De%20novo%20bacterial%20genome%20sequencing:%20millions%20of%20very%20short%20reads%20assembled%20on%20a%20desktop%20computer

1
De novo bacterial genome sequencing millions of
very short reads assembled on a desktop computer

• David Hernandez, Patrice François, Laurent
  Farinelli, Magne Østerås, Jacques Schrenzel

Presented by Lucas Lochovsky
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Outline

• Introduction
• Edenas Methodology
• Reducing Read Redundancy
• Overlap Graph Construction
• Transitive Edge Reduction
• Graph Cleanup
• Contig Production
• Results
• Assemblers
• Assembly tasks
• Additional Edena Analyses
• Graph Cleaning Effectiveness
• Effective Coverage Depth
• Conclusions

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Outline

• Introduction
• Edenas Methodology
• Reducing Read Redundancy
• Overlap Graph Construction
• Transitive Edge Reduction
• Graph Cleanup
• Contig Production
• Results
• Assemblers
• Assembly tasks
• Additional Edena Analyses
• Graph Cleaning Effectiveness
• Effective Coverage Depth
• Conclusions

4
1) Introduction

• NGS will allow us to explore strange new genomes,
  blah blah blah.
• WGS assemblers weve covered so far
• Medvedev-Brudno assembler
• Arachne
• AMOS-Cmp
• Velvet
• ALLPATHS
• Think youve seen it all?

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1) Introduction (contd)

• Edena De novo short read assembler
• Uses a classic overlap graph approach to assembly
• Anyone else get a feeling of déjà vu?
• Compare to other recently published NGS read
  assemblers
• De novo assembly of two bacterial genomes
  sequenced with the Illumina/Solexa platform

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Outline

• Introduction
• Edenas Methodology
• Reducing Read Redundancy
• Overlap Graph Construction
• Transitive Edge Reduction
• Graph Cleanup
• Contig Production
• Results
• Assemblers
• Assembly tasks
• Additional Edena Analyses
• Graph Cleaning Effectiveness
• Effective Coverage Depth
• Conclusions

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2) Edenas Methodology

• Built around a standard overlap-layout-consensus
  workflow
• Opted to use exact matching for overlap detection
• Reduce of spurious overlaps
• Faster than using approximate matching
• Also assume that all reads have the same length
• Is this assumption valid?

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2) Edenas Methodology (contd)

• Four major steps
• Remove redundant reads so that dataset size is
  more manageable
• Overlap detection and overlap graph construction
• Graph cleaning simplification and ambiguity
  resolution
• Produce contigs

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2) Edenas Methodology (contd)

• 1) Practice your 3 Rs Reducing Read Redundancy
• Illumina Genome Analyzer has high amount of
  over-sampling ? many redundant reads
• Reduce dataset so it contains only a single copy
  of each read ? non-redundant
• Index all reads into a prefix tree
• Identical reads will be mapped to the same key ?
  no duplicate reads in this structure

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2) Edenas Methodology (contd)

• Prefix trees are associative arrays for strings
  where all descendants of a node have a common
  prefix
• Reads and their reverse complements are
  considered the same read ? merged into the same
  tree key

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2) Edenas Methodology (contd)

• Ambiguous reads discarded, since they wont work
  with exact matching
• Opens up possibility of coverage gaps in read
  data (not explored by the authors)
• Original read data still useful for getting read
  frequencies
• Contig coverage depth
• Repeat identification

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2) Edenas Methodology (contd)

• 2) Overlap Graph Construction
• Non-redundant read dataset is indexed by a suffix
  array
• Déjà vu moment Almost exactly like suffix trees
  from MUMmer/MUMmerGPU!
• Information used to produce a bidirected overlap
  graph
• Déjà vu moment Just like the Medvedev-Brudno
  assembler! (which I presented!)

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2) Edenas Methodology (contd)

• This slide should be review for all of you!
• Bidirected graphs are kind of like directed
  graphs, except each edge has an orientation on
  each of its ends
• Gives rise to three types of edges
• Edges where one arrow points out of a vertex, and
  one arrow points into a vertex
• Edges with both arrows pointing out, and
• Edges with both arrows pointing in (easiest one
  to do in PowerPoint!)
• For a walk in a bidirected graph, for each vertex
  on that walk, the orientation of the edge
  entering the vertex must be opposite that of the
  edge leaving the vertex

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2) Edenas Methodology (contd)

• More review!
• In a bidirected overlap graph, each vertex is a
  double-stranded read
• Edges represent read overlaps
• Three possible ways that two double-stranded
  reads can overlap (corresponds to the three types
  of edges)
• Suppose we have two ds reads r1 and r2
• Each read can be oriented to the left or to the
• right
• The three possible overlaps are
• i) Both strands point in the same direction (both
  
• reads can point left, or both can point
  right,
• its the same overlap either way)
• ii) r1 points left and r2 points right
• iii) r1 points right and r2 points left

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2) Edenas Methodology (contd)

• Parameter Minimum overlap size
• Sensitivity vs. specificity tradeoff
• Small value Higher frequency of chance overlaps
  ? causes path branching in graph (sensitivity
  favoured)
• Large value Creates more dead-end (DE) paths,
  i.e. reads not extended by overlapping reads on
  one side (specificity favoured)

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2) Edenas Methodology (contd)

• 3a) Transitive Edge Reduction
• Simplifies paths by removing nonessential
  nodes/edges
• Generally speaking, a path of the form v1 ? v2 ?
  v3 can be reduced to v1 ? v3, representing the
  same sequence with fewer nodes
• Reduces graph complexity by the over-sampling
  rate c NL/G
• N Number of reads
• L Read length
• G Genome size

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2) Edenas Methodology (contd)

• For sequences, its about removing reads for
  which another read with the same sequence
  overlaps the first read to a greater extent

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2) Edenas Methodology (contd)

• 3b) Graph Cleanup
• Can have multiple paths branching off a single
  node (branching paths)
• Due to genomic repetitions, sequencing errors,
  and clonal polymorphisms
• Genomic repetitions cannot be fixed without
  additional information
• But the other two can be resolved

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2) Edenas Methodology (contd)

• Sequencing errors produce short dead-end (DE)
  paths
• Attempt to elongate branching nodes up to a
  certain depth md (minimum depth)
• Reads that cannot be extended to a depth of md
  are removed
• Experimentally determined that md10 is the best
  value

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2) Edenas Methodology (contd)
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2) Edenas Methodology (contd)

• Also disambiguate bubbles in the graph caused by
  single base substitutions (aka p-bubbles)
• Length of p-bubble is at most ms 4L - 2T - 1
• L Read length
• T Min. overlap size
• Explore each branching path up to length ms
  (guaranteed upper bound)
• Remove path with less coverage
• Polymorphisms can be retained for later analysis

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2) Edenas Methodology (contd)
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2) Edenas Methodology (contd)

• 4) Contig Production
• If run in strict mode, Edena starts generating
  contig sequences
• In non-strict mode, one more cleaning step is
  performed
• Longer overlaps more reliable than shorter ones
• Save only edges at branching nodes that have the
  highest overlap of all edges
• Produce contig sequence by following
  non-intersecting simple paths in overlap graph
• Nodes must have in-degree and out-degree of
  exactly one

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Outline

• Introduction
• Edenas Methodology
• Reducing Read Redundancy
• Overlap Graph Construction
• Transitive Edge Reduction
• Graph Cleanup
• Contig Production
• Results
• Assemblers
• Assembly tasks
• Additional Edena Analyses
• Graph Cleaning Effectiveness
• Effective Coverage Depth
• Conclusions

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3) Results

• Survivor WGS Assembly
• Four assemblers
• Two challenges
• One winner

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3) Results (contd)

• Contestant 1 SSAKE
• Indexes reads in a prefix tree based upon first
  eleven 5 bases
• Identify highest possible overlap between pairs
  of reads
• Use most highly-covered reads as starting points
  for read extension (i.e. assembly nucleation
  points)
• So far only used for partial genome sequencing
  for comparative metagenomic analysis (e.g.
  bacterial species distinction)

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3) Results (contd)

• Contestant 2 Velvet
• k-mer/q-gram/k-gram/q-mer de Bruijn graph
  representation of reads
• Contestant 3 SHARCGS
• Can accept base quality scores along with read
  data for read filtering (low quality reads
  discarded)
• Also filter out reads with low coverage
• Assembly performed with a prefix tree
• Contestant 4 Edena

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3) Results (contd)

• Reward Challenge
• Assemble the 2.82 Mbp genome sequence and the
  20.7 Kbp plasmid sequence of the Staphylococcus
  aureus MW2 strain from Illumina reads
• Immunity Challenge
• Assemble 1.55 Mbp genome sequence and the 3.66
  Kbp plasmid sequence of the Helicobacter
  acinonychis Sheeba strain from Illumina reads

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3) Results (contd)

• Staphylococcus aureus results
• Evaluated each assembler on the parameter
  configurations that produced the best results
• Edena Min. overlap size 21 bases
• Velvet k-mer value 23
• SHARCGS Max. gap span 14
• SSAKE Default parameters

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3) Results (contd)

• Compared contig assembly to published reference
  sequence
• Non-strict mode tends to produce longer contigs
  at the expense of additional misassemblies
• Velvet comparable to Edena strict

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3) Results (contd)

• SHARCGS unable to assemble significant contigs ?
  insufficient coverage depth
• SSAKE produced a large number of mismatches
  mostly at contig boundaries

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3) Results (contd)

• Authors also tried combining contig results from
  Edena and Velvet due to significant overlaps
  between their contigs
• N50 and mean contig size increased relative to
  original results
• Edena non-strict has similar influence on results
  as previously

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3) Results (contd)

• Helicobacter acinonychis results
• Best parameter settings
• Edena Min. overlap size 27 (strict), 26
  (non-strict)
• Velvet k-mer value 27
• SHARCGS Max. gap span 10 (also must remove last
  four bases from each read)
• SSAKE Default parameters

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3) Results (contd)

• Results similar to those from the previous
  assembly challenge

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3) Results (contd)

• Survivor WGS Assembly Conclusion
• Granted Immunity Edena, Velvet
• Sent to the Tribal Council SSAKE, SHARCGS

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Outline

• Introduction
• Edenas Methodology
• Reducing Read Redundancy
• Overlap Graph Construction
• Transitive Edge Reduction
• Graph Cleanup
• Contig Production
• Results
• Assemblers
• Assembly tasks
• Additional Edena Analyses
• Graph Cleaning Effectiveness
• Effective Coverage Depth
• Conclusions

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4) Additional Edena Analyses

• Graph Cleaning Effectiveness
• Demonstrate the effectiveness of DE path removal
  and p-bubble fixing
• Created an ideal read pool from the S. aureus MW2
  strain
• Consists of one read at every possible position
• No errors
• No polymorphisms
• Distinguish between positive and negative reads
• Positive reads have at least one exact occurrence
  in the reference sequence
• Negative reads have none

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4) Additional Edena Analyses (contd)

• Ideal dataset indicates branching nodes and
  p-bubbles caused by genomic repetition
• Anomalies in real datasets only due to negative
  reads
• Due to small quantity of branching nodes in the
  ideal dataset, branch removal procedure is
  extremely effective

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4) Additional Edena Analyses (contd)

• Though many p-bubbles consist of sequences made
  of negative reads, most cannot be explained by
  base calling errors
• Thought to correspond to underrepresented clonal
  polymorphisms

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4) Additional Edena Analyses (contd)

• Since there are no DE paths in the ideal dataset,
  expect that DE removal should remove all DE paths
  in real dataset (i.e. dead-ends correspond to
  negative reads)
• From tests with different md values (below),
  authors decided 10 was best
• Not so clear-cut to me

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4) Additional Edena Analyses (contd)

• Most DE paths have length 1
• Correspond to paths created by base calling
  errors
• Longer DE paths exist that do not appear to be
  caused by such errors
• Thought to be clonal polymorphisms in low
  abundance ? cant form a complete p-bubble

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4) Additional Edena Analyses (contd)

• Effective Coverage Depth
• Computed effective coverage depth according to
  formula from Lander and Waterman
• E N(L-T)/G
• N of usable reads
• L Read length
• T Req. overlap length
• G Genome size
• Can also estimate gaps in read coverage with Ne-E

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4) Additional Edena Analyses (contd)

• S. aureus sequencing
• Raw coverage depth 48x
• Effective coverage depth 14x
• H. acinonychis sequencing
• Raw coverage depth 284x
• Effective coverage depth 36x
• Statistics imply that there should be no gaps in
  H. acinonychis assembly, and only a few in S.
  aureus
• But each actual assembly contained several
  hundred gaps

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4) Additional Edena Analyses (contd)

• Statistics assume uniform read sampling
• Investigated underrepresented parts of genomes
• After alignment of reads to reference genome,
  extracted low coverage sequences
• These sequences have complex motifs and single
  base repeats ? cause difficulty in replication

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Outline

• Introduction
• Edenas Methodology
• Reducing Read Redundancy
• Overlap Graph Construction
• Transitive Edge Reduction
• Graph Cleanup
• Contig Production
• Results
• Assemblers
• Assembly tasks
• Additional Edena Analyses
• Graph Cleaning Effectiveness
• Effective Coverage Depth
• Conclusions

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5) Conclusions

• Edena holds up well against other recent
  assemblers, in both assembly quality and
  computational resources
• Some assemblers are partially complementary to
  each other (Edena and Velvet) ? can use together
  to produce results better than each individual
  assemblers results
• Rise of NGS paired read data will help produce
  longer contigs and clean up ambiguities

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Is Edena The One?

• The One that will herald the beginning of cost-
• effective whole genome assembly with NGS?
• Maybe you should ask the Oracle

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Thats all folks!

• Discussion Questions
• What were the strengths/weaknesses of the Edena?
  How would you improve it?
• How do you think Edena compares to the other
  assemblers tested? Would you test it against
  other assemblers not tested here?
• Given Edenas limitations, would you trust it for
  de novo genome assembly over traditional sequence
  assembly?
• Why did we have to discuss yet another NGS genome
  assembler today?