Our list of services is rapidly expanding. If you have a specific research question or analysis you wish to discuss that is not included below please contact

Jayden Lee, EMBA, PharmD, BCACP
Co-Founder and CGO, UGenome AI
jaydenlee@ugenome.io

1. Experimental design/statistical analysis consulting
For all our provided services we can provide consultation to generate maximum analytical insight from your experiments, ensuring the validity, reliability, and reproducibility of research findings. We can also perform analyses of data generated from these experiments if required.
Our consultation services include:
- Experimental Design: Ensuring proper statistical power, appropriate controls, and robust methodologies to enhance the validity of your results.
- Tailored Analysis Guidance: Providing recommendations on the most suitable analyses based on your specific experimental questions and research goals.
- Tool Design Support: Assisting in the design of experimental tools such as pLenti viral expression systems, plasmid and primer design, CRISPR gRNA, and HDR donor template design to ensure effective and efficient experimentation.

2. Public database analysis
If you are interested in analyzing publicly available data, we can help. Our team provides comprehensive support for accessing, processing, and interpreting data from various public databases, which can offer invaluable insights into your research. Public datasets allow researchers to validate their findings, identify novel patterns, and leverage existing large-scale studies to accelerate discovery without the cost of new data collection. We specialize in integrating and analyzing datasets from a variety of sources, providing you with actionable insights and saving you time and effort in data curation and processing.
- Database Access and Curation: We can assist in navigating large, complex databases such as GEO, TCGA, ClinVar, GTEx, and SRA
- Data Preprocessing: We provide data cleaning, normalization, and quality control to ensure that publicly available datasets are ready for meaningful downstream analysis.
- Integration of Multiple Datasets: Our team is skilled in combining different datasets from public repositories to enhance statistical power and enable cross-study comparisons.
- Bioinformatic Analysis: Whether you need differential expression analysis, variant calling, or population genetics analysis, we offer tailored pipelines to fit your research needs.
- Functional Annotation: We provide tools for functional annotation of genes, proteins, or variants from public data, helping you understand the biological relevance of your findings.

3. Genomic/Epigenomic services
Our team is skilled and experienced in the analysis of next-generation and 3rd generation sequencing data from whole genome, whole exome, ChIP-Seq, bisulfite sequencing, and reduced representation bisulfite sequencing experiments.

Whole exome/genome sequencing analysis
- Haplotype & mutation calling: Identify germline and somatic mutations in control and tumor DNA sequencing data.
- Copy number variant analysis: Identify gains and losses in gene copy numbers
- DNA ploidy analysis: Assess chromosomal aberrations and genome stability
- Structural variant analysis: Detect large genomic rearrangements
- Mutational signature identification: Uncover mutation patterns related to biological processes
- Haplotype phasing analysis: Determine the arrangement of variant alleles on chromosomes
- Gene fusion detection: Identify gene fusions that may drive disease progression

Bisulfite/reduced representation bisulfite sequencing
- Methylation Calling: Identify methylated cytosines (5mC) in the genome
- Methylation Data Aggregation: Combine methylation data across multiple regions or samples to generate meaningful insights into global methylation patterns
- Differential Methylation Analysis: Analyze and compare methylation patterns across samples
- Pathway enrichment: Identify if differentially methylated genes are enriched in specific biological pathways, helping to uncover functional implications of methylation changes.

ChIP-Seq
- Peak Calling: Identify genomic regions of protein-DNA binding where proteins are bound to DNA
- Peak Quality Assessment: Evaluate the quality of identified peaks to ensure they are reliable and biologically relevant
- Peak Annotation: Map identified peaks to genomic features (e.g., promoters, enhancers)
- Motif Discovery: Identify DNA sequence motifs within the peaks to uncover binding patterns for transcription factors or other regulatory elements.
- Differential Binding Analysis: Comparing binding events between conditions to identify changes in protein-DNA interactions
- Pathway enrichment: Link differential binding sites to enriched biological pathways to understand how changes in protein-DNA interactions may impact cellular functions

Customized Reference Genomes/Transcriptomes
Our licensed software, MAXX, can generate customized reference genomes using pre-defined mutations or gene fusions.
- Enhance sensitivity and specificity of mutation calling in NGS data
- Improve results from downstream bioinformatics analyses
- Improve DNA + RNA mutant allele frequency quantification

Pharmacogenetic Data Extraction
Our proprietary software, ProPEx, can extract pharmacogenetic genotype data from whole genome or exome sequencing data. We can generate pharmacogenetic reports of 11 pharmacogenes and their associated drugs, affecting >100 drugs on the market.

4. Transcriptomic services
Full analysis of bulk and single-cell RNA-seq data from raw FASTQ files.
- FASTQ QC: Assess the quality of raw RNA-seq reads to identify any sequencing issues
- Adapter trimming and quality filtering: Remove sequencing adapters and filter out low-quality reads from sequencing data
- Transcriptome alignment: Map reads to the reference genome or transcriptome
- Post alignment QC: Evaluating mapping quality
- Counts matrix generation: Create a matrix of gene counts from the aligned reads
- Normalization and batch correction: Correct for technical variability and batch effects
- Differential gene expression: Identify differentially expressed expressed genes between samples/conditions to identify potential biomarkers and regulatory changes
- Pathway enrichment: Identify pathways that are enriched in differentially expressed genes to give insight into underlying biological mechanisms or processes involved in a condition or experiment.
- Hierarchical and model-based clustering: Group genes or samples based on expression patterns to uncover co-expressed genes or sample subtypes
- Cell lineage and pseudotime inference (scRNA-Seq): Reconstruct cell developmental trajectories and identify the order in which cell states emerge.
- Cell clustering and population identification (scRNA-Seq): Grouping cells based on their gene expression profiles to identify distinct cell populations or subtypes
- Differential splicing analysis: Detect and analyze alternative splicing events, providing deeper insights into transcript diversity and regulation

5. Machine Learning Services
Leveraging advanced machine learning techniques, we can help you tackle complex research questions and uncover novel biological insights from your data. Our services are designed to integrate machine learning into genomic, transcriptomic, and clinical datasets for deeper understanding and discovery.
- Genotype-Phenotype Association: Uncover relationships between genetic variants and phenotypic traits
- Predicting Disease Risk: Build models to predict an individual’s likelihood of developing a disease based on genetic, clinical, or environmental factors, using algorithms like logistic regression, random forests, or deep learning.
- Variant Effect Prediction: Predict the functional impact of genetic variants, such as whether a variant is likely to be deleterious or benign.
- Haplotype Phasing and Imputation: Phase haplotypes and impute missing genotypes, improving the resolution of genomic data for downstream analysis.
- Gene Expression Prediction: Predict gene expression levels from genetic variants (eQTL analysis) or environmental factors to better understand gene regulation.
- Gene Interaction Networks: Discovering and modeling complex gene-gene interactions (epistasis) that contribute to traits or disease pathways, using tools like Bayesian networks or deep learning.
- Identifying Disease Mechanisms of Action: Integrate multi-omic datasets and identify key molecular mechanisms driving disease.
- Treatment Response Prediction: Develop predictive models to forecast how patients will respond to therapies, aiding in personalized medicine approaches.
- Diagnostic/Prognostic Biomarker Discovery: Identify biomarkers that can be used for disease diagnosis or to predict patient outcomes.
- Pre-Clinical Patient Selection Strategies: Using genomic, transcriptomic, or clinical data to predict which patients are likely to benefit from specific therapies during pre-clinical trials, optimizing patient stratification.

6. Proteomics (SomaLogic, olink, Luminex)
Analyze analytical and quantitative proteomic data using specialized software (SomaLogic, olink, Luminex, MAXQuant)
- Protein identification and quantification
- PTM analysis
- Protein-protein interaction
- Differential protein expression

7. Metabolomics (untargeted)
- Analyze untargeted metabolomics data
- Identify metabolites and their concentrations
- Understand metabolic pathways and disease mechanisms

For more information, contact:
Jayden Lee, EMBA, PharmD, BCACP
Co-Founder and CGO, UGenome AI
jaydenlee@ugenome.io