Case Studies: Real-World Uses of AutoSNPa in Genomic Research

Getting Started with AutoSNPa: Installation and First StepsAutoSNPa is an automated pipeline for single nucleotide polymorphism (SNP) analysis designed to streamline variant identification, filtering, and basic annotation. This guide walks you through installation, initial configuration, running your first analysis, and interpreting basic outputs. It’s written for researchers and bioinformaticians with basic familiarity with the command line and genomic data formats (FASTQ, BAM, VCF).

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1. System requirements and prerequisites

• Operating system: Linux (Ubuntu/CentOS) or macOS. Windows users should use WSL2 or a Linux virtual machine.
• Memory & CPU: At least 8 GB RAM and 4 CPU cores for small datasets; scale resources up for whole-genome analyses.
• Disk space: Minimum 20 GB free; larger datasets require substantially more space (100+ GB).
• Software prerequisites:
  • Python 3.8+
  • Conda (Miniconda/Anaconda) recommended for environment management
  • Git
  • Common bioinformatics tools (some may be installed automatically): BWA, SAMtools, bcftools, bedtools, and optionally Picard/GATK for advanced workflows

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2. Installation

There are two common ways to install AutoSNPa: via Conda (recommended) or from source.

Option A — Conda (recommended)

1. Install Miniconda or Anaconda if not already present.
2. Create and activate a new environment:

   
   conda create -n autosnpa_env python=3.9 -y conda activate autosnpa_env 

3. Install AutoSNPa (if available on a channel) and dependencies:

   
   conda install -c bioconda autosnpa -y 

4. Verify installation:

   
   autosnpa --help 

If AutoSNPa is not in conda channels, install dependencies via conda and use the source install below.

Option B — From source

1. Clone the repository:

   
   git clone https://github.com/username/AutoSNPa.git cd AutoSNPa 

2. Install Python dependencies:

   
   conda create -n autosnpa_env python=3.9 -y conda activate autosnpa_env pip install -r requirements.txt 

3. Install the package:

   
   pip install -e . 

4. Confirm the CLI is available:

   
   autosnpa --version 

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3. Configuration and reference data

AutoSNPa requires reference genome FASTA and associated index files, plus optional annotation databases.

• Obtain a reference FASTA (e.g., GRCh38 or GRCh37) and create indices:

  # example for BWA and samtools bwa index reference.fa samtools faidx reference.fa 

• Create a sequence dictionary (required by some tools):

  picard CreateSequenceDictionary R=reference.fa O=reference.dict 

• Common annotation sources: dbSNP VCF, ClinVar, and gene models (GTF/GFF).

Configure a YAML/JSON config file (example):

reference: /path/to/reference.fa bwa: /usr/bin/bwa samtools: /usr/bin/samtools threads: 4 output_dir: ./autosnpa_output 

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4. Input data formats

AutoSNPa accepts:

• Raw reads: paired or single FASTQ (gzip supported)
• Aligned reads: BAM/CRAM
• Existing variant files: VCF

Organize inputs in a simple directory structure:

project/   samples/     sample1_R1.fastq.gz     sample1_R2.fastq.gz   reference/     reference.fa 

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5. Running your first analysis

This example runs a simple pipeline: alignment (BWA), sorting/indexing (SAMtools), variant calling (bcftools), and basic filtering.

1. Basic command:

   
   autosnpa run  --sample sample1  --r1 samples/sample1_R1.fastq.gz  --r2 samples/sample1_R2.fastq.gz  --reference reference/reference.fa  --threads 4  --outdir autosnpa_output 

2. Typical pipeline steps (what AutoSNPa executes behind the scenes):

• Read alignment with BWA-MEM
• Convert SAM to BAM, sort and index with SAMtools
• Mark duplicates (Picard)
• Variant calling with bcftools mpileup + call
• Basic variant filtering (QUAL, depth, strand bias)

1. Output files to expect:

• autosnpa_output/sample1.sorted.bam and .bai
• autosnpa_output/sample1.raw.vcf.gz
• autosnpa_output/sample1.filtered.vcf.gz
• QC reports (read depth, mapping stats)

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6. Interpreting outputs

• BAM: check alignment quality with samtools flagstat and IGV.

  
  samtools flagstat sample1.sorted.bam 

• VCF: view variants with bcftools or convert to tabular form.

  
  bcftools view autosnpa_output/sample1.filtered.vcf.gz | head 

  Key VCF fields: CHROM, POS, REF, ALT, QUAL, FILTER, INFO (DP, AF).

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7. Common troubleshooting

• “Reference index not found”: ensure bwa index and samtools faidx exist for the reference.
• “Memory errors during mpileup”: reduce threads or increase RAM.
• Low variant yield: check read quality, coverage, and proper sample pairing.

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8. Tips & next steps

• Use known-sites (dbSNP) for base quality recalibration if adding GATK steps.
• For cohort analyses, run joint calling workflows to reduce false positives.
• Integrate annotation tools (SnpEff, VEP) to add gene/impact information to VCFs.

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9. Example minimal workflow script

#!/bin/bash set -e REF=reference/reference.fa SAMPLE=sample1 R1=samples/${SAMPLE}_R1.fastq.gz R2=samples/${SAMPLE}_R2.fastq.gz OUT=autosnpa_output bwa mem -t 4 $REF $R1 $R2 | samtools view -bS - | samtools sort -o $OUT/${SAMPLE}.sorted.bam samtools index $OUT/${SAMPLE}.sorted.bam bcftools mpileup -f $REF $OUT/${SAMPLE}.sorted.bam | bcftools call -mv -Oz -o $OUT/${SAMPLE}.raw.vcf.gz bcftools filter -s LOWQUAL -e '%QUAL<20 || DP<10' $OUT/${SAMPLE}.raw.vcf.gz -Oz -o $OUT/${SAMPLE}.filtered.vcf.gz tabix -p vcf $OUT/${SAMPLE}.filtered.vcf.gz 

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10. Resources and help

• Check the AutoSNPa README and GitHub issues for known bugs and community tips.
• Use conda-forge/bioconda channels for dependency updates.
• For specific errors, capture logs and post minimal reproducible examples when seeking help.