LoRDEC: hybrid correction of long reads
Overview
In a nutshell, LoRDEC is a program for error correcting long sequencing reads using short reads. It implements a hybrid correction approach. It uses little memory and is very efficient. Most importantly it scales up to process very large data sets. It can be applied to long reads obtained with either Pacific Biosciences SMRT sequencing (SMRT = Single Molecule Real Time) or with Oxford Nanopore MINion technology.
Why is LoRDEC algorithm different?
- It is efficient and can process large read data sets, included from eukaryotic or vertebrate species, on a usual computing server, and even works on desktop/laptop computers.
- It adopts a novel graph based approach: it builds a succinct De Bruijn Graph (DBG) representing the short reads, and seeks a corrective sequence for each erroneous region of a long read by traversing chosen paths in the graph.

For the sake of efficiency, LoRDEC analyzes reads through their k-mers. If a k-mer is error-free, we call it solid (circles).
Long read is viewed by LoRDEC as a series of blocks of error-free k-mers (drawn as rectangles), separated by regions of erroneous k-mers (horizontal line). To bridge erroneous regions, LoRDEC finds path in a De Bruijn graph of the short reads (shown as directed paths of circles outside the horizontal line). Such correction paths are shown as directed paths of circles outside the horizontal line.
More infos
LoRDEC is a bioinformatics software published and initially developed in collaboration with Leena Salmela of University of Helsinki (Finland).
LoRDEC processes data coming from high throughput sequencing machines of the second and third generations. These data are called sequencing reads, or simply reads for short. Technically speaking it processes short reads and long reads to correct errors in the long reads.
Third generation DNA sequencing technologies yield long, but erroneous sequencing reads, while second generation technologies yield short read with low error rate. Hence, the need for correcting the long reads.
LoRDEC has been accepted by the Elixir Service Delivery Plan in 2019, and since then LoRDEC is registered in the ELIXIR bio.tools database under this entry), and documented following the EDAM ontology.
LoRDEC has been used in large genome and transcriptome projects by numerous genomic institutes over the world. Some infos about the first hundred such projects can be found at https://www.lirmm.fr/~rivals/lordec/.
High Performance Computing
We also provide scripts for a parallel use of LoRDEC on High Performance Computing servers. If you want to correct long reads but lack short reads, then you can look at LoRMA, which is non hybrid error correction tool for long reads.

- More information at including FAQ at https://www.lirmm.fr/~rivals/lordec/
- Official releases page at our gitlab server: https://gite.lirmm.fr/lordec/lordec-releases/wikis/home

NB: Elixir is a European organisation that coordinates life science resources from across Europe, and the Institut Français de Bioinformatique is the French node of Elixir.
Funding
This work was supported by
- Academy of Finland [grant number 267591]
- ANR Colib’read (ANR-12-BS02-0008)
- Défi MASTODONS SePhHaDe from CNRS
- Labex NumEV



Other tools
FastME 2.0
FastME is a software package for the fast and accurate inference of phylogenetic trees from distance matrices. It implements algorithms based on the Balanced Minimum Evolution (BME) principle, a distance-based criterion closely related to the Neighbor Joining (NJ) method. The goal of the BME framework is to identify the phylogenetic tree that minimizes the total…
CompPhy
CompPhy: a web-based collaborative platform for comparing phylogenies CompPhy is a web platform dedicated to the collaborative handling of phylogenetic trees. Users can freely manage collections of trees and communicate on a common project. By collaborative, we mean that several users connected to the same project can manipulation at the same time trees from shared…
EPIK: Precise and scalable evolutionary…
EPIK is a program dedicated to « Phylogenetic Placement » (PP) of metagenomic or metabarcoding reads on a reference tree. It is similar in spirit and technically the successor of RAPPAS (Linard et al. 2020). EPIK achieves identical or slightly better accuracy than RAPPAS and outperforms it in speed and flexibility. In many aspects the documentation of RAPPAS…