Biologically inspired ChaosNet architecture for Hypothetical Protein Classification

ChaosNet is a type of artificial neural network framework developed for classification problems and is influenced by the chaotic property of the human brain. Each neuron of the ChaosNet architecture is the one-dimensional chaotic map called the Generalized Luroth Series (GLS). The addition of GLS as neurons in ChaosNet makes the computations straightforward while utilizing the advantageous elements of chaos. With substantially less data, ChaosNet has been demonstrated to do difficult classification problems on par with or better than traditional ANNs. In this paper, we use Chaosnet to perform a functional classification of Hypothetical proteins [HP], which is indeed a topic of great interest in bioinformatics. The results obtained with significantly lesser training data are compared with the standard machine learning techniques used in the literature

A Combined Approach for Genome Wide Protein Function Annotation/Prediction

Background Today large scale genome sequencing technologies are uncovering an increasing amount of new genes and proteins, which remain uncharacterized. Experimental procedures for protein function prediction are low throughput by nature and thus can't be used to keep up with the rate at which new proteins are discovered. On the other hand, proteins are the prominent stakeholders in almost all biological processes, and therefore the need to precisely know their functions for a better understanding of the underlying biological mechanism is inevitable. The challenge of annotating uncharacterized proteins in functional genomics and biology in general motivates the use of computational techniques well orchestrated to accurately predict their functions. Methods We propose a computational flow for the functional annotation of a protein able to assign the most probable functions to a protein by aggregating heterogeneous information. Considered information include: protein motifs, protein sequence similarity, and protein homology data gathered from interacting proteins, combined with data from highly similar non-interacting proteins (hereinafter called Similactors). Moreover, to increase the predictive power of our model we also compute and integrate term specific relationships among functional terms based on Gene Ontology (GO). Results We tested our method on Saccharomyces Cerevisiae and Homo sapiens species proteins. The aggregation of different structural and functional evidence with GO relationships outperforms, in terms of precision and accuracy of prediction than the other methods reported in literature. The predicted precision and accuracy is 100% for more than half of the input set for both species; overall, we obtained 85.38% precision and 81.95% accuracy for Homo sapiens and 79.73% precision and 80.06% accuracy for Saccharomyces Cerevisiae species protein

Combining Homolog and Motif Similarity Data with Gene Ontology Relationships for Protein Function Prediction

Uncharacterized proteins pose a challenge not just to functional genomics, but also to biology in general. The knowledge of biochemical functions of such proteins is very critical for designing efficient therapeutic techniques. The bot- tleneck in hypothetical proteins annotation is the difficulty in collecting and aggregating enough biological information about the protein itself. In this paper, we propose and evaluate a protein annotation technique that aggregates different biological infor- mation conserved across many hypothetical proteins. To enhance the performance and to increase the prediction accuracy, we incorporate term specific relationships based on Gene Ontology (GO). Our method combines PPI (Protein Protein Interactions) data, protein motifs information, protein sequence similarity and protein homology data, with a context similarity measure based on Gene Ontology, to accurately infer functional information for unannotated proteins. We apply our method on Saccharomyces Cerevisiae species proteins. The aggregation of different sources of evidence with GO relationships increases the precision and accuracy of prediction compared to other methods reported in literature. We predicted with a precision and accuracy of 100% for more than half proteins of the input set and with an overall 81.35% precision and 80.04% accurac

Spatial Metagenomic Analysis in Understanding the Microbial Diversity of Thar Desert

The arid and semi-arid regions of Rajasthan are one of the most extreme biomes of India, possessing diverse microbial communities that exhibit immense biotechnological potential for industries. Herein, we sampled study sites from arid and semi-arid regions of Thar Desert, Rajasthan, India and subjected them to chemical, physical and metagenomics analysis. The microbial diversity was studied using V3–V4 amplicon sequencing of 16S rRNA gene by Illumina MiSeq. Our metagenomic analyses revealed that the sampled sites consist mainly of Proteobacteria (19–31%) followed by unclassified bacteria (5–21%), Actinobacteria (3–25%), Planctomycetes (5–13%), Chloroflexi (2–14%), Bacteroidetes (3–12%), Firmicutes (3–7%), Acidobacteria (1–4%) and Patescibacteria (1–4%). We have found Proteobacteria in abundance which is associated with a range of activities involved in biogeochemical cycles such as carbon, nitrogen, and sulphur. Our study is perhaps the first of its kind to explore soil bacteria from arid and semi-arid regions of Rajasthan, India. We believe that the new microbial candidates found can be further explored for various industrial and biotechnological applications

Intriguing Role of Proline in Redox Potential Conferring High Temperature Stress Tolerance

Proline is a proteinogenic amino acid synthesized from glutamate and ornithine. Pyrroline-5-carboxylate synthetase and pyrroline-5-carboxylate reductase are the two key enzymes involved in proline synthesis from glutamate. On the other hand, ornithine-δ-aminotransferase converts ornithine to pyrroline 5-carboxylate (P5C), an intermediate in the synthesis of proline as well as glutamate. Both proline dehydrogenase and P5C dehydrogenase convert proline back to glutamate. Proline accumulation is widespread in response to environmental challenges such as high temperatures, and it is known to defend plants against unpropitious situations promoting plant growth and flowering. While proline accumulation is positively correlated with heat stress tolerance in some crops, it has detrimental consequences in others. Although it has been established that proline is a key osmolyte, its exact physiological function during heat stress and plant ontogeny remains unknown. Emerging evidence pointed out its role as an overriding molecule in alleviating high temperature stress (HTS) by quenching singlet oxygen and superoxide radicals. Proline cycle acts as a shuttle and the redox couple (NAD+/NADH, NADP+/NADPH) appears to be highly crucial for energy transfer among different cellular compartments during plant development, exposure to HTS conditions and also during the recovery of stress. In this review, the progress made in recent years regarding its involvement in heat stress tolerance is highlighted

Root and Leaf Anatomy, Ion Accumulation, and Transcriptome Pattern under Salt Stress Conditions in Contrasting Genotypes of Sorghum bicolor

Roots from salt-susceptible ICSR-56 (SS) sorghum plants display metaxylem elements with thin cell walls and large diameter. On the other hand, roots with thick, lignified cell walls in the hypodermis and endodermis were noticed in salt-tolerant CSV-15 (ST) sorghum plants. The secondary wall thickness and number of lignified cells in the hypodermis have increased with the treatment of sodium chloride stress to the plants (STN). Lignin distribution in the secondary cell wall of sclerenchymatous cells beneath the lower epidermis was higher in ST leaves compared to the SS genotype. Casparian thickenings with homogenous lignin distribution were observed in STN roots, but inhomogeneous distribution was evident in SS seedlings treated with sodium chloride (SSN). Higher accumulation of K+ and lower Na+ levels were noticed in ST compared to the SS genotype. To identify the differentially expressed genes among SS and ST genotypes, transcriptomic analysis was carried out. Both the genotypes were exposed to 200 mM sodium chloride stress for 24 h and used for analysis. We obtained 70 and 162 differentially expressed genes (DEGs) exclusive to SS and SSN and 112 and 26 DEGs exclusive to ST and STN, respectively. Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis unlocked the changes in metabolic pathways in response to salt stress. qRT-PCR was performed to validate 20 DEGs in each SSN and STN sample, which confirms the transcriptomic results. These results surmise that anatomical changes and higher K+/Na+ ratios are essential for mitigating salt stress in sorghum apart from the genes that are differentially up- and downregulated in contrasting genotypes

Overexpression of a Plasma Membrane Bound Na+/H+ Antiporter-Like Protein (SbNHXLP) Confers Salt Tolerance and Improves Fruit Yield in Tomato by Maintaining Ion Homeostasis

A Na+/H+ antiporter-like protein (NHXLP) was isolated from Sorghum bicolor L. (SbNHXLP) and validated by overexpressing in tomato for salt tolerance. Homozygous T2 transgenic lines when evaluated for salt tolerance, accumulated low Na+ and displayed enhanced salt tolerance compared to wild-type plants (WT). This is consistent with the amiloride binding assay of the protein. Transgenics exhibited higher accumulation of proline, K+, Ca2+, improved cambial conductivity, higher PSII, and antioxidative enzyme activities than WT. Fluorescence imaging results revealed lower Na+ and higher Ca2+ levels in transgenic roots. Co-immunoprecipitation experiments demonstrate that SbNHXLP interacts with a Solanum lycopersicum cation proton antiporter protein2 (SlCHX2). qRT-PCR results showed upregulation of SbNHXLP and SlCHX2 upon treatment with 200 mM NaCl and 100 mM potassium nitrate. SlCHX2 is known to be involved in K+ acquisition, and the interaction between these two proteins might help to accumulate more K+ ions, and thus maintain ion homeostasis. These results strongly suggest that plasma membrane bound SbNHXLP involves in Na+ exclusion, maintains ion homeostasis in transgenics in comparison with WT and alleviates NaCl stress

Genome-wide in silico analysis of dehydrins in Sorghum bicolor , Setaria italica and Zea mays and quantitative analysis of dehydrin gene expressions under abiotic stresses in Sorghum bicolor

Dehydrins (DHNs) are highly hydrophilic, thermo stable, calcium dependent chaperons involved in plant developmental processes as well as in diverse abiotic stresses. A systematic survey resulted in the identification of 7 dehydrins (DHNs) in Setaria italica and Zea mays, but 6 in Sorghum bicolor. They are classified into 5 sub-groups, namely YnSKn, SKn, KnS, S, and YnS. DHNs of Sorghum exhibit 1 ortholog with Oryza sativa and Z. mays and 3 with S. italica. Unlike other DHNs, SbDHN5 has been found as an ordered protein with many phosphorylation sites. Network analyses of novel YnS subgroup showed interaction with HSP70 and FKBP genes. In silico promoter analysis revealed the presence of abscisic acid (ABA), drought, salt, low temperature stress-responsive elements. The miRNA target analysis revealed DHNs are targeted by 51 miRNAs responsive to abiotic stresses. High transcript expressions of DHNs were observed in root, stem and leaf compared to inflorescence in S. bicolor. All DHN genes exhibited high levels of expression in stem under cold, heat, salt, and drought stresses. In contrast to other DHNs, the SbDHN2 of YnS subgroup, exhibited the highest expression, under multiple stresses in all the tissues indicating its involvement against a wide array of abiotic stresses

Ten Simple Rules for Organizing a Virtual Conference—Anywhere

1 International Institute of Tropical Agriculture, Nairobi, Kenya, 2 Faculty of Life Sciences, The University of Manchester, Manchester, United Kingdom, 3 Department of Computer and Information Sciences, Covenant University, Ota, Nigeria, 4 Institute of Bioinformatics, Johannes Kepler University, Linz, Austria, 5 Moroccan Society for Bioinformatics Institute, Morocco, 6 South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa, 7 University of Cape Town, Cape Town, South Africa, 8 University of Notre Dame, South Bend, Indiana, United States of America, 9 Biotechnology Unit, University of Buea, Buea, South West Region, Cameroon, 10 International Livestock Research Institute, Nairobi, Kenya, 11 Biosciences Eastern and Central Africa, Nairobi, Kenya, 12 International Center of Insect Physiology and Ecology, Nairobi, Kenya, 13 Bioinformatics Organization, Hudson, Massachusetts, United States of America, 14 Bioinformatics Team, Center for Development of Advanced Computing, Pune University Campus, Pune, India, 15 Harvard School of Public Health, Boston, Massachusetts, United States of Americ

Machine Learning Heuristics on Gingivobuccal Cancer Gene Datasets Reveals Key Candidate Attributes for Prognosis

Delayed cancer detection is one of the common causes of poor prognosis in the case of many cancers, including cancers of the oral cavity. Despite the improvement and development of new and efficient gene therapy treatments, very little has been carried out to algorithmically assess the impedance of these carcinomas. In this work, from attributes or NCBI’s oral cancer datasets, viz. (i) name, (ii) gene(s), (iii) protein change, (iv) condition(s), clinical significance (last reviewed). We sought to train the number of instances emerging from them. Further, we attempt to annotate viable attributes in oral cancer gene datasets for the identification of gingivobuccal cancer (GBC). We further apply supervised and unsupervised machine learning methods to the gene datasets, revealing key candidate attributes for GBC prognosis. Our work highlights the importance of automated identification of key genes responsible for GBC that could perhaps be easily replicated in other forms of oral cancer detection.publishedVersionPeer reviewe