Learn how to perform multiple sequence alignment (MSA) using ClustalW, from retrieving sequences to interpreting results. This guide covers essential steps, such as gap penalties and phylogenetic analysis, and explores how MSA reveals evolutionary relationships and functional insights in genomic research.
Microarray analysis examines gene expression differences between diseased and healthy tissues by labeling and hybridizing cDNA on a chip. Emission levels are measured to determine gene activity, and public tools like GEO NCBI enable comparison of patient and control data. Statistically significant changes reveal potential therapeutic targets and disease mechanisms.
This blog covers the basics of PCR, the role of primers, key criteria for primer design, and the significance of SNPs. It also explains how to design primers for SNP mutations using various bioinformatics tools.
In this blog we will learn about transcription factors, their types, functions, and their key role in regulating gene expression. Discover methods and tools for predicting transcription factors and understanding their influence on gene activity.
In this blog, I offer a detailed, step-by-step guide on using bioinformatics tools for multiepitope vaccine design. Covering everything from antigen selection to epitope validation, this resource is tailored to help researchers efficiently design vaccines that target multiple epitopes, ensuring a strong immune response.
This blog covers the basics of Python and R along with difference between these languages. If you’re stepping into the world of data science, you’ve likely come across two powerhouse programming languages: Python is suitable for machine learning while R is better for statistical analysis.