How to feed BWA output into RNA-seq analysis?

Prepare BWA Output

• Ensure that your BWA output is in SAM format, as this is commonly required for downstream RNA-seq analysis. If not already in SAM format, convert your BWA output (typically in BAM format) into SAM using SAMtools:

 

• Use the command: samtools view -h -o output.sam input.bam to convert BAM to SAM.

Sort and Index the SAM File

• Convert the SAM file to a BAM file, sorting it in the process, to prepare for indexing and further processing.

 

• Run: samtools sort -o sorted\_output.bam output.sam to sort the file.

 

• Index the sorted BAM file using: samtools index sorted\_output.bam to facilitate random access to the data.

QC Check of the Alignment

• Perform quality checks on the alignment to ensure reliable data for RNA-seq analysis.

 

• Use tools like Qualimap or Samtools stats to evaluate the alignment's overall quality, looking at metrics like mapping rates and coverage.

Generate Read Counts

• To proceed with RNA-seq analysis, generate read counts from the aligned BAM file. Programs like HTSeq-count or featureCounts can be utilized for this task.

 

• Ensure you have an appropriate annotation file (GTF or GFF) that corresponds to your reference genome.

 

• Example with HTSeq-count: Run htseq-count -f bam -r pos -s no sorted\_output.bam genes.gtf > counts.txt.

Normalize Data

• Normalization of read counts is crucial for accurate differential expression analysis. Consider methods like DESeq2 or edgeR for normalization.

 

• These tools account for sequencing depth and library size to facilitate meaningful comparison across samples.

Proceed to Differential Expression Analysis

• With normalized read counts, begin differential expression analysis to understand gene expression profiles among different conditions.

 

• Tools like DESeq2 or edgeR provide frameworks to conduct statistical tests on the normalized data, highlighting differentially expressed genes.

Visualization and Interpretation

• Visualize the results using tools such as R with packages like ggplot2 or specific plotting functions within DESeq2.

 

• Generate plots like heatmaps, PCA, or MA plots to aid in the interpretation of the RNA-seq data and results.