What Can We Do
What Can We Do?
Analyzing large, multi-source data sets can be challenging, but it also provides a unique opportunity to gain novel insights into complex biological systems. At CD Genomics, our bioinformatics platform is designed to help researchers and companies analyze complex data sets and identify meaningful connections between different data sources. We have developed our own algorithms and pipelines and used established tools and methods to analyze data from different sources and provide comprehensive insights into complex biological systems.
Our bioinformatics platform can handle various types of data, including genomics, transcriptomics, proteomics, metabolomics, and preclinical data. We specialize in integrating and analyzing data from multiple sources, using machine learning and statistical techniques to identify patterns and correlations.
Data Types That We Can Analyze
Data Types That We Can Analyze
Our bioinformatics platform has carried out analysis across multi-omics platforms, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and microbiome data. We also specialize in integrating non-omic data, such as outcome data, biomarker assay data (e.g., microarray, RT-PCR, flow cytometry), and quantitated imaging data.
We can accept data in most formats and from any location. Depending on the size of the data file, we can use email, cloud transfer or physical receipt via hard-drives. Our team of bioinformaticians is experienced in handling large data sets and can work with you to determine the most appropriate method for data transfer.
Long-Read Sequencing Data Analysis Services
At CD Genomics, we prioritize delivering comprehensive and accurate long-read sequencing data analysis with efficiency and precision. As demand for advanced bioinformatics services grows, we ensure that each stage of our service is executed with speed and expertise.
1
Primary Analysis Basecalling and Quality Control
We start with basecalling, converting raw signal data into high-quality sequence data. Our quality control process, utilizing tools such as FastQC and PycoQC, ensures that your data is accurate and reliable, setting a solid foundation for subsequent analyses.
2
Secondary Analysis Genome Assembly and Variant Calling
Our secondary analysis includes constructing comprehensive genome assemblies from long-read data. We employ advanced algorithms for consensus sequence generation and error correction, followed by accurate variant calling to identify genetic variations such as SNPs and indels.
3
Tertiary Analysis Structural Variants and Gene Regulation
In the tertiary analysis phase, we delve deeper into your data to uncover structural variants and analyze gene regulation. Our services include detailed analysis of alternative splicing, base modifications, and pathway enrichment, providing insights into complex biological processes and interactions.
4
Advanced Analysis and Visualization Custom Pipelines and Visualization
We offer advanced analysis options and custom pipeline development tailored to your specific research needs. Our visualization services ensure that complex data is presented in an understandable and actionable format, facilitating meaningful interpretation and discovery.
5
Real-Time Support and Confidentiality Ongoing Communication and Data Security
Throughout the analysis process, we maintain real-time communication to address any issues and ensure smooth progress. Our rigorous confidentiality protocols protect the security and privacy of your data, providing peace of mind throughout your research journey.
Ready to advance your research with the latest in long-read sequencing analysis?
Bioinformatic Analysis Workflows
Bioinformatic Analysis Workflows
Languages such as Linux, Perl, Python, and R and biological information analysis software are used for data analysis and chart making.
Data Input
- Raw data is assessed for quality using various metrics (e.g., Phred score, adapter contamination).
- Data is checked for completeness and quality.
Data Preprocessing
- Raw data is preprocessed to remove adapters, trim low-quality bases, and correct sequencing errors.
Alignment
- Clean reads are aligned to a reference genome or transcriptome using appropriate alignment tools (e.g., HISAT2, STAR).
Quantification
- Aligned reads are quantified to obtain gene or transcript expression levels (e.g., FPKM, TPM).
Differential Expression Analysis
- Statistical methods are used to identify differentially expressed genes or transcripts between different conditions (e.g., DESeq2, edgeR).
Functional Enrichment Analysis
- Differentially expressed genes are annotated with Gene Ontology (GO) terms and pathway information to identify biological processes and pathways that are significantly enriched.
Visualization
- Results are visualized using various plots (e.g., volcano plots, heatmaps, PCA plots) to facilitate interpretation.
Report Generation
- A comprehensive report is generated summarizing the analysis results, including key findings, visualizations, and statistical results.
Featured Data Mining Services
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Microbiome Data Mining
Analyze microbial datasets to understand complex interactions within various environments or host systems.
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Microarray Expression Data Mining
Explore gene expression data to identify key patterns and relationships across different biological conditions.
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GWAS Data Mining
Pinpoint genetic variants associated with diseases and traits using Genome-Wide Association Studies.
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Clinical Data Mining
Extract actionable insights from clinical datasets to enhance research outcomes and support evidence-based medical decisions.
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Medical Data Mining
Leverage medical data to discover insights that improve patient care, identify risk factors, and optimize treatment strategies.
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Omics Data Mining
Focus on single-omics datasets, such as genomics, proteomics, or metabolomics, for targeted research insights.
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Multi-omics Data Mining
Integrate multiple omics datasets for a comprehensive view of biological processes, aiding in the understanding of disease mechanisms.
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Cancer Data Mining
Specialize in mining cancer datasets to discover biomarkers, therapeutic targets, and insights into cancer biology for precision oncology.
Single Database Data Mining Services
Leverage individual public databases like ArrayExpress, GEO, or TCGA to extract relevant data and gain specific research insights.
Integrated Database Mining Services
Combine data from multiple databases, such as GEO, TCGA, and UCSC Xena, to conduct comprehensive analyses and uncover deeper biological insights.
Advantages
Advantages of Bioinfomatics Service
CD Genomics offers comprehensive bioinformatics services designed to provide high-quality results at various scales. Whether you're working on a single project or require ongoing support, our services are adaptable to your needs.
- Cost-Effective for Large-Scale Projects
- High-Quality and Detailed Reports
- Consistent and Reproducible Results
- Expertise in Complex Data Analysis
- Minimized Errors and Data Loss
- Broad Range of Analytical Services
CD Genomics provides bioinformatics solutions across a variety of research fields, supporting a broad range of applications. Our services are tailored to address complex challenges in multiple domains:
What types of raw data do you accept?
