copyright © 2004 synamatix sdn bhd (538481-u) please dial: +44 870 22 333 65 pin: 444888 please...

Copyright © 2004 Synamatix sdn bhd (538481-U)

Please dial: +44 870 22 333 65

Pin: 444888 Please note that this is a UK number

Challenges of data management and analysis

from 2nd generation sequencing platforms

October 10 2006

Copyright © 2006 Synamatix sdn bhd (538481-U)To listen to the webcast please dial: +44 870 22 333 65 Pin: 444888 Please note that this is a UK number

PresentersPresenters

Colin Hercus25 mer mapping

Zayed Albertyn100 mer & polony mapping


IntroductionIntroduction


Personal Genome and Personalised Personal Genome and Personalised medicinemedicine

The Human Genome3 billion “pieces” – in every cell..

First Genome took 16yrs

Cost US$3 billion

Late 2006-2007New technologies

emerging..

Cost: US$1000Time: 1 day!


Variety of approaches towards ULCSVariety of approaches towards ULCS


MethodsMethods


Command line interface

CORE Database platform

SynaRex Bulk

SynaProbe Bulk

SynaSearch Bulk

SynaMer

SynaFrag

SXSequenceRefs

SXLRESearch

SXFuzzyPatternSearch

Sxpet

SXParse

Data analysi

s

Develop Tools

Another 20+ apps

Graphical Interface


How?How?


What do we

know about

data ?

Similarity

& association

Common PATTERNS and

functionality


A T G C

A T G C A T G A A T……

AT TG GCCAGAAA AT TGAT

ATG TGC GCACAT

ATG

TGAGAA AAT

ATGC TGCA

ATGCA

GCATCATG

TGCAT


Q* logN base AQ* logN base A

Size of database

Speed milliseconds

1 10 100 1000

100

200

300

400

500

600

700

800

900

Conventional

SynaBASE


Case Study - Comparison of Human v Mouse genome

3 yrs

SynaBASE BLAST

6h

PatternHunter

22days


ResultsResults


Read mappingRead mapping

Variety of novel methods for genome sequencingShorter reads with higher coverage

25mers - Solexa100-200mers – 454Polony reads

Larger volumes of sequence dataError rates much higher than Sanger methodComputationally Intractable for conventional bioinformatics applications


Mapping 25 mersMapping 25 mers


Mapping 25mersMapping 25mersSynaBASE API method SXSSASearch() can be used to rapidly map short oligos to a genome using un-gapped alignments

Suitable for finding substitution differences but not insert/delete differences

Gapped alignment of short oligos using a modified version of the SXSSASearch() method

SXSSASearch:

does not use heuristics and is guaranteed to find all matches to an oligo given the scoring matrix and a thresholduses a weight matrix with position dependent scores for each base

Mapping

25 mers

# SXOligoSearch# Thu Sep 14 16:22:07 2006# $Id: SXOligoSearch.cpp,v 1.28 2006/07/17 07:31:57 Exp $# SXOligoSearch chr22 dummy.txt

>Read-0:21200326AAGTAGCCAAGAGCATGCCC.........T.......... + chr22:21200327-21200346 20

>Read-1:21200835GTCTCCACAAGAAAATACAA.................... + chr22:21200836-21200855 20

>Read-2:21200982TGTATTCTGCAGAACTGATA...C..........G..... + chr22:21200983-21201002 20


Very fast and flexible approachVery fast and flexible approach

Example: 350,000 reads can be mapped in 125sec - 3 per ms

Makes approach suitable for reads that have varying quality over their length

Mismatch penalty can be reduced towards the 3’ end of reads

0 25

1.0

Quality or Probability of being correct

Mapping

25 mers


Mismatches and quality scoresMismatches and quality scoresIf a read maps to 2 locations:

One with a mismatch in the low quality 3’ end and one with a mismatch near the 5’ end. The position of the mismatch and quality should be taken into account when selecting the best mapping and for SNP qualification

In the example above the first reported alignment would likely be taken as the correct one as the mismatch is in a low quality base

To optimize performance the search process starts by searching for an exact match and the threshold is increased until at least one match is found

If a read maps to multiple locations then it may be from a repeat and may be ignored when determining putative SNPs

Mapping

25 mers


Finding SNPs IFinding SNPs ISNP identification should take into account:

Known SNPsWhether the species is Haploid, Diploid, etc.Quality of reads by base positionBackground SNP rateIf the SNP is within a documented exon, then translation neutral SNPs can be distinguished

Example 1: the reads all have a mismatch corresponding to the same position in the genome indicating a possible SNP

Mapping

25 mers


Finding SNPs IIFinding SNPs II

Example 2: One read has a mismatch and two reads match

The mismatch corresponds to a low quality base position in the read so the mismatch could be interpreted as insignificant and not reported. If the species is diploid and it is known from a SNP library that some individuals carry a SNP for a ‘C’ at this position. In this case there is an increased probability of this individual carrying the SNP on one of the two chromosome copies. Some SNPs cause disease only if they exist in both copies of the chromosome while others can cause disease even if only one copy carries the SNP


SummarySummary

Mapping of short reads achieved at very high throughput – less than 1ms

Position specific scoring allows variable quality reads to be mapped

Statistical analysis of mismatches to qualify SNPs

Mapping

25 mers


Mapping 100 mersMapping 100 mers


MultiPass Strategy for Mapping Sequence data to Genomes using SynaBASE

Search 4% mutated reads against the Human genome SynaBASE using high stringency parameters

Analysis Steps

Reduce repeat filtering stringency

SynaSearch matches ~61 % on first pass

Repeat the search by reducing filter score to identify shorter alignments e.g. score < 30

1st Pass

2nd Pass

3rd Pass

Mapping

120 mers


Input Sequence Reads: Input Sequence Reads: ~ 1.7 million @ 6X coverage ~ 1.7 million @ 6X coverage

of Hs chr22of Hs chr22

Mapping

120 mers


Mapping

120 mers


Analysis of ResultsAnalysis of Results

View read placement along chromosomeCalculate mapping efficiency1.7m reads mapped to human genome in 53 min 22 seconds

Mapping

120 mers


Simulation Mapping ResultsSimulation Mapping Results

Dataset

% Reads Mappe

d

No.Queries

Matched

Mean Aligned Length

Mean Percent

ID

Hits Per

Query

Minutes

Original

100.00 1738713 119.84 100.00 1.30 66.85

Pass 1 -4 %

Mutated

61.38 1067225 119.17 97.37 1.15 53.53

Pass 2 -4 %

Mutated

25.56 444415 119.32 96.23 1.24 24.42

Pass 3 -4 %

Mutated

9.76 169698 119.28 97.12 1.17 7.01

Mapping

120 mers


Chr22 mapping overviewChr22 mapping overview

Chr22 sequence position

Rea

d D

ensi

ty C

ou

nt

Red – forward

Green – Reverse complement

Mapping

120 mers


Human Chr2 Human Chr2

Chr2 sequence position

Rea

d D

ensi

ty C

ou

nt

Red – forward

Green – Reverse complement

Mapping

120 mers


Viewing ResultsViewing Results

Gbrowse: Community-based system to view resultsNumerous customisations to show sequence coverageAnalyze read mappings in the context of

Known genesRepeats and variations (SNP)Comparative genomics

Mapping

120 mers


Mapping

120 mers


RAB36 RAS Oncogene Family on chromosome 22RAB36 RAS Oncogene Family on chromosome 22Mapping

120 mers


Areas of lower read coverage

Mapping

120 mers


ConclusionsConclusions

Very significant performance improvements compared to MegaBLAST – <100ms per read

Very high coverage attained by using multi-pass strategy

Over 95% coverageRemaining 5% are repeats

High specificity – less matches per read

Enables multiple human genomes to be processed per day

Mapping

120 mers


Mapping Polony reads Mapping Polony reads

5 mers5 mers


Polony sequencing read mappingPolony sequencing read mapping

Convert genomic sequences to spectraSample random probe sets from random chromosomal regionsFilter probe sets using probe intensity spectraQuery probe sets against genome database

5 mers polony reads


Probe set Generation

Sample probe intensities from spectra using normal distribution(Mean 2000 / SD 250)

Generate 10,000 random 200bp reads from Hs chr22

Method Verification

Filter probes based on intensity thresholds for each error rate

Generate Overlapping segments for Hs chr22 @ 5X Coverage

Sequence to spectrum conversion using 512 bit translation

Sequence to spectrum conversion using 512 bit translation

Reference Database Generation

Simulate error rates at 1-7% in probe sequence

Build SynaBASE for querying with probe sets

Alignment search remainder of probes against reference SynaBASE of Hs

chr22

Analyze score and % identities for all probe sets at various intensity

thresholds


Error Rate

Threshold Hits (%)

No of queries

that found no

result

No of queries that match within 128

bases (Accuracy %)

No of queries that match the incorrect

target

Elapsed Time (s)

Avg. Query Time (ms)

0% 0 100.00 0 10000(100.00) 0 58.793 5.79 1% 2310 100.00 0 10000(100.00) 0 54.756 5.48 2% 2420 100.00 0 10000(100.00) 0 61.547 6.15 3% 2490 100.00 0 9995( 99.95) 5 68.223 6.82 4% 2550 99.99 1 9996( 99.96) 3 66.968 6.70 5% 2590 99.81 19 9968( 99.68) 13 114.028 11.40 6% 2630 99.41 59 9882( 98.82) 59 160.873 16.09 7% 2670 99.81 19 9815( 98.15) 166 166.932 16.69

OverallOverall 5 mers polony reads


AdvantagesAdvantages

Time taken to conduct 0% to 4% searches – around 6msEnhanced performance to the SynaBASE engine & associated algorithms100% hits matched for 1-3 % error margin data~15 million searches against a reference genome in 1 day

5 mers polony reads


ConclusionConclusion


SummarySummary

SynaBASE used as database PLATFORMUnique, leads to massive increases in speed and scalability

Applied to the 3 main classes of reads from 2nd generation sequencing platforms

100s of fold faster than conventional approaches

Specificity and accuracy enhanced due to exhaustive nature of SynaBASE


Thank you

Please email questions to:

[email protected]

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