achim tresch computational biology ‘omics’ - analysis of high dimensional data

Achim TreschComputational Biology

‘Omics’

- Analysis of high

dimensional Data

Next Generation Sequencing

Today:

Illumina NGS platform, Fastq files

Sequence bioinformatics:Hash tablesSuffix arrays

Burrows-Wheeler transform

Illumina

Slides from Kurt Strueber Genome Center MPIPZ Cologne

• RNA-seq, ChIP-seq, Methyl-seq

…CCATAGGCTATATGCGCCCTATCGGCAATTTGCGGTATAC…GCGCCCTA

GCCCTATCGGCCCTATCG

CCTATCGGACTATCGGAAA

AAATTTGCAAATTTGC

TTTGCGGTTTGCGGTA

GCGGTATA

GTATAC…

TCGGAAATTCGGAAATTT

CGGTATAC

TAGGCTATA

GCCCTATCGGCCCTATCG

CCTATCGGACTATCGGAAA

AAATTTGCAAATTTGC

TTTGCGGT

TCGGAAATTCGGAAATTTCGGAAATTT

AGGCTATATAGGCTATATAGGCTATAT

GGCTATATGCTATATGCG

…CC…CC…CCA…CCA…CCAT

ATAC…C…C…

…CCAT…CCATAG TATGCGCCC

GGTATAC…CGGTATAC

GGAAATTTG

…CCATAGGCTATATGCGCCCTATCGGCAATTTGCGGTATAC…ATAC……CC

GAAATTTGC

Goal: identify variations

Goal: classify, measure significant peaks

Short Read Applications

• Genotyping

Reference genome

Short reads

Illumina

Slides taken from Michael MainUniversity of Colorado

Hash tables

• The simplest kind of hash table is an array of records.

• This example has 701 records.

[ 0 ] [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

An array of records

. . .

[ 700]

Hash tables

• Each record has a special field, called its key.

• In this example, the key is a long integer field called Number.

[ 0 ] [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

. . .

[ 700]

[ 4 ]

Number 506643548

Hash tables

• The number might be a person's identification number, and the rest of the record has information about the person.

[ 0 ] [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ]

. . .

[ 700]

[ 4 ]

Number 506643548

Hash tables

• When a hash table is in use, some spots contain valid records, and other spots are "empty".

[ 0 ] [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 700]Number 506643548Number 233667136Number 281942902 Number 155778322

. . .

Hash tables

• In order to insert a new record, the key must somehow be converted to an array index.

• The index is called the hash value of the key.


. . .

Number 580625685

In our case: The keys are short sequences, and the records contain their location in the genome

Inserting a new record

• Typical way create a hash value:


. . .

Number 580625685

(Number mod 701)

What is (580625685 mod 701) ?


• Typical way to create a hash value:


. . .

Number 580625685

(Number mod 701)

What is (580625685 mod 701) ?3


• The hash value is used for the location of the new record.

Number 580625685


. . .

[3]


• The hash value is used for the location of the new record.


. . .Number 580625685


• Here is another new record to insert, with a hash value of 2.


. . .Number 580625685

Number 701466868

My hashvalue is

[2].

Collisions

• This is called a collision, because there is already another valid record at [2].


. . .Number 580625685

Number 701466868

When a collision occurs,

move forward until you

find an empty spot.

Collisions

• This is called a collision, because there is already another valid record at [2].


. . .Number 580625685 Number 701466868

The new record goes

in the empty spot.

Collisions

[ 0 ] [ 1 ] [ 2 ] [ 3 ] [ 4 ] [ 5 ] [ 700]Number 506643548Number 233667136Number 281942902 Number 155778322Number 580625685 Number 701466868

. . .

If the keys were short sequences, where would you place the sequenceATACCG?(NB: this is an ill-posed question)

A Quiz

• The data that's attached to a key can be found fairly quickly.


. . .Number 580625685 Number 701466868

Number 701466868

Searching for a Key

• Calculate the hash value.• Check that location of the array

for the key.


. . .Number 580625685 Number 701466868

Number 701466868

My hashvalue is

[2].

Not me.

Searching for a Key

• Keep moving forward until you find the key, or you reach an empty spot.


. . .Number 580625685 Number 701466868

Number 701466868

My hashvalue is

[2].Not me.

Searching for a Key

• Keep moving forward until you find the key, or you reach an empty spot.


. . .Number 580625685 Number 701466868

Number 701466868

My hashvalue is

[2].Yes!

Searching for a Key

• When the item is found, the information can be copied to the necessary location.


. . .Number 580625685 Number 701466868

Number 701466868

My hashvalue is

[2].Yes!

Searching for a Key

Hash tables store a collection of records with keys.

The location of a record depends on the hash value of the record's key.

When a collision occurs, the next available location is used.

Searching for a particular key is generally quick.

THE END

Summary

Suffix Arrays

• Suffix arrays were introduced by Manber and Myers in 1993

• More space efficient than suffix trees• A suffix array for a string x of length m is an array of

size m that specifies the lexicographic ordering of the suffixes of x.

Idea: Every substring is a prefix of a suffix

Example of a suffix array for acaaacatat$

3415792681011

Starting position of

that suffix in the search

string

Suffix Arrays

• Naive in place construction– Similar to insertion sort– Insert all the suffixes into the array one by one

making sure that the new inserted suffix is in its correct place

– Running time complexity:• O(m2) where m is the length of the string

• Manber and Myers give a O(m log m) construction in their 1993 paper.

Suffix Array Construction

• O(n) space where n is the size of the database string• Space efficient. However, there’s an increase in

query time• Lookup query

– Binary search– O(m log n) time; m is the size of the query– Can reduce time to O(m + log n) using a more efficient

implementation

Suffix Array Construction

find(Pattern P in SuffixArray A):

i = 0 lo = 0, hi = length(A) for 0<=i<length(P):

Binary search for x,y

where P[i]=S[A[j]+i] for lo<=x<=j<y<=hi

lo = x, hi = y

return {A[lo],A[lo+1],...,A[hi-1]}

Suffix Array Search

Search ‘is’ in mississippi$ 0 11 i$

1 8 ippi$

2 5 issippi$

3 2 ississippi$

4 1 mississippi$

5 10 pi$

6 9 ppi$

7 7 sippi$

8 4 sissippi$

9 6 ssippi$

10 3 ssissippi$

11 12 $

Examine the pattern letter by letter, reducing the range of occurrence each time.

- First letter i: occurs in indices from 0 to 3- Second letter s: occurs in indices from 2 to 3

Done. Output: issippi$ and ississippi$

Suffix Array Search

• It can be built very fast.• It can answer queries very fast:

– How many times ATG appears?

• Disadvantages: – Can’t do approximate matching– Hard to insert new stuff dynamically

(need to rebuild the array)

Summary

• http://pauillac.inria.fr/~quercia/documents-info/Luminy-98/albert/JAVA+html/SuffixTreeGrow.html

• http://home.in.tum.de/~maass/suffix.html• http://homepage.usask.ca/~ctl271/857/suffix_tree.shtml• http://homepage.usask.ca/~ctl271/810/

approximate_matching.shtml• http://www.cs.mcgill.ca/~cs251/OldCourses/1997/topic7/• http://dogma.net/markn/articles/suffixt/suffixt.htm• http://www.csse.monash.edu.au/~lloyd/tildeAlgDS/Tree/

Suffix/

Links

Bowtie: A Highly Scalable Tool for Post-Genomic

Datasets

(Slides by Ben Langmead)


• Genotyping

• RNA-seq, ChIP-seq, Methyl-seq


GCCCTATCGGCCCTATCG

CCTATCGGACTATCGGAAA

AAATTTGCAAATTTGC

TTTGCGGTTTGCGGTA

GCGGTATA

GTATAC…

TCGGAAATTCGGAAATTT

CGGTATAC

TAGGCTATA

GCCCTATCGGCCCTATCG

CCTATCGGACTATCGGAAA

AAATTTGCAAATTTGC

TTTGCGGT



GGCTATATGCTATATGCG


ATAC…C…C…


GGTATAC…CGGTATAC

GGAAATTTG


GAAATTTGC

Goal: identify variations

Goal: classify, measure significant peaks


Finding the alignments is typically the performance bottleneck


GCCCTATCGGCCCTATCG

CCTATCGGACTATCGGAAA

AAATTTGCAAATTTGC

TTTGCGGTTTGCGGTA

GCGGTATA

GTATAC…

TCGGAAATTCGGAAATTT

CGGTATAC

TAGGCTATA

GCCCTATCGGCCCTATCG

CCTATCGGACTATCGGAAA

AAATTTGCAAATTTGC

TTTGCGGT



GGCTATATGCTATATGCG


ATAC…C…C…


GGTATAC…CGGTATAC

GGAAATTTG


GAAATTTGC

Short Read Alignment

• Given a reference and a set of reads, report at least one “good” local alignment for each read if one exists– Approximate answer to: where in genome did read

originate?

…TGATCATA… GATCAA

…TGATCATA… GAGAAT

better than

• What is “good”? For now, we concentrate on:

…TGATATTA… GATcaT

…TGATcaTA… GTACAT

better than

– Fewer mismatches is better

– Failing to align a low-quality base is better than failing to align a high-quality base

Indexing

• Genomes and reads are too large for direct approaches like dynamic programming

• Indexing is required

• Choice of index is key to performance

Suffix tree Suffix array Seed hash tablesMany variants, incl. spaced seeds

Indexing

• Genome indices can be big. For human:

• Large indices necessitate painful compromises1. Require big-memory machine2. Use secondary storage

> 35 GBs > 12 GBs > 12 GBs

3. Build new index each run4. Subindex and do multiple

passes

Burrows-Wheeler Transform

Burrows WheelerMatrix

Last column contains the characters

preceding the characters in the first column

BWT(T)

a c a a c g $$ a c a a c gg $ a c a a ca c g $ a c aa a c g $ a cc a a c g $ aa c a a c g $

Rotate string one by one in each row

Sort suffixes lexicographically

Text T

Burrows-Wheeler Transform• Reversible permutation used originally in compression

• Once BWT(T) is built, all else shown here is discarded– Matrix will be shown for illustration only

• In long texts, BWT(T) contains more repeated character occurrences than the original text easier to compress!

BurrowsWheelerMatrix

Last column

BWT(T)T


• Property that makes BWT(T) reversible is “LF Mapping”– ith occurrence of a character in Last column is

same text occurrence as the ith occurrence in First column

BWT(T)

Burrows WheelerMatrix

Rank: 2

Rank: 2

Burrows M, Wheeler DJ: A block sorting lossless data compression algorithm. Digital Equipment Corporation, Palo Alto, CA 1994, Technical Report 124; 1994


• To recreate T from BWT(T), repeatedly apply rule:T BWT[ LF(i) ] + T; i = LF(i)– Where LF(i) maps row i to row whose first

character corresponds to i’s last per LF Mapping

• Could be called “unpermute” or “walk-left” algorithm

Final T

BWT in Bioinformatics

• Oligomer counting– Healy J et al: Annotating large genomes with exact word

matches. Genome Res 2003, 13(10):2306-2315.

• Whole-genome alignment– Lippert RA: Space-efficient whole genome comparisons

with Burrows-Wheeler transforms. J Comp Bio 2005, 12(4):407-415.

• Smith-Waterman alignment to large reference– Lam TW et al: Compressed indexing and local alignment

of DNA. Bioinformatics 2008, 24(6):791-797.

Comparison to Maq & SOAP

• PC: 2.4 GHz Intel Core 2, 2 GB RAM• Server: 2.4 GHz AMD Opteron, 32 GB RAM• Bowtie v0.9.6, Maq v0.6.6, SOAP v1.10• SOAP not run on PC due to memory constraints• Reads: FASTQ 8.84 M reads from 1000 Genomes (Acc:

SRR001115)• Reference: Human (NCBI 36.3, contigs)

CPU timeWall clock

time

Readsper

hour

Peak virtual memory footprint

Bowtiespeedu

p

Reads aligned (%)

Bowtie –v 2 (server) 15m:07s 15m:41s 33.8 M 1,149 MB - 67.4

SOAP (server) 91h:57m:35s 91h:47m:46s 0.08 M 13,619 MB 351x 67.3

Bowtie (PC) 16m:41s 17m:57s 29.5 M 1,353 MB - 71.9

Maq (PC) 17h:46m:35s 17h:53m:07s 0.49 M 804 MB 59.8x 74.7

Bowtie (server) 17m:58s 18m:26s 28.8 M 1,353 MB - 71.9

Maq (server) 32h:56m:53s 32h:58m:39s 0.27 M 804 MB 107x 74.7

• Bowtie delivers about 30 million alignments per CPU hour

TopHat: Bowtie for RNA-seq

• TopHat is a fast splice junction mapper for RNA-Seq reads. It aligns RNA-Seq reads using Bowtie, and then analyzes the mapping results to identify splice junctions between exons.– Contact: Cole Trapnell ([email protected])– http://tophat.cbcb.umd.edu

Nicolas Delhomme, EMBL Heidelberg

University of Umeå

Acknowledgements

NGS Exercises were designed by

achim tresch computational biology ‘omics’ - analysis of high dimensional data

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