Whole Exome Sequencing (WES): Principle, Steps, Uses microbiologystudy

Whole Exome Sequencing (WES) is a method of sequencing used to study the regions of the genome that code for proteins. These protein-coding regions within the genome are known as exons and they make up less than 2% of our entire genome, but they are very important because they contain about 85% of the genetic variants linked to different diseases.

Whole Exome Sequencing (WES) focuses on the exome and provides a more targeted approach compared to whole-genome sequencing (WGS), which sequences the entire genome including non-coding regions. Since most disease-related mutations are found in the exome, this makes WES an effective tool for diagnosing genetic conditions and understanding disease mechanisms. WES is especially useful in research and clinical settings to identify both common and rare genetic variants.

Principle of Whole Exome Sequencing 

The principle of WES involves selectively capturing and sequencing the exons which are parts of the genome that code for proteins. Exons represent a small fraction of the genome but contain the majority of disease-related variants. This makes WES a faster and cost-effective alternative to sequencing the whole genome for genetic analysis and identifying genetic variants that may contribute to different disorders. It also simplifies the data analysis process.

WES uses exome enrichment to capture the protein-coding regions from a library of adapter-ligated sequences by using special probes that bind to the exon sequences. These enriched exonic regions are sequenced using different high-throughput sequencing platforms to obtain detailed information on the coding regions. Finally, the data is analyzed to identify variants that could affect protein functions. This helps to understand different genetic conditions.

Process/Steps of Whole Exome Sequencing

1. Sample Preparation: The process begins with obtaining high-quality genomic DNA from various biological samples of interest. Different extraction methods are used with the salting-out method and spin column-based methods being the common ones. After the DNA is extracted, the quality and quantity of the DNA is checked to make sure it is ready for sequencing.
2. Library Preparation: DNA extraction is followed by library preparation for sequencing. At first, the extracted genetic material is fragmented into smaller pieces. This can be done either by sonication or by using enzymatic digestion methods. Then the ends of the fragmented DNA are repaired by removing overhanging nucleotides and ligated with special adapters which help the fragments bind to the sequencing platform.
3. Exome Enrichment: Then, the exonic regions are enriched using different capture methods. The most common method is aqueous-phase hybridization capture which involves hybridization to probes. Here, the DNA fragments are ligated to biotinylated probes that specifically target the exons. The hybridized fragments are then pulled down using biotin-streptavidin-based magnetic beads. Uncaptured or non-exonic regions of the DNA are washed away. The enriched exonic fragments are then eluted and amplified to prepare them for sequencing. There are different other capture methods including solid-phase or array-based hybridization and polymerase-mediated capture.
4. Sequencing: The enriched exome library is sequenced using different high-throughput sequencing platforms. This process produces millions of short overlapping reads from the DNA fragments that must be pieced together to reconstruct the exonic regions. Sequencing both the forward and reverse strands creates paired-end reads which provide longer-range information than single reads and improve alignment accuracy when mapping the reads to the reference genome. Once the sequencing is done, the raw data is ready for analysis.
5. Data Analysis: The raw sequenced data are pre-processed and aligned to the reference genome. During sequencing, multiple reads can be generated from the template leading to amplification biases introduced by PCR. These PCR duplicates are removed from the aligned reads. After alignment, different computational tools are used in variant calling to identify genetic variants, such as single nucleotide variants (SNVs) and insertions/deletions in the exonic regions. These variants are then filtered and annotated to identify genes that may be involved in potentially pathogenic variants. Further analysis helps to identify the specific genetic mutations responsible for different conditions.

Advantages of Whole Exome Sequencing 

• WES provides comprehensive coverage of coding regions.
• WES generates a smaller and more manageable dataset which makes it quicker and easier to analyze compared to whole-genome sequencing. This reduces processing time and data storage requirements.
• WES can analyze all coding regions of the genome simultaneously making it efficient for identifying genetic variants in protein-coding regions across the genome. 
• WES is scalable and allows large-scale screening which is useful for discovering mutations in genetic research. 
• It is cost-effective compared to whole-genome sequencing. It is affordable and practical for many research applications.

Limitations of Whole Exome Sequencing 

• WES only focuses on the exons and does not capture potentially functional non-coding elements. This misses clinically significant variants that may occur outside of the exonic regions. 
• It can suffer from issues with read depth and errors in aligning sequences to the reference genome.
• It has a limited ability to detect structural variations including copy number variants (CNVs) and indels which are often poorly resolved and aligned. Detecting CNVs using WES is difficult due to the non-uniform depth of coverage across different regions of the exome.
• The exome capture step in the WES can introduce technical biases. It cannot capture all regions of interest. Many target regions resist enrichment and complete capture cannot be achieved.
• It can uncover secondary or incidental findings such as mutations unrelated to the condition being tested which raises significant ethical and practical issues about handling these findings.

Applications of Whole Exome Sequencing 

• WES can be used in the diagnosis of genetic disorders. It is useful for finding the genetic causes of diseases that have many different known genetic factors.
• It can be used in prenatal screening to detect fetal abnormalities that could lead to developmental disorders or congenital conditions. This helps in determining the inheritance of genetic abnormalities.
• WES is also used in detecting inherited mutations that cause Mendelian disorders.
• It is also used to study novel and rare genetic variants which can be associated with different diseases. This is useful in identifying potential therapeutic targets.
• It can be used to understand how genetic variations affect an individual’s response to drugs which helps in the development of new drugs personalized to individual genetic profiles.
• It is used in studying complex diseases and also used in cancer research to discover mutations that contribute to cancer development.

Whole Exome Sequencing (WES) vs. Whole Genome Sequencing (WGS) 

Feature	Whole Exome Sequencing (WES)	Whole Genome Sequencing (WGS)
Sequencing coverage	It specifically targets and sequences only the protein-coding regions.	It covers the entire genome including coding and non-coding regions.
Data size and complexity	It produces smaller datasets which requires less data processing and is easier to analyze.	It generates larger datasets which requires higher computational and storage needs.
Cost	It is less expensive and is a cost-effective choice for genetic analysis.	It is more expensive compared to WES as it sequences the entire genome.
Advantages	It focuses on exonic regions where most disease-causing mutations occur which makes it easier to detect mutations that directly affect proteins.	It can detect mutations across the entire genome and can analyze both well-studied and less-understood regions without prior knowledge of the disease.
Limitations	It does not cover non-coding regions so it may miss important regulatory mutations.	It produces large amounts of data that increases costs and makes analysis more difficult.
Applications	It is commonly used in clinical settings to identify variants in protein-coding regions that are linked to diseases.	It is ideal for research settings for novel variant discovery, studying structural variants and non-coding regions.
Use cases	It is recommended for targeted studies focusing on protein-coding regions.	It is recommended for projects that require detailed analysis of the entire genome and have larger resources.

References

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