1. Introduction to RNA-seq
Paul Gardner
July 6, 2015
Paul Gardner RNA-seq intro

2. Where on the bio/math spectrum do you lie?
Paul Gardner RNA-seq intro

3. What is RNA?
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IUPAC ambiguity chars:
Paul Gardner RNA-seq intro

4. Crick’s “central dogma of molecular biology”
Paul Gardner RNA-seq intro

5. Types of RNA
Protein coding RNA:
Messenger RNA
Noncoding RNA:
Ribosomal RNA & Transfer RNA
Spliceosomal RNAs (U1, U2, U4, U5, & U6)
SRP RNA (protein export)
RNase P RNA
snoRNAs & microRNAs (David Humphreys)
Cis-regulatory RNA (riboswitches,
thermosensors, leaders)
Self-splicing introns
“Long” non-coding RNAs (lncRNA)
Clustered regularly interspaced short
palindromic repeats (CRISPR)
RNAs of Unknown Function (RUFs)
Paul Gardner RNA-seq intro

6. What is RNA-seq?
Martin & Wang (2011) Next-generation transcriptome
assembly. Nature Reviews Genetics.
Paul Gardner RNA-seq intro

7. Run the best statistical test in the universe:
Eye-ball results: positive & negative controls
Remember: only RNAs expressed under exp. conditions will
be observed
Paul Gardner RNA-seq intro

8. Applications and extensions of RNA-seq
Applications
Genome annotation (mRNAs, ncRNAs, spliceforms, UTRs)
Quantification (Listen to Alicia Oshlack)
Extensions
Infer RNA structure (SHAPE) (Lucks et al. (2011))
RNA:RNA (CLASH) (Travis et al. (2014))
RNA:protein (RIP-seq) (Cook et al. (2015))
Paul Gardner RNA-seq intro

9. RNA-seq identifies 1,000s of new RNAs
SraB yceD rpmF E.RUF plsX
E.coli K12
E.coli E24377A
C.rodentium
S.enterica
K.pneumoniae
rmf RNA motif rmf P.RUF pyrD
S.maltophilia
X.axonopodis
secY X.RUF rpsM
D. Enterobacteriaceae RUF E. Pseudomonas RUF
F. Xanthomonadaceae RUF
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GNRA tetraloop
0-48 BPs
A. Enterobacteriaceae RUF
B. Pseudomonas RUF
C. Xanthomonadaceae RUF
Gaps
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1,000-4,999
>5,000
Number of RNA-seq reads
80%
90% 70%
40%
nucleotide
present
nucleotide
identity
N
N 90%
N 80%
covarying mutations
base pair annotations
compatible mutations
no mutations observed
R = A or G. Y = C or U.
Legend
70%
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TAGGCATATTTTTTTCCATCAGATATAGCGTATTGATGATAGCCATTTTAAACTATGCGC−−−TTCGTTTTGCAGGTTGATGTTTGTTATCAGCACTGAACGAAAATAAAGCAGTAACCCGCAATGTGTGCGAATTATTGGCAAAAGGCAACCACAGGCTGCCTTTTTCTTTGACTCTATGACGTTACAAAGTTAATATGCGCGCCCTATGCAAAAGGTAAAATTACCCCTGACTCTCGATCCGGTTCGTACGGCTCAAAAACGCCTTGATTACCAGGGTATCTATACCCCTGATCAGGTTGAGCGCGTCGCCGAATCCGTAGTCAGTGTGGACAGTGATGTGGAATGCTCCATGTCGTTCGCTATCGATAACCAACGTCTCGCAGTGTTAAACGGCGATGCGAAGGTGACGGTAACGCTCGAGTGTCAGCGTTGCGGGAAGCCGTTTACTCATCAGGTCTACACAACGTATTGTTTTAGTCCTGTGCGTTCAGACGAACAGGCTGAAGCACTGCCGGAAGCGTATGAACCGATTGAGGTTAACGAATTCGGTGAAATCGATCTGCTTGCAATGGTTGAAGATGAAATCATCCTCGCCTTGCCGGTAGTTCCGGTGCATGATTCTGAACACTGTGAAGTGTCCGAAGCGGACATGGTCTTTGGTGAACTGCCTGAAGAAGCGCAAAAGCCAAACCCATTTGCCGTATTAGCCAGCTTAAAGCGTAAGTAATTGGTGCTCCCCGTTGGATCGGGGATAAACCGTAATTGAGGAGTAAGGTCCATGGCCGTACAACAGAATAAACCAACCCGTTCCAAACGTGGCATGCGTCGTTCCCATGACGCGCTGACCGCAGTCACCAGCCTGTCTGTAGACAAAACTTCTGGTGAAAAACACCTGCGTCACCACATCACTGCCGACGGTTACTACCGCGGCCGCAAGGTCATCGCTAAGTAATCACGCA−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−TCTGC−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−GTGATGAAGCTTAGTGAGGATTTTCCCCAGGCAACTGGGGAAAGACCAAACCGGGCGGCGACGATACCTTGACACGTCTAACCCTGGCGTTAGATGTCATGGGAGGGGATTTTGGCCCTTCCGTGACAGTGCCTGCAGCATTGCAGGCACTGAATTCTAATTCGCAACTCACTCTTCTTTTAGTCGGCAATTCCGACGCCATCACGCCATTACTTGCTAAAGCTGACTTTGAACAACGTTCGCGTCTGCAGATTATTCCTGCGCAGTCAGT
ATGGCGCAGGCTGGCATTGGTAACCTCGGCGGCGGGCTCGGCAAGTTCACGGAACTTCGCCAGCGGTTGCTGTTCGTCCTCGGGGCATTGATCGTTTATCGCATCGGCTGCTATGTGCCGGTGCCTGGCGTGAATCCCGATGCCATGCTTTCGTTGATGCAGGCGCAGGGCGGCGGCATCGTGGACATGTTCAACATGTTCTCGGGCGGCGCCCTGCACCGTTTCAGTATTTTTGCATTGAACGTGATGCCGTATATCTCGGCATCGATCGTGATCCAGTTGGCCACGCACATCTTTCCCGCCCTCAAGGCGATGCAGAAAGAAGGCGAATCGGGCCGACGCAAGATCACCCAATATTCGCGCATCGGTGCGGTGTTGCTGGCGGTGGTGCAGGGCGGCAGTATCGCGCTGGCACTGCAGAACCAGACCGCCCCTGGTGGCGCTCCGGTGGTGTATGCGCCGGGCATGGGCTTCGTGCTCACCGCGGTGATCGCTTTGACCGCTGGTACCATCTTCCTGATGTGGGTAGGCGAGCAGGTTACCGAGCGCGGCATCGGTAACGGCGTATCGCTGATCATCTTTGCCGGCATCGTGGCTGGCCTGCCGTCGGCGGCCATCCAGACGGTCGAAGCCTTCCGCGAAGGCAATCTGAGCTTCATTTCGCTGTTGTTGATCGTCATCACCATCCTGGCGTTCACGCTGTTCGTCGTGTTTGTCGAGCGTGGGCAGCGGCGGATCACGGTCAACTACGCGCGCCGCCAGGGCGGTCGCAATGCGTACATGAACCAGACCTCGTTCTTGCCGCTCAAGCTGAACATGGCCGGTGTGATTCCGCCGATCTTTGCGTCCAGCATCCTGGCATTCCCGGCAACGTTGTCGATGTGGTCGGGTCAGGCTGC−−ATCGG−GTGGTATCGGCTCGTGGCTGCAGAAGATTGCCAACGCGCTTGGCCCCGGTGAGCCGGTACACATGCTGGTCTTCGCTGCGCTGATCATCGGTTTTGCATTCTTCTACACCGCGCTGGTGTTCAACTCGCAGGAAACCGCCGACAACCTCAAGAAATCGGGCGCGCTGATTCCGGGCATCCGTCCAGGCAAGGCCACCGCAGATTACGTCGATGGCGTACTGACGCGCCTGACAGCTGCCGGTTCGTTGTACCTGGTAATCGTCTGCCTGCTGCCGGAAATCATGCGCACGCAGCTCGGCACTTCGTTCCACTTCGGGGGCACCTCGCTATTGATTGCAGTGGTGGTGGTGATGGACTTCATTGCGCAGATCCAGGCGCACCTGATGTCGCACCAGTATGAGAGCTTGCTGAAGAAGGCCAACCTCAAGGGCGGCTCACGCGGCGGTCTTGCGCGCGGTTAAGTGGTACACTAGATCTTCATC−−−−−−ACGTGAAGACGGC−CTGGTTCCCGGGCCACGATCTTCCGATCAGAAGGGCGGCTCGCGCGACG−TCTCGCGCGCGGGTGTGACGGGGTGGTTCTGTGCGGGAGTAGCACAGGCGATTC−GGAGTGGTTTTCTGGATCAGCACCGTCCGGCGCCGGAGCGAGGGCACACTCCCCACGCCGGGTCCATGGAACCTCTGGTTCCACGGGCTTCAAAGCAATCCGAGGCCTTGCTATAATTCCGAGTTCACTTT−−TGATCCATCCTGCCGGATGG−−−CGCCTGGG−−−CGCTGTCGGGCCATCACTCAGTTGGAGAATCGCGTCATGGCGCGTATTGCAGGCGTCAACCTGCCAGCCCAGAAGCACGTCTGGGTCGGGTTGCAAAGCATCTACGGCATCGGCCGTACCCGTTCAAAGAAGCTCTGCGAATCCGCAGGCGTTACCTCGACCACGAAGATTCGTGATCTGTCCGAACCCGAAATCGAGCGCCTGCGCGCCGAAGTCGGCAAGTATGTCGTCGAAGGCGACCTGCGCCGCGAAATCGGTATCGCGATCAAGCGACTGATGGACCTCGGCTGCTATCGCGGTCTGCGTCATCGCCGTGGTCTTCCGCTGCGTGGTCAGCGCACCCGTACCAACGCCCGCACCCGCAAGGGTCCGCGCAAGGCGATCAGGAAGTAA
Lindgreen et al. (2014) Robust identification of noncoding RNA from transcriptomes requires
phylogenetically-informed sampling. PLOS Computational Biology.
Paul Gardner RNA-seq intro

10. Some open questions
How much transcription is ”functional”?
What’s a good negative control for transcriptome
experiments?
What causes variation in [protein]:[mRNA] ratios?
Lu, Vogel et al. (2007) Absolute protein expression profiling estimates the relative contributions of transcriptional
and translational regulation. Nature Biotechnology
Paul Gardner RNA-seq intro

11. Thanks & a plug
QMB 2015
Computational genomics
Proteins, animal genetics, genomes & disease, microbes &
disease
Paul Gardner RNA-seq intro