Genome assembly: then and now — v1.0

Genome assembly: then and nowKeith Bradnam

Image from Wellcome Trust

Image from flickr.com/photos/dougitdesign/5613967601/

Contents

Sequencing 101

Genome assembly: then

Genome assembly: now

Assemblathon 1

Assemblathon 2

Assemblathon 3

http://flickr.com/photos/dougitdesign/5613967601/


More info

✤ http://assemblathon.org

✤ http://arxiv.org

✤ http://twitter.com/assemblathon

Assemblathon 2 paper has been reviewed, just dealing with reviewer's comments.

http://assemblathon.org

http://assemblathon.org

http://arxiv.org

http://arxiv.org

http://twitter.com/assemblathon



Sequencing 101A, C, G, T...

Image from nlm.nih.gov

Fred Sanger



Read

Most sequencing technologies start with a sequencing read. A read could be as short as 25 bp (Solexa sequencing from a few years ago), or >15,000 bp (PacBio with latest chemistry).

Read pair

Most sequencing is done with pairs of connected reads, separated by a short interval whose length is known. Read pairs can also overlap with each other.

Read pair

Mate pair

Mate pairs, also known as jumping pairs, have much larger inserts (thousands or tens of thousands of bp), but it is hard to make good mate pair libraries. Having very large inserts is very useful for the purposes of genome assembly.

Sequence a whole lot of read pairs, and hopefully they will overlap with each other and allow you to start making contiguous sequences...

Contigs

...which are better known as contigs.

Mate pairs — or other information — can hopefully be used to connect contigs together into scaffolds. The unknown gap between contigs is replaced with unknown bases (Ns).

ScaffoldNNNNNNNNNNNNNNNNNNN

Mate pairs — or other information — can hopefully be used to connect contigs together into scaffolds. The unknown gap between contigs is replaced with unknown bases (Ns).

Assembly size

NNNNNNNNNNNNNNNNNNN

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Assembly size is simply the sum of all scaffolds or contigs that are included in the final genome assembly. If you are calculating the assembly size from scaffolds, then some fraction of that final size will come from the Ns in scaffold sequences.

Here we have a toy genome assembly, with 12 scaffolds totaling 200 Mbp.

Assembly size

NNNNNNNNNNNNNNNNNNN

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200 Mbp

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Assembly size is simply the sum of all scaffolds or contigs that are included in the final genome assembly. If you are calculating the assembly size from scaffolds, then some fraction of that final size will come from the Ns in scaffold sequences.

Here we have a toy genome assembly, with 12 scaffolds totaling 200 Mbp.

N50 length

NNNNNNNNNNNNNNNNNNN

NNNNNNNNNNN

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The most widely used measure to describe genome assemblies is the N50 lengths of scaffolds or contigs. This is essentially a weighted mean, designed to be more informative than a crude mean length (which is not very useful if you end up with thousands of very short contigs). To calculate the N50 scaffold length, start with the length of the longest scaffold...

N50 length

NNNNNNNNNNNNNNNNNNN

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The most widely used measure to describe genome assemblies is the N50 lengths of scaffolds or contigs. This is essentially a weighted mean, designed to be more informative than a crude mean length (which is not very useful if you end up with thousands of very short contigs). To calculate the N50 scaffold length, start with the length of the longest scaffold...

N50 length

NNNNNNNNNNNNNNNNNNN

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If this length does not exceed 50% of the total assembly size (50% is why it is N50), proceed to the next longest scaffold, and add the length to a running total.

N50 length

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Now we have exceeded 50% of the total assembly size.

N50 length

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The length of the contig or scaffold that takes you past 50% is what is reported as the N50 length. So here, we have an N50 length of 20 Mbp.

N50 length

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N50 may be more robust than using a simple mean length, but it can still be easily manipulated. What if we excluded the two shortest scaffolds from our assembly?

N50 length

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N50 may be more robust than using a simple mean length, but it can still be easily manipulated. What if we excluded the two shortest scaffolds from our assembly?

N50 length

NNNNNNNNNNNNNNNNNNN

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Now the total assembly size is 10 Mbp smaller, which is only 5%, but the N50 increases to 25 Mbp...a 25% increase in size. If these were two different assemblies and you only saw an N50 of 25 Mbp vs N50 of 20 Mbp, you might think the first assembly was much better.

N50 length

NNNNNNNNNNNNNNNNNNN

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190 Mbp

Now the total assembly size is 10 Mbp smaller, which is only 5%, but the N50 increases to 25 Mbp...a 25% increase in size. If these were two different assemblies and you only saw an N50 of 25 Mbp vs N50 of 20 Mbp, you might think the first assembly was much better.

N50 for two assemblies

Here are another two fictional assemblies. The first assembly now has a lower N50 value, but this is purely because it contains more sequence (which are albeit short scaffolds). Do you want more sequence in your assembly, or fewer but longer sequences?


208 Mbp 190 Mbp



208 Mbp 190 Mbp

N50 = 15 Mbp N50 = 25 Mbp


NG50 for two assemblies

208 Mbp 190 Mbp

We prefer a measure called NG50. This does not use the assembly size, but instead uses the known (or estimated) genome size (the 'G' in NG50 refers to the Genome).


Expected genome size = 250 Mbp




The NG50 of these two assemblies is now the same. We think that NG50 is a fairer way of comparing genome assemblies that might differ in their total size.

NG50 = 15 Mbp NG50 = 15 Mbp



The NG50 of these two assemblies is now the same. We think that NG50 is a fairer way of comparing genome assemblies that might differ in their total size.

How do I describe thee? Let me count the ways

Apart from assembly size, and N50/NG50 length, there are many other ways to describe a genome assembly.


Metric Description

Assembly size With or without very short contigs?

N50 / NG50 For contigs and/or scaffolds

Coverage When compared to a reference sequence

Errors Base errors from alignment to reference sequence and/or input read data

Number of genes From comparison to reference transcriptome and/or set of known genes

Apart from assembly size, and N50/NG50 length, there are many other ways to describe a genome assembly.


Metric Description

Assembly size With or without very short contigs?

N50 / NG50 For contigs and/or scaffolds

Coverage When compared to a reference sequence

Errors Base errors from alignment to reference sequence and/or input read data

Number of genes From comparison to reference transcriptome and/or set of known genes

And many, many more...Apart from assembly size, and N50/NG50 length, there are many other ways to describe a genome assembly.

Genome assemblyBack in the day...

How were genomes assembled back in the late 1990s when genome sequencing projects were starting to make the news?


Genome sequencing projects often had a fantastic amount of supporting material which helped put the genome together. They were further helped by targeting genomes which had low heterozygosity. And of course this was all done with Sanger sequencing which gave long, accurate reads.

Genetic maps ✓ Physical maps ✓Understanding of target genome ✓Haploid / low heterozygosity genome ✓Accurate & long reads ✓Resources (time, money, people) ✓


Genome sequencing projects often had a fantastic amount of supporting material which helped put the genome together. They were further helped by targeting genomes which had low heterozygosity. And of course this was all done with Sanger sequencing which gave long, accurate reads.

So what was the result of spending millions of dollars to assemble genomes of well-characterized species,with accurate long reads, and detailed maps???

So hopefully this gave us a useful set of finished genomes, right?

✤ 2000: published genome size = 125 Mbp

✤ 2007: genome size = 157 Mbp


Arabidopsis thaliana

Many published genome sizes are sometimes based on estimates which can be wrong. As they sequenced more and more of the Arabidopsis genome, they had to revise how big it was. So between 2000 and 2007 they produced more sequence but paradoxically it became less complete because the estimate of the size went up. Now it has come back down again. But the genome remains unfinished.




✤ Amount sequenced = 119 Mbp






✤ Amount sequenced = 119 Mbp

✤ Ns = 0.2% of genome



Drosophila melanogaster

✤ Genome published 1998

✤ Heterochromatin finished 2007

The fly genome was 'finished' in 1998. But this was only really the easy-to-sequence portion of the genome (the euchromatin). The trickier heterochromatin was sequenced as a separate project that didn't finish until almost a decade later. The fly genome remains unfinished.

Drosophila melanogaster


✤ Heterochromatin finished 2007

✤ Ns = 4% of genome

The fly genome was 'finished' in 1998. But this was only really the easy-to-sequence portion of the genome (the euchromatin). The trickier heterochromatin was sequenced as a separate project that didn't finish until almost a decade later. The fly genome remains unfinished.

Caenorhabditis elegans


✤ 2004: last N removed

The worm genome has no unknown bases in it. However, since the publication of the genome sequence the genome has continued to be refined as errors are corrected. The last batch of changes all occurred just last November. So after almost 15 years of post-genome-publication, we can still find over 1,400 errors in one of the best characterized genome sequences that exists.




✤ 1998–2013: genome sequence changes






✤ 558 insertions

✤ 230 deletions

✤ 614 substitutions






✤ 558 insertions

✤ 230 deletions

✤ 614 substitutions

} Nov 2012


Saccharomyces cerevisiae


✤ 12 Mbp genome

✤ 1,653 changes to genome since 1997

Likewise in yeast. The first eukaryotic genome sequence continues to receives fixes to correct the sequence. The last set of changes were made in 2011. These changes affected coding sequences, not just intergenic and intronic DNA.

Saccharomyces cerevisiae


✤ 12 Mbp genome

✤ 1,653 changes to genome since 1997

✤ Last changes made in 2011

Likewise in yeast. The first eukaryotic genome sequence continues to receives fixes to correct the sequence. The last set of changes were made in 2011. These changes affected coding sequences, not just intergenic and intronic DNA.

Genetic maps ✓ Physical maps ✓Understanding of target genome ✓Haploid / low heterozygosity genome ✓Accurate & long reads ✓Resources (time, money, people) ✓


And all of this was done in an era when we had all of these supporting materials.

Genetic maps ✗

Physical maps ✗

Understanding of target genome ✗

Haploid / low heterozygosity genome ✗

Accurate & long reads ✗

Resources (time, money, people) ✗

Genome assembly: now

We don't have these now! Genome sequencing no longer requires an international consortium, rather it could be a project for a Grad student.

Assembling & finishinga genome is not easy!

It was never easy, even when we access to lots of resources to help us put together genomes. And it is not easy now. Don't be fooled into thinking that because there are many published genome sequences, that these sequences represent the absolute ideal genome sequence.

AssemblathonsA new idea is born

Image from flickr.com/photos/dullhunk/4422952630

The Assemblathon was born out of the Genome 10K project.

If you sequence 10,000 genomes......you need to assemble 10,000 genomes

The Assemblathon was born out of the Genome 10K project.

How many assembly tools are out there?

There are many, many tools out there for assembling, or helping to assemble, a genome sequence. It seems reasonable to ask...which is the best?


Ray

Celera

MIRA

ALLPATHS-LGSGA

CurtainMetassembler

Phusion

ABySS

Amos

Arapan

CLC

Cortex

DNAnexus

DNA Dragon EULER

EdenaForge

GeneiousIDBA

Newbler

PRICE

PADENA

PASHAPhrap

TIGR

Sequencher

SeqMan NGen

SHARCGS

SOPRA

SSAKE

SPAdesTaipan

VCAKE

Velvet

Arachne

PCAP

GAM

MonumentAtlas

ABBA

Anchor

ATAC

Contrail

DecGPU GenoMinerLasergene

PE-Assembler

Pipeline Pilot

QSRA

SeqPrep

SHORTY

fermiTelescoper

QuastSCARPA Hapsembler

HapCompass

HaploMerger

SWiPS

GigAssembler

MSR-CA



Ray

Celera

MIRA

ALLPATHS-LGSGA

CurtainMetassembler

Phusion

ABySS

Amos

Arapan

CLC

Cortex

DNAnexus

DNA Dragon EULER

EdenaForge

GeneiousIDBA

Newbler

PRICE

PADENA

PASHAPhrap

TIGR

Sequencher

SeqMan NGen

SHARCGS

SOPRA

SSAKE

SPAdesTaipan

VCAKE

Velvet

Arachne

PCAP

GAM

MonumentAtlas

ABBA

Anchor

ATAC

Contrail

DecGPU GenoMinerLasergene

PE-Assembler

Pipeline Pilot

QSRA

SeqPrep

SHORTY

fermiTelescoper

QuastSCARPA Hapsembler

HapCompass

HaploMerger

SWiPS

GigAssembler

MSR-CA

Which is the best?


Comparing assemblers

✤ Can't fairly compare two assemblers if they:

However, it is not always straightforward to compare two tools if they were used on different species or on different datasets from the same species.



✤ produced assemblies from different species





✤ assembled same species, but used sequence data from different NGS platforms






✤ used same NGS platform but different sequence libraries






✤ used same NGS platform but different sequence libraries

✤ Even using different options for the same assembler may produce very different assemblies!


A genome assembly competition

That's where the Assemblathon came in.

An attempt to standardize some aspects of the genome assembly process

Genome assembly contests

Others have been trying to do the same thing. E.g. GAGE, and dnGASP.

✤ 2010–2011

✤ Used synthetic data

✤ Small genome (~100 Mbp)

✤ We knew the answer!

Assemblathon 1

It is easier to judge a tool when you know what the final answer should look like. However, many people that work on developing assemblers would prefer to work with real data...

Here we go again

...which is where Assemblathon 2 came in.

Type of data Number of genomes

Size of genomes

Do we know the answer?

Assemblathon 1 Synthetic 1 Small ✓

Assemblathon 2 Real 3 Large ✗

Melopsittacus undulatus

Boa constrictor constrictorMaylandia zebraA budgie, a cichlid fish from Lake Mawali, and a reptile.

Bird

SnakeFishLet's simplify the names for the rest of the talk.

Why these three species?

There is no special reason why these species were used. People had a need to sequence the genomes, and some companies were willing to donate sequences.

Why these three species?

Because they were there

There is no special reason why these species were used. People had a need to sequence the genomes, and some companies were willing to donate sequences.

Species Estimated genome size Illumina Roche 454 PacBio

Bird 1.2 Gbp 285x(14 libraries)

16x(3 libraries)

10x(2 libraries)

Fish 1.0 Gbp 192x(8 libraries)

Snake 1.6 Gbp 125x(4 libraries)

Assemble this!

Lots of sequence data was provided for the bird. Mate pair and read pair libraries were available for all Illumina datasets.

Who took part?

Lots of teams took part. Not just from the big sequencing/genome centers.

Who took part?

21 teams43 assemblies

52,013,623,777 bp of sequence

Lots of teams took part. Not just from the big sequencing/genome centers.

Species Competitive entries

Evaluation entries

Bird 12 3

Fish 10 6

Snake 12 0

Entries

There were evaluation entries (not eligible to be declared the winner) allowed in addition to competition entries (only 1 per team).

Goals

✤ Assess 'quality' of assemblies

Goals


✤ Define quality!

Goals


✤ Define quality!

✤ Produce ranking of assemblies for each species

Goals


✤ Define quality!

✤ Produce ranking of assemblies for each species

✤ Produce ranking of assemblers across species?

Who did what?

Person/group Jobs

Me, Ian, and Joseph Fass Perform various analyses of all assemblies

David Schwarz et al. Produce & evaluate optical maps

Jay Shendure et al. Produce Fosmid sequences (bird & snake only)

Martin Hunt & Thomas Otto Performed REAPR analysis

Dent Earl & Benedict Paten Help with meta-analysis of final rankings

flickr.com/photos/jamescridland/613445810

Hard to get agreement on how best to interpret the results. Some analyses and interpretations in the Assemblathon 2 paper end up being compromises.

91 co-authors!

flickr.com/photos/jamescridland/613445810

Hard to get agreement on how best to interpret the results. Some analyses and interpretations in the Assemblathon 2 paper end up being compromises.

Results!

Lots of results!

A screen grab of my master spreadsheet that contains all of the numerical results.

102 different metrics!

10 key metrics

We focused on 10 of 102 metrics that we thought were a) useful and b) captured different aspects of an assembly's quality.

Key Metric Description

1 NG50 scaffold length

2 NG50 contig length

3 Amount of assembly in 'gene-sized' scaffolds

4 Number of 'core genes' present

5 Fosmid coverage

6 Fosmid validity

7 Short-range scaffold accuracy

8 Optical map: level 1

9 Optical map: levels 1–3

10 REAPR summary score

The 10 key metrics.

1) Scaffold NG50 lengths

✤ Can calculate NG50 length for each assembly

✤ But also calculate NG60, NG70 etc.

✤ Plot all results as a graph

An N50 (or NG50) value on its own doesn't tell you that much. Ideally you should always be aware of the total assembly size and the distribution of lengths when comparing assemblies. You can do this by not only calculating NG50, but NG1..NG100. NG1 would be the length of scaffold that captures 1% of the estimated genome size (when summing scaffolds from longest to shortest).

1) Scaffold NG50 lengths

Scaffold length is on a log axis and team identifiers are shown in the legend.

The black dashed line shows the NG50 value, but the point where each series starts on the left shows the lengths of the longest scaffolds. Also, if the NG100 value is greater than zero, then that assembly is bigger than the known/estimated genome size.

2) Contig vs scaffold NG50

We did the same thing for contig NG50 as well as scaffold NG50. The two measure are sometimes, but not always, correlated. The two highlighted data points show outliers for bird assemblies, reflecting assemblies that are good at making long contigs *or* good at making long scaffolds, but not both.

3) Gene-sized scaffolds

It is great to have long scaffolds, but maybe for many questions that you might be interested in (e.g. studying codon usage bias), you only need to have scaffolds that have a good chance of capturing a full-length gene.


✤ Do assemblers get a little too excited by length?




✤ How long is 'long enough' for a scaffold?





✤ What if you just wanted to find genes?





✤ What if you just wanted to find genes?

✤ Average vertebrate gene = ~25 Kbp



The blue line shows the percentage of the estimated genome size that is present in scaffolds of 25 Kbp or longer. Most assemblies, even if they have a much shorter *average* scaffold length, may contain many scaffolds that are long enough to contain a single gene.

4) Core genes

A previously developed tool (CEGMA) was used to see how many 'core genes' (extremely, highly conserved) are present in each assembly. Note that CEGMA finds genes where a full-length (or nearly full-length) gene is present within a single scaffold. Many core genes might be present, but split across scaffolds.

4) Core genes

✤ Used CEGMA tool


4) Core genes

✤ Used CEGMA tool

✤ CEGMA = set of 458 'Core Eukaryotic Genes' (CEGs)


4) Core genes

✤ Used CEGMA tool

✤ CEGMA = set of 458 'Core Eukaryotic Genes' (CEGs)

✤ How many full-length CEGs are in each assembly?


4) Core genes

These results show the number of CEGMA genes that were present in any one assembly as a percentage of all possible CEGMA genes (i.e. those present across all assemblies for each species).

4) Core genes

Core genes (out of 458)Core genes (out of 458)

Species Best individual assembly

Across all assemblies

Bird 420 442

Fish 436 455

Snake 438 454

In the three species, most of the core genes were present across all assemblies, but individual assemblies typically lacked several core genes.

ABYSS MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDBCM MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDCRACS MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDCURT MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDGAM MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVMLFYEVRKIKNVEDMERAC MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDPHUS MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDRAY MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDSGA MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVEDSYMB MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVMLFYEVRKIKNVEDSOAP MNTVLTRANSLFAFSLSVMAALTFGCFITTAFKERTVPVSIAVSRVML-------KNVED ************************************************ *****

ABYSS FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------BCM FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------CRACS FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------CURT FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------GAM FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNNLPHTHIMERAC FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------PHUS FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------RAY FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------SGA FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------SYMB FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------SOAP FTGPGERSDLGIITFNISANILYYKHSSLFPNIFDWNVKQLFLYLSAEYSTKNN------ ******************************************************

ABYSS ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGBCM ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGCRACS ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGCURT ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGGAM YGHALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLK------------------MERAC ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGPHUS ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGRAY ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGSGA ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGSYMB ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAGSOAP ---ALNQVVLWDKIILRGDDPNLLLKDMKSKYFFFDDGNGLKGNRNVTLTLSWNVVPNAG ***************************************

ABYSS ILPLVTGAGHISVPFPDTYKMTKSYBCM ILPLVTGAGHISVPFPDTYKMTKSYCRACS ILPLVTGAGHISVPFPDTYKMTKSYCURT ILPLVTGAGHISVPFPDTYKMTKSYGAM -------------------------

4) Core genes

Example of one core gene predicted in bird assemblies. CEGMA gene predictions are available as supplementary material with the paper.

5) Fosmid coverage

5) Fosmid coverage

✤ Had to first assemble Fosmids

5) Fosmid coverage


✤ Looked at repeat content & coverage across Fosmids

5) Fosmid coverage



✤ Aligned assembly scaffolds to Fosmids

5) Fosmid coverage



✤ Aligned assembly scaffolds to Fosmids

✤ Only had Fosmids for bird and snake

5) Fosmid coverage

Looked at coverage of Fosmids by aligning some of the input reads to the Fosmids. Occasionally we see small gaps in coverage. These represent Fosmid assembly errors or regions of the genome not captured by the input read data. We aligned scaffolds to the Fosmids and see that most assemblies contain most of the Fosmids, but repeats complicate the picture.

5) Fosmid coverage

Most of the Fosmid sequences were used as 'Trusted' reference sequences by which to assess the assemblies.

5) Fosmid coverage

✤ Only used regions of Fosmids that were validated by one or more assemblies


5) Fosmid coverage

✤ Only used regions of Fosmids that were validated by one or more assemblies

✤ Validated Fosmid Regions (VFRs)

✤ 99% of bird Fosmids

✤ 89% of snake Fosmids


5 & 6) Coverage & Validity

COMPASS tool by Joe Fass

The COMPASS tool compared the Validated Fosmid Regions (VFRs) to the scaffolds to calculate four measures, two of which ('coverage' and 'validity') were used as key metrics.

5 & 6) Coverage & Validity

Some COMPASS results from the bird assemblies. Multiplicity is high when the assemblies were large (compared to the estimated genome size).

Validated Fosmid Region

7) Short-range scaffold accuracy

We also used the VFRs in another way. We took pairs of 100 nt 'tag' sequences from either end of consecutive 1000 nt fragments across all VFR sequences.


100 nt 100 nt


We also used the VFRs in another way. We took pairs of 100 nt 'tag' sequences from either end of consecutive 1000 nt fragments across all VFR sequences.



The start coordinates of each pair of tag sequences should map 900 nt apart in the assemblies and hopefully both tags map only to the same scaffold. We combined both of these into one 'summary score' metric.


Map pairs of 'tag' sequences to assembly scaffolds






How many map as a pair to one scaffold?





How many map as a pair to one scaffold?

How many map at expected distance apart (900 ± 2 bp)?



Expected distance apart (900 bp)Expected distance apart (900 bp)

Species Shortest Longest

Bird 702 bp 41,949 bp

Snake 673 bp 46,813 bp

Most pairs of tags mapped to the same scaffold, and at the expected distance apart, but there were a few notable exceptions.


The red line indicates the theoretical maximum summary score that could be achieved.

8 & 9) Optical maps

For optical map analysis, scaffolds had to be a certain minimum length *and* possess enough restriction enzyme sites.

8 & 9) Optical maps

✤ Stretch out DNA


8 & 9) Optical maps

✤ Stretch out DNA

✤ Cut with restriction enzymes


8 & 9) Optical maps

✤ Stretch out DNA


✤ Note lengths of fragments


8 & 9) Optical maps

✤ Stretch out DNA



✤ Compare to in silico digest of scaffolds


8 & 9) Optical maps

✤ Stretch out DNA



✤ Compare to in silico digest of scaffolds

✤ Not all scaffolds suitable for analysis


8 & 9) Optical maps

Image from University of Wisconsin-Madison

An example of an optical map. After cutting, each DNA fragment is measured to estimate its length. Optical map results were divided into three categories (levels 1–3).

8 & 9) Optical maps

White bars: total length of scaffolds that were suitable for optical map analysis. Dark blue: global alignments of scaffolds to maps (these are the best quality). Light blue: global alignments with more permissive thresholds. Orange bars: local alignments. We used level 1 (dark blue) as one key metric and levels 1+2+3 as a second key metric. The MLK assembly is good, *relatively* speaking (high percentage of suitable scaffolds are in level 1 category), but we record scores on an absolute basis (MERAC highest for level 1, SOAP highest for levels 1+2+3).

8 & 9) Optical maps

Fish optical map results were much worse than in bird, with very few assemblies having scaffolds with 'level 1' global alignments to the optical map. SGA had the most level 1 coverage, but a much lower amount of sequence that was alignable at any level (1, 2, or 3).

8 & 9) Optical maps

Snake optical map results were intermediate compared to bird and fish.

10) REAPR summary score

REAPR is a tool that aligns input reads to scaffolds and looks for base errors and regions which might represent misassemblies (where scaffolds should ideally be split in two). These two facets are combined into one summary score.

What does this all mean?

102 metricsper assembly

10 key metrics

1 finalranking

Using the 10 key metrics, we combined the results to produce a single score for each assembly by which to rank them.

Assembly Number of core genes Rank Z-score

CRACS 438 1 +0.68

SYMB 436 2 +0.59

PHUS 435 3 +0.54

BCM 434 4 +0.49

SGA 433 5 +0.44

MERAC 430 6 +0.30

ABYSS 429 7 +0.25

SOAP 428 8 +0.21

RAY 422 9 –0.08

GAM 415 10 –0.41

CURT 360 11 –3.02

Although we did take an average rank from the 10 individual rankings, we preferred to use a Z-score approach. Each assembly was scored based on the total number of standard deviations from the average of each metric. This rewards/penalizes assemblies with very high/low scores in individual metrics. The above results are from the CEGMA metric in bird.

This graph shows the final rankings of bird assemblies based on their sum Z-scores. Assemblies in red are the evaluation entries. The error bars reflect what would be the highest and lowest sum Z-score if we had used any combination of 9 key metrics rather than 10. Note that the highest ranked bird assembly was an evaluation assembly by Baylor College of Medicine, their competitive entry ranked number 2.

In fish, BCM ranked 1st though the error bars suggest there is much variability. The lack of Fosmid data means that there is only 7 key metrics rather than 10.

Snake seemed to the only species that outwardly looked like one assembler outperformed all others (SGA, in this case). We will return to this issue. Note that there were no evaluation entries for snake.

Another way of looking at all of this data is to plot the Z-scores for each metric as a heat map (red = higher Z-scores).

A parallel coordinates plot is another way of trying to show all of the information at once.

What does this all mean?

No really, what does this all mean?

Still a bit hard to make sense of the overall rankings. What are the main findings from our paper?

Some conclusions

✤ Very hard to find assemblers that performed well across all 10 key metrics

✤ Assemblers that perform well in one species, do not always perform as well in another

✤ Bird & snake assemblies appear better than fish

✤ No real 'winner' for bird and fish

SGA — best assembler for snake?

Even if we had happened to use 9 key metrics rather than 10, and even if we threw out the metric where SGA performed the best, it would still probably rank 1st. So is that the end of the story?

Description Rank of snake SGA assembly

NG50 scaffold length 2

NG50 contig length 5

Amount of assembly in 'gene-sized' scaffolds 7

Number of 'core genes' present 5

Fosmid coverage 2

Fosmid validity 2

Short-range scaffold accuracy 3

Optical map: level 1 2

Optical map: levels 1–3 1

REAPR summary score 2

SGA only ranked 1st in one of the ten key metrics and ranked 7th in another. So it is a good assembler *on average*. But if one of these metrics was highly important to you, you may want to use an assembler that ranked higher in that metric.

Best assembler across species?

Not all assemblers were used for all species, but many teams submitted entries for 2 or 3 of the species. In theory, if a team submitted an entry for all species, and if their assembler ranked 1st in all metrics, they could achieve 1st place twenty-seven times (10 + 10 + 7 for fish). So what was the best assembler across species, as judged by total number of 1st places? It is BCM. But Ray comes 4th with three 1st places.

Best assembler across species?

Assembler Number of 1st places (out of 27)

BCM 5

Meraculous 4

Symbiose 4

Ray 3

Excluding evaluation entries

Not all assemblers were used for all species, but many teams submitted entries for 2 or 3 of the species. In theory, if a team submitted an entry for all species, and if their assembler ranked 1st in all metrics, they could achieve 1st place twenty-seven times (10 + 10 + 7 for fish). So what was the best assembler across species, as judged by total number of 1st places? It is BCM. But Ray comes 4th with three 1st places.

Ray performance

Species Final ranking

Bird 7

Fish 7

Snake 9

However, Ray ranks much lower when looking at its performance across all key metrics. So some assemblers do very well in specific measures, and not so well in others and other assemblers do moderately well across lots of metrics (e.g. SGA).

We found it interesting that the best bird assembly was the evaluation entry by Baylor College of Medicine. What is different about this entry compared to their competitive entry?

Assembler Final rank

NGS data used in

assembly

CoverageZ-score

ValidityZ-score

NG50 Contig Z-score

BCM - evaluation 1 Illumina +

454 +2.0 +1.4 +1.5

BCM - competitive 2 Illumina +

454 + PacBio –0.3 –0.8 +2.7

BCM bird assemblies

The only difference is that the BCM competitive entry included PacBio data, and somehow this led to the paradoxical situation where including more sequenced produced a lower measures for coverage and validity (from the Fosmids), though one key metric (NG50 contig length) did improve.

BCM evaluation scaffoldNNNNNNNNNNNNNNNNNNN

BCM used PacBio data to help fill in the gaps in their scaffolds.


BCM competition scaffoldNNNNNNNNNNNNNNNNNNN



BCM competition scaffoldNNNNNNNNNNNNNNNNNNN

PacBio sequence



BCM competition scaffoldCGTCGNNATCNNGGTTACG

Errors in the PacBio sequence were penalized by the choice of alignment program used to align Fosmids to scaffolds.


BCM competition scaffoldCGTCGNNATCNNGGTTACG

Mismatches from PacBio sequence penalized alignment score more than matching unknown bases

Errors in the PacBio sequence were penalized by the choice of alignment program used to align Fosmids to scaffolds.

The choice of one command-line option,used by one tool in the calculation of one key metric...

...probably made enough difference to dropthe PacBio-containing assembly to 2nd place.

This was actually down to the use of a single command-line option to the lastz alignment program. If we had not chosen this option, the PacBio-containing entry would have probably ranked 1st among all bird assemblies.

Other conclusions

✤ Different metrics tell different stories

✤ Heterozygosity was a big issue for bird & fish assemblies

✤ Final rankings very sensitive to changes in metrics

✤ N50 is a semi-useful predictor of assembly quality

The last point may disappoint some. Despite looking at many different metrics, N50 scaffold length still does a reasonable job of predicting overall quality. However...

...the outliers in this relationship should be noted. The highlighted bird assembly had the second highest scaffold N50 length, but ranked 6th among bird assemblies.

Inter-specific differences matter

Biological differences may account for differences in assembler performance between different species. However, the input data for each species was also very difference and this may play a role as well (some assemblers perform prefer certain short-insert sizes).


✤ The three species have genomes with different properties

✤ repeats

✤ heterozygosity



✤ The three species have genomes with different properties

✤ repeats

✤ heterozygosity

✤ The three genomes had very different NGS data sets

✤ Only bird had PacBio & 454 data

✤ Different insert sizes in short-insert libraries


The Big Conclusion

The Big Conclusion

"You can't always get what you want"Sir Michael Jagger, 1969

What comes next?

What comes next?

There may be an Assemblathon 3. This will be decided at the next Genome 10K workshop (in April, 2013).

What comes next?

3?

There may be an Assemblathon 3. This will be decided at the next Genome 10K workshop (in April, 2013).

A wish list for Assemblathon 3

If there is to be an Assemblathon 3, here are some things that we have learned from Assemblathon 2.


✤ Only have 1 species




✤ Teams have to 'buy' resources using virtual budgets





✤ Factor in CPU time/cost?






✤ Agree on metrics before evaluating assemblies!







✤ Encourage experimental assemblies








✤ Use new FASTG genome assembly file format








✤ Use new FASTG genome assembly file format

✤ Get someone else to write the paper!


~ fin ~

Genome assembly: then and now — v1.0

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