Disrupting Multi-Omics Data Analysis: 5 Key Highlights of BioStrand
There are currently several programs underway across the globe, such as the 1+ Million Genomes initiative in the EU and the research program in the United States, designed to push the boundaries of genomic research . Programs like these, with the core objective to sequence at least one million genomes across the population, will finally be able to provide genomic data at scale and leverage the potential of genomics to advance the prevention, diagnosis, and treatment of a range of diseases.
However, collating valuable omics data and creating the infrastructure to share the information with healthcare companies, medical professionals, genetics researchers, etc., is just the first step in the process. The eventual value that this data enables will depend to a large extent on the capabilities of the omic sequence comparison and analysis tools available in today’s genetics research marketplace.
As the availability of genome, transcriptome and proteome sequences for research expands, it also drives the critical need for more efficient, accurate, and robust omics comparison tools and solutions. But contemporary technologies and conventional approaches may not be adequate and may even hamstring efforts to convert the data into value. Today, there is a clear lag between omics data generation and omics data analysis.
Take the COVID-19 genome, for example, with nearly a million known sequences to date. The most cited genome comparison tools for identifying identical sequences are currently capable of aligning a maximum of 1000,000 analogous sequences. The need of the hour, therefore, is for a Google-like approach to omics data analysis, so to speak, that indexes all relevant data in the public domain to enable the instant identification of exact-match sequences across vast omics data sets. If omics data analysis is to keep pace with omics data generation, there is an exigent need for an easy-to-use powerful solution that fundamentally transforms genetic research.
It is this need that the BioStrand SaaS platform addresses with a revolutionary cloud-based solution that is not just about accelerating conventional processes but fundamentally reinvents the approach to genetic research. Our multifaceted Retrieve & Relate application for sequence analysis enables faster and more accurate data analysis and delivers the most complete and comprehensive results possible.
Our transformative approach to omics data analysis also addresses several of the following key concerns and challenges prevalent in the industry today.
Speed and accuracy at scale
One of the key reasons for the lag between omics data generation and omics data analysis is the fact that conventional sequence comparison and analysis tools are outdated in terms of accuracy and effectiveness. Often, researchers and scientists are forced to sacrifice scalability for accuracy. More importantly, current techniques rely on statistical approximations which in turn can result in the propagation of errors.
Our next-generation analysis tools leverage the principles of indexing and exact matching used in computer science to deliver faster results at lower costs and with high accuracy. With exact matching, all data available, across multiple silos and databases, can now be analyzed at high speed and at scale without going through the cumbersome process of separately comparing and analyzing multiple sets of sequences across multiple data sources, including proprietary enterprise data.
Our simple, quick, comprehensive, and accurate solution frees up researchers and scientists to focus on finding new associations and discoveries without concerns about security, accuracy, and cost, and holds significant implications for pharmaceutical and biomedical R&D.
Accessible technology, comprehensive functionality
There are two huge challenges related to current genetic research — one, there is no single tool to analyze DNA, RNA, and proteins together, at all levels (the process, instead, relies on several disparate and complex processes and stages tools); and two, while the number of tools for data analyses is indeed growing, most of them are designed for research experts and experienced bioinformaticians and require extensive manual optimization.
When operating next to each other, without interacting, these techniques are lacking a vital dimension containing a bulk of untapped resources, targets and perhaps, solutions to many standing problems. Integrating data from separate omics techniques performed on the same cohort of samples is often referred to as vertical data integration, and it is not an easy task.
The BioStrand approach addresses both these challenges with one stroke. Out of the box, the solution has the capability to surface relationships between DNA, RNA, and proteins together, at all levels. Most importantly, the platform democratizes genetic research by making technology simple, intuitive, and accessible, mitigating the need for specialized skills. The solution drastically reduces the time-to-discovery for R&D departments and researchers with a simple solution that does not come with its own steep learning curve.
Faster time to value, insight & innovation
The BioStrand solution compresses multiple and often disparate stages of traditional omics data analysis into one simple, intuitive and user-friendly interface with the technology doing all the heavy lifting in the background. It eliminates all the usual challenges of building complex pipelines, finding access to multiple databases, and navigating the steep learning curve of a disparate tool environment. The solution actualizes the principle of ‘Data in, Results out’ to streamline and accelerate knowledge extraction and time-to-value.
Search is multi-domain and is as simple as inputting text or pasting bio-sequences with the results displayed on three levels: DNA, RNA, and AA. Drill down, filter, and extrapolate through the results and combine multiple dimensions, such as taxonomy or ontology, to quickly discover novelty functional relationships. Take a microscopic view down to the sequence level or discover other useful visual applications such as ontology maps, frequency tables, or multiple sequence alignment views.
In short, the platform is designed to maximize researchers’ view of their data with integrated, comprehensive, and accurate results that accelerate time-to-insight and -value.
There are a lot of variables that determine the cost of genetic sequence comparison and analysis, including the size of datasets, the length of sequences, access to computing services, cloud service subscriptions, etc. As a result, cost management has become a significantly time-consuming phase of the genetic research process. Cost implications can also have an adverse impact on research as the project scope may often be defined by budget rather than by eventual value.
In the BioStrand model, licenses have a fixed fee allowing researchers and R&D departments to budget more efficiently with the focus squarely on value.
Disrupting genomic data analysis
The current approach to genomic data analysis, with its approximations, errors, and its reliance on specialized skillsets and capital-intensive compute capabilities, leaves a lot of value on the table. It is essentially a compromised process with neither the accuracy nor the scalability required to cope with voluminous datasets of the Big Data age. As a result, genetic research is expensive, time-consuming, and, in the end, not exactly accurate.
The BioStrand SaaS platform fundamentally transforms genetic research with a validated, easy-to-use solution that affords more control, delivers timely and accurate results, accelerates R&D cycles, and delivers insights faster.
Register for future blogs
Image source: AdobeStock © Sunny studio 93394019
Originally published at https://blog.biostrand.be.