SEARCH · Search bioRxiv

Results for “Genomics”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,351 records · Page 75Linked to original sources

bioRxiv2019

The landscape of chloroplast genome assembly tools

BackgroundChloroplasts are intracellular organelles that enable plants to conduct photosynthesis. They arose through the symbiotic integration of a prokaryotic cell into an eukaryotic host cell and still contain their own genomes with distinct genomic information. Plastid genomes accommodate essential genes and are regularly utilized in biotechnology or phylogenetics. Different assemblers that are able to assess the plastid genome have been developed. These assemblers often use data of whole genome sequencing experiments, which usually contain reads from the complete chloroplast genome. ResultsThe performance of different assembly tools has never been systematically compared. Here we present a benchmark of seven chloroplast assembly tools, capable of succeeding in more than 60% of known real data sets. Our results show significant differences between the tested assemblers in terms of generating whole chloroplast genome sequences and computational requirements. The examination of 105 data sets from species with unknown plastid genomes leads to the assembly of 20 novel chloroplast genomes. ConclusionsWe create docker images for each tested tool that are freely available for the scientific community and ensure reproducibility of the analyses. These containers allow the analysis and screening of data sets for chloroplast genomes using standard computational infrastructure. Thus, large scale screening for chloroplasts within genomic sequencing data is feasible.

bioinformatics↗

bioRxiv2019

Genomes of intracellular Leishmania parasites directly sequenced from patients

Whole genome sequencing (WGS) is increasingly used for molecular diagnosis and epidemiology of infectious diseases. Current Leishmania genomic studies rely on DNA extracted from cultured parasites, which might introduce sampling and biological biases into the subsequent analyses. Up to now, direct analysis of Leishmania genome in clinical samples is hampered by high levels of human DNA and large variation in parasite load in patient samples. Here, we present a method, based on target enrichment of Leishmania donovani DNA with Agilent SureSelect technology, that allows the analysis of Leishmania genomes directly in clinical samples. We validated our protocol with a set of artificially mixed samples, followed by the analysis of 63 clinical samples (bone marrow or spleen aspirates) from visceral leishmaniasis patients in Nepal. We were able to identify genotypes using a set of diagnostic SNPs in almost all of these samples (97%) and access comprehensive genome-wide information in most (83%). This allowed us to perform phylogenomic analysis, assess chromosome copy number and identify large copy number variants (CNVs). Pairwise comparisons between the parasite genomes in clinical samples and derived in vitro cultured promastigotes showed a lower aneuploidy in amastigotes as well as genomic differences, suggesting polyclonal infections in patients. Altogether our results underline the need for sequencing parasite genomes directly in the host samples.\n\nAuthor summaryVisceral leishmaniasis (VL) is caused by parasitic protozoa of the Leishmania donovani complex and is lethal in the absence of treatment. Whole Genome Sequencing (WGS) of L. donovani clinical isolates revealed hitherto cryptic population structure in the Indian Sub-Continent and provided insights into the epidemiology and potential mechanisms of drug resistance. However, several biases are likely introduced during the culture step. We report here the development of a method that allows determination of parasite genomes directly in clinical samples, and validate it on bone marrow and splenic aspirates of VL patients in Nepal. Our study sheds a new light on the biology of Leishmania in the human host: we found that intracellular parasites of the patients had very low levels of aneuploidy, in sharp contrast to the situation in cultivated isolates. Moreover, the observed differences in genomes between intracellular amastigotes of the patient and the derived cultured parasites suggests polyclonality of infections, with different clones dominating in clinical samples and in culture, likely due to fitness differences. We believe this method is most suitable for clinical studies and for molecular tracking in the context of elimination programs.

microbiology↗

bioRxiv2019

GAD: a Python script for dividing genome annotation files into feature-based files

Nowadays, manipulating and analyzing publicly available genomic datasets become a daily task in bioinformatics and genomics laboratories. The release of several genome sequencing projects prompts bioinformaticians to develop automated scripts and pipelines which analyze genomic datasets in particular gene annotation pipelines. Handling genome annotation files with fully-featured programs used by non-developers is necessary, furthermore, accelerating genomic data analysis with a focus on diminishing the genome annotation and sequence files based on specific features is required. Consequently, to extract genome features from GTF or GFF3 in a precise manner, GAD script (https://github.com/bio-projects/GAD) provides a simple graphical user interface which interpreted by all python versions installed in different operating systems. GAD script contains unique entry widgets which are capable to analyze multiple genome sequence and annotation files by a click. With highly influential coded functions, genome features such upstream genes, downstream genes, intergenic regions, genes, transcripts, exons, introns, coding sequences, five prime untranslated regions, and three prime untranslated regions and other ambiguous sequence ontology terms will be extracted. GAD script outputs the results in diverse file formats such as BED, GTF/GFF3 and FASTA files which supported by other bioinformatics programs. Our script could be incorporated into various pipelines in all genomics laboratories with the aim of accelerating data analysis.

bioinformatics↗

bioRxiv2019

Complete genome assembly of the Wolbachia endosymbiont of the horn fly Haematobia irritans irritans: a supergroup A strain with multiple horizontally acquired cytoplasmic incompatibility genes

The horn fly, Haematobia irritans irritans, is a hematophagous parasite of livestock distributed throughout Europe, Africa, Asia, and the Americas. Welfare losses on livestock due to horn fly infestation are estimated to cost between USD 1-2.5 billion annually in North America and Brazil. The endosymbiotic bacterium Wolbachia pipientis is a maternally inherited manipulator of reproductive biology in arthropods and naturally infects laboratory colonies of horn flies from Kerrville, USA and Alberta, Canada, but has also been identified in wild-caught samples from Canada, USA, Mexico and Hungary. Reassembly of PacBio long-read and Illumina genomic DNA libraries from the Kerrville H. i. irritans genome project allowed for a complete and circularised 1.3 Mb Wolbachia genome (wHae). Annotation of wHae yielded 1249 coding genes, 34 tRNAs, three rRNAs, and five prophage regions. Comparative genomics and whole genome Bayesian evolutionary analysis of wHae compared to published Wolbachia genomes suggests that wHae is most closely related to and diverged from Wolbachia supergroup A strains known to infect Drosophila spp. Whole-genome synteny analyses between wHae and closely related genomes indicates that wHae has undergone convoluted genome rearrangements while maintaining high nucleotide identity. Comparative analysis of the cytoplasmic incompatibility (CI) genes of wHae suggests two phylogenetically distinct CI loci and acquisition of another CifB homolog from phylogenetically distant supergroup A Wolbachia strains suggesting horizontal acquisition of these loci. The wHae genome provides a resource for future examination of the impact Wolbachia may have in both biocontrol and potential insecticide resistance of horn flies.\n\nImportanceHorn flies, Haematobia irritans, are obligate hematophagous parasites of cattle having significant effects on production and animal welfare. Control of horn flies mainly relies on the use of insecticides, but issues with resistance have increased interest in development of alternative means of control. Wolbachia pipientis is an endosymbiont bacterium known to have a range of effects on host reproduction such as induction of cytoplasmic incompatibility, feminization, male killing, and also impacts on vector transmission. These characteristics of Wolbachia have been exploited in biological control approaches for a range of insect pests. Here we report the assembly and annotation of the circular genome of the Wolbachia strain of the Kerrickville, USA horn fly (wHae). Annotation of wHae suggests its unique features including the horizontal acquisition of additional transcriptionally active cytoplasmic incompatibility loci. This study will provide the foundation for future Wolbachia-induced biological effect studies for control of horn flies.

microbiology↗

bioRxiv2019

Learning mutational signatures and their multidimensional genomic properties with TensorSignatures

Mutational signature analysis is an essential part of the cancer genome analysis toolkit. Conventionally, mutational signature analysis extracts patterns of different mutation types across many cancer genomes. Here we present TensorSignatures, an algorithm to learn mutational signatures jointly across all variant categories and their genomic context. The analysis of 2,778 primary and 3,824 metastatic cancer genomes of the PCAWG consortium and the HMF cohort shows that practically all signatures operate dynamically in response to various genomic and epigenomic states. The analysis pins differential spectra of UV mutagenesis found in active and inactive chromatin to global genome nucleotide excision repair. TensorSignatures accurately characterises transcription-associated mutagenesis, which is detected in 7 different cancer types. The analysis also unmasks replication- and double strand break repair-driven APOBEC mutagenesis, which manifests with differential numbers and length of mutation clusters indicating a differential processivity of the two triggers. As a fourth example, TensorSignatures detects a signature of somatic hypermutation generating highly clustered variants around the transcription start sites of active genes in lymphoid leukaemia, distinct from a more general and less clustered signature of Pol{eta}-driven translesion synthesis found in a broad range of cancer types. Key findingsO_LISimultaneous inference of mutational signatures across mutation types and genomic features refines signature spectra and defines their genomic determinants. C_LIO_LIAnalysis of 6,602 cancer genomes reveals pervasive intra-genomic variation of mutational processes. C_LIO_LIDistinct mutational signatures found in quiescent and active regions of the genome reveal differential repair and mutagenicity of UV- and tobacco-induced DNA damage. C_LIO_LIAPOBEC mutagenesis produces two signatures reflecting highly clustered, double strand break repair-initiated and lowly clustered replication-driven mutagenesis, respectively. C_LIO_LISomatic hypermutation in lymphoid cancers produces a strongly clustered mutational signature localised to transcription start sites, which is distinct from a weakly clustered translesion synthesis signature found in multiple tumour types. C_LI

cancer biology↗

bioRxiv2025

A high-quality genome assembly and integrative data portal (Phabase) for the Mesoamerican black bean (Phaseolus vulgaris cv. Negro Jamapa)

Common bean (Phaseolus vulgaris L.) is one of the major legume crops worldwide, constituting a fundamental part of the diet in several countries. In central and south Mexico, the Mesoamerican Negro Jamapa variety is the one of most widely consumed cultivar, and it has notably served as a working model for functional genomics to address the molecular responses to stresses and during the nitrogen-fixing symbiosis with rhizobia. Despite this, the genome sequence of this landrace is not yet available, and the transcriptomic data generated by different research groups on this species are scattered. This prompted us to carry out a de novo sequencing, assembly and gene prediction for Negro Jamapa genome, using Hi-Fi PacBio technology and advanced genomic tools, respectively. Herein, we present a high-quality genome assembly for Negro Jamapa at chromosome level. The assembled genome is 522 Mb length, with an N50 of 45Mb, 89X coverage and BUSCO completeness of 98.4%, parameters that are considerably better than the available Mesoamerican reference genome. In parallel, we built an expression atlas of P. vulgaris from various tissues and growth conditions, by re-mapping publicly available RNA-seq and small RNA-seq data produced in recent years by distinct research groups. This analysis is available for the scientific community in the new user-friendly portal developed in this study, Phabase (https://phabase.ccg.unam.mx/). This portal includes several tools such as BLAST, gene and microRNA expression atlas, JBrowse, download section and integration with other Phaseolus spp. genomes. Significance statementIn this study we report the first genome sequence of Phaseolus vulgaris Negro Jamapa, one of the most widely consumed bean varieties and a key experimental model for multiple research groups. We also developed Phabase (Phabase), an integrated database hosting this genome together with BLAST, JBrowse, and a comprehensive expression atlas. This resource enables functional genomics, comparative analyses, and accelerated breeding of common bean.

plant biology↗

bioRxiv2025

Bacteriophage PAK_P3 genome structuration and dynamics during infection of Pseudomonas aeruginosa reveal specific interactions patterns

Bacteriophages, or phages, are highly abundant and diverse genomic entities that play an important role in microbial ecology, evolution, and horizontal gene transfer. While the dynamic changes in genome organization in cellular organisms have been well described, the 3D folding of phage genomes during the infection of their host is extremely limited. Understanding how phage genomes fold, invade and rearrange the genome of their host to functionally organize the optimal expression of their genes remains unknown. Here, we explore the spatial dynamics of the virulent double-stranded DNA phage PAK_P3 during infection of its host Pseudomonas aeruginosa and reveal how its genome rapidly decondenses to adopt a specific 3D organization that reflects its transcriptional program. Concomitantly, the host genome decondenses as gene expression wanes and transcription induced domains vanish. We also uncover specific and discrete bridging of the host and the phage genomes, showing how the phage genome exploits the spatial genome architecture of its host to succeed in its infection cycle. Our data highlights an unprecedented level of genome folding and gene expression regulation during a viral infection.

microbiology↗

bioRxiv2026

Selection shapes the evolution of genome size in a globally invasive plant

O_LIBiological invasions provide powerful natural experiments for understanding how genome architecture responds to novel climatic environments. Transposable elements (TEs) can rapidly restructure genomes, yet their role in adaptive genome size evolution during invasion remains poorly understood. C_LIO_LIHere, we examined genome size and TE abundance in 439 individuals of globally invasive common ragweed (Ambrosia artemisiifolia L.) across native (North American) and invasive (European, Australian) ranges. By integrating whole-genome resequencing, flow cytometry, and trait versus genetic differentiation comparison (QST-FST), we tested whether genome size evolution is shaped by selection, climate, and life-history traits. C_LIO_LIGenome size was significantly larger in Australian genotypes, driven by increased TE and rRNA (ribosomal RNA) abundance. Crucially, trait versus genetic differentiation comparison provided evidence of divergent selection on genome size in North American and European populations, but not in Australia. Genome size was correlated with mean annual temperature (MAT) across all ranges, linking genomic traits to environmental variables. C_LIO_LIGenome size evolution during invasion can be rapid, adaptive, and range-specific, with TE-driven genome expansion emerging as a potential genomic response to the demographic and environmental pressures accompanying colonization of novel environments. C_LI

evolutionary biology↗

bioRxiv2026

Embarrassingly_FASTA: Enabling Recomputable, Population-Scale Pangenomics by Reducing Commercial Genome Processing Costs from $100 to less than $1

Computational preprocessing has become the dominant bottleneck in genomics, frequently exceeding sequencing costs and constraining population-scale analysis, even as large repositories grow from tens of petabytes toward exabyte-scale storage to support World Genome Models. Legacy CPU-based workflows require many hours to days per 30x human genome, driving many repositories to distribute aligned or derived intermediates such as BAM and VCF files rather than raw FASTQ data. These intermediates embed reference- and model-dependent assumptions that limit reproducibility and impede reanalysis as reference genomes, including pangenomes, continue to evolve. Although recent work has established that GPUs can dramatically accelerate genomic pipelines, enabling large-cohort processing to shrink from years to days given sufficient parallelism, such workflows remain cost-prohibitive. Here, we introduce Embarrassingly_FASTA, a GPU-accelerated preprocessing pipeline built on NVIDIA Parabricks that fundamentally changes the economics of genomic data management. By rendering intermediate files transient rather than archival, Embarrassingly_FASTA enables retention of raw FASTQ data and reliable use of highly discounted ephemeral cloud infrastructure such as spot instances, reducing compute spend from [~]$17/genome (CPU on-demand) to <$1/genome (GPU spot), and commercial secondary-analysis pricing from [~]$120/genome to compute spend under $1/genome. We demonstrate the impact of this efficiency using a simulated large-cohort pangenome build-up (using variant-union accumulation as a proxy for diversity growth) in Caenorhabditis elegans and humans, highlighting the long tail of unsampled human genetic diversity. Beyond GPU kernels, Embarrassingly_FASTA contributes a transient-intermediate lifecycle and spot-friendly orchestration that makes FASTQ retention and routine recomputation economically viable. Embarrassingly_FASTA thus provides enabling infrastructure for recomputable, population-scale pangenomics and next-generation genomic models. Non-Expert DescriptionReading a persons complete DNA sequence has become fast and inexpensive, but turning that raw data into something scientists can analyze is now one of the biggest obstacles in modern genetics. Today, processing a single genome can take many hours or even days, which makes it difficult and expensive to study large populations or reanalyze data when better methods become available. As a result, many databases store only partially processed DNA instead of the original data, limiting future discoveries. In this work, we present a new system that dramatically speeds up this processing step using graphics processing units (GPUs), the same type of hardware used in modern artificial intelligence. With our approach, a human genome can be processed in about 35 minutes instead of more than 15 hours, and at a fraction of the cost. This makes it practical to keep the original DNA data and reprocess it whenever new tools or reference genomes become available, rather than being locked into outdated results. We also show that this speed and affordability allow researchers to explore genetic diversity at an unprecedented scale. By analyzing both human genomes and those of a small worm species commonly used in research, we demonstrate how new genetic variations continue to emerge as more individuals are studied, especially in humans, where much diversity remains unexplored. Overall, our work helps remove a major barrier to studying DNA at population scale and lays the foundation for future genetic models that could better explain disease, evolution, and human health. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/703356v2_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@11b8fc3org.highwire.dtl.DTLVardef@7b8b60org.highwire.dtl.DTLVardef@fb5a3borg.highwire.dtl.DTLVardef@1e11276_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2026

A comprehensive benchmark of discrepancies across microbial genome reference databases

Metagenomic analysis of microbial communities relies significantly on the quality and completeness of reference genomes, which allow researchers to compare sequencing reads against reference genome collections to reveal essential community characteristics. However, the reliability of these analyses is often compromised by substantial discrepancies across existing reference resources, including differences in genome content, assembly fragmentation, taxonomic representation, and metadata completeness. While these inconsistencies are known to introduce bias, the extent of divergence between major databases remains largely unknown. Here, we present a comprehensive benchmark of discrepancies across multiple widely used microbial genome reference resources. We developed the Cross-DB Genomic Comparator (CDGC), which utilizes reference genome alignments to systematically capture discrepancies in genome assemblies across reference databases. Applying this framework, we found that 99% of viral genomes were identical across databases, indicating strong consistency in viral reference resources. In contrast, fungal genomes showed substantially greater variability: although 82% of assemblies exhibited at least 90% similarity, only 7% were identical across databases. More concerning, we identified a subset of 461 assemblies with less than 50% similarity, suggesting the presence of technical artifacts, incomplete assemblies, or damaged genome files that require closer examination. Collectively, these results demonstrate that systematic cross-database benchmarking provides a critical mechanism for refining the accuracy of individual reference databases and advancing efforts towards more unified and reliable universal reference genomes.

bioinformatics↗

bioRxiv2026

Resistome, Virulome, Mobilome, And Biosynthetic Gene Clusters Adaptations of Acinetobacter Baumannii Mexican Strains Across the Pre- and of the COVID-19 Period: Insights from Whole-Genome Sequencing

BackgroundAcinetobacter baumannii is a critical multidrug-resistant pathogen whose genomic landscape in Mexico has been reshaped by the COVID-19 pandemic. While global studies have highlighted distinctive sequence type distributions, systematic analyses in Mexico remain limited. MethodsWe analyzed 194 genomes, including 47 newly sequenced post-COVID isolates (MIQ), alongside 147 publicly available genomes (HPG). Whole-genome sequencing was combined with phylogenetic reconstruction, resistome and virulome profiling based on gene presence and absence, mobilome analysis, and biosynthetic gene cluster (BGC) characterization. ResultsTwo major clades dominated by Oxford STs 758, 208, 417, and 369 were identified. Resistome profiling uncovered 128 distinct resistome profiles (combinations of genes) and 44 emerging antimicrobial resistance genes (ARGs), with an increased number of resistance genes in the strains obtained during the pandemic. Virulome analysis revealed enrichment of metabolic adaptation genes (argG, carA, ilvC) in MIQ strains. Mobilome profiling demonstrated enrichment of ISAbA1 and ISAbA3 elements, known to mobilize carbapenemase genes. BGC analysis showed conserved siderophores involved in virulence, alongside diversification of the secondary metabolite repertoires in MIQ genomes. Additional observations included geographic mixing of clades across Jalisco, Aguascalientes, and Mexico City and referral bias toward carbapenemase-positive isolates. ConclusionThe genomic landscape of A. baumannii in Mexico has diversified post-COVID, with evidence of inter-regional transmission, referral bias, virulome expansion, mobilome-driven ARG dissemination, and metabolic adaptation. These findings underscore the urgent need for coordinated genomic surveillance, functional and clinical validation of adaptation signals, and regionally integrated infection control strategies to mitigate resistance trajectories. Graphical abstractIntegrative genomic profiling of A. baumannii isolates from Mexico. Workflow summarizing the analysis of 194 genomes (147 historic public genomes, 47 novel MIQ strains). Clinical isolates were identified by MALDI Biotyper and tested with BD Phoenix M50. Whole genome sequencing enabled phylogenetic and MLST analyses, resistome and virulome profiling, mobilome characterization, and BGC identification using BIGSCAPE and antiSMASH. Outputs include phylogenetic clustering, resistance/virulence gene distributions, and biosynthetic potential across Mexican regions. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=84 SRC="FIGDIR/small/712163v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@74c87borg.highwire.dtl.DTLVardef@1160aa7org.highwire.dtl.DTLVardef@1564c1org.highwire.dtl.DTLVardef@89e91b_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology↗

bioRxiv2026

Genomic offsets predict observed kelp declines and suggest benefits of assisted migration in the Northeast Pacific

Kelp forests are widely distributed along temperate and polar coastlines worldwide and are among the worlds most productive and diverse marine ecosystems. Yet, due in part to ocean warming, they are declining and even disappearing in many parts of the world. While genomic tools can identify local adaptation and predict species responses to global change, these predictions have rarely been validated in the field, hampering their widespread use in conservation practice. Here, we applied a seascape genomics approach to investigate environmental adaptation in the two main canopy-forming species of the Northeast Pacific, Macrocystis tenuifolia and Nereocystis luetkeana. We leveraged whole-genome sequences of 598 individuals across 94 sites along the British Columbia and Washington coasts, together with 37 environmental variables. Both species showed genomic signatures of local adaptation, with distinct environmental drivers shaping adaptation in each species despite their co-occurrence across much of the studied area. Using gradient forests, we modelled the genetic turnover across environmental gradients and predicted populations vulnerability (genomic offset) under projected environmental conditions. Genomic offsets differed greatly among regions and were positively correlated with kelp declines observed to date, especially in Macrocystis, validating the link between genomic models and outcomes in the field and allowing us to translate genomic predictions into an ecologically meaningful metric: the risk of extirpation under global change. Our models predict that assisted migration could significantly attenuate kelps vulnerability to global change. Across environmentally heterogenous coastlines, short-distance migration can often substantially reduce future genomic-environmental mismatches, but in many cases, long-distance migration would be most beneficial. Our results highlight the potential of seascape genomics to predict vulnerability of populations to global change. Importantly, the validated link between our genomic models and ecological outcomes allows quantification of climate-driven extirpation risk and can inform conservation strategies to improve the resilience and sustainable management of these vulnerable ecosystems.

evolutionary biology↗

bioRxiv2026

Genomic sequence and structural variations accumulating between laboratory lineages of wild type C. elegans

Laboratory cultivation subjects model organisms to selective pressure and genetic drift that can result in the accumulation of many genomic and phenotypic differences over time. The nematode Caenorhabditis elegans has been used for research since the 1970s, and studies comparing the N2 Bristol and CB4856 Hawaiian isolates provided foundational knowledge about metazoan genome evolution. Most comparative genomics studies have used these isolates because their long-term geographical isolation promoted a high degree of genomic divergence within the species. Further, there is growing evidence of phenotypic differences between laboratory lineages of each wild type isolate after repeated independent lab cultivation of these strains. To examine the genomic divergence between different laboratory lineages the Bristol and Hawaiian backgrounds, we first generated de novo genome assemblies of two Bristol and two Hawaiian lineages from Illumina and PacBio sequencing reads. Following genome assembly, we quantified Single Nucleotide Polymorphisms (SNPs), short insertion/deletions (indels), and genomic structural variants (SVs). Between laboratory lineages of the Bristol isolate, we identified 25,432 SNPs, 5,202 indels, and 441 SVs. When aligning laboratory lineages of the Hawaiian isolate, we identified 4,518 SNPs, 1,188 indels, and 387 SVs. For both sets of comparisons, we find that SNPs and indels are broadly enriched in introns and depleted from coding sequences. In contrast to SNPs and indels, we find that genomic SVs are enriched in intergenic sequences. Taken together, our analyses reveal the accumulation of genomic divergence between lineages of Bristol and Hawaiian C. elegans from independent lab cultivation, and how these variants may underpin emergent phenotypic differences observed in the two most popularly used C. elegans wild type isolates. Author SummaryLaboratory model organisms, like natural populations, are subject to evolutionary pressures and genomic changes during prolonged laboratory cultivation. In this study we comprehensively quantify SNPs, indels, and SVs between independent lab cultivations of the C. elegans lineages of the Bristol and Hawaiian isolates.

genetics↗

bioRxiv2026

Evaluation of MeaSeq: comprehensive analysis and reporting of measles virus whole genome sequences.

Although vaccine-preventable, measles virus (MeV) continues to pose a significant public health challenge, with a substantial resurgence of cases worldwide. As whole-genome sequencing (WGS) becomes increasingly affordable and routinely adopted in public health laboratories, reliable and accessible analysis of next-generation sequencing (NGS) data is critical for outbreak investigation and molecular surveillance. Here, we present MeaSeq, a fast, user-friendly, open-source bioinformatics pipeline for MeV analysis using Illumina or Oxford Nanopore Technologies (ONT) NGS data. MeaSeq performs quality control assessments, consensus genome assembly and variant detection, optional genotype-specific reference selection, Distinct Sequence Identifier (DSId) assignment via user-provided databases or hashing, sub-consensus variant visualization, genome quality assessment, and standardized HTML reporting. We compared the performance of MeaSeq on NGS data generated from multiple sequencing platforms and targeted enrichment strategies against gold-standard Sanger data, reference genomes, and publicly available comparative data. This validation demonstrates that MeaSeq provides an accurate, reproducible, and accessible solution for routine MeV WGS analysis, supporting genomic surveillance and outbreak response workflows in public health and research settings. Impact StatementThe recent surge in measles cases worldwide, causing several countries to lose their measles elimination status, underscores the urgent need for effective and accessible genomic surveillance. Our manuscript introduces MeaSeq, a comprehensive and open-source bioinformatics pipeline specifically designed for analyzing MeV NGS data. MeaSeq includes MeV specific analyses such as genotype prediction from sequencing reads with optional genotype-specific reference selection; DSId assignment; quality control checks such as genome rule-of-six divisibility and gene CDS validation; subconsensus nucleotide analysis with mixed-site highlighting; and genomic plotting. By leveraging NGS technology, our pipeline can facilitate the identification of transmission chains and may provide critical insights into the dynamics of MeV outbreaks. This information is essential for public health officials and researchers to implement targeted interventions and optimize vaccine strategies. Additionally, the open-source nature of MeaSeq fosters collaboration and innovation within the scientific measles community along with providing access to a wider range of researchers. Data SummaryThe MeaSeq pipeline code is available on GitHub (https://github.com/phac-nml/measeq). Comparative datasets of publicly available WGS data were accessed through the NCBI Sequence Read Archive under the following BioProjects: PRJNA869081 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA869081) PRJNA480551 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA480551) PRJNA1017431 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1017431) PRJNA1241325 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1241325) PRJNA1174053 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1174053) PRJNA1293457 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1293457) PRJNA843031 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA843031) Whole-genome sequences were included in the validation analysis if they consisted of paired-end data (Illumina) and achieved [≥]95% genome completeness following trimming of the 5' and 3' untranslated regions (UTRs). This criterion ensured sufficient genome coverage for robust validation while allowing for limited missing data arising from regions of low sequencing depth or amplicon dropout. A complete list of sequences included in the validation, along with their accession numbers, is provided in Supplementary Table 1.

bioinformatics↗

bioRxiv2026

Towards a Unified Exact Solution of Rearrangement Small Parsimony for Natural Genomes

Phylogenetic reconstruction is a fundamental problem in comparative genomics. As a theoretical problem in rearrangement studies, this has been modelled as the Small Parsimony Problem (SPP), in which ancestral genome structures have to be determined minimizing the number of rearrangement events occurring throughout the phylogeny. This problem is of significant interest in microbial and cancer genomics, due to the prevalence and clinical importance of rearrangement events. Genome structures in this problem are expressed as sequences of markers, which are themselves oriented sequence features (such as genes) that abstract from non-structural variations. Recent research has focused on the problem under the natural genomes model, in which arbitrary variations in copy number of markers are allowed. Natural genomes are often studied under the DCJ-indel model, a model which has already been successfully applied to plasmid data. There also exist ILP solutions to a variant of the Small Parsimony Problem under the DCJ-indel model. However, these solutions are limited in their applicability, as they make some critical simplifications for tractability purposes: ancestral marker frequencies and precomputed putative ancestral adjancencies, with their predicted likelihoods, are assumed as input. This creates multiple problems from both a theoretical and practical perspective. Firstly, this simplification means that not the full state space is searched for a solution, but rather only the subset of genomes with the precomputed putative adjacencies, meaning an optimal solution to the exact SPP is not guaranteed. Secondly, marker frequencies are given externally, without any theoretical guarantees. Thirdly, the method used to precompute adjacencies relies on gene trees, which requires the use of genes as markers, when gene annotation is often unreliable, especially in regions with a lot of rearrangement. Additionally, this restricts the applicability of the approach to sets of genomes that are both divergent and large enough to be able to produce informative gene trees. This is, for example, rarely the case for plasmids, where nucleotide mutations are rarer than rearrangements and genomes are small. Hence, we revisit the problem to solve the exact SPP by introducing a cost to indel operations, which allows us to compute ranges of marker frequencies and derive theoretical results, that allow us to reduce the solution space that the ILP searches without sacrificing optimality. We show that this makes the problem tractable for the case of small and recently related genomes, first on simulated genomes, and then on a set of pathogenic plasmids which represent a realistic use case for the method.

bioinformatics↗

bioRxiv2026

Evaluating the impact of a sample-matched reference genome on single-cell transcriptomic inferences in Plasmodium falciparum

BackgroundPlasmodium falciparum field isolates exhibit genomic variation, including copy number variation and sequence divergence. In contrast, the P. falciparum 3D7 reference genome (Pf3D7) was derived from a long-term laboratory-adapted strain and does not fully reflect the genomic variation among field isolates. The extent to which the reference genome influences RNA-seq mapping and expression inference in natural infections remains unclear. ResultsWe generated both a reference genome and single cell RNA sequencing (scRNAseq) data from a P. falciparum-infected carrier in Mali. This new ML52 assembly was annotated using Companion with Pf3D7 as the reference. scRNAseq reads from the natural infection isolate were aligned to both the Pf3D7 genome and the isolate-specific ML52 genome, followed by locus-level alignment inspection. For most conserved genes, expression inference was concordant for both references, while genes showing reference-genome-dependent differences were investigated further. Some discrepancies were attributed to mapping artefacts or to reads aligning to unplaced genomic contigs that reflected divergent haplotypes from the co-infecting strains in the naturally infected carrier. While most multigene family loci showed concordant gene expression across both references, var genes exhibited considerable mis-mapping against 3D7 var loci as well as 2/3 of var reads not mapping at all to 3D7. ConclusionscRNAseq expression inference in P. falciparum is robust for conserved genes and most multigene families when comparing to a matched vs unmatched reference genome, but the extremely polymorphic var genes require a matched assembly in order to evaluate expression. These findings highlight the importance of the reference genome for var gene studies and should be considered when interpreting transcriptomic analyses in other organisms possessing highly variable antigenic loci.

bioinformatics↗

bioRxiv2026

Optimization of DNA Transformation in Mesoplasma florum and Identification of a Candidate Recipient Strain for Genome Transplantation

Genome transplantation is a key technology for synthetic genomics, enabling entire genomes to be transferred into recipient cells. Despite its importance, genome transplantation remains confined to a small number of wall-less Mollicute species and is often characterized by low efficiencies, limiting the development and testing of synthetic genomes. Because genome transplantation relies on the same polyethylene glycol mediated DNA delivery process used for plasmid transformation, improving DNA uptake is an important step toward more efficient transplantation systems. Here, we systematically optimized polyethylene glycol-mediated transformation in Mesoplasma florum, a fast-growing, non-pathogenic Mollicute with a reduced genome that represents an attractive chassis for synthetic genomics. We evaluated 20 parameters spanning cell physiology, DNA preparation, membrane conditioning, and recovery conditions. DNA topology emerged as the strongest determinant of transformation efficiency, with highly compacted DNA preparations producing up to three orders of magnitude more transformants than conventional plasmid preparations. Growth phase and polyethylene glycol concentration also strongly influenced transformation outcomes. To identify potential recipients for future genome transplantation experiments, we further screened ten strains belonging to the Mesoplasma lineage. Transformation efficiencies varied widely among strains, and Mesoplasma entomophilum W17 emerged as a particularly promising candidate. Moreover, W17 supported replication of plasmids carrying the M. florum oriC, suggesting compatibility between the replication systems of the two species. Together, these results establish an improved transformation workflow for M. florum, identify DNA topology as a major determinant of DNA uptake, and reveal a new candidate recipient for future genome transplantation studies. These advances provide practical tools and biological insights for synthetic genome engineering in Mollicutes.

synthetic biology↗

bioRxiv2026

GenDiMug: a generic inclusive multigraph framework to analyze genomic organizations and -omics data at multiple scales

After decades of investigation, analyses of genome organization remain challenging. This is because genome descriptions are very information-rich and usually rendered as a linear map of genomic features corresponding to an ordered list of position intervals on a nucleic acid macromolecular backbone. This formalism, while accurate, is not fully optimized to mathematically analyze all genome structural properties, in particular the relations between genomic features, nor to integrate large amounts of -omics data, such as transcriptomes, epigenomes, proteomes, meltomes or chromatin accessibility data, which are increasingly available. We therefore developed GenDiMug, a tool that uses a network representation of the genome as a backbone, providing a natural framework to integrate spatial and ontogenetic relationships between genomic features and multi-omics data. We illustrate how this generic approach can be applied to all sorts of genomes, both to visualize genomic topology at contig and chromosomal scales at base-pair resolution, and to analyze genome positional effects using various graph metrics. Using GenDiMug, we further reveal clumping of genes with similar expression levels in eukaryotic genomes and their possible circadian regulation in a photosynthetic alga.

bioinformatics↗