Harmonize in-house proteomics data and find relevant datasets from PRIDE and CPTAC, fit for downstream analysis and insight generation.
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Augment in-house proteomics datasets with ML-ready datasets from public sources like PRIDE, CPTAC, etc. through Polly’s data concierge services.
Our experts swiftly locate the datasets you need by querying through Polly’s metadata-annotated proteomics collection – all within minutes.
Let us handle the heavy lifting - we ensure every relevant study discovered includes vital information for your analysis, from data matrices to associated metadata and protein intensity tables.
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Automate data ingestion from your workflows (ELN, S3 bucket, CROs, and more) into Polly with our data importers.
Focus on discovery, not data wrangling! Polly automatically harmonizes your in-house datasets ensuring they adhere to your custom schema.
Integrate multi-modal datasets into one central Atlas to unveil hidden patterns, and expedite research breakthroughs.
Store, manage and analyze TBs of in-house and public proteomics data on Polly's secure compute infrastructure.
Eliminate the need to annotate individual datasets from in-house experiments or public databases.
Ensure precise annotation with 30+ ontology-backed metadata fields at dataset, sample and feature level using Polly’s harmonization engine.
Customize metadata fields and cohorts, data schema, or ontologies to best fit your specific analysis needs.
Our experts implement comprehensive QA checks to validate metadata and remove technical artifacts & variations in every dataset.
The metadata annotated or data engineering methods used on Polly are not a black box. Learn how each proteomics dataset was harmonized by downloading a detailed QA/QC report from your Atlas on Polly.
We use a comprehensive list of QA/QC checks to ensure every dataset is:
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Logical checks ensure that all dataset and sample-level metadata annotations contain non-NULL and non-blank values.
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Rigorous QA and QC checks to validate whether metadata attributes are in agreement with publication and are human-readable.
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Curated fields like disease, tissue, cell type, cell line, and organism follow their corresponding set of ontologies to preserve consistency in annotations.
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Poor-quality samples are filtered out ensuring subsequent analyses rely on robust and meaningful findings.
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Genomic data refers to the genetic information encoded in an organism's DNA, including sequences, mutations, and gene activity. Proteomic data looks at all the proteins produced by a genome, their functions, interactions, and changes, providing a deeper understanding of cellular processes.
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Proteomics data includes various types such as protein expression data, post-translational modifications, protein-protein interaction data, and protein quantification. These datasets are crucial for understanding the molecular mechanisms of diseases and identifying potential therapeutic targets.
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Proteomics gives insights into cellular functions by analyzing proteins, which are the direct effectors of genes. It helps in biomarker discovery, drug development, and personalized medicine, enhancing understanding of diseases and treatment efficacy.
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Proteomics data can be used for identifying disease biomarkers, understanding disease mechanisms, discovering new therapeutic targets, and enhancing personalized medicine by analyzing protein expression and interactions at a cellular level.
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Proteomics faces challenges such as incomplete protein coverage, difficulties in analyzing low-abundance proteins, and data complexity due to protein modifications. These limitations can affect the comprehensiveness and accuracy of results.
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The two major techniques used in proteomics are mass spectrometry (MS) and two-dimensional gel electrophoresis (2D-GE). Mass spectrometry is used to identify and quantify proteins, while 2D-GE separates proteins based on their size and charge for further analysis.
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