Streaming algorithms for real-time analysis of Oxford Nanopore sequencing data

Transcription

1 Streaming algorithms for real-time analysis of Oxford Nanopore sequencing data Minh Duc Cao Institute for Molecular Bioscience The University of Queensland London Calling 26 May 27, 26 Minh Duc Cao, The University of Queensland London Calling 26

2 Streaming algorithms Real-time analysis: Answer the biological questions quickly e.g., infection diagnosis Run sequencing only until the answers are obtained Decide complementary experiments Save time, save money Streaming algorithms: Process data input as a stream Continuously make inference and update the confident level Are robust to noise and scalable to massive data sets Minh Duc Cao, The University of Queensland London Calling 26

3 Real-time analysis workflow DNA extraction Library preparation Sequencing setup 2 hours 2.5 hours.5 hours Run simultaneously Scaffold Assemblies Fastq extraction Species typing Basecalling (Metrichor) Strain typing MinION sequencing Resistance profile Minh Duc Cao, The University of Queensland London Calling 26

4 Fastq extraction DNA extraction Library preparation Sequencing setup 2 hours 2.5 hours.5 hours Fastq extraction Basecalling (Metrichor) MinION sequencing Scaffold Species Strain Resistance Assemblies typing typing profile (Cao et al, 25): Bioinformatics, DOI:.93/bioinformatics/btv658 Minh Duc Cao, The University of Queensland London Calling 26

5 Scaffold and complete genome assemblies Stream of long reads BWA MEM Pre assemblies aligning repeats con nuing process Stream of alignment records pairing Stream of bridges connec ng Extending scaffolds output in real me (Cao et al, 26): biorxiv, DOI:./54783 Minh Duc Cao, The University of Queensland London Calling 26

6 MinION sequencing Sequence two K. pneumoniae strains with the MinION: Strain Phred Quality Scores Read Length Distribution Emp. errors BAA-246 Del: 9.5% (NDM- strain) Ins: 6.3% Mis: 5.3% Chemistry R7 Unaligned: Sep % 33-X Coverage 3883 Del: 7.9% (type strain) Ins: 6.% Mis: 2.9% Chemistry R7.3 Unaligned: Dec 24 % 8-X coverage Minh Duc Cao, The University of Queensland London Calling 26

7 Scaffolding and completing genome results Contigs K. pneumoniae BAA-246 (33X) K. pneumoniae 3883 (8X) Coverage (-fold) Coverage (-fold) 3.. S. cerevisae W33 (9X) Coverage (-fold).6.3 N5 (Mb) Minh Duc Cao, The University of Queensland London Calling 26

8 Scaffolding and completing genome results Contigs K. pneumoniae BAA-246 (33X) K. pneumoniae 3883 (8X) Coverage (-fold) Coverage (-fold) 3.. S. cerevisae W33 (9X) Coverage (-fold).6.3 N5 (Mb) Methods #Contig N5 (Mb) Errors /Kb CPUhrs #Contig N5 (Mb) Errors /Kb CPUhrs #Contig N5 (Mb) Misassemblies Misassemblies Misassemblies Errors /Kb CPUhrs SPAdes Hybrid SSPACE LINK npscarf NaS Nanocorr Minh Duc Cao, The University of Queensland London Calling 26

9 Pathogenicity island reconstruction SapF SapD SapC inta munim YqaJ rect ParA LexA CII ydau dnac IS26 aada sul ebr GCN5-like IS6 IS26 hin ltra umud umuc FRG Contigs Sequence data required to join 5Mb 5Mb 54Mb 54Mb 54Mb 54Mb 54Mb 65Mb 65Mb Minh Duc Cao, The University of Queensland London Calling 26

10 Pathogen identification (Cao et al, 25): biorxiv, DOI:./9356 Minh Duc Cao, The University of Queensland London Calling 26

11 Bacteria identification Proportion.5 Species typing K. pneumoniae ATCC BAA-246 2, 2 3 4,5, 5 K. pneumoniae ATCC , ,5, 5 Mixture 75% E.coli+25% S. aureus 2, ,5, 5 Yield (reads) Sequencing yield K. pneumoniae K. pneumoniae E. coli E. coli S. aureus Minh Duc Cao, The University of Queensland London Calling 26

12 Bacteria identification Proportion.5 Species typing K. pneumoniae ATCC BAA-246 2, 2 3 4,5, 5 K. pneumoniae ATCC , ,5, 5 Mixture 75% E.coli+25% S. aureus 2, ,5, 5 Yield (reads) Sequencing yield K. pneumoniae K. pneumoniae E. coli E. coli S. aureus Strain typing K. pneumoniae ATCC BAA-246 3, K. pneumoniae ATCC , Mixture 75% E.coli+25% S. aureus e) Mixture 75% E.coli+25% S. aureus 3, 3, Probability.5 2,,.5 2,,.5 2,,.5 2,, Yield (reads) K. pneumoniae ST K. pneumoniae ST3 E. coli ST73 E. coli ST526 S. aureus ST243 S. aureus ST2 S. aureus ST46 S. aureus ST29 Minh Duc Cao, The University of Queensland London Calling 26

13 Antibiotic resistance identification K. pneumoniae BAA-246 (NDM- strain): 27 genes Time genes Sens. Spec. Data (mins) (%) (%) (reads) 3 mpha 228 blashv stra blatem strb blactx blalen 263 sul2 blaoxa aac3 aac6 blacmy blacfe blalat blabil Time genes Sens. Spec. Data (mins) (%) (%) (reads) 9 QnrB 3844 aada oqxa teta oqxb dfra blaokp rmtc sul 322 sul fosa blandm Minh Duc Cao, The University of Queensland London Calling 26

14 A glimpse of R9 flowcells Sequence on R9 flowcell the mixture of E. coli ESBL (4%), E. faecium VRE-VanA (2%) S. aureus MRSA (2%) S. aureus VRSA (2%) Minh Duc Cao, The University of Queensland London Calling 26

15 A glimpse of R9 flowcells Time genes Data (mins) (reads) erma 23 ermg spc nora 5 Van 252 VanH VanS 2 dfra 2968 aac6 VanX tetu aada sul sul3 aadd 3 fusb 3964 mpha 4 VanY 5754 cat VanZ VanA Time genes Data (mins) (reads) 5 tetm 684 tets 2 aph msrc meca 5 catpc fosa blaoxa 24 blactx 538 cfr 48 blaz 242 vgaa 78 vgaalc 2684 ermc aac3 blacmy blalat blabil 2 dfra Minh Duc Cao, The University of Queensland London Calling 26

16 Summary and outlook Scaffold and complete bacterial assemblies with < 3-fold coverage Identify pathogen species and strain with reads (<.5 hours sequencing) Detect antibiotic resistance profile in a few hours of sequencing We expect the times to be significantly shortened: Higher throughput with upcoming models: MinION MkII, PromethION. Quicker library preparation Software available Minh Duc Cao, The University of Queensland London Calling 26

17 Acknowledgements Lachlan Coin Matthew Cooper Devika Ganesamoorthy Son Nguyen Alysha Elliott Tania Duarte Vivian Zhang Nick Hamilton Derek Benson Dominique Gorse Michael Thang Minh Duc Cao, The University of Queensland London Calling 26