How to analyze RNA-seq in Galaxy?

Prepare Your Data

• Start by collecting your RNA-seq data, typically in FASTQ format. Make sure your data is well-organized and that you know its origin (paired-end or single-end).

 

• Upload your FASTQ files to Galaxy, either by direct upload or using FTP if the files are large. Ensure your data is properly formatted and metadata is correctly associated.

Quality Control and Trimming

• In Galaxy, use tools like FastQC to assess the quality of your raw sequence data. This step helps identify issues like low-quality bases or adapter contamination.

 

• Use Trimmomatic or Cutadapt within Galaxy to trim adapters and low-quality regions from your reads, improving the reliability of downstream analysis.

Read Alignment

• Select an RNA-seq aligner such as HISAT2 for accurate alignment of your RNA-seq data to the reference genome. Set appropriate parameters for strand-specificity if applicable.

 

• Use Galaxy's history panel to manage your aligned data files. Verify that your alignments are successful by examining the output files, often in BAM format.

Quality Assessment of Alignment

• Perform post-alignment QC using tools like CollectRnaSeqMetrics and Samtools Flagstat. These tools help ensure your reads are aligning correctly and uniformly.

 

• Visualize your alignments using visualization tools like Integrated Genome Browser (IGB) in Galaxy to manually inspect the alignment quality.

Quantification of Gene Expression

• Utilize HTSeq-count or featureCounts in Galaxy to quantify reads mapped to genes, generating a count matrix suitable for differential expression analysis.

 

• Choose the appropriate annotation files (GTF or GFF3) matching your reference genome to ensure accurate counting.

Differential Expression Analysis

• Use Galaxy tools like DESeq2 or EdgeR for differential expression analysis. Input your count matrix and specify conditions/variables for comparison.

 

• Examine the results for differentially expressed genes, exporting tables and plots that can showcase the expression changes.

Interpretation and Visualization

• Make use of additional visualization tools such as heatmaps and Volcano plots in Galaxy to better understand the expression patterns and insights from your data.

 

• Consider integrating pathway analysis tools, such as GO analysis or KEGG, to associate differentially expressed genes with biological pathways and functions.

Export and Share Results

• Organize your final results, ensuring they are clearly documented. Export tables, plots, and relevant outputs from Galaxy for reporting or further offline analysis.

 

• Share your Galaxy history or workflows with collaborators by using Galaxy's sharing options, promoting reproducibility and collaborative discussion.