How to Standardise and Assemble Raw Data into Sequences: What Does it Mean for a Laboratory to Use Such Technologies?"

Transcription

1 How to Standardise and Assemble Raw Data into Sequences: What Does it Mean for a Laboratory to Use Such Technologies?" Dr Joseph Hughes 11th OIE Seminar Saskatoon - 17th June 2015

2 Cost per raw Megabase of DNA sequence $10, $1, $ $10.00 $1.00 Jul-98 Apr-01 Jan-04 Oct-06 Jul-09 Apr-12 Dec-14Sep-17 $0.10 Decreasing sequencing cost sequencingcosts $0.01 Democratization of sequencing

3 Applications of High throughput sequencing" Whole genome sequencing Genome variability within a host De-novo assembly of novel viruses Metagenomics of communities

4 Considerations for a genome assembly pipeline Flexible pipeline: Handling unknown genotypes or virus samples Platform independent: work with data from different platforms Virus independent: work on any virus Scalable to hundreds or thousands of samples Accuracy of SNP calling in the genome (outbreak analysis where samples are more closely related)

5 Pre-assembly " Processing" Check format (sff, fastq) Convert to FASTQ Remove adaptor contaminants Remove host genome contamination Quality & length trimming Assembly" De-novo assembly Reference assembly Validation Contig merge Classification Scaffolding contigs Post-assembly processing" Consensus Variant calling Annotation Genome comparison Known reference" Unknown reference"

6 Examples in Northern Italy: emergence of highly pathogenic avian influenza H7N1 Identify known molecular markers for viral pathogenicity in intra-host viral populations OIE & FAO reference lab for Influenza in the Netherlands: die-off of >1000 wild water frogs and newts Isolation, characterisation and relationship to known viruses of the Dutch frog killer Van Beurden et al. (2014). Genome Announc. hybrid Edible frog (Pelophylax kl. esculentus)

7 Monne et al. (2014). Journal of Virology Example 1: Characterization of HPAI signature mutations"

8 Pre-assembly processing" trim_galore and FastQC for quality control

9 Reference assemblers?" Hash based tools: Mosaik, Novoalign, Stampy, Tanoti Borrrows-Wheeler Transform-based tools: BWA, Bowtie2, NextGenMap HA M log10(doc) log10(doc) Too many to choose from position position NA NP 3.5 log10(doc) position NS log10(doc) position PA Bowtie2 and Stampy Tanoti log10(doc) log10(doc) position position 3.0 PB1 PB2 log10(doc) position log10(doc) position

10 Tablet - assembly

11 Variant calling detecting true mutations" Many tools LoFreq, Vphaser, DiversiTools Using replicates to validate mutations (e.g. FMDV experiments) Frequency of LPAI in HPAI samples Frequency of HPAI in LPAI samples X X X X X X X X X Samples PB2_I398T PB1_D154G Amino acid changes PB1_G216S PB1_E745K PA_T61I PA_K115N PA_K252E HA_A130T HA_T146A HA_E228A HA_T454A HA_R554K NP_A349T NP_N376S NA_K173R M1_A166V NS1_I136V NS1_N139D X X X X X NS1_-225R PB2_I398T PB1_D154G Amino acid changes One LPAI sample collected after the identification of HPAI with an HA cleavage site and multiple HPAI associated mutations at extremely low frequency PB1_G216S PB1_E745K PA_T61I PA_K115N PA_K252E HA_A130T HA_T146A HA_E228A HA_T454A HA_R554K NP_A349T NP_N376S NA_K173R M1_A166V NS1_I136V NS1_N139D NS1_-225R

12 Example 2: Isolation and Sequencing" From dead wild water frog in September 2013 Suspension from pooled internal organs Inoculated on BF-2 cells (Bluegill Fry cells fibroblast) DNA extracted using Dneasy kit (viral purity of 67% DNA sheared by sonication KAPA library preparation MiSeq (Illumina) Machine #2 test run: total run 26,700,000 reads including 50% PhiX (16Gb) 13,127,123 paired-end 300 bp reads from the sample (7.9 Gb)

13 Assembly" Abyss-pe de-novo assembler reconstructed the fullgenome in a single contig of 107,260 5 different regions had ambiguous/repetitive sequences Re-sequencing ambiguous regions with Sanger 1?????

14 Finishing assembly" CodonCode Aligner for assembling and checking the Sanger sequences SequencePatcher.pl to stitch the Sanger sequences into the de-novo contig icorn2 Final genome of 107,260 => 107,772bp

15 Annotating BLAST to find the most similar annotated genome Common Midwife Toad Virus (CMTV) from Spain Transfer of annotations from CMTV to the full genome (RATT) Identifies inappropriate start codons, frame-shifts Correcting of transferred models using Artemis

16 100 RGV JQ STIV EU FV3 KJ FV3 AY TFV AF CGSIV KF ADRV KF ADRV KC CMTV NL CMTV JQ ATV AY EHNV FJ ESV JQ kb

17 Standard formats" FASTQ quality score depends on the technology and base caller SAM soon v1.5 extensions

18 Genome standards 5 categories % genome covered >50% ~80-90% ~90-99% 100% 100% HTS coverage ~15-30 x >100 x RACE ~ x Ladner et al.(2014) mbio

19 Challenges: Rates of increase in data" 1,000, ,000 NGS (bp/$) Doubling time 5 months 100,000,000 10,000,000 1,000,000 Disk storage (Mbytes/$) 10,000 1, Hard disk storage (MB/$) Doubling time 14 months 100,000 10, DNA sequencing (bp/$) Pre-NGS (bp/$) Doubling time 19 months Year

20 Challenges: resources and technologies" Shift towards more data, labs need to have dedicated bioinformaticians Rule of thumb: invest as much in computers and data scientists as in sequencing equipment and lab technicians Non-uniform coverage, repeat regions, systematic biases, PCR errors, sequencing errors, sequence length

21 Director of OIE Collaborating Centre for Viral Genomics and Bioinformatics Director of Centre for Virus Research CVR bioinformatics team