid genomes were classified as draft, 472 as reference, and 135 as gold genomes. We also provide a free webtool to calculate the LAI of newly assembled genomes and the ability to save the result in the repository. The data repository is designed to fill in the gaps in the reported LAI of existing genomes, while the webtool is designed to help researchers calculate the LAI of their newly sequenced genomes.

Pseudomonas stutzeri phosphite dehydrogenase (PTDH) catalyzes the oxidation of phosphite to phosphate in the presence of NAD, resulting in the formation of NADH. The regeneration of NADH by PTDH is greater than any other enzyme due to the substantial change in the free energy of reaction (G°′ = − 63.3 kJ/mol). Presently, improving the stability of PTDH is for a great importance to ensure an economically viable reaction process to produce phosphite as a byproduct for agronomic applications. The binding site of NAD⁺ with PTDH includes thirty-four residues; eight of which have been previously mutated and characterized for their roles in catalysis. In the present study, the unexplored twenty-six key residues involved in the binding of NAD⁺ were subjected to in silico mutagenesis based on the physicochemical properties of the amino acids. The effects of these mutations on the structure, stability, activity, and interaction of PTDH with NAD⁺ were investigated using molecular docking, molecular dynamics simulations, free energy calculations, and secondary structure analysis. We identified seven novel mutations, A155I, G157I, L217I, P235A, V262I, I293A, and I293L, that reduce the compactness of the protein while improving PTDH stability and binding to NAD⁺.