1. AMOS Assembly Validation and Visualization
Michael Schatz
October 7, 2008

2. Lander-Waterman Statistics
Sequencing as a random process
E(#contigs) = Ne-cσ
E(contig size) = L[((ecσ – 1)/c) - (1- σ)]
L = read length
T = minimum overlap
G = genome size
N = number of reads
c = coverage (NL / G)
σ = 1 – T/L

3. Assembly Reality Contigs are never as large as expected
High coverage is a necessary but not sufficient condition
Sequencing is basically random, but sequence composition is not
Repeats control the quality of the assembly
Assemblers break contigs at ambiguous repeats
Highly repetitive genomes will be highly fragmented
Assemblers make mistakes
Mis-assemblies confuse all downstream analysis
Tension between overlap error rate and repeat resolution

4. Assembly Evaluation

5. Assembly Evaluation

6. Genome Assembly Forensics
Computationally scan an assembly for mis-assemblies.
Data inconsistencies are indicators for mis-assembly
Some inconsistencies are merely statistical variations
amosvalidate
Load Assembly Data into Bank
Analyze Mate Pairs & Libraries
Analyze Depth of Coverage
Analyze Normalized K-mers
Analyze Read Alignments
Analyze Read Breakpoints
Load Mis-assembly Signatures into Bank
AMOS
Bank

7. Mis-assembly types Compression A R B Expansion A R B Correct
Rearrangement A R B C D
Correct
Mis-assembly
Inversion A R C B
Basic mis-assemblies can be combined into more complicated patterns:
Insertions, Deletions, Giant Hairballs

8. 1. Analyze Mate Pairs Evaluate mate “happiness” across assembly
Happy = Correct orientation and distance
Finds regions with multiple:
Compressed Mates
Expanded Mates
Invalid orientation (same   or outie  )
Missing Mates
Linking mates (mate in a different scaffold)
Singleton mates (mate is not in any contig)

9. Mate-Happiness: Deletion
Deletion: Excise reads between collapsed repeats
Truth
Misassembly: Compressed Mates, Linking Mates

10. Mate-Happiness: Insertion
Insertion: Additional reads between flanking repeats
Truth
Misassembly: Expanded Mates, Missing Mates

11. Mate-Happiness: Inversion
Inversion: Flipping of reads
Truth
Misassembly: Misoriented Mates

12. Mate-Happiness: Rearrangement
Rearrangement: Reordering of reads
Truth
Misassembly: Misoriented, Compressed, Stretched Mates

13. C/E Statistic
Easy to detect mis-oriented or missing mates, but individual compressed or expanded mates are expected.
Does the distribution of inserts spanning a given position differ from the rest of the library?
Record large differences as potential misassemblies
Even if each individual mate is “happy”
Compute the statistic at all positions
(Local Mean – Global Mean) / (Global Stdev / √N)
> +3 indicates significant expansion
< -3 indicates significant compression
Introduced by Jim Yorke’s group at UMD

14. Sampling the Genome 2kb 4kb 6kb 8 inserts: 3kb-6kb Local Mean: 4048
C/E Stat: ( ) = +0.33
(400 / √8)
Near 0 indicates overall happiness
0kb

15. C/E-Statistic: Expansion
0kb
2kb
4kb
6kb
8 inserts: 3.2kb-6kb
Local Mean: 4461
C/E Stat: ( ) = +3.26
(400 / √8)
C/E Stat ≥ 3.0 indicates Expansion

16. C/E-Statistic: Compression
0kb
2kb
4kb
6kb
8 inserts: 3.2 kb-4.8kb
Local Mean: 3488
C/E Stat: ( ) = -3.62
(400 / √8)
C/E Stat ≤ -3.0 indicates Compression

17. 2. Read Coverage
Find regions of contigs where the depth of coverage is unusually high
Collapsed Repeat Signature
Can detect collapse of 100% identical repeats
AMOS Tool: analyzeReadDepth
2.5x mean coverage A R1 B R2 A
R1 + R2 B

18. 3. Normalized K-mers
Not all repeats are mis-assembled, but most mis-assemblies occur at repeats.
Detect repeats by noticing increased read k-mer coverage.
2 copy repeat will have ~2x the average read k-mer coverage
N copy repeat will have ~Nx the average read k-mer coverage
Normalize read k-mer coverage with consensus k-mer coverage to highlight just the repeats with “missing” copies.
The sequence of an N copy repeat should occur N times in the consensus.

19. Normalized K-mers Correct assembly of 2 copy repeat A R1 B R2
Read K-mers
Consensus K-mers
Normalized K-mers

20. Normalized K-mers Mis-assembly of 2 copy repeat
Effective even if reads are left as singletons A B
R1 + R2
Read K-mers
Consensus K-mers
Normalized K-mers

21. 4. Read Alignment
Multiple reads with same conflicting base are unlikely
1x QV 30: 1/1000 base calling error
2x QV 30: 1/1,000,000 base calling error
3x QV 30: 1/1,000,000,000 base calling error
Regions of correlated SNPs are likely to be assembly errors or interesting biological events
Highly specific metric
AMOS Tools: analyzeSNPs & clusterSNPs
Locate regions with high rate of correlated SNPs
Parameterized thresholds:
Multiple positions within 100bp sliding window
2+ conflicting reads
Cumulative QV >= 40 (1/10000 base calling error)
A G C A G C A G C A G C A G C A G C C T A C T A C T A C T A C T A

22. 5. Read Breakpoints
Align singleton reads to consensus sequences.
A consistent breakpoint shared by multiple reads can indicate a collapsed repeat.
Initially developed to detect collapsed repeat in Bacillus Anthracis.
375
BAPDN53TF 786bp
665
BAPDF83TF bp
428
BAPCM37TR 697bp
668
BAPBW17TR 1049bp
144337
16S rRNA
146944
144203
145515
146226
147021 RA RB

23. Performance
Combining signatures into suspicious regions greatly improves specificity.

24. Hawkeye

25. Hawkeye Goals Interactively explore and analyze Libraries
Insert Sizes, Read Length, Inserts
Scaffolds & Contigs
Sizes, Composition, Sequence
Multiple Alignment, SNP Barcode
Read Coverage, k-mer Coverage
Inserts
Happiness, Coverage, CE Statistic
Reads
Clear Range, Quality Values, Chromatograms
Features
Arbitrary regions of interest
Including Mis-assembly Signatures!!!
Overview
Zoom & Filter
Details on Demand

26. Launch Pad

27. Histograms & Statistics
Insert
Size
Read
Length GC
Content
Overall
Statistics
Bird’s eye view of data and assembly quality

28. Scaffold View Statistical Plots Scaffold Features Inserts Overview
Control Panel
Details

29. Insert Happiness Happy Stretched Compressed Both mates present
Oriented Correctly &&
|Insert Size – Library.mean| <= Happy-Distance * Library.sd
Stretched
Insert Size > Library.mean + Happy-Distance * Library.sd
Compressed
Insert Size < Library.mean - Happy-Distance * Library.sd
Misoriented
Same or Outies
Linking
Read’s mate is in some other scaffold
Singleton
Read’s mate is a singleton
Unmated
No mate was provided for read
Both mates present
Only 1 read present

30. Standard Feature Types
[B] Breakpoint
Alignment ends at this position
[C] Coverage
Location of unusual mate coverage (asmQC)
[S] SNPs
Location of Correlated SNPs
[U] Unitig
Used to report location of surrogate unitigs in CA assemblies
[X] Other
All other Features
Loading Features:
$ loadFeatures bankname featfile
Featfile format:
Contigid type end5 end3 comment

31. Contig View Consensus & Position Scrollable Read Tiling
Read Orientation
Discrepancy
Highlight
Summary
Navigation
Contig
Quick Select
Regular Expression
Consensus Search

32. Contig View Expanded Quality Values Normalized Chromatogram
Chromatograms are loaded from specified directories,
or on demand from Trace Archive.

33. Misassembly Walkthough:
Assembly Reports
Misassembly Walkthough:
Correlated SNPs
Contigs
Features
Reads
Scaffolds
Full Integration: “Double click takes you there”

34. SNP View
Zoom Out
SNP Sorted
Reads
Polymorphism
View

35. SNP Barcode SNP Sorted Reads
Colored Rectangle indicate the positions and composition of the SNPs

36. Scaffold View Coverage CE Statistic SNP Feature Happy Stretched
Compressed
Misoriented
Linking

37. Collapsed Repeat Read Coverage Spike -5.5 CE Dip Compressed Mates
Cluster
68 Correlated SNPs

38. Confirmed Misassembly
Fixed
Collapsed repeat
Compressed mates (-5.5 CE Stat)
Correlated SNPs (68 Positions within 1400bp)
Spike in Read Coverage

39. Fixing the collapsed repeat
Select reads and mates in region of collapse.
AMOS: findMissingMates, select-reads
Reassemble those reads with a stricter unitigger error rate.
AMOS: minimus
Patch the collapsed region of the original assembly with corrected version.
AMOS: stitchContigs

40. stitchContigs Before After
The multiple alignment of the patch replaces the multiple alignment between the two stitch reads. Otherwise, reads left of the left stitch read and right of the right stitch read are taken directly from the original master contig in their original multiple alignment, so the master contig and the stitched contig are identical except near the compression point.

41. More Information AMOS Webpages: Contact AMOS Acknowledgements
Contact AMOS
amos-help [ at ] lists.sourceforge.net
Acknowledgements
Adam Phillippy
Ben Shneiderman
Steven Salzberg
Art Delcher
Mihai Pop