Genome Assembly: the art of trying to make one BIG thing from millions of very small things

Genome Assembly: the art of trying to make one BIG thing from millions of

very small things

Keith Bradnam

@kbradnam

Image from Wellcome Trust

Genome Assembly: the art of trying to make one BIG thing from millions of

very small things

Keith Bradnam

@kbradnam

Image from Wellcome Trust

This was a talk given at UC Davis on 2015-01-28, presented to an audience of graduate students.

Author: Keith Bradnam, Genome Center, UC Davis This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

flickr.com/incrediblehow/

Overview

1. What is genome assembly?

2. Why is it difficult?

3. Why is it important?

4. How do we know if an assembly is any good?

What is genome assembly?

A genome assembly is an attempt to accurately represent an entire genome sequence from a

large set of very short DNA sequences.

Using a piece of bioinformatics software is just like running an experiment. Just because you get an answer, it doesn't mean it will be the right answer. You should always be prepared to tweak some parameters and re-run the experiment.

The ideal goal would be to end up with complete sequences for each chromosome at each level of ploidy. E.g. diploid genomes would be assembled as two sets of genome sequences.

'Large' is a relative term. We would expect that advances in sequencing technology would mean that the number of sequences needed to assemble a genome is only ever going to decrease.

'Short' is also a relative term. As technology improves, we expect to see our input sequences get longer and longer until the steps of sequencing and assembly essentially merge into one process.

It's a bit like trying to do the hardest jigsaw puzzle you can imagine!

This is a jigsaw that I did for the benefit of your education! There are lots of analogies that can be made between assembling genomes, and assembling jigsaws.

Sometimes we assemble regions of jigsaws that are locally accurate, but globally misplaced (the top region circled in red). Sometimes we also assemble regions and leave them to one side as we don't know where they should go. Many 'finished' genome assemblies include sets of 'unanchored' sequences that are not positioned on any chromosome.

Let's keep working on our jigsaw.

The hardest parts of a jigsaw tend to be repetitive regions (skies, sea, forests etc.). The same is true for genome assemblies.

Sometimes we can use information to pair together two different completed sections of a jigsaw. In this case, we can use our understanding of what a bridge looks like to give us an approximate spacing between the two completed sections at the top of this puzzle. We do similar things with genome assemblies and also end up inserting approximately sized gaps between regions of sequence.

Is this good enough?

For a jigsaw, we would never ever call this 'finished', but for a genome assembly this would represent an almost perfect sequence! All of the main details are present, you can identify what the picture is showing (San Francisco), the edges are detailed enough that we can accurately calculate the size of the jigsaw, and the parts that are missing are mostly minor details.

Jigsaws often end up with a few missing pieces meaning that it is impossible to complete the puzzle. Genome assemblies also end up with missing pieces because they were never in the input set of sequences to begin with. This is because not all sequencing technologies capture all locations in a genome.

With the exception of bacterial genomes, we never reach this point with genome assembly. All published eukaryotic genomes are incomplete and contain errors. Maybe yeast (Saccharomyces cerevisiae) and worm (Caenorhabditis elegans) are the best examples we have a of near-complete reference genome for a eukaryotic species.

Why is it difficult?

World's largest jigsaw puzzle

• Made by University of Economics of Ho Chi Minh City

• 551,232 pieces

• 15 x 23 meters

World's largest jigsaw puzzle

• Made by University of Economics of Ho Chi Minh City

• 551,232 pieces

• 15 x 23 meters

The world's largest jigsaw has nothing on the world's largest genome assembly…

World's largest assembled genome

• Lobolly pine (Pinus taeda)

• 22 Gbp genome!

• ~80% repetitive

• 64x coverage

from tulsalandscape.com

• 22 Gbp genome!

• ~80% repetitive

• 64x coverage

• 22 Gbp genome!

• ~80% repetitive

• 64x coverage

This gargantuan effort featured the work of many people at UC Davis, led by the efforts of David Neale's group.

What does 64x coverage mean?

Over 1.4 trillion bp of DNA were sequenced!

What does 64x coverage mean?

Over 1.4 trillion bp of DNA were sequenced!

I.e. they had to use 64x times as much input DNA as they ended up with in the final output. Imagine if baking a cake was like this, and you had to use 64x as many ingredients in order to make one cake.

Some genome assembly projects are done with >100x coverage.

Biological challenges for genome assembly

Problem Description

RepeatsMany plant and animal genomes mostly consist of

repetitive sequences, some of which are longer than length of sequencing reads.

Ploidy For many species, you have at least two copies of the genome present. Level of heterozygosity is important.

Lack of reference genome

Reference-assisted assembly is a much easier problem than de novo assembly. Even having genome from a

closely related species can help.

Biological challenges for genome assembly

Problem Description

RepeatsMany plant and animal genomes mostly consist of

repetitive sequences, some of which are longer than length of sequencing reads.

Ploidy For many species, you have at least two copies of the genome present. Level of heterozygosity is important.

Lack of reference genome

Reference-assisted assembly is a much easier problem than de novo assembly. Even having genome from a

closely related species can help.

Ploidy is often a much bigger problem for plant genomes. E.g. some wheat species are hexaploid. Genome assembly is sometimes performed on a genome for which we already have a reference (e.g. if you sequenced your own genome, you could align it to the human reference sequence). Otherwise, we are talking about de novo assembly which is much, much harder.

from amazon.com

Returning to the jigsaw analogy…every jigsaw puzzle comes with a picture of the puzzle on the box. This is a luxury not always available to genome assemblers.

When we are doing de novo assembly, it is a bit like doing a jigsaw without knowing what it will look like.

Even with de novo assembly, we may have a distant relative with a known genome sequence that can help with the assembly. A bit like assembling a jigsaw using a blurred picture as a guide.

Jigsaws tell you how many pieces are in the puzzle (and what the dimensions of the puzzle will be). We don't always know this for genome assembly. There are measures for determining how big a genome might be, but these methods can sometimes be misleading.

Other challenges for genome assembly

Problem Description

Cost In 2014 Illumina claimed the $1,000 genome barrier had been broken (if you first spend ~$10 million on hardware).

Library prep A critical, and often overlooked, step in the process.

Sequence diversity

Illumina, 454, Ion Torrent, PacBio, Oxford Nanopore: which mix of sequence data will you be using?

Hardware Some genome assemblers have very high CPU/RAM requirements. Might need specialized cluster.

Expertise Not always easy to even get assembly software installed, let alone understand how to run it properly.

Software There is a lot of choice out there.

Problem Description

Sequence diversity

The PRICE genome assembler has 52

command-line options!!!

The PRICE genome assembler has 52

command-line options!!!

This is probably not the most complex, nor the most simple, genome assembler that is out there. But how much time do you have to explore some of those 52 parameters that could affect the resulting genome assembly?

Problem Description

Sequence diversity

There are over 125 different tools available to help assemble a genome!

Not all of these are comprehensive genome assemblers, some are tools to help with specific aspects of the assembly process, or to help evaluate genome assemblies etc.

Still, this represents a bewildering amount of choice.

These six assembly tools were published in one month in 2014!

Before you assemble…

• You should remove adapter contamination

• You should remove sequence contamination

• You should trim sequences for low quality regions

Before you assemble…

• You should remove adapter contamination

• You should remove sequence contamination

• You should trim sequences for low quality regions

After we have generated the raw sequence data, we still must run a few basic steps to clean up our data prior to assembly. How straightforward are these steps?

Tools for removing adapter contamination from sequences

There are at least 34 different tools!

One of these tools has 27 different command-line options

Tools for removing adapter contamination from sequences

There are at least 34 different tools!

One of these tools has 27 different command-line options

Even the first step of removing adapter contamination is something for which you could spend a lot of time researching different software choices.

Why is it important?

Saccharomyces cerevisiae

• 12 Mbp genome

• Published in 1997

• First eukaryotic genome sequence

Saccharomyces cerevisiae

• 12 Mbp genome

• First eukaryotic genome sequenceNot the first published genome — there were several bacterial genomes sequenced in the preceding couple of years — but this was the first eukaryotic genome sequence. Furthermore, this genome sequence has undergone continual improvements and corrections since publication (the last set of changes were in 2011).

Caernorhabditis elegans

• ~100 Mbp genome

• First animal genome sequence

Arabidopsis thaliana• First plant genome sequence

• Size?

• 2000 = 125 Mbp

• 2007 = 157 Mbp

• 2012 = 135 Mbp

Arabidopsis thaliana• First plant genome sequence

• Size?

• 2000 = 125 Mbp

• 2007 = 157 Mbp

• 2012 = 135 MbpAs alluded to earlier, we don't always know for sure how big (or small) a genome is. The Arabidopsis genome size has been corrected upwards and downwards since publication. The amount of sequenced information as of today is about 119 Mbp. And this is for the best understood plant genome that we know about it!

Homo sapiens• ~3 Gbp genome

• Finished?

• 'working draft' announced in 2000

• 'working draft' published in 2001

• completion announced in 2003

• complete sequence published in 2004

Homo sapiens• ~3 Gbp genome

• Finished?

• 'working draft' announced in 2000

• 'working draft' published in 2001

• completion announced in 2003

• complete sequence published in 2004The human genome has also undergone improvements since the (many) announcements regarding its completion (or near completion). There are only a small number of species for which there is dedicated group of people who seek to continually improve the genome sequence and get closer to 'the truth'.

The 100,000 genomes project

There are lots of ongoing genome sequencing projects

i5k Insect and other Arthropod Genome Sequencing Initiative

The 100,000 genomes project

There are lots of ongoing genome sequencing projects

i5k Insect and other Arthropod Genome Sequencing Initiative

Bigger numbers must be better, right? Some projects sequence genomes to align back to a reference to look for the differences, others seek to characterize genomes for which we have very little genomic information. The 100,000 genomes project in England heralds the start of the mass sequencing of patients to understand disease.

We no longer have one genome per species

• We have genome sequences representing different strains and varieties of a species

• We have multiple genomes from different tissues of the same individual (e.g. cancer genomes)

• We potentially will have genomes from different time points or life stages of an individual

We no longer have one genome per species

• We have genome sequences representing different strains and varieties of a species

• We have multiple genomes from different tissues of the same individual (e.g. cancer genomes)

• We potentially will have genomes from different time points or life stages of an individual

Imagine having your genome sequenced at birth from several different tissues and getting 'genome health checks' throughout your life.

There is no point sequencing so many genomes if we can't accurately assemble them!

Sequencing genomes is relatively easy. Putting that information together in a meaningful way so as to make it useful to others…that's not so easy.

Bad genome assemblies #1

Length of 10 shortest sequences: 100, 100, 99, 88, 87, 76, 73, 63, 12, and 3 bp!

The average vertebrate gene is about 25,000 bp

Length of 10 shortest sequences: 100, 100, 99, 88, 87, 76, 73, 63, 12, and 3 bp!

The average vertebrate gene is about 25,000 bp

Everyone wants long sequences in a genome assembly. This may not always matter, but in most cases they should hopefully be long enough to contain at least one gene.

These data are from a vertebrate genome sequence that someone asked me to look at. Over half of the genome assembly was represented by sequences less than 150 bp! This is not much use to anyone.

Ns = 90.6% !!!

Genome sequences usually contain

unknown bases (Ns)

Ns = 90.6% !!!

Genome sequences usually contain

unknown bases (Ns)

From another assembly that I was asked to look at. Even the 9% of the genome which wasn't an 'N' was split into tiny little fragments. Completely unusable information.

Has anyone compared different assemblers to work out which is the best?

I was wondering whether you would ask this…

A genome assembly competition

This was a genome assembly assessment exercise that I was involved with.

@assemblathon

@assemblathonIt spawned a sequel.

Published in Gigascience, 2013

3 species 21 teams

43 assemblies 52 Gbp of sequence!

• Assess 'quality' of genome assemblies

• Identify the best assemblers

• First need to define quality!

Who makes the best pizza in Davis?

An easy question to ask, but maybe not as straightforward as it seems…

Freshest?

Cheapest?

Biggest?

Gluten free?

Healthiest

Choice of toppings?

Delivery time?

Tastiest?

Freshest?

Cheapest?

Biggest?

Gluten free?

Healthiest

Choice of toppings?

Delivery time?

Tastiest?

'Best' is subjective. If you are intolerant to gluten, then the best pizza place will be the one that makes gluten-free pizzas.

Freshest?

Cheapest?

Biggest?

Gluten free?

Healthiest

Choice of toppings?

Delivery time?

Tastiest?

Freshest?

Cheapest?

Biggest?

Gluten free?

Healthiest

Choice of toppings?

Delivery time?

Tastiest?

Even if you focus on who makes the best 'tasting' pizzas, this is still very subjective.

Who makes the best genome assembly?

Image from flickr.com/dullhunk/

But surely this is not such a subjective topic when it comes to genome assembly?

Longest contigs?

Fewest errors?

Lowest CPU demands?Best deals with repeats?

Contains most genes?

Fastest?

Best resolves heterozygosity?

Easiest to install?

Longest scaffolds?

Longest contigs?

Fewest errors?

Lowest CPU demands?Best deals with repeats?

Contains most genes?

Fastest?

Best resolves heterozygosity?

Easiest to install?

Longest scaffolds?

It is less subjective, but there are still many different ways we can think of when trying to determine what makes a good genome assembly.

And the winner is…

• No winner!

• Some assemblers seemed to work well for one species, but not for other species

• Some assemblies were good, as measured by one metric, but not when measured by others

And the winner is…

• No winner!

• Some assemblers seemed to work well for one species, but not for other species

• Some assemblies were good, as measured by one metric, but not when measured by others

This result was disappointing to many who was hoping that we would provide a resounding endorsement for assembler 'X'.

How do we know if anassembly is any good?

The fundamental input to a genome assembly is a set of sequencing reads.

Technology Date Typical read lengths

Sanger ~1970–2000 750–1,000 bp

Solexa/Illumina ~2005 ~25 bp

Illumina ~2014 ~150–250 bp

Pacific Biosciences ~2014 10–15 Kbp

Oxford Nanopore ~2014 5–??? Kbp

Technology Date Typical read lengths

Sanger ~1970–2000 750–1,000 bp

Solexa/Illumina ~2005 ~25 bp

Illumina ~2014 ~150–250 bp

Pacific Biosciences ~2014 10–15 Kbp

Oxford Nanopore ~2014 5–??? Kbp

Different technologies produce reads with very different length distributions, and these technologies also increase the length of reads over time. Perhaps more importantly, different technologies have different error profiles (where errors occur in reads and types of error).

Read pair

Insert size is known (approximately)

Read pair

Insert size is known (approximately)

Typically, we work with pairs of reads separated by a short distance (< 1,000 bp) or even overlapping. The insert size is not exact but can be modeled by a distribution of sizes.

Mate pair (jumping pair)

Much larger insert size

Mate pair (jumping pair)

Much larger insert size

Mate pairs are produced using a different preparation method and can be separated by several thousand bp. These become very useful in genome assembly.

Should be able to make one contiguous sequence from overlapping paired reads

Contig

Should be able to make one contiguous sequence from overlapping paired reads

ContigFor some sequencing technologies with long reads, you can simply see if there are enough overlapping reads such that you can form a contiguous sequence, or contig. For short read technologies such as Illumina, different mathematical approaches are used to form contigs (e.g. De Bruijn graph approaches).

Use mate pair information to link contigs as part of a scaffolding process

Scaffold

ScaffoldHopefully, you will have some mate pairs where one read from the pair matches one contig, and the other matches another contig. You can then create a scaffold sequence which spans the two contigs.

Scaffold

NNNNNNNNNNNNNN

Scaffold

NNNNNNNNNNNNNN

The unknown region between contigs is replaced with Ns to represent unknown bases. The length of these regions are sometimes approximations.

Making contigs is a different process to making scaffolds

Some assemblers do a better job at making contigs than they do at combining those contigs into scaffolds. Sometimes you can use different tools to do each step.

Assembly size = sum length of scaffolds

209 Mbp

Assembly size = sum length of scaffolds

209 Mbp

Let's consider a fictional assembly with a few scaffolds and contigs. The first thing we calculate is the assembly size. This is simply the sum length of all sequences included in the assembly.

Mean scaffold length is rarely used as a metric

Most genome assemblies contain a lot of very short contigs

Mean scaffold length is rarely used as a metric

Most genome assemblies contain a lot of very short contigs

At one extreme, an assembly could include every read that wasn't included in a contig. More likely, you will end up with some very short contigs which may not be useful. Contigs/scaffolds below a user-defined length threshold are often excluded from assemblies. All of these short sequences lower the mean length.

N50 length

The length of the sequence which takes the sum length of all sequences past 50% of the total assembly size

This is the most widely-used metric to assess genome assembly quality…sometimes it is the only metric.

N50 length

The length of the sequence which takes the sum length of all sequences past 50% of the total assembly size

This is the most widely-used metric to assess genome assembly quality…sometimes it is the only metric.

This was first described in the human genome paper. It has since been mentioned in just about every paper that has ever described a new genome sequence.

Calculating N50

Assembly size = 209 Mbp

332222

Calculating N50

332222

It is sometimes easier to see how N50 is calculated by showing an example. Let's start with the longest scaffold and add the lengths to a running total. We want to stop when we have seen >50% of the total assembly size (i.e. >104.5 Mbp).

Calculating N50

332222

Running total = 50 Mbp

Calculating N50

332222

Calculating N50

332222

Calculating N50

332222

N50 length = 35 Mbp

Mean length = 16 Mbp

Calculating N50

332222

N50 length = 35 Mbp

Mean length = 16 MbpAfter looking at three scaffolds we now know what the N50 scaffold length is This will always be much higher than the mean length.

Different assembly of the same genome

N50 length = 40 Mbp

Different assembly of the same genome

N50 length = 40 MbpLet's assume we tweaked the parameters of our assembly software to exclude the shortest scaffolds. This makes a smaller assembly but increases the N50 length. This means that it is possible to boost N50 simply by throwing away sequences.

NG50 length

Like N50, but rather than use assembly size in the calculation, use known (or estimated) genome size

NG50 length

Like N50, but rather than use assembly size in the calculation, use known (or estimated) genome size

In the Assemblathon contests, we used a new measure which enables a fairer comparison between different assemblies (of the same genome).

N50 length = 35 MbpAssembly size = 209 Mbp Assembly size = 185 Mbp

N50 length = 40 Mbp

Assume genome size is 240 Mbp

NG50 length = 35 Mbp NG50 length = 35 Mbp

N50 length = 35 MbpAssembly size = 209 Mbp Assembly size = 185 Mbp

N50 length = 40 Mbp

Assume genome size is 240 Mbp

NG50 length = 35 Mbp NG50 length = 35 MbpIf we knew what the actual genome size was (e.g. 240 Mbp) we can calculate the NG50 scaffold length and see that it is the same for both assemblies.

NG50 length

Use NG50 when making comparisons between genome assemblies because N50 can be biased

And be warned…some people obsess over N50!

Metrics

Metric Notes

Assembly size How does it compare to expected size?

Number of sequences How fragmented is your assembly?

N50 length (contigs & scaffolds)

Making contigs and making scaffolds are two different skills.

NG50 scaffold length Becoming more common to see this used.

Coverage How much of some reference sequence is present in your assembly?

Errors Errors in alignment of assembly to reference sequence or to input read data.

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

Metric Notes

Assembly size How does it compare to expected size?

Number of sequences How fragmented is your assembly?

N50 length (contigs & scaffolds)

Making contigs and making scaffolds are two different skills.

NG50 scaffold length Becoming more common to see this used.

Coverage How much of some reference sequence is present in your assembly?

Errors Errors in alignment of assembly to reference sequence or to input read data.

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

This is a very brief summary that lists just some of the ways in which you could describe your genome assembly.

Assembly size

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

A B C D E F G H I J K L M

Assemblathon 2 bird genome assemblies

Assembly size

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

A B C D E F G H I J K L M

Assemblathon 2 bird genome assemblies

In Assemblathon 2, one assembly of the bird genome (a parrot) was very, very small. Conversely, one assembly was almost twice the size of the estimated genome (~1.2 Gbp). Bigger is not always better when it comes to assembly size.

Using core genes

• All genomes perform some core functions (transcription, replication, translation etc.)

• Proteins involved tend to be highly conserved

• They should be present in every genome

This was an approach developed by our lab, originally to find a handful of genes in a newly sequenced genome which could be used to train a species-specific gene finder. We then adapted the technique to assess the gene space of a draft genome.

What is CEGMA?

• CEGMA (Core Eukaryotic Gene Mapping Approach)

• defines a set of 248 'Core Eukaryotic Genes' (CEGs)

• CEGs identified from genomes of: S. cerevisiae, S. pombe, A. thaliana, C. elegans, D. melanogaster, and H. sapiens

• How many full-length CEGs are present in an assembly?

What is CEGMA?

• CEGMA (Core Eukaryotic Gene Mapping Approach)

• defines a set of 248 'Core Eukaryotic Genes' (CEGs)

• CEGs identified from genomes of: S. cerevisiae, S. pombe, A. thaliana, C. elegans, D. melanogaster, and H. sapiens

• How many full-length CEGs are present in an assembly?We expect that these 248 genes to be present in all eukaryotes. CEGMA uses a combination of software tools to find these genes. The number of core genes present is assumed to reflect the proportion of all genes that are present in the assembly. Sometimes genes are split across contigs or scaffolds, CEGMA can find some of these and reports them as partial matches.

Here are N50 scaffold lengths and number of core genes present in a variety of genomes that I have looked at. There is a lot of variation. Some assemblies might give you longer sequences (higher N50 values), but this is no guarantee that those assemblies will contain more gene sequences. Likewise, assemblies with more gene sequences may not necessarily have longer sequences.

Should you use CEGMA?

• CEGMA is not easy to install

• It is old and somewhat out of date

• You could use other transcript/protein data sets instead of CEGMA

Should you use CEGMA?

• CEGMA is not easy to install

• It is old and somewhat out of date

• You could use other transcript/protein data sets instead of CEGMA

The principle of CEGMA could be used with a variety of different data. Maybe there are a small number of full-length mRNAs available for your species of interest. If you have multiple genome assemblies, you could simply see how they differ with respect to the presence of those genes.

Other tools for evaluating assemblies

FRCbam (2012) REAPR (2013) kPAL (2014)

Other tools for evaluating assemblies

FRCbam (2012) REAPR (2013) kPAL (2014)

Just as it seems increasingly popular to develop new genome assemblers, there is a growing demand (and supply) for tools to evaluate genome assemblies. Here are three recent ones.

Summary

In conclusion…• Genome assembly is not a solved problem

• If possible, try different genome assemblers

• Don't rely on one metric to assess quality

• Different metrics assess different aspects of quality

• Look at your genome assembly!

In conclusion…• Genome assembly is not a solved problem

• If possible, try different genome assemblers

• Don't rely on one metric to assess quality

• Different metrics assess different aspects of quality

• Look at your genome assembly!The last point is worth repeating. Is your genome 91% N? Do you have 3 bp sequences in your assembly? These are easy things to check

And remember, all genome assemblies should be thought of as 'work in progress'!

Further resources

http://acgt.me

@assemblathon

Further resources

http://acgt.me

@assemblathonI use the Assemblathon twitter account to tweet links to papers and resources that describe tools relevant to the field of genome assembly. Normally only a few tweets a day. My ACGT blog contains some posts relating to genome assembly, and I try to write these with more of a general audience in mind.

Genome Assembly: the art of trying to make one BIG thing from millions of very small things

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