China 
Africa 
Explore chapters and articles related to this topic
Big Data and Transcriptomics
Published in Shampa Sen, Leonid Datta, Sayak Mitra, Machine Learning and IoT, 2018
Sudharsana Sundarrajan, Sajitha Lulu, Mohanapriya Arumugam
The next generation sequence analysis largely depends on the bioinformatics tools to support the analysis process. Some of the tools and databases, which are commonly utilized for the processing and analysis are summarized in Table 5.6.
DeepCOVID-19: A model for identification of COVID-19 virus sequences with genomic signal processing and deep learning
Published in Cogent Engineering, 2022
Emmanuel Adetiba, Joshua A. Abolarinwa, Anthony A. Adegoke, Tunmike B. Taiwo, Oluwaseun T. Ajayi, Abdultaofeek Abayomi, Joy N. Adetiba, Joke A. Badejo
In this article, the development of a DeepCOVID-19 identification pipeline has been presented. The model was developed based on GSP, deep learning and genomic datasets of Severe Acute Respiratory Syndrome CoV-2 (SARS-CoV-2), Severe Acute Respiratory Syndrome CoV (SARS-CoV), and Middle East Respiratory Syndrome CoV (MERS-CoV) with a validation accuracy of 98.33% obtained with the transfer learning-based approach in the second experiment. Based on this value of accuracy and the statistical comparison of our results, DeepCOVID-19 can successfully differentiate the genomes of the three Coronavirus strains despite their very high similarity. This provides an innovative pipeline for an alignment-free-based sequence analysis with GSP and deep learning, which is a vital contribution to the bioinformatics body of knowledge. This pipeline can be extended and/or adapted to other sequence analysis problems. Thus, our future studies will involve an exploration of the efficacy of the model for other critical bioinformatics tasks.
Sequestration of mercury and cadmium by a heavy metals' tolerant Bacillus cereus strain JS-3: a microbial preparedness for terrestrial and anthropogenic hazards
Published in Bioremediation Journal, 2022
Amanpreet Kaur, Jashan Nirwan, Anupreet Singh Tiwana, Amrit Singh, Rupinder Pal Singh, Anoop Verma, Saurabh Gupta
Bacterial isolate (JS-3) was characterized in terms of its morphology, physiology and inherited biochemical characteristics. JS-3 has rod-shaped morphology and is Gram-positive, motile, but does not produce pigment. Physiologically, JS-3 can tolerate high salt concentration (10% w/v) but cannot tolerate high temperatures. JS-3 tolerates pH ranging from 5 to 8. Bacterial culture can be sustained and grown under aerobic conditions, is catalase-positive, oxidase negative, lacks nitrate reductase enzyme and hence is an obligate aerobic organism (Table 1). The 16S rRNA gene sequence analysis of the isolated and multiple-sequence alignment (BLAST) specified 99% homology to Bacillus cereus with 98% alignment coverage of over 1.5 kb. The sequence was assigned the GenBank accession number KM924148. Classification of JS-3, belonging to the genus Bacillus (Figure 5), was established using the Ribosomal Database Project (RDP II) Classifier (Cole et al. 2007). For phylogenetic profiling, additional sequences were retrieved from GenBank and the Ribosomal Database Project (Benson et al. 2018). Sequences were selected based randomly on commonly found Bacillus and other strains in wastewater and industrial effluents. These sequences and JS-3 sequences were aligned utilizing Clustal W (Larkin et al. 2007), and phylogenetic tree with evolutionary relationships with 17 additional sequences was constructed by maximum likelihood method using Molecular Evolutionary Genetics Analysis (MEGA-X) software (Kumar et al. 2018).
EightyDVec: a method for protein sequence similarity analysis using physicochemical properties of amino acids
Published in Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2022
Ranjeet Kumar Rout, Saiyed Umer, Sabha Sheikh, Sanchit Sindhwani, Smitarani Pati
The ClustalW platform is considered to be one of the most useful sequence alignment methods for protein and DNA sequence analysis (Zhang et al. 2011). We have utilised the ClustalW multiple sequence alignment results and our proposed method EightyDVec’s results in form of distance matrices. To examine the linear correlation among the EightyDVec and ClustalW methods, the parametric-based correlation analysis has been used. The greater the correlation coefficient between two sequences represents the stronger the linear correlation. For the dataset, the results have been reported in Table 8. On comparing the results with those in Table 3, it has been found that the biological and evolutionary relationship listed above as per the known phylogeny relationship.