Pierre Peterlongo

This study evaluates Sourmash's ability to profile microbial strains from long-read metagenomes, showing accurate gene content recovery and abundance estimation.

The paper introduces a method to interpolate and extrapolate node counts in colored compacted de Bruijn graphs to compare pangenomes while accounting for genome number variability and rare sequence influence.

The paper introduces kmcomp, a lossless compression method for k-mer matrices that enables fast random access, achieving significant storage savings with minimal query overhead.

Kaminari improves approximate k-mer query performance by using k-mer minimizers and integer compression, outperforming Bloom filters in memory and speed with minimal false positive impact.

The paper introduces the Backpack Quotient Filter (BQF), a data structure for querying k-mer abundance with improved space efficiency compared to the Counting Quotient Filter.

The paper introduces a tool called back_to_sequences that efficiently finds sequences containing specific k-mers from large datasets.

Assembling the Sequence Read Archive (27.3 million accessions) with over 100-fold compression enables minutes-scale k-mer search and mining of enzymes, plasmids, proteins and viral reactivation.

Cdbgtricks improves the efficiency of updating compacted de Bruijn graphs by indexing them for fast k-mer location and subgraph queries.

The paper introduces kmindex for fast indexing of complex genomic datasets and ORA for real-time querying of the Tara Oceans metagenomes.

The paper introduces fimpera, an algorithm that improves counting Approximate Membership Query data structures by reducing false positives and filter size while maintaining precision.

The paper evaluates metagenomic software tools using realistic datasets, highlighting improvements in assembly and taxon profiling while identifying remaining challenges in virus/archaea detection and reproducibility.

kmtricks improves Bloom filter construction for large sequencing data by efficiently counting k-mers and preserving low-abundant ones, enhancing metagenomics analysis.

StrainFLAIR uses variation graphs to estimate strain-level abundances in metagenomic samples, showing effectiveness on both simulated and real datasets.

findere reduces false-positive rates in approximate membership queries for k-mers without increasing space or memory usage.

The paper introduces metaVaR, a reference-free method for metagenomic population genomics using metavariant species models to estimate genomic differentiation and detect natural selection loci.

DiscoSnp-RAD efficiently detects small variants in RAD-Seq data, revealing population structure and outperforming existing tools in speed.

SVJedi genotypes known structural variants from long read sequencing data with high accuracy, outperforming existing methods for long reads.

The paper investigates population-scale allele-specific expression (psASE) in Oithona similis using metagenomic and metatranscriptomic data, finding that psASE is influenced by population genomic differentiation and linked to natural selection.

ELECTOR evaluates long reads correction methods by providing efficient and comprehensive metrics to assess quality improvements.

The paper introduces Bcool, a short-read correction method using compacted de Bruijn graphs, achieving higher accuracy and scalability than existing k-mer-based approaches.

This paper introduces a de novo clustering algorithm for long-read transcriptomics data, showing improved performance for Oxford Nanopore reads and non-model species.

The paper introduces a method to separate host and symbiont reads in holobiont transcriptomes, improving assembly quality and enabling functional analysis.

DiscoSnp++ detects SNPs and small indels from raw NGS data without a reference genome, achieving high precision and recall on simulated and real datasets.

The paper benchmarks computational metagenomics software using complex datasets, showing strengths and limitations in assembly, binning, and taxonomic profiling.

This paper reviews computational pan-genomics, discussing its status, potential, and challenges across multiple biological disciplines.