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Single-cell RNA sequencing (scRNAseq) distinguishes the transcriptional states of individual cells, offering a high-resolution understanding of cellular diversity within tissues. Unlike bulk RNA-seq, which averages gene expression across many cells, scRNAseq provides a detailed view of individual cellular transcriptional states, highlighting their inherent heterogeneity within complex tissues. This has made it instrumental in uncovering rare cell types, pinpointing cell-to-cell variability, and mapping developmental pathways in intricate tissues. As biological research advances, there has been an exponential growth in scRNAseq data generation, which is critical to understanding cell-to-cell variability and tracing developmental lineages in complex tissues. Yet, the vast potential of this data is often constrained by its dispersed and siloed storage across various platforms and literature, compelling researchers to spend significant amounts of time locating, accessing, and standardizing this data.

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DeepLife's Omics Catalog aims to resolve this by offering a centralized repository, ensuring datasets are not only accessible but also are presented in a standardized, analyzable format, streamlining the research process and maximizing the value drawn from scRNAseq. We offer a comprehensive, dynamic, and continuously updated repository designed to meet the specific needs of biotechnology and R&D professionals.

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One of the distinguishing features of DeepLife’s Omics Catalog is its dashboard-style user interface. This provides a macro view of all scRNA-seq datasets covering multiple therapeutic areas and allows detailed exploration of 49 metadata fields for each dataset including dataset availability, file type availability, sequencing technology, sequencing type, number of runs, disease, organ, tissue, cell & tissue type. The interface also provides a workspace allowing multiple users within the same team to interact with the dataset, leaving comments and suggestions for the DeepLife team during the processing phase. Crucial details, including the associated disease, sequencing assay, organ, tissue, and cell type involved, are readily available for the user, thus securing the selection of the ideal dataset to be processed. To ensure seamless integration into ongoing research workflows, the Omics Catalog adopts widely recognized ontologies such as MESH, MONDO, UMLS, and specialized variants like CL, UBERON, and DOID.

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To ensure the reliability of the data, the Omics Catalog incorporates quality-related metrics for the raw data files such as Phred score q20, Phred score q30 etc. This feature facilitates the selection of robust and dependable datasets for downstream analysis. The Catalog presently includes over 90% of all single-cell datasets from GEO dating back to 2017; all ingested datasets receive an expert review report that provides clear quality metrics, helping users select the most suitable datasets for further downstream analysis.

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Our in-house team continuously updates datasets within the Omics Catalog, which can also be integrated with proprietary external data. As we continue to expand the depth and breadth of the Omics Catalog beyond scRNAseq, we are incorporating proteomics, genomics, and metabolomics, ensuring a holistic understanding of cellular processes. By linking the genetics information with functional proteins and resultant metabolites, we aim to provide a multi-dimensional view of cellular health and function.With DeepLife's Omics Catalog, you have a dynamic, reliable, and extensive data resource at your disposal, coupled with a user-friendly interface capable of enhancing data interoperability, streamlining access to datasets, and accelerating the analytical process for discoveries in research, precision medicine and interconnected disciplines.

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In addition to data accumulation, the Omics Catalog represents the detailed gene expression landscape across a diverse set of conditions and tissues, capturing the nuances of individual cellular states. The catalog is more than an archive; it's our foundational stepping stone towards Atlasing. Using this vast dataset, we construct organ-level cellular atlases, which facilitate the detailed characterization of individual cell types, their specific functions, and their interactions within intricate biological systems. This enables us to shed light on the nuanced cellular hierarchies, their spatial relationships, and the underpinning molecular mechanisms. As we delve into this next chapter, we invite researchers to anticipate a journey from data integration to an intricate, high-resolution atlas that will revolutionize our understanding of cellular complexity. For further engagement and to explore potential partnerships, contact us, and stay tuned for our next step!

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