blog.bilgehan.nlThe Payoff: Finding Meaning with Differential Expression AnalysisThis is the moment we've been working towards. We have journeyed from raw, messy sequencing reads, through meticulous quality control, alignment, and quantification. The result of that journey is a clean, simple counts matrix: a table where rows are ...Aug 16, 2025·4 min read
blog.bilgehan.nlCounting the Evidence: Quantifying Gene ExpressionIn our last post, we successfully aligned our clean reads to the genome, generating BAM files that tell us the genomic origin of each read. We're now one step away from the exciting biological questions. The next task is to convert this mapping infor...Aug 14, 2025·3 min read
blog.bilgehan.nlFinding a Home: Aligning RNA-Seq Reads to the GenomeWe've meticulously cleaned our raw data. Now, we need to figure out where each read came from by aligning it to a reference genome. For RNA-Seq, this is tricky because of splicing, where introns are removed from the final transcript. This means we ne...Aug 14, 2025·4 min read
blog.bilgehan.nlGarbage In, Garbage Out: A Practical Guide to QC for RNA-SeqTitle: Garbage In, Garbage Out: A Practical Guide to QC for RNA-Seq In our last post, we organized our raw FASTQ files and saw how Snakemake can automate merging. Now, we must confront a critical truth of bioinformatics: no data is perfect. This is w...Aug 14, 2025·4 min read
blog.bilgehan.nlDeconstructing the Data: A Guide to Raw RNA-Seq FilesIn our last post, we made the case for using workflow managers. Now, let's apply that philosophy to a real-world scenario: analyzing RNA-sequencing (RNA-Seq) data. This process, which takes us from raw sequencer output to biological insight, will be ...Aug 14, 2025·3 min read
