MERCURIUS™ DRUG-seq Data Analysis:
From FASTQ to Differential Expression

High-throughput transcriptomics can generate thousands of expression profiles in a single experiment, but turning raw sequencing data into reliable biological results requires the right analytical workflow.

Whether you are analyzing your first MERCURIUS™ DRUG-seq experiment or looking to establish a reproducible workflow for larger compound perturbation studies, this session will provide a practical framework for moving confidently from sequencing output to interpretable transcriptional responses.

In this practical webinar, we will walk through the key steps for analyzing MERCURIUS™ DRUG-seq data, from raw FASTQ files to quality-controlled count matrices and differential expression results.

Using real MERCURIUS™ DRUG-seq data and practical examples, we will explain what to look for at each stage, how to recognize common technical issues, and how to distinguish meaningful treatment responses from experimental artifacts.

What you will learn

• How MERCURIUS™ DRUG-seq sequencing data are processed from FASTQ files to gene expression count matrices
• Which quality-control metrics matter most for MERCURIUS™ DRUG-seq experiments
• How to identify sample outliers, plate effects, and technical artifacts
• Key considerations for normalization, experimental design, and differential expression analysis
• How to identify compound-induced transcriptional responses and generate perturbation signatures
• Best practices for obtaining robust and interpretable results from high-throughput DRUG-seq experiments

Who should attend?

This webinar is designed for scientists working with or considering MERCURIUS™ DRUG-seq, including bioinformaticians, computational biologists, screening scientists, and researchers who want a practical understanding of how high-throughput transcriptomic data are processed and analyzed.

No advanced bioinformatics expertise is required.

Live Q&A will follow the presentation.