PCR Amplification and In-Silico Analysis of Putative Boiling Stable Protein Encoding Genes from Invasive Alien Plant Lantana camara

DNA barcoding merupakan metode identifikasi spesies menggunakan potongan DNA pendek yang disebut barcode DNA. Gen matK merupakan gen standar untuk penentuan barcode DNA tanaman. Penelitian ini bertujuan untuk menentukan barcode DNA tumbuhan rumput macan (L. camara L.) berdasarkan gen matK, serta melakukan analisis in-silico terhadap produk gen matK tumbuhan rumput macan (L. camara L.) dengan kerabat terdekatnya. Gen matK L. camara L. telah berhasil diamplifikasi dengan Polymerase Chain Reaction (PCR) menggunakan primer forward matK-1RKIM-f dan primer reverse matK-3FKIM-r. Analisis terhadap sekuens matK L. camara L. menunjukkan bahwa barcode DNA tumbuhan rumput macan (L. camara L.) terdiri dari 843 nukleotida. Selanjutnya, hasil analisis in-silico menunjukkan bahwa matK Lantana camara L. bersifat basa, stabil, dan dapat berinteraksi baik dengan air.DNA barcoding is a method of species identification using short pieces of DNA called DNA barcode. matK is a standard gene to determine DNA barcode of a plant. The aim of this research was to determine the DNA barcode of Rumput Macan plant (Lantana camara L.) based on matK gene, as well as in-silico analysis of the product matK gene Rumput Macan (L camara L.) with its closest relatives. L. camara L. matK gene was successfully amplified by Polymerase Chain Reaction (PCR) using forward primer MATK-1RKIM-f and reverse primer MATK-3FKIM-r. Analysis of the matK sequence of L. camara L. showed that the barcode DNA of rumput macan plant (L. camara L.) consisting of 843 nucleotides. Furthermore, the result of in-silico analysis showed that the matK of L camara L. is alkaline, stable, and able to interact well with water.

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RT-PCR Amplification and in-silico Analysis of Water Stress-Responsive Hydrophilic TaDreb, TaDHN and TaWCS20 Genes from Triticum aestivum

Russian Agricultural Sciences ◽

10.3103/s1068367421030162 ◽

2021 ◽

Vol 47 (3) ◽

pp. 218-232

Author(s):

Arun Dev Sharma ◽

Gurmeen Rakhra ◽

Dhiraj Vyas

Keyword(s):

Triticum Aestivum ◽

Water Stress ◽

In Silico ◽

Pcr Amplification ◽

In Silico Analysis ◽

Rt Pcr ◽

Silico Analysis

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Geographical, Molecular, and Computational Analysis of Migraine-Causing Genes

Journal of Computational Biophysics and Chemistry ◽

10.1142/s2737416521500204 ◽

2021 ◽

Vol 20 (04) ◽

pp. 391-403

Author(s):

Muhammad Naveed ◽

Bakhtawar Bukhari ◽

Nadia Afzal ◽

Haleema Sadia ◽

Bisma Meer ◽

...

Keyword(s):

In Silico ◽

Computational Analysis ◽

Pcr Amplification ◽

In Silico Analysis ◽

Bivariate Analysis ◽

Headache Disorders ◽

Gender And Age ◽

Migraine Pathophysiology ◽

Silico Analysis

Migraine is a re-occurring type of headache and causes moderate-to-severe pain that is troubling or pulsing. The pain occurs in half of the head, and common symptoms are photophobia, phonophobia, nausea, depression, anxiety, vomiting, etc. This study evaluates the prevalence of migraine and responsible genes through molecular modeling in the region of Bahawalpur, Pakistan. This research was aimed to determine the prevalence of migraine-causing genes in the population of Bahawalpur and also to do molecular and in-silico analysis of migraine-causing gene as no similar research was conducted before. The disease was characterized and diagnosed under the criteria of the Second Edition of the International Classification of Headache Disorders and molecular identification of migraine-causing genes, i.e. GRIA1, GRIA3, and ESR1, by PCR amplification. The total number of samples collected for migraine patients was 230, out of which 30 were positive for PCR amplification of the genes GRIA1, GRIA3, and ESR1. Therapeutic potentials of commercial drugs, namely Cyclobenzaprine, Divalproex, Ergotamine, and Sumatriptan, were analyzed in silico through molecular docking. Ergotamine demonstrated the highest binding affinity of [Formula: see text]8.4 kcal/mol for the target molecule and, hence, the highest potential. The bivariate analysis showed that the prevalence of migraine concerning gender and age was significantly correlated ([Formula: see text], [Formula: see text]). It was observed that almost 31.4% of women suffered from headaches daily, 70% weekly, 28.1% monthly, and 23.5% rarely. Comparatively, only 8.3% of males suffered from daily headaches, 34% weekly, 12.8% monthly, and 14.9% rarely. The study shows promising results and encourages future researchers to conduct such a comprehensive epidemiological study on an even larger population to justify a more precise association of risk factors involved in migraine pathophysiology.

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In Silico Analysis of Usher Encoding Genes in Klebsiella pneumoniae and Characterization of Their Role in Adhesion and Colonization

PLoS ONE ◽

10.1371/journal.pone.0116215 ◽

2015 ◽

Vol 10 (3) ◽

pp. e0116215 ◽

Cited By ~ 17

Author(s):

Fida Khater ◽

Damien Balestrino ◽

Nicolas Charbonnel ◽

Jean François Dufayard ◽

Sylvain Brisse ◽

...

Keyword(s):

Klebsiella Pneumoniae ◽

In Silico ◽

In Silico Analysis ◽

Encoding Genes ◽

Silico Analysis

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

Seminars in Oncology ◽

10.1053/j.seminoncol.2020.02.012 ◽

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

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In Silico Analysis of Single Nucleotide Polymorphism in Human Prothrombin Gene

10.1055/s-0039-1680245 ◽

2019 ◽

Author(s):

I. Farah ◽

A. El-Mubark ◽

M. Osman ◽

A. Soliman ◽

F. Ali ◽

...

Keyword(s):

Single Nucleotide Polymorphism ◽

In Silico ◽

In Silico Analysis ◽

Nucleotide Polymorphism ◽

Single Nucleotide ◽

Prothrombin Gene ◽

Silico Analysis

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

Current Medicinal Chemistry ◽

10.2174/0929867327666200727114410 ◽

2020 ◽

Vol 27 (38) ◽

pp. 6523-6535 ◽

Cited By ~ 3

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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