Helsinki genome project-20151210-amb
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A presentation I held in the first day of the special workshop: "Current Challenges 2015 - Next Generation Sequencing (592003) 1-3 ECTS" (7-11 December 2015) at University of Helsinki Viikki Campus http://www.helsinki.fi/dpps/currentchallenges2015.htm The purpose was to inform Finnish researchers present on the pros and cons to take into consideration before starting a genome project and show the resources available at SciLifeLab.
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Helsinki genome project-20151210-amb
- 2. Workshop Outline • Introducing SciLifeLab • The important considerations of all genome projects • The annotation and assembly platforms • A vision into the future
- 3. Survey
- 6. Survey • How many of you have used sequencing facilities? • Assembled a genome? • Planning to start a genome project?
- 7. Survey • How many of you have used sequencing facilities? • Assembled a genome? • Planning to start a genome project? • Have worked with NGS data?
- 8. Survey • How many of you have used sequencing facilities? • Assembled a genome? • Planning to start a genome project? • Have worked with NGS data? • Just curious about NGS?
- 9. Things to consider • Repeats • Heterozygosity • Size of your genome • GC content • Access to material and specifically HMW DNA • Access to a good computational cluster • Good bioinformaticians / lab technicians
- 10. Things to consider • Repeats • Heterozygosity • Size of your genome • Access to material and specifically HMW DNA • Access to a good computational cluster • Good bioinformaticians / lab technicians WHAT IS YOUR SCIENTIFIC QUESTION?
- 11. Variation space • Repeats • Heterozygosity • Size of your genome • Access to material and specifically HMW DNA • Access to a good computational cluster • Good bioinformaticians / lab technicians WHAT IS YOUR SCIENTIFIC QUESTION?
- 12. Things to consider • Repeats • Heterozygosity • Size of your genome • Access to material and specifically HMW DNA • Access to a good computational cluster • Good bioinformaticians / lab technicians WHAT IS YOUR SCIENTIFIC QUESTION? http://www.intechopen.com/books/recent-advances-in-autism- spectrum-disorders-volume-i/discovering-the-genetics-of-autism
- 13. Things to consider • Repeats • Heterozygosity • Size of your genome • Access to material and specifically HMW DNA • Access to a good computational cluster • Good bioinformaticians / lab technicians WHAT IS YOUR SCIENTIFIC QUESTION? Alkan C., Coe B.P., Eichler E.E.. Nature Rev Genetics (2011)
- 14. Things to consider • Repeats • Heterozygosity • Size of your genome • Access to material and specifically HMW DNA • Access to a good computational cluster • Good bioinformaticians / lab technicians WHAT IS YOUR SCIENTIFIC QUESTION?
- 15. Things to consider • Repeats • Heterozygosity • Size of your genome • Access to material and specifically HMW DNA • Access to a good computational cluster • Good bioinformaticians / lab technicians WHAT IS YOUR SCIENTIFIC QUESTION? Ward L.D. & Kellis M. Nat Biotechnology (2012)
- 16. About me Álvaro Martínez Barrio, PhD Alvaro.Martinez.Barrio@scilifelab.se linkedin.com/in/ambarrio @ambarrio • PhD Bioinformatics 2010 • Postdoc Pop Genetics / Comp Biol 2014, L. Andersson + H. Ronne • Herring: Illumina, SOLiD, Moleculo, PacBio • Species Plant: 454, SOLiD, Illumina • Species Seal (~3Gb): Illumina • Species Beetle: Illumina, PacBio
- 17. Figure 1 | Cost-effectiveness of Pool-seq. The accuracy of allele frequency estimates is compared for whole-genome sequencing of pools of individuals (Pool-seq) and whole-genome sequencing of individuals using the ratio of the standard deviation (SD) of the estimated allele frequency with both methods. The same number of reads is used for both sequencing strategies. A value smaller than one indicates that Pool-seq is more accurate than sequencing of individuals. a | The influence of the pool size is shown. A larger pool size results in higher accuracy of Pool-seq, but Pool-seq still produces more accurate allele frequency estimates even for pool sizes of 50 individuals in most Nature Reviews | Genetics 0.4 10 20 30 Number of individuals sequenced seperately SDpool/SDindividuals SDpool/SDindividuals Number of individuals sequenced seperately 40 50 0.5 0.6 0.7 0.8 0.9 1.0 1.1a b 0.4 10 20 30 40 50 0.5 0.6 0.7 0.8 0.9 1.0 1.1 Pool size Coverage per sequenced individual Deviation in DNA content from each individual in the pool 100 20× 0% 100 20× 30% 100 5× 30% 100 1× 30% Pool size Coverage per sequenced individual Deviation in DNA content from each individual in the pool 500 5× 30% 100 5× 30% 50 5× 30% Schlötterer C., Tobler R., Kofler R. and Nolte V. Nature Rev Genetics (2014) Why pooling?
- 19. SciLifeLab (promotion slides)SciLifeLab National service Local scientific center SciLifeLab Director (July 2015) Olli Kallioniemi Co-director Kerstin Lindblad-TohVision: To be an internationally leading center that develops, uses and provides access to advanced technologies for molecular biosciences with focus on health and environment. www.scilifelab.se 2010: Strategic research initiative 2013: National resource 2015: New management and chairman
- 20. SciLifeLab platforms SciLifeLab National Genomics Infrastructure National Bioinformatics Infrastructure Sweden Joakim Lundeberg Ann-Christine Syvänen Ulf Gyllensten Bengt Persson Clinical Diagnostics … Lars Engstrand Computer resources free for Swedish researchers VR SNIC Ongoing merge of BILS, WABI and more; complete 2016. National, distributed
- 23. 23 The Bioinformatics Platform 2016 Funding • The Research Council • SciLifeLab • KAW foundation • Host universities Applied at the Research Council as continued national infrastructure 2016-2023. Decision late 2015. Custom-tailored support Tools Training Today ~70 FTE
- 24. 24 Long-term Support Wallenberg Advanced Bioinformatics Infrastructure www.scilifelab.se/facilities/wabi/ Björn Nystedt Thomas Svensson Tailored solutions – high impact Siv AnderssonGunnar von Heijne Applied bioinformatics: 500h free support/project • Variant analyses in health and disease • Transcriptomics • Single-cell analyses • Epigenetics • Metagenomics Directors Managers Swedens strongest unit for analyses of large-scale genomic data (24 FTE) National committee reviews and selects projects based on scientific quality Staff in Stockholm, Uppsala, Lund, Gothenburg, Linköping, Umeå.
- 25. WABI personnel (2013-2014) Johan Reimegård Mikael Huss Åsa Björklund Pär Engström Jakub Orzechowski Westholm Estelle Proux- Wéra Sanella Kjellqvist Diana Ekman Pall Olason Anna Johansson Marcel Martin Alvaro Martinez Barrio Per Unneberg
- 31. Project)growth) 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 100 200 300 400 Active Projects Numberofactiveprojects ● ● UPPMAX UPPNEX
- 32. 2009:$ $13,152$MSEK$from$KAW$and$SNIC$ $ 2012:$$ $23.8$MSEK$from$KAW/SNIC$ $ 2014:$ $20$+$20$MSEK$from$KAW$for$WGS$ $ $ $SNIC$receives$47.8$MSEK$from$VR$to$handle$sensiBve$data$ $ UPPNEX)history)
- 43. Just a word on N50… N50 typically refers to a con<g (or scaffold) length But… • The original defini<on is the number of con<gs needed to reach half of the genome size (L50 is the length) • Many programs use the total assembly size as a proxy for the genome size; this is some<mes completely misleading: Use NG50! • PI:s don’t understand N50 anyway; use something more intui<ve J: - con<gs larger than 1 kbp sum to 93% of the genome size - con<gs larger than 10 kbp sum to 48% of the genome size - con<gs larger than 100 kbp sum to 19% of the genome size Genome Assembly Genome size Assembly size NG50 N50 3 con<gs 100 kbp 5 con<gs 30 kbp Just a word on N50
- 46. Pre-assembly • Quality trimming • (Error correction) • Kmer analysis • De novo repeat library
- 47. Quality trimming DeBruijn-graph assemblers are in principle sensi<ve to errors since they do not take base quality values into account • Trim adapters (e.g. Cutadapt) • Filter on quality, both 5’ and 3’ end! (e.g. Trimmoma<c) • Consider hard-trimming of 5’ end • Error correc<on (e.g. Quake) • Inspect (e.g. FastQC) Plots by Olof Karlberg Quality trimming
- 48. Reads vs kmers 1 read: 100 bp …….. Kmers: k=21bp N= (L – k + 1) (100bp – 21 bp + 1) 80 Base coverage * (L-k+1) = Kmer coverage! ! ! L! Ex: !50X * (100-21+1) = 40X (i.e. kmer coverage is 80% of base coverage) ! ! 100! Reads vs Kmers
- 50. Digging into the kmers Genome size • Remove low-copy kmers • Iden<fy the coverage peak • Divide total nb of kmers by peak Genome size = Ktot/Cpeak! ! Here: ! 1.4 Gbp = 80 G / 55 ! ! Note: Ktot = Nb reads * (L-k+1)! ! ! “Cpeak 20 million dis<nct kmers occure 55 <mes in all reads combined” Base coverage = Cpeak ! ! (L-k+1)/L! Here:! 69X = 55 ! (100 – 21 +1)/100! Interpreting kmer graphs (1/2)
- 56. Fig4.1 17-mer depth distribution Table4.2 17-mer Data statistics K K-mer_NO. Peak_depth Genome Size Used Bases Used Reads X 17 22,425,038,045 32 700,782,438 26,827,492,429 275,153,399 38.28 Total 26.82Gb data was retained for 17-mer analysis .the 17-mer frequency distribution derived from the sequenced reads was plotted in Fig1, the peak of the 17-mer distribution is about 32, and the total K-mer count is 22,425,038,045, then the genome size can be estimated ( by 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 10 20 30 40 50 60 70 80 90 100 Percentage(%) Depth(X) Fig4.1 17-mer depth distribution Table4.2 17-mer Data statistics K K-mer_NO. Peak_depth Genome Size Used Bases Used Reads X 17 22,425,038,045 32 700,782,438 26,827,492,429 275,153,399 38.28 Total 26.82Gb data was retained for 17-mer analysis .the 17-mer frequency distribution derived from the sequenced reads was plotted in Fig1, the peak of the 17-mer distribution is about 32, and the total K-mer count is 22,425,038,045, then the genome size can be estimated ( by 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 10 20 30 40 50 60 70 80 90 100 Percentage(%) Depth(X) 1.6 1.8 2 Heterozygosity with kmer graphs
- 57. Fig4.1 17-mer depth distribution Table4.2 17-mer Data statistics K K-mer_NO. Peak_depth Genome Size Used Bases Used Reads X 17 22,425,038,045 32 700,782,438 26,827,492,429 275,153,399 38.28 Total 26.82Gb data was retained for 17-mer analysis .the 17-mer frequency distribution derived from the sequenced reads was plotted in Fig1, the peak of the 17-mer distribution is about 32, and the total K-mer count is 22,425,038,045, then the genome size can be estimated ( by 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 10 20 30 40 50 60 70 80 90 100 Percentage(%) Depth(X) Fig4.1 17-mer depth distribution Table4.2 17-mer Data statistics K K-mer_NO. Peak_depth Genome Size Used Bases Used Reads X 17 22,425,038,045 32 700,782,438 26,827,492,429 275,153,399 38.28 Total 26.82Gb data was retained for 17-mer analysis .the 17-mer frequency distribution derived from the sequenced reads was plotted in Fig1, the peak of the 17-mer distribution is about 32, and the total K-mer count is 22,425,038,045, then the genome size can be estimated ( by 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 10 20 30 40 50 60 70 80 90 100 Percentage(%) Depth(X) 1.6 1.8 2 Fig4.2 Hybrid effect on K-mer distribution. The X axis is the depth of 17-mer and Y axis is the ratio of 17-mer. The Epi is the 17-mer curve of herring. The H_0.01067 means that the heterozygosis rate is 1.067%, and H_0.012 is 1.2%, H_0.015 is 1.5%. From this figure, we can see that with the heterozygosis rate increasing, the sub-peak is becoming more apparent at the position of the half of the expected K-mer depth on the X axis. We can get the conclusion that the heterozygosis rate of herring genome is about 1.5%. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 20 40 60 80 Percentage(X) Depth(X) H_0.01067 Epi H_0.012 H_0.015 Heterozygosity with kmer graphs
- 58. Fig4.1 17-mer depth distribution Table4.2 17-mer Data statistics K K-mer_NO. Peak_depth Genome Size Used Bases Used Reads X 17 22,425,038,045 32 700,782,438 26,827,492,429 275,153,399 38.28 Total 26.82Gb data was retained for 17-mer analysis .the 17-mer frequency distribution derived from the sequenced reads was plotted in Fig1, the peak of the 17-mer distribution is about 32, and the total K-mer count is 22,425,038,045, then the genome size can be estimated ( by 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 10 20 30 40 50 60 70 80 90 100 Percentage(%) Depth(X) Fig4.1 17-mer depth distribution Table4.2 17-mer Data statistics K K-mer_NO. Peak_depth Genome Size Used Bases Used Reads X 17 22,425,038,045 32 700,782,438 26,827,492,429 275,153,399 38.28 Total 26.82Gb data was retained for 17-mer analysis .the 17-mer frequency distribution derived from the sequenced reads was plotted in Fig1, the peak of the 17-mer distribution is about 32, and the total K-mer count is 22,425,038,045, then the genome size can be estimated ( by 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 0 10 20 30 40 50 60 70 80 90 100 Percentage(%) Depth(X) 1.6 1.8 2 Fig4.2 Hybrid effect on K-mer distribution. The X axis is the depth of 17-mer and Y axis is the ratio of 17-mer. The Epi is the 17-mer curve of herring. The H_0.01067 means that the heterozygosis rate is 1.067%, and H_0.012 is 1.2%, H_0.015 is 1.5%. From this figure, we can see that with the heterozygosis rate increasing, the sub-peak is becoming more apparent at the position of the half of the expected K-mer depth on the X axis. We can get the conclusion that the heterozygosis rate of herring genome is about 1.5%. 0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 0 20 40 60 80 Percentage(X) Depth(X) H_0.01067 Epi H_0.012 H_0.015 The heterozygosity was estimated to be 1.5% Heterozygosity with kmer graphs
- 61. Repeat library and repeat quantification Create a de novo repeat library • Run a low-coverage (e.g. 0.1X) assembly (e.g. RepeatExplorer or Trinity) • Filter contaminants and mito/chloro • [ Make non-redundant (e.g. Cdhit) ] • QuanJfy the (high) repeat content by an independent subset of reads - Mapping (e.g. bwa), or - Mask with RepeatMasker
- 62. Repeat library and repeat quantification Create a de novo repeat library • Run a low-coverage (e.g. 0.1X) assembly (e.g. RepeatExplorer or Trinity) • Filter contaminants and mito/chloro • [ Make non-redundant (e.g. Cdhit) ] • QuanJfy the (high) repeat content by an independent subset of reads - Mapping (e.g. bwa), or - Mask with RepeatMasker A!real!example! Coverage! %GC! 5!Mbp!mitochondrion!in!spruce!
- 63. Repeat library from low coverage dataRepeat library from low coverage data R R R’ R R’’ Overlaps? Sparse seq data
- 64. Repeat library from low coverage dataRepeat library from low coverage data R R R’ R R’’ Overlaps? Assembled con<gs Sparse seq data
- 65. Repeat library from low coverage dataRepeat library from low coverage data R R R’ R R’’ Overlaps? Assembled con<gs Sparse seq data Warning! Beware of contamina<ons, plas<ds etc
- 66. Repeat library from low coverage dataQuan<fy your repeat seqs R R R’ R R’’ Independent set of sparse data Screen reads with repeat seqs 33% of all bases in the reads are covered by repeat seqs ó 33% of the genome is “repeated” Warning! The quan<fica<on depends heavily on the size of the original read set
- 67. Classifying repeats LTR Gypsy/Copia LINE/SINE DNA elements … This is very tricky… Classifying the repeat library directly • RepeatMasker • Repeat protein domain search (h=p://www.repeatmasker.org/cgi-bin/RepeatProteinMaskRequest) Problems • No close homologs in databases • Rapid evoluHon of repeats (like transposable elements, TEs) • Non-autonomous TEs do not contain proteins SoluHons • Fetch intact ORF:s from hits in assembly • Extend assembly matches and get more complete elements • Check match alignment profiles in assembly (LINEs conserved at 3’ end but not at 5’..) => OWen slow, manual, species-specific soluHons
- 71. 0 20 40 60 80 100 050100150200250300350 Coverage NumberofMb'sinhg19 454 Illumina SOLiD average coverage _C%:(!)#&1-#! ! "#$%#&'#! The current (in hg18 the The six type 1&! 00 4 umina OLiD average coverage • Stephan C. Schuster (Penn U)
- 75. Short Reads (Illumina) - graph assembly adapter removal quality trimming de Bruijn or string graph construction error correction T T A T T scaffolding contigs read pairs NNNNNN read mapping Long Reads (PacBio) - HGAP assembly read length reads read self-correction overlap-layout-consensus assembly consensus calling with quiver assembled genome ATCGTT-CCGAGTCTCCCCGCAATCGCAAGCG-TTTCAT CGAGTCT-CGCGCAATCGCAAGCG-TTTC ATCGTT-CCGAGTCTCCCCGCCATC TT-CCGAGACTCCCCGCAATCGCAAGCGATT GTTTCCGAGTCTCCCCGCAATCGCTAGCG-TTGCAT 1 2 3 1 pre-processing 2 assembly 3 finishing/polishing the overall assembly strategy is the same… …but the data and tools are fundamentally different
- 78. Short Reads (Illumina) - graph assembly adapter removal quality trimming de Bruijn or string graph construction error correction T T A T T scaffolding contigs read pairs NNNNNN read mapping Long Reads (PacBio) - HGAP assembly read length reads read self-correction overlap-layout-consensus assembly consensus calling with quiver assembled genome ATCGTT-CCGAGTCTCCCCGCAATCGCAAGCG-TTTCAT CGAGTCT-CGCGCAATCGCAAGCG-TTTC ATCGTT-CCGAGTCTCCCCGCCATC TT-CCGAGACTCCCCGCAATCGCAAGCGATT GTTTCCGAGTCTCCCCGCAATCGCTAGCG-TTGCAT 1 2 3 1 pre-processing 2 assembly 3 finishing/polishing the overall assembly strategy is the same… …but the data and tools are fundamentally different Many!instruments…!to Assembler#Name# Algorithm# Input# Arachne! OLC! Sanger! CAP3! OLC! Sanger! TIGR! Greedy! Sanger! Newbler! OLC! 454/Roche! Edena! OLC! Illumina! SGA! OLC! Illumina! MaSuRCA! De!Bruijn/OLC! Illumina! Velvet! De!Bruijn! Illumina! ALLPATHS! De!Bruijn! Illumina/PacBio! ABySS! De!Bruijn! Illumina! SOAPdenovo! De!Bruijn! Illumina! CLC! De!Bruijn! Illumina/454! CABOG! OLC! Hybrid!! • Currently!efforts!ongoing!to!
- 81. de Bruijn
- 82. de Bruijn
- 87. Short Reads (Illumina) - graph assembly adapter removal quality trimming de Bruijn or string graph construction error correction T T A T T scaffolding contigs read pairs NNNNNN read mapping Long Reads (PacBio) - HGAP assembly read length reads read self-correction overlap-layout-consensus assembly consensus calling with quiver assembled genome ATCGTT-CCGAGTCTCCCCGCAATCGCAAGCG-TTTCAT CGAGTCT-CGCGCAATCGCAAGCG-TTTC ATCGTT-CCGAGTCTCCCCGCCATC TT-CCGAGACTCCCCGCAATCGCAAGCGATT GTTTCCGAGTCTCCCCGCAATCGCTAGCG-TTGCAT 1 2 3 1 pre-processing 2 assembly 3 finishing/polishing the overall assembly strategy is the same… …but the data and tools are fundamentally different Many!instruments…!to Assembler#Name# Algorithm# Input# Arachne! OLC! Sanger! CAP3! OLC! Sanger! TIGR! Greedy! Sanger! Newbler! OLC! 454/Roche! Edena! OLC! Illumina! SGA! OLC! Illumina! MaSuRCA! De!Bruijn/OLC! Illumina! Velvet! De!Bruijn! Illumina! ALLPATHS! De!Bruijn! Illumina/PacBio! ABySS! De!Bruijn! Illumina! SOAPdenovo! De!Bruijn! Illumina! CLC! De!Bruijn! Illumina/454! CABOG! OLC! Hybrid!! • Currently!efforts!ongoing!to! ! • No!easy!way!to!determine!best! assembly/assembler! • implemented!heuris4cs!are!the! key!issue! • Choice!of!approach!depends!on! data!being!assembled!
- 88. Short Reads (Illumina) - graph assembly adapter removal quality trimming de Bruijn or string graph construction error correction T T A T T scaffolding contigs read pairs NNNNNN read mapping Long Reads (PacBio) - HGAP assembly read length reads read self-correction overlap-layout-consensus assembly consensus calling with quiver assembled genome ATCGTT-CCGAGTCTCCCCGCAATCGCAAGCG-TTTCAT CGAGTCT-CGCGCAATCGCAAGCG-TTTC ATCGTT-CCGAGTCTCCCCGCCATC TT-CCGAGACTCCCCGCAATCGCAAGCGATT GTTTCCGAGTCTCCCCGCAATCGCTAGCG-TTGCAT 1 2 3 1 pre-processing 2 assembly 3 finishing/polishing the overall assembly strategy is the same… …but the data and tools are fundamentally different Many!instruments…!to Assembler#Name# Algorithm# Input# Arachne! OLC! Sanger! CAP3! OLC! Sanger! TIGR! Greedy! Sanger! Newbler! OLC! 454/Roche! Edena! OLC! Illumina! SGA! OLC! Illumina! MaSuRCA! De!Bruijn/OLC! Illumina! Velvet! De!Bruijn! Illumina! ALLPATHS! De!Bruijn! Illumina/PacBio! ABySS! De!Bruijn! Illumina! SOAPdenovo! De!Bruijn! Illumina! CLC! De!Bruijn! Illumina/454! CABOG! OLC! Hybrid!! • Currently!efforts!ongoing!to! CABOG! OLC! Hybrid!! • Currently!efforts!ongoing!to! establish!best!prac4ces! • Assemblathons!and!GAGE!to! evaluate!exis4ng!solu4ons…!
- 89. Some recommendations • Large eukaryote genome, Illumina data: Allpaths-LG (needs specific libraries), SOAPdenovo, SGA, Masurca, DISCOVAR • Large eukaryote genome, additional longer reads: Masurca, Newbler, CABOG • Small eukaryote or prokaryote genome, Illumina data: Spades, Masurca, SOAPdenovo, Abyss, Velvet, DISCOVAR • Small eukaryote or prokaryote genome, mixed data: MIRA, Spades, Masurca, Newbler • Need to run in parallel: Abyss, Rai • Amplified data (Single Cell Genomics): Spades
- 90. Standard contiguity metrics Just a word on N50… N50 typically refers to a con<g (or scaffold) length But… • The original defini<on is the number of con<gs needed to reach half of the genome size (L50 is the length) • Many programs use the total assembly size as a proxy for the genome size; this is some<mes completely misleading: Use NG50! • PI:s don’t understand N50 anyway; use something more intui<ve J: - con<gs larger than 1 kbp sum to 93% of the genome size - con<gs larger than 10 kbp sum to 48% of the genome size - con<gs larger than 100 kbp sum to 19% of the genome size Genome Assembly Genome size Assembly size NG50 N50 3 con<gs 100 kbp 5 con<gs 30 kbp
- 91. The devil is in the repeatsats and Short Reads reover C R A B Mathema,cally best result:
- 93. ATCGGGTATATAG-CCTA! ||||||| || || ||||! ATCGGGTGTACAGCCCTA! ! ? A B A & B A: B: Collapsable repeat errors (worst!)
- 95. Gaps
- 96. Gaps
- 97. CCS vs CLR
- 98. CCS vs CLR
- 99. CCS vs CLR
- 100. other options for assembling PacBio reads https:/ /github.com/PacificBiosciences/Bioinformatics-Training/wiki/Large-Genome- Assembly-with-PacBio-Long-Reads
- 101. Hybrid assemblies
- 102. Gaps
- 104. • PacBio data cannot (currently) be assembled in its raw state • several strategies exist for correcting reads prior to assembly • correction without complementary technology used to be difficult – until recently, was limited by computational power and SMRT cell throughput PacBio data is noisy Koren & Philippy Curr Op Micro 2014
- 105. Hybrid assemblers (for PacBio) 105 other options for assembling PacBio reads
- 106. Hybrid assemblers 106 other options for assembling PacBio reads Zimin A.V., Marçais G., Puiu D., Roberts M., Salzberg S.L., Yorke J.A. Bioinformatics (2013)
- 107. Hybrid assemblers 107 other options for assembling PacBio reads Zimin A.V., Marçais G., Puiu D., Roberts M., Salzberg S.L., Yorke J.A. Bioinformatics (2013)
- 108. Pure PacBio Short Reads (Illumina) - graph assembly adapter removal quality trimming de Bruijn or string graph construction error correction T T A T T scaffolding contigs read pairs NNNNNN read mapping Long Reads (PacBio) - HGAP assembly read length reads read self-correction overlap-layout-consensus assembly consensus calling with quiver assembled genome ATCGTT-CCGAGTCTCCCCGCAATCGCAAGCG-TTTCAT CGAGTCT-CGCGCAATCGCAAGCG-TTTC ATCGTT-CCGAGTCTCCCCGCCATC TT-CCGAGACTCCCCGCAATCGCAAGCGATT GTTTCCGAGTCTCCCCGCAATCGCTAGCG-TTGCAT 1 2 3 1 pre-processing 2 assembly 3 finishing/polishing the overall assembly strategy is the same… …but the data and tools are fundamentally different
- 109. Pure PacBio
- 112. Pure PacBio
- 113. Pure PacBio other options for assembling PacBio reads
- 114. Pure PacBio other options for assembling PacBio reads
- 115. Pure PacBio
- 118. Finishing/Polishing (Olli-Pekka) quiver isn’t perfect using Pilon to polish remaining indels • makes use of short read mapping to identify potential indels, SNPs, ambiguous bases, local misassemblies $ java -Xmx16G –jar path/to/pilon-1.8.jar --genome path/to/fasta --unpaired path/to/mapping.bam --output sample_name --changes --variant --tracks --mindepth 100 Pilon removed 128 remaining indels in 3.8 Mbp genome despite
- 119. Finishing/Polishing (Olli-Pekka) quiver isn’t perfect using Pilon to polish remaining indels • makes use of short read mapping to identify potential indels, SNPs, ambiguous bases, local misassemblies $ java -Xmx16G –jar path/to/pilon-1.8.jar --genome path/to/fasta --unpaired path/to/mapping.bam --output sample_name --changes --variant --tracks --mindepth 100 Pilon removed 128 remaining indels in 3.8 Mbp genome despite
- 122. LETTER doi:10.1038/nature15714 Single-molecule sequencing of the desiccation- tolerant grass Oropetium thomaeum Robert VanBuren1 *, Doug Bryant1 *, Patrick P. Edger2,3 , Haibao Tang4,5 , Diane Burgess2 , Dinakar Challabathula6 †, Kristi Spittle7 , Richard Hall7 , Jenny Gu7 , Eric Lyons4 , Michael Freeling2 , Dorothea Bartels6 , Boudewijn Ten Hallers8 , Alex Hastie8 , Todd P. Michael9 & Todd C. Mockler1 Plant genomes, and eukaryotic genomes in general, are typically repetitive, polyploid and heterozygous, which complicates genome assembly1 . The short read lengths of early Sanger and current next-generation sequencing platforms hinder assembly through complex repeat regions, and many draft and reference genomes are fragmented, lacking skewed GC and repetitive intergenic sequences, which are gaining importance due to projects like the Encyclopedia of DNA Elements (ENCODE)2 . Here we report the whole-genome sequencing and assembly of the desiccation- tolerant grass Oropetium thomaeum. Using only single-molecule real-time sequencing, which generates long (>16 kilobases) reads with random errors, we assembled 99% (244megabases) of the Oropetium genome into 625 contigs with an N50 length of 2.4megabases. Oropetium is an example of a ‘near-complete’ draft genome which includes gapless coverage over gene space as well as intergenic sequences such as centromeres, telomeres, transposable elements and rRNA clusters that are typically unassembled in draft genomes. Oropetium has 28,466 protein-coding genes and 43% repeat sequences, yet with 30% more compact euchromatic regions it is the smallest known grass genome. The Oropetium genome demonstrates the utility of single-molecule real-time sequencing for assembling high-quality plant and other eukaryotic genomes, and serves as a valuable resource for the plant comparative genomics community. The genomes of Arabidopsis3 , rice4 , poplar, grape and Sorghum5 were first sequenced using high-quality and reiterative Sanger-based approaches producing a series of ‘gold standard’ reference genomes. The advent of next-generation sequencing (NGS) technologies reduced and comparative genomics, although draft genomes are now avail- able for most agriculturally important grasses1 . The largest genome assemblies, such as maize (2,300megabases (Mb))7 , barley (5,100Mb)8 and wheat (hexaploid, 17,000Mb)9 are highly fragmented as a result of the inability of current sequencing technologies to span complex repeat regions. Near-finished reference genomes are available for rice4 , Sorghum5 and Brachypodium10 , but more high-quality grass genomes are needed for comparative genomics and gene discovery. Here we pres- ent the ‘near-complete’ draft genome of the grass Oropetium thomaeum, the first high-quality reference genome from the Chloridoideae sub- family. The draft genome is near complete because we were able to sequence through complex repeat regions that are unassembled in most draft genomes. Oropetium has the smallest known grass genome at 245Mb and is also a resurrection plant that can survive the extreme water stress such as loss of >95% of cellular water (Fig. 1)11 . Single-molecule real-time (SMRT) sequencing (Pacific Biosciences) produces long and unbiased sequences, which enables assembly of complex repeat structures and GC- and AT-rich regions that are often unassembled or highly fragmented in NGS-based draft genomes. We generated ~72× sequencing coverage of the Oropetium genome using 32 SMRT cells on the PacBio RS II platform (which is equivalent to <1 week of sequencing time and <US$10,000 in reagents). The resulting sequence had a read N50 length of over 16kilobases (kb), and there was 10× coverage of reads over 20kb in length (Extended Data Fig. 1a). The raw reads were error-corrected using the hierarchical genome assembly process (HGAP), and the longest reads (>16kb) were assembled using Celera assembler followed by two rounds of genome polishing using Quiver12 . The assembly contains 650 contigs spanning 99% (244Mb) OPEN
- 123. Know how to annotate
- 124. Annotation (Jarkko) BILS assembly and annota1on service 1 Henrik Lantz Team leader Mahesh Panchal Assembly Jacques Dainat Annota1on Mar1n Norling Assembly Lucile Soler Annota1on 5 PhDs, all in Uppsala • Annota1on 2 years, assembly 1 year • Not driving own research, focusing on support • 80 h of free support to all projects - submiPed by customer • Dedicated compute cluster for annota1on, ~160 cores • Assemblies run on shared cluster, ~3200 cores • All organisms - all types of data • Close contact with sequencing facili1es
- 125. Annotation (Jarkko) BILS assembly and annota1on service 1 Henrik Lantz Team leader Mahesh Panchal Assembly Jacques Dainat Annota1on Mar1n Norling Assembly Lucile Soler Annota1on 5 PhDs, all in Uppsala • Annota1on 2 years, assembly 1 year • Not driving own research, focusing on support • 80 h of free support to all projects - submiPed by customer • Dedicated compute cluster for annota1on, ~160 cores • Assemblies run on shared cluster, ~3200 cores • All organisms - all types of data • Close contact with sequencing facili1es Annota1on/Assembly technology Assembly Perl/Make pipeline • Pre-assembly – Quality control – kmer analyses • Assembly – Different assembly programs • Assembly valida1on – FRCbam – Quast – Own tools Annota-on • Maker-MPI – proteins – RNA-seq • Refinement scripts • Func1onal annota1on – Blast – Synteny 2
- 126. Know how to validate
- 127. Assembly validationly!valida4on…!is!it!important?! Some4mes,!easy!ques4ons!are!the!most!difficult:! • Is!my!de!novo!assembly!correct?! • What!assembler!I!need!to!use?! • I!just!used!all!the!possible!assemblers!one! can!think!of….!How!I!pick!up!one!now?! n!genes?! ugh!to!! ?! Assembly!valida4on…!is!it!important?! Some4mes,!easy!ques4ons!are!the!most!difficult:! • Is!my!de!novo!assembly!correct?! • What!assembler!I!need!to!use?! • I!just!used!all!the!possible!assemblers!one! can!think!of….!How!I!pick!up!one!now?! • Does!my!assembly!contain!genes?! • Is!my!assembly!good!!enough!to!! perform!gene!annota4on?! ! ! !
- 129. Evaluating assemblies with referenceEvalua4ng!assemblies!with!a!reference! Coun4ng!errors!not!always!possible:! • Reference!almost!always!absent.! • Error! types! are! not! weighted! accordingly.! Visualiza4on!is!useful,!however:! • No!automa4on! • !Does!not!scale!on!large!genomes! WOW….!Looks!like!that!it!is!difficult!even! with!the!answer!
- 130. Evaluating assemblies without referenceEvalua4ng!assemblies!without!a!reference! • Sta4s4cs!(N50,!etc.)! • Congruency!with!raw!sequencing!data:! • Alignments! • QAtools! • FRCbam! • REAPR! • Gene!space!! • CEGMA! • reference!genes! • transcriptome! There!is!no!a!real!recipe,!or!a!tool.!We!can!only!suggest!some! best!prac4ce.!!
- 131. Your reads are often the best source to validate your assemblies • Check again your insert sizes (Picard Tools, http://picard.sourceforge.net) ! ! ! ! ! • Plotting coverage x %GC x length Post!assembly…!am!I!on!the!right!track?! • Check!lib!insert!sizes!(use!PicardTools!h`p://picard.sourceforge.net/)! • PE! • MP! Your!genome! Mitochondrion! Contamina4ons! 0 2000 4000 6000 8000 02004006008001000 Insert Size Histogram for All_Reads in file MP_on_masurca_sorted.bam Insert Size Count FR RF TANDEM 0 100 200 300 400 500 02000400060008000 Insert Size Histogram for All_Reads in file PE_on_masurca_sorted.bam Insert Size Count FR RF TANDEM 0 2000 4000 6000 8000 10000 0200400600800 Insert Size Histogram for All_Reads in file 7_130425_AD1YUEACXX_P469_101_index12_trimmed−to−assembly.abyss.scaf_onlyAligned.bam Insert Size Count FR RF TANDEM • Failed!MP!or!bad! assembly?! • Plot!cov!vs!%GC!vs!length! ! Look! at! the! plots! and!at!the!tables,! duplica4on! rate! is! an! important! measure.!! You!need!to!check! i f! t h e! p l o t ( s )! coincides! with! what!you!expect.! 0.0 0.2 0.4 0.6 0.8 1.0 0100200300400500 GC coverage coverage Frequency 0 100 200 300 400 500 050100150 0 100 200 300 400 500 01020304050 cov len(kbp) 0 10 20 30 40 50 0246810 cov len(kbp) Plopng!coverage!and!GC!content! 0 coverage Frequency 0 100 200 300 400 500 050100150 age!and!GC!content!
- 132. Your reads are often the best source to validate your assemblies • Check again your insert sizes (Picard Tools, http://picard.sourceforge.net) ! ! ! ! ! • Plotting coverage x %GC x length Post!assembly…!am!I!on!the!right!track?! • Check!lib!insert!sizes!(use!PicardTools!h`p://picard.sourceforge.net/)! • PE! • MP! Your!genome! Mitochondrion! Contamina4ons! 0 2000 4000 6000 8000 02004006008001000 Insert Size Histogram for All_Reads in file MP_on_masurca_sorted.bam Insert Size Count FR RF TANDEM 0 100 200 300 400 500 02000400060008000 Insert Size Histogram for All_Reads in file PE_on_masurca_sorted.bam Insert Size Count FR RF TANDEM 0 2000 4000 6000 8000 10000 0200400600800 Insert Size Histogram for All_Reads in file 7_130425_AD1YUEACXX_P469_101_index12_trimmed−to−assembly.abyss.scaf_onlyAligned.bam Insert Size Count FR RF TANDEM • Failed!MP!or!bad! assembly?! • Plot!cov!vs!%GC!vs!length! ! Look! at! the! plots! and!at!the!tables,! duplica4on! rate! is! an! important! measure.!! You!need!to!check! i f! t h e! p l o t ( s )! coincides! with! what!you!expect.! 0.0 0.2 0.4 0.6 0.8 1.0 0100200300400500 GC coverage coverage Frequency 0 100 200 300 400 500 050100150 0 100 200 300 400 500 01020304050 cov len(kbp) 0 10 20 30 40 50 0246810 cov len(kbp) Plopng!coverage!and!GC!content! 0 coverage Frequency 0 100 200 300 400 500 050100150 age!and!GC!content!
- 133. Data congruencyData!congruency! Idea:!Map!read:pairs!back!to!assembly!and!look!for!discrepancies!like:! • no!read!coverage! • no!span!coverage! • too!long/short!pair!distances! Reads! can! be! aligned! back! to! the! assembly! to! iden4fies! “suspicious”! features.! But!what!we!do!with!this!features?! FRCbam (Vezzi et al. 2012)
- 134. Data congruency FRCbam (Vezzi et al. 2012) Features! 4!coverage!related!features:! • LOW_COV_PE,!HIGH_COV_PE,!LOW_NORM_COV_PE,!and!HIGH_NORM_COV_PE! ! ! ! ! ! 4!features!for!compression/expansion!event!(CE!stats)! • COMPR_PE,!STRECH_PE,!COMPR_MP,!and!STRECH_MP! ! ! ! 6!features!on!suspicious!pair/mate!orienta4ons:! • HIGH_SINGLE_PE,!and!HIGH_SINGLE_MP! • HIGH_SPAN_PE,!and!HIGH_SPAN_MP! • HIGH_OUTIE_PE,!and!HIGH_OUTIE_MP! ! A R1,2 B A R1,2 C B A R1 B R2 C AGAGCTAGC AGAGCTAGC AGATCTCGC AGATCTCGC Reads! can! be! aligned! back! to! the! assembly! to! iden4fies! “suspicious”!features.!
- 135. FRCurve FRCurve! FRCbam!predicted!“Assemblathon!2”!outcome! The!Feature!Response!Curve!(FRCurve)!characterizes!the!sensi4vity! (coverage)! of! the! sequence! assembler! as! a! func4on! of! its! discrimina4on!threshold!(number!of!features!).! Feature!Response!Curve:! • Overcomes!limits!of!standard! indicators!(i.e.!N50)! • Captures!trade:off!between! quality!and!con4guity! • Deeply!connected!to!ROC!curves! • Features!can!be!used!to!iden4fy! problema4c!regions! • Single!features!can!be!plo`ed!to! iden4fy!assembler:specific!bias! 0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 5,000 5,500 6,000 6,500 7,000 7,500 8,000 0 20 40 60 80 100 120 Feature threshold approximatecoverage(%) Feature Space rhody TOTAL SGA Ray CLC SOAPdenovo ALLPATHS-LG PB ABySS MSRA-CA CABOG PB CABOG VELVET ALLPATHS-LG
- 136. Features and PCA Features!and!PCA! −5 −4 −3 −2 −1 −2−1012 PCA1 PCA2 ●bifido ecoli entero eubac fragilis kleb staphylocossus strep ●swig tim●bifido ecoli entero fragilis ●fuso7 kleb staphylocossusstrep ●swig tim ●bifido clap clap19 ecoli entero fragilis fusonuke kleb strep ●swig tim ●bifido ecoli entero eubac fragilis kleb staphylocossus strep ●swig ●bifido ecolientero eubac ●swig tim ●bifido ecoli entero eubac fragilis kleb staphylocossus strep ● swig clap clap19 ecoli enteroeubac fragilis ●fuso7 kleb staphylocossus strep ●swig tim entero eubac ●fuso7 strep ● swig ●bifido ecoli entero eubac fragilis kleb strep ●swig ●bifido ecoli entero eubac kleb staphylocossus strep ● swig ●bifido ecoli entero eubac fragilis kleb staphylocossus strep ●swig −4 −2 0 2 4 −6−4−20246 PCA1 PCA2 ●bifido clap clap19 copro ecoli egg enteroeubac fragilis ● fuso7 fusonuke kleb staphylocossus strep ●swig tim●bifido clap clap19 copro ecoli egg entero eubac fragilis ●fuso7 fusonuke kleb staphylocossusstrep●swig tim ●bifido clap clap19 copro ecoli egg entero eubac fragilis ●fuso7 fusonuke kleb strep ●swig tim ●bifido clap clap19 ecoli egg entero eubac fragilis ● fuso7 fusonuke kleb staphylocossus strep ●swig ●bifido clap clap19 copro ecoli egg entero eubac ● fuso7 fusonuke strep ●swig tim ●bifido clap clap19 copro ecoli egg entero eubac fragilis ● fuso7 fusonuke kleb staphylocossus strep ● swig tim ●bifido clap clap19 copro ecoli egg enteroeubac fragilis ●fuso7 fusonuke kleb ● rhody staphylocossus strep ●swig tim ● bifido copro ecoli egg entero eubac fragilis ●fuso7 fusonuke kleb strep ● swig tim ●bifido clap clap19 copro ecoli egg entero eubac fragilis ● fuso7 fusonuke kleb staphylocossus strep●swig tim ●bifido clap clap19 copro ecoli egg entero eubac fragilis ●fuso7 fusonuke kleb staphylocossus strep ● swig ●bifido clap clap19 copro ecoli egg entero eubac fragilis ●fuso7 fusonuke kleb staphylocossus strep ●swig Assembled!18!bacterial!genomes! with!11!assemblers!! (illumina!+!PacBio!data)! PCA!performed!on!features:! • Assemblies!of!the!same!organism! (family)!tend!to!cluster;! • No!clear!difference!when!using! PacBio!data;!
- 137. REAPR (Hunt et al. 2013) REAPRREAPR! REAPR!(Hunt!et!al.!2013)! Uses!same!principle!of!FRCurve:! • Iden4fies!suspicious/erroneous! posi4ons! • Breaks#assemblies#in#suspicious# posi.ons# • The!“broken!assembly”!is!more! fragmented!but!hopefully!more! corrected!(Reapr!cannot!make! things!worse…)!
- 138. Conserved core (species) gene space Gene!space! CEGMA#(h`p://korflab.ucdavis.edu/datasets/cegma/)! HMM:s!for!248!core!eukaryo4c!genes!aligned!to!your! assembly!to!assess!completeness!of!gene!space! “complete”:!70%!aligned! “par4al”:! !30%!aligned! ! ! Similar#idea#based#on#aa#or#nt#alignments#of# • Golden!standard!genes!from!own!species! • Transcriptome!assembly! • Reference!species!protein!set! Use!e.g.!GSNAP/BLAT!(nt),!exonerate/SCIPIO!(aa)!! !
- 139. Other external validation methodsOther!External!Valida4on!Methods! ! Restric4on!Map! ◦ Representa4on! of! the! cut! sites! on! a! given! DNA! molecule! to! provide! spa4al! informa4on!of!gene4c!loci! Op4cal!maps!can!be!used!to!check!assembly!correctness:! Long!PacBio!Reads!can!be!used!as!well!
- 143. Things that are not there 100Mb 1 2 3 4 5 6 7 8 9 10 11 12 13 1415 16 1718 1920 2122 X Closed gap Inversion Complex event High Low STR Density Extended Data Figure 3 | Genome distribution of closed gaps and insertions. Chromosome ideogram heatmap depicts the normalized density of inserted CHM1 base pairs per 5-Mb bin with a strong bias noted near the end of most chromosomes. Locations of structural variants and closed gaps are given by coloured diamonds to the left of each chromosome: closed gap sequences (red), inversions (green), and complex events (blue). RESEARCH LETTER Chaison M.J.P et al. Nature (2014) yhigh-throughputDNAsequencing(ChIP-seq)analysis(Supplemen- aryInformation).Weidentifiedasignificant15-foldenrichmentofshort andemrepeats(STRs)whencomparedtoarandomsample(P,0.00001) Fig. 1a). A total of 78% (39 out of 50) of the closed gap sequences were omposedof10%ormoreofSTRs.TheSTRswerefrequentlyembedded n longer, more complex, tandem arrays of degenerate repeats reach- ng up to 8,000 bp in length (Extended Data Fig. 1a–c), some of which ore resemblance to sequences known to be toxic to Escherichia coli16 . ecause most human reference sequences17,18 have been derived from ones propagated in E. coli, it is perhaps not surprising that the appli- ation of a long-read sequence technology to uncloned DNA would esolvesuchgaps.Moreover,thelengthandcomplexdegeneracyofthese TRs embedded within (G1C)-rich DNA probably thwarted efforts to ollow up most of these by PCR amplification and sequencing. Next, we developed a computational pipeline (Extended Data Fig. 2) o characterize structural variation systematically (structural variation efined here as differences $50 bp in length, including deletions, dupli- ations, insertions and inversions7 ). Structural variants were discovered y mapping SMRT sequencing reads to the human reference genome11 P = 0.02712 P = 0.00003 P < 0.00001 0 25 50 75 100 (G+C)content Reference flank Gap closure Tandem repeat P < 2.2 × 10–16 0.00 0.25 0.50 0.75 1.00 Gaps Reference Proportionofregionwithsimplerepeats a b G ap onlyTandem repeatsG ap w ithout tandem repeats Sam pled reference igure 1 | Sequence content of gap closures. a, Gap closures are enriched or simple repeats compared to equivalently sized regions randomly sampled om GRCh37. b, Human genome gaps typically consist of (G1C)-rich equence (yellow) flanking complex (A1T)-rich STRs (green) (empirical value; Supplementary Information). Red line indicates genomic (G1C) ontent.
- 144. Things that are not there Steinberg K.M. et al. Genome Research (2014)Figure 5. Overview of the Chr 11 (NC_018922.2) 1.9-Mb region, exhibiting three alignment bins with a large number of PacBio ‘‘cliff’’ reads where the alignment coverage dropped off sharply. WGS component (light green lines) boundaries flanked by such reads are marked with red dashed lines. The ends of each component at the boundary are labeled with letters to show orientation. Pairs of alignments corresponding to three different PacBio reads are marked in yellow, green, and dark blue. These alignments overlap by < 10% on each of the reads. The split alignments for these three reads suggest that the two WGS components marked in purple should be inverted and translocated as indicated by the arrow at the top of the image. The other PacBio reads in these bins exhibit the same pattern of split alignments, which supports the proposed reordering and orientation of the WGS components. The bottom light green lines show a proposed tiling path with the orientation corrected; the letters indicate where each end of the initial tiling path components should be placed. CHM1 assembly of the human genome Cold Spring Harbor Laboratory Presson November 16, 2014 - Published bygenome.cshlp.orgDownloaded from
- 145. Summary • Genome size and repeat content can be estimated w/o an assembly. • Adapters and trim low QV is good unless the assembly program does EC itself. • Assess the levels of heterozygosity in your target genome before you assemble (or sequence) it and set your expectations accordingly. • Choose an assembler that excels in the area you are interested in (e.g., coverage, continuity) and do libraries for it. • Interested in doing just coding potential analyses? (e.g., training a gene finder, studying codon usage bias, looking for intron-specific motifs) => Consider studying exome assemblies. • Or consider a proxy, studying a specie that it is sufficiently close evolutionary which genome is quite good in quality.
- 146. Summary • Genome size and repeat content can be estimated w/o an assembly. • Adapters and trim low QV is good unless the assembly program does EC itself. • Assess the levels of heterozygosity in your target genome before you assemble (or sequence) it and set your expectations accordingly. • Choose an assembler that excels in the area you are interested in (e.g., coverage, continuity, or number of error free bases). • Interested in doing just coding potential analyses? (e.g., training a gene finder, studying codon usage bias, looking for intron-specific motifs) => Consider studying exome assemblies. • Or consider a proxy, studying a specie that it is sufficiently close evolutionary which genome is quite good in quality. Settle down an assembly so Science can continue!
- 147. Know the future
- 152. Acknowledgements • Olga Vinnere Pettersson • Björn Nysted • Ola Spujth • Henrik Lantz • Jacques Daimat • Francesco Vezzi • BGI • Jon Badalamenti (Bond Lab) • Stephan C. Schuster (Penn U)