CoLoRMap: Correcting Long Reads by Mapping short reads

Haghshenas, Ehsan; Hach, Faraz; Şahinalp, S. Cenk; Chauve, Cédric

doi:10.1093/bioinformatics/btw463

Cited by 61 publications

(65 citation statements)

References 45 publications

(49 reference statements)

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“…Several error correction algorithms for long reads have been proposed, including PacBioToCA ([6]; the algorithm from the Celera assembler [13]), LSC [8], Proovread [14], CoLoRMap [15], the algorithm from the Cerulean assembler [11], ECTools [16], LoRDEC [17], Jabba [18], DAGCon ([7]; from HGAP assembler), LoRMA [19] and the algorithms from the FALCON and Sprai assemblers (not published). The long read error correction algorithms can be grouped into three classes.…”

Section: Introductionmentioning

confidence: 99%

“…This validation approach is thus called the adjacent alignment based validation approach . Some of the remaining algorithms can also address the error richness problem to an extent by making alignments of several passes with different parameter settings [14, 19] or by aligning one pair of paired-end short reads by referencing the alignments of the other pair [15]. However, none of the existing algorithms could address the lack of reference data problem.…”

Section: Introductionmentioning

confidence: 99%

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HALC: High throughput algorithm for long read error correction

Bao

Lan

2017

BMC Bioinformatics

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BackgroundThe third generation PacBio SMRT long reads can effectively address the read length issue of the second generation sequencing technology, but contain approximately 15% sequencing errors. Several error correction algorithms have been designed to efficiently reduce the error rate to 1%, but they discard large amounts of uncorrected bases and thus lead to low throughput. This loss of bases could limit the completeness of downstream assemblies and the accuracy of analysis.ResultsHere, we introduce HALC, a high throughput algorithm for long read error correction. HALC aligns the long reads to short read contigs from the same species with a relatively low identity requirement so that a long read region can be aligned to at least one contig region, including its true genome region’s repeats in the contigs sufficiently similar to it (similar repeat based alignment approach). It then constructs a contig graph and, for each long read, references the other long reads’ alignments to find the most accurate alignment and correct it with the aligned contig regions (long read support based validation approach). Even though some long read regions without the true genome regions in the contigs are corrected with their repeats, this approach makes it possible to further refine these long read regions with the initial insufficient short reads and correct the uncorrected regions in between. In our performance tests on E. coli, A. thaliana and Maylandia zebra data sets, HALC was able to obtain 6.7-41.1% higher throughput than the existing algorithms while maintaining comparable accuracy. The HALC corrected long reads can thus result in 11.4-60.7% longer assembled contigs than the existing algorithms.ConclusionsThe HALC software can be downloaded for free from this site: https://github.com/lanl001/halc.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1610-3) contains supplementary material, which is available to authorized users.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HALC: High throughput algorithm for long read error correction

Bao

Lan

2017

BMC Bioinformatics

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“…For example, the idea of context-aware seeds may improve long-read mapping. Long reads suffer from high error rates [3,4,13]. Finding error-free seeds for long reads is very challenging [5].…”

Section: Discussionmentioning

confidence: 99%

Context-Aware Seeds for Read Mapping

Xin

Shao

Kingsford

2019

Preprint

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Motivation: Most modern seed-and-extend NGS read mappers employ a seeding scheme that requires extracting t non-overlapping seeds in each read in order to find all valid mappings under an edit distance threshold of t. As t grows (such as in long reads with high error rate), this seeding scheme forces mappers to use more and shorter seeds, which increases the seed hits (seed frequencies) and therefore reduces the efficiency of mappers. Results: We propose a novel seeding framework, context-aware seeds (CAS). CAS guarantees finding all valid mapping but uses fewer (and longer) seeds, which reduces seed frequencies and increases efficiency of mappers. CAS achieves this improvement by attaching a confidence radius to each seed. We prove that all valid mappings can be found if the sum of confidence radii of seeds are greater than t. CAS generalizes the existing pigeonhole-principle-based seeding scheme in which this confidence radius is implicitly always 1. Moreover, we design an efficient algorithm that constructs the confidence radius database in linear time. We experiment CAS with E. coli genome and show that CAS reduces seed frequencies by up to 25.4% when compared with the state-of-the-art pigeonhole-principle-based seeding algorithm, the Optimal Seed Solver.

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“…Given the distinct characteristics of long reads, i.e., significantly higher error rates and lengths, specialized algorithms are needed to correct them. Till date, several error correction tools for long reads have been developed including PacBioToCA [17], LSC [18], ECTools [19], LoRDEC [20], proovread [21], NaS [22], Nanocorr [23], Jabba [24], CoLoRMap [25], LoRMA [26], HALC [27], FLAS [28], FMLRC [29], HG-CoLoR [30] and Hercules [31].…”

Section: Introductionmentioning

confidence: 99%

A comprehensive evaluation of long read error correction methods

Zhang

Jain

Aluru

2019

Preprint

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Background: Third-generation single molecule sequencing technologies can sequence long reads, which is advancing the frontiers of genomics research. However, their high error rates prohibit accurate and efficient downstream analysis. This difficulty has motivated the development of many long read error correction tools, which tackle this problem through sampling redundancy and/or leveraging accurate short reads of the same biological samples. Existing studies to asses these tools use simulated data sets, and are not sufficiently comprehensive in the range of software covered or diversity of evaluation measures used. Results:In this paper, we present a categorization and review of long read error correction methods, and provide a comprehensive evaluation of the corresponding long read error correction tools. Leveraging recent real sequencing data, we establish benchmark data sets and set up evaluation criteria for a comparative assessment which includes quality of error correction as well as run-time and memory usage. We study how trimming and long read sequencing depth affect error correction in terms of length distribution and genome coverage post-correction, and the impact of error correction performance on an important application of long reads, genome assembly. We provide guidelines for practitioners for choosing among the available error correction tools and identify directions for future research.Conclusions: Despite the high error rate of long reads, the state-of-the-art correction tools can achieve high correction quality. When short reads are available, the best hybrid methods outperform non-hybrid methods in terms of correction quality and computing resource usage. When choosing tools for use, practitioners are suggested to be careful with a few correction tools that discard reads, and check the effect of error correction tools on downstream analysis. Our evaluation code is available as open-source at https://github.com/haowenz/LRECE.

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CoLoRMap: Correcting Long Reads by Mapping short reads

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 61 publications

References 45 publications

HALC: High throughput algorithm for long read error correction

HALC: High throughput algorithm for long read error correction

Context-Aware Seeds for Read Mapping

A comprehensive evaluation of long read error correction methods

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