What is whole genome sequencing?
Whole genome sequencing (WGS) is a next-generation sequencing (NGS) method used to analyze genetic variation across an organism’s entire genome.
By sequencing both coding and non-coding regions, WGS supports broad variant discovery, comparative genomics, microbial characterization, metagenomic profiling and de novo genome assembly. The method is useful across fields such as:
- Human genetics
- Cancer research
- Infectious disease research
- Microbiology
- Agriculture
- Plant and animal genomics
- Environmental research
What can whole genome sequencing detect?
Diverse applications of whole genome sequencing
Whole genome sequencing is useful across applications where broad genomic context is needed. These examples show how researchers use WGS and what each application can help investigate:
- Broad variant discovery (such as human genetics research): Detect genome-wide variants
- Rare disease research: Identify causal variants that may fall outside known genes or panels
- Cancer research: Detect SNVs, indels, CNVs, SVs or mutational signatures, and support analysis of intra-tumor heterogeneity in single-cell WGS
- Microbial genomics: Support strain typing, outbreak analysis, AMR marker detection and genome comparison
- De novo assembly: Assemble genomes when a reference genome is unavailable or incomplete
- Metagenomics: Provide broad taxonomic and functional profiling
- Plant and animal genomics: Discover variants linked to agricultural or biological traits, or study genetic diversity across breeds, strains, cultivars or wild populations
- Model organism research: Confirm strain identity, characterize engineered lines and identify background or spontaneous variants that may explain experimental phenotypes
How whole genome sequencing compares with whole exome sequencing and targeted sequencing
For studies focused on a small number of genes or known regions, targeted sequencing may provide deeper coverage at a lower sequencing burden. For studies focused on coding regions, whole exome sequencing (WES) may be sufficient.
WGS, WES and targeted sequencing differ in genomic scope, cost, sequencing depth, analysis complexity and discovery potential.
| Method | What it analyzes | Best suited for | Key considerations |
|---|---|---|---|
| Whole genome sequencing | Coding and non-coding regions across the genome |
|
Requires more sequencing (and resulting costs for sequencing reagents) and data analysis than narrower methods |
| Whole exome sequencing | Primarily protein-coding regions | Studies focused on coding variants |
May miss regulatory, intronic, intergenic and some structural variants |
| Targeted sequencing | Selected genes, panels or genomic regions |
High-depth analysis of known targets |
Limited discovery outside selected regions |
Limitations of whole genome sequencing
WGS is a powerful approach for analyzing structural variants, CNVs and mitochondrial variants that WES may not capture well, but there are still limitations to WGS to consider.
- Very low-frequency variants could be missed without sufficient depth
- Highly repetitive or difficult-to-map regions may not yield accurate results
- Some large or complex structural variants could be missed, depending on read length and analysis tools
- Epigenetic modifications are not identified unless the workflow is designed to detect them
- Sample or library prep bias can reduce confidence in regions with poor or uneven coverage
- Low-abundance organisms or clones may be missed when their abundance is below detection thresholds
Method selection is also a workflow decision
Choosing between WGS, WES and targeted sequencing affects more than sequencing scope. The choice also influences sample input requirements, library preparation strategy, coverage targets, indexing needs, sequencing cost, data storage and analysis complexity.
For whole genome sequencing, library preparation has a direct impact on genome representation. A streamlined, reproducible library prep workflow can help researchers reduce variability, support uniform coverage and improve confidence in downstream analysis.
PCR-free vs PCR-amplified WGS library prep
WGS library preparation workflows generally include amplification of DNA fragments. However, PCR-free workflows are also available.
- PCR-amplified workflows may be needed when DNA input is limited or sample quality is challenging
- PCR-free library preparation can help reduce amplification-related bias and duplicate reads when sufficient high-quality DNA is available
For any method, the best workflow starts with a clear focus on the research questions to be answered: how much of the genome needs to be examined, which variant types matter, how many samples will be processed, and what level of sensitivity is required?
Choosing the right WGS workflow for your project
A successful WGS workflow depends on more than sequencing alone. All the following parameters influence the quality of the final results and must be considered to match the project requirements:
- Input DNA quality
- Fragmentation method
- Library preparation kit
- Coverage requirements
- Bias control
- Throughput
- Data analysis
We offer solutions designed to support reliable whole genome sequencing workflows from sample preparation through sequencing-ready libraries.
Plan your experiment and implement it in your lab
How much coverage do you need for whole genome sequencing?
The right coverage depth depends on the study goal, sample type and variant class of interest. Higher coverage can improve results in several situations.
- Increase confidence in variant detection, especially for low-frequency variants, heterogeneous samples or complex genomes
- Support more accurate consensus generation
- Help detect structural variants and reduce the risk of false-negative results
Lower coverage may be sufficient for some screening, population-scale or microbial applications.
Coverage considerations by application
| Application | Coverage considerations |
|---|---|
| Human germline WGS |
Often designed for high-confidence genome-wide variant discovery. Coverage should support the variant classes and confidence thresholds required by the study. |
| Cancer WGS |
May require tumor-normal pairing and higher effective coverage depending on tumor purity, heterogeneity and variant allele frequency. |
| Microbial WGS |
Coverage depends on genome size, isolate purity, strain complexity and whether the goal is assembly, variant calling or resistance marker analysis. |
| Metagenomic WGS |
Depth depends on sample complexity, host background, microbial abundance and whether low-abundance organisms or genes must be detected. |
| De novo assembly |
Often requires careful library strategy, sufficient depth and attention to read length and genome complexity. |
| Low-pass WGS |
Uses lower coverage when broad genomic information is needed across many samples, often with imputation or population-level analysis. |
| Plant and animal WGS |
Coverage depends on genome size, ploidy, repeat content, heterozygosity and research objective. |
See how CDPHE evaluated updated extraction and library prep options for bacterial whole genome sequencing for enteric pathogens in the PulseNet surveillance network. This webinar compares their current workflow with alternate extraction and library preparation methods using EZ2 Connect and QIAseq FX DNA Library Kit, highlighting key quality metrics, workflow tradeoffs and compatibility with existing foodborne pathogen surveillance protocols.