Computational biologist and Basepair founder, Dr. Amit Sinha (@ausinha) helps viewers navigate the world of RNA-Seq analysis. Topics include: Introduction to RNA-Seq, tools and workflows for analysis, visualization and figures, Q & A. More info at: https://www.basepairtech.com/

1. RNA-Seq Analysis: Everything
You Always Wanted to
Know...and then some
Amit Sinha, PhD
amit@basepairtech.com
Founder: www.basepairtech.com
Instructor: Harvard Med School

2. Overview
1. Introduction
2. Analysis Steps
3. Computing options
4. Demo
5. Q n A
2

3. Why RNA-Seq
Unbiased estimation of transcription
Applications
Differential expression
Novel transcripts, splice junctions, gene fusions
Variants
3

4. Raw data after sequencing
• Fastq format
• For paired-end, 2 files
• 1-5 Gb each
@SOLEXA2:5:1:3:169#0/1
CCAAATAATAGTTTGTTTTTTTGATATCTATA
+
aa``_b__a`b`aaa`aaaaaaa_a_a`a`a`
@SOLEXA2:5:1:3:1063#0/1
CAGTTCTTAAAGCTCACAAAGATGGTTTGAAA
+
bbbaabbbbbababbbbaaaba```bbaaaa
@SOLEXA2:5:1:3:902#0/1
GGCATAACATATCTTCCAAATCCATGTATTTC
+
aabbbbbbbbbbbbbbbb`bbbbbabaabaab
@SOLEXA2:5:1:3:1072#0/1
AAGAAACAGAACTTGAATTTTCTTTAACTCAC
+
baaaaaaaaaaaaaa``aaaaaaaaaaa`aaa
4

5. Differential expression pipeline
5
Alignment Differential expression

6. Steps in RNA-Seq analysis
QC
• Check sequencing quality, no sample
contamination
Alignment
• Map short reads to the genome
Metrics
• Ensure your reads are mapping to coding
regions
Transcript
count
• Count number of reads on each transcript
Differential
expression
• Statistical significance of expression count
6

7. Tools for RNA-Seq analysis
QC
• FastQC, FASTX
Alignment
• Tophat, STAR
Metrics
• Picard, Cummerbund?
Transcript
count
• Rsamtools, htseq-count, featurecounts
Differential
expression
• Cuffdiff, DESeq, VOOM
7

8. QC
8

9. Alignment
9

10. Post-alignment QC
10

11. Read counts
11
Gene Count
TP53 456
BRCA1 897
HOXA9 765

12. Differential expression
12
Heatmap Volcano plot

13. Downstream analysis
-0.4 -0.2 0.0 0.2
-0.4-0.20.00.2
Principle Component 2
PrincipleComponent3
LSK
CMP
MEP
GMP
LGMP-GMP
LGMP-HSC
Geneset
Enrichment Clustering
(PCA)
Network
Analysis
14

14. Options for analysis
Desktop Local server Web-based
High control
Low computing power
Small storage space
Can’t handle large loads
Computing and storage
Low cost
Need tech expertise
Quota limits
Scalable
Cost-effective
Higher uptime
No direct access
Name
File
Run
15

15. Analysis scripts for the server
16

16. Upload Data
Web-based software
17
1 Choose workflow2 Run analysis3

17. Free trial for webinar attendees
18
https://www.basepairtech.com
code: webinar-101

18. Questions?