SNP 的VCF获取

尝试下全英文写作
Finding SNPs (Single Nucleotide Polymorphisms) in a genome involves multiple bioinformatics steps. Below is a general workflow for identifying SNPs from raw sequencing data:

1. Prepare Input Data

• Input: High-quality DNA sequencing reads (e.g., Illumina paired-end data).
• Tools: FastQC, Trimmomatic, fastp.
• Process:
  1. Quality Check: Use tools like FastQC to assess the quality of raw reads.
  2. Read Trimming: Remove low-quality bases, adapter sequences, and short reads to ensure high data quality.

2. Map Reads to a Reference Genome

• Input: Cleaned reads and a reference genome.
• Tools: BWA, BWA-MEM2, Bowtie2, or HISAT2.
• Process:
  1. Index the reference genome:

     bwa index reference.fasta
     

  2. Align the reads to the reference genome:

     bwa mem reference.fasta reads_R1.fastq reads_R2.fastq > aligned_reads.sam
     

3. Convert, Sort, and Index Alignments

• Tools: SAMtools, Picard.
• Process:
  1. Convert SAM to BAM:

     samtools view -bS aligned_reads.sam > aligned_reads.bam
     

  2. Sort the BAM file:

     samtools sort aligned_reads.bam -o sorted_reads.bam
     

  3. Index the sorted BAM file:

     samtools index sorted_reads.bam
     

4. Remove Duplicates (Optional)

• Tools: Picard

• Process:

  java -jar picard.jar MarkDuplicates \
       I=sorted_reads.bam \
       O=dedup_reads.bam \
       M=dedup_metrics.txt
  
  

• Tools:Sambamba(recommend)

• Process:

sambamba markdup -r -t 10 \
 ${sample}_sort.bam \
 ${sample}_mkdup.bam

5. Call Variants

• Tools: GATK (HaplotypeCaller), FreeBayes, or bcftools.
• Process:
  1. Generate a gvcf (genomic VCF) with GATK HaplotypeCaller:

     gatk HaplotypeCaller \
          -R reference.fasta \
          -I dedup_reads.bam \
          -O output.g.vcf.gz \
          -ERC GVCF
     

  2. Optionally, combine multiple samples into a single VCF using CombineGVCFs or similar tools.
  3. Perform joint genotyping to generate the final variant calls:

     gatk GenotypeGVCFs \
          -R reference.fasta \
          -V combined.g.vcf.gz \
          -O final_variants.vcf.gz
     

6. Filter Variants

• Tools: GATK VariantFiltration, bcftools filter.
• Process:
  1. Apply filters to remove low-quality SNPs:

     gatk VariantFiltration \
          -R reference.fasta \
          -V final_variants.vcf.gz \
          --filter-expression "QD < 2.0 || FS > 60.0 || MQ < 40.0" \
          --filter-name "basic_snp_filter" \
          -O filtered_variants.vcf.gz
     

7. Annotate SNPs

• Tools: SnpEff, ANNOVAR, VEP.
• Process:
  1. Annotate SNPs with their effects on genes or regulatory regions:

     snpEff ann database_name filtered_variants.vcf.gz > annotated_variants.vcf
     

8. Visualize and Validate SNPs

• Tools: IGV (Integrative Genomics Viewer), bcftools stats.
• Process:
  1. Validate SNPs visually using tools like IGV to confirm alignment accuracy.
  2. Generate statistics on the variants:

     bcftools stats filtered_variants.vcf.gz > variant_stats.txt
     

Output

• Final File: A VCF file (filtered_variants.vcf.gz) containing high-confidence SNPs, optionally annotated with functional information.

Key Considerations

• Ensure the reference genome is appropriate for the species being studied.
• Adjust variant calling and filtering parameters based on sequencing depth and quality.
• Use biological replicates or pooled data for more robust SNP detection.