How Clovertex Can Help With Your Multiomics Initiatives

HPC enabled cloud services

Development, scaling & optimization

Analytics and advanced interpretation

Hypercare, support, and bioinformatics services
Datalake and database management for bioinformatics on cloud

Infrastructure Engineering

Scientific cloud computing services, including high-performance computing (HPC), can solve many challenges, including data storage, organized workflows, environment configuration, and batch processing. A strong HPC infrastructure helps end-users design pipelines using parallel computing instead of the traditional sequential computing. This results in significant decrease in time and resource utilization with an increase in the scale of innovation.

Clovertex brings significant expertise in building high-performance computing architecture for deploying and processing a variety of pipelines and large-scale multi-omics data. Our team of experienced engineers can successfully implement various workflows in HPC environments both, on-cloud, and on-premises. We provide strategic guidance for selection of best infrastructure and migration support for Bioinformatics solutions.

Pipeline Engineering

Pipeline engineering involves data ingestion, pre-processing, analysis, storage, and report generation when providing solutions for consumer & clinical genomics, hospitals, and pharmaceutical companies. Next-generation sequencing and multi-omics studies generate peta-byte scale of data, which can overwhelm traditional pipeline workflows developed in Linux-based shell scripts.

The use of workflow managers such as Nextflow, Cromwell and containerized solutions, offer easy-to-deploy solutions which can be tailor-made to the requirements of a pipeline. This solution provides insights into performance metrics, job status tracking, job automation, and many other factors. These reports can be helpful in pipeline optimization. The integration of cloud services such as AWSBatch, ECS, Parallel Clusters with pipeline engineering further increases the efficiency and scalability.

Clovertex brings expertise in both scientific and technical skills for developing multi-omics-based pipeline engineering practices. Our team can deploy a system of workflow managers for on-cloud or on-premises servers and modularize the entire pipeline workflow for bulk data and multiple user-based requirements. Our experience includes working with DNA/RNA sequencing, Microarray, Single-cell, Epigenetics, Metagenomics, and Drug biomarker-based pipelines. We also incorporate best practices in proteomics and metabolomics using advanced science and technology as required in the healthcare industry.

Data Analytics and Visualization

Data analytics transforms raw data into conclusive findings and visualizations for ease of understanding. Data obtained from multi-omics experiment are increasingly being used in the research of drug discovery and precision medicine; however, with the data being generated in petabyte scale, analytics has traditionally created a unique set of challenges when transforming the raw data on an efficient time scale. The development of latest Bioinformatics and Computer Science algorithms to process huge volumes of data has dramatically improved the process of transforming data into insightful results, which can significantly help in drug discovery and precision medicine.

Artificial Intelligence, Machine Learning and Data Science are being extensively used in a wide variety of applications, including disease predictions, establishing the mechanism of action of compounds, studying gene regulation, and molecular profiling of various diseases. Natural Language Processing (NLP) can also help researchers to identify, relate, and analyze datasets from public repositories with a solution that combines NLP techniques, biomedical ontologies, and the R statistical framework to simplify the association of samples from large-scale repositories to their ontology-based annotations.

At Clovertex, our team of data analytics experts has vast experience in providing deep-dive guidance in Artificial Intelligence and Machine Learning for multi-omics and Drug discovery-based solutions. We assist in building decision-making skills, implementing technological practices and interpreting data for multiple predictions. We also aim towards supporting non-technical users in understanding and utilizing data driven approaches for multi-omics studies.

​​Web App Based Solutions
for Multiomics

Webapp solutions provide a platform to all users (technical and non-technical background) to implement pipelines and perform interpretation of results for multiple experiments and case studies. The UI can also be deployed in a client environment which can be used by multiple users and allows bulk job management.

Web app-based solutions are commonly used to solve several computational problems related to multi-omics. Shiny and Django-based applications are commonly connected as front-end layers for any algorithm deployed in high-performance computing (HPC) or Amazon Web Services (AWS) based environments. These solutions can be easily integrated into data lake where different scientific data can be analyzed and visualized.

Clovertex is actively engaged in developing various web apps using R-Shiny, Python-Djnago and html angular (full stack UI) based frameworks for data visualization and interpretation. This can be experimental data or genomics annotation results for patient meta-data, clinical, population, disease-specific, and drug repurposing data. We build UI-based solutions for scientists to enable ease of use and better understanding.

Professional Services and
Scientific Solutions

In Professional Services, scientists use variety of open-source and commercial application continuously to generate, process and visualize scientific data. The data can either be generated in-house or obtained from Clinical Research Organizations. Regular support, system maintenance, data migration and application upgrades can be cumbersome and challenging for both scientists and enterprise IT to meet the scientific requirements. Enterprise IT, who in-general are equipped with regular IT skills will either not have the product knowledge and scientific skills required to configure and maintain the scientific applications. Therefore, scientific computing IT personnel specializing in scientific workflows and product knowledge are required to handle and manage the software, systems, and data. We also offer services for Rconnect/JupyterNotebook/Scientific package managers in in-house environments in terms of package management and version control.

In scientific solutions, many researchers work in the field of cheminformatics, structural biology, drug discovery. Here, they face challenges related to computational infrastructure, latest scientific deployment solutions and decision making for achieving best results. This restricts them to maintain industry standards in terms of best computational practices and scientific involvement.

At Clovertex, we aim towards providing solution and support for structural biology, protein interaction pathways, chemoinformatics and drug design based case studies. We also offer subject matter expertise for scientific solutions for non-omics-based bioinformatics services.

Metadata Management

Managing bulk data (raw data, analyzed data, pre-processed data or unstructured healthcare data) in Multiomics is a big challenge. Many cloud-based solutions, data lake and databases are used to address this problem by organizing and storing the data and making it accessible on a common platform. It produces reliable results quickly and can track data from sequencing runs over time and across experiments to improve efficiency, allowing researchers to collaborate and use analyzed data across domain and functionalities.

At Clovertex, we provide cloud data storage and management solutions for healthcare and multi-omics. We help customers to create their own data lake with interactive APIs and dashboard UIs which are easily accessible and useful for cross-domain research and analytics. We create data framework in cloud and offer support for data migration, architecture scaling and deployment for multi-omics-based solutions.