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Phytoplankton blooms fuel marine food webs with labile dissolved carbon and also lead to the formation of particulate organic matter composed of living and dead algal cells. These particles contribute to carbon sequestration and are sites of intense algal-bacterial interactions, providing diverse niches for microbes to thrive. We analyzed 16S and 18S ribosomal RNA gene amplicon sequences obtained from 51 time points and metaproteomes from 3 time points during a spring phytoplankton bloom in a shallow location (6-10 m depth) in the North Sea. Particulate fractions larger than 10 µm diameter were collected at near daily intervals between early March and late May in 2018. Network analysis identified two major modules representing bacteria co-occurring with diatoms and with dinoflagellates, respectively. The diatom network module included known sulfate-reducing Desulfobacterota as well as potentially sulfur-oxidizing Ectothiorhodospiraceae. Metaproteome analyses confirmed presence of key enzymes involved in dissimilatory sulfate reduction, a process known to occur in sinking particles at greater depths and in sediments. Our results indicate the presence of sufficiently anoxic niches in the particle fraction of an active phytoplankton bloom to sustain sulfate reduction, and an important role of benthic-pelagic coupling for microbiomes in shallow environments. Our findings may have implications for the understanding of algal-bacterial interactions and carbon export during blooms in shallow-water coastal areas., (© The Author(s) 2024. Published by Oxford University Press on behalf of FEMS.)

Background: Argiope 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., Findings: We 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., Conclusions: We 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., (© The Author(s) 2021. Published by Oxford University Press GigaScience.)

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., (© The Author(s) 2021. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics.)

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., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

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.

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.