Bioinformatics Pipeline for Screening Mouse Sequences for B16-F10 Lineage

Answered using ChatGPT 4o by ChatGPT4o

Question:

How to screen mouse sequences (fastq) for membership to the B16-F10 melanoma lineage using bioinformatic pipelines.

Answer:

Screening mouse sequences (FASTQ files) for membership to the B16-F10 melanoma lineage involves several bioinformatics steps. Here’s a general pipeline you can follow:

1. Quality Control

First, ensure the quality of your raw sequencing data.

  • Tools: FastQC, MultiQC
  • Steps:
    • Run FastQC on your FASTQ files to generate quality reports.
    • Use MultiQC to aggregate the reports for easier interpretation.
fastqc sample_R1.fastq sample_R2.fastq
multiqc .

2. Trimming

Remove low-quality bases and adapter sequences.

  • Tools: Trimmomatic, Cutadapt
  • Steps:
    • Use Trimmomatic to trim adapters and low-quality bases.
trimmomatic PE -phred33 sample_R1.fastq sample_R2.fastq \
  sample_R1_paired.fastq sample_R1_unpaired.fastq \
  sample_R2_paired.fastq sample_R2_unpaired.fastq \
  ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36

3. Alignment

Align the reads to the mouse reference genome.

  • Tools: BWA, Bowtie2
  • Reference Genome: GRCm38 (Mouse Genome)
  • Steps:
    • Index the reference genome.
    • Align the reads to the reference genome.
bwa index GRCm38.fa
bwa mem GRCm38.fa sample_R1_paired.fastq sample_R2_paired.fastq > sample_aligned.sam

4. Convert SAM to BAM and Sort

Convert the SAM file to BAM format and sort it.

  • Tools: SAMtools
  • Steps:
    • Convert SAM to BAM.
    • Sort the BAM file.
samtools view -Sb sample_aligned.sam > sample_aligned.bam
samtools sort sample_aligned.bam -o sample_sorted.bam

5. Mark Duplicates

Mark duplicate reads to avoid biases in downstream analysis.

  • Tools: Picard
  • Steps:
    • Mark duplicates in the BAM file.
picard MarkDuplicates I=sample_sorted.bam O=sample_dedup.bam M=sample_metrics.txt

6. Variant Calling

Identify variants that are characteristic of the B16-F10 melanoma lineage.

  • Tools: GATK, FreeBayes
  • Steps:
    • Call variants using GATK HaplotypeCaller.
gatk HaplotypeCaller -R GRCm38.fa -I sample_dedup.bam -O sample_variants.vcf

7. Filter Variants

Filter the called variants to retain high-confidence variants.

  • Tools: GATK
  • Steps:
    • Apply variant filters.
gatk VariantFiltration -R GRCm38.fa -V sample_variants.vcf -O sample_filtered_variants.vcf \
  --filter-expression "QD < 2.0 || FS > 60.0 || MQ < 40.0" --filter-name "basic_snp_filter"

8. Compare with B16-F10 Specific Variants

Compare the filtered variants with known B16-F10 melanoma lineage-specific variants.

  • Tools: bcftools, BEDTools
  • Steps:
    • Obtain a list of known B16-F10 specific variants.
    • Use bcftools or BEDTools to intersect your variants with the known B16-F10 variants.
bcftools isec -p isec_output -n=2 sample_filtered_variants.vcf B16-F10_known_variants.vcf

9. Interpret Results

Analyze the intersection results to determine the membership of your sequences to the B16-F10 melanoma lineage.

10. Visualization (Optional)

Visualize the results for better interpretation.

  • Tools: IGV, Integrative Genomics Viewer
  • Steps:
    • Load the BAM and VCF files into IGV for visualization.
igv.sh

Summary

This pipeline involves quality control, trimming, alignment, duplicate marking, variant calling, filtering, and comparison with known B16-F10 variants. Each step uses specific bioinformatics tools to ensure accurate and reliable results. Adjust the parameters and tools based on your specific requirements and data characteristics.