Bioinformatics Case Study
Metabolite Identification
Why is this important?
Metabolite Identification allows us to analyze hundreds to thousands of metabolites moving beyond the scope of standard clinical chemistry techniques. In turn, we can help with disease diagnosis and monitoring, understanding cellular phenomena, and driving toward personalized therapies.
Details
Compay Name: Top 20 global private biomedical company
Date: March 2024
Metabolite Identification. Company requested metabolome analysis of bacterial culture samples (BSL2 level organism) grown under 2 different conditions: 3 replicates each for a total of 6 samples. With our service partner — Robust Diagnostics — we provided sample preparation, profiling, and metabalome analysis.
Bioinformatics Case Study
Bone Metastasis Risk Analysis in Breast Cancer
Why is this important?
Bone metastasis in breast cancer occurs when cancer cells spread from the breast (tissue) to the bones. This change indicates rapid cancer spread. We want to identify this possibility beforeit happens leading to better treatment.
Details
Company: Startup focusing on breast cancer detection
Date: May 2023
Bone metastasis risk in breast cancer treatment
- Client had access to RNA-seq data from breast cancer patients before treatment with Nintenab*
- UGenome pre-processed and aligned the RNA-seq data to a reference genome.
- UGenome converted the aligned RNA-seq data to transcripts per million and calculated a MeCo score for each patient.
*Nintedanib is used to treat idiopathic pulmonary fibrosis
Bioinformatics Case Study
Survival Analysis with a gene signature in cancer
Why is this important?
Gene signatures — a set of genes with a unique pattern — help cancer screenings because they can distinguish populations making specific treatments possible.
Details
Company: Bioinformatics Service Company
Date: October 2023
Survival Analysis with a gene signature in cancer
- Client needed to assess gene signatures for better cancer identification.
- We completed an exploratory analysis to assess distributional differences to check for sample to sample variability and presence of any outlier samples.
- Lastly, we performed univariate and multivariate survival analyses using a data-driven cutoff to optimize the cutpoint that maximizes the statistical significance of the difference between low and high expressing patients that corresponds with survival outcomes.
Why work with UGenome?
We are built on values with a drive to help you get a better treatment option for your immediate need and the people you ultimately serve: fellow humans.
Ben Stansfield
UGenome’s Manager of Computational Biology
“As a two-time kidney transplant recipient, generations of strangers, doctors, nurses, and of course my donors — my parents Stephen and Nancy — gave me a second chance at life. We want to be a company that helps revolutionize others’ lives whether we even meet those people or not. We know they have value and they are loved.”