JMIR bioinformatics and biotechnology最新文献

{"title":"The Easy-to-Use SARS-CoV-2 Assembler for Genome Sequencing: Development Study.","authors":"Martina Rueca,&nbsp;Emanuela Giombini,&nbsp;Francesco Messina,&nbsp;Barbara Bartolini,&nbsp;Antonino Di Caro,&nbsp;Maria Rosaria Capobianchi,&nbsp;Cesare Em Gruber","doi":"10.2196/31536","DOIUrl":"https://doi.org/10.2196/31536","url":null,"abstract":"<p><strong>Background: </strong>Early sequencing and quick analysis of the SARS-CoV-2 genome have contributed to the understanding of the dynamics of COVID-19 epidemics and in designing countermeasures at a global level.</p><p><strong>Objective: </strong>Amplicon-based next-generation sequencing (NGS) methods are widely used to sequence the SARS-CoV-2 genome and to identify novel variants that are emerging in rapid succession as well as harboring multiple deletions and amino acid-changing mutations.</p><p><strong>Methods: </strong>To facilitate the analysis of NGS sequencing data obtained from amplicon-based sequencing methods, here, we propose an easy-to-use SARS-CoV-2 genome assembler: the Easy-to-use SARS-CoV-2 Assembler (ESCA) pipeline.</p><p><strong>Results: </strong>Our results have shown that ESCA could perform high-quality genome assembly from Ion Torrent and Illumina raw data and help the user in easily correct low-coverage regions. Moreover, ESCA includes the possibility of comparing assembled genomes of multisample runs through an easy table format.</p><p><strong>Conclusions: </strong>In conclusion, ESCA automatically furnished a variant table output file, fundamental to rapidly recognizing variants of interest. Our pipeline could be a useful method for obtaining a complete, rapid, and accurate analysis even with minimal knowledge in bioinformatics.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":" ","pages":"e31536"},"PeriodicalIF":0.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8924907/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40307905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Novel Molecular Networks and Regulatory MicroRNAs in Type 2 Diabetes Mellitus: Multiomics Integration and Interactomics Study.","authors":"Manoj Khokhar, Dipayan Roy, Sojit Tomo, Ashita Gadwal, Praveen Sharma, Purvi Purohit","doi":"10.2196/32437","DOIUrl":"10.2196/32437","url":null,"abstract":"<p><strong>Background: </strong>Type 2 diabetes mellitus (T2DM) is a metabolic disorder with severe comorbidities. A multiomics approach can facilitate the identification of novel therapeutic targets and biomarkers with proper validation of potential microRNA (miRNA) interactions.</p><p><strong>Objective: </strong>The aim of this study was to identify significant differentially expressed common target genes in various tissues and their regulating miRNAs from publicly available Gene Expression Omnibus (GEO) data sets of patients with T2DM using in silico analysis.</p><p><strong>Methods: </strong>Using differentially expressed genes (DEGs) identified from 5 publicly available T2DM data sets, we performed functional enrichment, coexpression, and network analyses to identify pathways, protein-protein interactions, and miRNA-mRNA interactions involved in T2DM.</p><p><strong>Results: </strong>We extracted 2852, 8631, 5501, 3662, and 3753 DEGs from the expression profiles of GEO data sets GSE38642, GSE25724, GSE20966, GSE26887, and GSE23343, respectively. DEG analysis showed that 16 common genes were enriched in insulin secretion, endocrine resistance, and other T2DM-related pathways. Four DEGs, MAML3, EEF1D, NRG1, and CDK5RAP2, were important in the cluster network regulated by commonly targeted miRNAs (hsa-let-7b-5p, hsa-mir-155-5p, hsa-mir-124-3p, hsa-mir-1-3p), which are involved in the advanced glycation end products (AGE)-receptor for advanced glycation end products (RAGE) signaling pathway, culminating in diabetic complications and endocrine resistance.</p><p><strong>Conclusions: </strong>This study identified tissue-specific DEGs in T2DM, especially pertaining to the heart, liver, and pancreas. We identified a total of 16 common DEGs and the top four common targeting miRNAs (hsa-let-7b-5p, hsa-miR-124-3p, hsa-miR-1-3p, and has-miR-155-5p). The miRNAs identified are involved in regulating various pathways, including the phosphatidylinositol-3-kinase-protein kinase B, endocrine resistance, and AGE-RAGE signaling pathways.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":" ","pages":"e32437"},"PeriodicalIF":0.0,"publicationDate":"2022-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135235/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48397752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Convolutional Neural Network-Based Automatic Classification of Colorectal and Prostate Tumor Biopsies Using Multispectral Imagery: System Development Study.","authors":"Remy Peyret, Duaa alSaeed, Fouad Khelifi, Nadia Al-Ghreimil, Heyam Al-Baity, Ahmed Bouridane","doi":"10.2196/27394","DOIUrl":"10.2196/27394","url":null,"abstract":"<p><strong>Background: </strong>Colorectal and prostate cancers are the most common types of cancer in men worldwide. To diagnose colorectal and prostate cancer, a pathologist performs a histological analysis on needle biopsy samples. This manual process is time-consuming and error-prone, resulting in high intra- and interobserver variability, which affects diagnosis reliability.</p><p><strong>Objective: </strong>This study aims to develop an automatic computerized system for diagnosing colorectal and prostate tumors by using images of biopsy samples to reduce time and diagnosis error rates associated with human analysis.</p><p><strong>Methods: </strong>In this study, we proposed a convolutional neural network (CNN) model for classifying colorectal and prostate tumors from multispectral images of biopsy samples. The key idea was to remove the last block of the convolutional layers and halve the number of filters per layer.</p><p><strong>Results: </strong>Our results showed excellent performance, with an average test accuracy of 99.8% and 99.5% for the prostate and colorectal data sets, respectively. The system showed excellent performance when compared with pretrained CNNs and other classification methods, as it avoids the preprocessing phase while using a single CNN model for the whole classification task. Overall, the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images.</p><p><strong>Conclusions: </strong>The proposed CNN architecture was detailed and compared with previously trained network models used as feature extractors. These CNNs were also compared with other classification techniques. As opposed to pretrained CNNs and other classification approaches, the proposed CNN yielded excellent results. The computational complexity of the CNNs was also investigated, and it was shown that the proposed CNN is better at classifying images than pretrained networks because it does not require preprocessing. Thus, the overall analysis was that the proposed CNN architecture was globally the best-performing system for classifying colorectal and prostate tumor images.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":"abs/2301.13151 1","pages":"e27394"},"PeriodicalIF":0.0,"publicationDate":"2022-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135179/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68432734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review.","authors":"Glauco Cardozo,&nbsp;Salvador Francisco Tirloni,&nbsp;Antônio Renato Pereira Moro,&nbsp;Jefferson Luiz Brum Marques","doi":"10.2196/40473","DOIUrl":"https://doi.org/10.2196/40473","url":null,"abstract":"<p><strong>Background: </strong>In recent decades, the use of artificial intelligence has been widely explored in health care. Similarly, the amount of data generated in the most varied medical processes has practically doubled every year, requiring new methods of analysis and treatment of these data. Mainly aimed at aiding in the diagnosis and prevention of diseases, this precision medicine has shown great potential in different medical disciplines. Laboratory tests, for example, almost always present their results separately as individual values. However, physicians need to analyze a set of results to propose a supposed diagnosis, which leads us to think that sets of laboratory tests may contain more information than those presented separately for each result. In this way, the processes of medical laboratories can be strongly affected by these techniques.</p><p><strong>Objective: </strong>In this sense, we sought to identify scientific research that used laboratory tests and machine learning techniques to predict hidden information and diagnose diseases.</p><p><strong>Methods: </strong>The methodology adopted used the population, intervention, comparison, and outcomes principle, searching the main engineering and health sciences databases. The search terms were defined based on the list of terms used in the Medical Subject Heading database. Data from this study were presented descriptively and followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses; 2020) statement flow diagram and the National Institutes of Health tool for quality assessment of articles. During the analysis, the inclusion and exclusion criteria were independently applied by 2 authors, with a third author being consulted in cases of disagreement.</p><p><strong>Results: </strong>Following the defined requirements, 40 studies presenting good quality in the analysis process were selected and evaluated. We found that, in recent years, there has been a significant increase in the number of works that have used this methodology, mainly because of COVID-19. In general, the studies used machine learning classification models to predict new information, and the most used parameters were data from routine laboratory tests such as the complete blood count.</p><p><strong>Conclusions: </strong>Finally, we conclude that laboratory tests, together with machine learning techniques, can predict new tests, thus helping the search for new diagnoses. This process has proved to be advantageous and innovative for medical laboratories. It is making it possible to discover hidden information and propose additional tests, reducing the number of false negatives and helping in the early discovery of unknown diseases.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":"3 1","pages":"e40473"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9828303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10540975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonfungible Tokens as a Blockchain Solution to Ethical Challenges for the Secondary Use of Biospecimens: Viewpoint.","authors":"Marielle S Gross, Amelia J Hood, Robert C Miller","doi":"10.2196/29905","DOIUrl":"10.2196/29905","url":null,"abstract":"<p><p>Henrietta Lacks' deidentified tissue became HeLa cells (the paradigmatic learning health platform). In this article, we discuss separating research on Ms Lacks' tissue from obligations to promote respect, beneficence, and justice for her as a patient. This case illuminates ethical challenges for the secondary use of biospecimens, which persist in contemporary learning health systems. Deidentification and broad consent seek to maximize the benefits of learning from care by minimizing burdens on patients, but these strategies are insufficient for privacy, transparency, and engagement. The resulting supply chain for human cellular and tissue-based products may therefore recapitulate the harms experienced by the Lacks family. We introduce the potential for blockchain technology to build unprecedented transparency, engagement, and accountability into learning health system architecture without requiring deidentification. The ability of nonfungible tokens to maintain the provenance of inherently unique digital assets may optimize utility, value, and respect for patients who contribute tissue and other clinical data for research. We consider the potential benefits and survey major technical, ethical, socioeconomic, and legal challenges for the successful implementation of the proposed solutions. The potential for nonfungible tokens to promote efficiency, effectiveness, and justice in learning health systems demands further exploration.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":" ","pages":"e29905"},"PeriodicalIF":0.0,"publicationDate":"2021-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168237/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42689875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of a novel c.3080delC JAG1 gene mutation associated with Alagille syndrome by whole-exome sequencing (Preprint)","authors":"D. Panwar, Dr. Kumar Gautam Singh, Ms. Shruti Mathur, Mr. Bhagwati Prasad, Ms. Anita Joshi, Dr. Vandana Lal, A. Thatai","doi":"10.2196/preprints.33946","DOIUrl":"https://doi.org/10.2196/preprints.33946","url":null,"abstract":"\u0000 BACKGROUND\u0000 Alagille syndrome is an autosomal dominant disorder associated with variable clinical phenotypic features including cholestasis, congenital heart defects, vertebral defects, and dysmorphic facies.\u0000 \u0000 \u0000 OBJECTIVE\u0000 Whole-exome sequencing (WES) has become technically feasible due to the recent advances in next-generation sequencing technologies, therefore offering new opportunities for mutations/genes identification.\u0000 \u0000 \u0000 METHODS\u0000 Next-generation sequencing (NGS) - Whole-exome sequencing was used to identify pathogenic variants of the proband. In this paper, we have uncovered a novel JAG1 mutation associated with Alagille syndrome in a 5 years old girl presented with conjugated hyperbilirubinemia and infantile cholestasis.\u0000 \u0000 \u0000 RESULTS\u0000 The exome sequencing analysis revealed the presence of a novel JAG1 heterozygous c.3080delC variant in exon 25. The detected mutation determines a stop codon (p.P1027RfsTer9) in the gene sequence, encoding a truncated protein. Our exome observations were confirmed through Sanger sequencing as well.\u0000 \u0000 \u0000 CONCLUSIONS\u0000 Here, we report a case of a patient diagnosed with Alagille syndrome, and our finding emphasis the detection of novel JAG1 mutation associated with Alagille syndrome variants thereby, establishing the genetic diagnosis of the disease.\u0000 \u0000 \u0000 CLINICALTRIAL\u0000 N/A\u0000","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49021753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Potential Vaccine Candidates Against SARS-CoV-2 to Fight COVID-19: Reverse Vaccinology Approach","authors":"E. Gupta, R. Mishra, Ravi Ranjan Kumar Niraj","doi":"10.2196/32401","DOIUrl":"https://doi.org/10.2196/32401","url":null,"abstract":"Background The recent emergence of COVID-19 has caused an immense global public health crisis. The etiological agent of COVID-19 is the novel coronavirus SARS-CoV-2. More research in the field of developing effective vaccines against this emergent viral disease is indeed a need of the hour. Objective The aim of this study was to identify effective vaccine candidates that can offer a new milestone in the battle against COVID-19. Methods We used a reverse vaccinology approach to explore the SARS-CoV-2 genome among strains prominent in India. Epitopes were predicted and then molecular docking and simulation were used to verify the molecular interaction of the candidate antigenic peptide with corresponding amino acid residues of the host protein. Results A promising antigenic peptide, GVYFASTEK, from the surface glycoprotein of SARS-CoV-2 (protein accession number QIA98583.1) was predicted to interact with the human major histocompatibility complex (MHC) class I human leukocyte antigen (HLA)-A*11-01 allele, showing up to 90% conservancy and a high antigenicity value. After vigorous analysis, this peptide was predicted to be a suitable epitope capable of inducing a strong cell-mediated immune response against SARS-CoV-2. Conclusions These results could facilitate selecting SARS-CoV-2 epitopes for vaccine production pipelines in the immediate future. This novel research will certainly pave the way for a fast, reliable, and effective platform to provide a timely countermeasure against this dangerous virus responsible for the COVID-19 pandemic.","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41873622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Isolating SARS-CoV-2 Strains From Countries in the Same Meridian: Genome Evolutionary Analysis.","authors":"Emilio Mastriani, Alexey V Rakov, Shu-Lin Liu","doi":"10.2196/25995","DOIUrl":"10.2196/25995","url":null,"abstract":"<p><strong>Background: </strong>COVID-19, caused by the novel SARS-CoV-2, is considered the most threatening respiratory infection in the world, with over 40 million people infected and over 0.934 million related deaths reported worldwide. It is speculated that epidemiological and clinical features of COVID-19 may differ across countries or continents. Genomic comparison of 48,635 SARS-CoV-2 genomes has shown that the average number of mutations per sample was 7.23, and most SARS-CoV-2 strains belong to one of 3 clades characterized by geographic and genomic specificity: Europe, Asia, and North America.</p><p><strong>Objective: </strong>The aim of this study was to compare the genomes of SARS-CoV-2 strains isolated from Italy, Sweden, and Congo, that is, 3 different countries in the same meridian (longitude) but with different climate conditions, and from Brazil (as an outgroup country), to analyze similarities or differences in patterns of possible evolutionary pressure signatures in their genomes.</p><p><strong>Methods: </strong>We obtained data from the Global Initiative on Sharing All Influenza Data repository by sampling all genomes available on that date. Using HyPhy, we achieved the recombination analysis by genetic algorithm recombination detection method, trimming, removal of the stop codons, and phylogenetic tree and mixed effects model of evolution analyses. We also performed secondary structure prediction analysis for both sequences (mutated and wild-type) and \"disorder\" and \"transmembrane\" analyses of the protein. We analyzed both protein structures with an ab initio approach to predict their ontologies and 3D structures.</p><p><strong>Results: </strong>Evolutionary analysis revealed that codon 9628 is under episodic selective pressure for all SARS-CoV-2 strains isolated from the 4 countries, suggesting it is a key site for virus evolution. Codon 9628 encodes the P0DTD3 (Y14_SARS2) uncharacterized protein 14. Further investigation showed that the codon mutation was responsible for helical modification in the secondary structure. The codon was positioned in the more ordered region of the gene (41-59) and near to the area acting as the transmembrane (54-67), suggesting its involvement in the attachment phase of the virus. The predicted protein structures of both wild-type and mutated P0DTD3 confirmed the importance of the codon to define the protein structure. Moreover, ontological analysis of the protein emphasized that the mutation enhances the binding probability.</p><p><strong>Conclusions: </strong>Our results suggest that RNA secondary structure may be affected and, consequently, the protein product changes T (threonine) to G (glycine) in position 50 of the protein. This position is located close to the predicted transmembrane region. Mutation analysis revealed that the change from G (glycine) to D (aspartic acid) may confer a new function to the protein-binding activity, which in turn may be responsible for attaching the virus to","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":"2 1","pages":"e25995"},"PeriodicalIF":0.0,"publicationDate":"2021-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7837406/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38781957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Novel Coronavirus Enigma: Phylogeny and Analyses of Coevolving Mutations Among the SARS-CoV-2 Viruses Circulating in India.","authors":"Anindita Banerjee, Rakesh Sarkar, Suvrotoa Mitra, Mahadeb Lo, Shanta Dutta, Mamta Chawla-Sarkar","doi":"10.2196/20735","DOIUrl":"10.2196/20735","url":null,"abstract":"<p><strong>Background: </strong>The RNA genome of the emerging novel coronavirus is rapidly mutating, and its human-to-human transmission rate is increasing. Hence, temporal dissection of their evolutionary dynamics, the nature of variations among different strains, and understanding the single nucleotide polymorphisms in the endemic settings are crucial. Delineating the heterogeneous genomic constellations of this novel virus will help us understand its complex behavior in a particular geographical region.</p><p><strong>Objective: </strong>This is a comprehensive analysis of 95 Indian SARS-CoV-2 genome sequences available from the Global Initiative on Sharing All Influenza Data (GISAID) repository during the first 6 months of 2020 (January through June). Evolutionary dynamics, gene-specific phylogeny, and the emergence of the novel coevolving mutations in 9 structural and nonstructural genes among circulating SARS-CoV-2 strains across 12 different Indian states were analyzed.</p><p><strong>Methods: </strong>A total of 95 SARS-CoV-2 nucleotide sequences submitted from India were downloaded from the GISAID database. Molecular Evolutionary Genetics Analysis, version X software was used to construct the 9 phylogenetic dendrograms based on nucleotide sequences of the SARS-CoV-2 genes. Analyses of the coevolving mutations were done in comparison to the prototype SARS-CoV-2 from Wuhan, China. The secondary structure of the RNA-dependent RNA polymerase/nonstructural protein NSP12 was predicted with respect to the novel A97V mutation.</p><p><strong>Results: </strong>Phylogenetic analyses revealed the evolution of \"genome-type clusters\" and adaptive selection of \"L\"-type SARS-CoV-2 strains with genetic closeness to the bat severe acute respiratory syndrome-like coronaviruses. These strains were distant to pangolin or Middle East respiratory syndrome-related coronavirus strains. With regard to the novel coevolving mutations, 2 groups have been seen circulating in India at present, the \"major group\" (66/95, 69.4%) and the \"minor group\" (21/95, 22.1%) , harboring 4 and 5 coexisting mutations, respectively. The \"major group\" mutations fall in the A2a clade. All the minor group mutations, except 11083G>T (L37F, NSP6 gene), were unique to the Indian isolates.</p><p><strong>Conclusions: </strong>This study highlights the rapidly evolving SARS-CoV-2 virus and the cocirculation of multiple clades and subclades. This comprehensive study is a potential resource for monitoring the novel mutations in the viral genome, interpreting changes in viral pathogenesis, and designing vaccines or other therapeutics.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":"1 1","pages":"e20735"},"PeriodicalIF":0.0,"publicationDate":"2020-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38866237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Docking Using Chimera and Autodock Vina Software for Nonbioinformaticians.","authors":"Sania Safdar Butt, Yasmin Badshah, Maria Shabbir, Mehak Rafiq","doi":"10.2196/14232","DOIUrl":"10.2196/14232","url":null,"abstract":"<p><p>In the field of drug discovery, many methods of molecular modeling have been employed to study complex biological and chemical systems. Experimental strategies are integrated with computational approaches for the identification, characterization, and development of novel drugs and compounds. In modern drug designing, molecular docking is an approach that explores the confirmation of a ligand within the binding site of a macromolecule. To date, many software and tools for docking have been employed. AutoDock Vina (in UCSF [University of California, San Francisco] Chimera) is one of the computationally fastest and most accurate software employed in docking. In this paper, a sequential demonstration of molecular docking of the ligand fisetin with the target protein Akt has been provided, using AutoDock Vina in UCSF Chimera 1.12. The first step involves target protein ID retrieval from the protein database, the second step involves visualization of the protein structure in UCSF Chimera, the third step involves preparation of the target protein for docking, the fourth step involves preparation of the ligand for docking, the fifth step involves docking of the ligand and the target protein as Mol.2 files in Chimera by using AutoDock Vina, and the final step involves interpretation and analysis of the docking results. By following the guidelines and steps outlined in this paper, researchers with no previous background in bioinformatics research can perform computational docking in an easier and more user-friendly manner.</p>","PeriodicalId":73552,"journal":{"name":"JMIR bioinformatics and biotechnology","volume":" ","pages":"e14232"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11168238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45566561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}