Aug 2026· Molecular biology and evolution· 0 citations
Medicine
TL;DR
A step-by-step protocol for snpArcher, a Snakemake-based workflow that takes raw sequencing reads and a reference genome as input and produces a filtered, joint-called VCF suitable for downstream population genomic analysis, is presented.
Abstract
Population genomic studies in non-model organisms increasingly depend on whole-genome resequencing, yet translating raw reads into reliable variant callsets remains a practical challenge due to the complexity of multi-step bioinformatics pipelines and the absence of species-specific best practices. Here we present a step-by-step protocol for snpArcher, a Snakemake-based workflow that takes raw sequencing reads and a reference genome as input and produces a filtered, joint-called VCF suitable for downstream population genomic analysis. We guide users through six phases: installation and environment setup, sample sheet creation, run configuration, execution on local or high-performance computing systems, quality control review using an interactive HTML dashboard, and downstream analysis, focusing on postprocessing and filtering. The QC dashboard aggregates individual-level metrics including principal component analysis, relatedness estimation, depth-missingness diagnostics, and admixture analysis to help identify batch effects, contamination, cryptic relatedness, and outlier samples before downstream analysis. We demonstrate the impact of sequential filtering steps on the site frequency spectrum and demographic inference using a dataset of 137 burrowing owl (Athene cunicularia) genomes, showing how removal of low-coverage individuals, sex-linked scaffolds, and regions of excess heterozygosity eliminates artifacts that would otherwise bias inference of population size history. This protocol is intended as a practical companion to the original snpArcher publication, enabling researchers working with non-model organisms to produce and evaluate analysis-ready variant callsets in a reproducible manner.
Scalable and reproducible genomic workflows that support both individual- and population-level analyses are critically needed in precision medicine. We present WGS2IBI, a cloud-based, modular workflow that integrates whole-genome sequencing (WGS) preprocessing, population-level variant screening, and the previously developed Individualized Bayesian Inference (IBI) framework within a reproducible analysis pipeline. Implemented using the Common Workflow Language (CWL) and Docker, WGS2IBI ensures portability and reproducibility across computational environments.
Benchmarking on the Jackson Heart Study (JHS) TOPMed Freeze 9 cohort demonstrated efficient large-scale genomic processing and analysis. Preprocessing reduced 102 million variants to 18 million variants in approximately two hours at a total cost of $20.83. Population-level analyses using Global Search and Fisher’s exact test (FET) were completed for $0.44 and $11.44, respectively. Individual-level analysis using IBIwas completed for $3.66. Analyses across additional TOPMed cohorts showed predictable scaling across cohort and chromosome sizes. Deployment on both BioData Catalyst (BDC) and the Gabriella Miller Kids First (KF) Data Resource Center via CAVATICA further demonstrated portability across two major NIH cloud ecosystems. To provide a biologically meaningful use case beyond workflow benchmarking, we also applied WGS2IBI to real hypertension data from 1,821 unrelated Framingham Heart Study (FHS) participants, where IBI-prioritized variants were enriched for lower minor allele frequency and included variants mapping to genes with prior blood-pressure relevance.
WGS2IBI provides a scalable, reproducible, and accessible workflow resource for WGS analysis, enabling efficient population- and individual-level genetic studies without local installation. Dual deployment on BDC and KF expands usability across diverse NIH genomic ecosystems, supporting both adult and pediatric research communities. This workflow lowers practical computational barriers for large-scale WGS studies and enables integrated evaluation of individual- and population-level genetic analyses.
Yasaman J. Soofi, Md Asad Rahman, Jinxing Ren et al.· BMC Bioinformatics· 0 citations
SVPLEX is a Nextflow pipeline for cohort-level structural variant detection from short-read whole-genome sequencing data. The pipeline implements six different structural variant callers with different strengths and weaknesses, integrating different levels of evidence for SVs, and generates a merged consensus callset across the analysis cohort. Callset filtering is achieved by leveraging consensus among multiple individual callers and by ensuring that deletion and duplication calls are supported by observable changes in read depth. The output merged cohort SV callset can then be used to assess cohort-specific variation, remove technical artefacts, and serve as input for rare disease variant prioritisation workflows. SVPLEX is user-friendly, reproducible, scalable, and can be executed flexibly on either a local workstation, a high-performance compute (HPC) cluster, or deployed on cloud infrastructure. The required inputs are alignment files for the cohort of interest, and the output is a single merged cohort structural variant VCF. SVPLEX is available on GitHub (bahlolab/SVPLEX) and is licensed under the MIT open-source licence.
Information about segregating haplotypes and structural variation (SV) can be extremely rich for a variety of applications in population genomics but remains largely inaccessible for many non-model species. Of the available methods, linked-read sequencing is especially promising for its low cost and scalability, but its adoption remains limited. One existing linked-read method is Haplotagging, which barcodes sequencing reads to reconstruct long molecules that encode haplotype information, with the potential to generate phased whole-genome data and detect structural variants. In this study, we present BLink-seq, a novel Haplotagging method that is compatible with standard short-read next-generation sequencing platforms, is locally reproducible with low-cost reagents, and is scalable for high-throughput sample processing. We optimized library preparation parameters, explored their relationship to linked-read library metrics, and validated phasing performance and structural variant detection in two evolutionary extremes: an experimental Drosophila melanogaster cross of inbred lines carrying known inversions, and four Atlantic silverside (Menidia menidia) parent-offspring trios sourced from highly outbred, wild-caught populations. We then applied our protocol to a cohort of 376 silversides to demonstrate its scalability and potential for SV detection and genotype imputation. Using BLink-seq, we generated chromosome-scale phased blocks and identified known inversions in both validation datasets. We discovered previously uncharacterized structural complexity within a known adaptive inversion on silverside chromosome 11, demonstrating that linked-read data can refine our understanding of SV architecture beyond what short reads alone can resolve. Finally, we provide a user guide for researchers interested in using BLink-seq.
Azwad R Iqbal, Pavel V. Dimens, J. Rick et al.· bioRxiv· 0 citations
MKMC (Multi-sample Kmer Counter), a scalable, reference-free toolkit for RNA-seq analysis that leverages k-mer–based statistics to detect biological variation without requiring alignment, is presented.
L. Mboning, Maciej Dlugosz, Marek Kokot 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
Large-scale, multi-center projects have become common in the era of rapid technological development, but protocol standardization remains challenging. In whole-exome sequencing (WES), various exome enrichment kits exhibit variable efficiency across genomic regions, leading to systematic, non-biological batch effects, much stronger than other technical factors. We propose a workflow to minimize the effect of WES capture inconsistencies in single-nucleotide variation (SNV) data. The pipeline consists of quality control, mapping to the genome, SNV calling, joint genotyping, and imputing genotypes using reference haplotypes. SNVs are then aggregated into gene-level features measuring the burden of deleterious variants. Finally, a gene-level imputation is performed using a customized algorithm. Namely, if the detection rate of a gene is low in samples enriched with a given capture kit but high in samples enriched with other kits, missing values in the former group are imputed, as such differences are unlikely to reflect true biology. As a benchmark, we conducted a study on over a thousand breast cancer cases across 11 cohorts, using eight exome capture kits. We demonstrated that the proposed pipeline leads to a considerable decrease in the batch effect signal, potentially increasing the likelihood of finding true biological signals.
Laura Jarosz, M. Ochocki, Julia Merta et al.· Methods and Protocols· 0 citations
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