Algorithms and Data Compression

The paper introduces CBL, a dynamic and exact k-mer set representation that outperforms existing methods and supports in-place set operations.

The paper introduces a new sequence retrieval method using pattern mining from cross-species omic data, showing it can retrieve homologous sequences with 50% identity without alignment.

The paper develops algorithms to find high-density clusters in graph representations of pangenomes by solving a path-finding problem in weighted directed graphs.

The paper introduces Genozip Deep, a method for losslessly co-compressing FASTQ and BAM files, achieving significant compression improvements over existing methods.

This paper introduces a new sketching method for genomic data that reduces space and memory usage while maintaining efficiency and accuracy compared to existing tools.

The paper introduces a new compression tool for colored de Bruijn graphs, achieving better compression than existing tools across various datasets.

The paper introduces Aird-ComboComp, a compression framework for mass spectrometry data that improves compression ratios and decompression speed compared to existing methods.

The paper introduces SMT, a data structure for efficient kmer counting and motif discovery in genomic data, showing its effectiveness in handling noisy datasets and large-scale analysis.

The paper introduces μ-PBWT, a memory-efficient method for haplotype matching, reducing memory usage by up to 25 times and enabling storage of large datasets on a commodity laptop.

The paper introduces masked superstrings as a unified framework for k-mer set representations, showing they are NP-hard to compute and proposing greedy heuristics for efficient implementation.

The paper introduces a recursive prefix-free parsing method to improve the efficiency of building pangenome alignment indexes, reducing construction time and memory usage.

This paper introduces masked minimizers, a unifying framework for sequence sketching methods, and proposes a new metric to evaluate and optimize them for better genomic analysis.

The paper presents efficient data structures for storing and querying the positional Burrows-Wheeler Transform (PBWT) to improve memory usage for large haplotype datasets.

Genozip 14 improves BAM and CRAM compression, achieving up to 2.7x better compression than CRAM 3.1 across various genomic data types.

The paper introduces k-MEMs and extends the MONI index to efficiently find them for pangenome-to-pangenome comparison.

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 introduces a method using IBLTs and syncmers to efficiently estimate Jaccard similarity and identify differing k-mers between genomic datasets.

This paper introduces an improved weighted k-mer dictionary that compresses k-mer counts more efficiently than existing methods.

This paper introduces the Spectral Burrows-Wheeler Transform (SBWT) and develops succinct index structures for efficient k-spectrum membership queries in genomic data.

AGC provides a compact representation of assembled genomes, reducing storage space by 2-3 orders of magnitude while enabling efficient extraction of contig parts.

The paper improves the memory and runtime complexity of the WFA algorithm and integrates its scoring system into standard alignment frameworks.

ABRIDGE compresses SAM alignment files with lossless and lossy options, achieving higher compression ratios than existing tools for RNA-Seq and DNA-Seq.

The paper introduces Matchtigs, a method to compress kmer sets with a polynomial algorithm and greedy heuristic, achieving significant size reduction and performance improvements in downstream applications.

This paper introduces Counting de Bruijn graphs for efficiently indexing and querying sequencing data, achieving smaller storage and faster construction compared to existing methods.

The paper introduces a new sequence alignment algorithm called block aligner that is faster than existing methods while maintaining accuracy on realistic datasets.

The paper introduces NIQKI, a novel indexing method for genomic databases that improves query speed and scalability compared to existing approaches.

The paper introduces BionetBF, a novel Bloom Filter for fast membership identification in biological networks, showing high performance and low memory usage.

MetaProFi is a Bloom filter-based tool that stores and queries sequence data, enabling efficient and accurate identification of functionally relevant genetic variants by supporting amino acid and translated nucleotide sequences.

MONI is a pangenomics index that efficiently finds maximal exact matches by combining prefix-free parsing with threshold calculation, outperforming existing tools in speed and memory usage.

The paper introduces VSTseed, a method for generating periodic spaced seeds that can locate reads with substitutions in a reference sequence, and provides SIMD instructions for efficient processing.