https://researchdata.se/sv/catalogue Search results sv This item holds the processed data and analysis outputs of a comparative study of the root response to cold in six genotypes: Norway spruce (Picea abies), Scots pine (Pinus sylvestris), silver birch (Betula pendula), aspen (Populus tremula) and the Arabidopsis thaliana ecotypes Col-0 and Ost-0 (Osthammar). Fine roots were sampled before and 6 h, 24 h, 3 d and 10 d after transfer to 5 degrees C (133 RNA-seq samples). The data include gene expression and differential expression for every genotype, orthology, comparative co-expression (ComPlEx) results for all 15 genotype pairs, the conserved co-expression core (133 orthogroups) with orthology and sample-stability labels, per-genotype co-expression networks, Gene Ontology and transcription-factor annotation, and the Salmon quantification behind the counts. They are provided both as R objects and as tab-separated text tables, with a browser page (explore.html) that needs no installation. Unpack the archive: tar -xzf data.tar.gz. Then start with README_RESOURCE.md in the unpacked folder. To look up a gene, open explore.html in a web browser. The tab-separated tables in data/open/ hold the same content as the R objects and can be read with any software; DATA_DICTIONARY.md describes every table and column, and USAGE_EXAMPLES.md gives worked examples in R and Python. The R objects (.RData, .rda, .RDS) need R (https://www.r-project.org); the DESeq2 objects need the Bioconductor package DESeq2. Thu, 15 Oct 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-32747586 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-32747586 Umeå universitet Tuuli Aro Elena M. van Zalen Alexander Vergara Camilla Canovi Vikash Kumar Jamie McCann Ellen Dimmen Chapple Jason Hill Torgeir R. Hvidsten Vaughan Hurry Nathaniel Street Since 1986, replicate lines of the seed beetle Acanthoscelides obtectus have been kept under two experimental evolution regimes: Early (E; lines EI–EIV), where beetles reproduce only during the first two days of adult life, and Late (L; lines LV–LVIII), where egg laying is only possible after day 10. After over 200 generations, late lines have evolved a nearly doubled lifespan. This item contains the processed genome-wide data shown in the external genome browser, in five groups: - Selective sweeps: candidate regions under consistent selection in each regime. - Copy-number variants (CNVs): regime-level coverage profiles and CNV calls. - Quantitative trait locus (QTL) mapping: LOD (logarithm of the odds) profiles for lifespan and body weight, a recombination map, and founder coverage from an Early x Late intercross. - Gene annotation and expression: a gene-level annotation, Drosophila orthologs and differentially expressed genes. - Region quality: masks of unreliable regions, GC content and mappability. Groups 1, 2 and 5 are derived from pooled whole-genome sequencing (Pool-seq) of the eight lines. Each line was sequenced as one female and one male pool of 50 individuals, giving 8 pooled samples per regime and 16 in total. Code related to the analyses is available from the GitHub repository: https://github.com/Goran-Arnqvist/UUBeetleLab (https://github.com/Goran-Arnqvist/UUBeetleLab) 1. Selective sweepsCandidate regions with consistent signatures of a selective sweep across populations: 574 regions in the Early regime and 458 in the Late regime. - Sweep detection per sample. Read pileups were generated for each of the 16 pooled samples with SAMtools v0.1.19, and each sample was analysed independently with Pool-HMM v1.4.4. Pool-HMM identifies regions in which the allele frequency spectrum (AFS) is skewed towards extreme allele frequencies, as expected after a selective sweep, and uses a hidden Markov model to call candidate loci. Parameters: initial theta 0.005, 100 chromosomes per pool, mutation-rate parameter k = 1e-7, every tenth polymorphic position analysed. The X chromosome (CAVLJG010000002.1) was not analysed. - Consistency within each regime. The 8 per-sample call sets of a regime (4 lines x 2 sexes) were intersected with bedtools multiinter (BEDTools v2.31). A region was kept if it was called in at least 6 of the 8 samples. Requiring support across independently evolving replicate lines prioritises regions associated with the selection regime over signals specific to one line. 2. Copy Number Variants CNVs were called by depth of coverage. Standard CNV callers assume a fixed ploidy, which pooled samples do not have, so the two regimes were used as mutual controls: a CNV is only called where coverage differs consistently between all Early and all Late lines. - Coverage. Read depth per non-overlapping 100 base pair (bp) bin was computed for each pooled sample with mosdepth v0.3.3, excluding reads with mapping quality below 20 or with any SAM (Sequence Alignment/Map) flag in 3852. - Filtering and bias correction. Bins were removed if they overlapped the coverage masks of group 5, had GC content below the 5th or above the 99th percentile, or had mappability below 0.10. Depth was corrected for GC and then mappability bias with weighted LOESS (locally estimated scatterplot smoothing) regression. - Regime contrast. Female and male depths were summed per line, and the median across the four lines of each regime was taken per bin. The log2(Late / Early) ratio of the medians was denoised with a stationary wavelet transform (Haar wavelet, 5 levels, SureShrink soft thresholds). - Segmentation. The denoised ratio was segmented with circular binary segmentation (CBS; R package PSCBS v0.68.0, undo threshold 4 standard deviations). - CNV calls. Segments shorter than 1 kb or with more than 60% missing bins were removed. A segment was kept if the fold change between regimes was at least 1.5 and a permutation test over the 8 lines gave P = 0.95, coverage >= 0.80) and intersected with protein-coding genes. The score column is the log2 fold change (positive = higher expression in Early lines); pvalue_bh_fdr is the false discovery rate; color_fold_change is a display colour, mapped directly from the original log2 fold change to a colourmap. The protein accession of a gene (e.g. CAK1619843.1) links the three file types: it is the Name of protein-coding genes in the annotation, the ID in the ortholog file and the geneID in the DEG files. 5. Region qualityMasks of unreliable regions built for this repeat-rich genome (over 60% repeats), used to filter the sweep, CNV and Gene Ontology analyses. Unlike the other files, these cover all 3,796 sequences of the assembly. - Coverage masks. Read depth of the 16 pooled samples in 1,000 bp windows (100 bp step); regions above the 99th or below the 7th percentile, consistently across samples of both regimes. - Mapping-quality mask. Regions where fewer than 40% of reads have mapping quality >= 10. - Assembly mask. Regions within 150 bp of an assembly gap, and contigs shorter than 20 kb. - GC content. Fraction (0–1, not a percentage) of G and C bases per 100 bp bin. - Mappability. Paired-end reads simulated from the reference (dicey v0.3.3, insert size 400 bp) and mapped back with BWA-MEM2 v2.2.1. Values are simulated-read depth (0–300); divide by 300 for mappability on a 0–1 scale. FilesEach .gz file has a tabix index with the same name plus .tbi (not listed below). The complete file list and file sizes are provided in MANIFEST.txt. 1. Selective sweeps - selective_sweeps_consistent_Early.bed.gz: Candidate sweep regions, Early; BED3, no header - selective_sweeps_consistent_Late.bed.gz: Candidate sweep regions, Late; BED3, no header 2. Copy-number variants - coverage_median_corrected_100bp_{Early,Late}.bw: Median across {Early,Late} lines of the summed female + male corrected depth - coverage_log2_ratio_Late_vs_Early_100bp.bw: log2(Late / Early); positive = higher coverage in Late - coverage_log2_ratio_Late_vs_Early_100bp_denoised.bw : As above, wavelet-denoised (used for segmentation) - CNV_segmentation_log2_ratio_Late_vs_Early.bw: All CBS segments (mean log2 ratio), before filtering - CNV_calls_{Early,Late}.bed.gz: Final CNV calls, Early (3,001), Late (3,779). Columns: chrom, start, end, call (deletion/gain), cn_deviation (population copy number minus 2) - CNV_calls_{Early, Late}.bw: Early/Late calls as bigWig (value = cn_deviation) 3. QTL mapping, recombination and founder coverage Files ending in _markers.bw hold the original LOD values at marker positions. Each has a display-only version with the same name ending in _100kb_interpolated.bw (not listed). - QTL_LOD_lifespan_IM_markers.bw: Lifespan, interval mapping - QTL_LOD_bodyweight_IM_markers.bw: Body weight, interval mapping - QTL_LOD_lifespan_MQM_markers.bw: Lifespan, MQM; all six QTLs - QTL_LOD_bodyweight_MQM_chr{2-22.9cM,4-25.3cM,4-28.3cM,6-12.6cM,9-90.5cM}_markers.bw: Body-weight MQM LOD profiles for QTLs on chromosomes 2, 4, 6, and 9 at the indicated cM positions. - recombination_rate_cM_per_Mb_100kb.bw: Local recombination rate per 100 kilobase (kb), unsmoothed - recombination_rate_cM_per_Mb_2Mb_smoothed.bw: As above, 2 Megabase (Mb) moving average - F0_coverage_mean_100bp_{E2,L5}.bw: Mean normalized depth of the four {E2,L5} founders - F0_coverage_log2_ratio_L5_vs_E2_100bp.bw: log2(L5 / E2); positive = higher in L5; capped at -5 to 5 4. Gene annotation and expression - Aobtectus_GCA_963669975.1_genes_eggNOG.gff3.gz: One feature per gene, with eggNOG-mapper annotation - Aobtectus_GCA_963669975.1_Drosophila_orthologs.gff3.gz: D. melanogaster orthologs - DEGs_Early_vs_Late_*.gff3.gz: Differentially expressed genes (DEGs) between Early and Late lines, for female abdomen, female head + thorax, male abdomen, and male head + thorax. 5. Region quality - excluded_regions_high_coverage.bed.gz: Abnormally high coverage; BED3, no header - excluded_regions_low_coverage.bed.gz Abnormally low coverage; BED3, no header - excluded_regions_low_mapping_quality.bed.gz: Low mapping quality; BED3, no header - excluded_regions_assembly_gaps_small_contigs.bed.gz: Near assembly gaps, or small contigs; BED3, no header - GC_fraction_100bp.bw: GC content per 100 bp bin, as a fraction (0–1) - mappability_100bp.bw: Simulated-read depth per 100 bp bin (0–300) Item thumbnail adapted from: Udo Schmidt, CC BY-SA 2.0, via Wikimedia Commons. Mon, 05 Oct 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-34059444 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-34059444 Uppsala universitet Athanasios Toros Mats Pettersson Angela P. Fuentes-Pardo Alexandre Rego Carl-Johan Rubin Mirko Đorđević Uroš Savković Biljana Stojković Elina Immonen Marek Kučka Yingguang Frank Chan Leif Andersson Göran Arnqvist This repository contains files associated with the analysis performed during the Norway spruce and Scots pine genome assembly and annotation, associated epigenetic analyses, and population genetics in Norway spruce. The repository contains four folders that contain files relating to different sections in the associated publication: - Genome - Orthogroups - Epigenetics - Co-expression - Population genetics Further details are provided in Readme files within each folder. These files are referred to in the associated publication as “Data Availability” as a link to the Data Availability statement where details of this repository are provided. The two genomes were assembled using PacBio HiFi reads and Micro-C (Norway spruce) or Hi-C (Scots pine) chromatin contact data. The chromatin contact data was also used to perform analysis of global compartmentalisation of chromatin and to detect Topologically Associating Domains (TADs) and chromatin loops. The genomes were annotated using collections of RNA-Seq data and PacBio IsoSeq reads. A comparative co-expression network analysis was performed for data profiling wood formation in both species to identify conserved and diverged patterns of gene co-expression. This was linked to analysis of orthogroups to examine evidence for neo- and sub-functionalisation among paralogs. In Norway spruce and extensive set of individuals were re-sequenced using Illumina short read data. Subsets of those individuals were used to perform environmental GWAS, detect signals of selection and to identify geographically segregating gene presence/absence variants. The associated ENA Umbrella project is PRJEB88492. Tue, 14 Jul 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28737623 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28737623 Umeå universitet Nathaniel Street Overview Clinical symptoms that persist for at least three months after infection by SARS-CoV-2, i.e. post-acute sequelae of SARS-CoV-2 (PASC), is an escalating global health problem. The mechanisms underlying post-COVID are still unclear, in particular there is a lack of large studies concerning patients with chronic symptoms persisting for several years after a mild COVID-19 infection. The aim of this study was to investigate possible molecular signatures and persistent SARS-CoV-2 gene fragments in patients with PASC up to 28 months after a mild infection. Summary We analyzed the gene expression profile in PBMCs from 60 middle-aged post-COVID patients and 50 age-matched controls, all of whom experienced a mild SARS-CoV-2 infections between March 2020 and February 2022. The uploaded data consist of count table and sample information and can be used for gene expression analysis of patients and controls. Generation of Data Sequencing libraries were prepared from 500ng/μg of polyA selected RNA using the TruSeq stranded mRNA library preparation kit (cat# 20020595, Illumina Inc.). Unique dual indexes (cat# 20022371, Illumina Inc.) were used. The library preparation was performed according to the manufacturers’ protocol (#1000000040498). Sequencing was performed using paired-end 150 bp read length on a NovaSeq X Plus system, 10B flow cell and XLEAP-SBS sequencing chemistry. Samples were analyzed with the nf-core RNA sequencing pipeline release 3.15.1 (nf-co.re/rnaseq). In brief, the pipeline processes raw data from FastQ inputs, aligns the reads, generates counts relative to genes or transcripts and performs extensive quality-control of results. Data Count Data: Samples were analyzed with the nf-core RNA sequencing pipeline release 3.15.1 (nf-co.re/rnaseq). In brief, the pipeline processes raw data from FastQ inputs, aligns the reads, generates counts relative to genes or transcripts and performs extensive quality-control of results. Sample Data: Information regarding SampleName, Sample and Condition Tue, 29 Apr 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28832492 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28832492 Uppsala universitet Joakim Klar Serena Fineschi Niklas Dahl Despite their importance, the pathways and enzymes associated with the biosynthesis of many specialized metabolites are yet to be characterized. The integration of multi-omics data represents a powerful approach to link genes encoding enzymes and their regulatory factors to metabolite biosynthesis. However, suitable multi-omics data resources for this approach are scarce. Here we present a data resource comprising Liquid Chromatography Mass Spectrometry (LC-MS) and mRNA-Sequencing data from European aspen (Populus tremula L.) including tissues sampled from different organs, and genotypes that produce contrasting sets of salicinoid phenolic glucosides (SPGs), that are specialized metabolites characteristic for Salicaceae. Tue, 25 Mar 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27336156 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27336156 Umeå universitet Sara M. Rydman Kathryn Robinson Jenna Lihavainen Benedicte R. Albrectsen Stefan Jansson Nathaniel Street This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper "Anti-correlation of LacI association and dissociation rates observed in living cells" (see Related Material). The raw data consists of microscopy images and qPCR files. The analysis and code consist of data analysis, output from the analysis, its post-processing, simulations, and which figure the data was used in. Experimental data, and analysis scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis, code & figure generation to combine for specific figures. For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition and which figure the data was used in. All experiments were performed at 30 degrees Celsius. Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt. Thu, 02 Jan 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-26425576 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-26425576 Uppsala universitet Vinodh Kandavalli Spartak Zikrin Johan Elf Daniel Jones This is the sequencing data from direct RNA long-read sequencing by Oxford Nanopore Technologies of the chronic lymphocytic leukemia cell line HG3 (HG-3). Total RNA was extracted using the RNAeasy kit (Qiagen, Hilden, Germany). Sequencing libraries were generated from ~5 ?g of high-quality RNA (RIN >9.7) as assessed by Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA). The direct RNA Sequencing kit (SQK- RNA002; Oxford Nanopore Technologies, Oxford, UK) was used according to the manufacturerÕs recommendations. Sequencing libraries were sequenced on a PromethION (Oxford Nanopore Technologies, Oxford, UK) using PromethION R9.4.1 flow cells (Oxford Nanopore Technologies, Oxford, UK). Basecalling was performed using Guppy v6.1.2 and passed FASTQ files were analyzed using nf-core/nanoseq v1.1.0 pipeline1. The FASTQ files were aligned to the reference genome hg19 using Minimap2 v2.1717. The resulting bam and index file are deposited here. The data has been used in the publication by Hagerstrand and Oder et al. The non-canonical BAF chromatin remodeling complex is a novel target of spliceosome dysregulation in SF3B1-mutated chronic lymphocytic leukemia, Leukemia 2024 (https://doi.org/10.1038/s41375-024-02379-4). Researchers are welcome to use the data contained in the dataset for any scientific projects according to license CC BY-NC-ND 4.0. Wed, 04 Sep 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-21694799 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-21694799 Karolinska Institutet Daniel Hägerstrand Blaz Oder Zahra Haider Richard Rosenquist Brandell Background to the study: We have produced a chromosome-scale genome assembly generated using long-read sequencing, optical and high-density genetic maps containing 39,894 annotated genes with functional annotations for 73,765 transcripts in 37,184 genes. We conducted whole-genome resequencing of the Umeå Aspen (UmAsp) collection comprising 227 aspen individuals. We utilised the assembly and existing whole genome re-sequencing data to perform genome-wide association analyses (GWAS) using Single Nucleotide Polymorphisms (SNPs) in the UmAsp, Swedish Aspen (SwAsp) and Scottish Aspen (ScotAsp) collections for leaf physiognomy phenotypes. We conducted Assay of Transposase Accessible Chromatin sequencing (ATAC-Seq) and identified genomic regions of accessible chromatin, and subset SNPs to these regions, which improved the GWAS detection rate. We identified candidate long non-coding RNAs in leaf samples and quantified their expression in an updated co-expression network, which we used to explore the functions of candidate genes identified from the GWAS. This data set comprises: the ATAC-Seq peaks from the ATAC-Sequencing of aspen leaves, 'Aspen leaf ATAC_Seq peaks.zip'; and the gene expression matrix of mean values per aspen genotype from the SwAsp collection, 'Gene_Expression_matrix_genotype_mean.tsv'. We provide a zipped directory for each of 'ScotAsp.zip', 'SwAsp.zip' and 'UmAsp.zip' providing the raw leaf image scans, the cropped leaf images and raw data files from the LAMINA leaf shape analyses of these images, and the processed data files and genotypic BLUP values for each of these ScotAsp, SwAsp and UmAsp collections. We provide the GWAS associations of SNPs ranked by decreasing P-value until the 1000th gene for each of the 26 leaf physiognomy traits for each collection, i.e. 'ScotAsp top-ranked GWAS results', 'SwAsp top-ranked GWAS results' and 'UmAsp top-ranked GWAS results'. The single nucleotide polymorphism (SNP) data for each of the aspen collections is in 'ScotAsp_biallelic_Het.HWE.recode.vcf.gz', 'SwAsp_AfterBatchRemoval_biallelic_Het.HWE.recode.vcf.gz' and 'UmAsp_biallelic_Het.MAF.HWE.recode_.vcf.gz'. Thu, 22 Aug 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-25335448 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-25335448 Umeå universitet Kathryn Robinson Bastian Schiffthaler Hui Liu Sara Westman Martha Rendon Anaya Teitur Kalman Vikash Kumar Camilla Canovi Carolina Bernhardsson Nicolas Delhomme Jerry Jenkins Jing Wang Niklas Mähler Kerstin Richau Victoria Stokes Stuart A'Hara Joan Cottrell Kizi Coeck Tim Diels Klaas Vandepoele Chanaka Mannapperuma Eung-Jun Park Stephane Plaisance Stefan Jansson Pär Ingvarsson Nathaniel Street This dataset contains Spatial Transcriptomics (ST) data matching with Matrix Assisted Laser Desorption/Ionization - Mass Spetrometry Imaging (MALDI-MSI). This data is complementary to data contained in the same project. FIles with the same identifiers in the two datasets originated from the very same tissue section and can be combined in a multimodal ST-MSI object. For more information about the dataset please see our manuscript posted on BioRxiv (doi: https://doi.org/10.1101/2023.01.26.525195).  This dataset includes ST data from 19 tissue sections, including human post-mortem and mouse samples. The spatial transcriptomics data was generated using the Visium protocol (10x Genomics). The murine tissue sections come from three different mice unilaterally injected with 6-OHDA. 6-OHDA is a neurotoxin that when injected in the brain can selectively destroy dopaminergic neurons. We used this mouse model to show the applicability of the technology that we developed, named Spatial Multimodal Analysis (SMA). Using our technology on these mouse brain tissue sections we were able to detect both dopamine with MALDI-MSI and the corresponding gene expression with ST. This dataset includes also one human post-mortem striatum sample that was placed on one Visium slide across the four capture areas. This sample was analyzed with a different ST protocol named RRST (Mirzazadeh, R., Andrusivova, Z., Larsson, L. et al. Spatially resolved transcriptomic profiling of degraded and challenging fresh frozen samples. Nat Commun 14, 509 (2023). https://doi.org/10.1038/s41467-023-36071-5), where probes capturing the whole transcriptome are first hybridized in the tissue section and then spatially detected. Each tissue section contained in the dataset has been given a unique identifier that is composed of the Visium array ID and capture area ID of the Visium slide that the tissue section was placed on. This unique identifier is included in the file names of all the files relative to the same tissue section, including the MALDI-MSI files published in the other dataset included in this project. In this dataset you will find the following files for each tissue section: - raw files: these are the read one fastq files (containing the pattern *R1*fastq.gz in the file name), read two fastq files (containing the pattern *R1*fastq.gz  in the file name) and the raw microscope images (containing the pattern *Spot*.jpg in the file name). These are the only files needed to run the Space Ranger pipeline, which is freely available for any user (please see the 10x Genomics website for information on how to install and run Space Ranger); - processed data files: we provide processed data files of two types: a) Space Ranger outputs that were used to produce the figures in our publication; b) manual annotation tables in csv format produced using Loupe Browser 6 (csv tables with file names ending _RegionLoupe.csv, _filter.csv, _dopamine.csv, _lesion.csv, _region.csv patterns);  c) json files that we used as input for Space Ranger in the cases where the automatic tissue detection included in the pipeline failed to recognize the tissue or the fiducials. Using these processed files the user can reproduce the figures of our publication without having to restart from the raw data files. The MALDI-MSI analyses preceding ST was performed with different matrices in different tissue section. We used 1) 9-aminoacridine (9-AA) for detection of metabolites in negative ionization mode, 2) 2,5-dihydroxybenzoic acid (DHB) for detection of metabolites in positive ionization mode, 3) 4-(anthracen-9-yl)-2-fluoro-1-ethylpyridin-1-ium iodide (FMP-10), which charge-tags molecules with phenolic hydroxyls and/or primary amines, including neurotransmitters. The information about which matrix was sprayed on the tissue sections and other information about the samples is included in the metadata table. We also used three types of control samples: - standard Visium: samples processed with standard Visium (i.e. no matrix spraying, no MALDI-MSI, protocol as recommended by 10x Gemomics with no exeptions)  - internal controls (iCTRL): samples not sprayed with any matrix, neither processed with MALDI-MSI, but located on the same Visium slide were other samples were processed with MALDI-MSI - FMP-10-iCTRL: sample sprayed with FMP-10, and then processed as an iCTRL. This and other information is provided in the metadata table. Tue, 16 May 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-22778920 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-22778920 Kungliga Tekniska högskolan Marco Vicari Reza Mirzazadeh Anna Nilsson Patrik Bjärterot Ludvig Larsson Hower Lee Mats Nilsson Julia Foyer Markus Ekvall Paulo Czarnewski Xiaoqun Zhang Per Svenningsson Per Andrén Lukas Käll Joakim Lundeberg Coverage traks for  5´ monophosphorylated mRNA decay intermediates (5´P) in bacterial species from vaginal swabs. Bedgraph files provide information for the 5'P coverage for the forward and reverse strand. Datasets are provided for 8 samples (r1 to r8). Tue, 04 Apr 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-22305991 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-22305991 Karolinska Institutet Susanne Huch Lilit Nersisyan Valerie D Valeriano Juan Du Jing Wang Lars Engstrand Vicent Pelechano