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Polly: An Ecosystem that Enables Cell Metabolism Labs
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Kanika Sori
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April 6, 2018
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Discovery of a drug target requires expertise from different arms of a cell metabolism lab. A typical cell metabolism lab has a core biology facility that performs wet lab experiments, separate metabolomics and genomics core facilities, and a bioinformatics group to aid the core facilities. This is a case study of one such lab where Polly, a cloud-based data analytics platform, increased the throughput by bringing down hypothesis generation time from months to days.
Myra Johnson is a Research Assistant working with TargetA lab, a cell metabolism facility at Dale Medical School, that is interested in identifying drug targets for Diabetes type II patients.
Myra is primarily responsible for running experiments that her supervisor, Togo Stewart, designs. She prepares samples and runs them through the mass spec machine and collects raw data. She views and curates the peaks using El-Maven, a data processing engine for large-scale metabolomic experiments. Finally, she exports her table of peaks of interest to Polly and spots interesting metabolites across samples with Polly FirstView’s heatmap. She also looks at the intensity and fractional enrichment plots of metabolites of glycolysis cycle that pique her interest.
Next, she opens the project on Polly where she exports her processed data, and opens an iPython notebook to run the script that Sam D’Silva, a bioinformatician in her lab, wrote a year ago. This code allows Myra to normalise intensity values and re-plot intensity distributions. She converts the results into a knowledge repo that she shares with Togo to get his feedback. Polly enables Togo to review Myra’s work in one single ecosystem. Polly proves very fruitful in Myra’s and Togo’s discussion where Myra is quickly able to pull up data, peaks or plots Togo asked for. Togo is also able to dig deep into Myra’s data whenever he needs without her guiding him through her files and analysis.
Later, Myra extracts output data of the script and feeds it into IsoCorrect, an application on Polly, for natural abundance corrections. At the output screen, she hovers over suggested apps and uses PollyPhi Relative LCMS Dashboard to visualise key metabolites as canonical pathway visualisations. She exports these plots to a PowerPoint presentation that she can use in TargetA’s upcoming lab meeting.
Meanwhile, Togo re-evaluates Myra’s findings and generates a hypothesis – application of drug X2910 prevents the backflow of pyruvate to glucose and stimulates glycogen synthesis in mouse liver cells.
He is excited about it and asks Myra to repeat the experiment and confirm her findings before they present the hypothesis to Matt Pitt, a biologist from a core biology department. She takes the next two days to reproduce the analysis and confirms the hypothesis. She updates her presentation and is ready for the lab meeting scheduled for the next morning.
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Polly provides pre-processed, harmonized datasets that enable AI/ML model training for patient classification. It supports feature selection, dimensionality reduction, and validation workflows to build robust predictive models for precision medicine applications.
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Polly analyzes both single-cell and bulk multi-omics data to identify stage-specific genetic markers. By applying machine learning algorithms to detect patterns in gene expression, Polly helps researchers map lineage differentiation and gain insights into disease progression.
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Polly builds disease-specific atlases by:
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Polly integrates genomics, transcriptomics, proteomics, and clinical data into a unified, multi-dimensional view of patient populations. This helps researchers uncover complex biological relationships and enhances predictive modeling for patient subgroups.
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Yes, Polly automatically processes raw, unstructured data from public sources, addressing missing values, batch effects, and inconsistencies. Its machine learning–driven pipelines filter out noise and standardize data, ensuring higher-quality datasets for seamless analysis.
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Polly's harmonization engine normalizes, processes, and integrates diverse datasets using standard ontologies and metadata frameworks. This ensures consistency, removes batch effects, and enhances the reliability of downstream analyses for precise patient classification.
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Polly streamlines patient stratification by:
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Researchers encounter several challenges, including:
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Patient stratification is the process of categorizing patients into subgroups based on genetic, molecular, or clinical characteristics. This approach is crucial for precision medicine because it identifies which patient populations are most likely to respond to specific treatments, thereby improving therapeutic outcomes and reducing the risk of adverse effects.
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Polly provides access to a curated repository of RNA-seq datasets that are consistently processed and enriched with metadata. This harmonization allows researchers to efficiently search for datasets with similar transcriptional profiles, facilitating transcriptome profiling and biomarker identification.
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Polly utilizes signature reversal and multivariate gene expression signatures to predict potential drug combinations. By analyzing publicly available transcriptomics data and drug signatures, Polly can identify drugs or compounds that may have therapeutic effects by reversing disease signatures.
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Polly ranks similar datasets using cosine similarity scores, which measure how closely a dataset's transcriptional profile matches the query signature. This helps researchers quickly find relevant datasets for further analysis and validation.
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Researchers define the biological process of interest, select a dataset, preprocess the data, identify differentially expressed genes, and validate the signature. Polly’s platform streamlines this process with expert support and ML-ready datasets.
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Polly's RNA-Seq Atlas addresses challenges in extracting associated signatures from public databases by providing a curated resource of RNA-seq datasets collected from the Gene Expression Omnibus (GEO). This richly curated resource helps researchers to find datasets with similar transcriptional profiles to their gene sets of interest.
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Gene signature comparison analyzes gene expression patterns to identify disease-related signatures. It helps researchers find drugs that can reverse disease signatures, aiding in therapeutic discoveries.
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