2019-07-03
Brad Langhorst - New England Biolabs
Talk Goals
- Convey utility of UMIs in RNA-seq
- Examine technical factors associated with apparent transcript level differences
- Compare Transcript levels +/- UMI with expectation
- Explore correlation of transcript features with measured abundance
- For me: Hear about what other features should we consider?
What is a UMI
- Universal Molecular Identifier
- (aka MID, Molecular barcode, UID)
- Semi-random synthetic sequence of DNA bases
- Probability of any specific sequence = 1/4^n
- n = number of bases
- n=1 1/4 A,C,G, or T
- n=2 1/16 AA,AC,AG,AT,CA,CC ... GT,TA,TC,TG,TT
- n=3 1/256 ...
- We use 12 bp UMIs = 1/16,777,216
- When combined with transcript identity, probability of false collision is very low (function of transcript abundance)
- Sequencing errors might produce related UMIs
RNA-Seq Workflow Overview
RNA-Seq Workflow Overview
UMI added here
technical factors related to observed abundance
RNA-Seq Workflow Overview
UMIs duplicated here
technical factors related to observed abundance
Technical Factors
- Pre - UMI
- Priming bias (hexamers may not be random)
- Fragmentation bias (too easy = low, too hard = low)
- A-tailing bias (lower efficiency = low)
- Post - UMI
- Size selection/cleanups (bead binding, differential elution)
- PCR (too easy = high, too hard = low)
Uneven Amplification
BRCA1
p53
Original
Library
+ UMI
Fragments
2
1
3
4
5
6
7
9
1
2
3
4
5
PCR
1
1
1
2
2
3
4
4
4
4
4
4
5
6
7
8
9
5
6
7
8
9
1
2
3
4
5
1
2
3
4
5
2
3
4
5
4
4
4
5
9
9
8
8
25/18 = 1.8
9/5 = 1.4
BRCA1/p53 ratio
4/3 = 1.3
Identification of Duplication
Dedup
1
1
1
2
2
8
9
8
9
9
9
8
Using UMI
No UMI
BRCA1
Alignment
1
2
9
= 12
= 3
1
1
1
2
2
8
9
8
9
9
9
8
1
2
8
9
= 4
= 12
BRCA1
BRCA1
BRCA1
+/-UMI Experiment Design
C. Devoe, K. Krishnan, D. Rodriguez
Goal: Compare counts of transcripts with and without UMI
- 10 ng total RNA (~ 300 cells, human/mouse blood)
- NEBNext Ultra II RNA library prep + UMI adapters
- 12 PCR cycles
- Sequenced on Illumina NovaSeq 4000 S2, 2x75bp
Experimental Results
C. Devoe, K. Krishnan, D. Rodriguez
Experimental Results
C. Devoe, K. Krishnan, D. Rodriguez
Experimental Results
Hard to Amplify
Easy to Amplify
C. Devoe, K. Krishnan, D. Rodriguez
RNA Mixture Experiment
G. Naishadham
Goal: Compare counts of transcripts to expectation
- 10 ng Input (~ 300 cells, cell line Blood/Brain RNA)
- Defined mixtures 1:3 and 3:1 to generate expected values
- NEBNext Ultra II RNA library prep + UMI adapters
- 12 PCR cycles
- Sequenced on Illumina NovaSeq 4000 S2, 2x75bp
RNA Mixture Experiment
G. Naishadham
Galaxy Workflow
G. Naishadham
Challenges: 3 reads R1, R2 + UMI
memory = f(umilen)*num reads
Experimental Results
G. Naishadham
Experimental Results
G. Naishadham
Need MOAR Reads!
Do transcript features explain variable amplification?
- GC
- Fragment Length
- RNA structure stability
- K-mer enrichment
- Others?
Transcript GC%
Increasing Duplication
transcripts with > 100 reads
Fragment Lengths
Increasing Duplication
G. Naishadham
transcripts with > 100 reads
Mean Free Energy
Increasing Duplication
Increasing Predicted Stability
transcripts with > 100 reads
G. Naishadham
Other Factors Under Consideration
- Complexity (Shannon information content)
- Maximum 200 bp folding energy
- Other ideas ?
UMIs in RNA-Seq
- We see differences in amplification between transcripts
- We can partially correct these using UMIs
- We don't yet understand mechanism but we're not done digging yet
PS: I'm hiring soon - see the job board!