https://researchdata.se/sv/catalogue Search results sv Underlying data, co-expression analysis outputs and results for the study "Comparative genomics of cold-temperature responses in boreal tree roots". Includes per-species root cold-stress expression matrices (Arabidopsis Col-0 and Ost-0, aspen, birch, Norway spruce, Scots pine), differential expression results, the comparative co-expression (ComPlEx) outputs and 15 pairwise comparisons, the conserved orthogroup core and cliques, supercluster/GO results, transcription-factor lists, annotation and sample metadata. Tue, 18 Aug 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 Ellen Dimmen Chapple Torgeir R. Hvidsten Vaughan Hurry Nathaniel Street Sequence-specific binding of proteins to DNA is essential for accessing genetic information. Here, we derive a simple equation for target-site recognition, which uncovers a previously unrecognized coupling between the macroscopic association and dissociation rates of the searching protein. Importantly, this relationship makes it possible to recover the relevant microscopic rates from experimentally determined macroscopic ones. This post contains kinetic data for the lac repressor and code to analyse and interpret the data in light of this theory. File list:- data_pbm.zip - Data-set for output generated using a protein binding microarray. - data_tirf.zip - Data-set for output generated using TIRF based single molecule localisation microscopy. - data_cas9.zip - Data-set for cas9 binding collected from literature. - data_spr.zip - Data-set for output generated using surface plasmon resonance based methods. - figure_scripts.zip - Set of scripts used to generate all figures. See README file inside for details. Tue, 21 Dec 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-17099687 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-17099687 Uppsala universitet Emil Marklund Guanzhong Mao Jinwen Yuan Spartak Zikrin Eldar Abdurakhmanov Sebastian Deindl Johan Elf Dataset from Jernbom Falk, A., Galletly, C., Just, D. et al. Autoantibody profiles associated with clinical features in psychotic disorders. Transl Psychiatry 11, 474 (2021). Abstract:Autoimmune processes are suspected to play a role in the pathophysiology of psychotic disorders. Better understanding of the associations between auto-immunoglobulin G (IgG) repertoires and clinical features of mental illness could yield novel models of the pathophysiology of psychosis, and markers for biological patient stratification. We undertook cross-sectional detection and quantification of auto-IgGs in peripheral blood plasma of 461 people (39% females) with established psychotic disorder diagnoses. Broad screening of 24 individuals was carried out on group level in eight clinically defined groups using planar protein microarrays containing 42,100 human antigens representing 18,914 proteins. Autoantibodies indicated by broad screening and in the previous literature were measured using a 380-plex bead-based array for autoantibody profiling of all 461 individuals. Associations between autoantibody profiles and dichotomized clinical characteristics were assessed using a stepwise selection procedure. Broad screening and follow-up targeted analyses revealed highly individual autoantibody profiles. Females, and people with family histories of obesity or of psychiatric disorders other than schizophrenia had the highest overall autoantibody counts. People who had experienced subjective thought disorder and/or were treated with clozapine (trend) had the lowest overall counts. Furthermore, six autoantibodies were associated with specific psychopathology symptoms: anti-AP3B2 (persecutory delusions), anti-TDO2 (hallucinations), anti-CRYGN (initial insomnia); anti-APMAP (poor appetite), anti-OLFM1 (above-median cognitive function), and anti-WHAMMP3 (anhedonia and dysphoria). Future studies should clarify whether there are causal biological relationships, and whether autoantibodies could be used as clinical markers to inform diagnostic patient stratification and choice of treatment. This dataset contains relative continuous as well as binarized autoantibody data, with associated cytokine and clinical data. These data constitute sensitive personal information that fall under the GDPR. Therefore, access to data and related code is restricted. The data can be made available for validation purposes, upon reasonable request and in accordance with GDPR. A reasonable request should contain:1) Name of PI and host organisation2) Contact details3) Scientific purpose of data access request4) Commitment to inform when the data has been used in a publication5) Commitment not to host or share the data outside the requesting organisation6) Statement of non-commercial use of data Wed, 15 Sep 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-16451112 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-16451112 Kungliga Tekniska högskolan August Jernbom Cherrie Galletly David Just Catherine Toben Bernhard Baune Scott Clark Dennis Liu Peter Nilsson Anna Månberg Oliver Schubert Data Set Description Single cell RNA sequencing (Samrt-Seq3) and Whole exome sequencing from multiple regions of individual tumors from Breast Cancer patients and also single cell RNA seq for two ovarian cancer cell lines. The dataset contains raw sequencing data for various high-throughput molecular tests performed on two sample types: tumor samples from two breast cancer patients and cell lines derived from High-grade serous carcinoma Patients. The breast cancer data comes from two patients: patient 1 (BCSA1) has two tumor regions A-B and patient 2 (BCSA2) has five regions(A-E). For a normal sample and each region from each patient Whole Exome Sequencing was performed using Twist Biosciences Human Exome Kit by the SNP&SEQ Technology platform, SciLifeLab, National Genomics Infrastructure Uppsala, Sweden. Also for each patient, EPCAM+ CD45- sorted cells from all the regions where sorted to a 384 well plate, and Smart-Seq3 libraries were prepared at Karolinska Institutet and sequenced at National Genomics Infrastructure Uppsala, Sweden. The HGSOC cell-line data comes from OV2295R2 and TOV2295R cell lines described in Laks et al Cell 2019 Nov 14; 179(5): 1207–1221.e22 doi: 10.1016/j.cell.2019.10.026 . The cell line Smart-Seq3 libraries were prepared from two 384 well plates at Karolinska Institutet and sequenced at National Genomics Infrastructure Uppsala, Sweden. Terms for accessThis dataset is to be used for research on intratumor heterogeneity and subclonal evolution of tumors. To apply for conditional access to the dataset in this publication, please contact datacentre@scilifelab.se (mailto:datacentre@scilifelab.se) . Fri, 27 Aug 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15082398 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15082398 Kungliga Tekniska högskolan Seong-Hwan Jun Hosein Toosi Jeff Mold Camilla Engblom Xinsong Chen Ciara O’Flanagan Michael Hagemann-Jensen Rickard Sandberg Johan Hartman Samuel Aparicio Andrew Roth Jens Lagergren ### Dataset description The data here is provided to support the publication 'RecA finds homologous DNA by reduced dimensionality search’. The supporting data is largely of two types: (1) Image sequences from automated widefield microscopy of live cells in a microfluidic device, and (2) STED superresolution images of fixed and immunostained cells For automated widefield microscopy, the 'data' folder contains the raw microscopy images, together with the output from the image processing pipeline - stabilised and cropped phase channel ('PreprocessedPhase' directory) and segmentation mask done on the 'PreprocessedPhase' images ('SegmentedChannels' contains a single mask of growth channels, 'SegmentedPhase' has cell segmentation output for each frame of the 'PreprocessedPhase'). Our custom image processing pipeline referred to above is found in the folder 'image_analysis_code/ImAnalysis'. The 'plotting_code' directory contains subdirectories with the figure number and panel. In each subdirectory there is a code used to generate the panel. To run those scripts, the paths pointing at the data in the scripts will have to be changed to match the location of the 'data' directory on the machine at which the script is executed. The 'image_analysis_code' directory has to be added to the MATLAB path in order for the code to work. The images and data from selected cells in each relevant experiment is stored in a .mat file with the name starting with 'Tt'. This structure can be loaded into the Matlab workspace and the images, segmentation outlines, and the DSB annotation (if relevant) can be accessed. comments: Fig. S2c script 'plot_spots_and_sace_table.m' uses 'detectSpotsSingleCell' function that relies on the path hardcoded in expInfoObj structure. The path in 'detectSpotsSingleCell' line 311 of the function will have to be modified to match the directory with the microscope images. Same for Fig. S5f, function 'detectFilamentsSingleCell' has to be modified on line 203 The code was developed and run using Matlab R2020b, python3.8.5, pytorch1.5.1, some plots require Matlab gramm library or OriginPro 2020. ### experiments, short descriptions, and folders structure (the names of the experiments were automatically generated by the BIOVIA electronic lab notebook software) EXP-20-BV3202 - DSB repair measurements in ParB cells exp2 exp5, exp6, exp7 EXP-20-BV3206 - control - DSB repair measurements in cells without chromosomal cut-site exp1, exp3 exp4 EXP-20-BV3207 - DSB repair measurements in recA-SYFP2 background exp1, exp5, exp6 EXP-20-BV3209 - control - DSB repair in strain with recG and ruvC deletions exp1 EXP-20-BV3210 - DSB measurements in mutants - focus on automatic spot counting exp1 (wt), exp3 (recA), exp4 (recB), exp5 (wt), exp6 (recA), exp7 (recB), exp8 (wt) EXP-20-BV3214 - DAPI staining of chromosome exp7 EXP-20-BV3219 - fast (20s/frame) imaging of RecA-SYFP2 during DSB repair exp3 EXP-20-BV3220 - malI experiments during DSB exp2, exp3, exp4 - malO at -45 kb (yahA) exp5, exp6 - malO at 170 kb (ybbD) exp7, exp9 - malO at ygaY EXP-20-BV3221 - control - measuring number of RecA filaments in cells with recB deletion (vs wt strain on the same chip) exp1, exp2 EXP-20-BV3224 - control - measuring DSB repair dynamics in the RecA-alfa background exp1, exp2 EXP-21-BT2884 - control - measuring DSB repair dynamics in a strain with malO-cs (instead of ParS-cs) therun, exp2 EXP-21-BV3233 - control - measuring repair dynamics in a strain with pars-cs-malO exp2 List of figures and experiments Figure 1: a - EXP-20-BV3210 b - n/a c - EXP-20-BV3202 d - EXP-20-BV3202,EXP-20-BV3206,EXP-20-BV3210 e - EXP-20-BV3202 f - EXP-20-BV3202 g - EXP-20-BV3202 Figure 2: a - EXP-20-BV3220 b - n/a c - EXP-20-BV3220 d - EXP-20-BV3220 e - EXP-20-BV3220 Figure 3: a - EXP-20-BV3207 b - EXP-20-BV3219 c - EXP-20-BV3207 f - EXP-20-BR5273 g - EXP-20-BR5273 h - EXP-20-BR5273 i - EXP-20-BR5273 j - EXP-20-BR5273 Figure 4: a - n/a b - EXP-20-BV3202, EXP-20-BV3207, EXP-20-BV3220 Figure ED1: a - EXP-20-BV3210 Figure ED2: a - EXP-20-BV3210 b - EXP-20-BV3206, EXP-20-BV3210 c - EXP-20-BV3210 d - EXP-20-BV3202 e - EXP-20-BV3202 f - EXP-20-BV3209 Figure ED3: a - EXP-21-BT2884 b - EXP-21-BV3233 Figure ED4: a - EXP-20-BV3220 b - EXP-20-BV3220 c - EXP-20-BV3220 d - EXP-20-BV3220 e - none Figure ED5: a - EXP-20-BV3207 b - n/a c - EXP-20-BV3207 d - EXP-20-BV3207 e - EXP-20-BV3207 f - EXP-20-BV3221 g - EXP-20-BV3202, EXP-20-BV3207, EXP-20-BV3224 h - EXP-20-BV3207 i - EXP-20-BV3207 Figure ED6: a - EXP-20-BR5273 b - n/a c - EXP-20-BR5273 d - EXP-20-BR5273 e - EXP-20-BR5273 f - EXP-20-BR5273 g - EXP-20-BR5273 h - EXP-20-BR5273 i - EXP-20-BR5273 k - EXP-20-BR5273 Figure ED7: EXP-20-BR5273 Figure ED8: EXP-20-BV3214 Sun, 22 Aug 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-14815802 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-14815802 Uppsala universitet Jakub Wiktor Arvid Heden Gynnå Prune Leroy Jimmy Larsson Giovanna Coceano Ilaria Testa Johan Elf This is the Supplemental Data 6 of the MapToCleave study. The file folder contains detailed feature information of MapToCleave processed and unprocessed miRNA precursors. There are seven feature files for each miRNA precursor. The file with the suffix “.HEK.statistics” provides basic information on the hairpin. The file with the suffix “.fold” provides RNA secondary structure in dot bracket format predicted by RNAfold. The file with the suffix “.str” provides printed RNA secondary structure in txt format. The file with suffix “.HEK.unopened_5p_ref” and “.HEK.unopened_3p_ref” provides positional information of base pairing indicated by “-” and symmetric mismatch bulge indicated by “*”, “>” asymmetric bulge with more nucleotides at the 5p strand or “ Wed, 18 Aug 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15134739 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15134739 Stockholms universitet Wenjing Kang Bastian Fromm Inna Biryukova Marc Friedländer This is the Supplementary Data 9 of the MapToCleave study. ​​The file folder contains detailed feature information of MirGeneDB highly and lowly expressed miRNA precursors of 20 species. There are six feature files for each miRNA precursor. The file with the suffix “.statistics” provides basic information on the hairpin. The file with the suffix “.fold” provides RNA secondary structure in dot bracket format predicted by RNAfold. The file with suffix “.unopened_5p_ref” and “.unopened_3p_ref” provides positional information of base pairing indicated by “-” and symmetric mismatch bulge indicated by “*”, “>” asymmetric bulge with more nucleotides at the 5p strand or “ Wed, 18 Aug 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15134862 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15134862 Stockholms universitet Wenjing Kang Bastian Fromm Inna Biryukova Marc Friedländer This is the Supplemental Data 13 of the MapToCleave study. This dataset is related to Figure 6E. The GHG feature is profiled based on the different definitions that are described in the method section “Identifying the presence of the GHG feature using different definitions”. The file “GHG_feature_MapToCleave.txt” shows the presence of the GHG features in the MapToCleave processed and unprocessed miRNA precursors. The file “GHG_feature_MirGeneDB.txt” shows the presence of the GHG features in MirGeneDB human miRNA precursors. The files “in_vivo_highly_expressed_miRNA_precursor_IDs.txt” and “in_vivo_lowly_expressed_miRNA_precursor_IDs.txt” show the identity of miRNA precursors that were used to generate the plot of “in vivo expression” in the panel “miRNA processing efficiency” of Figure 6E. The files “Chromatin_pri-miRNA_highly_processed_miRNA_precursor_IDs.txt” and “Chromatin_pri-miRNA_lowly_processed_miRNA_precursor_IDs.txt” show the identity of miRNA precursors that were used to generate the plot of “Chromatin pri-miRNA” in the panel “miRNA processing efficiency” of Figure 6E. Wed, 18 Aug 2021 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15144339 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-15144339 Stockholms universitet Wenjing Kang Bastian Fromm Inna Biryukova Marc Friedländer