Genome assembly with SPAdes. Andrey Prjibelski Center for Algorithmic Biotechnology SPbU
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1 Genome assembly with SPAdes Andrey Prjibelski Center for Algorithmic Biotechnology SPbU
2 Introduction
3 Why to assemble? 3
4 Why to assemble? Sequencing data Billions of short reads Sequencing errors Contaminants 4
5 Why to assemble? Sequencing data Billions of short reads Sequencing errors Contaminants Ha rd to pe rfo rm an aly sis 5
6 Why to assemble? Sequencing data Billions of short reads Sequencing errors Contaminants Ha rd to pe rfo rm an aly sis Assembly Corrects sequencing errors Much longer sequences Each genomic region is presented only once May introduce errors 6
7 Assembly basics
8 De novo whole genome assembly 8
9 De novo whole genome assembly 9
10 De novo whole genome assembly 10
11 De novo whole genome assembly 11
12 Early days Sanger sequencing Long reads Low coverage Overlap-Layout-Consensus (OLC) Find overlaps between all reads (BLAST) Order reads according to the overlaps Merge reads into consensus string 12
13 NGS and OLC Overlap-Layout-Consensus is not applicable Hard to find overlaps between short reads Impossible to scale to such amount of reads De Bruijn graph approach (Pevzner et al., 2001) (Zerbino et al., 2008) String Graph approach (Meyers, 2005) (Simpson, Durbin 2011) 13
14 De Bruijn graph in a nutshell Не that mischief hatches, mischief catches Sequencing Не that mischief mischief hatches, hatches, mischief, mischief catches 14
15 De Bruijn graph in a nutshell, mischief catches mischief hatches, Не that mischief hatches, mischief He, hatches that mischief catches 15
16 De Bruijn graph ACGTCCGTAA 16
17 De Bruijn graph ACGTCCGTAA k=2 AC 17
18 De Bruijn graph ACGTCCGTAA k=2 AC CG 18
19 De Bruijn graph ACGTCCGTAA k=2 AC CG GT 19
20 De Bruijn graph ACGTCCGTAA k=2 AC CG GT TC 20
21 De Bruijn graph ACGTCCGTAA k=2 AC CG GT CC TC 21
22 De Bruijn graph ACGTCCGTAA k=2 AC CG GT CC TC 22
23 De Bruijn graph ACGTCCGTAA k=2 AC CG GT CC TC 23
24 De Bruijn graph ACGTCCGTAA k=2 AC CG GT CC TC TA 24
25 De Bruijn graph ACGTCCGTAA k=2 AC CG GT TA CC TC AA 25
26 De Bruijn graph ACGTCCGTAA k=2 ACG AC CG GT TA CC TC AA 26
27 De Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT TA CC TC AA 27
28 De Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT TA GTC CC TC AA 28
29 De Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT TA GTC TCC CC TC AA 29
30 De Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT TA GTC CCG TCC CC TC AA 30
31 De Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT TA GTC CCG TCC CC TC AA 31
32 De Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GTA GT TA GTC CCG TAA TCC CC TC AA 32
33 De Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GTA GT TA GTC CCG TAA TCC CC TC AA 33
34 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GTA GT TA GTC CCG TAA TCC CC TC AA 34
35 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GTA GT TA GTC CCG TAA TCC CC TC AA 35
36 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA GTC CCG TCC CC TC AA 36
37 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA GTC CCG TCC CC TC AA 37
38 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA CCG GTCC CC AA 38
39 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA CCG GTCC CC AA 39
40 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA GTCCG AA 40
41 Condensed de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA GTCCG AA 41
42 What about real data? CCGTTG TGCAGG GTTGCA k=3 42
43 What about real data? CCG CCGTTG TGCAGG GTTGCA k=3 43
44 What about real data? CCG CGT CCGTTG TGCAGG GTTGCA k=3 44
45 What about real data? CCG GTT CGT CCGTTG TGCAGG GTTGCA k=3 45
46 What about real data? CCG CGT GTT TTG CCGTTG TGCAGG GTTGCA k=3 46
47 What about real data? CCG CGT GTT TTG CCGTTG TGCAGG GTTGCA k=3 TGC GCA CAG AGG 47
48 What about real data? CCG CGT GTT TTG CCGTTG TGCAGG GTTGCA CCGT k=3 TGC GCA CAG AGG 48
49 What about real data? CCG GTT CGT TTG CCGTTG TGCAGG GTTGCA CCGT CGTT k=3 TGC GCA CAG AGG 49
50 What about real data? CCG GTT CGT TTG CCGTTG TGCAGG GTTGCA CCGT CGTT GTTG k=3 TGC GCA CAG AGG 50
51 What about real data? CCG GTT CGT TTG CCGTTG TGCAGG GTTGCA CCGT CGTT GTTG k=3 TGCA TGC GCA GCAG CAGG CAG AGG TTGC 51
52 What about real data? CCG GTT CGT TTG CCGTTG TGCAGG GTTGCA CCGT CGTT GTTG k=3 TGCA TGC GCA GCAG CAGG CAG AGG TTGC 52
53 What about real data? CCG GTT CGT TTG CCGTTG TGCAGG GTTGCA CCGT CGTT GTTG k=3 TGCA TGC GCA GCAG CAGG CAG AGG TTGC 53
54 What about real data? CCG GTT CGT TTG CCGTTG TGCAGG GTTGCA CCGT CGTT GTTG k=3 TGCA TGC GCA GCAG CAGG CAG AGG TTGC 54
55 What about real data? CCG GTT CGT TTG CCGTTG TGCAGG GTTGCA CCGT CGTT GTTG k=3 TGCA TGC GCA GCAG CAGG CAG AGG TTGC 55
56 What about real data? CCG CGT GTT TTG CCGTTG TGCAGG GTTGCA k=3 TGC CAG GCA CCG CCGTTGGCAGG AGG AGG 56
57 Repeats in de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA GTCCG AA 57
58 Repeats in de Bruijn graph ACGTCCGTAA k=2 ACG AC CGT CG GT GTAA GTCCG AA 58
59 Repeats in de Bruijn graph ACGTCCGTAA k=2 CGT2 ACG AC CG GT GTAA GTCCG AA 59
60 Eulerian path with multiplicities ACGTCCGTAA k=2 CGT2 ACG AC CG GT GTAA GTCCG AA 60
61 Oh, repeats... Ribosomal operons (5-8 kbp) ALU, SINEs < 1 kbp, extremely high multiplicity LINEs >> 1 kbp, high multiplicity Tandem repeats 61
62 Oh, repeats... NCBI contains assemblies with 100K+ scaffolds! These are not the genomes I wanted you to assemble Gene Meyers 62
63 Resolving repeats 63
64 Resolving repeats 64
65 Resolving repeats? 65
66 Paired reads AACCCGTACGTTTTGCAAACGACCGTAACCAAATTGG AACCCGTACGT...TAACCAAATTGG insert size 66
67 Resolving repeats 67
68 Resolving repeats 68
69 Resolving repeats 69
70 Resolving repeats 70
71 Long reads to the rescue 71
72 Long reads to the rescue 72
73 Long reads to the rescue 73
74 Long reads to the rescue 74
75 Real life Part of E.coli genome, K = 99 75
76 SPAdes assembler
77 SPAdes genome assembler Data types Small genomes (bacteria, fungi) Standard data sets MDA single-cell data sets Sequencing technologies Illumina IonTorrent 77
78 SPAdes genome assembler Hybrid assembly Paired-end reads Mate-pairs PacBio Oxford Nanopore Sanger Previous assemblies Works with high-coverage projects (1000x+) Fast & efficient User-friendly 78
79 SPAdes genome assembler Requirements System 64-bit Linux Mac OS Python 2.4 or higher bioinf.spbau.ru/spades/ 79
80 SPAdes first steps spades.py 80
81 SPAdes first steps spades.py spades.py --help spades.py --test 81
82 SPAdes first steps spades.py spades.py --help spades.py --test -o <output_dir> 82
83 Input data formats FASTA:.fasta /.fa FASTQ:.fastq /.fq Gzipped:.gz Unmapped BAM (IonTorrent):.bam 83
84 Input data options Unpaired reads Illumina unpaired IonTorrent 84
85 Input data options Unpaired reads Illumina unpaired IonTorrent -s single.fastq -s single1.fastq -s single2.fastq... 85
86 Input data options Paired-end reads Interlaced pairs in one file >left_read_id ACGTGCAGG >right_read_id GCTTCGAGG Separate files file1.fastq file2.fastq >left_read_id >right_read_id ACGTGCAGG GCTTCGAGG 86
87 Input data options Paired-end reads Interlaced pairs in one file --pe1-12 file.fastq Separate files --pe1-1 file1.fastq --pe1-2 file2.fastq 87
88 Input data options Paired-end reads Interlaced pairs in one file --pe1-12 file.fastq Separate files --pe1-1 file1.fastq --pe1-2 file2.fastq --pe1-s unpaired.fastq 88
89 SPAdes pipeline 89
90 Pipeline options Run only error correction (I will use my own assembler) --only-error-correction 90
91 Pipeline options Run only error correction (I will use my own assembler) --only-error-correction Run only assembler (input reads are already corrected or quality-trimmed) --only-assembler 91
92 Pipeline options Run only error correction (I will use my own assembler) --only-error-correction Run only assembler (input reads are already corrected or quality-trimmed) --only-assembler Run mismatch correction after the assembly --careful 92
93 SPAdes performance options Number of threads -t N Maximal available RAM (GB) SPAdes will terminate if exceeded -m M 93
94 Restarting SPAdes SPAdes / system crashed --continue -o your_output_dir 94
95 Restarting SPAdes SPAdes / system crashed --continue -o your_output_dir You forgot some options --restart-from check_point ec error correction as assembly mc mismatch correction k## --- specific k value 95
96 Why to create new assembler? 96
97 Single-cell sequencing
98 How to sequence bacteria? 98
99 How to sequence bacteria? Sequencing requires a lot of DNA 99
100 How to sequence bacteria? Conventional sequencing >99% of bacteria cannot be cultivated in the lab 100
101 How to sequence bacteria? Conventional sequencing Metagenomics Reads from different genomes mixed in one data set Hard to assemble and classify resulting sequences 101
102 Metagenomics Metagenomics: sequencing of whole bacterial community Reads from dozens of different genomes mixed in one data set Different coverage for different bacteria Presence of different strains Conservative genomic regions Hard to assemble and classify resulting sequences 102
103 How to sequence bacteria? Conventional sequencing Metagenomics Single cell Needs whole genome amplification 103
104 Single-cell sequencing via MDA Multiple Displacement Amplification Random hexamer primers 104
105 Single-cell sequencing via MDA Multiple Displacement Amplification Random hexamer primers Phi29 DNA polymerase strand displacement 105
106 Single-cell sequencing via MDA Multiple Displacement Amplification Random hexamer primers Phi29 DNA polymerase strand displacement 106
107 Challenges in single-cell assembly E. coli isolate dataset E.coli single-cell dataset 107
108 Challenges in single-cell assembly A cutoff threshold will eliminate about 25% of valid data in the single cell case, whereas it eliminates noise in the normal multicell case
109 Challenges in single-cell assembly Insert size deviation Chimeric reads Isolate dataset 0.01% Single-cell dataset ~2%
110 Single-cell assembly techniques
111 How to select K? SPAdes is iterative assembler Uses several K values iteratively Output is from the last iteration K is selected automatically Read length Data type Setting K manually (not recommended) -k 21,33,55 111
112 Why iterative? Small K 112
113 Why iterative? Small K Tangled graph Unresolved short repeats Short contigs ACGGATC TTGGAAG k=2 k=4 113
114 Why iterative? Small K Tangled graph Unresolved short repeats Short contigs ACGGATC TTGGAAG k=2 k=4 ACGGATC ACGG GGA GATC TTGGAAG TTGG GAAG 114
115 Why iterative? Small K Tangled graph Unresolved short repeats Short contigs Large K 115
116 Why iterative? Small K Tangled graph Unresolved short repeats Short contigs Large K Many gaps ACCGT k=2 GTAAT k=4 116
117 Why iterative? Small K Tangled graph Unresolved short repeats Short contigs Large K Many gaps ACCGT k=2 ACCGT GCATT GTAAT k=4 ACCGT GTAAT 117
118 Why iterative? Small K Tangled graph Unresolved short repeats Short contigs Large K Many gaps ACCGT k=2 ACCGCATT GTAAT k=4 ACCGT GTAAT 118
119 Why iterative? Small K Tangled graph Unresolved short repeats Short contigs Large K Many gaps Iterative run Contigs constructed with small K re used as reads to close gaps Last iteration has larger K to resolve short repeats 119
120 Iterative SPAdes run Reads De Bruijn graph construction Condensed de Bruijn graph Increase k-mer size Intermediate contigs Graph simplification Final assembly graph Smaller k-mer sizes are needed for reconstructing low-coverage regions Larger k-mer sizes are needed for resolving short repeats 120
121 What about sequencing errors? CCGTTG CGTTAC GTTGCA TGCAGG 121
122 What about sequencing errors? CCGTTG CGTTAC GTTGCA TGCAGG CCG CGT GTT TTA TAC TTG TGC GCA CAG AGG 122
123 What about sequencing errors? CCGTTG CGTTAC GTTGCA TGCAGG CCG CGT GTT TTA TAC TTG TGC GCA CAG AGG 123
124 What about sequencing errors? CCGTTG CGTTAC GTTGCA TGCAGG TAC GTTAC CCG CCGTT GTT GTTGCAGG AGG 124
125 More about sequencing errors CCGTTG CGTTACAG GTTGCA TGCAGG CCG CGT GTT TTA TAC ACA TTG TGC GCA CAG AGG 125
126 More about sequencing errors CCGTTG CGTTACAG GTTGCA TGCAGG CCG CGT GTT TTA TAC ACA TTG TGC GCA CAG AGG 126
127 More about sequencing errors CCGTTG CGTTACAG GTTGCA TGCAGG GTTACAG CCG CCGTT GTT CAG GTTGCAG CAGG AGG 127
128 More about sequencing errors CCGTTG CGTTACAG GTTGCA TGCAGG CCG CCGTT GTTGCAG GTT CAG CAGG AGG 128
129 Real life 129
130 Real life 130
131 Single-cell sequencing via MDA Multiple Displacement Amplification Random hexamer primers Phi29 DNA polymerase strand displacement
132 Chimeric junctions CCGTTG CGTTGC GTTGCA ATTTAA TTAAAG TAAAGG TTGTAA 132
133 Chimeric junctions CCGTTG CGTTGC GTTGCA ATTTAA TTAAAG TAAAGG TTGTAA 133
134 Chimeric junctions CCGTTG CGTTGC GTTGCA ATTTAA TTAAAG TAAAGG TTGTAA CCG CGT GTT TTG ATT TTT TTA TAA TGC AAA GCA AGG AAG 134
135 Chimeric junctions CCGTTG CGTTGC GTTGCA ATTTAA TTAAAG TAAAGG TTGTAA CCG CGT GTT TTG TGT ATT TTT TTA TAA GTA TGC AAA GCA AGG AAG 135
136 Assembling single-cell data Add option --sc 136
137 Visualizing assembly graph SPAdes outputs assembly graph and contigs paths in 2 formats FASTG +.paths files GFA 137
138 Bandage 138
139 Thank you! Questions? Cite Bankevich et al., SPAdes: A New Genome Assembly Algorithm and Its Applications to Single-Cell Sequencing. JCB, Nurk et al., Assembling single-cell genomes and mini-metagenomes from chimeric MDA products. JCB, Prjibelski et al., ExSPAnder: a universal repeat resolver for DNA fragment assembly. Bioinformatics,
140 Extra slides
141 Mate-pairs 141
142 Mate-pairs 142
143 Mate-pairs 143
144 Mate-pairs 144
145 Mate-pairs 145
146 Mate-pairs Conventional mate-pairs: 146
147 Input data options Mate-pair reads Cannot be used separately Interlaced pairs in one file --mp1-12 mp.fastq Separate files --mp1-1 mp1.fastq --mp1-2 mp2.fastq 147
148 Nextera mate-pairs Conventional mate-pairs: Illumina Nextera mate-pairs: 148
149 Input data options High-quality mate-pair reads (e.g. Nextera MP) Can be used separately Interlaced pairs in one file --hqmp1-12 hqmp.fastq Separate files --hqmp1-1 hqmp1.fastq --hqmp1-2 hqmp2.fastq 149
150 Input data options Read-pairs orientation Forward-reverse Default for paired-end and high quality mate-pair Reverse-forward Default for conventional mate-pairs Forward-forward --pe1-fr / --pe1-rf / --pe1-ff --mp2-fr / --mp2-rf / --mp2-ff 150
151 Pacific Biosciences Up to 70 kbp long Much cheaper than Sanger 10-20% error rate
152 Oxford NanoPores In 2010 announced whole genome sequencing Sequencer as small as USB stick Longest reported read 200 kbp 15-30% error rate
153 Hybrid assembly options PacBio CLR --pacbio pb.fastq Oxford Nanopore reads --nanopore nanopore_reads.fastq Sanger reads --sanger sanger.fastq Additional contigs --trusted-contigs contigs.fa --untrusted-contigs contigs.fa 153
154 PacBio only assembly Thm: Perfect assembly possible iff a) errors random b) sampling is Poisson c) reads long enough 2 solve repeats. Note: e-rate not needed Gene Meyers twitter
155 PacBio only assembly D. melanogaster Assembled with Illumina N50 = 100 kbp Assembled with PacBio P5 N50 = 21 Mbp Assembled new highly repetitive regions
156 Input data types Standard Illumina default Single-cell data sets --sc IonTorrent data --iontorrent 156
157 Compiling SPAdes Requirements 64-bit Linux based OS g cmake zlib bzlib./spades_compile.sh PREFIX=/usr/local./spades_compile 157
158 SPAdes warnings and errors Provided at the end of log Don t forget to attach spades.log and params.txt 158
159 SPAdes for Cloud Platforms SPAdes runs on: Illumina BaseSpace DNAnexus TorrentServer Galaxy (available from Galaxy Tool Shed) 159
160 OLC
161 Early days Sanger sequencing Long reads Low coverage Overlap-Layout-Consensus (OLC) Find overlaps between all reads BLAST and similar algorithms Ignore "insufficient" overlaps At least 40bp >94% similarity 161
162 Overlap graph B A C D E B E C D A 162
163 Layout A C B D E E A B C D 163
164 Layout A C B D E E A B C D 164
165 Layout A C B D E A B C E D 165
166 Layout A C B D E A B C E D 166
167 Consensus A B C D E 167
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