RNAseq data analysis using Galaxy
out of 5 stars
02 Jul 2021 | Contributor(s): Matthew Escobar, Sam S Donovan, Irina Makarevitch, Bill Morgan, Sabrina Robertson | doi:10.25334/XHW8-7189
This is a bioinformatics exercise intended for use in a computer lab setting with life science majors.
Yeti or not: Do they exist?
31 Dec 2020 | Contributor(s): Keith Johnson, Adam Kleinschmit, Jill Rulfs, William (Bill) Morgan | doi:10.25334/GDSW-P773
Through this 4-part bioinformatics case study, students will be led through the forensic analysis of putative Yeti artifacts based on published findings.
NIBLSE Collaborative Community Model for developing, disseminating, and assessing bioinformatics learning resources
08 Dec 2020 | Contributor(s): William (Bill) Morgan, Adam Kleinschmit, Anne Rosenwald, Eric Triplett, Mark A. Pauley, William Tapprich | doi:10.25334/QTEK-P378
The Network for Integrating Bioinformatics into Life Sciences Education (NIBLSE) has cultivated a collaborative community model to facilitate the development, dissemination, and assessment of bioinformatics learning resources.
Needleman - Wunsch Algorithm Exercise
22 Apr 2020 | Contributor(s): Michael Sierk, Sam S Donovan, Neal Grandgenett, Bill Morgan, Mark A. Pauley, Elizabeth F Ryder | doi:10.25334/H7KQ-F202
This exercise is used in a sophomore-junior level bioinformatics course. The algorithm is introduced to the students who then complete the exercise.
Checking Normality in R with swirl
13 Jan 2020 | Contributor(s): Bill Morgan | doi:10.25334/PQ79-N018
How to use the "three-prong" approach to check for normality
NIBLSE Incubators: A community-based model for the development of bioinformatics learning resources
21 May 2019 | Contributor(s): Michael Sierk, Sam S Donovan, Bill Morgan, Hayley Orndorf, Mark A. Pauley, Sabrina Robertson, Elizabeth F Ryder, William Tapprich | doi:10.25334/Q4QX7T
Presentation on NIBLSE incubators at the 2019 Great Lakes Bioinformatics Conference
15 May 2019 | Contributor(s): Michael Sierk, Sam S Donovan, Bill Morgan, Hayley Orndorf, Mark A. Pauley, Sabrina Robertson, Elizabeth F Ryder, William Tapprich | doi:10.25334/Q42F3K
29 Mar 2019 | Contributor(s): Michael Sierk, Sam S Donovan, Bill Morgan, Hayley Orndorf, Mark A. Pauley, Sabrina Robertson, Elizabeth F Ryder, William Tapprich | doi:10.25334/Q4XF1K
Presentation given at GLBIO 2019 on NIBLSE Incubators
Development of the NIBLSE Learning Resource Collection and Incubators
16 Mar 2018 | Contributor(s): Bill Morgan, Sam S Donovan, Hayley Orndorf, Sabrina Robertson, Elizabeth Ryder, Michael Sierk, Anne Rosenwald, Liz Dinsdale, Eric Triplett, Mark A. Pauley, William Tapprich | doi:10.25334/Q4TX1G
Talk on the NIBLSE Resource Collection and Incubators given at several professional development confernces
Incubators: A community based model for improving the usability of bioinformatics learning resources
19 Jan 2018 | Contributor(s): Hayley Orndorf, Bill Morgan, Neal Grandgenett, Mark A. Pauley, Elizabeth Ryder, Michael Sierk, Robin Wright, Anne Rosenwald, Liz Dinsdale, Eric Triplett, Sam S Donovan | doi:10.25334/Q4KQ30
Poster on Incubators, a NIBLSE and QUBES collaboration presented at Great Lakes Bioinformatics Conference - Chicago, IL
13 Nov 2017 | Contributor(s): Matthew Escobar, Sam S Donovan, Irina Makarevitch, Bill Morgan, Sabrina Robertson | doi:10.25334/Q42H38
RNA-seq Analysis
05 Jun 2017 | Contributor(s): Bill Morgan, Matthew Reeder
In this computer lab module, students learn how to process an RNA-seq data set to identify differentially expressed genes (DEGs). The samples for this data set were collected from yeast cells expressing either a control gene or a pathogen...
13 Oct 2016 | Contributor(s): Michael Sierk, Sam S Donovan, Neal Grandgenett, Bill Morgan, Mark A. Pauley, Elizabeth Ryder
03 Aug 2016 | Contributor(s): Michael Sierk
03 Aug 2016 | Contributor(s): Bill Morgan, Matthew Reeder
In this computer lab module, students learn how to process an RNA-seq data set to identify differentially expressed genes (DEGs). The samples for this data set were collected from yeast cells expressing either a control gene or a pathogen effector gene.