Biology subjects

Majidian, S.

Publications and source records attributed to Majidian, S..

10 recordsLinked to original sources

bioRxiv2026

The Genome In A Bottle HG002 assembly-based variant benchmark set enables comprehensive benchmarking of small and structural variants.

High-accuracy, high-resolution benchmark data are fundamental to improving the accuracy and generalizability of sequencing technologies and associated algorithms. Most existing benchmarks for DNA sequencing rely on mapping to a single linear reference, omitting inaccessible and highly repetitive complex genomic regions. Here, we present the Genome in a Bottle (GIAB) Consortium's HG002 v5.0q benchmark set, a more comprehensive variant benchmark derived from a fully phased, highly curated, and accurate diploid assembly of HG002. Our approach leverages recent advances in long-read sequencing and assembly to provide confident small- and structural-variant calls across previously inaccessible regions, including segmental duplications, homopolymers, tandem repeats, and complex immune loci. The v5.0 benchmark significantly expands the callable genome relative to v4.2.1, adding x200 Mbp of sequence and increasing variant detection by 18 % for small variants and 3-fold for structural variants compared to previous benchmarks. The benchmark regions include more variants in repetitive regions, including 1.2 x segmental duplications, 1.8 x tandem repeats, and 2.0 x homopolymers, compared to v4.2.1. Separate and combined small- and structural-variant benchmarks are available for three versions of the human reference genome (GRCh37, GRCh38, and CHM13). We validate the reliability of this benchmark set by comparing multiple sequencing technologies and variant calling pipelines and curating differences. The v5.0q benchmark provides a critical resource for the development and validation of next-generation sequencing technologies and analysis methods.

genomics↗

bioRxiv2026

Rapid phylogenomic analysis for viral surveillance and metagenomic profiling with Omni2Tree

Phylogenomic surveillance is limited not by sequencing throughput, but by the difficulty of converting heterogeneous raw data into reliable evolutionary inference, particularly for low-titer and contaminated viral field samples. Here we present Omni2Tree, an assembly-free framework that reconstructs viral phylogenies directly from raw sequencing reads and generates easily shareable interactive reports and genome-wide entropy profiles to identify diversification. In H5N1 benchmark analyses, Omni2Tree maintained accurate placement and topological stability even under low coverage, unlike assembly or reference based methods. Omni2Tree generated an annotated phylogeny for 64-sample H5N1 field surveillance dataset from the eastern USA in under 3 hours. Omni2Tree recovered known phylogenetic structure and key variability insights across 1,328 hepatitis C virus and 707 human cytomegalovirus datasets, and resolved co-infecting respiratory viruses in clinical metagenomic samples. By enabling direct analysis from raw reads, Omni2Tree supports faster, more portable, and more decentralized phylogenomic surveillance across outbreak, clinical, and resource-limited settings.

bioinformatics↗

bioRxiv2025

Movi Color: fast and accurate long-read classification with the move structure

The number of reference genomes is rapidly increasing, thanks to advances in long-read sequencing and assembly. While these collections can improve the sensitivity and specificity of classification methods, this requires highly efficient compressed indexes. K-mer-based approaches like Kraken 2 are efficient but limit the analysis to a fixed k-mer length. This is hard for the user to set ahead of time, and suboptimal settings can harm sensitivity and specificity. Methods that use compressed full-text indexes like SPUMONI2 and Cliffy lift this constraint, but are less efficient than k-mer-based tools. Further, these methods either cannot report a full listing of genomes where a match occurs, or cannot scale to large reference databases. We propose new methods and algorithms that use compressed full-text indexes to enable multi-class and taxonomic classification. Unlike past compressed-indexing methods for classification, ours uses the move structure, which is extremely fast thanks to its locality of reference. Our method, called Movi Color, augments the main table of the Movi index. Specifically, Movi Color assigns a "color" to each run of the Burrows-Wheeler Transform according to the subset of genomes from which the run suffixes originated. When the reference is highly repetitive - as is typical when indexing pangenomes or reference databases - only certain colors occur, creating opportunities to compress the index. For species-level classification, Movi Color achieves over 1.6x higher precision and 2x higher recall than Kraken 2 and Metabuli. At the genus level, it achieves 70% higher precision and 80% higher recall. Movi Colors read processing time is 7-20x faster than Metabuli and is a comparable to Kraken 2. Although Movi Color uses more memory than both Kraken 2 and Metabuli, its speed-accuracy trade-off makes it well-suited for real-time or high-throughput scenarios.

bioinformatics↗

bioRxiv2025

EvANI benchmarking workflow for evolutionary distance estimation

1Advances in long-read sequencing technology has led to a rapid increase in high-quality genome assemblies. These make it possible to compare genome sequences across the Tree of Life, deepening our understanding of evolutionary relationships. Average nucleotide identity (ANI) is a distance measure that has been applied to species delineation, building of guide trees, and searching large sequence databases. Since computing ANI is computationally expensive, the field has increasingly turned to sketch-based approaches that use assumptions and heuristics to speed this up. We propose a suite of simulated and real benchmark datasets, together with a rank-correlation-based metric, to study how these assumptions and heuristics impact distance estimates. We call this evaluation framework EvANI. With EvANI, we show that ANIb is the ANI estimation algorithm that best captures tree distance, though it is also the least efficient. We show that k-mer based approaches are extremely efficient and have consistently strong accuracy. We also show that some clades have inter-sequence distances that are best computed using multiple values of k, e.g. k = 10 and k = 19 for Chlamydiales. Finally, we highlight that approaches based on maximal exact matches may represent an advantageous compromise, achieving an intermediate level of computational efficiency while avoiding over-reliance on a single fixed k-mer length.

genomics↗

bioRxiv2024

Orthology inference at scale with FastOMA

The surge in genome data, with ongoing efforts aiming to sequence 1.5M eukaryotes in a decade, could revolutionise genomics, revealing the origins, evolution, and genetic innovations of biological processes. Yet, traditional genomics methods scale poorly with such large datasets. Addressing this, "FastOMA" provides linear scalability, enabling the processing of thousands of eukaryotic genomes within a day. FastOMA maintains the high accuracy and resolution of the well-established OMA approach in benchmarks. FastOMA is available at https://github.com/DessimozLab/FastOMA/.

bioinformatics↗

bioRxiv2024

Inferring the selective history of CNVs using a maximum likelihood model

Copy number variations (CNVs) - structural variations generated by deletion and/or duplication that result in change in DNA dosage - are prevalent in nature. CNVs can drastically affect the phenotype of an organism and have been shown to be both involved in genetic disorders and be used as raw material in adaptive evolution. Unlike single-nucleotide variations, the often large and varied effects of CNVs on phenotype hinders our ability to infer their selective advantage based on the population genetics data. Here, we present a likelihood-based approach, dubbed PoMoCNV, that estimates the evolutionary parameters of CNVs based on population genetics data. As a case study, we analyze the genomics data of 40 strains of Caenorhabditis elegans, representing four different populations. We take advantage of the data on chromatin accessibility to interpret the evolutionary parameters of CNVs inferred by PoMoCNV. We further test the reliability of PoMoCNV by estimating the evolutionary parameters of CNVs for mutation-accumulation experiments in C. elegans with varying levels of genetic drift. SignificanceInferring the evolutionary parameters of copy number variations (CNVs) based on population genetics data is crucial to understand their role in evolution. However, given the diversity in the size and effects of CNVs, such inference poses a challenge. We developed a likelihood-based approach called PoMoCNV to address this issue.

bioinformatics↗

bioRxiv2023

The GIAB genomic stratifications resource for human reference genomes

Stratification of the genome into different genomic contexts is useful when developing bioinformatics software like variant callers, to assess performance in difficult regions in the human genome. Here we describe a set of genomic stratifications for the human reference genomes GRCh37, GRCh38, and T2T-CHM13v2.0. Generating stratifications for the new complete CHM13 reference genome is critical to understanding improvements in variant caller performance when using this new complete reference. The GIAB stratifications can be used when benchmarking variant calls to analyze difficult regions of the human genome in a standardized way. Here we present stratifications in the CHM13 genome in comparison to GRCh37 and GRCh38, highlighting expansions in hard-to-map and GC-rich stratifications which provide useful insight for accuracy of variants in these newly-added regions. To evaluate the reliability and utility of the new stratifications, we used the stratifications of the three references to assess accuracy of variant calls in diverse, challenging genomic regions. The means to generate these stratifications are available as a snakemake pipeline at https://github.com/ndwarshuis/giab-stratifications.

genomics↗

bioRxiv2022

Read2Tree: scalable and accurate phylogenetic trees from raw reads

The inference of phylogenetic trees is foundational to biology. However, state-of-the-art phylogenomics requires running complex pipelines, at significant computational and labour costs, with additional constraints in sequencing coverage, assembly and annotation quality. To overcome these challenges, we present Read2Tree, which directly processes raw sequencing reads into groups of corresponding genes. In a benchmark encompassing a broad variety of datasets, our assembly-free approach was 10-100x faster than conventional approaches, and in most cases more accurate--the exception being when sequencing coverage was high and reference species very distant. To illustrate the broad applicability of the tool, we reconstructed a yeast tree of life of 435 species spanning 590 million years of evolution. Applied to Coronaviridae samples, Read2Tree accurately classified highly diverse animal samples and near-identical SARS-CoV-2 sequences on a single tree--thereby exhibiting remarkable breadth and depth. The speed, accuracy, and versatility of Read2Tree enables comparative genomics at scale.

bioinformatics↗

bioRxiv2021

OMAMO: orthology-based model organism selection

SummaryThe conservation of pathways and genes across species has allowed scientists to use non-human model organisms to gain a deeper understanding of human biology. However, the use of traditional model systems such as mice, rats, and zebrafish is costly, time-consuming and increasingly raises ethical concerns, which highlights the need to search for less complex model organisms. Existing tools only focus on the few well-studied model systems, most of which are complex animals. To address these issues, we have developed Orthologous Matrix and Model Organisms, a software and a website that provide the user with the best simple organism for research into a biological process of interest based on orthologous relationships between the human and the species. The outputs provided by the database were supported by a systematic literature review. Availability and implementationhttps://omabrowser.org/omamo/, https://github.com/DessimozLab/omamo Contactchristophe.dessimoz@unil.ch and sina.majidian@unil.ch

bioinformatics↗

bioRxiv2020

Hap10: reconstructing accurate and long polyploid haplotypes using linked reads

BackgroundHaplotype information is essential for many genetic and genomic analyses, including genotype-phenotype associations in human, animals and plants. Haplotype assembly is a method for reconstructing haplotypes from DNA sequencing reads. By the advent of new sequencing technologies, new algorithms are needed to ensure long and accurate haplotypes. While a few linked-read haplotype assembly algorithms are available for diploid genomes, there are no algorithms yet for polyploids. ResultsThe first haplotyping algorithm designed for 10X linked reads generated from a polyploid genome is presented, built on a typical short-read haplotyping method, SDhaP. Using the input aligned reads and called variants, the haplotype-relevant information is extracted. Next, reads with the same barcodes are combined to produce molecule-specific fragments. Then, these fragments are clustered into strongly connected components which are then used as input of a haplotype assembly core in order to estimate accurate and long haplotypes. ConclusionsHap10 is a novel algorithm for haplotype assembly of polyploid genomes using linked reads. The performance of the algorithms is evaluated in a number of simulation scenarios and its applicability is demonstrated on a real dataset of sweet potato.

bioinformatics↗