Genome Assembly Forensics

Genome Assembly Forensics and Visualisation

Nathan S. Watson-Haigh

Fri 11th May 2012, ACPFG Journal Club

Schatz, M.C. et al., 2007. Hawkeye: an interactive visual analytics tool for genome assemblies. Genome Biology, 8(3), p.R34.Phillippy, A.M., Schatz, M.C. & Pop, M., 2008. Genome assembly forensics: finding the elusive mis-assembly. Genome Biology, 9(3), p.R55.Schatz, M.C. et al., 2011. Hawkeye and AMOS: Visualizing and Assessing the Quality of Genome Assemblies. Briefings in

Bioinformatics. Available at: http://bib.oxfordjournals.org/content/early/2011/12/23/bib.bbr074.

Overview

• Genome Assembly• N50/N90/N95• Paired-end and Matepair Reads• Mis-assembly Signatures• Assembly Validation and Manual Editing

Genome Assembly – Shotgun Reads

aligned shotgun reads

DNA being sequenced

Genome Assembly – Repeats



reads from different repeats can’t be

resolved

double coverage


Genome Assembly – Diploid

Assembly Metrics – N50

• The N50 is the most widely reported metric for de novo assemblies

• It is a single measure of the contig length size distribution of an assembly– If contigs are sorted into descending length order, the

n50 is the size of the contig above which the assembly contains at least 50% of the total length of all the contigs

– Commonly reported with the N90 and N95


+ = N50

+ = N90

+ = N95


• The N50 is the most widely reported metric for de novo assemblies

• It is a single measure of the contig length size distribution of an assembly– If contigs are sorted into descending length order, the

n50 is the size of the contig above which the assembly contains at least 50% of the total length of all the contigs

– Commonly reported with the N90 and N95• These stats DO NOT imply anything about

assembly quality– Could simply concatenate contigs together to get a

better N50!!

Paired-end Reads

Matepair Reads

Paired-end and Matepair Reads

Paired-end Matepair

reverse compliment

So, Why are Pairs so Useful?

So, Why are Pairs so Useful?

Pairs are Useful – Orientation and Separation





Incorrect orientationIncorrect distance

Mis-assembly Signatures – Collapsed Tandem Repeat

Correct alignment

Incorrect alignment

Mis-assembly Signatures – Collapsed Tandem Repeat

Mis-assembly

Correct assembly

Mis-assembly Signatures – Collapsed (small) Tandem Repeat

Mis-assembly

Correct assembly

Mis-assembly Signatures – Collapsed Repeat

Mis-assembly

Correct assembly

Mis-assembly Signatures – Rearrangement

Mis-assembly

Correct assembly

Automated Assemblies Are One Thing, Good Assemblies Are Another

• Given the computer resources you can generate an automated assembly in a few weeks– Not necessarily good– Need to optimise assembly parameters

• For small organisms (< ~15Mbases)– Commodity hardware– OLC assemblers

• For larger genomes– More RAM (10-100’s Gbytes) for OLC assemblers– De Bruijin Graph assemblers– Read Mapping step to generate contig read alignments


• Automated assemblies need to be checked for mis-assemblies– Need paired-end/matepair reads– Need viewers to visualise paired-end data– Need editors to break/join/reassemble parts of the

assembly deemed to be inconsistent with read pair info– Need enough computer hardware to allow all this data to

be loaded – especially with large volumes of Illumina paired-end data


• Very time consuming and laborious to check/edit– Small assemblies (< ~15Mbases)

• Several weeks/few months to move 1 scaffold/contig at a time

– Large assemblies need a team to do the same thing• Need enough RAM to load all the paired-end data• Need ways to identify regions requiring closer inspection• identify possible mis-assemblies

• Major hurdles– Software inadequacies– Time– File formats! Grrrr!

Software Inadequacies

Software Contig View

Scaffold View

Editing Reassemble Clipping Info

Other

SeqMan Pro

9 9 6 6 6 $$, buggy, not for large assemblies (32bit), 1 template size

Gap5 6 NA 9 NA 8 Free, join editor, contig comparator, poor visual support for many contigs, shuffle pads, ACE, multiple template sizes

Consed 6 6 5 9 6 Free/US$2500/US$10k, poor visual support for many contigs, multiple templates sizes

Hawkeye 9 9 NA NA 7 Leverages AMOS, automated detection of mis-assemblies, large assemblies, modular

SeqMan Pro – Strategy View

SeqMan Pro



Scaffold View


Other

SeqMan Pro





Gap5 – Template View

Gap5 – Contig Comparator

Gap5 – Join Editor

Gap5 – Contig Editor



Scaffold View


Other

SeqMan Pro





Consed – Assembly View

Consed – Contig Viewer/Editor



Scaffold View


Other

SeqMan Pro





Scaffold/Contig Length Distribution

Library Stats

• A measure of the deviation of local distribution of insert sizes to the global distribution of insert sizes– 0 indicates no deviation– ≤ 3 indicates much

compression– ≥3 indicates much

expansion

Compression-Expansion (CE) Statistic

Insert Coverage Read Coverage

500bp inserts 3kb inserts

20kb inserts

AMOSvalidate

• An assembly analysis pipeline to identify possible mis-assemblies– Paired-end data

• CE stats• Incorrect orientation• Missing mate

– Coverage– SNP density– Singletons

Hawkeye Cons

• Poor support for correcting mis-assemblies once detected



Scaffold View


Other

SeqMan Pro





Closing Remarks

• Software exist to allow manual editing of assemblies– Time consuming– Different tools have different features– Most fall over with assemblies > ~15Mbases or with

many contigs/scaffolds (10k-100k)

Closing Remarks

• Ideal Tool– Contig/scaffold viewer capable of displaying

compressed/expanded mates, which contigs mates map to when they are off contig/scaffold (like SeqMan Pro and Hawkeye)

Closing Remarks



– Contig join editor for manual alignment and editing of contigs (like Gap5)

Gap5 – Join Editor

Closing Remarks




– Visualise clipped regions with consensus mismatches (like Gap5)


Closing Remarks




– Visualise clipped regions with consensus mismatches (like Gap5)

– Automated analysis of assembly to identify regions requiring attention (like AMOSvalidate) and a way to navigate to those regions for editing

– Minimise mouse-clicks and keyboard presses!!

Newbler Plant Genome Assemblies

• Pretty conservative in contig construction• Seems to split out repetitive regions into their

own contigs pretty well• Heterozygsity issues

– SNP alignment issues– Indels break contigs– Hidden in clipped regions– Manual joining of neighbouring contigs can reduce

scaffolded contig numbers by 60-70%– Many unscaffolded contigs have high sequence

similarity to scaffolded contigs – could collapse these and reduce the number of unscaffolded contigs by 50%


Education

Genome Assembly Forensics