Using k-mers with errors for Nanopore read analysis - Quentin - - PowerPoint PPT Presentation

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Using k-mers with errors for Nanopore read analysis - Quentin - - PowerPoint PPT Presentation

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Centre de Recherche en Informatique, Signal et Automatique de Lille Using k-mers with errors for Nanopore read analysis - Quentin Bonenfant Laurent No Hlne T ouzet {quentin.bonenfant , laurent.noe , helene.touzet} @univ-lille.fr

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Using k-mers with errors for Nanopore read analysis

  • Quentin Bonenfant

Laurent Noé Hélène T

  • uzet

{quentin.bonenfant , laurent.noe , helene.touzet} @univ-lille.fr

CRIStAL – UMR CNRS 9189 – BONSAI team

Centre de Recherche en Informatique, Signal et Automatique de Lille

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Quentin Bonenfant - SeqBio 2018 2

Overview

1) K-mers 2) Long read sequencing 3) K-mers with errors 4) Use case: Nanopore adapters 5) Results 6) Conclusion

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K-mers

ATCAGTCAGCGGGTATCTACTGCACCTATCGAGCTTTTTT

  • Substring of size k
  • Used for:

– Assembly (SPAdes → De Bruijn Graph) – Mapping (Bowtie2 → Burrows-Wheeler T

ransform)

– Overlapping (Minimap2 → Minimizers) – … k=8

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Long read sequencing

10-15% of errors

ATCAGTCAGCGGGGTATCTACTC---CACCTATCGAGCTTTTTTATCT ||||||||||| |||| |||| ||||||||||||||| ||||| ATCAGTCAGCG---TATCGACTCTAGCACCTATCGAGCTTT--TATCT Insertion Deletion Substitution

k-mer

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Long read sequencing

How to account for sequencing errors? → k-mers with errors → d: max number of errors

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K-mers with errors ?

AATTCCGG

d=1 k=8

ACTTCCGG AATTC-GG AATTTCCGG ...

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How ?

  • Using dynamic programming

→ Large computational cost

  • Indexing all neighbours

→ Memory expensive / long to compute

  • Research with errors in an index

→ 01*0 seeds

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01*0 seeds

  • Approximate seeds
  • Lossless
  • Principle:

– Choose a value for d – Split k-mer in d+2 blocks – Search blocks in the index

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01*0 seeds

Pigeonhole principle 4 pigeons (d) 6 holes (d+2) → At least 2 holes are empty

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01*0 seeds

Example Finding “AUCAGUGCAAAUGCUCAAGA” d=3 k= 20 → Split in 5 blocs of size 4

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AUCA GUGC AAAU GCUC AAGA |||| ||| | || || | |||| AUCA AUGC A-AU GCGC AAGA AUCA GUGC AAAU GCUC -AAGA ||| |||| | || |||| |||| AUC- GUGC AUAU GCUC AAAGA AUCA GUGC AAAU GCUC AAGA |||| | | |||| || | |||| AUCA GAGA AAAU GC-C AAGA

01*0 seeds

1 1 1 1 0 1 0

1) 2) 3)

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01*0 seeds

  • First implementation

– BWOLO (2014) – BWT

Vroland C, Salson M, Bini S, Touzet H. Approximate search of short patterns with high error rates using the 01 ⁎ 0 lossless seeds. Journal of Discrete Algorithms 37, 2016

  • SeqAn implementation

– Optimum Search Scheme (2018) – Bidirectional BWT

Kiavash K, Pockrandt C, Torkamandi B, Luo H, and Reinert K. FAMOUS: Fast Approximate String Matching Using OptimUm Search Schemes. Recomb-Seq 2018

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Use case: Motif inference for Nanopore adapters

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Nanopore adapters sequence

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Nanopore adapters sequence

  • Sequencing adapters sequence
  • Porechop

– https://github.com/rrwick/Porechop/ – Adapter trimming – Known adapters database

  • Can we guess the adapter sequence from the

reads?

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Our method

  • Identify k-mers composing the adapter

– Higher frequency at the start / end of reads

  • Reconstruct adapter from k-mers
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Frequency of k-mers

k=16

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Counting k-mers with errors

  • Select the 500 most frequent 16-mers
  • Count all occurences with d=2 errors
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Counting result example

K-mer count TTCAGTTACGTATTGC 2761 TCAGTTACGTATTGCT 2716 CTATCTTCGGCGTCTG 2628 TCTATCTTCGGCGTCT 2628 CTTCGTTCAGTTACGT 2626 CGTTCAGTTACGTATT 2612 GTTCAGTTACGTATTG 2567 CTCTATCTTCGGCGTC 2563 GCTCTATCTTCGGCGT 2509 CTGTCGCTCTATCTTC 2491

Exact k-mers

0 err 1err 2err K-mer 2761 4403 5844 TTCAGTTACGTATTGC 2626 4324 6002 CTTCGTTCAGTTACGT 2612 4420 5905 CGTTCAGTTACGTATT 2716 4361 5813 TCAGTTACGTATTGCT 2567 4423 5837 GTTCAGTTACGTATTG 2359 4276 5895 TCGTTCAGTTACGTAT 2447 4048 5591 TTCGTTCAGTTACGTA 2628 3999 4775 CTATCTTCGGCGTCTG 2628 3934 4748 TCTATCTTCGGCGTCT 2563 3900 4649 CTCTATCTTCGGCGTC

K-mers with errors

k=16 d=0 k=16 d=2

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k-mer ranks plot

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How adapter sequence is built

K-mers k=16 TTCAGTTACGTATTGC CTTCAGTTACGTATTG CGTTCAGTTACGTATT TCAGTTACGTATTGCT GTTCAGTTACGTATTG GTTACGTATTGCTGTT TTACGTATTGCTGTTC CAGTTACGTATTGCTG TACGTATTGCTGTTCT CTCTATCTTCGGCGTC AGTTACGTATTGCTGT

T T C A G T T A C G T A T T G C T C A G T T A C G T A T T G C T C T T C A G T T A C G T A T T G C A G T T A C G T A T T G C T G C T T C A G T T A C G T A T T G C T G T T C T A G T T A C G T A T T G C T G T G T T A C G T A T T G C T G T T T T A C G T A T T G C T G T T C T A C G T A T T G C T G T T C T

1 2 3 4 5 6 7 8 9 10 11

Rank

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Dataset 1

  • Consortium ANR ASTER

– Algorithms and software for third generation

sequencing

  • Prep and sequencing: Genoscope

– Specie: Mouse (Mus musculus) – Tissue: brain – Sample T

ype: 1D cDNA

– Flowcell: R9.4 – ENA/SRA : PRJEB25574

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Experiment

  • Sample size

– 10,000 reads, 100 fjrst bases – k = 16, d = 2

  • Run the workfmow on 100 samples
  • Compare results for both counting methods

(k-mers with and without errors)

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Results with exact k-mers

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Results with approximate k-mers

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The MEME approach

  • MEME

Multiple EM for Motif Elicitation

Bailey and Elkan, Fitting a mixture model by expectation maximization to discover motifs in biopolymers, ISMB 1994.

  • Experiment

– 1000 random reads, fjrst 100 bases – Repeated on 5 samples

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MEME results

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Results

Exact k-mers Approximate k-mers MEME

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Dataset 2

  • Nanopore wgs consortium

– Oxford Nanopore human reference datasets

https://github.com/nanopore-wgs-consortium/NA12878

  • Data from RNA project

– https://github.com/nanopore-wgs-consortium/NA12878/blob/mast

er/nanopore-human-transcriptome/fastq_fast5_bulk.md

– Sample T

ype: 1D cDNA

– Cell line: GM12878 human cell line (Ceph/Utah pedigree) – Kit : SQK-PCS108 – Flowcell: R9.4 – File: Bham_Run1_20171115_1D.pass.depup.fastq

  • Same experiment
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Exact k-mers results

67 different adapters found

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Approximate k-mers results (WGS)

T-ACTTGCCTGTCGCTCTATCTTC

PCR adapters 3 (start) ←

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Approximate k-mers results (WGS)

Exact k-mers Approximate k-mers

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Implementation

  • C++ with SeqAn library

(optimal search schemes)

  • Computation time

For 10k reads Exact k-mers : <1 second K-mers with errors: 10-20 seconds MEME: >180h

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Porechop wrapper

  • Integration in Porechop (Python)

Custom wrapper* allow integration in Porechop workfmow easily by adding inferred adapters to adapter database

  • T

wo case studies: → discovering the adapter sequence if unknown → checking (if known) adapter is present and correctly sequenced (quality check)

  • C++ and Python code available on demand
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Conclusion

  • Our goal was to test the effjciency of 01*0 seeds using

k-mers approach on noisy reads

  • Our experiment showed

– More consistent results with approximate k-mers – Practical running time for real data

→ k-mers with errors can improve results at low cost

  • Could be used as an alternative to minimizers ?
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End

Thank you for your attention

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Potential adapters

Exact k-mers

TCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCAGGTGTTTAACCTTTTTG 39 TCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCGGGTGTTTAACCTG 16 TCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCGGGTGTTTAACCTC 14 TCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCAGGTGTTTAACCTG 13 TCAGTTACGTATTGCTCTTGCCTGTCGCTCTATCTTCGGCGTCTGCTTGGGTGTTTAACCTTTTTG 6 TCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCAGGTGTTTAACCTC 4 TTTGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCAGGTGTTTAACCTG 2 CTTGTACTTCGTTCAGTTACGTATTG 2 CTTGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCAGGTGTTTAACCTG 1 CTTGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCGGGTGTTTAACCTC 1 CTTGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGGCAGGTGTTTAACCTTTTTG 1 TTTGTACTTCGTTCAGTTACGTATTG 1

K-mers with errors

ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTTTTTG 83 ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTCT 9 ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTTTTA 4 ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTTTTTA 1 ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTCTT 1 ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTGTTT 1 ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTTTTG 1

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Workfmow

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Porechop analysis

Set best read: start end K-mer_Error 98.7 84.6 SQK-NSK007 100.0 81.8 SQK-MAP006 96.6 100.0 SQK-MAP006 Short 100.0 100.0 PCR adapters 1 100.0 100.0 PCR tail 1 96.4 92.9 PCR tail 2 93.3 93.1 1D^2 part 2 100.0 100.0

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Aligning adapters

Kmer_error_start / SQK-NSK007_Y_Top

  • ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTG

AATGTACTTCGTTCAGTTACGTATTGCT--------------- ATATTGCGGCGTCTGCTTGGGTGTTTAACCTTTTTG

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Aligning adapters

Kmer_error_start/SQK-MAP006_Short_Y_Top_LI32

  • ATGTACTTCGTTCAGTTACGTATTGCTTTTCTGTTGGTGCTG
  • ATATTGCGGCGTCTGCTTGGGTGTTTAACCTTTTTG
  • -----CGGCGTCTGCTTGGGTGTTTAACCT-----
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Aligning adapters

Kmer_error_start/PCR_tail_1_start

  • ATGTACTTCGTTCAGTTACGTATT-GCTTTTCTGTTGGTG
  • ----------------------TTAACCTTTCTGTTGGTG

CTGATATTGCGGCGTCTGCTTGGGTGTTTAACCTTTTTG CTGATATTGC-----------------------------