Introduction

A mapping-based transcriptome analysis pipeline is a structured workflow used for analyzing RNA sequencing (RNA-seq) data. This type of analysis helps scientists understand gene expression by mapping RNA-seq reads back to a reference genome. Here’s a general outline of what a mapping-based transcriptome analysis pipeline typically involves:

  1. Quality Control (QC):
    • Raw Data QC: Assess the quality of the raw sequencing reads using tools like FastQC. This helps identify issues such as adapter contamination, low quality scores, or overrepresented sequences.
    • Trimming: Based on the QC report, trim adapters and remove low-quality bases from the reads using tools like Trimmomatic or Cutadapt.
  2. Read Mapping:
    • Alignment: Map the processed reads to a reference genome using aligners that are optimized for RNA-seq data, such as STAR, HISAT2, or Bowtie2. These tools account for RNA splicing by aligning reads across exon junctions.
    • Post-Mapping QC: Tools like Qualimap or RSeQC can be used to assess the quality of the alignment, such as the percentage of reads mapped, mapping distribution, and potential biases.
  3. Assembly and Quantification:
    • Transcript Assembly: For organisms with a well-annotated reference genome, this step might involve using the alignments to assemble reads into transcripts using tools like StringTie or Cufflinks.
    • Quantification: Estimate the expression levels of genes and transcripts. This can be done using the assembled transcripts or directly from the aligned reads using software like featureCounts or HTSeq.
  4. Differential Expression Analysis:
    • Analyze the quantified data to identify genes or transcripts that are differentially expressed across different conditions or treatments. Tools like DESeq2, edgeR, or limma are commonly used for this purpose.
  5. Functional Analysis:
    • Annotation: Map the genes and transcripts to known databases to understand their potential functions using tools like Blast2GO or DAVID.
    • Pathway Analysis: Identify enriched pathways or gene ontology (GO) terms to understand the biological processes impacted by the observed differential expression.
  6. Visualization and Reporting:
    • Generate plots and visualizations to represent data quality, read distribution, expression levels, and differential expression results using tools like ggplot2 in R or Python’s matplotlib.
    • Prepare comprehensive reports and potentially interactive web interfaces for exploring the results.
  7. Optional Advanced Analysis:
    • Isoform discovery and alternative splicing analysis.
    • Fusion gene detection or SNP calling.
    • Integration with other types of genomic data.

This pipeline can be modular, and depending on specific research needs, steps can be added or omitted. Additionally, numerous bioinformatics tools exist for each step, allowing researchers to tailor the pipeline to best fit their data and research questions.

Given that you have a de novo assembled genome of a non-model organism and a set of raw transcriptome reads, you can follow a comprehensive transcriptome analysis pipeline tailored for non-model organisms with limited reference data. This will help you perform tasks like assessing gene expression, identifying novel transcripts, and possibly discovering alternative splicing events or genetic variations. Here’s a step-by-step guide:

Pipeline

1. Pre-processing of RNA-Seq Reads

  • Quality Control: Use FastQC to analyze the quality of your raw Illumina reads.

  • Trimming: Use Trimmomatic to trim adapters and remove low-quality bases. This step is crucial to ensure the quality of the downstream analysis.

    trimmomatic PE -phred33 input_forward.fq.gz input_reverse.fq.gz \
output_forward_paired.fq.gz output_forward_unpaired.fq.gz \
output_reverse_paired.fq.gz output_reverse_unpaired.fq.gz \
ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

2. Alignment to the Reference Genome

  • Align Reads: Use an RNA-seq aware aligner like HISAT2 or STAR. For de novo assemblies, HISAT2 is a good choice because it can handle genomes without a prior annotation efficiently.

    hisat2-build reference.fasta reference_index
hisat2 -p 8 -x reference_index -1 output_forward_paired.fq.gz -2 output_reverse_paired.fq.gz -S output.sam

3. Conversion of SAM to BAM, Sorting, and Indexing

  • Convert SAM to BAM, sort, and index the alignment files using SAMtools.

    samtools view -bS output.sam > output.bam
samtools sort output.bam -o output_sorted.bam
samtools index output_sorted.bam

4. Transcript Assembly and Quantification

  • Assembly: Use StringTie to assemble transcripts. This step can integrate both your GFF file and the novel information derived from the reads.

    stringtie output_sorted.bam -G annotation.gff -o assembled_transcripts.gtf -l prefix

  • Quantification: Quantify gene and transcript abundances using StringTie or featureCounts to prepare for differential expression analysis.

    stringtie -e -B -p 8 -G assembled_transcripts.gtf -o gene_abundances.gtf output_sorted.bam

5. Differential Expression Analysis

  • Prepare count matrices and perform differential expression analysis using DESeq2 or edgeR in R. Ensure to prepare a proper design matrix if you have multiple conditions or replicates.

6. Functional Annotation

  • Annotation: Since this is a non-model organism, you may need to use tools like BLAST to find homologous genes in related species and infer potential functions.
  • Pathway Analysis: Use tools like KEGG or Reactome for pathway analysis if applicable.

7. Visualization and Reporting

  • Visualize the quality metrics, alignment statistics, transcript distributions, and differential expression results using tools like ggplot2 in R.
  • Prepare a detailed report summarizing the methods, results, and interpretations.

8. Optional Advanced Analysis

  • Depending on your specific research questions, consider exploring alternative splicing with tools like rMATS or SUPPA2, or genetic variation analysis using tools integrated within the GATK suite for RNA-seq data.

This pipeline assumes command-line familiarity and access to sufficient computational resources. Each step may require specific parameter adjustments based on your data characteristics and the quality of the de novo assembly. Additionally, consider running pilot analyses on subsets of your data to refine parameters before processing the entire dataset.