The growth of genomic data has revolutionized the landscape of healthcare, research, and biotechnology. Over the past decade, advances in technology have led to a massive surge in genomic sequencing, creating an unprecedented wealth of data.
The cost of sequencing a human genome has plummeted from nearly $100 million in the early 2000s to less than $1,000 today, opening up new possibilities in personalized medicine, disease research, and bioinformatics.
Challenges faced by researchers
With advancements in computing and the availability of AI/ML based techniques, researchers still continue to face significant challenges in analyzing genomics data. These challenges include:
Data Storage and Management
As sequencing becomes cheaper, more genomes are being sequenced resulting in an exponential growth of genomic data.
This genomic data needs to be securely stored and shared in a storage system that can seamlessly scale.
Metadata
Storing large amounts of genomic data just solves one part of the puzzle.
The data also needs to be discoverable so that it can be used for exploratory analysis when researchers are designing and conducting experiments.
This is a problem faced by many organizations today as they often complain “We don’t even know what data we have. But our cloud storage has Petabytes of data”.
Computational Power
Analyzing such large amounts of data requires significant compute power. While Cloud has made it very easy to spin up compute when required, researchers still struggle to get access to compute when required.
Researchers have to work on broken internal processes to request capacity from IT teams in the form of tickets and internal systems. This introduces significant delays in timely access to compute.
With the availability of multiple Compute Modalities, such as, Batch, Kubernetes, High Performance Computing, significant time is being spent in arriving at the right compute solution that is optimal for a Workflow/Pipeline.
Workflows / Pipelines
To analyze genomic data at scale, researchers require access to Workflows / Pipelines that can process large volumes of genomic data.
Researchers spend vast amounts of their time in identifying these workflows, making them work with the diverse data formats, learning various workflow definition languages (such as Nextflow, Snakemake, WDL, CWL), and struggle with ensuring reproducibility.
Analytics
Gaining insights from genomics data requires usage of various data exploration tools to visualize results and perform tertiary analysis.
Significant time gets spent by researchers in figuring out the tools that are required, getting them to work with the Workflows and managing different versions, dependencies, etc.
Introducing Quark – A self-service Bioinformatics Platform
Quark, our self-service Bioinformatics platform addresses all of the above challenges and makes it extremely simple to generate actionable insights from genomics data.
Using Quark, Scientists & Bioinformaticians can run Multi Omics, Computational Biology, AI/ML experiments through a purpose-built, scientist-friendly self-service platform.
Quark for Bench Scientists
Quark makes it super simple for Bench Scientists to start deriving insights from genomics data.
With no coding or scripting expertise required, in just 3 simple steps, Bench Scientists can use Quark to get insights from their data.
- Step 1: Select a Pipeline from a catalog of ready to run pipelines. Quark comes pre-built with lots of readily available pipelines for Drug Discovery, Genomics, Epigenomics, Immuno-oncology and others.
- Step 2: Provide input data and start a large scale experiment. Whether it is a single sample or hundreds of samples, Quark will seamlessly provision and scale the required compute to run your experiments.
- Step 3: Start getting insights from the data through the out of the box Analytics that Quark provides.
Quark for Bioinformaticians
Quark enables Bioinformaticians to easily build new pipelines and collaborate with fellow researchers.
Visual Pipeline Builder
Building multi step genomics workflows requires deep understanding of Workflow Definition Languages such as Nextflow, WDL, CWL with steep learning curves.
Quark’s Pipeline Builder enables Bioinformaticians to easily build new pipelines without any coding or scripting expertise.
Using a drag and drop interface, new pipelines can be easily built in a matter of a few minutes by simply wiring together different tools. Quark provides all the popular tools (e.g. for QC, Variant Calling, etc.) that can be easily connected with each other to build complex workflows.
Reproducible Development Environments
For expert Bioinformaticians who like to build new pipelines by writing code, Quark provides development environments that can be easily created.
Quark’s development environments ensure reproducibility through a powerful versioning system that keeps track of all changes.
Bioinformaticians can choose different starter environments, define required packages (support for multiple package managers), connect required datasets. All seamlessly tracked through a version control system.
Powerful Analytics for everyone
Quark’s mission is to empower researchers so that they spend more time doing Science. To enable this, Quark provides powerful analytics features out of the box.
Quark’s Analytics feature enables researchers to:
- Perform Patient Stratification through powerful Gene & Variant search
- Build cohorts to identify trait specific biomarkers
- Perform rich analytics to generate insights
- Differential Gene Expression Analysis
- Gene Enrichment Analysis (Pathway, GO Enrichment, Disease Enrichment)
- Quality Control for Groups
- Mutant Allele Fraction
- Genomic Alteration
- Survival Benefit Analysis
Ready to experience the power of self-service Bioinformatics?
Quark has enabled numerous researchers to easily derive actionable insights from data.
If you would like to see Quark in action, request for a demo here. Or you can learn more about Quark’s features here.