🧬 WIA-BIO-001

Genome Sequencing Data Standard
Unlocking the Blueprint of Life Through Standardized Genomic Data

πŸ“‹ Specification

Complete technical specification for genome sequencing data formats and protocols

πŸ”¬ Simulator

Interactive genome data simulator - test sequencing workflows in 99 languages

πŸ“– eBook (English)

Comprehensive guide to genome sequencing standards and best practices

πŸ“– ν•œκΈ€ μ „μžμ±…

κ²Œλ†ˆ μ‹œν€€μ‹± ν‘œμ€€ μ™„λ²½ κ°€μ΄λ“œ

Overview

The WIA-BIO-001 standard defines a comprehensive framework for genome sequencing data management, ensuring interoperability, reproducibility, and quality across genomic research and clinical applications. This standard addresses the entire lifecycle of genomic data from sequencing to analysis and sharing.

3.2B
Base Pairs in Human Genome
20,000+
Protein-Coding Genes
99.9%
Genetic Similarity Between Humans
$600
Cost of Whole Genome Sequencing (2024)

Key Features

πŸ” Data Format Standardization

FASTQ, BAM/SAM, VCF/BCF formats with strict quality controls and metadata requirements

πŸ“Š Quality Metrics

Phred scores, coverage depth, mapping quality, variant confidence metrics

πŸ§ͺ Sequencing Technologies

Support for Illumina, PacBio, Oxford Nanopore, and emerging platforms

πŸ”’ Privacy & Security

HIPAA compliance, encryption, de-identification, consent management

🌐 Interoperability

GA4GH compatibility, FAIR principles, cross-platform data exchange

πŸ“ˆ Variant Calling

SNP, indel, CNV, SV detection with population frequency annotations

Data Formats & Structure

FASTQ Format (Raw Reads)

@SEQ_ID
GATTTGGGGTTCAAAGCAGTATCGATCAAATAGTAAATCCATTTGTTCAACTCACAGTTT
+
!''*((((***+))%%%++)(%%%%).1***-+*''))**55CCF>>>>>>CCCCCCC65

VCF Format (Variants)

##fileformat=VCFv4.3
##reference=GRCh38
#CHROM  POS     ID      REF ALT QUAL    FILTER  INFO    FORMAT  SAMPLE
chr1    12345   rs12345 A   G   99      PASS    DP=100  GT:GQ   0/1:99
chr2    67890   .       C   T   85      PASS    DP=85   GT:GQ   1/1:85

Core Data Elements

Element Format Description Required
Sample ID String (UUID) Unique identifier for biological sample Yes
Sequencing Platform Enum Illumina, PacBio, Nanopore, etc. Yes
Reference Genome String GRCh38, GRCh37, T2T-CHM13 Yes
Coverage Depth Float Average read depth (30x, 60x, etc.) Yes
Quality Scores Phred Scale Base quality (Q20, Q30 thresholds) Yes
Variant Annotations JSON/VCF Clinical significance, population frequencies Recommended

Use Cases

πŸ₯ Clinical Diagnostics

Scenario: A 35-year-old patient with family history of breast cancer undergoes germline sequencing.

  • Whole exome sequencing (WES) targeting 20,000+ genes
  • Detection of BRCA1/BRCA2 pathogenic variants
  • Clinical-grade quality metrics (>100x coverage, >99% accuracy)
  • ACMG variant classification and reporting
  • Secure PHI handling and genetic counseling integration

πŸ”¬ Cancer Genomics

Scenario: Tumor-normal paired sequencing for precision oncology.

  • Somatic mutation detection (SNVs, indels, CNVs, fusions)
  • Tumor mutational burden (TMB) calculation
  • Actionable variant identification for targeted therapy
  • Microsatellite instability (MSI) status assessment
  • Integration with drug response databases (OncoKB, CIViC)

🧬 Population Genomics

Scenario: Large-scale cohort study of 100,000 participants.

  • Genome-wide association studies (GWAS) for complex traits
  • Rare variant analysis and burden testing
  • Ancestry inference and population structure
  • Polygenic risk score development
  • Data sharing through controlled-access repositories

πŸ‘Ά Prenatal & Newborn Screening

Scenario: Non-invasive prenatal testing (NIPT) and newborn genomic screening.

  • Cell-free DNA sequencing for aneuploidy detection
  • Newborn screening for 300+ genetic disorders
  • Rapid turnaround for critical findings (<48 hours)
  • Parental consent and counseling protocols
  • Long-term data storage for future reanalysis

Quality Control Pipeline

Pre-Processing Quality Checks

  1. Read Quality Assessment: FastQC analysis for per-base quality, GC content, adapter contamination
  2. Adapter Trimming: Remove sequencing adapters and low-quality bases (Trimmomatic, Cutadapt)
  3. Contamination Screening: Check for human, bacterial, viral contamination
  4. Read Alignment: BWA-MEM, Bowtie2 to reference genome with MAPQ >20 threshold

Post-Alignment Quality Metrics

Metric Acceptable Range Clinical Grade Tools
% Mapped Reads >95% >98% SAMtools, Picard
Mean Coverage >30x >100x Mosdepth, GATK
% Bases at Q30 >90% >95% FastQC, Qualimap
Insert Size 300-500bp 350Β±50bp Picard CollectInsertSizeMetrics
Duplicate Rate <20% <10% Picard MarkDuplicates
Ti/Tv Ratio 2.0-2.1 2.0-2.1 bcftools stats

Variant Calling Best Practices

GATK Best Practices Pipeline

# 1. Mark duplicates
gatk MarkDuplicates -I aligned.bam -O marked.bam -M metrics.txt

# 2. Base quality score recalibration
gatk BaseRecalibrator -I marked.bam -R reference.fa --known-sites dbsnp.vcf -O recal.table
gatk ApplyBQSR -I marked.bam -R reference.fa --bqsr-recal-file recal.table -O recal.bam

# 3. Call variants
gatk HaplotypeCaller -R reference.fa -I recal.bam -O variants.vcf

# 4. Variant quality score recalibration
gatk VariantRecalibrator -V variants.vcf --resource:hapmap,known=false,training=true,truth=true,prior=15.0 hapmap.vcf \
  -an QD -an MQ -an FS -mode SNP -O snp.recal --tranches-file snp.tranches

# 5. Apply filters
gatk ApplyVQSR -V variants.vcf --recal-file snp.recal --tranches-file snp.tranches -mode SNP -O filtered.vcf
πŸ’‘ Best Practice Tip: Always use the latest reference genome (GRCh38/hg38) and dbSNP build. Maintain separate pipelines for germline vs. somatic variant calling with appropriate sensitivity/specificity trade-offs.

Privacy & Security

HIPAA Compliance for Genomic Data

Genomic Privacy Challenges

⚠️ Re-identification Risk: Even de-identified genomic data can potentially be re-identified through:
  • Genealogical database matching (GEDmatch, 23andMe)
  • Triangulation with public databases (1000 Genomes, gnomAD)
  • Rare variant fingerprinting
Implement differential privacy, secure multi-party computation, and controlled access tiers.

Integration & Interoperability

GA4GH Standards Alignment

GA4GH Standard Purpose WIA-BIO-001 Integration
htsget Streaming genomic data access Required for federated queries
Phenopackets Clinical phenotype representation Recommended for variant interpretation
VRS (Variation Representation) Unambiguous variant nomenclature Required for variant exchange
DRS (Data Repository Service) Cloud-agnostic data access Required for data repositories
Passports & AAI Authentication & authorization Required for controlled access

Implementation Guide

Quick Start

npm install @wia/bio-genome-sequencing

import { GenomeData, VariantCaller, QualityControl } from '@wia/bio-genome-sequencing';

// Load FASTQ files
const reads = await GenomeData.loadFastq('sample_R1.fastq.gz', 'sample_R2.fastq.gz');

// Quality control
const qc = new QualityControl(reads);
const report = await qc.generateReport();
console.log(`Mean Quality: ${report.meanQuality}`);

// Align to reference
const aligned = await reads.align('GRCh38', { threads: 8 });

// Call variants
const caller = new VariantCaller({ mode: 'germline', minQuality: 30 });
const variants = await caller.call(aligned);

// Export VCF
await variants.exportVCF('output.vcf.gz');

εΌ˜η›ŠδΊΊι–“ (Hongik Ingan)

Broadly Benefiting Humanity Through Genomic Understanding