Brad Langhorst
Product Development Group Leader - New England Biolabs
Sequencing Finishing and the Future 2020-12-03
Brad Langhorst - New England Biolabs
Talk Goals
- Illustrate 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
What is a UMI
- Universal Molecular Identifier (a.k.a. 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 11 bp UMIs = 1/4,194,304
- When combined with transcript identity, probability of 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 contributing to observed abundance
RNA-Seq Workflow Overview
UMIs duplicated here
Example Molecule
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, D. Posfai, 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 adaptors
- 12 PCR cycles
- Sequenced on Illumina NovaSeq 6000 S2, 2x75bp
Experimental Results
Experimental Results
Experimental Results
Hard to Amplify
Easy to Amplify
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 adaptors
- 12 PCR cycles
- Sequenced on Illumina NovaSeq 6000 S2, 2x75bp
RNA Mixture Experiment
G. Naishadham
Expected Results
G. Naishadham
Experimental Results
G. Naishadham
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 for differences using UMIs
- Implications for power assessment in differential expression
- We don't yet understand mechanism but we're not done digging yet
PS: I'm hiring soon - see the job board!
SFAF2020 Unique Molecular Indices Improve RNA-seq Experiments
By Brad Langhorst
