The single-cell genomics analysis introduces researchers with the concept of cellular heterogeneity and identifies functional cell subpopulations within customers’ single-cell dataset. Specialized solutions of our analysis team ensure optimal de-noising of the gene expression data, enabling effective extraction of biological insights.
The professional analysis service aims to cater for customer’s interests in specific biological and clinical questions, performing analysis in a customized way. In addition to the basic services in the promotion version, our standard analysis pipeline incorporates cutting-edge public or proprietary bioinformatics tools to identify rare cell types, perform cell lineage tracking, infer developmental trajectories, spatial distributions and inter-cellular interactions of single cells.
Based on all the analysis content in the professional edition and utilizing the in-house single-cell genomics research expertise, we will help researchers accelerate their research by providing experimental design and taking advantage of public data, and help extract additional biological or clinical insights from single-cell data.
10X Genomics 3’/5’ single-cell transcriptome technology reveals differences in gene expression at the single-cell level, effectively solving the biological problem of tissue heterogeneity, such as tumors.
The 10X 5’ product with V(D)J profiling offers comprehensive, scalable solutions for measuring immune repertoire information and gene expression from the same cell.
SMART-seq2 single-cell RNA-seq technology can amplify full-length transcripts and is widely used in the in-depth study of single-cell transcriptomes.
The 10X Chromium Single Cell ATAC-seq provides a comprehensive, scalable approach to determine the regulatory landscape of chromatin in hundreds to thousands of cells in a single sample.
Omni Single Cell Database
Omni Single Cell Database is a large compendium of single-cell genomics data that addresses important aspects of drug discovery research. It contains comprehensive data representing a wide collection of diseased and healthy tissues, at the single cell level, which can facilitate target identification and prioritization, target efficacy, and safety prediction.
Currently, Omni Single Cell Database covers >1800 datasets from >800 studies, representing>20k clusters and more than 25 million cells. We query PubMed monthly to identify scRNA-seq publications pertaining to Cancer, Immunology, Neuroscience, and Developmental Biology, with high impact journals given higher priority. References of publications are examined to identify additional studies on similar topics. Priorities are also given to those studies or topics frequently requested by users.
OmniBrowser is a web portal with a focus on scRNA-seq data exploration, providing a rich collection of commonly used tools for data visualization and analysis. This tool set is designed for experimental biologists with limited programming skills, while still being valuable for bioinformatics researchers. Omni Single Cell Database, the underlying database of OmniBrowser is currently the largest scRNA-seq dataset collection in the world, with >1800 datasets from >800 publications in publication consistency. To query such datasets, OmniBrowser provides a series of web-based tools, including gene expression query, dimensional reduction visualization, differential analysis & across-dataset comparison, similar gene search, association analysis with sample metadata, GO analysis.
OmniAnalyzer is a standalone application featuring scRNA-seq data analyses with multi-dataset integration capability. It processes single-cell sequencing data quickly and massively. Running in the Microsoft Windows environment, OmniAnalyzer is an executable program after simple installation. OmniAnalyzer enables interactive data visualizations, custom study imports (including alignment and all downstream processing), and analytical capabilities more amenable to a dedicated client application.
OmniAnalyzer has a few unique features that complement OmniBrowser: load local datasets for analysis，customizable preprocessing and clustering，selection and annotation of cells，TCR/BCR-based analysis，Total-seq based analysis，cross-datasets analysis.
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