In the sprawling universe of modern science, bioinformatics stands as one of the most transformative and empowering fields of our time. It is where biology meets computation, where data becomes meaning, and where curiosity becomes discovery. If...
In the genomic era, the ability to predict the virulence potential of pathogens has become an indispensable part of infectious disease research. With the exponential growth of microbial genome data, bioinformatics tools now enable scientists to...
Bioinformatics is now at the heart of modern biology and medicine. From decoding genomes and predicting antimicrobial resistance, to developing personalized medicine and advancing evolutionary research, computational skills are no longer optional —...
Bioinformatics is a rapidly growing field at the intersection of biology, computer science, mathematics, and statistics. As data volumes increase, as well as the diversity of data types (genomics, proteomics, metabolomics, imaging, single‑cell data,...
If you’re starting out in bioinformatics or looking to sharpen your computational biology skills, having the right learning resources makes all the difference. Here’s my curated list of 10 must-read books — from beginner-friendly introductions to...
Single Nucleotide Polymorphisms (SNPs) are the most common type of genetic variation in humans—and many other organisms. A single base change in the DNA sequence (for example, an A instead of a G) can influence everything from our eye color to...
In statistics and bioinformatics, you’ll often see results reported with p-values, FDR, and q-values (q-scores). But what do these terms mean, and how are they different? Let’s break them down with simple definitions and a step-by-step example.
In today’s era of big biology, we’re generating more data than ever before—genomes, transcriptomes, proteomes, metabolomes, microbiomes… you name it. But raw biological data doesn’t speak for itself. Making sense of it requires more than traditional...
High-throughput sequencing has become indispensable in cell biology, enabling detailed insights into chromatin structure, gene expression, and regulatory dynamics. Yet, when faced with unexpectedly low mapping rates to the human genome,...
DESeq2 is a powerful and widely-used R package that identifies differentially expressed genes (DEGs) from RNA-seq data. Whether you're comparing treated vs untreated samples, disease vs healthy conditions, or wild-type vs mutant strains, DESeq2...