Abstract Accurate taxonomic classification in metagenomics is often compromised by low-complexity sequences, which lead to chance matches that in turn cause sequences to be misclassified. Here we pres...

Genetic variation among microbial strains of the same species can profoundly influence their phenotypes, ecological functions, and impacts on human health. Traditionally, the relative abundance of a s...

Background The gut microbiome has been widely studied in the context of obesity, and yet the reported associations vary widely across populations and analytical approaches. In Asian populations where ...

Welcome to the -viz suite of tools: classic bioinformatics tools, reimagined for the browser. agviz · bamviz · genbankviz · phispyviz Each of our tools is a classic bioinformatics tool, but reimagi…

Pangenomes are genome references that integrate sequences from multiple individuals into graph-based or multi-haplotype representations, capturing genetic variation beyond a single linear reference. H...

Existing notions of pangenome coordinates rely on hard-to-compute multiple sequence alignments. On the other hand, pangenome-wide exact unique matches (multi-MUMs) can be computed efficiently, and rep...

AbstractMotivation. Estimating mutation rates between evolutionarily related sequences is a central problem in molecular evolution. Due to the rapid expans

Nanopore sequencing provides signal data corresponding to the nucleotide motifs sequenced. Through machine learning-based methods, these signals are translated into long-read sequences that overcome t...

Folddisco enables protein structural motif search in million scale databases.

Randomness is a powerful tool in the design and analysis of algorithms and data structures for nucleotide sequence data. Nucleotide sequences are not themselves random but are often randomized using hash functions. Despite their widespread use in genomics, there is no comprehensive review of the types of hash functions used and their various applications. In this survey intended for bioinformatic methods developers, we divide hash functions into four categories: scattering hash functions, permutations, minimum perfect hash functions, and locality-sensitive hash functions. For each category, we provide examples of both general-use hash functions that have been applied in nucleotide sequence analysis and hash functions that have been designed specifically for nucleotide sequence analysis. We highlight their salient properties, commonalities, differences, and application areas.

Nature - Telomere-to-Telomere Assembly Using HERRO-Corrected Simplex Nanopore Reads

AbstractMotivation. Single-cell sequencing data analysis requires robust quality control (QC) to mitigate technical artifacts and ensure reliable downstrea

Nature Biotechnology - A long-read metagenome assembly method recovers circular and complete genomes better than existing tools.

Nanopore sequencing enables real-time, long-read analysis by processing raw signals as they are produced. A key step, segmentation of signals into events, is typically handled algorithmically, struggl...

A key step in sequence similarity search is to identify shared seeds between a query and a reference sequence. A well-known tradeoff is that longer seeds offer fast searches but reduce sensitivity in ...

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A scientific plotting library in Rust. Contribute to Psy-Fer/kuva development by creating an account on GitHub.