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ELECTOR: EvaLuation of Error Correction Tools for lOng Reads

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Submitted on 21 Nov 2018

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ELECTOR: EvaLuation of Error Correction Tools for lOng Reads

Lolita Lecompte, Camille Marchet, Pierre Morisse, Antoine Limasset, Pierre Peterlongo, Arnaud Lefebvre, Thierry Lecroq

To cite this version:

Lolita Lecompte, Camille Marchet, Pierre Morisse, Antoine Limasset, Pierre Peterlongo, et al.. ELEC- TOR: EvaLuation of Error Correction Tools for lOng Reads. JOBIM 2018 - Journées Ouvertes Biolo- gie, Informatique et Mathématiques, Jul 2018, Marseille, France. pp.1-2. �hal-01929900�

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ELECTOR: EvaLuation of Error Correction Tools for lOng Reads

Lolita Lecompte1, Camille Marchet1, Pierre Morisse2, Antoine Limasset3, Pierre Peterlongo1, Arnaud Lefebvre2, Thierry Lecroq2

1Univ Rennes, Inria, CNRS, IRISA, F-35000 Rennes, France 2Normandie Univ, UNIROUEN, LITIS, 76000 Rouen, France 3Université Libre de Bruxelles

1. Introduction

I Long read technologies, Pacific Biosciences and Oxford Nanopore, have high error rates (from 9% to 30%)

I Multiple error correction methods exist

I Important to assess the correction stage for downstream analyses

I Only one tool: LRCstats [1]

shows global correction gain

does not give access to correctors de- tailed behavior

high computation times

Developing methods allowing to evaluate error correction tools with precise and reliable statistics is therefore a crucial need.

2. Contribution

We propose ELECTOR, a novel tool that enables the evaluation of long read correction methods:

I provide more metrics than LRCstats on the correction quality I scale to very long reads and large datasets

I compatible with a wide range of state-of-the-art error correction tools (hybrid/self)

3. Output statistics

Assembly using Miniasm If reference, remapping

I Recall I Nb of contigs using BWA

I Precision I N50 I Average identity

I Overall correct bases rate I N75 I Genome coverage

I GC content before and after correction I Nb of breakpoints

I Number of trimmed and/or split corrected reads I NGA50

I Mean missing size in trimmed/split reads I NGA75

4. Methods

1. Multiple alignment of triplets: {ref erence read, uncorrected read, corrected read}

2. Seed-MSA strategy: multiple sequence alignment (MSA) using partial order graphs [2]

coupled to a seed strategy comparable to MUMmer or Minimap.

I Faster and scalable

5. Heuristic performances

Dataset from E. coli, simulated with Sim- LoRD [3], and corrected with MECAT.

I Dataset: reads with a 10kb mean length, a 15% error rate, and a coverage of 100x.

Strategy MSA seed-MSA

Recall 84.505% 84.587%

Precision 88.347% 88.278%

Correct bases rate 95.290% 95.250%

Time 107h 42m

Similar results using both strategies.

A substantial gain in time is achieved using the seed-MSA strategy.

6. Results: ELECTOR vs. LRCstats

Dataset from E. coli, simulated with SimLoRD, composed of reads with a 8kb mean length, a 18%

error rate, and a coverage of 20x.

Method Original Nanocorr daccord

ELECTOR LRCstats ELECTOR LRCstats ELECTOR LRCstats

Error rate 15.837 17.9267 0.339 0.3983 0.422 0.4498

Recall N/A - 0.98503 - 0.98836 -

Precision N/A - 0.99424 - 0.98468 -

Deletions 847,315 3,635,647 46,596 56,708 58,110 72,547 Insertions 10,393,229 13,038,057 237,798 279,970 306,930 336,686 Substitutions 5,611,023 671,040 143,605 45,783 72,265 25,643

Trimmed / split reads N/A - 1,612 - 123 -

Mean missing size N/A - 341 - 3,026 -

%GC 50.7 - 50.8 - 50.8 -

Time 13min 3h53 13min 3h52 13min 3h50

Results of these experiments show that the metrics computed by ELECTOR are comparable to LRCstats outputs, but also highlight several novelties.

LRCstats, besides having low performance results, also fails to evaluate correction’s detailed impact on big datasets and on very long reads.

7. Conclusion

I Novel and open-source method for fast long read correction assessment I Compatible with hybrid and self cor-

rectors

I Numerous metrics for correction quality (recall/precision)

I Downstream analyses assessment (mapping/assembly)

I Time-saving, scaling computation

[1] Sean La, Ehsan Haghshenas, and Cedric Chauve. LRCstats, a tool for evaluating long reads correction methods. Bioinformatics, 33(22):3652–3654, 2017.

[2] Christopher Lee, Catherine Grasso, and Mark F Sharlow. Multiple sequence alignment using partial order graphs. Bioinformatics, 18(3):452–464, 2002.

[3] Bianca K Stöcker, Johannes Köster, and Sven Rahmann. SimLoRD: Simulation of Long Read Data. Bioinformatics, 32(17):2704–2706, 2016. github.com/kamimrcht/ELECTOR Contact: camille.marchet@irisa.fr

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