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Introduction to Annotation Data Packages in Bioconductor Accurate gene and transcript annotation is the foundation of many bioinformatics workflows, including RNA-seq analysis, functional genomics, and variant annotation.
In the R/Bioconductor ecosystem, dedicated annotation data packages make it easy for researchers to access, query, and leverage gene models sourced from major biological databases.
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AI is transforming every field — and bioinformatics is no exception. From designing drug molecules in minutes to writing entire pipelines, generative AI is making it faster than ever to process biological data. But here’s the truth:
AI doesn’t understand biology — you do.
That’s why, in this new era, your value isn’t replaced by AI — it’s multiplied by your ability to judge, validate, and improve what AI produces.
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Suna was 28. She had melanoma. Chemo left her wrecked—her hair gone, her strength gone, and her hope fading. Doctors gave her weeks.
Then they tried something different.
It didn’t poison the tumor. It didn’t cut or burn. It woke up her immune system.
Her own T-cells found the cancer. Attacked it. Killed it.
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What is partial correlation Partial correlation measures the relationship between two variables while controlling for the effect of one or more other variables.
Suppose you want to know how X and Y are related, independent of how both are influenced by Z. Partial correlation helps answer:
If we remove the influence of Z, is there still a connection between X and Y?
body { text-align: justify} Today’s guest blog post on multiOmics integration is written by Aditi Qamra and edited by Tommy.
If you want to do a guest posting in my blog which gets 30k views per month, feel free to contact me on LinkedIn.
Aditi is a senior data scientist working on biomarker discovery and early product development at Roche, using multimodal clinical and genomic data. She has a PhD and postdoc in epigenomics of solid tumors and enjoys upskilling herself in stats topics.
You Can Change Your Appetites Linear algebra, statistics, machine learning—these used to feel abstract to me. I had zero experience of bioinformatics when I was studying my PhD in a wet lab.
I memorized formulas without truly understanding them. But over time, I found the right resources that made these concepts click, especially in the context of bioinformatics.
I wrote a blog post: My opinionated selection of books/urls for bioinformatics/data science curriculum six years ago, and many links are broken.
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In my last blog post, I showed you how to download TCGA RNAseq count data and do PCA and make a heatmap. It is interesting to see some of the LUSC samples mix with the LUAD samples and vice versa.
In this post, we will continue to use PCA to do more Exploratory data analysis (EDA).
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In my last post, I showed you how to use PCA for bulk RNAseq data.
Today, let’s see how we can use it for scATACseq data.
Download the example dataset from 10x genomics https://support.10xgenomics.com/single-cell-atac/datasets/1.1.0/atac_pbmc_5k_v1
The dataset is 5k Peripheral blood mononuclear cells (PBMCs) from a healthy donor (v1.0).
Download the atac_pbmc_5k_v1_filtered_peak_bc_matrix.tar.gz file and unzip it.
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what is PCA? Principal Component Analysis (PCA) is a mathematical technique used to reduce the dimensionality of large datasets while preserving the most important patterns in the data.
It transforms the original high-dimensional data into a smaller set of new variables called principal components (PCs), which capture the most variation in the data.
In a biotech startup, an early data strategy is key to ensure public and private data remain useful and valuable. As AI hype reaches new heights, I want to emphasize that a data strategy must precede any AI strategy.
Data is the oil of the AI engine. Unfortunately, the real-world data are usually messy and not AI-ready. Without a robust data strategy, you are building an AI system on a shaky foundation.