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RNA-seq

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Fastqc Analysis & Hisat Alignments Using Cyverse (Part 2), Ray A. Enke Oct 2018

Fastqc Analysis & Hisat Alignments Using Cyverse (Part 2), Ray A. Enke

Ray Enke Ph.D.

Part 2 of this in class exercise uses CyVerse Discovery Environment (DE) for the following:
  • view the output files of FastQC analysis
  • create custom data tracks from HISAT alignment files for visualization in the UCSC Genome Browser

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Fastqc Analysis & Hisat Alignments Using Cyverse (Part 1), Ray A. Enke Oct 2018

Fastqc Analysis & Hisat Alignments Using Cyverse (Part 1), Ray A. Enke

Ray Enke Ph.D.

This in class exercise demonstrates the basic features of the CyVerse Discovery Environment (DE) cyberinfrastructure and also provides a tutorial for setting up FastQC analysis of next generation sequencing reads as well as HISAT alignment of eukaryotic RNA-seq FASTQ files.

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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 1-Cyverse De, Fastqc, Trimmomatic (Updated 11/17), Ray A. Enke, Melika Rahmani-Mofrad Dec 2016

Genomics Rna-Seq Analysis Part 1-Cyverse De, Fastqc, Trimmomatic (Updated 11/17), Ray A. Enke, Melika Rahmani-Mofrad

Ray Enke Ph.D.

This in class exercise is designed to teach students about the basic features of CyVerse Discovery Environment (DE) as well as how to run FastQC and Trimmomatic steps of a general eukaryotic RNA-seq analysis pipeline using CyVerse DE apps.

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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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Rna Sequencing Analysis Of The Developing Chicken Retina, Christophe Langouet-Astrie*, Annamarie Meinsen*, Emily R. Grunwald*, Stephen Turner, Raymond A. Enke Nov 2016

Rna Sequencing Analysis Of The Developing Chicken Retina, Christophe Langouet-Astrie*, Annamarie Meinsen*, Emily R. Grunwald*, Stephen Turner, Raymond A. Enke

Ray Enke Ph.D.

RNA sequencing transcriptome analysis using massively parallel next generation sequencing technology provides the capability to understand global changes in gene expression throughout a range of tissue samples. Development of the vertebrate retina requires complex temporal orchestration of transcriptional activation and repression. The chicken embryo (Gallus gallus) is a classic model system for studying developmental biology and retinogenesis. Existing retinal transcriptome projects have been critical to the vision research community for studying aspects of murine and human retinogenesis, however, there are currently no publicly available data sets describing the developing chicken retinal transcriptome. Here we used Illumina RNA sequencing …

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Pathway Analysis Of Differentially Expressed Genes (Computational), Raymond A. Enke May 2016

Pathway Analysis Of Differentially Expressed Genes (Computational), Raymond A. Enke

Ray Enke Ph.D.

This class tested protocol will guide students through the steps for the following activities:
  • Conduct gene ontology (GO) pathway analysis of statistically and biologically significant differentially expressed genes (DEGs)
  • Sort and organize lists of DEGs in MS Excel spreadsheets based on biological function

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Analysis Of Rna-Seq Alignments Using Dna Subway Green Line (Computational), Raymond A. Enke May 2016

Analysis Of Rna-Seq Alignments Using Dna Subway Green Line (Computational), Raymond A. Enke

Ray Enke Ph.D.

This class tested protocol will guide students through the steps for the following activities:
  • Review basic steps of RNA-Seq bioinformatics analysis in DNA Subway Green Line
  • View and run basic analytics of RNA-Seq data set in DNA Subway Green Line

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Creating Custom Rna-Seq Data Tracks In The Ucsc Genome Browser (Computational), Raymond A. Enke May 2016

Creating Custom Rna-Seq Data Tracks In The Ucsc Genome Browser (Computational), Raymond A. Enke

Ray Enke Ph.D.

This class tested protocol will guide students through the steps for the following activities:
  • Create your own custom data tracks in the UCSC Genome Browser
  • Visualize RNA-Seq TopHat alignment data as custom tracks in the UCSC Genome Browser
  • Integrate RNA-Seq alignment data with other genome-wide data sets

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Integration Of Rna-Seq Data Analysis Into Undergraduate Lab Teaching Modules, Raymond A. Enke Jan 2016

Integration Of Rna-Seq Data Analysis Into Undergraduate Lab Teaching Modules, Raymond A. Enke

Ray Enke Ph.D.


Undergraduate students learn about Next Generation Sequencing (NGS) platforms in courses, but often have difficulty understanding the impact of these techniques without hands-on experience analyzing actual NGS datasets.  The NSF-supported Infrastructure & Training to Bring NGS Analysis Into Undergraduate Education project focuses on streamlining RNA-sequencing (RNA-seq) data analysis using a combination of bioinformatics and wet lab workflows tailored for implementation into diverse undergraduate laboratory classroom settings. These modular workflows can be applied to a variety of novel or publicly available RNA-seq datasets.
 
To create a course-based undergraduate research experience focusing on NGS that complements my research program, I …

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