Biology subjects

Paynter, V. M.

Publications and source records attributed to Paynter, V. M..

4 recordsLinked to original sources

bioRxiv2026

MirMachine 2: a scalable, evolutionarily informed pipeline for microRNA annotation and comparative genomics across thousands of animal genomes

Genome sequencing is rapidly outpacing the annotation of conserved regulatory elements, limiting the evolutionary and comparative insights that can be extracted from expanding genome collections. MicroRNAs are among the most conserved and phylogenetically informative genes, yet automated annotation has remained difficult to scale while preserving evolutionary interpretability. Here we present MirMachine 2, an evolutionarily informed framework that combines curated reference models, lineage-aware scoring, and adaptive filtering to enable robust genome-wide microRNA annotation at scale. Applying this to thousands of animal genomes reveals that many apparent absences of conserved microRNAs reflect methodological bias rather than biological loss, particularly in underrepresented lineages. By enabling consistent and interpretable comparison of microRNA complements across large datasets, MirMachine 2 establishes scalable microRNA annotation as a practical foundation for genome-scale evolutionary and comparative genomics.

bioinformatics↗

bioRxiv2024

MirGeneDB 3.0: Improved taxonomic sampling, uniform nomenclature of novel conserved microRNA families, and updated covariance models.

We present a major update of MirGeneDB (3.0), the manually curated animal microRNA gene database. Beyond moving to a new server and the creation of a computational mirror, we have expanded the database with the addition of 33 invertebrate species, including representatives of 5 previously unsampled phyla, and 6 mammal species. MirGeneDB now contains entries for 21, 822 microRNA genes (5, 160 of these from the new species) belonging to 1743 microRNA families. The inclusion of these new species allowed us to refine both the evolutionary node of appearance of a number of microRNA genes/families, as well as MirGeneDBs phylogenetically informed nomenclature system. Updated covariance models of all microRNA families, along with all smallRNA read data are now downloadable. These enhanced annotations will allow researchers to analyze microRNA properties such as secondary structure and features of their biogenesis within a robust phylogenetic context and without the database plagued with numerous false positives and false negatives. In light of these improvements, MirGeneDB 3.0 will assume the responsibility for naming conserved novel metazoan microRNAs. MirGeneDB is part of RNAcentral and Elixir Norway, and is publicly and freely available at master.cloud.mirgenedb.org. Key PointsO_LIMajor update to the manually curated and uniformly named microRNA gene database MirGeneDB C_LIO_LI114 animal species, >1700 microRNA families and [~]20 000 genes searchable, browsable and downloadable C_LIO_LINew features to download all smallRNA read data and updated covariance models for each family C_LI

evolutionary biology↗

bioRxiv2022

Accurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachine

The annotation of microRNAs, an important class of post-transcriptional regulators, depends on the availability of transcriptomics data and expert knowledge. This led to a large gap between novel genomes made available and high-quality microRNA complements. Using >16,000 microRNAs from the manually curated microRNA gene database MirGeneDB, we generated trained covariance models for all conserved microRNA families. These models are available in MirMachine, our new tool for the annotation of conserved microRNA complements from genomes only. We successfully applied MirMachine to a wide range of animal species, including those with very large genomes, additional genome duplications and extinct species, where smallRNA sequencing will be hard to achieve. We further describe a microRNA score of expected microRNAs that can be used to assess the completeness of genome assemblies. MirMachine closes a long-persisting gap in the microRNA field facilitating automated genome annotation pipelines and deeper studies on the evolution of genome regulation, even in extinct organisms. HighlightsO_LIAn annotation pipeline using trained covariance models of microRNA families C_LIO_LIEnables massive parallel annotation of microRNA complements of genomes C_LIO_LIMirMachine creates meaningful annotations for very large and extinct genomes C_LIO_LImicroRNA score to assess genome assembly completeness C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=129 SRC="FIGDIR/small/517654v2_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1ee2bf1org.highwire.dtl.DTLVardef@18719c1org.highwire.dtl.DTLVardef@abcfb6org.highwire.dtl.DTLVardef@1d1c368_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

bioRxiv2020

PAN-INDIA 1000 SARS-CoV-2 RNA Genome Sequencing Reveals Important Insights into the Outbreak

The PAN-INDIA 1000 SARS-CoV-2 RNA Genome Sequencing Consortium has achieved its initial goal of completing the sequencing of 1000 SARS-CoV-2 genomes from nasopharyngeal and oropharyngeal swabs collected from individuals testing positive for COVID-19 by Real Time PCR. The samples were collected across 10 states covering different zones within India. Given the importance of this information for public health response initiatives investigating transmission of COVID-19, the sequence data is being released in GISAID database. This information will improve our understanding on how the virus is spreading, ultimately helping to interrupt the transmission chains, prevent new cases of infection, and provide impetus to research on intervention measures. This will also provide us with information on evolution of the virus, genetic predisposition (if any) and adaptation to human hosts. One thousand and fifty two sequences were used for phylodynamic, temporal and geographic mutation patterns and haplotype network analyses. Initial results indicate that multiple lineages of SARS-CoV-2 are circulating in India, probably introduced by travel from Europe, USA and East Asia. A2a (20A/B/C) was found to be predominant, along with few parental haplotypes 19A/B. In particular, there is a predominance of the D614G mutation, which is found to be emerging in almost all regions of the country. Additionally, mutations in important regions of the viral genome with significant geographical clustering have also been observed. The temporal haplotype diversities landscape in each region appears to be similar pan India, with haplotype diversities peaking between March-May, while by June A2a (20A/B/C) emerged as the predominant one. Within haplotypes, different states appear to have different proportions. Temporal and geographic patterns in the sequences obtained reveal interesting clustering of mutations. Some mutations are present at particularly high frequencies in one state as compared to others. The negative estimate Tajimas D (D = -2.26817) is consistent with the rapid expansion of SARS-CoV-2 population in India. Detailed mutational analysis across India to understand the gradual emergence of mutants at different regions of the country and its possible implication will help in better disease management.

genomics↗