Reference-quality diploid genomes without de novo assembly

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1 Reference-quality diploid genomes without de novo assembly Michael Schatz January 16, 2018 PAG Bioinformatics / #PAGVI

2 Why NT de novo? De novo is necessary for the first genome of a species, but is it really necessary for genomes 2 through N? 1. De novo is slow: Large inputs and intermediate files. Algorithms are complex to compare all the reads to each other. Mammalian genomes need thousands of core hours and terabytes of space 2. De novo is demanding: Make the libraries just right, sequence the reads just right, set the parameters just right, launch and relaunch to optimize 3. De novo is unpredictable: Add a little more (or a little less) data, and your contig N50 drops in half. Errors creep in ranging from SNPs to SVs. Heuristics break when the data or genome structure are unexpected. 4. De novo is just the beginning. Annotation from scratch is really hard, and to use it (variant analysis/selection analysis/regulatory analysis) you will probably need to align to a reference anyways.

Why NT de novo? De novo is necessary for the first genome of a species, but is it really necessary for genomes 2 through N? 1. De novo is slow: Large inputs and intermediate files.

3 Why NT de novo? De novo is necessary for the first genome of a species, but is it really necessary for genomes 2 through N? 1. De novo is slow: Large inputs and intermediate files. Algorithms are complex to compare all the reads to each other. Mammalian genomes need thousands of core hours and terabytes of space 2. De novo is demanding: Make the libraries just right, sequence the reads Wouldn't it be great if we could assemble just right, set the parameters just right, launch and relaunch to optimize high quality genome sequences and capture all the genetic diversity in a 3. De novo is unpredictable: Add a little more (or a little less) data, and your contig N50 drops in half. Errors creep in ranging from SNPs to SVs. Heuristics break species when without the data or this genome complexity? structure are unexpected. 4. De novo is just the beginning. Annotation from scratch is really hard, and to use it (variant analysis/selection analysis/regulatory analysis) you will probably need to align to a reference anyways.

4 Reference-Guided Assembly 1. High quality reference Contig N50 over 1Mbp Scaffold N50 over 10Mbp High Quality Gene Annotation (See VGP definition) Your sample is sufficiently similar (~99% identity) 2. Sample specific data SNPs and Indels: Illumina-based (Illumina PE or 10) Structural Variants: Long Reads (PacBio or NT) Phasing Data: 10 and/or HiC; trios when available Data requirements similar to de novo, but less demanding, more accurate, and more predictable Comparative Genome Assembly ( AMScmp ) Pop et al (2004) Briefings in Bioinformatics. Sep;5(3):

5 CrossStitch my.mat.fa HQ Reference In collaboration with Sedlazeck, Gingeras, Guido, Ring, & Gerstein labs my.pat.fa

6 CrossStitch HQ Reference A -> C A/C -> A C Call SNPs + Indels Phase SNPs + Indels GAT -> GAT/ -> GAT Call SVs Phase SVs & Local Assembly Phased SNPs, Indels & SVs.vcf my.mat.fa Assemble Diploid Sequence In collaboration with Sedlazeck, Gingeras, Guido, Ring, & Gerstein labs my.pat.fa

7 CrossStitch HQ Reference A -> C A/C -> A C Call SNPs + Indels Phase SNPs + Indels GAT -> GAT/ -> GAT Call SVs Phase SVs & Local Assembly Phased SNPs, Indels & SVs.vcf my.mat.fa Assemble Diploid Sequence In collaboration with Sedlazeck, Gingeras, Guido, Ring, & Gerstein labs my.pat.fa

8 Phasing Results Phase Block 1 Phase Block 2 Unphased 1e+01 1e+03 1e+05 1e+07 1e+00 1e+02 1e+04 1e+06 1e+08 NA12878 ptimal phase block length increases with read length Read length (log10 bp) N50 Phase block size (log10 bp) Short Reads: ~1kbp Long Reads: ~500kbp Linked Reads: ~10Mbp Hi-C: 100Mbp Hi-C: 145Mbp N50 J (Healthy human, varies significantly by sample) HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies Edge, P, Bafna, V, Bansal, V (2016) Genome Research. doi: /gr

9 CrossStitch HQ Reference A -> C A/C -> A C Call SNPs + Indels Phase SNPs + Indels GAT -> GAT/ -> GAT Call SVs Phase SVs & Local Assembly Phased SNPs, Indels & SVs.vcf my.mat.fa Assemble Diploid Sequence In collaboration with Sedlazeck, Gingeras, Guido, Ring, & Gerstein labs my.pat.fa

10 SVs using Short, Long and Linked Reads Sniffles 17,139 PacBio Main Diagonal Calls per tool Falcon 7,857 12,241 uter triplets Concordance by Technology LongRanger SuperNova 2,823 3,394 1,946 2,837 3,785 1, Genomics 18,862 Inner triplets Concordance by Assembly Concordance by Mappers SURVIVR2 MegaHit 3,291 1,858 2,163 1,529 2, ,646 1,378 6, ,855 Illumina verall: Lonnnnnnng reads give the most variants with the best concordance J

NGMLR + Sniffles BWA-MEM: NGMLR: NGMLR: Convex gap

with larger SVs Sniffles: Scan within and between

Inv, Trans) Mendelian concordance >95%, experimental

complex structural variations using single molecule

11 NGMLR + Sniffles BWA-MEM: NGMLR: NGMLR: Convex gap penalty to balance frequent small sequencing errors with larger SVs Sniffles: Scan within and between split reads to accurately find SVs (Ins, Del, Dup, Inv, Trans) Mendelian concordance >95%, experimental validation also very high Accurate detection of complex structural variations using single molecule sequencing Sedlazeck, Rescheneder et al (2017) biorxiv https //doi.org/ /

12 No more false positives! Illumina data Truncated reads: Missing pairs PacBio data NT data Insertion detected by long reads Accurate detection of complex structural variations using single molecule sequencing Sedlazeck, Rescheneder et al (2017) biorxiv https //doi.org/ /

13 No more false positives! Illumina data Truncated reads: Missing pairs PacBio data NT data Insertion detected by long reads Accurate detection of complex structural variations using single molecule sequencing Sedlazeck, Rescheneder et al (2017) biorxiv https //doi.org/ /

14 CrossStitch HQ Reference A -> C A/C -> A C Call SNPs + Indels Phase SNPs + Indels GAT -> GAT/ -> GAT Call SVs Phase SVs & Local Assembly Phased SNPs, Indels & SVs.vcf my.mat.fa Assemble Diploid Sequence In collaboration with Sedlazeck, Gingeras, Guido, Ring, & Gerstein labs my.pat.fa

Local Assembly and SV Phasing Transfer the phasing of the short read variants to the long reads The phased long reads allow the SVs to be phased Phased Short Read Variants

15 Local Assembly and SV Phasing Transfer the phasing of the short read variants to the long reads The phased long reads allow the SVs to be phased Phased Short Read Variants Phased Long Read Variants Phase SVs: Make sure SVs are associated with the correct haplotype Local Assembly: Refine sequence of insertions, resolve complex nested variants

16 CrossStitch HQ Reference A -> C A/C -> A C Call SNPs + Indels Phase SNPs + Indels GAT -> GAT/ -> GAT Call SVs Phase SVs & Local Assembly Phased SNPs, Indels & SVs.vcf my.mat.fa Assemble Diploid Sequence In collaboration with Sedlazeck, Gingeras, Guido, Ring, & Gerstein labs my.pat.fa

17 Assembling a Perfect Personalized Diploid Genome Carefully stitch the phased variants into the reference genome at the right position to create a pair of phased chromosome fasta files Phased SNPs, Indels & SVs.vcf G A T T A C A G A T T A C A

18 Assembling a Perfect Personalized Diploid Genome Carefully stitch the phased variants into the reference genome at the right position to create a pair of phased chromosome fasta files Phased SNPs, Indels & SVs.vcf sub G C T G C T T A C A A - A sub inversion G T A A insertion del

19 Assembling a Perfect Personalized Diploid Genome Carefully stitch the phased variants into the reference genome at the right position to create a pair of phased chromosome fasta files Phased SNPs, Indels & SVs.vcf sub G C T G C T T A C A A - A sub inversion G T A A insertion Stitching based on AlleleSeq pipeline enhanced for SVs (Rozowsky et al, 2011) Maintains a mapping from reference to personal genome coordinates to make lift over of annotation straightforward to compute Using 10 + HiC + PacBio, assemble essentially perfect diploid human genomes with haplotypes spanning entire chromosomes Phased diploid genome can be aligned or aligned against just like a de novo genome assembly del

Applications Expression & Regulation Population Genetics Polyploidy

Analyze differential expression in CNVs Discover new regulatory regions

Variations Identified SVs in >900 accessions using short reads Assembling

Studying heterozygosity in sugarcane Have a high quality PacBio-based

20 Applications Expression & Regulation Population Genetics Polyploidy Foundation for mapping functional data Discover novel genes and gene fusions Analyze differential expression in CNVs Discover new regulatory regions Analyze allele-specific expression Framework for GWAS of Structural Variations Identified SVs in >900 accessions using short reads Assembling the top 50 lines using long & linked reads Perform GWAS of breeding traits Studying heterozygosity in sugarcane Have a high quality PacBio-based assembly of PJ2878 using FALCN (140kbp N50) Developing new methods for phasing (9-14 copies of each chromosome)

Reference-quality Genomes without de novo assembly

species Reference-free or mapping to a distant

first genome of a species has been assembled,

Use the right combination of data to capture and

create a high quality personalized genome that are

than a de novo assembly The personalized diploid

evolutionary analysis in many species CrossStitch

21 Reference-quality Genomes without de novo assembly De novo assembly is essential for exploring new species Reference-free or mapping to a distant reference is difficult to impossible But once the first genome of a species has been assembled, shouldn t the second genome be a little easier? Use the right combination of data to capture and phase all types of variants vernight analysis to create a high quality personalized genome that are more accurate, more predictable, and easier to use than a de novo assembly The personalized diploid genome will be a platform for functional and evolutionary analysis in many species CrossStitch FALCN SURVIVR NGM+Sniffles Ribbon Assemblytics

Acknowledgements Schatz Lab Mike Alonge Amelia Bateman Charlotte Darby

Rhyker Ranallo-Benavide *Your Name Here* Baylor Medicine Fritz

DNAnexus Maria Nattestad CSHL Gingeras Lab Jackson Lab Lippman Lab Lyon

Skiena Lab Patro Lab GRC Roderic Guido Alessandra Breschi Anna Vlasova

Taylor Lab Timp Lab Wheelan Lab Cornell Susan McCouch Lyza Maron Mark

22 Acknowledgements Schatz Lab Mike Alonge Amelia Bateman Charlotte Darby Han Fang Michael Kirsche Sam Kovaka Laurent Luo Srividya Ramakrishnan T. Rhyker Ranallo-Benavide *Your Name Here* Baylor Medicine Fritz Sedlazeck University of Vienna Arndt von Haeseler Philipp Rescheneder DNAnexus Maria Nattestad CSHL Gingeras Lab Jackson Lab Lippman Lab Lyon Lab Martienssen Lab McCombie Lab Tuveson Lab Ware Lab Wigler Lab SBU Skiena Lab Patro Lab GRC Roderic Guido Alessandra Breschi Anna Vlasova Yale Gerstein Lab JHU Battle Lab Langmead Lab Leek Lab Salzberg Lab Taylor Lab Timp Lab Wheelan Lab Cornell Susan McCouch Lyza Maron Mark Wright ICR John McPherson Karen Ng Timothy Beck Yogi Sundaravadanam PacBio Greg Concepcion

23 Thank Sunset Room, 9am-5pm, tomorrow

The Resurgence of Reference Quality Genome Sequence

The Resurgence of Reference Quality Genome Sequence Michael Schatz Jan 12, 2016 PAG IV @mike_schatz / #PAGIV Genomics Arsenal in the year 2015 Sample Preparation Sequencing Chromosome Mapping Summary &