Learn More
The de Bruijn graph data structure is widely used in next-generation sequencing (NGS). Many programs, e.g. de novo assemblers, rely on in-memory representation of this graph. However, current techniques for representing the de Bruijn graph of a human genome require a large amount of memory (≥30 GB). We propose a new encoding of the de Bruijn graph, which(More)
SUMMARY Counting all the k-mers (substrings of length k) in DNA/RNA sequencing reads is the preliminary step of many bioinformatics applications. However, state of the art k-mer counting methods require that a large data structure resides in memory. Such structure typically grows with the number of distinct k-mers to count. We present a new streaming(More)
BACKGROUND The process of generating raw genome sequence data continues to become cheaper, faster, and more accurate. However, assembly of such data into high-quality, finished genome sequences remains challenging. Many genome assembly tools are available, but they differ greatly in terms of their performance (speed, scalability, hardware requirements,(More)
MOTIVATION Genome assembly tools based on the de Bruijn graph framework rely on a parameter k, which represents a trade-off between several competing effects that are difficult to quantify. There is currently a lack of tools that would automatically estimate the best k to use and/or quickly generate histograms of k-mer abundances that would allow the user(More)
MOTIVATION Efficient and fast next-generation sequencing (NGS) algorithms are essential to analyze the terabytes of data generated by the NGS machines. A serious bottleneck can be the design of such algorithms, as they require sophisticated data structures and advanced hardware implementation. RESULTS We propose an open-source library dedicated to genome(More)
The analysis of next-generation sequencing data from large genomes is a timely research topic. Sequencers are producing billions of short sequence fragments from newly sequenced organisms. Computational methods for reconstructing whole genomes/transcriptomes (de novo assemblers) are typically employed to process such data. However, these methods require(More)
Nowadays, metagenomic sample analyses are mainly achieved by comparing them with a priori knowledge stored in data banks. While powerful, such approaches do not allow to exploit unknown and/or "unculturable" species, for instance estimated at 99% for Bacteria. This work introduces Compareads, a de novo comparative metagenomic approach that returns the reads(More)
MOTIVATION Insertions play an important role in genome evolution. However, such variants are difficult to detect from short-read sequencing data, especially when they exceed the paired-end insert size. Many approaches have been proposed to call short insertion variants based on paired-end mapping. However, there remains a lack of practical methods to detect(More)
Detecting single nucleotide polymorphisms (SNPs) between genomes is becoming a routine task with next-generation sequencing. Generally, SNP detection methods use a reference genome. As non-model organisms are increasingly investigated, the need for reference-free methods has been amplified. Most of the existing reference-free methods have fundamental(More)
MOTIVATION As the quantity of data per sequencing experiment increases, the challenges of fragment assembly are becoming increasingly computational. The de Bruijn graph is a widely used data structure in fragment assembly algorithms, used to represent the information from a set of reads. Compaction is an important data reduction step in most de Bruijn graph(More)