The rapid growth in publicly available genome assemblies has made selecting genomes suitable for downstream analyses increasingly challenging. Differences in assembly and annotation quality can influence gene completeness, duplication rates, contiguity, repeat representation, and other characteristics. Assessing genome quality therefore requires integrating multiple complementary quality metrics that are often generated by independent tools. Here, we present nf-core/genomeqc, a workflow for assessing and comparing genome assemblies. The pipeline accepts RefSeq/GenBank accessions for automatic genome and annotation retrieval, or local genome (FASTA) and annotation (GFF3/GTF) files. It integrates complementary analyses of assembly contiguity, gene completeness, annotation quality, repeat content and other quality metrics using tools such as BUSCO, QUAST, Merqury, and AGAT, before combining the results on a phylogenetic tree for visualisation and comparison across species. GenomeQC is implemented in Nextflow within the nf-core framework, providing an accessible, reproducible, scalable and community-driven workflow for genome quality assessment.
The GenomeCompendium is released, a public database and interactive analysis tool for complete prokaryotic genomes and it is shown that complex, repeat-rich genomes are more common than previously estimated.
Tiberiu Totu, Garance Jaques, B. Heiniger et al.· bioRxiv· 0 citations
Whole-genome comparison is fundamental for validating genome assemblies and investigating genome evolution, yet identifying suitable reference genomes and interpreting chromosome-scale synteny from often noisy nucleotide alignments remain challenging. We introduce Hobrac, an automated workflow that addresses these two major bottlenecks by combining automated reference genome selection with gene-based structural comparisons. Starting from a genome assembly and its taxon identifier, Hobrac identifies suitable reference genomes, complements nucleotide alignments with conserved BUSCO orthologues, and generates publication-quality visualizations. The workflow produces dotplots, ribbon-plots and synteny visualization that can be explored interactively or offline. Hobrac is freely available at https://github.com/Genoscope-LBGB/hobrac.
B. Istace, F. Denoeud, Emilie Téodori et al.· bioRxiv· 0 citations
Genome-wide association studies (GWAS) play a crucial role in unraveling the genetic foundations of complex traits in plants but are also hampered by the application of heterogeneous tools, incompatible file formats and disparate computational environments. Existing GWAS frameworks are often restricted to a single linear reference genome, limiting the capacity for the analysis of structural variations and presence/absence variations (PAV) within plant populations. These issues pose obstacles to reproducibility, scalability, and comprehensive investigations. Here, we present PlantOmicsGWAS, an open-source Python framework for reproducible plant genome-wide association analysis and genomic prediction. It integrates reference indexing, FASTQ quality control, alignment, variant calling, VCF normalization, PLINK conversion, linkage disequilibrium analysis, population-structure estimation, association testing, marker scoring, genomic prediction, and visualization within a unified Linux and HPC workflow. The framework supports conventional linear-reference analyses and includes an optional pangenome-oriented module for working with multiple assemblies and graph-derived variation. Using a Vitis benchmark dataset containing 120 accessions and 118,247 graph-derived variants, PlantOmicsGWAS reduced manual workflow fragmentation and generated standardized association outputs. This tool provides a modular and extensible platform for plant GWAS and pan-GWAS workflows while retaining compatibility with established command-line tools and common genotype formats. The GWAS workflow described herein is adaptable to a range of sequencing methods and plant genomes, bridging research on crop related issues across various biological levels, from the individual organism to entire populations. PlantOmicsGWAS implements Bayesian sparse linear mixed modeling (BSLMM) through GEMMA for multi-trait association discovery, while also supporting FaST-LMM, regression-based approaches, and machine-learning algorithms (Random Forest, XGBoost) as benchmarking alternatives. The PlantOmicsGWAS, a versatile toolkit is available at GitHub https://github.com/plantomicsgwas1-boop/PlantOmicsGwas_V1 and on Linux and HPC platform (https://pypi.org/project/PlantOmicsGwas/1.0.2/). Working Model
Falak Sher Khan, Ahmed S. Yassin, Shams ur Rehman et al.· bioRxiv· 0 citations
Continuous progress in genome sequencing and assembly, coupled with the growing availability of massive resources of long-read genomic sequences and high-quality molecular markers, based on single nucleotide polymorphisms (SNPs) and insertions-deletions (indels), demands accurate methods to easily, accurately and readily determine marker positions across genome versions. This is particularly important for applications such as development and updating of genotyping array. However, existing tools often require an associated reference genome for the molecular markers and additional adaptations are needed to map the short context sequences of these markers when no such reference genome is available. To overcome these limitations, our aim was to develop an original pipeline.
AlignMarkers is a robust bioinformatics pipeline designed to accurately place molecular markers on genome assemblies without requiring information on initial positions on a reference genome, using sequence alignment. It can also operate on coordinate-based files to perform liftover-like analyses, providing an alternative when genome-to-genome alignments could not be generated. It accepts multiple input file formats (VCF, BED, FASTA, CSV) and is optimized for sequences of ≥ 100 base pairs. Context sequences are retrieved, when only coordinates are provided, and then aligned to target genomes using Minimap2, followed by a stringent filtering process ensuring alignment uniqueness, high sequence identity, and verification of the expected nucleotide. AlignMarkers is built with Nextflow and Python and integrates established tools such as Minimap2, Samtools, and Bedtools. It generates comprehensive reports and visualizations to facilitate result interpretation. We evaluated AlignMarkers using 80,000 randomly selected positions across the version 2
Pisum sativum
genome assembly of cultivar Cameor, showing that increasing flanking sequence length improves placement accuracy and reduces multimapping, especially in repeat-rich regions. We further benchmarked AlignMarkers on 100,000
Solanum lycopersicum
SNPs and indels against CrossMap and bcftools/liftover, showing high concordance in the outputs of all tools. These results demonstrate the robustness and reliability of the pipeline when used with different marker resources.
AlignMarkers is a reliable and user-friendly solution for transferring marker positions. It supports multiple applications and facilitates the management of large sets of molecular markers for various purposes, including the construction of genotyping platforms.
Introduction: Mitochondrial genomes recovered from public sequencing repositories offer cost-effective molecular resources without additional laboratory work. Building on a previously validated targeted Basic Local Alignment Search Tool–Contig Assembly Program version 3 (BLAST–CAP3) workflow for single-gene assembly, the present study demonstrates its extension to complete mitochondrial genome draft assembly using a publicly available whole-genome sequencing (WGS) dataset of Gryllus bimaculatus.
Materials and methods: Mitochondrial reads were retrieved using custom National Center for Biotechnology Information (NCBI) nucleotide Basic Local Alignment Search Tool (BLASTN) parameters against the published G. bimaculatus mitochondrial genome reference, followed by CAP3 de novo assembly on Galaxy Europe.
Results: Assembly yielded a single complete contig of 15,954 base pairs (bp) with 99.574% nucleotide identity to the reference (E-value = 0.0; bit score = 29,085). Independent read mapping validation confirmed that all 15,954 positions of the assembled contig were covered by at least 2× read depth (mean 44.5×; maximum 92×), with 4731 of the 4749 deduplicated reads (99.6%) successfully mapped. A critical obstacle, failure of standard CAP3 parameters in the A+T-rich control region (~1240 bp) due to the tandem repeat architecture, was resolved by reducing the overlap length cutoff to 10–15 bp. Self-dotplot analysis confirmed that tandem repeats were confined to positions ~14,650–15,450 bp with a repeat unit spacing of ~150–200 bp.
Conclusions: This study documents a practical and potentially transferable solution for animal mitochondrial genome draft assembly from public sequencing data. The complete pipeline requires no local software installation and is executable using web-based tools within a single working day.
Y.M. Gupta· Academia Molecular Biology a...· 0 citations
Interpretation of genomic variants, positions, and regions depends on reliable annotation—adding evidence such as predicted effect, conservation, population frequency, and gene-level context—yet the underlying resources are numerous, versioned, and assembly-specific. We present the Genomic Annotation Infrastructure (GAIn), a platform that generates transparent, reproducible annotations via declarative pipelines that define annotation tasks as ordered lists of components, called annotators, that produce annotation attributes using genomic resources from Genomic Resource Repositories (GRRs). We provide two public GRRs: a main repository containing more than 250 heterogeneous genomic resources, and a separate GRR-ENCODE repository containing resources derived from thousands of ENCODE (Encyclopedia of DNA Elements) project experiments. Users can use the annotation pipelines we made available, author custom annotation pipelines, and execute annotation tasks with these pipelines via GAIn’s web and command-line interfaces. The web interface can be used without any setup, but it relies on shared computational infrastructure and imposes limits on the size of annotation tasks. The command-line interface requires setup but supports arbitrarily large annotation tasks through simple-to-use parallelization and offers a broader set of features. For example, command-line GAIn can be extended by using custom GRRs or creating custom annotators via its plugin architecture. In addition, GAIn’s re-annotation feature, which updates annotations as they evolve, substantially simplifies maintaining annotations in a large genomics analysis project. GAIn’s resource management, explicit versioning, and pipeline abstraction provide an auditable, maintainable, and efficient foundation for modern genomic annotation across reference assemblies and use cases.
Murat Cokol, Lubomir Chorbadjiev, Yoon-ha Lee et al.· bioRxiv· 0 citations
