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Discipline 03 of 18 38 Tools Stage 3: Variant Discovery & Structural Variation

🔍 3. Variant Calling (Germline, Somatic, SVs, CNVs & Visual Inspection)

De novo haplotype assembly, deep convolutional pileup imaging, Bayesian population calling, subclonal tumor-normal calling, and structural variation discovery.

Essential Software: GATK HaplotypeCaller DeepVariant FreeBayes Mutect2 Strelka2 Manta
Comparative Analysis Production Benchmarks

Algorithm Architecture & Decision Matrices

Detailed comparison cards highlighting algorithmic trade-offs, strengths, weaknesses, and recommended production setups.

Germline Small Variants (SNV/Indel)
GATK HaplotypeCaller DeepVariant (CNN) FreeBayes Strelka2 Octopus Platypus Clair3 (ONT/HiFi) Longshot PEPPER-Margin-DeepVariant
De novo haplotype assembly, deep convolutional pileup imaging, Bayesian population calling, and long-read SNP resolution.
Somatic Cancer & Tumor-Normal
GATK Mutect2 Strelka2 Somatic VarScan2 VarDict LoFreq Pisces ClairS (Long-Read Somatic) MuSE / SomaticSniper
Low-VAF subclonal mutations, targeted panel and amplicon calling, liquid biopsy error models, orientation bias filtering.
Structural Variants (SVs > 50 bp)
Manta DELLY Sniffles2 cuteSV LUMPY GRIDSS SvABA SURVIVOR SVIM / pbsv / Smoove
Deletions, duplications, inversions, translocations, split-read assembly, long-read clustering, multi-caller merging.
Copy Number, Clonality & SV Inspection
CNVkit Control-FREEC GATK gCNV FACETS ExomeDepth CNVnator ichorCNA PURPLE / PyClone-VI Samplot / Ribbon
Ploidy/purity estimation, low-pass tumor fraction, WES exon depth CNVs, and read-level breakpoint visualization.
Hardware & Execution Profiles

Primary Software & Algorithms in Variant Calling & Discovery

Direct command-line recipes, Conda environment specifications, memory footprints, and license metadata.

Germline & Somatic Caller
💻 16-32GB RAM Java • Apache Spark

GATK4

Docs ↗ By Broad Institute
(License: BSD-3-Clause)

Gold-standard genome analysis toolkit for germline (HaplotypeCaller) and somatic (Mutect2) variant discovery, Base Quality Score Recalibration (BQSR), and VQSR.

Formats: Input: BAM, CRAM, FASTA • Output: VCF, gVCF
💻 8-16 CPU Cores • 16-32 GB RAM
conda install -c bioconda gatk4
Deep Learning CNN Variant Caller
⚡ GPU Required Python / C++ • TensorFlow / GPU

DeepVariant

Docs ↗ By Google Health & Genomics
(License: BSD-3-Clause)

Deep convolutional neural network that calls genetic variants by transforming aligned BAM reads into multi-channel pileup images. Achieves precision scores >99.9%.

Formats: Input: BAM, CRAM, FASTA • Output: VCF, gVCF
💻 16 CPU Cores • 32 GB RAM • NVIDIA GPU (Optional)
docker pull google/deepvariant:latest
VCF/BCF Manipulation
💻 < 8GB RAM C • HTSlib

bcftools

Docs ↗ By Petr Danecek & Heng Li
(License: MIT / GPL-3)

Blazing-fast utility suite for calling, filtering, sorting, merging, and querying VCF/BCF variant call format files. Includes mpileup genotype caller and consensus builder.

Formats: Input: VCF, BCF, BAM • Output: Indexed VCF, BCF, FASTA
💻 2-4 CPU Cores • 2-4 GB RAM
conda install -c bioconda bcftools
Haplotype-based Caller
💻 < 8GB RAM C++ • Bayesian Haplotype Models

FreeBayes

Docs ↗ By Erik Garrison & Gabor Marth
(License: MIT)

Bayesian genetic variant detector designed to find SNPs, indels, MNPs, and complex haplotypes smaller than sequencing read length directly from short read alignments.

Formats: Input: BAM • Output: VCF
💻 4-8 CPU Cores • 8-16 GB RAM
conda install -c bioconda freebayes
Long-Read SV Caller
💻 16-32GB RAM Python / C++ • Clustering Algorithms

Sniffles2

Docs ↗ By Smok et al. (Fritz Sedlazeck Lab)
(License: MIT)

Fast and accurate structural variant caller for long reads (PacBio HiFi and Oxford Nanopore). Calls deletions, duplications, inversions, and translocations with multi-sample genotype calling.

Formats: Input: BAM, CRAM • Output: VCF, .snf files
💻 8 CPU Cores • 16 GB RAM
conda install -c bioconda sniffles
Short-Read SV Discovery
💻 16-32GB RAM C++ / Python • Graph Assembly

Manta

Docs ↗ By Illumina
(License: GPL-3.0)

Somatic and germline structural variant and indel caller for paired-end short reads. Combines discordant paired-end alignment evidence with split-read graph assembly.

Formats: Input: BAM, CRAM • Output: VCF
💻 8-16 CPU Cores • 16-32 GB RAM
conda install -c bioconda manta
Variant Effect Predictor
💻 16-32GB RAM Perl / C • Cache Indexes

Ensembl VEP

Docs ↗ By William McLaren et al. (EMBL-EBI)
(License: Apache-2.0)

Determines the clinical consequence of genomic variants in coding and non-coding regions. Annotates SIFT, PolyPhen, gnomAD allele frequencies, ClinVar, and splice sites.

Formats: Input: VCF • Output: Annotated VCF, HTML, JSON
💻 4-8 CPU Cores • 16-32 GB RAM (Local cache)
conda install -c bioconda ensembl-vep
Variant Annotation & Filtering
💻 < 8GB RAM Java • Pre-built Genome DBs

SnpEff & SnpSift

Docs ↗ By Pablo Cingolani
(License: LGPL-3.0)

Rapid genomic variant annotation and functional effect prediction toolbox. Calculates consequence categories (missense, nonsense, frameshift) and filters multi-sample VCFs.

Formats: Input: VCF • Output: Annotated VCF, HTML summary
💻 4 CPU Cores • 8-16 GB RAM
conda install -c bioconda snpeff
Structural Variant Caller
💻 < 8GB RAM C++ • Paired-End & Split-Read

Delly

Docs ↗ By Tobias Rausch et al. (EMBL)
(License: BSD-3-Clause)

Integrated structural variant discovery tool utilizing paired-end discordant mapping and split-read clipping to resolve somatic and germline SV breakpoints at base-pair precision.

Formats: Input: BAM, CRAM • Output: BCF, VCF
💻 4-8 CPU Cores • 8-16 GB RAM
conda install -c bioconda delly
Genomic Interval Arithmetic
💻 < 8GB RAM C++ • Interval Trees

bedtools

Docs ↗ By Aaron Quinlan Lab (Utah)
(License: GPL-2.0)

The Swiss-army knife for genomic interval arithmetic. Enables rapid intersection, merging, coverage calculation, subtracting, and windowing across BED, GFF, VCF, and BAM genome annotations.

Formats: Input: BED, VCF, GFF, BAM • Output: Filtered BED, Intersected intervals
💻 2-4 CPU Cores • 4-8 GB RAM
conda install -c bioconda bedtools
Recommended Workflow Progression

Proceed to Stage 4: Variant Annotation & Clinical Impact

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