1. Velvet / Curtain
Matthias Haimel
EBI is an Outstation of the European Molecular Biology Laboratory.

2. 
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3. Overview
• De Bruijn Graph
• Velvet
• Theory
• Practice
• Data formats and quality
• Velvet
• Simulation data
• Multiple insert lengths
• Curtain
• Theory
• Practice
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4. De Bruijn graph
• A concept in combinatorial mathematics
• In combinatorics, de bruijn graph is usually fully connected
• http://en.wikipedia.org/wiki/De_Bruijn_graph
• de bruijn sequence
• Related concept
• Path through graph
• Velvet
• de Bruijn inspired graph structure
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5. De Bruijn graph (Velvet)
• Representation of
• a sequence based on short words (k-mers)
• overlaps between words
• K-mer: word of length k
• K=5
GCCTTCCA
• k-1 overlap
GCCTT GCCTT GCCTT
CCTTC CCTTC CCTTC
CTTCC CTTCC
TTCCA
...
GCCTTCCA GCCTTCCA GCCTTCCA
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6. De Bruijn graph (Velvet)
GCCTTCCAATTT
GCCTTCAAATTT
C A
CTTC TTCC .....
CAATT
T
CCT TC
G CT
C AATTT
A A
CTTC TTCA ..... AAATT
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7. De Bruijn graph representations (Velvet)
TTCA
ATTC TCAG
Error free, no repeat,
no polymorphism
Repeat > kmer length
SNP, variant, < kmer length
Structural variant, inversion
Structural variant, deletion…
…
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8. Example
TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG
AGTCGAG CTTTAGA CGATGAG CTTTAGA
GTCGAGG TTAGATC ATGAGGC GAGACAG
GAGGCTC ATCCGAT AGGCTTT GAGACAG
AGTCGAG TAGATCC ATGAGGC TAGAGAA
TAGTCGA CTTTAGA CCGATGA TTAGAGA
CGAGGCT AGATCCG TGAGGCT AGAGACA
TAGTCGA GCTTTAG TCCGATG GCTCTAG
TCGACGC GATCCGA GAGGCTT AGAGACA
TAGTCGA TTAGATC GATGAGG TTTAGAG
GTCGAGG TCTAGAT ATGAGGC TAGAGAC
AGGCTTT ATCCGAT AGGCTTT GAGACAG
AGTCGAG TTAGATT ATGAGGC AGAGACA
GGCTTTA TCCGATG TTTAGAG
CGAGGCT TAGATCC TGAGGCT GAGACAG
AGTCGAG TTTAGATC ATGAGGC TTAGAGA
GAGGCTT GATCCGA GAGGCTT GAGACAG
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9. Example
Read: GTCGAGG
GTCG
(1x)
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10. Example
Read: GTCGAGG
GTCG TCGA
(1x) (1x)
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11. Example
Read: GTCGAGG
GTCG TCGA CGAG
(1x) (1x) (1x)
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12. Example
Read: GTCGAGG
GTCG TCGA CGAG GAGG
(1x) (1x) (1x) (1x)
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13. Example
New read: CGAGGCT
GTCG TCGA CGAG GAGG
(1x) (1x) (2x) (1x)
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14. Example
Read: CGAGGCT
GTCG TCGA CGAG GAGG
(1x) (1x) (2x) (2x)
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15. Example
Read: CGAGGCT
GTCG TCGA CGAG GAGG AGGC
(1x) (1x) (2x) (2x) (1x)
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16. Example
Read: CGAGGCT
GTCG TCGA CGAG GAGG AGGC GGCT
(1x) (1x) (2x) (2x) (1x) (1x)
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17. Example
New read: TCGACGC
GTCG TCGA CGAG GAGG AGGC
(1x) (2x) (2x) (2x) (1x)
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18. Example
Read: TCGACGC
GTCG TCGA CGAG GAGG AGGC
(1x) (2x) (2x) (2x) (1x)
CGAC GACG ACGC
(1x) (1x) (1x)
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19. Example
etc…
GATT
(1x)
TGAG ATGA GATG CGAT CCGA TCCG ATCC GATC AGAT
(9x) (8x) (5x) (6x) (7x) (7x) (7x) (8x) (8x)
AGAA
(1x)
GCTC CTCT TCTA CTAG
(2x) (1x) (2x) (2x)
TAGT AGTC GTCG TCGA CGAG GAGG AGGC GGCT TAGA AGAG GAGA AGAC GACA ACAG
(3x) (7x) (9x) (10x) (8x) (16x) (16x) (11x) (16x) (9x) (12x) (9x) (8x) (5x)
GCTT CTTT TTTA TTAG
(8x) (8x) (8x) (12x)
CGAC GACG ACGC
(1x) (1x) (1x)
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20. Example
After simplification…
GATT
AGAT
GATCCGATGAG AGAA
GCTCTAG
TAGTCGA CGAG
GAGGCT GGCT TAGA AGAGA AGACAG
GCTTTAG
CGACGC
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21. Example
Tips removed…
AGAT
GATCCGATGAG
GCTCTAG
TAGTCGA CGAG
GAGGCT GGCT TAGA AGAGA AGACAG
GCTTTAG
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22. De Bruijn graph biology extensions (Velvet)
• Handling of reverse strand
• DNA is read in two directions
• Paired-end data
• Handling small differences, which are “uninteresting”
• Errors in sequencing technology
• Memory
• regularly use 80, 100GB real memory
• easily get to 1TB real memory requirements
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23. Read variety
• Short reads ~75bp
• Illumina / Solexa
• SOLiD (colour space)
• Long reads 500-1000 bp
• 454 read
• Sanger capillary reads
• Paired-end reads
• Short reads
• short insert length
• Mate pair reads
• Short reads
• long insert length
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24. Paired-End
Mate Pair
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25. Short paired-end / mate pair reads
?
Velvet expect Illumina paired-end orientation: (L-> <-R)
L R paired-end
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26. Short paired-end / mate pair reads
Illumina mate-pair orientation: (<-L R->)
L R
mate pair
reverse complement
L R paired-end
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27. Velvet algorithms
• Remove Bubbles
• Tour Bus
• Velvet parameters
• -max_branch_length
• -max_divergence
• -max_gap_count
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28. Example
AGAT
GATCCGATGAG
GCTCTAG
TAGTCGA CGAG
GAGGCT GGCT TAGA AGAGA AGACAG
GCTTTAG
GCTC CTCT TCTA CTAG
(2x) (1x) (2x) (2x)
GGCT TAGA
(11x) (16x)
GCTT CTTT TTTA TTAG
(8x) (8x) (8x) (12x)
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29. Example
Bubbles removed… by TourBus
AGAT
GATCCGATGAG
TAGTCGA CGAG
GAGGCT GGCT GCTTTAG TAGA AGAGA AGACAG
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30. Example
Final simplification…
AGATCCGATGAG
TAGTCGAG GAGGCTTTAGA AGAGACAG
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31. Example
TAGTCGAGGCTTTAGATCCGATGAGGCTTTAGAGACAG
Final simplification…
AGATCCGATGAG
TAGTCGAG GAGGCTTTAGA AGAGACAG
One possible walk through the graph ...
TAGTCGAG
GAGGCTTTAGA
AGATCCGATGAG
GAGGCTTTAGA
AGAGACAG
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32. N50
• Total
• N90
• N50
• N10
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33. N50
• Total
• 4,295,113bp
• N90
• 439bp
• N50
• 3,119bp
• N10
• 13,519bp
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34. N50
• N50 is the length of the smallest contig
• contains the fewest (largest) contigs
• combined length represents at least 50% of the assembly
• N10
• > 10 % of the largest contigs
http://www.broadinstitute.org/crd/wiki/index.php/N50
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35. Velvet practical: Part 1
• Compile
• Single end (ERX001300)
• K-mer length
• Coverage cut-offs
• Whole genome sequence as input???
• Staphylococcus aureus MRSA252
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36. Velvet algorithms
• Long read information
• Rock Band
• Velvet parameters
• -long_mult_cutoff
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37. Velvet algorithms
• Paired-end information
• Pebble
• Velvet parameters
• -min_pair_count
Once all distances and variance computed,
Simple greedy extension from main contigs out
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38. Paired-end in Velvet
• Hugely improves quality of assembly
• Insert length greater than repeat
• greater than the length of the most common genomic repeat
• Mixed insert length improves results
• Short: helps for local assembly
• Long: get over repeats
• Large genomes
• Very memory intensive
• Calculation intensive
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39. Data formats and quality
• Fasta • Fastq
• .fasta • .fastq
• .fa • .fq
• ? • ?
Header
>read_1 @SEQ_ID
TATAATATTTAT... GATTTGGGGTTCAAAGC
Sequence +
!''*((((***+))%%%
Quality
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40. FASTQ paired
@SRR022863.1.F
ATATAGATGTACATAAATTAGTTGAAGTATATGAACG
+
.F .R IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIAIIII
/1 /2 @SRR022863.1.R
TTCACCCATTTTATCCATGATTTTGTTCTTTCTCTTC
+
IIIIIHIIIIIIII3III.,IIII&II6II-))&'I0
@SRR022863.1.F @SRR022863.1.R
ATATAGATGTACATAAATTAGT... TTCACCCATTTTATCCATGATTTTGTT...
+ +
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIAIIII IIIIIHIIIIIIII3III.,IIII&II6II-))&'I0
@SRR022863.2.F @SRR022863.2.R
TTATGAATTATTAATAAGTGCT... CATAAAAAAAGAAAATGTACTCTTTAC...
+ +
IIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIIII IIII)0&A,%.&9$8I4+A;I)4II)&%-I$I%#)II
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41. Quality score
• Velvet does NOT use quality score!!!
• Error correction of de Bruijn graph
• p
• the probability that the corresponding base call is incorrect
• Phred quality score
• 10 -> 1 in 10
• 40 -> 1 in 10,000.
• Odds ratio
• earlier versions of solexa pipeline
• differs mainly at lower levels
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42. Quality encoding
• !''*((((***+))%%%
• One value per base
• Integer mapping based on ASCII encoding
• probability of incorrect base call
• Sanger format • Illumina 1.5+
• Phred score • Phred score
• ASCII 33 – 126 -> 0 – 93 • ASCII 59 – 126 -> -5 – 62
• Rarely exceeds 60 • Only 2 – 40 expected
• ! = 33 -> 0 • ! = 33 -> (does not exist)
• b = 66 -> 33 • b = 66 -> 2
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43. Quality encoding
• wikipedia
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44. Quality trimming
Good / Bad ?
Quality score
Bp position in Read
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45. Quality trimming
• Fixed length trimming
• Cut-off at position x
• Adaptive trimming
• Quality score cut-off
• Minimum sequence length
• Sliding window
• Window size
• Quality score cut-off
• Use average quality value of window
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46. Velvet practical: Part 2
• Paired-end (SRX008042)
• Explore parameters
• Set cut-offs
• Analyse quality score (SRX008042)
• Trimming reads
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47. Velvet modules
• Columbus (since Velvet 1.0)
• use reference sequence
• assist with alignment information
• local re-sequencing
• structural variants
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48. Velvet modules
• Oases
• De novo transcriptome assembler
• uses preliminary Velvet assembly
• clusters contigs into loci
• construct transcript isoforms using paired-end / long read
information
• confidence score: describes uniqueness of a transcript in a locus
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49. Read Simulation - Why?
• Controlling the data
• Contamination
• Coverage distribution
• Sequencing errors
• Genome size
• Insert length
• Insert length distribution
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50. Read Simulation - Why?
• Make results comparable
• Assemblers
• Parameters
• Algorithms
• Assembly strategies
• Genome specific “features”
• Robust
• Introduce errors
• Simulate SNPs
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51. Real data vs. simulation
Mario Caccamo
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52. Real data vs. simulation
Mario Caccamo
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53. Velvet practical: Part 3
• Velvet
• Long Reads
• Hybrid Assembly
• Mixed insert length libraries
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54. Curtain
• assembly pipeline
• Paired-end assembly for large genomes
• Group related Contigs
• Uses velvet to assemble groups of related reads
• Iterative approach
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55. Curtain
Genome assembly Pipeline
Curtain
Contigs
Map Group Fill
Assemble Collect
Reads Contigs Bins
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56. Curtain
Curtain Contigs
Map Group Fill
AssembleCollect
Reads Contigs Bins
• Set of input Contigs
• Use established assemblers
• Velvet unpaired
• Cortex
• SGA
• ...
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57. Curtain
Curtain Contigs
Map Group Fill AssembleCollect
Reads Contigs Bins
• Map reads to input contigs
• SAM file support
• bwa
• maq
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58. Curtain
Curtain Contigs
Map Group Fill AssembleCollect
Reads Contigs Bins
• Group Contigs using Paired-end information
1 2 3 4 5
bin mapping read & read pair
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59. Curtain
Curtain Contigs
Map Group Fill
Reads Contigs Bins AssembleCollect
• Assemble each bin
• Run velvet using paired-end information
• bin specific parameters
• Run each bin individually velvet
• Highly parallelizable
• Collect results
• Start next iteration ………………….
Results
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60. Curtain
• Low memory footprint
• Scalable for large genomes
• Make use of cluster
• Available
• www.ebi.ac.uk/egt
• http://code.google.com/p/curtain/
• Future announcements
• http://groups.google.com/group/curtain-assembler
• Future work
• Long read support
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61. Curtain practical
• Run Curtain for Staphylococcus
• Simulation data
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62. Thanks ...
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