https://researchdata.se/sv/catalogue Search results sv FIB-SEM Cells were grown on Sapphire discs and high-pressure frozen in 100 µm deep golden carriers in an HPM100 (Leica microsystems, Wetzlar, Germany) with 20% BSA in ASW as cryo-protectant. The cells Freeze substitution was performed as in Guo et al¹⁰, but with shorter washing steps for the sake of time. After freeze substitution, the samples were infiltrated with TAAB embedding resin hard grade (TAAB Laboratories, Aldermaston, England). The sample was mounted on an SEM-stub with epoxy and silver glue. The sample was further coated with 5nm Pt to reduce charging. Volumes were acquired using a Scios dualbeam (FEI, Eindhoven, The Netherlands) with the electron beam operating at 2 kV/0.2 nA, detected with the T1 In-lens detector. To automate the acquisition, we use the Auto Slice and View 4 software provided with the microscope. A 700 nm protective layer of platinum was deposited on the selected area before milling. The volume was further registered and processed by the ImageJ plugins Linear alignment by SIFT and Multistackreg. After registration, the volumes were converted to mrc-files, and the header was modified to recover the pixel size that got lost during conversion. The resolution of the two volumes and the number of slices were: 9235_1 (8.43 nm x 8.43 nm x 8.0 nm), 1,725 slices, and 9235_2 (6.744 nm x 6.744 nm x 7.0 nm), 1,300 slices. Segmentation The A. flamelloides BUSS2 cells were segmented by a combination of manual segmentation and Deep Learning Segmentation in Microscopy Image Browser v2.91 beta 45¹¹,¹². Training sets were assembled using the Segment Anything Model 2 (SAM 2). The training dataset for endoplasmatic reticulum and endocytic compartments was assembled by using the whole model and gradually using the different individual models to cut out the model. The final cut out introduced to the mask and used for mask-restricted BW thresholding to yield the respective training slices. Briefly, Desulfobacter sp. symbionts, whole cells, symbiosome, endoplasmatic reticulum and endocytic compartments and hydrogenosomes were manually annotated throughout the volume every 50-slices where the FIB-SEM volume was abundant in those structures. Image segments and the central model (patch size 256x256) were extracted 5 slices deep as and used to train using the 2.5D semantic segmentation approach using 5 slices deep in Z2C+DLv3 architecture with the ResNet50 model. The predicted structures were manually refined extensively. Nucleus – segmented using the SAM2 model interactive 3D model. Nuclear pores – The nucleus model was dilated by 15 pixels and BW thresholding was used to select the pixels that corresponded to nuclear pores and manually refined. Plasma membrane and symbiosome membrane– the whole cell and symbiosome model respectively were used to create an eroded mask to create a 5 px cutout of the respective membranes. Sharply shifting membrane sections were manually refined. Microtubule Organizing Centre and microtubules – the MTOC was manually segmented using SAM2. Microtubules were traced manually using a 3 px brush. Other structures (digestive vacuoles/inclusions/secondary symbionts) – manually segmented using the SAM2 model. Symbiosome pores – symbiosome openings were identified manually in all three orientations any labeled using 3D balls. Three model files (exported from MIB¹¹,¹²) were compiled for each volume, one for the whole cell, one for the full symbiosome and one with the remaining segmentations (9235_1, 10 segments: nucleus, nuclear pores, endoplasmatic reticulum and endocytic compartments, hydrogenosomes, symbiosome membrane, other structures (digestive vacuoles/inclusions/secondary symbionts), Desulfobacter sp. symbionts, plasma membrane, microtubule organizing centre and microtubules, symbiosome pores and 9235_2, 7 segments: nucleus, nuclear pores, hydrogenosomes, symbiosome membrane, Desulfobacter sp. symbionts, plasma membrane, symbiosome pores.) The segmentations were rendered in Dragonfly v.2022.2.0 Build 1399. Tue, 14 Apr 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-31894429 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-31894429 Uppsala universitet Jon Jerlström-Hultqvist This data set contains supporting data tracks for the manuscript: "Breaking insect genome records: sequencing of Stylops ater (Strepsiptera) reveals a minute, compact genome with a reduced set of genes". Assembly and gene annotation are available on ENA under the umbrella project PRJEB71963. Here we publish the following additional resources: - repeatmasker.gff - repeat track generated via: a repeat library was modelled with the RepeatModeler2 [1] v2.0.2a package. As repeats can be part of actual protein-coding genes, the candidate repeats modelled by RepeatModeler were vetted against our protein set (minus transposons) to exclude any nucleotide motif stemming from low-complexity coding sequences. From the repeat library, identification of repeat sequences present in the genome was performed using RepeatMasker (https://www.repeatmasker.org/) v4.1.5 [2] - repeatrunner.gff - repeat track generated via: RepeatRunner [3]. RepeatRunner is a program that integrates RepeatMasker with BLASTX allowing analysing highly divergent repeats and divergent portions of repeats and identifying divergent protein coding portions of retro-elements and retroviruses not detected by RepeatMasker. - trna.gff - tRNA track - have been predicted through tRNAscan version 1.4 [4]. - rfam.gff - ncRNA track - As the main source of information we use the RNA family database Rfam (version 14.9) [5]. Rfam provides curated co-variance (CM) models, which can be used together with the Infernal [6] package to predict ncRNAs in genomic sequences. By default, the set of CM profiles is limited by us to only included broadly conserved, eukaryotic ncRNA families. /! In general, Rfam-derived ‘annotations’ should rather be seen as ‘predictions’. With the exception of some very well conserved ncRNA families, many of the resulting Rfam predictions need to be considered with some care. - ST_1.gff3 - Transcriptome assembly of Illumina RNA-seq library ST_1 (ENA: SAMEA12922144, ERX11689259) assembled using our in-house pipeline transcript_assembly (https://github.com/NBISweden/pipelines-nextflow/tree/master/subworkflows/transcript_assembly) [7]. To minimise gene fusions in this parasite genome the maximum intron length was reduced from 500000 to 20000 (hisat2 --max-intronlen 20000). Otherwise default parameter were used. - ST_2.gff3 - Transcriptome assembly of Illumina RNA-seq library ST_2 (ENA: SAMEA12922144, ERX11689261) assembled using our in-house pipeline transcript_assembly (https://github.com/NBISweden/pipelines-nextflow/tree/master/subworkflows/transcript_assembly) [7]. To minimise gene fusions in this parasite genome the maximum intron length was reduced from 500000 to 20000 (hisat2 --max-intronlen 20000). Otherwise default parameter were used. - rc2_evidence_abinitio.gff - gene models created from second MAKER run (rc2), combining evidence (from run 1 or rc1) and ab initio predictors. Specifically, AUGUSTUS was used for the rc2_evidence_abinitio.gff - rc2_evidence_genemark.gff - gene models created from second MAKER run (rc2), combining evidence (from run 1 or rc1) and ab initio predictors. Specifically, GeneMark was used for the rc2_evidence_genemark.gff References: [1] - Flynn JM, Hubley R, Goubert C, Rosen J, Clark AG, Feschotte C, Smit AF. (2020) RepeatModeler2 for automated genomic discovery of transposable element families. Proceedings of the National Academy of Sciences. 117 (17) 9451-9457. https://doi.org/10.1073/pnas.1921046117 [2] - Smit AFA, Hubley R, Green, P. (2013-2015) RepeatMasker Open-4.0. [3] - Yandell Lab: https://www.yandell-lab.org/software/repeatrunner.html [4] - Lowe TM, Eddy SR. (1997) tRNAscan-SE: A program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Research 25(5): 955–964. https://doi.org/10.1093/nar/25.5.955 (https://doi.org/10.1093/nar/25.5.955) [5] - Ioanna Kalvari, Eric P Nawrocki, Nancy Ontiveros-Palacios, Joanna Argasinska, Kevin Lamkiewicz, Manja Marz, Sam Griffiths-Jones, Claire Toffano-Nioche, Daniel Gautheret, Zasha Weinberg, Elena Rivas, Sean R Eddy, Robert D Finn, Alex Bateman, Anton I Petrov, Rfam 14: expanded coverage of metagenomic, viral and microRNA families, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D192–D200, https://doi.org/10.1093/nar/gkaa1047 [6] - The recommended citation for using Infernal 1.1 is E. P. Nawrocki and S. R. Eddy, Infernal 1.1: 100-fold faster RNA homology searches (http://eddylab.org/publications.html#Nawrocki13c) , Bioinformatics 29:2933-2935 (2013). [7] - Github: https://github.com/NBISweden/pipelines-nextflow/tree/master/subworkflows/transcript_assembly Fri, 14 Nov 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30604043 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30604043 Naturhistoriska riksmuseet Johannes Bergsten Martin Pippel Meri Lähteenaro Julia Heintz Genevieve Diedericks Henrique G. Leitão Carlos Leyton Rotella Mahesh Binzer-Panchal Christian Tellgren-Roth Mai-Britt Mosbech Hannes Svardal Alice Mouton Giulio Formenti Ann M. Mc Cartney Henrik Lantz Olga Vinnere Pettersson Abstract of the paper "Experimental evidence for antagonistic effects of sperm competition in two sea urchin species": Sperm competition occurs when sperm from two or more males compete for fertilization of a batch of eggs. This form of sexual selection has been extensively studied in internal fertilizers, where complete fertilization is common. However, our understanding of sperm competition in external fertilizers remains limited. To investigate how sperm competition affects fertilization under sperm limitation in external fertilizers, we conducted controlled experiments on two sea urchin species: Paracentrotus lividus and Dendraster excentricus. In a series of repeated tests, eggs from a single female were exposed either to sperm from three individual males separately (non-competitive treatment) or to a pooled sample from three males (competitive treatment). We measured fertilization success as the proportion of eggs fertilized after two hours and assessed sperm motility as percentage of motile sperm and curvilinear velocity (VCL). Subsequently, for P. lividus, we estimated the sperm concentrations at which fertilization success was 50% of maximum observed fertilization success (F₅₀), and the fertilization efficiency of the sperm (Fₑ), i.e. the likelihood that a sperm - egg encounter results in a fertilizing contact, using the fertilization kinetics model of Styan (1998). For D. excentricus, we measured fertilization success at a sperm concentration that should yield 50% fertilization success. Our results show that sperm competition reduced fertilization success and increased F₅₀ compared to the non-competitive treatment. This suggests that there are antagonistic effects of sperm competition in external fertilizers that may compromise fertilization of eggs, at least under conditions of sperm limitation. Description of files: fertility_data_cleaned.csv: data on Dendraster excentricus fertilization success means for non-competitive- and sperm competition treatment; used for linear mixed-effects models and basic statistics fertility_data_cleaned.xlsx : see above Fertility_FHL_means.csv: used for plotting and calculate basic statistics Fertility_FHL_means.xlsx : see above fertility_vigo_cleaned.csv: data on Paracentrotus lividus fertilization success means for non-competitive- and sperm competition treatment; used for modelling F50 and Fe after Styan (1998), linear mixed-effects models and basic statistics fertility_vigo_cleaned.xlsx: see above Fertility_Vigo_means.csv: calculate statistics Fertility_Vigo_means.xlsx: see above FHL_FandF2.txt: look at differences between post 2h- and 24h fertilization success in D. excentricus motility_data_cleaned.csv : perm motility data on D. excentricus, used for linear-mixed effects models and basic statistics motility_data_cleaned.xlsx: see above Motility_Fertility_FHL_Means.csv: motility and fertilization data on D. excentricus, used to check if motility parameters affected fertilization success Motility_Fertility_FHL_Means.xlsx: see above Motility_Fertility_Vigo_Means.xlsx: : motility and fertilization data on P. lividus, used to check if motility parameters affected fertilization success Motility_Fertility_Vigo_Means.csv: see above Motility_Urchins.csv: used for plotting motility data in both sea urchin species motility_vigo_cleaned.csv: sperm motility data on P. lividus Size_Correlation_F.csv: test sizes and fertlilization success of both sea urchin species, to check if test size affects fertilzation success Size_Correlation_M.csv: test sizes and fertlilization success of both sea urchin species, to check if test size affects sperm motility Size_Correlation_M.xlsx: see above Code_Analysis.r: R-Code for statistical analysis Mon, 13 Oct 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30305140 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30305140 Göteborgs universitet Luisa Kumpitsch Lara Maleen Beckmann Sophie E Hanson Estefanía Paredes Jose González Fernández Lucas Langer Jan Terje Lifjeld Erica H. Leder Jonathan N. Havenhand Kraken2 Arthopod Reference Database v.1Kraken2 (v2.1.2) database containing all 2,593 reference assemblies for Arthropoda available on NCBI as of March 2023. This database was built for and used in the analysis of shotgun sequencing data of bulkDNA from Malaise trap samples collected by the Insect Biome Atlas, in the context of the manuscript "Small Bugs, Big Data: Metagenomics for arthropod biodiversity monitoring" by authors: López Clinton Samantha, Iwaszkiewicz-Eggebrecht Ela, Miraldo Andreia, Goodsell Robert, Webster Mathew T, Ronquist Fredrik, van der Valk Tom (for submission to Ecology and Evolution). For custom database building, Kraken2 requires all headers in reference assembly fasta files to be annotated with "kraken:taxid|XXX" at the end of each header. Where "XXX" is the corresponding National Center for Biotechnology Information (NCBI) taxID of the species. The code used to add the taxID information to each fasta file header, and update the accession2taxid.map file required by Kraken2 for database building, is available in this GitHub repository (https://github.com/SamanthaLop/Small_Bugs_Big_Data) (also linked under "Related Materials" below). ContentBelow is a list of the files in this item (in addition to the README and MANIFEST files), and their description. The first three files (marked with a *) are required to run Kraken2 classifications using the database. - * hash.k2d.gz - A hash file with all minimiser to taxon mappings (855 GB). - * opts.k2d - A file containing all options used when building the Kraken2 database (64 B). - * taxo.k2d - A file containing the taxonomy information used to build the database (385.9 KB). - seqid2taxid.map.gz - A file containing contig accession numbers and their corresponding taxids (810.6 MB). Note that this file is needed by Kraken2 when building the database, and as it was updated during custom building, it has been included for reference, but it is not required to use the database for classification. - genome_assembly_metadata.tsv - NCBI-generated table (tsv format, gzipped) of all reference assemblies for Arthropoda as of March 2023, which were used in the database construction. This includes columns: Assembly Accession, Assembly Name, Organism Name, Organism Infraspecific Names Breed, Organism Infraspecific Names Strain, Organism Infraspecific Names Cultival, Organism Infraspecific Names Ecotype, Organism Infraspecific Names Isolate, Organism Infraspecific Names Sex, Annotation Name, Assembly Stats Total Sequence Length, Assembly Level, Assembly Submission, and WGS project accession. How to use the database- Download the hash.k2d.gz, opts.k2d, and taxo.k2d files to the same directory (e.g. /PATH/TO/DATABASE/). - Unzip the hash.k2d.gz file. - Install or load Kraken2 to run classification on sequencing data using the database. - When running Kraken2, indicate the path to the directory (not the individual files) with the --db flag (e.g. kraken2 --db /PATH/TO/DATABASE/ ...). Note that the whole database must be loaded into memory by Kraken2 to be able to classify any sequencing reads, so ensure you have access to enough memory before running (the uncompressed hash file is around 1.1 TB). We also recommend using the Kraken2 option --memory-mapping, as it ensures the database is loaded once for all samples, instead of once for each individual sample, saving considerable time and resources. For more information on using Kraken2, see the Kraken2 wiki manual (https://github.com/DerrickWood/kraken2/wiki/Manual) . This database was built by Samantha López Clinton (samantha.lopezclinton@nrm) and Tom van der Valk (tom.vandervalk@nrm.se). Mon, 18 Aug 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-29666605 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-29666605 Naturhistoriska riksmuseet Samantha López Clinton Tom van der Valk This repository includes data files and scripts used in the publication "Seascape genomics identify adaptive barriers correlated to tidal amplitude in the shore crab Carcinus maenas" published in Molecular Ecology in 2022. In this publication we assessed 12 sites in the native range of the European shore crab Carcinus maenas spanning >2000 km, and examined genetic structure and selection to tidal gradient using 24,000 Single Nucleotide Polymorphisms (SNPs) derived from 2b-RAD sequencing. Additionally, we performed biophysical modelling, and gene expression analyses of candidate clock genes. Data in this repository includes final filtered vcf files for all, neutral and outlier loci. Raw sequence data is archived in GenBank's SRA: BioProject ID PRJNA797386 and BioSample IDs SAMN25002278-SAMN25002565. New population genomic and biophysical modelling script are provided as well, while scripts for the 2b-RAD bioinformatic analysis are available at: https://github.com/z0on/2bRAD_denovo, and scripts for demographic inferences are available at: https://github.com/alanlm-speciation/moments_optimization. Abstract: Most marine invertebrates disperse during a planktonic larval stage that may drift for weeks with ocean currents. A challenge for larvae of coastal species is to return to coastal nursery habitats. Shore crab (Carcinus maenasL.) larvae are known to show tidal rhythmicity in vertical migration in tidal areas and circadian rhythmicity in micro-tidal areas, which seems to increase successful coastal settlement.We studied genome-wide differentiation based on 24,000 SNPs of 12 native populations of shore crab sampled from a large tidal amplitude gradient from macro-tidal (ca. 8 m) to micro-tidal (ca.0.2 m).Dispersal and recruitment success of larvae was assessed with a Lagrangian biophysical model, which showed a strong effect of larval behavior on long-term connectivity, and dispersal barriers that partly coincided with different tidal environments. The genetic population structure showed a subdivision of the samples into three clusters, which represent micro-, meso- and macro-tidal areas. The genetic differentiation was mostly driven by 0.5% outlier loci, whichshowed strong allelic clines located at the limits between the three tidal areas. Demographic modelling suggested that the two genetic barriers have different origins. Differential gene expression of two clock genes (cyc and pdp1) further highlighted phenotypic differences among genetic clusters that are potentially linked to the differences in larval behaviour. Taken together, our seascape genomic study suggest that tidal regime acts as a strong selection force on shore crab population structure, consistent with larval behaviour affecting dispersal and recruitment success. Thu, 27 Jan 2022 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-17836025 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-17836025 Göteborgs universitet Marlene Jahnke Per-Olav Moksnes Alan Le Moan Gerrit Martens Per R Jonsson