What's New @ HSLS MolBio

Spring 2023 CRC Next Generation Sequencing Workshops

High throughput sequencing has brought abundant sequence data along with a wealth of new “-omics” protocols, and this explosion of data can be as bewildering as it is exciting. The Center for Research Computing’s (CRC) multi-day hands-on workshops give researchers the research, open-sourced tools to plan and execute successful bioinformatics and genomics experiments.

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CRC Workshops Spring 2023

The Center for Research Computing’s (CRC’s) Spring 2023 workshops have added some new features, including a series of foundational Python workshops in preparation for industry-sponsored workshops on data science. CRC’s first workshop will be the twice-yearly CRC Ecosystem On-ramp held Jan.

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December 7: Gene Expression Visualization using PartekFlow and Cytoscape

Wednesday, December 7, 1-4 p.m., online

Register for Gene Expression Visualization using PartekFlow and Cytoscape

This workshop is on the visualization of gene expression data using HSLS-licensed Partek Flow software as well as open-access Cytoscape software.

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Nov 16 – CITE-Seq and Spatial Transcriptomics Data Analysis with Partek Flow

Wednesday, November 16, 1-4 p.m., online

Register for CITE-Seq and Spatial Transcriptomics Data Analysis with Partek Flow

In this workshop, we will cover multiomics data analysis –  CITE Seq and Spatial Transcriptomics

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Nov 17 – Bridging histology and genomics with Visium Spatial profiling data analysis tutorial

Date: November 17, 2022
Time: 1:30 p.m.- 3:30 p.m. ET

Register for Bridging histology and genomics with Visium Spatial profiling data analysis tutorial, presented by 10x Genomics and Partek.

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October 26 – Gene Expression Data Mining and Analysis

Wednesday, October 26, 1-4 p.m., online

Register for Gene Expression Data Mining and Analysis

This workshop will focus on Gene Expression Omnibus (GEO) repository. We will learn how to find high throughput gene expression studies – bulk RNA-seq/ scRNA-seq /microarray from GEO, retrieve gene expression count data, and generate a list of differentially expressed genes (DEG) between two conditions (disease state vs.

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