BAINOM-nX for Biomedical Research
State-of-the-art bioinformatics platform for every stage of multi-omic based research.
From analysis to creation of tangible outcomes.
Insights obtained from multi-omic clinical and biomedical data hold the key to accelerate biomedical research.
However, the multi-omic data is large, heterogenous, and importantly extraction of meaningful insights require specific technical skills, scientific know-how, and computational infrastructure.
Furthermore, large amount of omic data with potentially targetable, but yet unanalyzed features exists in several scientific databases. The full potential of multi-omics hence remain untapped.
Bainom provides a unique solution to democratize multi-omic data and its analysis – BAINOM-nX for Biomedical Research.
Using our platform, researchers can utilize our basic research oriented, customized, and iterative Bioinformatics as an automated service (BaaS) to process, homogenize, and find interpretable explanations in their multi-omic data.
In addition, an array of integrated applications in our platform allow seamless creation of shareable knowledge (research articles, grant applications) and tangible outcomes (databases, pipelines) from the analyzed multi-omic data.
Benefits to the User
1. Bioinformatics as an automated service (BaaS)
At-scale, deep bioinformatics covering every aspect of -omic analysis and inference, arranged as standalone modules. Options to choose full, end-to-end OR specific analysis as per your needs.
2. Tangible Outcomes
Full support to prepare publication quality figures. Systematic writing system to collaboratively prepare research articles, grants, or other tangible assets.
3. Interactive Dashboard
Interactive dashboard to explore and infer -omic data in detail. Collaborate in-platform, add comments, unstructured information, ask questions, chat, or video call.
4. Hypothesis Generation
Seamlessly search all available -omic dataspace to generate hypothesis for your disease of interest. Guide experiment design by shortlisting most interesting targets.
5. Understand Mechanisms
Develop sophisticated machine learning or mathematical models to understand the biological processes at unprecedented depths.
Explore the full potential of multi-omics
1. Data upload & storage
Login/signup with secure multi-step authentication.
Upload -omic or clinical data in any format, processing stage, or size. Data can be ported from both on-premise systems or a cloud platform.
Custom settings to chose data privacy levels.
Raw -omic data files (e.g., FASTQ) structured and securely stored in cloud with anytime access.
Novel, compressed formats for infrequently used data files, significantly reducing storage costs.
2. QC & pre-processing
Systematic quality control of raw data coming from any high-throughput sequencing pipeline.
Sample-type specific pipelines for data cleanup operations; demultiplexing, adapter removal, filtering of concatamers and PCR duplicates.
200+ AI workflows optimized for processing data from different next generation sequencing experiments.
AI workflows include all standard, widely used pipelines along with advanced pipelines curated from scientific literature for granular processing tasks.
Detailed read/interval quality metrics, outlier detection, and imputation for missing data points.
Intuitive dashboard to explore descriptive statistics of processed data.
Actionable versions of processed data (e.g, BAM, BED, bedgraph) readily available for download or analysis.
3. Genome assembly
Cloud based computational resources for de novo genome assembly from short read (e.g., using SPAdes) and long read (e.g, using Smartdenovo) sequencing data.
Iterative computation using multiple genome assemblers and consolidation to generate least fragmented assembly.
Seamless detection, classification, and annotation of DNA structural-functional elements using parallel gene finding algorithms.
4. Mapping to genome
Alignment of sequencing reads to the reference genome or transcriptome.
Pre-compiled, verified genome indexes for 85+ organisms.
Hyper-permutation of mapping parameters (e.g., gap penalty, read length) for selection of best alignment method for every data set.
Visual exploration of mapped -omic and other clinical data in an interactive genome browser.
Quantification of mapping statistics, read coverage distribution, and mapping quality.
Repeat masking, tagging of conserved regions, identification of functional elements in the genome in reference to mapped data.
5. Precise genome-wide annotation
100+ features describing complete -omic structural and functional attributes (e.g., gene structure – CDS, 3′ UTR, transcripts, homologs)
Features carefully consolidated from 75+ high-quality scientific repositories.
Validated annotation for features that contain multiple entries in the repositories.
Inclusion of all standard (e.g, gene structure), advanced (e.g., non-coding DNA variations), and novel (e.g., RNA regulatory signals) -omic features, allowing in-depth parsing of -omic data.
Options to choose features for further investigation with suggestions for best combination of features to analyze based on input data/query.
6. Peak calling & Feature extraction
Cutting-edge statistical methods for peak calling that quantify read coverage at any region of interest across the genome.
Nucleotide resolution peak calling for each genomic feature, providing unprecedented accuracy and details.
Ready-to-go extraction pipelines for all actionable -omic features, ranging from DNA variations (SNPs, InDels, CNVs) to RNA processing (expression, splicing, modifications).
Platform modularity allows rapid creation of new algorithms for custom feature extraction.
Detailed metagenomics and analysis of read distributions across genome for any selected feature.
7. In-depth feature exploration
Data harmonized for easy inter-feature or inter-sample comparisons.
User-friendly interface highlighting significant genes or functional units (e.g., microbes, metabolites) corresponding to each feature with options to go into details.
Multi-layered analytics & info sections for systematically investigating the details of each feature, down to the nucleotide level.
Feature specific plots organized in a customizable publication quality format with summary of important conclusions at each stage.
Options to re-arrange, modify, and format plots OR custom tools to generate additional plots.
8. Multivariate Analysis
Systems biology based approach for studying holistic biological patterns.
Prompts to select interesting or most significant genes (or functional units) for machine learning based multivariate analysis.
Unsupervised machine learning for normalization, integration, and classification of features to reveal functional patterns.
Multiple, parallelly deployed interpretive machine learning methods aggregated to output robust patterns.
Interactive interface to study each sub-class, identify top gene candidates, and obtain details about the contribution of each feature.
9. Functional assessment
Shortlisted genes further refined in the context of holistic biological networks by aggregating major pathway analysis algorithms (e.g., KEGG, GO, GSEA).
Graph theory based computation of multiple network level parameters (e.g., connectivity, neighbors) to identify gene interactome.
Machine learning based prediction of gene importance for the condition under study by: knock-down effects on survival, testing alternate modes-of-action, assessing impact of known isoforms and secondary gene-gene interactions.
Composite modeling to suggest most relevant sub-networks or regulatory modules.
10. Understand patterns & develop predictive models
In-depth investigation of underlying biological principles governing each functional state.
Supervised machine learning model building, training, and testing for merging DNA/RNA structural-regulatory parameters with functional classes to identify underlying regulatory processes.
At-scale, cloud based, reinforcement learning to infer the contributions of each DNA/RNA parameter towards regulation of each functional class.
Computation of millions of ML model permutations to arrive at most robust explanations for the biological process.
Parallel testing of all major ML models. Conversion capabilities for ML models into standalone software, pipelines.
Enterprise grade training abilities of ML models on large datasets.
11. Develop mathematical models
Development of comprehensive mathematical models describing the multi-dimensional biological regulation.
Inclusion of every explanatory feature determining the differential or temporal variations in functional outcomes.
Explicit model design and validation using differential equations, integration, and algebraic equations.
Simulation of model behavior and inter-feature associations.
Computation of system-wide rates, effect size, or contributions of each feature, gene, or functional unit
12. Bainom Tensor
Bainom Tensor – a data silo of pre-analyzed, clinically actionable -omic patterns.
Bainom Tensor composed of processed -omic data for every disease and matched controls, compiled by deep mining & in-depth analysis of >100 major scientific databases.
Up-to-date information encompassing 10 billion+ data points corresponding to -omic data, with regular updates to include all new scientific developments.
All actionable data organized in ready to integrate formats and is fully searchable.
13. Feature prioritization & refinement
Prioritization and ranking of significant genes or functional units (e.g., metabolites, microbiome) by matching with all, up-to-date actionable patterns included in the Bainom data silo.
Deep learning based gene scoring to tag the most interesting genes or functional units for detailed analysis.
Validation of observed patterns in all publicly available data for a disease of interest OR development of alternate hypotheses by identifying similar patterns in other diseases.
14. Target contextualization & interpretation
Shortlisted genes matched with all available scientific literature (articles, guidelines, case studies) to reveal contemporary knowledge about each gene.
Search capabilities for all ongoing and completed clinical trials involving the targets of interest.
Up-to-date, digestible information about the molecular connections, regulation, and therapeutic potential for each gene of interest.
Ordered, scored list of top genes presented as an intuitive interface with multiple filtering and sorting functionalities.
Options to obtain more information for each gene and create custom reports of intervenable targets.
15. Hypothesis generation by deep mining
Bainom data silo of pre-analyzed, clinically actionable -omic patterns for every disease and matched controls, compiled by deep mining & in-depth analysis of >100 major scientific databases.
ab-initio search and identification of druggable targets in Bainom data silo, without any data input.
Prioritization and ranking of significant genes in the disease of interest.
Shortlisted gene targets matched with all available scientific literature (articles, guidelines, case studies), and clinical trials to reveal contemporary knowledge about each gene.
Ordered, scored list of top gene targets presented as an intuitive interface with multiple filtering and sorting functionalities.
Options to obtain more information for each gene and create custom reports of druggable targets.
16. Computational structural biology
Computational designing of full-length protein 3D structure from available crystal structures of every gene of interest.
ab initio protein structure modeling for genes with low confidence structural information.
AI guided, at-scale, docking and molecular dynamic (MD) simulations of gene-drug interactions.
Development and refinement of novel small molecules through high-throughput docking and MD simulations while considering evolutionary, functional, and structural parameters.
17. Interactive reporting
Interactive and multi-layered user interface for every stage of analysis.
Information organized intuitively for seamless sharing and collaboration.
Stringent data security guidelines.
Minimalistic, responsive and user friendly interface.
Powerful data visualization using cutting-edge data analytic applications.
Customizable analysis pipelines allow addition or modification of workflows.
18. Tools to create tangible outcomes
Custom options for generation of publication quality figures.
Integrated tools and full support for drafting research articles and grant applications.
Cloud based interface for easy collaboration and live tracking of work.
Conversion of inferences into databases, predictive ML models, computational pipelines, or software.
Realize the full potential of your multi-omic data.
Work with us to seamlessly decode multi-omic patterns for every stage of drug discovery and development.
Explore our other products
Interested in drug discovery? Access our drug discovery module geared towards multi-omic centric solutions for every stage of drug discovery and development.
By exploiting our expertise and analytics platform, we are deep mining a rationally selected subset of the vast multi-omic dataspace (e.g., RNA processing, protein structure, missense mutations). By shortlisting intervenable candidates through this mining, we are identifying and validating hitherto unknown druggable targets for difficult-to-treat and rare diseases.
Want to identify biomarkers and develop new diagnostics? Explore our work on discovering new biomarkers OR collaborate to perform de novo biomarker discovery in your data.
By integrated, multi-omic analysis of clinical databases, we identify novel biomarkers for precision therapy and therapy risk stratification of patients. Through this, we aim to create sensitive predictive models and diagnostic tests for cancers and other genetic diseases.
In practical terms, Bainom Platform functions as a GPS for drug discovery and development – it guides the user through the complex and constantly developing landscape of multi-omic data – starting from extraction of clinical or biological features – arriving at actionable inferences and suggestions.
Bainom is an early-stage biotech developing the next generation of artificial intelligence (AI) and machine learning (ML) driven bioinformatics platform that makes actionable multi-omic data and its inference accessible to all, with critical applications in drug discovery, precision health, and biomedical research.
Our systems biology-oriented platform, based on the inter-disciplinary expertise of the core team creates a one-stop bioinformatics ecosystem. We achieve this by combining the largest collection of cloud-based AI/ML bioinformatic workflows, unique multi-omic data silos of clinically actionable features, deep mining of all available -omics dataspace, and dynamic, multi-layered visualization of inferred patterns.
In parallel, by exploiting our interdisciplinary computational/experimental biology experience and analytics platform, we identify and validate hitherto unknown biomarkers and druggable targets for difficult-to-treat and rare diseases.
Our mission is to transform therapeutics of difficult-to-treat diseases and accelerate discovery of new treatments through AI/ML driven solutions that unleash the full clinical potential of multi-omic data.
CONTACT
General: info@bainom.com | Business: business@bainom.com
Founder: Deepak Sharma – deepak.sharma@bainom.com | +1 216-319-1124
Operations: Justin Padilla – justin.padilla@bainom.com
Copyright @ 2023 Bainom