Accurate self-correction of errors in long reads using de Bruijn graphs

deBGR: an efficient and near-exact representation of the weighted de Bruijn graph

MOTIVATION

Almost all de novo short-read genome and transcriptome assemblers start by building a representation of the de Bruijn Graph of the reads they are given as input. Even when other approaches are used for subsequent assembly (e.g. when one is using 'long read' technologies like those offered by PacBio or Oxford Nanopore), efficient k -mer processing is still crucial for accurate assembly, and state-of-the-art long-read error-correction methods use de Bruijn Graphs. Because of the centrality of de Bruijn Graphs, researchers have proposed numerous methods for representing de Bruijn Graphs compactly. Some of these proposals sacrifice accuracy to save space. Further, none of these methods store abundance information, i.e. the number of times that each k -mer occurs, which is key in transcriptome assemblers.

RESULTS

We present a method for compactly representing the weighted de Bruijn Graph (i.e. with abundance information) with essentially no errors. Our representation yields zero errors while increasing the space requirements by less than 18-28% compared to the approximate de Bruijn graph representation in Squeakr. Our technique is based on a simple invariant that all weighted de Bruijn Graphs must satisfy, and hence is likely to be of general interest and applicable in most weighted de Bruijn Graph-based systems.

AVAILABILITY AND IMPLEMENTATION

https://github.com/splatlab/debgr .

CONTACT

rob.patro@cs.stonybrook.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

HALC: High throughput algorithm for long read error correction

BACKGROUND

The 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.

RESULTS

Here, 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.

CONCLUSIONS

The HALC software can be downloaded for free from this site: https://github.com/lanl001/halc .

Reproducibility of Digital PCR Assays for Circulating Tumor DNA Analysis in Advanced Breast Cancer

Circulating tumor DNA (ctDNA) analysis has the potential to allow non-invasive analysis of tumor mutations in advanced cancer. In this study we assessed the reproducibility of digital PCR (dPCR) assays of circulating tumor DNA in a cohort of patients with advanced breast cancer and assessed delayed plasma processing using cell free DNA preservative tubes. We recruited a cohort of 96 paired samples from 71 women with advanced breast cancer who had paired blood samples processed either immediately or delayed in preservative tubes with processing 48–72 hours after collection. Plasma DNA was analysed with multiplex digital PCR (mdPCR) assays for hotspot mutations in PIK3CA, ESR1 and ERBB2, and for AKT1 E17K. There was 94.8% (91/96) agreement in mutation calling between immediate and delayed processed tubes, kappa 0.88 95% CI 0.77–0.98). Discordance in mutation calling resulted from low allele frequency and likely stochastic effects. In concordant samples there was high correlation in mutant copies per ml plasma (r² = 0.98; p<0.0001). There was elevation of total cell free plasma DNA concentrations in 10.3% of delayed processed tubes, although overall quantification of total cell free plasma DNA had similar prognostic effects in immediate (HR 3.6) and delayed (HR 3.0) tubes. There was moderate agreement in changes in allele fraction between sequential samples in quantitative mutation tracking (r = 0.84, p = 0.0002). Delayed processing of samples using preservative tubes allows for centralized ctDNA digital PCR mutation screening in advanced breast cancer. The potential of preservative tubes in quantitative mutation tracking requires further research.