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!

Improving Transcriptome Analysis with Molecular Indices and Galaxy

By Brad Langhorst

Improving Transcriptome Analysis with Molecular Indices and Galaxy

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