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Spatial transcriptomics is an emerging field that bridges molecular biology and anatomy. Over the last decade, a battery of assays have been developed that profile gene expression in-situ, i.e, measuring the abundances of mRNA molecules in cells and tissues while retaining information about their spatial locations. This workshop will introduce the basic concepts, major techniques, […]
The goal of the workshop is to enable the participants to submit and rationally evaluate results of the AlphaFold protein structure predictions. The class will cover the basic principles of the structural biology of proteins, protein structure visualization (in pymol) and protein structure prediction. The participants will have opportunity to generate AlphaFold predictions for a […] |
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As we step into the era of precision medicine, the need for extensive genomic data analysis is more pressing than ever. With the advent of high-throughput technologies, a plethora of genomic, transcriptomic, and epigenomic datasets from cancer patients have been sequenced, providing a wealth of data for systematic bioinformatics analysis. This workshop is tailored to […] |
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This 3-day interactive workshop introduces the overarching principles guiding generative modeling and specifically Large-Scale Language Models (LLM), their application in Python for inference, and specific use-cases in Genomics. Experience with Python is necessary, and basic knowledge about ML workflows is preferred. At the end of this workshop, you WILL be comfortable with loading, inferencing and […] |
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RNA-seq I aims to provide an introduction and the basics tools to process raw RNA-seq data on a cluster machine (Hoffman2). The workshop can serve also as a starting point to develop a gene expression project. This workshop is divided in three days that will cover major steps of processing RNA-seq. To facilitate learning, the […]
In order to make inferences based on genomic data, it is useful to understand the evolutionary forces that underlie observed genetic variation. The field of population genetics offers theoretical tools to test predictions, gain intuition, and even generate training data for statistical and deep learning models. However, population genetics simulations have historically been slow and […] |
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