The analysis of genomic and transcriptomic data involves several steps. First, the raw sequencing data is processed using various bioinformatics tools to align and map reads to a reference genome. For genomic data, variants are called to identify mutations, insertions, deletions, and copy number variations. For transcriptomic data, gene expression levels are quantified, and differential expression analysis is performed to identify genes that are upregulated or downregulated in cancer. Advanced statistical methods and machine learning algorithms are often used to interpret the complex datasets.
