“In science, the insights of the past are digested and incorporated into the present in the same way that the genetic material of our ancestors is incorporated into the fabric of our body.”
— Harold L. Burstyn
Without a hint of exaggeration, genetic data is the spine of personalized healthcare. As such, advancements in genomics are the key to new tiers of medical care. Today, genetic research applications have expanded beyond biology into psychology, sociology, demography, statistics, and economics. Genetic information, combined with data analysis and computing power, can be applied to multiple areas of research.
In November 2019, EurekAlert reported that scientists were developing a method to standardize genetic data analysis. A major challenge and opportunity is next-generation sequencing that can detect if a patient has a mutation. This requires special quality metrics to ensure confidence in data accuracy.
The development of precision medicine relies upon combining whole genomic data and data analysis tools. Clinicians need state-of-the-art solutions based on advanced computation and ML. Molecular technology is nearing maturity and will bring new methodologies and approaches. These innovations will demand new ways to analyze data sets.
In this article, we scratch only the surface of a very complex topic. If you are reading this, it means you are not a stranger to research and understanding the value data conveys. We will cover the basics of whole genome analysis, genomic data storage, and the advantages software can bring to healthcare projects.
Whole Genome Analysis
Whole genome analysis (WGA) is a tool designed to acquire the full genomic code of a human or any other organism. WGA’s power is in its ability to generate immense data sets from sequence reads. Best of all, WGA is getting more and more accessible.
Obviously, researchers need to analyze this data and interpret it. Data processing involves multiple steps using software. Genomic variations are compared to reference genomes. This can take weeks using traditional approaches, but new innovations such as cloud computing makes whole genome analysis fast and comparatively cheap.
“Eventually we’ll be able to sequence the human genome and replicate how nature did intelligence in a carbon-based system.”
— Bill Gates, co-founder of Microsoft, investor, and philanthropist
Genomic Data Storage
The amount of data available is growing geometrically. With DNA, it’s soaring. This rapid increase raises an array of security, legal, ethical and technical challenges. In addition to data accumulation, analysis, and distribution, data storage is critical and requires novel tech solutions.
Technology Record reports that the human genome sequence of a single person requires approximately 100 GB of storage space. The number of sequenced genes is rapidly increasing, and scientists will need exabytes of storage capacity. Researchers have high hopes for cloud computing because of its ability to scale quickly and limitlessly. In 2014, Google launched Cloud Life Sciences (formerly Google Genomics), which allows scientists to store DNA recordings on server farms. Microsoft Genomics and AWS have developed their own services.
“If you get a personal genome, you should be able to get personal cell lines, stem cell derived from your adult tissues, that allow you to bring together synthetic biology and the sequencing so that you can repair parts of your body as you age or repair things that were inherited disorders.”
— George M. Church; Professor and Researcher in Genetics; Harvard, MIT, and Harvard Medical School
Whole Sequencing
In a nutshell, whole genome sequencing (WGS) provides holistic genetic analysis. It is a comprehensive method developed for analyzing the whole genome. Genetic information is crucial for identifying mutations and inherited disorders, and tracking the progress of diseases. Some mutations can drive cancer. Therefore, sequencing must be able to process massive amounts of data and be cost-effective. Next-generation sequencing (NGS) is predominantly associated with human genome sequencing.
WGS has many benefits:
- Huge volumes of data combined with high speed
- High-resolution, base-by-base genome pictures
- Ability to capture missed variants of various size
- Identification of the potential causative variants
WGS falls into the following methods:
- Phased sequencing
- Large WGS
- De novo sequencing
- Small WGS
“Automating DNA sequencing can save time and money and reduce human error, allowing genomic-based personalized healthcare to work miracles where conventional methods fall short. We expect doctors will apply genomic data in clinical practice, and sequencing will become a routine task for hospitals. Our responsibility is to provide tools to make this happen.”
— Vlad Medvedovsky, Founder and Chief Executive Officer at Proxet, custom software development solutions company.
Benefits Of Building Genetic Data Analyzing Software And Tools
Before delving into the perks of software, let’s review the benefits of genome analytics. Why are genomic analytics important?
The analysis of genomics involves three stages:
- Stage 1: A physical sample is converted into raw sequence of data with the help of sequencers
- Stage 2: A computationally-intensive procedure is used to put the base pairs in order
- Stage 3: Genetic data extracts meaning
This meaning is extracted by comparing the genetic data with references; the more references available, the better. For example, experiments were carried out to measure the correlation between nutritional requirements and ancestry. It turned out that diet depends on genomics and specific ancestry. This discovery means that dieticians can develop nutritional programs that can enhance health and tackle diseases proactively, long before they are ever detected.
Every development in genomics presents new challenges, such as privacy. Genetic companies often sell or share genomic data with third parties. This can lead to life-saving discoveries, but compromises patient confidentiality. Human information and health data has significant commercial value, which is why governments make great efforts to get ownership of it.
Given that each country has its own regulatory framework and pre-approved processes for genetic data sharing, we expect to see guidelines on genetic ethics and data encryption in order to mitigate the risks. Obviously, open data sharing is not an option.
We at Proxet have found ways to connect genomics and algorithms, artificial intelligence, and machine learning. So far, our software has provided the following advantages to healthcare companies:
- a variety of reporting options
- due flexibility as to research specificity and clinical needs
- real mutation detection and ignoring of calling errors
- simplicity and swiftness of the process
- utmost accuracy
- direct trace comparison
- streamlined workflow
- enhanced visualization, interpretation, and control
- biologist-friendly
Genetic testing of data provides insights that become the grounds for new discoveries. The latest innovations allow genetics to approach genomes agnostically, working with dense, granular data sets. In the 2010s, the field has moved from hypothesis-driven research to large-scale, genome-wide scans. In the 2020s, the field will come to rely on software tools where security plays a key role. Software development teams can deliver easy-to-use, out-of-the-box, and deeply personalized solutions that tackle specific needs. A dependable digital partner with expertise can push your healthcare project to the next level.
