assemble…or - irisa.fr · epgv seminar - lusignan - april 2013 8 . minia approach-• use a bloom...

Assemble… or not

Colloque EPGV 2013 Pierre Peterlongo

This talk A « bird eye » view of the GenScale NGS work

EPGV Seminar - Lusignan - April 2013 2

This talk A « bird eye » view of the GenScale NGS work

Dominique Lavenier

Rayan Chikhi

Guillaume Rizk

Claire Lemaitre

Raluca Uricaru

And many collaborators, … EPGV Seminar - Lusignan - April 2013 3

Minia, de novo assembly of a human genome on a

laptop Guillaume Rizk, Rayan Chikhi

Space-efficient and exact de Bruijn graph representation based on a Bloom filter Algorithms in Bioinformatics, 2012 - Springer


Contiging, overview Reads

TCAGGCAG ACTCAGCA ATATAATA …

k-‐‑mers

De-‐‑Bruijn Graph

…TCAGGCAGATCGATACTCAGCACAACGTATATAATA…

Contig, Unitig


De Bruijn Graph •  Very simple example: one read

Source: homolog.us


Contiging, overview Reads

TCAGGCAG ACTCAGCA ATATAATA …

k-‐‑mers

Contig, Unitig

De-‐‑Bruijn Graph

Memory bo,leneck

…TCAGGCAGATCGATACTCAGCACAACGTATATAATA…


Minia approach •  Use a BLOOM FILTER: probabilistic data-structure for

indexing k-mers:

Is ACGATCGACTCAGCAT indexed ? o  NO à We are sure ACGATCGACTCAGCAT is absent o  YES à We can not conclude : FALSE POSITIVES

•  Minia: o  Store some of the false positives :

•  Those that may be met while walking the graph.


Minia approach •  Use a BLOOM FILTER: probabilistic data-structure for

indexing k-mers and store the annoying false positives

•  (Knowing that AACGATCGACTCAGCA exists) Is ACGATCGACTCAGCAT indexed ?

o  NO à We are sure ACGATCGACTCAGCAT is absent o  YES à

•  if ACGATCGACTCAGCAT not a stored FP then present •  else, ACGATCGACTCAGCAT absent


Minia results

k = 25 : 13 bits per node.


Minia results


Minia results COMPARISON WITH OTHER HUMAN GENOME ASSEMBLIES


Minia results C. Elegans nematode –

33 million paired-‐‑end reads of length 100 bp (SRR065390)


Minia Consequences


•  Exploring parameter sets •  Developping light memory NGS applications:

o  Kissnp2 o  Mapsembler o  Utimate Gap Filler o  Kissplice o  Intl o  Minigraph o  …

Kissnp2 Search for SNPs on your laptop

•  Assembly difficulty: o  Repeats o  Polymorphism

•  Find SNPs: o  Usual approach: Map against a reference sequence (if exists).

•  (else) create a reference to map reads o  (else) Kissnp

Reads kissnp

>SNP_higher_path_1|score_27|high|left_contig_length_41|right_contig_length_45 atggcaadgggaataadcataadtadddctaaagtGACAACAGTCATTTTTTTCAAAGAACTTCAGTCTGGAACTATTATGTTGTTaggacatgtgatcdcatcaccagtatctcgaaatcctaaaada >SNP_lower_path_1|score_27|high|left_contig_length_41|right_contig_length_45 atggcaadgggaataadcataadtadddctaaagtGACAACAGTCATTTTTTTCAAAGAATTTCAGTCTGGAACTATTATGTTGTTaggacatgtgatcdcatcaccagtatctcgaaatcctaaaada


Kissnp2 Main idea

•  A SNP in the de-Bruijn Graph:


Kissnp2 Main idea

•  A SNP in the de-Bruijn Graph:

•  Algo o  Find a branching k-mer: two (or more) extension possibilities o  Walk the k-mers from one to another, until the full bubble is closed

o  Uses the Minia datastructure


Kissnp2 Yes, but…

•  Pros: o  No reference needed o  Don’t construct the full graph o  Works with 1, 2, 3, …, n datasets :

>SNP_higher_path_16|C1_2|C2_6|Q1_70|Q2_67 TAATGTTAAATGACGAGTTAATGGGTGCAGCACATGAACATGGCACATGTA >SNP_lower_path_16|C1_2|C2_5|Q1_70|Q2_68 TAATGTTAAATGACGAGTTAATGGGGGCAGCACATGAACATGGCACATGTA


Kissnp2 Yes, but…

•  Cons: 1.  A sequencing error = a SNP 2.  An approximate repeat = a SNP (with no filtration: 9 billion SNPs found in human genome !)

•  Avoid this bias: 1.  Use only k-mer with a minimal support (= coverage) 2.  Avoid approximate repeats:

•  Avoid k-mers with too big support (e.g. 1000) •  Extend found SNPs

>SNP_higher_path_16|C1_2|C2_6|Q1_70|Q2_67 …acgatcgacgacgctaacacatataccTAATGTTAAATGACGAGTTAATGGGTGCAGCACATGAACATGGCACATGTAgagagdacatatgtaacacgcagcat… >SNP_lower_path_16|C1_2|C2_5|Q1_70|Q2_68 …acgatcgacgacgctaacacatataccTAATGTTAAATGACGAGTTAATGGGGGCAGCACATGAACATGGCACATGTAgagagdacatatgtaacacgcagcat…


Kissnp2 In a nutshell

•  Read to SNPs o  Any number of read sets o  For each SNP found:

•  The average coverage per set •  The average quality of the SNP position •  Possibility to extend SNPs (left and right unitigs)

•  First results (publication in progress) o  Duck: 350 million reads : 4GB, 3h30, 527.208 SNPs (on genotoul) o  Duck: 3.500 million reads : 4GB, 5.5 days, 4.994.126 SNPs (on genotoul) o  Tick: 576 SNP found and selected w.r.t. coverage à 575 true positives.


Use / extricate polymorphism

•  Idea: o  Polymorphism o  Polyploidy o  Repeats o  …

… are the source of both: o  Huge volume of information

AND o  Huge mess in assembly process, and any NGS process.


Use / extricate polymorphism: a trail

•  From a graph of UNITIGS o  Produced by assemblers before contiging

•  Map set of reads…

A

T

C

G

Reads: set1

Reads: set2





A

T

C

G

•  Compute o  Coverage per set o  Quality per set





A

T

C

G

•  Compute o  Coverage per set o  Quality per set

•  « Phase » polymorphism o  Better assembly



•  « Phase » polymorphism o  Better assembly


Ultimate gap filler

Ref.

Reads


Ultimate gap filler

Ref.

Reads

1.  Detection of gap in the reference:


Ultimate gap filler

Ref.

Reads

1.  Detection of gap in the reference: 2.  Fill gaps (using pairs):


Take Home Message •  Assemble with no memory:

o  Minia

•  Find Polymorphism SNPs with no reference: o  Kissnp2 o  (kissplice)

•  Use and extricate polymorphism •  Detect and close gaps: (reads + ref) :

o  Ultimate Gap Filler

•  More infos and links: http://team.inria.fr/genscale


assemble…or - irisa.fr · epgv seminar - lusignan - april 2013 8 . minia approach-• use a bloom...

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