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Sour cherry (Prunus cerasusL.) 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 fruticosaPall.) and an unreduced (2n) pollen of the diploid ancestor sweet cherry (P. aviumL.). 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 NovaSeqTMshort-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. SubgenomePceS_a which originates fromP. aviumhas a length of 269 Mbp, whereas subgenomePceS_f which originates fromP. fruticosahas 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.

The evolutionary origins of Bilateria remain enigmatic. One of the more enduring proposals highlights similarities between a cnidarian-like planula larva and simple acoel-like flatworms. This idea is based in part on the view of the Xenacoelomorpha as an outgroup to all other bilaterians which are themselves designated the Nephrozoa (protostomes and deuterostomes). Genome data, which can help to elucidate phylogenetic relationships and provide important comparative data, remain sparse for early branching bilaterians. Here we assemble and analyse the genome of the simple, marine xenacoelomorphXenoturbella bocki, a key species for our understanding of early bilaterian and deuterostome evolution. Our highly contiguous genome assembly ofX. bockihas a size of ∼110 Mbp in 18 chromosome like scaffolds, with repeat content, and intron, exon and intergenic space comparable to other bilaterian invertebrates. We findX. bockito have a similar number of genes to other bilaterians and to have retained ancestral metazoan synteny. Key bilaterian signalling pathways are also largely complete and most bilaterian miRNAs are present. We conclude thatX. bockihas a complex genome typical of bilaterians, in contrast to the apparent simplicity of its body plan. Overall, our data do not provide evidence supporting the idea that Xenacoelomorpha are a primitively simple outgroup to other bilaterians and gene presence/absence data support a relationship with Ambulacraria.

Novel genomes are today often annotated by small consortia or individuals whose background is not from bioinformatics. This audience requires tools that are easy to use. This need had been addressed by several genome annotation tools and pipelines. Visualizing resulting annotation is a crucial step of quality control. The UCSC Genome Browser is a powerful and popular genome visualization tool. Assembly Hubs allow browsing genomes that are hosted locally via already available UCSC Genome Browser servers. The steps for creating custom Assembly Hubs are well documented and the required tools are publicly available. However, the number of steps for creating a novel Assembly Hub is large. In some cases the format of input files needs to be adapted which is a difficult task for scientists without programming background. Here, we describe the novel command line tool MakeHub that generates Assembly Hubs for the UCSC Genome Browser in a fully automated fashion. The pipeline also allows extending previously created Hubs by additional tracks.MakeHub is freely available for download from https://github.com/Gaius-Augustus/MakeHub.Contactkatharina.hoff@uni-greifswald.de

Funder: Howard Hughes Medical Institute, Funder: National Science Foundation; Grant(s): DBI:IIBR:CAREER #1943371, A global international initiative such as the Earth BioGenome Project (EBP) requires both agreement and coordination on standards to ensure that the collective effort generates rapid progress towards its goals. To this end, the EBP initiated five technical standards committees comprising volunteer members from the global genomics scientific community: Sample Collection and Processing, Sequencing and Assembly, Annotation, Analysis, and, IT and Informatics. The current versions of the resulting standards documents are available on the EBP website, with the recognition that opportunities, technologies and challenges may improve or change in the future requiring flexibility for the EBP to meet its goals. Here, we describe some highlights from the proposed standards, and areas where additional challenges will need to be met.

Introduced populations of invasive organisms have to cope with novel environmental challenges, while having reduced genetic variation caused by founder effects. The mechanisms associated with this "genetic paradox of invasive species" has received considerable attention, yet few studies have examined the genomic architecture of invasive species. Populations of the heart node ant Cardiocondyla obscurior belong to two distinct lineages, a New World lineage so far only found in Latin America and a more globally distributed Old World lineage. In the present study, we use population genomic approaches to compare populations of the two lineages with apparent divergent invasive potential. We find that the strong genetic differentiation of the two lineages began at least 40,000 generations ago and that activity of transposable elements (TEs) has contributed significantly to the divergence of both lineages, possibly linked to the very unusual genomic distribution of TEs in this species. Furthermore, we show that introgression from the Old World lineage is a dominant source of genetic diversity in the New World lineage, despite the lineages' strong genetic differentiation. Our study uncovers mechanisms underlying novel genetic variation in introduced populations of C. obscurior that could contribute to the species' adaptive potential.

Anthropogenic climate change is rapidly altering ecosystems, driving range shifts, range contractions, dwindling population sizes and local extinctions in many species. Some species, however, are expanding their ranges and seem to benefit from warming temperatures. This is the case for the wasp spider, Argiope bruennichi, which has undergone a range expansion from its historic range in the Mediterranean (“core”), now reaching as far as the Baltic states and Scandinavia (“edge”). The rate of this range expansion cannot be attributed to climate change alone, and it has been hypothesized that adaptive introgression lent the genetic variation upon which selection could act, enabling the rapid range expansion. In the present study, we first quantify the degree of local adaptation and phenotypic plasticity in cold tolerance in edge relative to core populations, and secondly investigate genomic and phenotypic turnover across the proposed introgression zone. With a reciprocal transplant common garden experiment, we provide strong support for the hypothesis that edge populations are locally adapted to colder winter conditions. We also find evidence of seasonal plasticity in the core populations, while edge populations have lost this plasticity. Our genome-wide analysis, using a combination of FST outlier and genetic-environment association tests, supports the hypothesis that adaptive introgression played a role in environmental adaptation.

Cherries are among the most popular fruits among consumers and are grown for industrial processing or fresh consumption. The cultivation and breeding of cherries faces new challenges in the future, not least due to climate change. Cultivation is becoming increasingly difficult due to changing climatic conditions, diseases and pests. Therefore, the market demands new varieties with high fruit quality and adaptation to locally changing conditions. Breeding for tree fruit is a long-term task, though, and tools to facilitate this process are unfortunately not yet available to a sufficient extent. Rapid progress in third-generation sequencing technologies enables breeders to explore and exploit the genomic information underlying economical important traits. We used Oxford Nanopore Technology PromethION platform and R10.3 pore type to sequence the genome of tetraploid ground cherry (Prunus fruticosa). The final sequence has been deposited at DDBJ/ENA/GenBank under the accession JAHHUK000000000. A specific description of the work can be viewed at https://biorxiv.org/cgi/content/short/2021.06.01.446499v1. The version described in this paper is version JAHHUK010000000. The data on this platform includes 17 files, supports the assembling and annotation procedure, and provides insights into this challenging work. As a result, five assemblies (MiniasmMinimap.fasta, medaka.fasta, raconpolished.fasta, purged.fasta, purged2.fasta) have been processed to generate a draft genome assembly. The final genome sequence was masked (genome.fa.masked.gz, genome.fa.out, genome.fa.tbl) and annotated using Braker 1 and Braker 2, GeMoMa pipeline and eight reference datasets from other Prunus species. The final structural annotation, coding sequences and protein predictions are provided for future studies (GeMoMa_Annotation_filtered_predictions_8ref_RNAseq_Braker.gff, GeMoMa_Annotation_filtered_predictions_8ref_RNAseq_Braker_assignment.tabular, GeMoMa_Annotation_filtered_predictions_8ref_RNAseq_Braker_cds-parts.fasta, GeMoMa_Annotation_filtered_predictions_8ref_RNAseq_Braker_proteins.fasta). The functional prediction was performed with InterProScan (Results IPS.xlsx). In addition, the sequences and annotation of the chloroplast (Pf_chloroplast_1.0.fasta, GeSeqJob-20210201-124142_utg000088l_segment0_1_GFF3.gff3) and mitochondrion (Pf_mitochondria_1.0, GeSeqJob-20210202-114557_utg001396l_trimmed_GFF3.gff3) are presented.

Background BRAKER 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. Results We 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. Conclusion TSEBRA 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.

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.

BackgroundArgiope bruennichi, the European wasp spider, has been investigated intensively as a focal species for studies on sexual selection, chemical communication, and the dynamics of rapid range expansion at a behavioral and genetic level. However, the lack of a reference genome has limited insights into the genetic basis for these phenomena. Therefore, we assembled a high-quality chromosome-level reference genome of the European wasp spider as a tool for more in-depth future studies.FindingsWe generated, de novo, a 1.67 Gb genome assembly of A. bruennichi using 21.8× Pacific Biosciences sequencing, polished with 19.8× Illumina paired-end sequencing data, and proximity ligation (Hi-C)-based scaffolding. This resulted in an N50 scaffold size of 124 Mb and an N50 contig size of 288 kb. We found 98.4% of the genome to be contained in 13 scaffolds, fitting the expected number of chromosomes (n = 13). Analyses showed the presence of 91.1% of complete arthropod BUSCOs, indicating a high-quality assembly.ConclusionsWe present the first chromosome-level genome assembly in the order Araneae. With this genomic resource, we open the door for more precise and informative studies on evolution and adaptation not only in A. bruennichi but also in arachnids overall, shedding light on questions such as the genomic architecture of traits, whole-genome duplication, and the genomic mechanisms behind silk and venom evolution.

In contrast to the western honey bee,Apis mellifera, other honey bee species have been largely neglected despite their importance and diversity. The genetic basis of the evolutionary diversification of honey bees remains largely unknown. Here, we provide a genome-wide comparison of three honey bee species, each representing one of the three subgenera of honey bees, namely the dwarf (Apis florea), giant (A. dorsata), and cavity-nesting (A. mellifera) honey bees with bumblebees as an outgroup. Our analyses resolve the phylogeny of honey bees with the dwarf honey bees diverging first. We find that evolution of increased eusocial complexity inApisproceeds via increases in the complexity of gene regulation, which is in agreement with previous studies. However, this process seems to be related to pathways other than transcriptional control. Positive selection patterns acrossApisreveal a trade-off between maintaining genome stability and generating genetic diversity, with a rapidly evolving piRNA pathway leading to genomes depleted of transposable elements, and a rapidly evolving DNA repair pathway associated with high recombination rates in allApisspecies. Diversification withinApisis accompanied by positive selection in several genes whose putative functions present candidate mechanisms for lineage-specific adaptations, such as migration, immunity, and nesting behavior.

The task of eukaryotic genome annotation remains challenging. Only a few genomes could serve as standards of annotation achieved through a tremendous investment of human curation efforts. Still, the correctness of all alternative isoforms, even in the best-annotated genomes, could be a good subject for further investigation. The new BRAKER2 pipeline generates and integrates external protein support into the iterative process of training and gene prediction by GeneMark-EP+ and AUGUSTUS. BRAKER2 continues the line started by BRAKER1 where self-training GeneMark-ET and AUGUSTUS made gene predictions supported by transcriptomic data. Among the challenges addressed by the new pipeline was a generation of reliable hints to protein-coding exon boundaries from likely homologous but evolutionarily distant proteins. In comparison with other pipelines for eukaryotic genome annotation, BRAKER2 is fully automatic. It is favorably compared under equal conditions with other pipelines, e.g. MAKER2, in terms of accuracy and performance. Development of BRAKER2 should facilitate solving the task of harmonization of annotation of protein-coding genes in genomes of different eukaryotic species. However, we fully understand that several more innovations are needed in transcriptomic and proteomic technologies as well as in algorithmic development to reach the goal of highly accurate annotation of eukaryotic genomes.

BackgroundArgiope bruennichi, the European wasp spider, has been studied intensively as to sexual selection, chemical communication, and the dynamics of rapid range expansion at a behavioral and genetic level. However, the lack of a reference genome has limited insights into the genetic basis for these phenomena. Therefore, we assembled a high-quality chromosome-level reference genome of the European wasp spider as a tool for more in-depth future studies.FindingsWe generated, de novo, a 1.67Gb genome assembly of A. bruennichi using 21.5X PacBio sequencing, polished with 30X Illumina paired-end sequencing data, and proximity ligation (Hi-C) based scaffolding. This resulted in an N50 scaffold size of 124Mb and an N50 contig size of 288kb. We found 98.4% of the genome to be contained in 13 scaffolds, fitting the expected number of chromosomes (n = 13). Analyses showed the presence of 91.1% of complete arthropod BUSCOs, indicating a high quality of the assembly.ConclusionsWe present the first chromosome-level genome assembly in the class Arachnida. With this genomic resource, we open the door for more precise and informative studies on evolution and adaptation in A. bruennichi, as well as on several interesting topics in Arachnids, such as the genomic architecture of traits, whole genome duplication and the genomic mechanisms behind silk and venom evolution.

Fluorescence-tags, commonly used to visualize the spatial distribution of proteins within cells, can influence the localization of the tagged proteins by affecting their stability, interaction with other proteins or the induction of oligomerization artifacts. To circumvent these obstacles, a protocol was developed to generate 50 nm thick serial sections suitable for immunogold labeling and subsequent reconstruction of the spatial distribution of immuno-labeled native proteins within individual bacterial cells. Applying this method, we show a cellular distribution of the staphylococcal alkaline shock protein 23 (Asp23), which is compatible with filament formation, a property of Asp23 that we also demonstrate in vitro.

Background 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. Results This article presents the software VARUS that selects, downloads and aligns reads from NCBI’s 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. Conclusions With 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.

Beginning in 2013, sea stars throughout the Eastern North Pacific were decimated by wasting disease, also known as "asteroid idiopathic wasting syndrome" (AIWS) due to its elusive aetiology. The geographic extent and taxonomic scale of AIWS meant events leading up to the outbreak were heterogeneous, multifaceted, and oftentimes unobserved; progression from morbidity to death was rapid, leaving few tell-tale symptoms. Here, we take a forensic genomic approach to discover candidate genes that may help explain sea star wasting syndrome. We report the first genome and annotation for Pisaster ochraceus, along with differential gene expression (DGE) analyses in four size classes, three tissue types, and in symptomatic and asymptomatic individuals. We integrate nucleotide polymorphisms associated with survivors of the wasting disease outbreak, DGE associated with temperature treatments in P. ochraceus, and DGE associated with wasting in another asteroid Pycnopodia helianthoides. In P. ochraceus, we found DGE across all tissues, among size classes, and between asymptomatic and symptomatic individuals; the strongest wasting-associated DGE signal was in pyloric caecum. We also found previously identified outlier loci co-occur with differentially expressed genes. In cross-species comparisons of symptomatic and asymptomatic individuals, consistent responses distinguish genes associated with invertebrate innate immunity and chemical defence, consistent with context-dependent stress responses, defensive apoptosis, and tissue degradation. Our analyses thus highlight genomic constituents that may link suspected environmental drivers (elevated temperature) with intrinsic differences among individuals (age/size, alleles associated with susceptibility) that elicit organismal responses (e.g., coelomocyte proliferation) and manifest as sea star wasting mass mortality.

International audience; Xenoturbella and the acoelomorph worms (Xenacoelomorpha) are simple marine animals with controversial affinities. They have been placed as the sister group of all other bilaterian animals (Nephrozoa hypothesis), implying their simplicity is an ancient characteristic ]; alternatively, they have been linked to the complex Ambulacraria (echinoderms and hemichordates) in a clade called the Xenambulacraria , suggesting their simplicity evolved by reduction from a complex ancestor. The difficulty resolving this problem implies the phylogenetic signal supporting the correct solution is weak and affected by inadequate modeling, creating a misleading non-phylogenetic signal. The idea that the Nephrozoa hypothesis might be an artifact is prompted by the faster molecular evolutionary rate observed within the Acoelomorpha. Unequal rates of evolution are known to result in the systematic artifact of long branch attraction, which would be predicted to result in an attraction between long-branch acoelomorphs and the outgroup, pulling them toward the root . Other biases inadequately accommodated by the models used can also have strong effects, exacerbated in the context of short internal branches and long terminal branches . We have assembled a large and informative dataset to address this problem. Analyses designed to reduce or to emphasize misleading signals show the Nephrozoa hypothesis is supported under conditions expected to exacerbate errors, and the Xenambulacraria hypothesis is preferred in conditions designed to reduce errors. Our reanalyses of two other recently published datasets produce the same result. We conclude that the Xenacoelomorpha are simplified relatives of the Ambulacraria.

BRAKER is a pipeline for highly accurate and fully automated gene prediction in novel eukaryotic genomes. It combines two major tools: GeneMark-ES/ET and AUGUSTUS. GeneMark-ES/ET learns its parameters from a novel genomic sequence in a fully automated fashion; if available, it uses extrinsic evidence for model refinement. From the protein-coding genes predicted by GeneMark-ES/ET, we select a set for training AUGUSTUS, one of the most accurate gene finding tools that, in contrast to GeneMark-ES/ET, integrates extrinsic evidence already into the gene prediction step. The first published version, BRAKER1, integrated genomic footprints of unassembled RNA-Seq reads into the training as well as into the prediction steps. The pipeline has since been extended to the integration of data on mapped cross-species proteins, and to the usage of heterogeneous extrinsic evidence, both RNA-Seq and protein alignments. In this book chapter, we briefly summarize the pipeline methodology and describe how to apply BRAKER in environments characterized by various combinations of external evidence.

We review software tools for gene prediction — the identification of protein-coding genes and their structure in genome sequences. The discussed approaches include methods based on RNA-Seq and current methods based on homology — comparative gene prediction and protein spliced alignments. Many methods require that their parameters are adjusted to the target species or its broader clade. These include ab initio gene finders, integrated approaches with ab initio components and some aligners. We also review current automatic methods for training for the common case that a bona fide training set of gene structures is not available before annotation.

Summary Six transcription regulatory genes of the Verticillium plant pathogen, which reprogrammed nonadherent budding yeasts for adhesion, were isolated by a genetic screen to identify control elements for early plant infection. Verticillium transcription activator of adhesion Vta2 is highly conserved in filamentous fungi but not present in yeasts. The Magnaporthe grisea ortholog conidiation regulator Con7 controls the formation of appressoria which are absent in Verticillium species. Vta2 was analyzed by using genetics, cell biology, transcriptomics, secretome proteomics and plant pathogenicity assays. Nuclear Vta2 activates the expression of the adhesin-encoding yeast flocculin genes FLO1 and FLO11. Vta2 is required for fungal growth of Verticillium where it is a positive regulator of conidiation. Vta2 is mandatory for accurate timing and suppression of microsclerotia as resting structures. Vta2 controls expression of 270 transcripts, including 10 putative genes for adhesins and 57 for secreted proteins. Vta2 controls the level of 125 secreted proteins, including putative adhesins or effector molecules and a secreted catalase-peroxidase. Vta2 is a major regulator of fungal pathogenesis, and controls host-plant root infection and H2O2 detoxification. Verticillium impaired in Vta2 is unable to colonize plants and induce disease symptoms. Vta2 represents an interesting target for controlling the growth and development of these vascular pathogens.

AUGUSTUS is a tool for finding protein-coding genes and their exon-intron structure in genomic sequences. It does not necessarily require additional experimental input, as it can be applied in so-called ab initio mode. However, extrinsic evidence from various sources such as transcriptome sequencing or the annotations of closely related genomes can be integrated in order to improve the accuracy and completeness of the annotation. AUGUSTUS can be applied to single genomes, or simultaneously to several aligned genomes. Here, we describe steps required for training AUGUSTUS for the annotation of individual genomes and the steps to do the actual structural annotation. Further, we describe the generation and integration of evidence from various sources of extrinsic evidence. © 2018 by John Wiley & Sons, Inc.

Metagenomic sequencing projects yield numerous sequencing reads of a diverse range of uncultivated and mostly yet unknown microorganisms. In many cases, these sequencing reads cannot be assembled into longer contigs. Thus, gene prediction tools that were originally developed for whole-genome analysis are not suitable for processing metagenomes. Orphelia is a program for predicting genes in short DNA sequences that is available through a web server application (http://orphelia.gobics.de). Orphelia utilizes prediction models that were created with machine learning techniques on the basis of a wide range of annotated genomes. In contrast to other methods for metagenomic gene prediction, Orphelia has fragment length-specific prediction models for the two most popular sequencing techniques in metagenomics, chain termination sequencing and pyrosequencing. These models ensure highly specific gene predictions. peerReviewed

Motivation: Gene finding in eukaryotic genomes is notoriously difficult to automate. The task is to design a work flow with a minimal set of tools that would reach state-of-the-art performance across a wide range of species. GeneMark-ET is a gene prediction tool that incorporates RNA-Seq data into unsupervised training and subsequently generates ab initio gene predictions. AUGUSTUS is a gene finder that usually requires supervised training and uses information from RNA-Seq reads in the prediction step. Complementary strengths of GeneMark-ET and AUGUSTUS provided motivation for designing a new combined tool for automatic gene prediction. Results: We present BRAKER1, a pipeline for unsupervised RNA-Seq-based genome annotation that combines the advantages of GeneMark-ET and AUGUSTUS. As input, BRAKER1 requires a genome assembly file and a file in bam-format with spliced alignments of RNA-Seq reads to the genome. First, GeneMark-ET performs iterative training and generates initial gene structures. Second, AUGUSTUS uses predicted genes for training and then integrates RNA-Seq read information into final gene predictions. In our experiments, we observed that BRAKER1 was more accurate than MAKER2 when it is using RNA-Seq as sole source for training and prediction. BRAKER1 does not require pre-trained parameters or a separate expert-prepared training step. Availability and implementation: BRAKER1 is available for download at http://bioinf.uni-greifswald.de/bioinf/braker/ and http://exon.gatech.edu/GeneMark/. Contact: katharina.hoff@uni-greifswald.de or borodovsky@gatech.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Made available in DSpace on 2015-10-21T20:31:01Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-04-24. Added 1 bitstream(s) on 2015-10-22T09:48:01Z : No. of bitstreams: 1 WOS000353676700001.pdf: 2518339 bytes, checksum: d1f8d18bd059b76504f3fcd9aa639a74 (MD5) National Institutes of Health (NIH) Agriculture and Food Research Initiative Competitive grant from the USDA National Institute of Food and Agriculture Research Council of Norway (NFR) PEW Charitable Trust University of East Anglia, UK Israel Science Foundation (ISF) Biotechnology and Biological Sciences Research Council, UK University of East Anglia University of Alabama College of Arts and Sciences Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Marie Curie International Outgoing Fellowship Swiss National Science Foundation New Faculty Initiative Grant (NFIG) from Illinois State University College of Arts and Sciences DFG Instituto Nacional de Bioinformatica (INB) from ISCIII in Spain Background: The shift from solitary to social behavior is one of the major evolutionary transitions. Primitively eusocial bumblebees are uniquely placed to illuminate the evolution of highly eusocial insect societies. Bumblebees are also invaluable natural and agricultural pollinators, and there is widespread concern over recent population declines in some species. High-quality genomic data will inform key aspects of bumblebee biology, including susceptibility to implicated population viability threats.Results: We report the high quality draft genome sequences of Bombus terrestris and Bombus impatiens, two ecologically dominant bumblebees and widely utilized study species. Comparing these new genomes to those of the highly eusocial honeybee Apis mellifera and other Hymenoptera, we identify deeply conserved similarities, as well as novelties key to the biology of these organisms. Some honeybee genome features thought to underpin advanced eusociality are also present in bumblebees, indicating an earlier evolution in the bee lineage. Xenobiotic detoxification and immune genes are similarly depauperate in bumblebees and honeybees, and multiple categories of genes linked to social organization, including development and behavior, show high conservation. Key differences identified include a bias in bumblebee chemoreception towards gustation from olfaction, and striking differences in microRNAs, potentially responsible for gene regulation underlying social and other traits.Conclusions: These two bumblebee genomes provide a foundation for post-genomic research on these key pollinators and insect societies. Overall, gene repertoires suggest that the route to advanced eusociality in bees was mediated by many small changes in many genes and processes, and not by notable expansion or depauperation. Illinois State Univ, Sch Biol Sci, Normal, IL 61790 USA ETH, Inst Integrat Biol, Expt Ecol, CH-8092 Zurich, Switzerland E Carolina Univ, Dept Biol, Greenville, NC 27858 USA Hebrew Univ Jerusalem, Alexander Silberman Inst Life Sci, Dept Ecol Evolut &Behav, Jerusalem, Israel Univ Ghent, Fac Sci, Lab Zoophysiol, B-9000 Ghent, Belgium Univ Otago, Dept Biochem, Labo Evolut &Dev Genet, Dunedin 9054, New Zealand Univ Otago, Dept Biochem, Natl Res Ctr Growth &Dev, Dunedin 9054, New Zealand Univ Missouri, Div Plant Sci, Div Anim Sci, Columbia, MO 65211 USA Univ Missouri, MU Informat Inst, Columbia, MO 65211 USA Georgetown Univ, Dept Biol, Washington, DC 20057 USA Arizona State Univ, Sch Life Sci, Tempe, AZ 85287 USA Univ Copenhagen, Dept Biol, Ctr Funct &Comparat Insect Gen, Copenhagen, Denmark Univ Hohenheim, Inst Anim Sci, D-70599 Stuttgart, Germany Univ Alabama, Dept Biol Sci, Tuscaloosa, AL 35487 USA Univ Illinois, Dept Entomol, Urbana, IL 61801 USA Univ Ghent, Fac Biosci Engn, Dept Crop Protect, Lab Agrozool, B-9000 Ghent, Belgium Univ Halle Wittenberg, Inst Biol, Wittenberg, Germany Univ Geneva, Sch Med, Dept Genet Med &Dev, CH-1211 Geneva, Switzerland Swiss Inst Bioinformat, CH-1211 Geneva, Switzerland MIT, Comp Sci &Artificial Intelligence Lab, Cambridge, MA 02139 USA MIT &Harvard, Broad Inst, Cambridge Ctr 7, Cambridge, MA 02142 USA Univ Munster, Inst Evolut &Biodivers, D-48149 Munster, Germany Ctr Genom Regulat CRG, Barcelona 08003, Spain UPF, Barcelona, Spain Ernst Moritz Arndt Univ Greifswald, Inst Math &Comp Sci, D-17487 Greifswald, Germany Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Genom Div, Berkeley, CA 94720 USA Natl Lib Med, Natl Ctr Biotechnol Informat, Bethesda, MD 20894 USA Norwegian Univ Food Sci, Dept Chem Biotechnol &Food Sci, N-1432 As, Norway Univ E Anglia, Sch Comp Sci, Norwich NR4 7TJ, Norfolk, England Univ Dusseldorf, Inst Evolut Genet, D-40225 Dusseldorf, Germany Univ Cologne, Inst Genet, Cologne, Germany Univ Sao Paulo, Dept Biol, Fac Filosofia Ciencias &Letras Ribeirao Preto, BR-14040901 Ribeirao Preto, Brazil Univ Edinburgh, Ashworth Labs, Inst Evolutionary Biol &Edinburgh Gen, Edinburgh EH9 3FL, Midlothian, Scotland Univ E Anglia, Sch Biol Sci, Norwich NR4 7TJ, Norfolk, England Royal Holloway Univ London, Sch Biol Sci, London, England Maynooth Univ, Dept Biol, Co, Kildare, Ireland Univ Bristol, Sch Biol Sci, Bristol BS8 1TQ, Avon, England CSIRO, Land &Water Flagship, Canberra, ACT, Australia Trin Coll Dublin, Sch Nat Sci, Dept Zool, Dublin, Ireland Zool Soc London, Inst Zool, London NW1 4RY, England ETH, Inst Integrat Biol, Theoret Biol, CH-8092 Zurich, Switzerland Swiss Inst Bioinformat, Lausanne, Switzerland Swiss Fed Inst Technol, Dept Biosyst Sci &Engn, Computat Evolut, Basel, Switzerland USDA ARS, Bee Res Lab, Washington, DC 20250 USA Univ Sao Paulo, Dept Genet, Fac Med Ribeirao Preto, BR-14040900 Ribeirao Preto, Brazil N Carolina State Univ, Ctr Res Sci Computat, Raleigh, NC 27695 USA Kyoto Univ, Grad Sch Agr, Lab Insect Ecol, Kyoto, Japan Univ Sao Paulo, Dept Biol Celular &Mol &Bioagentes Patogen, Fac Med Ribeirao Preto, BR-14040900 Ribeirao Preto, Brazil Inst Fed Educ Ciencia &Tecnol Sao Paulo, BR-15991502 Matao, Brazil Ohio State Univ, Dept Entomol, Wooster, OH 44791 USA Oxford Brookes Univ, Fac Hlth &Life Sci, Dept Biol &Med Sci, Oxford OX3 0BP, England Xian Jiaotong Liverpool Univ, Dept Biol Sci, Suzhou, Peoples R China Univ Hosp Halle Saale, Dept Lab Med, Halle, Germany German Ctr Integrat Biodivers Res iDiv, Leipzig, Germany Univ Southampton, Southampton, Hants, England Univ Leicester, Dept Biol, Leicester, Leics, England Univ Fed Sao Carlos, Ctr Ciencias Biol &Saude, Dept Genet &Evolucao, BR-13565905 Sao Carlos, SP, Brazil Univ Estadual Paulista, Fac Ciencias Agr &Vet, Dept Tecnol, BR-14884900 Jaboticabal, Brazil Univ N Carolina, Dept Biol, Greensboro, NC 27403 USA Natl Ctr Sci Res Demokritos, Inst Biosci &Applicat, Athens, Greece Univ Munich, Munich, Germany Baylor Coll Med, Dept Mol &Human Genet, Human Genome Sequencing Ctr, Houston, TX 77030 USA Univ Illinois, Roy J Carver Biotechnol, Urbana, IL 61801 USA Univ Illinois, Dept Crop Sci, Urbana, IL 61801 USA Univ Illinois, Inst Genom Biol, Urbana, IL 61801 USA MD Anderson Canc Ctr, Sch Hlth Profess, Mol Genet Technol Program, Unit 2, Houston, TX 77025 USA Univ Chicago, Dept Human Genet, Chicago, IL 60637 USA Univ Ghent, Dept Biochem &Microbiol, Lab Prot Biochem &Biomol Engn, B-9000 Ghent, Belgium Queen Mary Univ London, Sch Biol &Chem Sci, Dept Biol &Expt Psychol, London E1 4NS, England Guangzhou Univ, Sch Life Sci, Guangzhou, Guangdong, Peoples R China Johns Hopkins Univ, McKusick Nathans Inst Genet Med, Ctr Computat Biol, Baltimore, MD 21205 USA Univ Illinois, Neurosci Program, Dept Entomol, Carl R Woese Inst Genom Biol, Urbana, IL 61801 USA Departamento de Tecnologia, Faculdade de Ciências Agrárias e Veterinárias, Universidade Estadual Paulista, Jaboticabal, 14884-900, Brazi National Institutes of Health (NIH): DP1 OD006416 National Institutes of Health (NIH): U54 HG003273 Agriculture and Food Research Initiative Competitive grant from the USDA National Institute of Food and Agriculture: 2010-65106-21301 FAPESP: 11/03171-5 Marie Curie International Outgoing Fellowship: PIOF-GA-2011-303312 Swiss National Science Foundation: 31003A-125350 Swiss National Science Foundation: 31003A-143936

Xenacoelomorpha is, most probably, a monophyletic group that includes three clades: Acoela, Nemertodermatida and Xenoturbellida. The group still has contentious phylogenetic affinities; though most authors place it as the sister group of the remaining bilaterians, some would include it as a fourth phylum within the Deuterostomia. Over the past few years, our group, along with others, has undertaken a systematic study of the microscopic anatomy of these worms; our main aim is to understand the structure and development of the nervous system. This research plan has been aided by the use of molecular/developmental tools, the most important of which has been the sequencing of the complete genomes and transcriptomes of different members of the three clades. The data obtained has been used to analyse the evolutionary history of gene families and to study their expression patterns during development, in both space and time. A major focus of our research is the origin of ‘cephalized’ (centralized) nervous systems. How complex brains are assembled from simpler neuronal arrays has been a matter of intense debate for at least 100 years. We are now tackling this issue using Xenacoelomorpha models. These represent an ideal system for this work because the members of the three clades have nervous systems with different degrees of cephalization; from the relatively simple sub-epithelial net of Xenoturbella to the compact brain of acoels. How this process of ‘progressive’ cephalization is reflected in the genomes or transcriptomes of these three groups of animals is the subject of this paper.

Background: The first generation of genome sequence assemblies and annotations have had a significant impact upon our understanding of the biology of the sequenced species, the phylogenetic relationships among species, the study of populations within and across species, and have informed the biology of humans. As only a few Metazoan genomes are approaching finished quality (human, mouse, fly and worm), there is room for improvement of most genome assemblies. The honey bee (Apis mellifera) genome, published in 2006, was noted for its bimodal GC content distribution that affected the quality of the assembly in some regions and for fewer genes in the initial gene set (OGSv1.0) compared to what would be expected based on other sequenced insect genomes. Results: Here, we report an improved honey bee genome assembly (Amel_4.5) with a new gene annotation set (OGSv3.2), and show that the honey bee genome contains a number of genes similar to that of other insect genomes, contrary to what was suggested in OGSv1.0. The new genome assembly is more contiguous and complete and the new gene set includes 5000 more protein-coding genes, 50% more than previously reported. About 1/6 of the additional genes were due to improvements to the assembly, and the remaining were inferred based on new RNAseq and protein data. Conclusions: Lessons learned from this genome upgrade have important implications for future genome sequencing projects. Furthermore, the improvements significantly enhance genomic resources for the honey bee, a key model for social behavior and essential to global ecology through pollination. Funding for the project was provided by a grant to RG from the National Human Genome Research Institute, National Institutes of Health (NHGRI, NIH) U54 HG003273. Contributions from members of the CGE lab were supported by Agriculture and Food Research Initiative Competitive grant no. 2010-65205-20407 from the USDA National Institute of Food Agriculture. AKB was supported by a Clare Luce Booth Fellowship at Georgetown University

The prediction of protein coding genes is an important step in the annotation of newly sequenced and assembled genomes. AUGUSTUS is one of the most accurate tools for eukaryotic gene prediction. Here, we present WebAUGUSTUS, a web interface for training AUGUSTUS and predicting genes with AUGUSTUS. Depending on the needs of the user, WebAUGUSTUS generates training gene structures automatically. Besides a genome file, either a file with expressed sequence tags or a file with protein sequences is required for this step. Alternatively, it is possible to submit an externally generated training gene structure file and a genome file. The web service optimizes AUGUSTUS parameters and predicts genes with those parameters. WebAUGUSTUS is available at http://bioinf.uni-greifswald.de/webaugustus.

The evolutionary importance of hybridization and introgression has long been debated. Hybrids are usually rare and unfit, but even infrequent hybridization can aid adaptation by transferring beneficial traits between species. Here we use genomic tools to investigate introgression in Heliconius, a rapidly radiating genus of neotropical butterflies widely used in studies of ecology, behaviour, mimicry and speciation. We sequenced the genome of Heliconius melpomene and compared it with other taxa to investigate chromosomal evolution in Lepidoptera and gene flow among multiple Heliconius species and races. Among 12, 669 predicted genes, biologically important expansions of families of chemosensory and Hox genes are particularly noteworthy. Chromosomal organization has remained broadly conserved since the Cretaceous period, when butterflies split from the Bombyx (silkmoth) lineage. Using genomic resequencing, we show hybrid exchange of genes between three co-mimics, Heliconius melpomene, Heliconius timareta and Heliconius elevatus, especially at two genomic regions that control mimicry pattern. We infer that closely related Heliconius species exchange protective colour-pattern genes promiscuously, implying that hybridization has an important role in adaptive radiation. © 2012 Macmillan Publishers Limited.

Background Metagenomics is an approach to the characterization of microbial genomes via the direct isolation of genomic sequences from the environment without prior cultivation. The amount of metagenomic sequence data is growing fast while computational methods for metagenome analysis are still in their infancy. In contrast to genomic sequences of single species, which can usually be assembled and analyzed by many available methods, a large proportion of metagenome data remains as unassembled anonymous sequencing reads. One of the aims of all metagenomic sequencing projects is the identification of novel genes. Short length, for example, Sanger sequencing yields on average 700 bp fragments, and unknown phylogenetic origin of most fragments require approaches to gene prediction that are different from the currently available methods for genomes of single species. In particular, the large size of metagenomic samples requires fast and accurate methods with small numbers of false positive predictions. Results We introduce a novel gene prediction algorithm for metagenomic fragments based on a two-stage machine learning approach. In the first stage, we use linear discriminants for monocodon usage, dicodon usage and translation initiation sites to extract features from DNA sequences. In the second stage, an artificial neural network combines these features with open reading frame length and fragment GC-content to compute the probability that this open reading frame encodes a protein. This probability is used for the classification and scoring of gene candidates. With large scale training, our method provides fast single fragment predictions with good sensitivity and specificity on artificially fragmented genomic DNA. Additionally, this method is able to predict translation initiation sites accurately and distinguishes complete from incomplete genes with high reliability. Conclusion Large scale machine learning methods are well-suited for gene prediction in metagenomic DNA fragments. In particular, the combination of linear discriminants and neural networks is promising and should be considered for integration into metagenomic analysis pipelines. The data sets can be downloaded from the URL provided (see Availability and requirements section).

Background Gene prediction is an essential step in the annotation of metagenomic sequencing reads. Since most metagenomic reads cannot be assembled into long contigs, specialized statistical gene prediction tools have been developed for short and anonymous DNA fragments, e.g. MetaGeneAnnotator and Orphelia. While conventional gene prediction methods have been subject to a benchmark study on real sequencing reads with typical errors, such a comparison has not been conducted for specialized tools, yet. Their gene prediction accuracy was mostly measured on error free DNA fragments. Results In this study, Sanger and pyrosequencing reads were simulated on the basis of models that take all types of sequencing errors into account. All metagenomic gene prediction tools showed decreasing accuracy with increasing sequencing error rates. Performance results on an established metagenomic benchmark dataset are also reported. In addition, we demonstrate that ESTScan, a tool for sequencing error compensation in eukaryotic expressed sequence tags, outperforms some metagenomic gene prediction tools on reads with high error rates although it was not designed for the task at hand. Conclusion This study fills an important gap in metagenomic gene prediction research. Specialized methods are evaluated and compared with respect to sequencing error robustness. Results indicate that the integration of error-compensating methods into metagenomic gene prediction tools would be beneficial to improve metagenome annotation quality.