august 2008bioinformatics tools for comparative genomics of vectors1 genomes daniel lawson...

August 2008 Bioinformatics Tools for Comparative Genomics of Vectors

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Genomes

Daniel LawsonVectorBase @ EBI


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Bioinformatic Tools for Comparative Genomics of Vectors

Tuesday 10:30 - 13:00 Genome sequencing 14:00 - 16:00 Genome annotation 16:30 - 18:00 Practical

Wednesday 9:30 - 10:00 Review genome annotation 10:30 - 13:00 Comparative genomics I 14:00 - 16:00 Comparative genomics II 16:30 - 18:00 Practical

Thursday 8:30 - 9:00 Review comparative genomics 9:00 - 10:00 VectorBase lecture


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Tuesday Genome sequencing

Strategies New technologies ‘Finished’ versus ‘Accessible’ genomes

Genome annotation Aims and realistic goals Genefinding Adding value to the gene predictions (descriptions, xref to other data)

Practical Artemis practical IGGI assignments

Wednesday Thursday


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Tuesday Wednesday

Comparative genomics Gene synteny (ortholog/paralog determination) Feedback to genome annotation Genetrees

Practical ACT practical IGGI assignments

Thursday


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Tuesday Wednesday Thursday

VectorBase


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Some terminology

GenomeHereditary information of an organism encoded in the DNA

ChromosomeSingle large macromolecule of DNA

ContigSingle contiguous section of DNA (a set of overlapping DNA segments derived from a single genetic source)

Supercontig (or scaffold)Ordered (and orientated) assembly of contigs

CloneDefined segment of DNA to be used for some purpose

Expressed sequence tag (EST)Short sequence of a transcribed spliced nucleotide sequence. Widely used to identify gene transcripts


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Genome size & complexity

Issues for consideration when sequencing:

DNA source (haplotype issues)

Genome size

Repeat content

Duplications and inversions

Increasing complexity

Viruses Bacteria Protozoa Mammals Plants

Issues for consideration when annotating:

Genome size

Repeat content

Splicing (cis and trans)

Genefinding resources (e.g. ESTs)

Likely comparator species

Inverterbrates


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Genome sequencing

Sequencing involves:

DNA fragmenting into small pieces

Sequence determination

Assembly into large contiguous sequences

Problems occur:

Cloning steps

Bacterial transformation and amplification

Sequencing chemistry (GC compressions, homopolymer runs)

Assembly of repetitive regions


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123456

78910111213

Sequencing a Genome


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Most genome sequences are not complete (not finished). Whole Genome Shotguns are referred to as having an X-fold coverage.

Low coverage (2x) is sufficient for gene discovery and some regulatory element identification.

High coverage (6x) is good for gene annotation. There will still be some missing genes.

Finished sequence has no gaps and is presumed to contain all genes.

Sequence coverage


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Sequence strategies

Sequencing technologies and strategies for genomic sequencing are constantly changing (improving).

Genomic clones in an ordered ‘clone by clone’ approach

Whole Genome Shotgun (WGS)

Traditional Sanger sequencing long reads

New short-read technologies

Hybrid WGS strategies

Reduced representation WGS using short-read technologies

Mixture of Solexa/454 reads and large-insert clone ends

» How big a piece of DNA can we assembly with confidence?


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Finished sequence

Chromosome

4-5x shotgun sequence& computer assembly

Overlapping BACs354,510

Tiling set 29,298

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Draft sequence

……..TAGCTGTGTACGATGATC……….

~15 contigs per clone

4-5x more shotgunGap closureProblem solvingi.e. “Finishing”

1 contig

less than one error in 10,000

Sequencing the Human Genome


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Sequencing data


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Output from an automated DNA sequencing machine used by the Human Genome Project to determine the complete human DNA sequence.


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Advanced Technologies1992-1999Sequencer: gel ABI 373/3772 or 3 runs per day, 36 to 96 samples100kb of information per machine per day 80 people

2000Sequencer: capillary ABI 3700 8 runs per day, 96 samples400kb of information per machine per day 40 people

2004Sequencer: capillary ABI 3730xl15/40 runs per day, 96 samples2 Mb of information per machine per day 10 people


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Sequencing by synthesis

Solexa/Illumina sequencing platform. DNA fragments ligated with adaptors and attached to a flow cell. Solid state amplification of the sequence (approx. 1000 fold) to form dense (less

than 1 micron) spots. Can achieve very high spot densities (up to 10 million clusters per cm2). Use labeled reversible terminators and laser excitation to determine

incorporated bases No cloning step improves representation of the genome No issues relating to homopolymer runs

Read lengths are short, approx. 30-40 bp Throughput is in the order of 100 Mb per run 8 samples per flow cell


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Solexa sequencing


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Pyrosequencing (454)

Nebulized or adapter-ligated DNA fragments are attached to beads PCR amplification step Each DNA-bound bead is placed into picotiterplate where the DNA synthesis will

take place Measure incorporation of a nucleotide using the light produced via the luciferase

enzyme (nucleotide incorporation releases pyrophosphate which is converted to ATP by ATP sulfurylase and consumed by luciferase producing light).

However, the signal strength for homopolymer stretches is linear only up to eight consecutive nucleotides after which the signal falls-off rapidly

Can deal with high GC composition No cloning step improves representation of the genomic sequence

Read lengths are approx. 100 bp Throughput in currently in the order of 20 million bp per run


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Comparison of sequencing technologies

Platform Read length (bp) Throughput (Mb) Cost (cent/base)

Sanger 500-800 ~ 0.1 1

454 ~100† 20† 0.1

Solexa ~30 ~100 0.0001

† New FLX upgrades should increase read lengths to 300bp and throughput to approximately 100 MB


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New technologies need new assembly algorithms

Just as the the transition from ‘clone by clone’ approach to Whole Genome Shotgun spawned new algorithms for sequence assembly the increasing use of short-read technologies requires new assembly algorithm developments

Genomics clones (30-300 kb) Phrap

Chromosomes/Genomes using Sanger long-read technologies (<1000 Mb) TIGR assembler ARACHNE JAZZ PCAP Phusion

Genomes using short-read technologies (< 10 Mb) Velvet SHARCGS AbySS


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Some terminology

N50

Measure of genome assembly quality. The N50 value is defined as a value for which 50% of the sequenced nucleotides are represented in groups with length greater than this value. Commonly two N50 values are quoted:

N50 contig length - a measure of how well individual reads assemble

N50 supercontig length - a measure of the general quality of the assembly

ContigSingle contiguous section of DNA (a set of overlapping DNA segments derived from a single genetic source)

Supercontig (or scaffold)Ordered (and orientated) assembly of contigs


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High-throughput technology leads to lower quality assembled genomes

Few genomes are completely sequenced. The completion and quality assurance needed for bacterial genomes is expensive, for larger eukaryotes even more so.

‘Finishing’ is the process by which a WGS shotgun assembly is completed (determine the sequence from any physical or sequence gaps) and further polished to remove ambiguities in the base calls and attempt to accurately reflect repetitive regions.

New sequencing technologies provide better representation of the genome (by removing cloning steps) and deeper coverage but are harder to assemble because of the short-read lengths.

People now talk about the ‘accessible’ genome for a species. This simply means the output from a reasonably deep sequence shotgun after assembly and limited (mainly computational) processing and improvements.

» Trade off between throughput and product quality.


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Sequence substrates

What is the product of a genome assembly?

What is starting material for a genome annotation?

Completed chromosome/genome

Genomic clones

Ordered supercontigs

Unordered supercontigs

Clustered EST sequences†


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Sequencing substrates

Chromosome

Genomic clones

Supercontigs

Contigs

Unordered supercontigs

Clustered ESTs


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Genome sequencing

Annotation quality depends on:

Fragmentation of assembly

Sequencing errors

Poorly represented sequence regions

Extensive simple repeat sequences

Large number of transposon sequences

Haplotype problems

Contaminants (e.g. bacterial or viral sequences)


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Genome annotation - the goal!

Defining important features of the genome sequence Labelling/describing features of the genome 'Adding value' to the genome sequence

Annotation is an ongoing process Annotation is almost always incomplete

Set of ‘Best guess’ gene predictions Short description of the putative function for each prediction Species/Group dependant catalog of other data types


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Annotation from a genome project prospective

Initial ‘first pass’ annotation run prior to publication Subsequent curation is a collaboration with the community Focused on protein-coding genes ‘Best guess’ predictions Little emphasis on transposons or pseudogenes Predicting gene loci is more important than getting 100%

correct gene structure predictions


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Manual v Automated annotation

Genes

Genes

Genes

Genes


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Manual v Automated: Pros & Cons

Speed

Accuracy

Reproducibility

*

*

*

Met’s & STOPs

Coverage


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Manual (re)annotation - Bridges……

“Paint the Bridge” Classic “First-pass” annotation strategy Annotate genomic regions by walking through the chromosome/clone/slice Comprehensive but slow to deal with problem genes

“Painting by numbers” Identify problem genes by scripts to generate lists for manual appraisal Responsive to community submissions but only as good as the list

generation script


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Automated (re)annotation: Ensembl

Ensembl builds the bridge anew with each gene build Responsive to new data Questions of prediction “churn”


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Manual v Automated approaches

Involvement of the community to improve gene prediction accuracy and functional calls

Moderated submissions - (WormBase, FlyBase) Integration time is dependent on database release cycles

Direct submissions - (VectorBase) Presentation via DAS onto genome browser Moderated before integration Integration time is relatively slow

Indirect submissions - (EMBL/GenBank/DDBJ) Submissions to public nucleotide databases will get reflected in the

genome annotation - eventually! Processed to protein databases and then integrated


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Genome annotation - building a pipeline

Genome sequence

Map repeats

Genefinding

Protein-coding genes

Map ESTs Map Peptides

nc-RNAs

Functional annotation

Release


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Genome annotation - predicting genes

Blessed predictions

Community submissionsManual annotations

Species-specific predictions Similarity predictions

Transcript based predictions ab initio gene predictions

Canonical predictions

(Genewise) (Genewise)

(SNAP) (Exonerate)

(Apollo) (Genewise, Exonerate, Apollo)

Protein family HMMs(Genewise)

ncRNA predictions(Rfam)


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Annotation

august 2008bioinformatics tools for comparative genomics of vectors1 genomes daniel lawson...

Documents

bioinformatics tools

review comparative genomics

genome slide

vectorbase slide

ebi slide

review genome annotation

vectorbase lecture slide

dna fragmenting