Biology subjects

Stanke, M.

Publications and source records attributed to Stanke, M..

15 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↗

bioRxiv2025

Annotation of 200 Insect Genomes with BRAKER for Consistent Comparisons across Species

The annotation of genomes lags behind their sequencing and assembly. For example, of 5,092 insect species in GenBank, as one of the most widely used databases, only 375 currently have annotated genomes within this database. Additionally, species that were previously annotated can benefit from reannotation using RNA-Seq and protein data that have been added since their last annotation, as well as from state-of-the-art annotation methods, whose accuracy has improved. Heterogeneous annotations performed with different tools and protein databases can introduce artifactual differences when comparing gene sets or gene structures between species. Recently, the BRAKER3 annotation pipeline was introduced that integrates evidence from RNA-Seq and from a protein database. It was benchmarked as one of the most accurate annotation methods. Here, we introduce an automated genome annotation workflow that allows to annotate a list of species with BRAKER3 and VARUS for RNA-Seq retrieval, or in the absence of transcriptome data, with BRAKER2, with minimal manual intervention. We selected a diverse set of 200 insect species from different families, including 85 species previously lacking annotations in GenBank. Using currently available RNA-Seq and protein sequence data, we applied our automated workflow to annotate these genomes and conducted downstream analyses typically performed in comparative genomics studies. We present the resulting gene structures, protein sequences, gene ontology terms, orthologous gene groups and a species tree.

genomics↗

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↗

bioRxiv2021

Chromosome-level genome assembly of the functionally extinct northern white rhinoceros (Ceratotherium simum cottoni).

The northern white rhinoceros (NWR; Ceratotherium simum cottoni) is functionally extinct, with only two females remaining alive. Efforts to rescue the NWR have inspired the exploration of unconventional conservation methods, including the generation of artificial gametes from induced pluripotent stem cells and somatic cell nuclear transfer. To enable the technologies required for these approaches, we used complementary sequencing and mapping methods to generate a NWR chromosome-level reference genome that meets or exceeds the metrics proposed by the Vertebrate Genome Project. It represents 40 autosomes, an X and a partially-resolved Y chromosome, and the mitochondrial genome. We compared the NWR reference genome to the southern white rhinoceros (SWR) population that has been physically separated from the NWR for tens of thousands of years. Using short-read data from the SWR and optical mapping, we found that the two populations are very similar on both the chromosome level and mitochondrial genome level. The results of this study are encouraging for the efforts underway to rescue the NWR.

genomics↗

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

End-to-end Learning of Evolutionary Models to Find Coding Regions in Genome Alignments

1MotivationThe comparison of genomes using models of molecular evolution is a powerful approach for finding or towards understanding functional elements. In particular, comparative genomics is a fundamental building brick in building high-quality, complete and consistent annotations of ever larger sets of alignable genomes. ResultsWe here present our new program ClaMSA that classifies multiple sequence alignments using a phylogenetic model. It uses a novel continuous-time Markov chain machine learning layer, named CTMC, that is learned end-to-end together with (recurrent) neural networks for a learning task. We trained ClaMSA discriminately to classify aligned codon sequences that are candidates of coding regions into coding or non-coding and obtained six times fewer false positives for this task on vertebrate and fly alignments than existing methods at the same true positive rate. ClaMSA and the CTMC layer are general tools that could be used for other machine learning tasks on tree-related sequence data. AvailabilityFreely from https://github.com/Gaius-Augustus/clamsa.

bioinformatics↗

bioRxiv2020

BRAKER2: Automatic Eukaryotic Genome Annotation with GeneMark-EP+ and AUGUSTUS Supported by a Protein Database

Full automation of gene prediction has become an important bioinformatics task since the advent of next generation sequencing. The eukaryotic genome annotation pipeline BRAKER1 had combined self-training GeneMark-ET with AUGUSTUS to generate genes coordinates with support of transcriptomic data. Here, we introduce BRAKER2, a pipeline with GeneMark-EP+ and AUGUSTUS externally supported by cross-species protein sequences aligned to the genome. Among the challenges addressed in the development of the new pipeline was generation of reliable hints to the locations of protein-coding exon boundaries from likely homologous but evolutionarily distant proteins. Under equal conditions, the gene prediction accuracy of BRAKER2 was shown to be higher than the one of MAKER2, yet another genome annotation pipeline. Also, in comparison with BRAKER1 supported by a large volume of transcript data, BRAKER2 could produce a better gene prediction accuracy if the evolutionary distances to the reference species in the protein database were rather small. All over, our tests demonstrated that fully automatic BRAKER2 is a fast and accurate method for structural annotation of novel eukaryotic genomes.

bioinformatics↗

bioRxiv2020

Global, Highly Specific and Fast Filtering of Alignment Seeds

An important initial phase of arguably most homology search and alignment methods such as required for genome alignments is seed finding. The seed finding step is crucial to curb the runtime as potential alignments are restricted to and anchored at the sequence position pairs that constitute the seed. To identify seeds, it is good practice to use sets of spaced seed patterns, a method that locally compares two sequences and requires exact matches at certain positions only. We introduce a new method for filtering alignment seeds that we call geometric hashing. Geometric hashing achieves a high specificity by combining non-local information from different seeds using a simple hash function that only requires a constant and small amount of additional time per spaced seed. Geometric hashing was tested on the task of finding homologous positions in the coding regions of human and mouse genome sequences. Thereby, the number of false positives was decreased about million-fold over sets of spaced seeds while maintaining a very high sensitivity. An additional geometric hashing filtering phase could improve the run-time, accuracy or both of programs for various homology-search-and-align tasks.

bioinformatics↗

bioRxiv2019

VARUS: Sampling Complementary RNA Reads from the Sequence Read Archive

Vast amounts of next generation sequencing RNA data has been deposited in archives, accompanying very diverse original studies. The data is readily available also for other purposes such as genome annotation or transcriptome assembly. However, selecting a subset of available experiments, sequencing runs and reads for this purpose is a nontrivial task and complicated by the inhomogeneity of the data.\n\nThis article presents the software VARUS that selects, downloads and aligns reads from NCBIs Sequence Read Archive, given only the species binomial name and genome. VARUS automatically chooses runs from among all archived runs to randomly select subsets of reads. The objective of its online algorithm is to cover a large number of transcripts adequately when network bandwidth and computing resources are limited. For most tested species VARUS achieved both a higher sensitivity and specificity with a lower number of downloaded reads than when runs were manually selected. At the example of twelve eukaryotic genomes, we show that RNA-Seq that was sampled with VARUS is well-suited for fully-automatic genome annotation with BRAKER.\n\nWith VARUS, genome annotation can be automatized to the extent that not even the selection and quality control of RNA-Seq has to be done manually. This introduces the possibility to have fully automatized genome annotation loops over potentially many species without incurring a loss of accuracy over a manually supervised annotation process.

bioinformatics↗

bioRxiv2018

Multiple laboratory mouse reference genomes define strain specific haplotypes and novel functional loci

The most commonly employed mammalian model organism is the laboratory mouse. A wide variety of genetically diverse inbred mouse strains, representing distinct physiological states, disease susceptibilities, and biological mechanisms have been developed over the last century. We report full length draft de novo genome assemblies for 16 of the most widely used inbred strains and reveal for the first time extensive strain-specific haplotype variation. We identify and characterise 2,567 regions on the current Genome Reference Consortium mouse reference genome exhibiting the greatest sequence diversity between strains. These regions are enriched for genes involved in defence and immunity, and exhibit enrichment of transposable elements and signatures of recent retrotransposition events. Combinations of alleles and genes unique to an individual strain are commonly observed at these loci, reflecting distinct strain phenotypes. Several immune related loci, some in previously identified QTLs for disease response have novel haplotypes not present in the reference that may explain the phenotype. We used these genomes to improve the mouse reference genome resulting in the completion of 10 new gene structures, and 62 new coding loci were added to the reference genome annotation. Notably this high quality collection of genomes revealed a previously unannotated gene (Efcab3-like) encoding 5,874 amino acids, one of the largest known in the rodent lineage. Interestingly, Efcab3-like-/- mice exhibit severe size anomalies in four regions of the brain suggesting a mechanism of Efcab3-like regulating brain development.

genomics↗

bioRxiv2017

Comparative Annotation Toolkit (CAT) - simultaneous clade and personal genome annotation

The recent introductions of low-cost, long-read, and read-cloud sequencing technologies coupled with intense efforts to develop efficient algorithms have made affordable, high-quality de novo sequence assembly a realistic proposition. The result is an explosion of new, ultra-contiguous genome assemblies. To compare these genomes we need robust methods for genome annotation. We describe the fully open source Comparative Annotation Toolkit (CAT), which provides a flexible way to simultaneously annotate entire clades and identify orthology relationships. We show that CAT can be used to improve annotations on the rat genome, annotate the great apes, annotate a diverse set of mammals, and annotate personal, diploid human genomes. We demonstrate the resulting discovery of novel genes, isoforms and structural variants, even in genomes as well studied as rat and the great apes, and how these annotations improve cross-species RNA expression experiments.

bioinformatics↗

bioRxiv2017

Repeat associated mechanisms of genome evolution and function revealed by the Mus caroli and Mus pahari genomes

Understanding the mechanisms driving lineage-specific evolution in both primates and rodents has been hindered by the lack of sister clades with a similar phylogenetic structure having high-quality genome assemblies. Here, we have created chromosome-level assemblies of the Mus caroli and Mus pahari genomes. Together with the Mus musculus and Rattus norvegicus genomes, this set of rodent genomes is similar in divergence times to the Hominidae (human-chimpanzee-gorilla-orangutan). By comparing the evolutionary dynamics between the Muridae and Hominidae, we identified punctate events of chromosome reshuffling that shaped the ancestral karyotype of Mus musculus and Mus caroli between 3 to 6 MYA, but that are absent in the Hominidae. In fact, Hominidae show between four-and seven-fold lower rates of nucleotide change and feature turnover in both neutral and functional sequences suggesting an underlying coherence to the Muridae acceleration. Our system of matched, high-quality genome assemblies revealed how specific classes of repeats can play lineage-specific roles in related species. For example, recent LINE activity has remodeled protein-coding loci to a greater extent across the Muridae than the Hominidae, with functional consequences at the species level such as reproductive isolation. Furthermore, we charted a Muridae-specific retrotransposon expansion at unprecedented resolution, revealing how a single nucleotide mutation transformed a specific SINE element into an active CTCF binding site carrier specifically in Mus caroli. This process resulted in thousands of novel, species-specific CTCF binding sites. Our results demonstrate that the comparison of matched phylogenetic sets of genomes will be an increasingly powerful strategy for understanding mammalian biology.

genomics↗

bioRxiv2017

Wild tobacco genomes reveal the evolution of nicotine biosynthesis

Nicotine, the signature alkaloid of Nicotiana species responsible for the addictive properties of human tobacco smoking, functions as a defensive neurotoxin against attacking herbivores. However, the evolution of the genetic features that contributed to the assembly of the nicotine biosynthetic pathway remains unknown. We sequenced and assembled genomes of two wild tobaccos, Nicotiana attenuata (2.5 Gb) and N. obtusifolia (1.5 Gb), two ecological models for investigating adaptive traits in nature. We show that after the Solanaceae whole genome triplication event, a repertoire of rapidly expanding transposable elements (TEs) bloated these Nicotiana genomes, promoted expression divergences among duplicated genes and contributed to the evolution of herbivory-induced signaling and defenses, including nicotine biosynthesis. The biosynthetic machinery that allows for nicotine synthesis in the roots evolved from the stepwise duplications of two ancient primary metabolic pathways: the polyamine and nicotinic acid dinucleotide (NAD) pathways. While the duplication of the former is shared among several Solanaceous genera which produce polyamine-derived tropane alkaloids, the innovation and efficient production of nicotine in the genus Nicotiana required lineage-specific duplications within the NAD pathway and the evolution of root-specific expression of the duplicated Solanaceae-specific ethylene response factor (ERF) that activates the expression of all nicotine biosynthetic genes. Furthermore, TE insertions that incorporated transcription factor binding motifs also likely contributed to the coordinated metabolic flux of the nicotine biosynthetic pathway. Together, these results provide evidence that TEs and gene duplications facilitated the emergence of a key metabolic innovation relevant to plant fitness.

genomics↗