1. Day 4 Session 22: Questions and follow-up…. James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

2. Day 4 Session 23: RNA-seq processing, visualization, and QC
James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

3. RNA-seq Analysis Workflow
Data Filters
Intensity
Fold
Well Designed Study
Interpret Experiment
Normalized Vetted Data
Network building
High Quality RNA
Statistical Analysis
Additional QC analysis
Library Preparation
Pathway and Geneset Analysis
Differentially Expressed Gene List
Sequencing and QC analysis
Process Data
Align to genome
Normalize
Clustering and visualization
Raw Reads

4. Research Paradigm: Use Next Generation Sequencing (NGS) to study gene regulation
St John et al. (2014) The osteoblast to osteocyte transition: epigenetic changes and response to the vitamin D3 hormone. Mol. Endocrinol. 28:1150

5. Vitamin D Regulates Ca Metabolism by Regulating Genes in Multiple Tissues

6. Bone Marrow Precursors Differentiate into Osteoblast which then become Osteocytes
Osteoblasts form the collagen matrix that is mineralized to become bone.
Osteocytes respond to mechanical strain in bone to regulate osteoblast activity
95% of bone cells
Capulli et al. (2014) Arch Biochem Biophys 561:3

7. Osteoblasts become osteocytes (95% of bone cells!!!)
pOB = osteoblast precursor; OB = osteoblast; OS = osteoid; pOC = preosteocyte; OC = osteocyte; MB = mineralized bone

8. (IFNg inducible expression of SV40 Large T Ag)
IDG-SW3 cell Model
+ IFNg, 33oC
Proliferating Osteoblast precursors
“Permissive”
Isolate cells from bone of 3 mo old mice
No IFNg, 37oC
Ascobate
b glycerol PO4
Differentiation of primary bone cells
Immorto-Mouse
(IFNg inducible expression of SV40 Large T Ag)
“Differentiation”
=mineralization
Woo et al. (2011) J Bone Min Res 26:2634

9. Study Design: RNA-seq IDG-SW3 cells n = 3/group
Mouse bone precursor cell line
Immortomouse
IFNg inducible, heat labile SV40 Large T Ag
37oC
No IFNg
Ascorbate
b glycerol PO4
Study 1: Time course
Study 2: Time (3 vs 35 d) x Vit D (+/-)
Permissive
Culture
(33oC,
IFNg) 3 7 14 21 28 35
+/- 100 nM 1,25(OH)2 D
24 h prior
n = 3/group
+/- 100 nM 1,25(OH)2 D
24 h prior
GEO ID = GSE54783
St. John et al., 2014, Mol Endocrinol 28:1150
Fleet 2016

10. MultiDimensional Scaling (MDS) plot
Purple = subtype 1
Green = subtype 2
A means to visually assess similarities among samples.
Reflects euclidean distance of genes with highest log fold change among samples.
(similar samples group together)
Log FC dim 2
Log FC dim 1
Default QC from DEseq

11. Heat Map of 90 most Highly Expressed Genes by Sample
Cell
TRT A A B B

12. Thursday
BREAK #1

13. Day 4 Session 24: Differential Expression Analysis of RNA-seq data
James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

14. Sequence Read Archive (SRA)

15. The Statistical Challenge of NGS and RNA-seq Data
NGS NOT normally distributed - different statistical approach is needed.
Plot of sequence depth along a sequence = Poisson distribution.
BUT the variability between biological replicates = too much for the a Poisson distribution.
The Negative Bionomial distribution = good fit to RNA-seq data - can account for biological variability
Huang et al. (2015) Cancer Informatics 14S1:57

16. Count-based Analysis: edgeR, DEseq2
Transcript-Level Analysis: Cuffdiff2
Cumberbun in R to allow it to be used in R
Anders et al., 2013, Nat Protocol 8:1765
Trapnel et al., 2013, Nat Biotech 31:46

17. Day 4 Session 25: Special Lecture Practicalities of NGS Data
Nadia Atallah, PhD
Bioinformatician
Purdue University Center for Cancer Research

18. Day 4
Session :
Visualization and Functional Characterization of RNA-seq data
James C. Fleet, PhD
Distinguished Professor
Department of Nutrition Science
Pete Pascuzzi, PhD
Assistant Professor
Purdue Libraries

19. Thursday
BREAK #2

20. Criteria for Evaluating Bioinformatic Tools
Access
Appropriateness
Cost
Documentation
Exportability
Flexibility
Interpretability
Knowledge base
Methodologies
Scalability
Standardization
Sufficiency
Usability
UC Riverside visualization
Mullany et al. (2015) Cancer Inform. 14:

21. Large File Transfer Systems
(1) File Transfer Protocol (FTP)
(2) Globus: Direct Sharing and Transfer
(3) Cloud Systems
(4) Other