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DeepVariant: Redefining Genomic Analysis.

DeepVariant model Genetic variation detection DNA sequencing data Deep learning techniques

The field of genomic analysis concerns the investigation of DNA sequences in order to identify genetic variations. These variations within the DNA sequence may be either inherited or acquired and are frequently associated with a range of traits, drug responses and illnesses.

The emergence of DeepVariant

Using statistical methods is the traditional approach for performing genetic analysis. This method, meanwhile, might not be very accurate, especially when looking for uncommon genetic variants.

The DeepVariant model, developed by Google AI, has been specifically engineered to detect genetic variations in DNA sequencing data through deep learning techniques. The model’s training is centred around a robust dataset of established genetic variations, which facilitates its ability to compare new sequencing data with known variants. This process endows DeepVariant with a remarkable capacity for identifying variations with a high degree of precision.

Deep learning has recently become a powerful method for genetic research. Deep learning models surpass conventional techniques in precision owing to their ability to extract intricate patterns from data. In comparison to conventional methodologies, DeepVariant has been shown to exhibit significantly greater accuracy rates, reaching up to 99.9%. Beyond its accuracy, the DeepVariant technology is faster and more scalable.

Understanding the DeepVariant models

The DeepVariant models are based on a form of machine learning commonly known as deep learning. The deep learning models are trained on a vast dataset of established genetic variants. Upon presentation of new DNA sequencing data, the DeepVariant model utilizes its knowledge of the recognized genetic variants to determine prospective variants in the new data.

Initially, the DeepVariant model functions by fragmenting the DNA sequencing data into minute sections. These sections are then juxtaposed with the recognized genetic variants in the training dataset of the model. If there is a resemblance, the model will designate the segment as a potential variant.

The DeepVariant model applies various methods to determine the likelihood of a particular genetic variant. This involves assessing the statistical probability based on known mutation rates of specific DNA sequences. The model also factors in the quality of the DNA sequencing data. High-quality, error-free data increases the certainty of the identified variants, contributing to a more accurate outcome.

Furthermore, DeepVariant takes into account the genetic context, which refers to the DNA sequences surrounding the variant and their known functions or characteristics. This provides insight into whether the detected variant is in a region of significance, such as a protein-coding gene or regulatory element.

The model uses a scoring system. This scoring system, a numerical measure of confidence in each variant, takes these factors into account. With this system, a higher score signifies a more likely true genetic variant rather than a sequencing error, providing a ranked list of potential variants to guide subsequent analysis or investigation.

Evolution of DeepVariant models

Over time, the DeepVariant models have undergone significant evolution. Initially, the models were trained on short-read sequencing – a method that breaks down DNA into small pieces, usually 50 to 300 base pairs long, for separate analysis, which has inherent limitations in providing a comprehensive depiction of the DNA sequence. Consequently, this approach can result in the misidentification of variants.

However, in 2019, an update was made to the DeepVariant model whereby long-read sequencing, a technique where larger amounts of DNA, often thousands of base pairs long, are analyzed. This allows it to better handle repetitive regions and larger genetic variants that short-read sequencing may miss. This approach provides a more exhaustive representation of the DNA sequence, thus enabling the DeepVariant model to accurately identify more variants.

Despite the progress made, the DeepVariant model is still undergoing development with newer techniques being incorporated to enhance its accuracy in identifying variants.

DeepVariant: The Game-changer

DeepVariant has become a highly impactful tool in the realm of genomic analysis. Notably, it has been utilized to enhance the precision of clinical diagnostics, research endeavours and drug discovery efforts.

  • Regarding clinical diagnostics, DeepVariant has been leveraged to develop novel tests for rare diseases, along with identifying genetic variants that are linked to new drugs. For instance, it can identify a specific SNP in the CFTR gene that is known to cause Cystic Fibrosis, or an expanded CAG repeat in the HTT gene that leads to Huntington’s disease, thereby providing critical insights into patient diagnosis and treatment plans.
  • The utilization of DeepVariant in research has helped in scrutinizing the genetic foundations of various ailments and detecting previously unidentified genes associated with specific disorders. 
  • Lastly, DeepVariant serves as a valuable tool in drug discovery endeavours, facilitating the identification of genomic targets for innovative pharmaceuticals and aiding in the evaluation of their efficacy through clinical trials. For example, DeepVariant might identify an SNP (Single Nucleotide Polymorphism) leading to the overexpression of a certain protein that is associated with disease progression. This could then guide pharmaceutical companies to develop drugs that inhibit this protein, effectively treating the disease.

DeepVariant: The challenges and solutions

There exist various challenges that can impede the adoption and scaling of DeepVariant for genomic analysis aimed at achieving global health equity hence the need for effective solutions. Some of these challenges and their accompanying solutions include:

  • The compilation of recognized genetic variants is incessantly expanding, albeit keeping abreast with the latest updates can be challenging. One strategy to conquer this predicament is to gather data through crowd-sourcing from the research community. This would enable DeepVariant to be trained on a more extensive and diversified dataset, thereby refining its precision.
  • DeepVariant necessitates a robust computer to function, which could pose a hindrance for some institutions. A solution to this issue is to utilize cloud computing. Cloud computing platforms provide immediate access to potent computers, enabling DeepVariant to execute even if an organization lacks its own powerful computer.
  • DeepVariant is not yet entirely automated, thus requiring some human intervention, which is often time-consuming. An approach to overcome this hurdle is to implement machine learning to mechanise the analysis process. This would enhance the efficiency of utilizing DeepVariant and enable it to scale with greater ease.

Can DeepVariant do more?

DeepVariant isn’t limited to genetic variants. It’s also adept at recognizing structural variants such as deletions (where a part of the DNA is missing) and duplications (where a section of DNA is repeated). Both types can have significant effects on an individual’s health, altering gene function and contributing to various diseases. Angelman syndrome (an intellectual disability caused by deletion of chromosome 15) and MECP2 duplication syndrome (a neurological disorder caused by a duplication of genetic material on the X chromosome, which includes the MECP2 gene) are some of the diseases caused by these structural variants.

DeepVariant can also detect copy number variants – changes in the number of copies of a gene, which can influence how that gene functions and the traits it governs. Cancer, congenital disorders, schizophrenia, and autism are medical conditions that can result from copy number variants.

Scaling deep variant for global health equity

DeepVariant possesses the potential to advance global health equity by enhancing the accessibility of genomic analysis in developing countries.

Nevertheless, this objective encounters several challenges that need to be tackled. Costs associated with genetic analysis, a lack of qualified workers, and inadequate infrastructure in developing nations are a few of these difficulties.

There are diverse approaches to address these challenges:

  • One such approach is to diminish the cost of genomic analysis by using cloud computing and open-source software.
  • Another approach is to train more personnel in developing countries to utilize DeepVariant.
  • Finally, it is of utmost importance to construct the necessary facilities such as laboratories and data centres in less developed countries to facilitate genetic research and analysis.

It is possible to amplify the accessibility of genomic analysis to people in developing countries and enhance global health equity by addressing the barriers to the adoption and scaling of DeepVariant.

Additionally, there are other specific approaches to overcome the barriers to scaling DeepVariant for global health equity. These include:

  • Developing new methods for training DeepVariant models on smaller datasets, would make it feasible to use DeepVariant in countries with limited resources.
  • Developing new methods for running DeepVariant on less powerful computers would make it possible to use DeepVariant in more remote areas.

Making better decisions about patient care using DeepVariant would require the development of new and innovative solutions to the challenges hindering its adoption and scaling. With this, DeepVariant could become a more accessible and potent tool for genomic analysis, which could significantly impact global health equity.

Source: https://ai.googleblog.com/2020/09/improving-accuracy-of-genomic-analysis.html?m=1


Hiequity Team

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