To analyze gene expression data on luxbio.net, you start by uploading your raw sequencing files (like FASTQ) or a pre-processed count matrix directly to the platform’s secure cloud workspace. The system automatically validates the file format and integrity, and within minutes, your data is ready for a comprehensive analysis pipeline that includes quality control, normalization, differential expression, and advanced functional interpretation. The platform is designed for both bioinformaticians and biologists, offering a point-and-click interface for standard workflows while also allowing for custom R or Python scripting in integrated Jupyter notebooks for advanced users. A typical RNA-seq dataset with 6 samples (3 control, 3 treated) can be processed from raw reads to a list of significant differentially expressed genes in under an hour, leveraging the platform’s scalable computing infrastructure.
Getting Your Data into the System
Your first step is data ingestion. Luxbio.net supports a wide array of file formats, but the most common starting point is FASTQ files from your sequencer. The platform performs an automatic pre-flight check, generating a quality report using FastQC-like metrics. You’ll see a summary table like this directly in your project dashboard, which is crucial for deciding if you need to trim adapters or filter low-quality reads before alignment.
Table 1: Example Post-Upload Quality Metrics for a Single FASTQ File
| Metric | Value | Interpretation |
|---|---|---|
| Total Sequences | 35,000,000 | Good sequencing depth for most organisms. |
| % GC Content | 48% | Within expected range for human/mouse samples. |
| Sequence Length | 150 bp (paired-end) | Standard length for modern RNA-seq. |
| % Bases ≥ Q30 | 92.5% | High-quality sequencing run; minimal trimming needed. |
If your data is already aligned, you can upload a gene count matrix (e.g., a CSV file with genes as rows and samples as columns). The system automatically detects the gene identifier type (e.g., ENSEMBL, Entrez, Symbol) and offers to map them to a consistent annotation database. For large datasets, exceeding 50 GB, the platform provides a dedicated high-speed upload client that resumable, which is a lifesaver for unstable internet connections.
The Core Analysis Workflow: From Reads to Insights
Once your data is uploaded, the real analysis begins. Luxbio.net structures this into a logical, step-by-step pipeline. You can run the entire process with default parameters or dive deep into customizing each step.
Step 1: Alignment and Quantification. The platform uses ultra-fast aligners like STAR or HISAT2 to map your reads to a reference genome of your choice. Their reference library is extensive, covering over 50,000 genomes from Ensembl, NCBI, and UCSC. For quantification, the default tool is featureCounts, which generates raw read counts for each gene. A key feature here is the ability to process multiple samples in parallel, drastically reducing computation time. For a 12-sample experiment, alignment and quantification might take just 20-30 minutes.
Step 2: Quality Control and Exploratory Data Analysis. This is where you assess the technical quality of your experiment. Luxbio.net generates a multi-panel QC report. You’ll get metrics like total mapped reads (aim for >70-80%), the distribution of reads across genomic features (e.g., exons, introns), and sample-specific statistics. Crucially, it performs a Principal Component Analysis (PCA) and generates a heatmap of sample-to-sample distances. This visually reveals outliers and whether your biological replicates cluster together. If your treated samples don’t separate from controls in the PCA plot, it’s an early warning that the treatment effect might be weak.
Step 3: Normalization and Differential Expression. This is the heart of the analysis. The platform automatically normalizes your raw count data to account for differences in sequencing depth and RNA composition. The default method is the median-of-ratios method used by tools like DESeq2. For differential expression, you can choose from several robust algorithms, including DESeq2, edgeR, and limma-voom. You configure the comparison by simply selecting your control and treatment groups from a dropdown menu. The output is a detailed table of all genes, complete with log2 fold changes, p-values, and adjusted p-values (FDR). The interface allows you to instantly filter this list; for example, you can set a filter for |log2FC| > 1 and FDR < 0.05 to get your significantly changed genes.
Table 2: Top 5 Rows from a Typical Differential Expression Results Table
| Gene Symbol | Base Mean Expression | Log2 Fold Change | P-value | Adjusted P-value (FDR) |
|---|---|---|---|---|
| FOS | 1050.8 | +4.82 | 2.1E-15 | 5.5E-12 |
| JUN | 890.5 | +3.91 | 7.8E-14 | 1.1E-10 |
| EGR1 | 650.2 | +3.45 | 3.4E-11 | 2.9E-08 |
| MYC | 2200.1 | +2.15 | 0.00023 | 0.0087 |
| CDKN1A | 1100.6 | +2.01 | 0.00045 | 0.012 |
Advanced Analysis and Visualization Tools
After identifying differentially expressed genes (DEGs), Luxbio.net provides a suite of tools to understand their biological meaning. The platform is integrated with major annotation databases like GO (Gene Ontology), KEGG, and Reactome. With one click, you can run an over-representation analysis on your list of significant DEGs. The output isn’t just a table; it’s an interactive graph. You can see which biological processes (e.g., “inflammatory response”), molecular functions, or pathways are statistically enriched. The system provides a fold enrichment score and a p-value, and you can drill down to see exactly which genes in your list belong to that pathway.
Visualization is a key strength. The platform generates publication-ready figures directly within the interface. This includes Volcano plots (showing fold change vs. significance), MA plots, and heatmaps of normalized expression for the top DEGs across all samples. You can customize colors, labels, and dimensions before exporting the plot as a high-resolution PNG or PDF. For pathway analysis, it can generate pathway diagrams from KEGG where your DEGs are highlighted in color, providing an instant visual summary of which parts of a cellular process are affected.
Collaboration, Reproducibility, and Scaling Up
Luxbio.net isn’t just an analysis tool; it’s a collaborative research environment. You can share your entire project—data, code, and results—with colleagues with a few clicks. They can view your analysis, re-run it with different parameters, or build upon it. This is vital for reproducibility. Every action you take in the platform is logged, creating a complete audit trail. For grant applications or publications, you can generate a “Methods” section automatically, detailing every tool and parameter used.
For users dealing with massive datasets, like single-cell RNA-seq (scRNA-seq) from thousands of cells, the platform offers specialized workflows. These include cell clustering, trajectory inference (pseudotime analysis), and cell-type identification using reference databases. The underlying computing infrastructure scales seamlessly, so you don’t need to worry about memory or processing power, even for datasets with 100,000 cells. The pricing model is based on compute-hours and storage, making it cost-effective for labs of all sizes. They offer significant educational and non-profit discounts, and you can get started with a free tier that includes enough resources to analyze a small pilot dataset.