In silico Analysis of Genetic Diversity of Begomovirus using Homology Modelling

2010 ◽  
Vol 10 (3) ◽  
pp. 217-223 ◽  
Author(s):  
R. Prajapat ◽  
R.K. Gaur ◽  
R. Raizada ◽  
V.K. Gupta
Keyword(s):  
Genetic Diversity ◽  
In Silico ◽  
Homology Modelling ◽  
In Silico Analysis ◽  
Silico Analysis

scholarly journals Genetic diversity and species pattern of Trichoderma and Hypocrea in Manipur using in-silico analysis

2013 ◽  
Vol 9 (2) ◽  
pp. 106-111 ◽  
Author(s):  
Thongram Kamala ◽  
◽  
Sarangthem Indira Devi ◽  
Gourshyam Thingnam ◽  
Bharat Gopalrao Somkuwar
Keyword(s):  
Genetic Diversity ◽  
In Silico ◽  
In Silico Analysis ◽  
Species Pattern ◽  
Silico Analysis

scholarly journals Use of a Plasmodium vivax genetic barcode for genomic surveillance and parasite tracking in Sri Lanka

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Rajika L. Dewasurendra ◽  
Mary Lynn Baniecki ◽  
Stephen Schaffner ◽  
Yamuna Siriwardena ◽  
Jade Moon ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Sri Lanka ◽  
Plasmodium Vivax ◽  
In Silico ◽  
In Silico Analysis ◽  
Minor Allele ◽  
Allele Frequencies ◽  
Single Nucleotide ◽  
Parasite Populations ◽  
Silico Analysis

Abstract Background Sri Lanka was certified as a malaria-free nation in 2016; however, imported malaria cases continue to be reported. Evidence-based information on the genetic structure/diversity of the parasite populations is useful to understand the population history, assess the trends in transmission patterns, as well as to predict threatening phenotypes that may be introduced and spread in parasite populations disrupting elimination programmes. This study used a previously developed Plasmodium vivax single nucleotide polymorphism (SNP) barcode to evaluate the population dynamics of P. vivax parasite isolates from Sri Lanka and to assess the ability of the SNP barcode for tracking the parasites to its origin. Methods A total of 51 P. vivax samples collected during 2005–2011, mainly from three provinces of the country, were genotyped for 40 previously identified P. vivax SNPs using a high-resolution melting (HRM), single-nucleotide barcode method. Minor allele frequencies, linkage disequilibrium, pair-wise FST values, and complexity of infection (COI) were evaluated to determine the genetic diversity. Structure analysis was carried out using STRUCTURE software (Version 2.3.4) and SNP barcode was used to identify the genetic diversity of the local parasite populations collected from different years. Principal component analysis (PCA) was used to determine the clustering according to global geographic regions. Results The proportion of multi-clone infections was significantly higher in isolates collected during an infection outbreak in year 2007. The minor allele frequencies of the SNPs changed dramatically from year to year. Significant linkage was observed in sample sub-sets from years 2005 and 2007. The majority of the isolates from 2007 consisted of at least two genetically distinct parasite strains. The overall percentage of multi-clone infections for the entire parasite sample was 39.21%. Analysis using STRUCTURE software (Version 2.3.4) revealed the high genetic diversity of the sample sub-set from year 2007. In-silico analysis of these data with those available from other global geographical regions using PCA showed distinct clustering of parasite isolates according to geography, demonstrating the usefulness of the barcode in determining an isolate to be indigenous. Conclusions Plasmodium vivax parasite isolates collected during a disease outbreak in year 2007 were more genetically diverse compared to those collected from other years. In-silico analysis using the 40 SNP barcode is a useful tool to track the origin of an isolate of uncertain origin, especially to differentiate indigenous from imported cases. However, an extended barcode with more SNPs may be needed to distinguish highly clonal populations within the country.

scholarly journals Short Communication: Genetic diversity of the IGF2 gene as a source of genetic marker for halal authentication

2018 ◽  
Vol 10 (4) ◽  
pp. 203-209
Author(s):  
ANTI DAMAYANTI HAMDANI ◽  
MADIHAH . ◽  
RR. BHINTARTI SURYOHASTARI ◽  
DEWI PETI VIRGIANTI ◽  
RAHAYU FITRIAYANI WANGSA PUTRIE
Keyword(s):  
Genetic Diversity ◽  
Genetic Marker ◽  
In Silico ◽  
In Silico Analysis ◽  
Protein Structure Analysis ◽  
Multiple Sequence ◽  
Igf2 Gene ◽  
Halal Authentication ◽  
Silico Analysis ◽  
Peptide Marker

Hamdani AD, Madihah, Suryohastari RB, Virgianti DP, Putrie RFW. 2018. Genetic diversity of the IGF2 gene as a source of genetic marker for halal authentication. Nusantara Bioscience 10: 203-209. The main issue of halal authenticity is the availability of reliable and rapid analytical methods to identify animal species in raw and processed food. The two most popular procedures for identifying the source of the meat are protein-based and DNA-based methods, including mass spectroscopy (MS) using a peptide marker or PCR based DNA marker, respectively. This study aims to investigate the genetic diversity of insulin-like growth factor 2 (IGF2) gene, mainly from porcine (Sus scrofa) and bovine (Bos taurus) as a source of genetic marker for halal authentication by in-silico analysis using bioinformatic tools. Multiple sequence alignment and phylogenetic tree construction of IGF2 protein sequences from representative species of mammals, birds, and fishes showed the paraphyletic relationship between the IGF2 protein of S. scrofa and B. taurus. Protein structure analysis revealed differences in helical structures near the carboxyl end of the protein, while gene analysis showed different number of exon and motifs. By in-silico analysis, we have designed a peptide marker from amino acids at position of 93-107 (S. scrofa) and 93-112 (B. taurus) from peptide preptin region that resulted in different pattern of mass spectrum between the two species. We have also identified two DNA markers that can be detected by PCR using two primers sets designed from the IGF2 transcript sequences, to examine the presence of porcine in the sample. Thus, this study provides new genetic predictive markers, derived from the IGF2 gene, to identify the source of meat for halal authentication.

A systematic review with in silico analysis on transcriptomic profile of gallbladder carcinoma

2020 ◽  
Vol 47 (6) ◽  
pp. 398-408
Author(s):  
Sonam Tulsyan ◽  
Showket Hussain ◽  
Balraj Mittal ◽  
Sundeep Singh Saluja ◽  
Pranay Tanwar ◽  
...  
Keyword(s):  
Systematic Review ◽  
Gallbladder Carcinoma ◽  
In Silico ◽  
In Silico Analysis ◽  
Transcriptomic Profile ◽  
Silico Analysis

Enalos Suite of Tools: Enhancing Cheminformatics and Nanoinfor - matics through KNIME

2020 ◽  
Vol 27 (38) ◽  
pp. 6523-6535 ◽  
Author(s):  
Antreas Afantitis ◽  
Andreas Tsoumanis ◽  
Georgia Melagraki
Keyword(s):  
Data Analysis ◽  
Virtual Screening ◽  
In Silico ◽  
Model Development ◽  
In Silico Analysis ◽  
Material Design ◽  
Efficient Solutions ◽  
Biological Data ◽  
Cost Efficient ◽  
Silico Analysis

Drug discovery as well as (nano)material design projects demand the in silico analysis of large datasets of compounds with their corresponding properties/activities, as well as the retrieval and virtual screening of more structures in an effort to identify new potent hits. This is a demanding procedure for which various tools must be combined with different input and output formats. To automate the data analysis required we have developed the necessary tools to facilitate a variety of important tasks to construct workflows that will simplify the handling, processing and modeling of cheminformatics data and will provide time and cost efficient solutions, reproducible and easier to maintain. We therefore develop and present a toolbox of >25 processing modules, Enalos+ nodes, that provide very useful operations within KNIME platform for users interested in the nanoinformatics and cheminformatics analysis of chemical and biological data. With a user-friendly interface, Enalos+ Nodes provide a broad range of important functionalities including data mining and retrieval from large available databases and tools for robust and predictive model development and validation. Enalos+ Nodes are available through KNIME as add-ins and offer valuable tools for extracting useful information and analyzing experimental and virtual screening results in a chem- or nano- informatics framework. On top of that, in an effort to: (i) allow big data analysis through Enalos+ KNIME nodes, (ii) accelerate time demanding computations performed within Enalos+ KNIME nodes and (iii) propose new time and cost efficient nodes integrated within Enalos+ toolbox we have investigated and verified the advantage of GPU calculations within the Enalos+ nodes. Demonstration data sets, tutorial and educational videos allow the user to easily apprehend the functions of the nodes that can be applied for in silico analysis of data.

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