Hybrid and non hybrid error correction for long reads: LoRDEC and LoRMA
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1 Hybrid and non hybrid error correction for long reads: LoRDEC and LoRMA Eric Rivals Computer Science Lab & Institute Computational Biology, CNRS & Univ. Montpellier November 7, 2016 Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
2 Outline 1 Introduction 2 LoRDEC algorithm 3 LoRDEC experimental results Impact of parameters Scalability Correction of transcriptomic reads (RNA-seq) Correction of Oxford Nanopore MINIon reads 4 LoRDEC +LoRMA 5 LoRMA experimental results 6 Conclusion and future works Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
3 Introduction Outline 1 Introduction 2 LoRDEC algorithm 3 LoRDEC experimental results Impact of parameters Scalability Correction of transcriptomic reads (RNA-seq) Correction of Oxford Nanopore MINIon reads 4 LoRDEC +LoRMA 5 LoRMA experimental results 6 Conclusion and future works Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
4 Introduction Revolution in DNA sequencing Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
5 Introduction Third generation technologies PacBio: Pacific Biosciences up to 25 Kbp Oxford Nanopore MINion up to 50 Kbp Moleculo synthetic reads up to 10 Kbp Oxford Nanopore Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
6 Introduction Overview of sequencing techniques Name Read Lg Time Gb/run pros / cons 454 GS Flex d 0.7 long / indels Illumina HiSeq X 2*300 3 d 200 short/cost Illumina NextSeq 500 2*300 3 d 150 PE, single/idem SOLID (LifeSc) 85 8 d 150 long time Ion Proton h 100 new Illumina TrueSeq synthetic reads PacBio Sciences d 3 high error rate Oxford MINion d 0.8 high error rate Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
7 Introduction Context 3rd generation sequencing technologies yield longer reads PacBio Single Molecule Real Time sequencing: much longer reads (up to 25 Kb) but much higher error rates Error correction is required 1 self correction: using long reads only 2 hybrid correction: using short reads to correct long reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
8 Introduction Context 3rd generation sequencing technologies yield longer reads PacBio Single Molecule Real Time sequencing: much longer reads (up to 25 Kb) but much higher error rates Error correction is required 1 self correction: using long reads only 2 hybrid correction: using short reads to correct long reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
9 Introduction Context 3rd generation sequencing technologies yield longer reads PacBio Single Molecule Real Time sequencing: much longer reads (up to 25 Kb) but much higher error rates Error correction is required 1 self correction: using long reads only 2 hybrid correction: using short reads to correct long reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
10 Introduction Hybrid correction methods [Koren et al, Nat. Bio. 2012] Short reads are aligned to long reads a consensus is applied to correct part of the long read Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
11 Introduction Self correction methods [Chin et al, Nat. Met. 2013] Long reads are corrected with shorter reads from same technology Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
12 Introduction Other hybrid PacBio error correction programs PacBioToCA [Koren et al. 2012] AHA [Bashir et al. 2012] inside the assembler LSC [Au et al. 2012] compress homopolymers before alignment All follow an alignment based strategy (e.g. BLAST like) Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
13 Introduction Other hybrid PacBio error correction programs PacBioToCA [Koren et al. 2012] AHA [Bashir et al. 2012] inside the assembler LSC [Au et al. 2012] compress homopolymers before alignment All follow an alignment based strategy (e.g. BLAST like) proovread [Hackl et al. 2014]: alignment & chimera detection Jabba [Miclotte et al. 2015]: LoRDEC s approach + MEM based alignment variable length seeds for anchoring the LR on graph CoLoRMap [Haghshenas et al. 2016]: alignment & local assembly Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
14 Introduction Hybrid correction and assembly ECtools [Lee et al. biorxiv 2014] assemble SR into unitigs, assemble unitigs and LR with Celera Nanocorr [Goodwin et al. biorxiv 2014] recruit SR for a LR using BLAST, select SR with Longest Increasing Subsequence (LIS) compute consensus assembly with Celera NaS (Nanopore) [Madoui et al BMC Genomics 2015] recruit SR for each LR and reassemble the LR sequence complex pipeline Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
15 Introduction Hybrid correction and assembly ECtools [Lee et al. biorxiv 2014] assemble SR into unitigs, assemble unitigs and LR with Celera Nanocorr [Goodwin et al. biorxiv 2014] recruit SR for a LR using BLAST, select SR with Longest Increasing Subsequence (LIS) compute consensus assembly with Celera NaS (Nanopore) [Madoui et al BMC Genomics 2015] recruit SR for each LR and reassemble the LR sequence complex pipeline All need to assemble SR Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
16 Introduction Motivation LR correction programs require high computational resources and long running times on a supercomputer even for bacterial genome datasets. [Deshpande et al. 2013] Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
17 Introduction Motivation LR correction programs require high computational resources and long running times on a supercomputer even for bacterial genome datasets. [Deshpande et al. 2013] For a 1 Gb plant genome, correction of 18x PacBio with 160x Illumina required CPU hours with EC-tools! Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
18 Introduction Contributions LoRDEC a new and efficient hybrid correction algorithm based on De Bruijn Graphs (DBG) of short reads avoids the time consuming alignments (of SR on LR) LoRMA a complementary tool to LoRDEC for self correction of long reads a pipeline that iterates LoRDEC and apply LoRMA Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
19 Introduction Aperçu of raw and corrected PacBio reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
20 Introduction Aperçu of raw and corrected PacBio reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
21 LoRDEC algorithm Outline 1 Introduction 2 LoRDEC algorithm 3 LoRDEC experimental results Impact of parameters Scalability Correction of transcriptomic reads (RNA-seq) Correction of Oxford Nanopore MINIon reads 4 LoRDEC +LoRMA 5 LoRMA experimental results 6 Conclusion and future works Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
22 LoRDEC algorithm Algorithm overview 1 build a de Bruijn graph of the short reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
23 LoRDEC algorithm Algorithm overview 1 build a de Bruijn graph of the short reads the graph represents the short reads in compact form 2 take each long read in turn and attempt to correct it 1 correct internal regions, 2 correct end regions of the long read Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
24 LoRDEC algorithm Example of short read DBG of order 3 gac acg cga gag gga aac gaa agc caa gca S = {ggacgaa, cgaac, gacgag, cgagcaa, gcaacg} The DBG is built from the set of short reads (Illumina) using the GATB library. Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
25 LoRDEC algorithm Filtering k-mers of short reads Filtering k-mer rationale Because errors are randomly positioned Erroneous k-mers have low expected occurrence numbers Threshold based filter: minimum number of occurrences in short reads All k-mers present more are called solid k-mers and kept in the de Bruijn Graph Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
26 LoRDEC algorithm Example of filtered short read DBG of order 3 gac acg cga gag gac acg cga gag gga aac gaa agc aac gaa caa gca caa gca S = {ggacgaa, cgaac, gacgag, cgagcaa, gcaacg} Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
27 LoRDEC algorithm Long read sequence is partitioned head tail sources targets inner region : solid k-mers of the long read Solid k-mers are a priori correct piece of the sequences we correct the region between two solid k-mers Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
28 LoRDEC algorithm Long read is corrected with DBG bridge path path not found extension path s 1 t 1 s 2 t 2 s 3 For each putative region of a long read: align the region to paths of the de Bruijn graph find best path according to edit distance limited path search Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
29 LoRDEC algorithm LoRDEC: Correcting read ends Find a path in DBG starting from the extreme solid k-mer bridge path Maximize length of the prefix of the end to correct Minimize edit distance between the path and the prefix of the end s 1 t 1 path not found Find best extension maximizing an alignment score s 2 t 2 extension path s 3 Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
30 LoRDEC algorithm Correction algorithm 1 Correct inner region: 1 depth first search traversal of paths between source and target k-mers 2 node wise: minimal edit distance computation with seq region 2 Correct end region: 3 Paths optimisation: 1 build a graph of all correction paths for current read 2 finding a shortest path between the first and last solid k-mers Dijkstra algorithm Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
31 LoRDEC algorithm Trimming and splitting (optional) Classify each base as solid if it belongs to at least one solid k-mer and weak otherwise LoRDEC outputs solid bases in upper case characters and weak ones in lower case characters Corrected reads can be trimmed and/or split: 1 Trim weak bases from both ends of the read 2 Extract all runs of solid bases from the corrected reads Output of LoRDEC: >read1 acgtgagtagtcgagtagcgtagg TGGATCGAGCTAGggggt Trimmed read: >read1 GTAGTCGAGTagcgtagGTGGATCG AGCTAG Trimmed and split reads: >read1 1 GTAGTCGAGT >read1 2 GTGGATCGAGCTAG Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
32 LoRDEC algorithm LoRDEC correction pipline Filtering short-reads data for quality value and adapter presence cutadapt [Martin, 2012] Long reads correction with LoRDEC. Two parameters must be set : k-mer length default k = 19 threshold : minimum abundance for a k-mer to be solid that is, to be included in the de Bruijn graph Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
33 LoRDEC experimental results Outline 1 Introduction 2 LoRDEC algorithm 3 LoRDEC experimental results Impact of parameters Scalability Correction of transcriptomic reads (RNA-seq) Correction of Oxford Nanopore MINIon reads 4 LoRDEC +LoRMA 5 LoRMA experimental results 6 Conclusion and future works Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
34 LoRDEC experimental results Data sets E. coli Yeast Parrot Genome size 4.6 Mbp 12 Mbp 1.23 Gbp PacBio coverage 21x 129x 5.5x Illumina coverage 50x 38x 28x Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
35 LoRDEC experimental results Results: time and memory Data Method CPU time Elapsed time Memory Disk PacBioToCA 45 h 18 min 3 h 12 min E. coli LSC 39 h 48 min 2h 56 min LoRDEC 2 h 16 min 10 min PacBioToCA 792 h 41 min 21 h 57 min Yeast LSC 1200 h 46 min 130 h 16 min LoRDEC 56 h 8 min 3 h 37 min Parrot LoRDEC 568 h 48 min 29 h 7 min Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
36 LoRDEC experimental results Runtime, memory and disk usage Yeast PacBioToCA LSC LoRDEC CPU time (h) Memory (GB) Disk (GB) Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
37 LoRDEC experimental results Evaluation methods Two ways: 1 how do the reads align to the genome? 2 how do raw and corrected reads differ in their alignments? Using the Error Correction Toolkit [Yang et al. 2013] we compute Sensitivity = TP/(TP+FN) how well does the tool recognise erroneous positions? Gain = (TP-FP)/(TP+FN) how well does the tool remove errors without introducing new ones? Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
38 LoRDEC experimental results Error correction performance: E. coli Uncorrected PacBioToCA LSC LoRDEC Data Size Aligned Identity Genome coverage Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
39 LoRDEC experimental results Error correction performance: Parrot Uncorrected LoRDEC Data Size Aligned Identity Genome coverage Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
40 LoRDEC experimental results Sensitivity and gain results Data Method Sens. Gain PacBioToCA NA NA E. coli LSC LoRDEC PacBioToCA 1 NA NA Yeast LSC LoRDEC Parrot LoRDEC Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
41 LoRDEC experimental results Impact of parameters Parameters: E. coli Gain Gain Runtime Runtime (s) k Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
42 LoRDEC experimental results Scalability Scalability of LoRDEC E. coli Yeast Parrot CPU time (h) Memory (GB) Disk (GB) Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
43 LoRDEC experimental results Scalability Scalability of LoRDEC Mais transcriptome data Illumina HiSeq : 194 million of reads, 29 Tbp PacBio : reads, 168 Gbp LoRDEC time: 12 hours LoRDEC memory: 5 Gbytes Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
44 LoRDEC experimental results Correction of transcriptomic reads (RNA-seq) Chicken transcriptome with PacBio PacBio data Raws Corrected and trimmed # reads (x1000) # reads > 1Kbp (x1000) Max length of reads (kbp) Total length (Gbp) %GC Avg length (bp) Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
45 LoRDEC experimental results Correction of transcriptomic reads (RNA-seq) Chicken transcriptome with PacBio After correction and mapping with BWA-MEM on ref. transcriptome (1 RNA per gene) 5% more transcripts covered with uniquely mapping reads 80% id in alignments vs 66% before correction Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
46 LoRDEC experimental results Correction of transcriptomic reads (RNA-seq) Aperçu of raw and corrected PacBio RNA reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
47 LoRDEC experimental results Correction of transcriptomic reads (RNA-seq) Aperçu of raw and corrected PacBio RNA reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
48 LoRDEC experimental results Correction of Oxford Nanopore MINIon reads Correcting E. coli Nanopore data Raw reads + quast Corrected reads + quast Nanopore data Raw Corrected Nb reads Nb reads 1kbp Total length (Mbp) Unaligned bases (%) Genome fraction (%) Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
49 LoRDEC experimental results Correction of Oxford Nanopore MINIon reads MINion S. aureus data Mapping of reads with BWA-MEM onto the reference genome with appropriate options ref génome: 2.8 Mbp MINIon sequencing coverage 14x gain for k = 17 and s = 2 reaches 69% 99, 9 % genome covered by corrected reads 65 % genome at median coverage 8x 79% identity instead of 66 % without correction Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
50 LoRDEC +LoRMA Outline 1 Introduction 2 LoRDEC algorithm 3 LoRDEC experimental results Impact of parameters Scalability Correction of transcriptomic reads (RNA-seq) Correction of Oxford Nanopore MINIon reads 4 LoRDEC +LoRMA 5 LoRMA experimental results 6 Conclusion and future works Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
51 LoRDEC +LoRMA Overview of LoRDEC +LoRMA Modify LoRDEC to run on long reads only = LoRDEC Run LoRDEC iteratively with increasing k Polish the result with multiple alignments = LoRMA increase k PacBio reads LoRDEC LoRMA Corrected reads Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
52 LoRDEC +LoRMA LoRDEC Build a de Bruijn graph of the short reads For each long read: Classify k-mers: solid (= in the DBG) and weak Find paths in the DBG between the solid k-mers Minimize edit distance between the long read and the path s string AGTT TTCC ACGT ACGT CGTT GTTC TTCA TCAA CAAC C CAAC CGTA GTAA TAAC T CCCT AACC ACCC CCCT TAAG Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
53 LoRDEC +LoRMA LoRDEC Build a de Bruijn graph of the LONG reads Use a small k such that the genomic k-mers are expected to be found in the reads Use an abundancy threshold to differentiate between correct and erroneous k-mers For each long read: Classify k-mers: solid (= in the DBG) and weak Find paths in the DBG between the solid k-mers Minimize edit distance between the long read and the path s string Select a correcting path only if all possibilities have been explored. AGTT TTCC ACGT ACGT CGTT GTTC TTCA TCAA CAAC C CAAC CGTA GTAA TAAC T CCCT AACC ACCC CCCT TAAG Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
54 LoRDEC +LoRMA LoRMA Build a de Bruijn graph of the reads Annotate the graph by threading each read through the graph For each read find its friends, i.e. the most similar reads Use a multiple alignment of a read and its friends to correct the read AGTT TTCC CGTT GTTC TTCA TCAA CAAC ACGT AACC ACCC CCCT CGTA GTAA TAAC TAAG Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
55 LoRMA experimental results Outline 1 Introduction 2 LoRDEC algorithm 3 LoRDEC experimental results Impact of parameters Scalability Correction of transcriptomic reads (RNA-seq) Correction of Oxford Nanopore MINIon reads 4 LoRDEC +LoRMA 5 LoRMA experimental results 6 Conclusion and future works Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
56 LoRMA experimental results Evaluation method Process 1 Align the raw and corrected reads to the genome with BLASR 2 Consider a single best alignment. Compute following metrics total size of corrected reads total aligned size of corrected error rate of aligned regions (nb erroneous positions / aligned length) genome coverage Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
57 LoRMA experimental results Selfcorrection: E. coli with k = 19, 40, Original PBcR (self) LoRDEC*+LoRMA (%) Size Aligned GenomeCoverage 0 ErrorRate Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
58 LoRMA experimental results Selfcorrection and hybrid correction: E. coli Original (%) PBcR (self) LoRDEC*+LoRMA LoRDEC proovread PBcR (hybrid) Jabba Size Aligned GenomeCoverage 0 ErrorRate Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
59 LoRMA experimental results Selfcorrection: Yeast Original PBcR (self) LoRDEC*+LoRMA (%) Size Aligned GenomeCoverage 0 ErrorRate Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
60 LoRMA experimental results Selfcorrection and hybrid correction: Yeast Original (%) PBcR (self) LoRDEC*+LoRMA LoRDEC proovread PBcR (hybrid) Jabba Size Aligned GenomeCoverage 0 ErrorRate Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
61 LoRMA experimental results Selfcorrection: Resources PBcR (self) LoRDEC*+LoRMA Runtime(h) Memory(GB) Disk(GB) E. coli 0 Runtime(h) Memory(GB) Disk(GB) Yeast Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
62 LoRMA experimental results Selfcorrection and hybrid correction: Resources PBcR (self) LoRDEC*+LoRMA LoRDEC proovread PBcR (hybrid) Jabba Runtime(h) Memory(GB) Disk(GB) E. coli 0 Runtime(h) Memory(GB) Disk(GB) Yeast Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
63 Conclusion and future works Outline 1 Introduction 2 LoRDEC algorithm 3 LoRDEC experimental results Impact of parameters Scalability Correction of transcriptomic reads (RNA-seq) Correction of Oxford Nanopore MINIon reads 4 LoRDEC +LoRMA 5 LoRMA experimental results 6 Conclusion and future works Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
64 Conclusion and future works Take home message LoRDEC is at least 6 times faster than previous methods uses at least 93% less memory than previous methods corrects both PacBio & Nanopore reads scales up to vertebrate cases achieves similar accuracy as state-of-the-art methods. LoRDEC is freely available at Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
65 Conclusion and future works LoRDEC and LoRMA use GATB Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
66 Conclusion and future works Conclusions LoRDEC +LoRMA [Bioinformatics 2016]: DBG based initial correction of sequencing errors in long read data Further polishing with multiple alignments Accurate selfcorrection method, needs high coverage (75 ) Future: improve memory footprint and running time Freely available at Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
67 Conclusion and future works LoRDEC and LoRMA publications LoRDEC: accurate and efficient long read error correction L. Salmela, E. Rivals Bioinformatics, doi: /bioinformatics/btu538, 30 (24): , Accurate selfcorrection of errors in long reads using de Bruijn graphs L. Salmela, R. Walve, E. Rivals, E. Ukkonen Bioinformatics, doi: /bioinformatics/btw321, Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
68 Conclusion and future works Funding and acknowledgements Thank you for your attention! Questions? Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
69 Supplements Future works Improve time for LoRDEC* iteration and LoRMA Distinguish heterozygote regions Use sequence quality for correction Improve correction in case of branching Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
70 Supplements Limitations Assumption and limitations of LoRDEC It needs hybrid data sets does not use sequence quality prefers uniform sequencing use an abundance threshold for solid k-mers does not model the presence of haplotypes does not incorporate an error model Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
71 Supplements Limitations Alignment procedure on reference transcriptome Reference: one RNA per gene Evaluation of various mapping tools: BWA, Bowtie, BLASR, LAST, Agile et NUCMER Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
72 Additional results Error correction performance: Yeast Uncorrected PacBioToCA LSC LoRDEC Data Size Aligned Identity Genome coverage Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
73 Additional results Parameters: E. coli Gain Gain Runtime Runtime (s) Maximum error rate Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
74 Additional results Genome coverage: Parrot Percentage of Genome e-05 1e-06 Uncorrected 1e-07 Uncorrected (randomized) LoRDEC LoRDEC (randomized) 1e Cumulative read depth Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
75 Additional results Impact on assembly 51-mer distribution for yeast Number of kmers 1e+03 1e+05 1e+07 1e+09 k= 51 Number of kmers 1e+03 1e+05 1e+07 k= Abundance uncorrected Abundance corrected Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
76 Additional results Impact on assembly Impact of correction on assembly of E. coli PacBio Raw PacBio reads + Celera assembly Corrected Pacbio reads + Celera assembly Celera assemblies Raw Pacbio Corrected Pacbio Nb contigs Total length (Mbp) N50 (kbp) Unaligned bases (%) Genome fraction (%) Rivals (CNRS Univ. Montpellier) Long read correction November 7, / 67
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