Skip to main content
PLOS Computational Biology logoLink to PLOS Computational Biology
. 2020 Jun 26;16(6):e1007981. doi: 10.1371/journal.pcbi.1007981

The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies

Aleksey V Zimin 1,2,*, Steven L Salzberg 1,2,3,4
Editor: Christos A Ouzounis5
PMCID: PMC7347232  PMID: 32589667

Abstract

The introduction of third-generation DNA sequencing technologies in recent years has allowed scientists to generate dramatically longer sequence reads, which when used in whole-genome sequencing projects have yielded better repeat resolution and far more contiguous genome assemblies. While the promise of better contiguity has held true, the relatively high error rate of long reads, averaging 8–15%, has made it challenging to generate a highly accurate final sequence. Current long-read sequencing technologies display a tendency toward systematic errors, in particular in homopolymer regions, which present additional challenges. A cost-effective strategy to generate highly contiguous assemblies with a very low overall error rate is to combine long reads with low-cost short-read data, which currently have an error rate below 0.5%. This hybrid strategy can be pursued either by incorporating the short-read data into the early phase of assembly, during the read correction step, or by using short reads to “polish” the consensus built from long reads. In this report, we present the assembly polishing tool POLCA (POLishing by Calling Alternatives) and compare its performance with two other popular polishing programs, Pilon and Racon. We show that on simulated data POLCA is more accurate than Pilon, and comparable in accuracy to Racon. On real data, all three programs show similar performance, but POLCA is consistently much faster than either of the other polishing programs.


This is a PLOS Computational Biology Software paper.

Introduction

Third-generation sequencing platforms such as Single Molecule Real Time (SMRT) sequencing by Pacific Biosciences (PacBio) and nanopore sequencing by Oxford Nanopore Technologies (ONT) yield reads that can range in size from a few kilobases to more than a megabase. However, both technologies have a relatively high error rate of 8–15%. The types of errors differ between technologies, but with sufficiently deep coverage, most errors can be corrected by using reads to cross-check each other. Another strategy for error correction is to pair the long-read rata with short (100–250bp) Illumina reads, which have error rates below 0.5%. The hybrid strategy requires significantly lower coverage by the more-expensive long reads, which can be replaced by much-cheaper Illumina reads. Using a second technology has the additional advantage that systematic errors in the long reads might not be corrected even with deep coverage, and the Illumina reads can be used to correct these errors. Whole-genome assemblies assembled using a hybrid sequence strategy can thereby obtain an overall error rate of less than 1 error per 100 thousand bases [1].

There are two ways one can use Illumina data in a hybrid genome project. One can either use it early in the process to correct long reads, as is done in the PBcR [2] and MaSuRCA [3] assemblers, or one can use it after the long read assembly has been completed to improve the quality of the consensus by aligning the Illumina reads to the assembly. This latter approach is commonly referred to as “polishing” the consensus. There are several software tools available for polishing assemblies with Illumina data, with the most widely used ones being Pilon [4] and Racon [5]. In this paper we present a novel polishing tool called POLCA (POLishing by Calling Alternatives), which we are distributing with the MaSuRCA assembler package starting with version 3.3.5. The current version of POLCA described in this paper is available in MaSuRCA version 3.4.1. POLCA has three main advantages over the widely used tools Pilon and Racon: (1) it is very fast, (2) it uses very little memory, and (3) it makes more accurate corrections. As our experiments demonstrate, the polished sequence quality is better than the quality achieved by either Pilon or Racon. We also compare POLCA to two newer tools, ntEdit [6] and NextPolish [7]. Compared to the new tools, POLCA has comparable performance to NextPolish and it outperforms ntEdit by wide margin. Its speed, accuracy, and ease of use make POLCA a good tool for assembly polishing.

In the following we present our analysis of POLCA’s performance on three data sets. First, we use a simulated data set where we introduce known random errors into a genome and polish it with reads simulated from the same genome. This lets us compare the polished assembly to the “true” genome sequence. We then test our polishing methods on a set of bacterial genome assemblies produced from Oxford Nanopore data, and on a human genome assembled from PacBio data.

Design and implementation

There are at least two approaches to polishing the consensus sequence of an existing assembly. One is to recover the multi-alignment of the reads by aligning them to the genome assembly, and then re-doing the consensus calculation using the original or additional read data. A second approach is to align the reads to the consensus, identify any locations where the reads indicate a possible error, and then to fix those errors using the read sequences. The first approach, which is followed by Racon and Pilon, is more computationally expensive, but it may work better when assemblies contain a large number of errors. POLCA employs the latter approach.

POLCA is implemented as a bash script program that takes as input a file of Illumina reads and the target assembly to be polished. The outputs are the polished assembly and a VCF (variant call format) file containing the variants used for polishing. The basic outline of the script is to align the Illumina reads to the genome and then call short variants from the alignments. A variant call is treated as a putative error in the consensus if the count of the alternative allele observations is greater than 1 and at least twice the count of the reference allele. Each error is fixed by replacing the error variant with the highest scoring alternative allele suggested by the Illumina reads. The variants can be substitutions or insertions/deletions of one or more bases.

POLCA uses bwa mem [8] to align reads to the assembly, but another short-read aligner can easily be substituted. For variant calling, it uses FreeBayes [9] due to its stability and portability; however, by default FreeBayes can only use a single thread (processor). In POLCA we use shell level multiprocessing FreeBayes to run multiple instances of FreeBayes in parallel, thus significantly speeding up the variant calling. We also tuned its alignment and variant calling parameters to improve sensitivity, specificity, and speed for detecting consensus errors. The FreeBayes binary is included with the POLCA distribution as part of the MaSuRCA package. (Note that POLCA installs with MaSuRCA but can be run independently to polish assemblies produced with third-party assemblers.)

POLCA first builds an index of the target assembly, and then aligns the Illumina reads to the target with bwa. It then uses samtools to sort the alignment (bam) file. For variant calling we run FreeBayes in 5Mb batches, merging the variant call vcf files after all batches finish. We then process the assembly using the computed variant calls in parallel, where the number of batches is equal to the user-specified number of CPUs. We extract all target sequence names, sort them in lexicographic order and split the sorted list into batches. This helps balance the amount of target sequence in each batch, thus balancing the load on the CPUs. Parallel execution is achieved using the “xargs -P” command, which ensures compatibility between different Unix-based systems.

Results

To evaluate POLCA, we compared its performance to two widely used genome polishing tools, Pilon and Racon. We compared using three data sets: first, a simulated data set with Illumina-like reads based on the Arabidopsis thaliana genome, with simulated errors introduced into the genome sequence. The second experiment used a published human NA12878 assembly, sequenced and assembled from Pacific Biosciences SMRT data and available as GenBank accession GCA_001013985.1 [10]. The third experiment used several Klebsiella pneumoniae bacterial genomes sequenced with both Oxford Nanopore and Illumina data in a study in [1].

Simulated data experiments

The faux data set was based on the finished sequence of A. thaliana TAIR1.0 (GenBank accession GCA_000001735.1). We removed all N’s and non-ACGT characters from the genome sequence and called this sequence the “clean” genome. We then set up three experiments where we introduced random errors into the clean genome with probability e at each base. The errors themselves consisted of 90% substitutions, 5% insertions, and 5% deletions. The size of each insertion or deletion error was chosen uniformly at random from the range [1,20]. This ensured that approximately the same number of bases would appear in SNPs and indels (insertions or deletions). All substitutions were random bases differing from the true base; all insertions were random sequences of bases. The code for introducing errors into assembled genomes is included with the MaSuRCA package and its usage is described in the README.md file on GitHub. We created five simulated genomes with e taking values 0.0002, 0.0005, 0.001, 0.0025 and 0.005, which translated into genomic consensus error rates of approximately 0.037%, 0.094%, 0.18%, 0.46%, and 0.92%. We then simulated 30x coverage of the clean genome in simulated 250bp (“Illumina”) paired reads with a 1% error rate, using wgsim (https://github.com/lh3/wgsim) with parameters “-r 0 -e 0.01 -N 7200000–1 250–2 250”. Note that the simulated Illumina reads had an error rate approximately twice as high as that observed in real Illumina reads.

Table 1 compares the performance of POLCA to Pilon on a subset of three experiments; Fig 1 shows the comparisons for all five simulated error rates. Both POLCA and Pilon report all the corrections that they make, allowing us to evaluate the corrections precisely, computing the number of true positives (corrected errors, TP), false positives (corrections made where there was no error, FP), and false negatives (errors that were not corrected, FN). Racon and NextPolish do not report their corrections, so we omitted them from this comparison. (Note that ntEdit [6] performed much worse than any of the other methods, so we did not include it in any of the details results shown here.) Table 1 shows that polishing with POLCA leaves a smaller number of total errors than Pilon across all three error rates. Pilon fixed more substitution errors than POLCA in all three experiments, and in one it fixed more insertion/deletion errors, but in both categories it also introduced many new errors, which resulted in an overall lower error rate for POLCA.

Table 1. Results for error correction by POLCA and Pilon on an A. thaliana genome (total size 119Mb) with three different numbers of simulated errors.

Error rates ranged from 0.1% to 0.46%. Boldface indicates the better values for each experiment in each row.

POLCA Pilon
Experiment (error rate) Exp 1 (0.1%) Exp 2 (0.2%) Exp 3
(0.46%)
Exp 1 (0.1%) Exp 2 (0.2%) Exp 3
(0.46%)
Simulated substitution errors 53,726 107,244 267,896 53,726 107,244 267,896
Substitutions fixed (TP) 48,442 97,093 241,883 49,545 98,825 246,405
Substitutions missed (FN) 5,284 10,151 26,013 4,181 8,419 21,491
Substitution errors
introduced (FP)
4 27 68 2,019 3,887 9,471
Simulated indel errors 57,758 112,894 281,332 57,758 112,894 281,332
Indels fixed (TP) 54,802 107,588 268,702 55,463 107,576 261,279
Indels missed (FN) 2,956 5,306 12,630 2,295 5,318 20,053
Indel errors introduced (FP) 237 708 1,560 2,796 5,543 19,177
Total errors remaining after polishing 8,481 16,192 40,271 11,291 23,167 70,192

Fig 1.

Fig 1

Upper panel (a) shows the results for POLCA, Pilon, Racon, and NextPolish in correcting simulated errors for five different experiments with different numbers of errors introduced into an assembly of the Arabidopsis thaliana genome. Lower panel (b) shows the running times (wall clock time) of each program, measured on a 16-core AMD Opteron system with 128Gb of RAM, running with 16 threads. The run times do not include the time spent on mapping the reads, which was the same for all programs.

Racon and NextPolish do not provide base-by-base output, making it more challenging to compare corrections at the level of granularity shown in Table 1. Therefore, to evaluate Racon, Pilon, NextPolish and POLCA together, we used the Nucmer program from the MUMmer package [11] to align the polished sequence to the clean genome, and computed the alignment identity rate using the dnadiff software, also from the MUMmer package. We then estimated the number of bases in errors by multiplying this implied error rate by the clean genome size, 119,146,348 bp.

Fig 1A compares all four programs over the full range of simulated error rates. POLCA and NextPolish outperformed Racon and Pilon over the entire range of the error rates. POLCA was slightly better or equal to NextPolish for all error rates except for the highest, where NextPolish had a slight edge. POLCA and NextPolish were significantly faster than Pilon and Racon, as shown in Fig 1B. Note that here we only measured the time required for polishing, starting from the sorted, aligned reads, which were input to all three programs.

Human data

We then evaluated the performance of the polishing techniques on a real data set, using a previously published assembly of the NA12878 human genome, GenBank accession GCA_001013985.1. That assembly was produced from PacBio SMRT data [10], and as such it was likely to contain more consensus-level sequence errors than an assembly based on Illumina data. Alignment of this assembly to the GRCh38.p12 human reference genome with nucmer, followed by dnadiff to compute differences, yields an average alignment identity rate of 99.66%.

For polishing this assembly, we used Illumina data for the same subject, NA12878, from the Genome In A Bottle project [12], dataset 140115_D00360_0009_AH8962ADXX, which contains 553,657,530 149-bp reads.

Because the “true” sequence of the NA12878 genome is not known, we evaluated, for each of the three polishing programs, whether the polished genome yielded a better alignment to the GRCh38.p12 sequence. The NA12878 assembly polished with POLCA had the closest alignment by a small margin, with 99.752% identity to GRCh38, while the assemblies polished with NextPolish, Pilon and Racon had 99.750%, 99.746% and 99.749% identity respectively. Thus all four polishing programs gave very similar results in terms of accuracy, however, POLCA and NextPolish ran considerably faster, completing the task in 4 hours and less than 1 hour respectively, while Racon took 15h 39m and Pilon took far longer, 150h 16m.

We note that Pilon is designed to do more than correct single base substitutions and short indel errors, which explains its longer run times. It attempts to identify and correct mis-assembled or collapsed repeats as well, a much more computationally demanding problem.

Bacterial data

We tested the polishing approaches on four Klebsiella pneumoniae assemblies [1], for which all data as well as assemblies were made available at https://github.com/rrwick/Bacterial-genome-assemblies-with-multiplex-MinION-sequencing. We used Canu v1.5 [13] assemblies polished with Nanopolish [14] for isolates 1, 3, 4 and 5 as input to the polishing algorithms. The Canu assemblies were produced by the original authors [1] from Nanopore data alone and are available from the GitHub site. We used Illumina data from the corresponding isolates for polishing. The Illumina coverage depth for each isolate is shown in Table 2. We evaluated the polished assemblies by aligning them to the final, published sequences. The authors estimated that the error rates for those published sequences are below 0.00009%, i.e., the sequences are nearly perfect. We aligned the original Nanopore-only assemblies and the polished assemblies to the final sequences using MUMmer and then evaluated the average identity rate as described above for the Arabidopsis genomes. As shown in Table 2, POLCA performs as well as NextPolish and better than Pilon and Racon on these bacterial assemblies. All four programs improved the original (nanopore-only) assemblies substantially.

Table 2. Polishing results for four Klebsiella pneumoniae isolates.

The columns list average identity rates for 1-to-1 best alignments of the polished assemblies to the finished sequences of the isolates. In bold we highlight the best result and any result within 0.01% of the best.

Isolate barcode Illumina coverage depth Canu+Nanopolish Initial (%) POLCA (%) Pilon (%) Racon (%) NextPolish (%)
01 60x 99.62 99.96 99.96 99.76 99.94
03 38x 99.01 99.89 99.86 99.88 99.90
04 44x 99.79 99.93 99.89 99.88 99.94
05 68x 99.35 99.98 99.97 99.68 99.98

Combining polishing tools

Because the programs use different algorithms for error correction, we ran an additional experiment to determine if users might benefit from running combinations of the programs on the same genome. Using simulated Arabidopsis data with a consensus error rate of 0.18%, we ran all combinations of two programs, in both orders, to polish the sequence. Table 3 compares the performance of the various pairs of polishing programs in this experiment. The fewest total errors were achieved by running POLCA followed by NextPolish. POLCA alone produced the fewest errors of any single program, however results were further improved by adding NextPolish to the protocol.

Table 3. Total number of erroneous bases (lower is better) remaining in the Arabidopsis thaliana genome with 231,929 introduced errors after polishing by two methods run consecutively.

The program shown in each row was run first, followed by the program shown in each column. The “Single run” column shows the number of errors remaining after a single run of each program. Note that in some cases the total number of errors increases after running two programs consecutively, such as after using Pilon or Racon on assemblies polished with NextPolish or POLCA.

Single run POLCA Pilon Racon NextPolish
POLCA 13365 13363 13482 13961 13250
Pilon 18974 13360 16103 14217 13489
Racon 14646 14193 14686 14317 14194
NextPolish 13372 13372 13615 13721 13372

Availability

POLCA is distributed freely under the GPLv3 license as part of the MaSuRCA genome assembly toolkit at https://github.com/alekseyzimin/masurca.

Conclusion and future directions

POLCA provides an effective way to correct single-base substitution and short insertion/deletions errors in draft genome assemblies. On simulated data, it proved to be more accurate than Pilon and Racon and equivalent to the newer NextPolish method. POLCA was faster than Racon and Pilon, but slower than NextPolish. On simulated data, the most accurate polishing was achieved by using a combination of both POLCA and NextPolish. On real human and bacterial genome data, POLCA and NextPolish performed similarly, and better than Pilon and Racon, although POLCA appeared to be marginally better for human genome polishing. Our future plans for POLCA include continued maintenance to ensure the best performance with the latest sequencing data and speed improvements to stay competitive with the best available alternative software.

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

This work was supported in part by the USDA National Institute of Food and Agriculture https://nifa.usda.gov/ under grant 2018-67015-28199), by National Science Foundation https://www.nsf.gov/ grant IOS-1744309, and by National Institutes of Health https://www.nih.gov/ grants R01-HG006677 and R35-GM130151. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Wick RR, Judd LM, Gorrie CL, Holt KE. Completing bacterial genome assemblies with multiplex MinION sequencing. Microbial Genomics. 2017. October;3(10). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Berlin K, Koren S, Chin CS, Drake JP, Landolin JM, Phillippy AM. Assembling large genomes with single-molecule sequencing and locality-sensitive hashing. Nature Biotechnology. 2015. June;33(6):623 10.1038/nbt.3238 [DOI] [PubMed] [Google Scholar]
  • 3.Zimin AV, Puiu D, Luo MC, Zhu T, Koren S, Marçais G, et al. Hybrid assembly of the large and highly repetitive genome of Aegilops tauschii, a progenitor of bread wheat, with the MaSuRCA mega-reads algorithm. Genome Research. 2017. May 1;27(5):787–92. 10.1101/gr.213405.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, et al. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PloS ONE. 2014. November 19;9(11):e112963 10.1371/journal.pone.0112963 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Vaser R, Sović I, Nagarajan N, Šikić M. Fast and accurate de novo genome assembly from long uncorrected reads. Genome Research. 2017. May 1;27(5):737–46. 10.1101/gr.214270.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.Warren RL, Coombe L, Mohamadi H, Zhang J, Jaquish B, Isabel N, et al. ntEdit: scalable genome sequence polishing. Bioinformatics. 2019. November 1;35(21):4430–2. 10.1093/bioinformatics/btz400 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Hu J, Fan J, Sun Z, Liu S. NextPolish: a fast and efficient genome polishing tool for long read assembly. Bioinformatics (Oxford, England). 2019 Nov.Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome Research. 2017. May 1;27(5):722–36. 10.1101/gr.215087.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Li H, Durbin R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics. 2009. July 15;25(14):1754–60. 10.1093/bioinformatics/btp324 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Garrison E, Marth G. Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:1207.3907. 2012 Jul 17.
  • 10.Pendleton M, Sebra R, Pang AW, Ummat A, Franzen O, Rausch T, et al. Assembly and diploid architecture of an individual human genome via single-molecule technologies. Nature Methods. 2015. August;12(8):780 10.1038/nmeth.3454 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Marçais G, Delcher AL, Phillippy AM, Coston R, Salzberg SL, Zimin A. MUMmer4: a fast and versatile genome alignment system. PLoS Computational Biology. 2018. January 26;14(1):e1005944 10.1371/journal.pcbi.1005944 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Zook JM, Chapman B, Wang J, Mittelman D, Hofmann O, Hide W, et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nature Biotechnology. 2014. March;32(3):246 10.1038/nbt.2835 [DOI] [PubMed] [Google Scholar]
  • 13.Koren S, Walenz BP, Berlin K, Miller JR, Bergman NH, Phillippy AM. Canu: scalable and accurate long-read assembly via adaptive k-mer weighting and repeat separation. Genome research. 2017. May 1;27(5):722–36. 10.1101/gr.215087.116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Loman NJ, Quick J, Simpson JT. A complete bacterial genome assembled de novo using only nanopore sequencing data. Nature Methods. 2015. August;12(8):733 10.1038/nmeth.3444 [DOI] [PubMed] [Google Scholar]
PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007981.r001

Decision Letter 0

Christos A Ouzounis, William Stafford Noble

15 Apr 2020

Dear Dr. Zimin,

Thank you very much for submitting your manuscript "The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. 

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Christos A. Ouzounis

Associate Editor

PLOS Computational Biology

William Noble

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: This manuscript describes a new pipeline for correcting long-read genome assemblies using short-read sequences. POLCA was compared against Pilon and Racon and demonstrated advantages over both. Comments below are provided to improve the utility of the manuscript.

I attempted to install MaSuRCA-v3.3.5 on a local workstation running openSUSE 15.0 but received a fatal error regarding the lack of the file 'xlocale.h'. Apparently this file was removed since glibc 2.26 so I had to form a symbolic link to '/etc/local.h'. The authors should fix this in the next distribution, or at least provide this information in the Github site. It seems that someone else already reported installation errors (issues #148 and #151 in Github). Lack of clean installs will limit the use of this tool.

The use of Freebayes for correction, and then batching for parallel correction, is very much like the pipeline described in the Vertebrate Genome Project (https://github.com/VGP/vgp-assembly/tree/master/pipeline/freebayes-polish). Are the authors affiliated with VGP? If so, then please credit this github repository. If not, please explain how POLCA is different from the VGP pipeline.

In the first paragraph of Introduction, please provide a reference to support the overall error rate of <1/100000 bases in hybrid assemblies.

On page 6 in the first paragraph of Simulated data experiments, the authors describe how the errors were introduced in the simulated Illumina reads (wgsim) - please describe how random errors were introduced in the clean genome so that one may reproduce the experiment.

When BWA maps an Illumina read from a repetitive region, doesn't it map the read to one of the repeats that is chosen at random, therefore isn't it possible for this read to provide incorrect correction at repetitive regions? This is an important drawback of Illumina correction of large genome assemblies so it would be useful to know how POLCA take this into account.

On page 10, Table 3: To help the table 'stand alone', it would be useful to know the original number of errors in the genome (perhaps in the Table header) and a column at the beginning to show the number of errors after a single run of each algorithm. The reader could more easily compare the utility and advantage of a second round of polishing.

Minor corrections

On page 6 in the first paragraph, the authors probably want to change '00.1%' to '0.1%'.

In the third line on page 7, 'smaller' instead of 'small'.

Reviewer #2: The authors present POLCA, a new genome polishing tool that used Illumina data to improve genome assemblies performed using long read technologies such as Nanopore and PacBio. They state that their newly developed tool performs faster than RACON and Pilon, the two most widely used tools, and with a comparable or higher accuracy. The authors mention three main advantages of POLCA: its speed, its low memory usage and its accuracy. To demonstrate this, they use three different examples: simulated data, human data and bacterial data. Finally, they assess the effect of using a combination of strategies.

The authors performed several tests to demonstrate their claims and thus the usefulness of their tool. The manuscript is well written and easy to follow. Also, their tool is easy to install and use. While POLCA is distributed as part of the MaSuRCA package it can also be used independently, thus it is a flexible tool. Additionally, the fact that it is distributed along with a widely used genome assembler will allow it reaching a large number of potential users.

Major Points:

While the authors have compared their tool with the most widely used tools, there are some more recent tools such as NextPolish or ntEdit which also claim to be faster than RACON and Pilon and similar accuracy levels. I consider that the manuscript would benefit from comparing the performance of some of these new tools with POLCA.

Minor Points:

The authors mention that “Whole-genome assemblies assembled using a hybrid sequence strategy can thereby obtain an overall error rate of less than 1 error per 100 thousand bases.” How have they calculated this number, or which is the reference used for this calculation?

The authors state that they empirically calculated the allele frequency threshold used by POLCA to be 2. I think that the manuscript would benefit from a more comprehensive description of the information and methodology used to determine the threshold.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, PLOS recommends that you deposit laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007981.r003

Decision Letter 1

Christos A Ouzounis, William Stafford Noble

25 May 2020

Dear Dr. Zimin,

We are pleased to inform you that your manuscript 'The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Christos A. Ouzounis

Associate Editor

PLOS Computational Biology

William Noble

Deputy Editor

PLOS Computational Biology

***********************************************************

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Revision is improved and should be published

Reviewer #2: The authors have addressed all of the comments and the updated results further highlight the usefulness of their tool. I would only suggest slightly modifying the abstract to reflect the extra work performed by the authors to compare POLCA with the newer polishers and not only with the two most popular ones.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

**********

PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

PLoS Comput Biol. doi: 10.1371/journal.pcbi.1007981.r004

Acceptance letter

Christos A Ouzounis, William Stafford Noble

12 Jun 2020

PCOMPBIOL-D-20-00106R1

The genome polishing tool POLCA makes fast and accurate corrections in genome assemblies

Dear Dr Zimin,

I am pleased to inform you that your manuscript has been formally accepted for publication in PLOS Computational Biology. Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

Soon after your final files are uploaded, unless you have opted out, the early version of your manuscript will be published online. The date of the early version will be your article's publication date. The final article will be published to the same URL, and all versions of the paper will be accessible to readers.

Thank you again for supporting PLOS Computational Biology and open-access publishing. We are looking forward to publishing your work!

With kind regards,

Laura Mallard

PLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom ploscompbiol@plos.org | Phone +44 (0) 1223-442824 | ploscompbiol.org | @PLOSCompBiol

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Attachment

    Submitted filename: Response to reviews.docx

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.


    Articles from PLoS Computational Biology are provided here courtesy of PLOS

    RESOURCES