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Genomics

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Selected Works

2017

RNA-seq

Articles 1 - 3 of 3

Full-Text Articles in Life Sciences

Finding Function In The Unknown, Kelly Boyd, Emma Highland, Amanda Misch, Amber Hu, Sushma Reddy, Catherine Putonti Sep 2017

Finding Function In The Unknown, Kelly Boyd, Emma Highland, Amanda Misch, Amber Hu, Sushma Reddy, Catherine Putonti

Catherine Putonti

Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies.

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Genomics Rna-Seq Analysis Part 2_ Kallisto Indexing And Quantification (Updated 11/17), Ray A. Enke, Melika Rahmani-Mofrad Dec 2016

Genomics Rna-Seq Analysis Part 2_ Kallisto Indexing And Quantification (Updated 11/17), Ray A. Enke, Melika Rahmani-Mofrad

Ray Enke Ph.D.

This in class exercise is a hands on activity designed to teach students about how to run Kallisto indexing quantification using CyVerse DE apps as part of a eukaryotic RNA-seq analysis pipeline.

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Genomics Rna-Seq Analysis Part 3-Sleuth Data Visualization (Updated 11/17), Ray A. Enke, Scott Schumacker Dec 2016

Genomics Rna-Seq Analysis Part 3-Sleuth Data Visualization (Updated 11/17), Ray A. Enke, Scott Schumacker

Ray Enke Ph.D.

This in class exercise is a hands on activity designed to teach students about how to run Sleuth statistical modeling and RStudio data visualization package using Kallisto pseudoalignment output files as part of a eukaryotic RNA-seq analysis pipeline.

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