Content developed by Ben Rambo-Martin and Kristine Lacek
Slides
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Objectives
- Recognize common failure modes in influenza NGS analysis
- Interpret MIRA QC failures and apply appropriate remediation
- Understand the impact of DI particles and homopolymer-induced frameshifts
Common Problems in Influenza Bioinformatics
Failed MIRA QC: Low-Coverage / Incomplete Segment Coverage
- Not enough reads in your library — go back to the lab
- Ct <= 28?
- Gel image resolves segment bands cleanly?
- Proper sample number on your flow cell?
- MIRA <= v2.0.0?
- Reads are being subsampled
Failed MIRA QC: Minor Variant Count > 10
- In standard clinical samples, we have never observed >6 minor alleles (>=5% frequency) with the majority having 1-3, per segment
- Cell cultures regularly have high genetic variability which can result in high counts
- Could be a co-infection (e.g., both H1N1 and H3N2 infection at the same time)
- Most likely contamination!
Failed MIRA QC: Premature Stop-Codon
- ONT homopolymer issue may require manual correction
- DI particle induced alignment
DI Particles
Defective interfering (DI) particles can interfere with NGS analysis:
- Common in polymerase segments
- Coverage shape can spike at the ends or show “bat ears” pattern
- Can create erroneous indel mutations at coverage dropoff points
Frameshifts
- Prevalent in homopolymer regions of Oxford Nanopore Sequencing
- DAIS-Ribosome is frameshift-tolerant
- Shows as (~) mutation
- Convert back to nucleotide space and add or remove a base to fix
MIRA Demands High Quality Results
- Together we can stop the “Garbage In” side of the data analysis mantra: “Garbage In, Garbage Out”
- Built-in thresholds are for standardizing QC
- Amended consensus gives us extra information from a single sequence
- A mixed site may be under active or balancing selection in the host
- Consider each sample as a whole: if HA and NA pass but all other segments fail, consider why
- High priority samples can be useful even when QC thresholds are not met