Error detection and correction

Discrete mathematics

Transcriptome

De Bruijn sequence

Computer network

Mathematics

Biology

Multiple sequence alignment

Gene

Operating system

Algorithm

Data mining

Gene expression

Raw data

Polishing

Peptide sequence

Parallel computing

Composite material

Chemistry

Sequence assembly

Materials science

Programming language

Resource (disambiguation)

Sequence (biology)

Computer science

Computation

Process (computing)

Sequence alignment

Scalability

Genetics

Biochemistry

Speedup

Database

Scientific Reports

Third-generation sequencing technologies allow to sequence long reads of tens of kbp, that are expected to solve various problems. However, they display high error rates, currently capped around 10%. Self-correction is thus regularly used in long reads analysis projects. We introduce CONSENT, a new self-correction method that relies both on multiple sequence alignment and local de Bruijn graphs. To ensure scalability, multiple sequence alignment computation benefits from a new and efficient segmentation strategy, allowing a massive speedup. CONSENT compares well to the state-of-the-art, and performs better on real Oxford Nanopore data. Specifically, CONSENT is the only method that efficiently scales to ultra-long reads, and allows to process a full human dataset, containing reads reaching up to 1.5 Mbp, in 10 days. Moreover, our experiments show that error correction with CONSENT improves the quality of Flye assemblies. Additionally, CONSENT implements a polishing feature, allowing to correct raw assemblies. Our experiments show that CONSENT is 2-38x times faster than other polishing tools, while providing comparable results. Furthermore, we show that, on a human dataset, assembling the raw data and polishing the assembly is less resource consuming than correcting and then assembling the reads, while providing better results. CONSENT is available at https://github.com/morispi/CONSENT .

Scalable long read self-correction and assembly polishing with multiple sequence alignment