Biology subjects

Gabriel, L.

Publications and source records attributed to Gabriel, L..

9 recordsLinked to original sources

bioRxiv2026

Accurate ab initio gene prediction in eukaryotes with Tiberius in multiple clades

BackgroundEukaryotic genome annotation is currently bottlenecked by limitations in the generality, scalability or accuracy of computational methods and fewer than 20% of the genomes available at NCBI Datasets have associated gene annotations. Evidence-based pipelines such as BRAKER3 achieve high accuracy but require substantial extrinsic evidence and compute. Deep learning approaches have recently achieved large improvements in ab initio gene prediction accuracy. The end-to-end deep learning gene predictor Tiberius approaches the accuracy of evidence-based annotation on mammalian genomes without using any extrinsic evidence, but its published models were trained on mammals only, limiting its applicability across eukaryotes. ResultsWe extend Tiberius beyond mammals by training lineage-specific models for Mesan-giospermae, Fungi, Vertebrata, Insecta, Chlorophyta and Bacillariophyta, making the tool applicable to 92% of currently available eukaryotic assemblies. Across a benchmark of 33 species, Tiberius achieved higher exon-, transcript- and gene-level accuracy than the other evaluated ab initio methods, Helixer and ANNEVO, improving gene-level F1 score by 12-37 percentage points over Helixer and by 10-22 percentage points over ANNEVO, while also having the fastest runtimes overall. Compared with BRAKER3, which incorporates RNA-Seq and protein evidence, Tiberius approaches state-of-the-art accuracy in Mesangiospermae, Fungi, Bacillariophyta and Chlorophyta, while being on average 80 times faster when using a GPU. A reimplementation of the Tiberius backend reduced runtime by 31% compared to the previous implementation. Tiberius and its models are also available through a web server, which allows annotation of submitted assemblies without local resources. Furthermore, the Vertebrata model has been applied to annotate 2,948 vertebrate assemblies totalling nearly 6 trillion base pairs. ConclusionsTiberius is transferable well beyond Mammalia and reaches accuracy close to evidence-based annotation in several clades at a small fraction of the compute cost. This makes it a practical choice for highly accurate large-scale genome annotation, particularly when extrinsic evidence is unavailable or annotation throughput is limiting. Availability and implementationCode: https://github.com/Gaius-Augustus/Tiberius, web server: https://bioinf.uni-greifswald.de/tiberius.

bioinformatics↗

bioRxiv2024

Tiberius: End-to-End Deep Learning with an HMM for Gene Prediction

MotivationFor more than 25 years, learning-based eukaryotic gene predictors were driven by hidden Markov models (HMMs), which were directly inputted a DNA sequence. Recently, Holst et al. demonstrated with their program Helixer that the accuracy of ab initio eukaryotic gene prediction can be improved by combining deep learning layers with a separate HMM postprocessor. ResultsWe present Tiberius, a novel deep learning-based ab initio gene predictor that end-to-end integrates convolutional and long short-term memory layers with a differentiable HMM layer. Tiberius uses a custom gene prediction loss and was trained for prediction in mammalian genomes and evaluated on human and two other genomes. It significantly outperforms existing ab initio methods, achieving F1-scores of 62% at gene level for the human genome, compared to 21% for the next best ab initio method. In de novo mode, Tiberius predicts the exon-intron structure of two out of three human genes without error. Remarkably, even Tiberiuss ab initio accuracy matches that of BRAKER3, which uses RNA-seq data and a protein database. Tiberiuss highly parallelized model is the fastest state-of-the-art gene prediction method, processing the human genome in under 2 hours. Availability and Implementationhttps://github.com/Gaius-Augustus/Tiberius Contact{lars.gabriel@uni-greifswald.de, mario.stanke@uni-greifswald.de}

bioinformatics↗

bioRxiv2023

BRAKER3: Fully Automated Genome Annotation Using RNA-Seq and Protein Evidence with GeneMark-ETP, AUGUSTUS and TSEBRA

Gene prediction has remained an active area of bioinformatics research for a long time. Still, gene prediction in large eukaryotic genomes presents a challenge that must be addressed by new algorithms. The amount and significance of the evidence available from transcriptomes and proteomes vary across genomes, between genes and even along a single gene. User-friendly and accurate annotation pipelines that can cope with such data heterogeneity are needed. The previously developed annotation pipelines BRAKER1 and BRAKER2 use RNA-seq or protein data, respectively, but not both. A further significant performance improvement was made by the recently released GeneMark-ETP integrating all three data types. We here present the BRAKER3 pipeline that builds on GeneMark-ETP and AUGUSTUS and further improves accuracy using the TSEBRA combiner. BRAKER3 annotates protein-coding genes in eukaryotic genomes using both short-read RNA-seq and a large protein database, along with statistical models learned iteratively and specifically for the target genome. We benchmarked the new pipeline on genomes of 11 species under assumed level of relatedness of the target species proteome to available proteomes. BRAKER3 outperformed BRAKER1 and BRAKER2. The average transcript-level F1-score was increased by[~] 20 percentage points on average, while the difference was most pronounced for species withlarge and complex genomes. BRAKER3 also outperformed other existing tools, MAKER2, Funannotate and FINDER. The code of BRAKER3 is available on GitHub and as a ready-to-run Docker container for execution with Docker or Singularity. Overall, BRAKER3 is an accurate, easy-to-use tool for eukaryotic genome annotation.

bioinformatics↗

bioRxiv2023

GALBA: Genome Annotation with Miniprot and AUGUSTUS

The Earth Biogenome Project has rapidly increased the number of available eukaryotic genomes, but most released genomes continue to lack annotation of protein-coding genes. In addition, no transcriptome data is available for some genomes. Various gene annotation tools have been developed but each has its limitations. Here, we introduce GALBA, a fully automated pipeline that utilizes miniprot, a rapid protein- to-genome aligner, in combination with AUGUSTUS to predict genes with high accuracy. Accuracy results indicate that GALBA is particularly strong in the annotation of large vertebrate genomes. We also present use cases in insects, vertebrates, and a previously unannotated land plant. GALBA is fully open source and available as a docker image for easy execution with Singularity in high-performance computing environments. Our pipeline addresses the critical need for accurate gene annotation in newly sequenced genomes, and we believe that GALBA will greatly facilitate genome annotation for diverse organisms.

bioinformatics↗

bioRxiv2023

The structure of the tetraploid sour cherry 'Schattenmorelle' (Prunus cerasus L.) genome reveals insights into its segmental allopolyploid nature

Sour cherry (Prunus cerasus L.) is an economically important allotetraploid cherry species believed to have evolved in the Caspian Sea and Black Sea regions. How, when and where exactly the evolution of this species took place is unclear. It resulted from a hybridization of the tetraploid ground cherry (Prunus fruticosa Pall.) and an unreduced (2n) pollen of the diploid ancestor sweet cherry (P. avium L.). Some indications implement that the genome of sour cherry is segmental allopolyploid, but how it is structured and to what extent is unknown. To get an insight, the genome of the sour cherry cultivar Schattenmorelle was sequenced at ~400x using Illumina NovaSeqTM short-read and Oxford Nanopore long-read technologies (ONT R9.4.1 PromethION). Additionally, the transcriptome of Schattenmorelle was sequenced using PacBio Sequel II SMRT cell sequencing at ~300x. The final assembly resulted in a ~629 Mbp long pseudomolecule reference genome, which could be separated into two subgenomes each split into eight chromosomes. Subgenome PceS_a which originates from P. avium has a length of 269 Mbp, whereas subgenome PceS_f which originates from P. fruticosa has a length of 299.5 Mbp. The length of unassembled contigs was 60 Mbp. The genome of the sour cherry shows a size-reduction compared to the genomes of its ancestral species. It also shows traces of homoeologous sequence exchanges throughout the genome. Comparative positional sequence and protein analyses provided evidence that the genome of sour cherry is segmental allotetraploid and that it has evolved in a very recent event in the past.

genomics↗

bioRxiv2021

MPTHub: an open-source software for characterizing the transport of particles in biorelevant media

The study of the transport of particles in different environments plays an essential role in understanding interactions with humans and other living organisms. Importantly, obtained data can be directly used for multiple applications in fields such as fundamental biology, toxicology or medicine. Particle movement in biorelevant media can be readily monitored using microscopy and converted into time-resolved trajectories using freely available tracking software. However, translation into tangible and meaningful parameters is time-consuming and not always intuitive. Thus, we developed a new software - MPTHub - as an open-access, stand-alone, user-friendly tool for the rapid and reliable analysis of particle trajectories extracted from video microscopy. The software was programmed using Python and allowed to import and analyze trajectory data, and export relevant data such as individual and ensemble time-averaged mean square displacements and effective diffusivity, and anomalous transport exponent. Data processing was reliable, fast (total processing time of less than 10 sec) and required minimal memory resources (up to a maximum of around 150 MB in RAM). Demonstration of software applicability was conducted by studying the transport of different polystyrene nanoparticles (100-200 nm) in mucus surrogates. Overall, MPTHub represents a freely available software tool that can be used even by unexperienced users for studying the transport of particles in biorelevant media.

bioinformatics↗

bioRxiv2021

TSEBRA: Transcript Selector for BRAKER

BackgroundBRAKER is a suite of automatic pipelines, BRAKER1 and BRAKER2, for the accurate annotation of protein-coding genes in eukaryotic genomes. Each pipeline trains statistical models of protein-coding genes based on provided evidence and, then predicts protein-coding genes in genomic sequences using both the extrinsic evidence and statistical models. For training and prediction, BRAKER1 and BRAKER2 incorporate complementary extrinsic evidence: BRAKER1 uses only RNA-seq data while BRAKER2 uses only a database of cross-species proteins. The BRAKER suite has so far not been able to reliably exceed the accuracy of BRAKER1 and BRAKER2 when incorporating both types of evidence simultaneously. Currently, for a novel genome project where both RNA-seq and protein data are available, the best option is to run both pipelines independently, and to pick one, likely better output. Therefore, one or another type of the extrinsic evidence would remain unexploited. ResultsWe present TSEBRA, a software that selects gene predictions (transcripts) from the sets generated by BRAKER1 and BRAKER2. TSEBRA uses a set of rules to compare scores of overlapping transcripts based on their support by RNA-seq and homologous protein evidence. We show in computational experiments on genomes of 11 species that TSEBRA achieves higher accuracy than either BRAKER1 or BRAKER2 running alone and that TSEBRA compares favorably with the combiner tool EVidenceModeler. ConclusionTSEBRA is an easy-to-use and fast software tool. It can be used in concert with the BRAKER pipeline to generate a gene prediction set supported by both RNA-seq and homologous protein evidence.

bioinformatics↗

bioRxiv2021

The draft chromosome-level genome assembly of tetraploid ground cherry (Prunus fruticosa Pall.) from long reads

BackgroundCherries are stone fruits and belong to the economically important plant family of Rosaceae with worldwide cultivation of different species. The ground cherry, Prunus fruticosa Pall. is one ancestor of cultivated sour cherry, an important tetraploid cherry species. Here, we present a long read chromosome-level draft genome assembly and related plastid sequences using the Oxford Nanopore Technology PromethION platform and R10.3 pore type. FindingThe final assemblies obtained from 117.3 Gb cleaned reads representing 97x coverage of expected 1.2 Gb tetraploid (2n=4x=32) and 0.3 Gb haploid (1n=8) genome sequence of P. fruticosa were calculated. The N50 contig length ranged between 0.3 and 0.5 Mb with the longest contig being [~]6 Mb. BUSCO estimated a completeness between 98.7 % for the 4n and 96.1 % for the 1n datasets. Using a homology and reference based scaffolding method, we generated a final consensus genome sequence of 366 Mb comprising eight chromosomes. The N50 scaffold was [~]44 Mb with the longest chromosome being 66.5 Mb. The repeat content was estimated to [~]190 Mb (52 %) and 58,880 protein-coding genes were annotated. The chloroplast and mitochondrial genomes were 158,217 bp and 383,281 bp long, which is in accordance with previously published plastid sequences. ConclusionThis is the first report of the genome of ground cherry (P. fruticosa) sequenced by long read technology only. The datasets obtained from this study provide a foundation for future breeding, molecular and evolutionary analysis in Prunus studies.

genomics↗

bioRxiv2021

Enrichment of Zα domains at cytoplasmic stress granules is due to their innate ability to bind nucleic acids

Z domains are a subfamily of winged Helix-Turn-Helix (wHTH) domains found exclusively in proteins involved in the nucleic acids sensory pathway of vertebrate innate immune system and host evasion by viral pathogens. Interestingly, they are the only known protein domains that recognise the left-handed helical conformation of both dsDNA and dsRNA, known as Z-DNA and Z-RNA. Previously, it has been demonstrated that ADAR1 and ZBP1, two proteins possessing the Z domains, localize to cytosolic stress granules. It was further speculated that such localization is principally mediated by Z domains. To characterize and better understand such distinct and specific localization, we characterised the in vivo interactions and localization pattern for the amino terminal region of human DAI harbouring two Z domains (Z{beta}DAI). Using immunoprecipitation and mass spectrometry, we identified several interacting partners that were components of the complex formed by Z domains and RNAs. Differential interacting partners to wild-type Z, relative to mutant proteins, demonstrated that most of the physiologically relevant interactions are mediated by the nucleic acid binding ability of the Z{beta}. Further, we also show enrichment of selected complex components in cytoplasmic stress granules under conditions of stress. This ability is mostly lost in the mutants of Z{beta}DAI (Z{beta}DAI 4xmut) that lack nucleic-acid binding ability. Thus, we posit that the mechanism for the translocation of Z domain-containing proteins to stress granules is mainly mediated by the nucleic acid binding ability of their Z domains. Finally, we demonstrate that FUS and PSF/p54nrb, two RNA binding proteins with established roles in stress granules, interact with Z, which provides strong evidence for a role of these proteins in the innate immune system.

biochemistry↗