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RNASeq and Single Cell Sequencing (11/4 and 11/5)

by PICSciE/Research Computing

Training/Workshop Programming Languages Research & Data Analysis

Mon, Nov 4, 2024

4:30 PM – 6 PM EST (GMT-5)

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If you register for Part 1 on 11/4 then you will automatically be registered for Part 2 on 11/5.

RNA sequencing (rna-seq) is a sequencing technique used to analyze the presence and quantity of RNA in a biological sample at a given moment by the transcriptome. RNA-Seq is widely used to perform gene expression quantification to study cellular changes in organisms and microbiomes under different conditions. Single cell sequencing pipelines additionally allow biologists to perform differential expression analysis between groups of cells identified by known markers.The other genes in the identified groups of cells can be compared for more accurate analysis.

Learning objectives: Participants will learn to cleanup and analyze rna-seq sequence libraries and single cell differential expression data using some standard tools popular in the community. The workshop will use some specific tools to perform such analysis with focus on standard results expected from other such tools.

Knowledge prerequisites: Basic Linux and some experience with a scripting language (e.g., Bash, Python, R).

Hardware/software prerequisites: (1) Bring a laptop which can connect to the eduroam wireless network. You will also need to be able to Duo authenticate to use campus resources. (2) Have an SSH client installed on your laptop. (3) Register for an account on Adroit (https://forms.rc.princeton.edu/registration/?q=adroit). Make sure you can SSH to Adroit before the workshop.

Session format: Lecture and hands-on

Speakers

Abhishek Biswas's profile photo

Abhishek Biswas

Abhishek completed his doctoral work at Old Dominion University and worked at Oak Ridge National Laboratory as post-doctoral research associate before joining the Research Software Engineering group in at Princeton in June 2019. He is primarily associated with projects in Molecular Biology involving development of scalable high-performance metagenome binning pipeline and analysis of microscopy images.

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Co-hosted with: GradFUTURES

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