This dataset corresponds to GSE152641 — a whole-blood RNA-seq study of COVID-19 patients and healthy controls. OmicsDI +1

It includes expression data processed through edgeR on a Galaxy server — hence the title “COVID-19 DGE (GSE152641) edgeR Galaxy Server”.

The original GSE152641 study profiled peripheral blood from 62 SARS-CoV-2 (COVID-19) patients and 24 healthy controls, for a total of 86 samples. OmicsDI +1

The dataset captures host transcriptomic (gene expression) responses to SARS-CoV-2 infection, enabling analysis of differentially expressed genes (DEGs) in COVID-19 vs healthy individuals. OmicsDI +1

This resource can be used to: identify DEGs, perform immune-cell deconvolution / infiltration analysis, compare COVID-19 transcriptomic signatures with other viral infections, perform downstream pathway analysis, co-expression analysis, or machine learning / biomarker discovery.

Because the original study also compared COVID-19 responses to other viral infections (six viruses: influenza, RSV, HRV, Ebola, Dengue, SARS), the dataset is useful for comparative transcriptomic studies of immune response across infections, though here only the COVID-19 whole-blood data from GSE152641 are included. OmicsDI +1

The data are human (Homo sapiens) whole-blood bulk RNA-seq. OmicsDI +1

The underlying gene expression matrix is a count matrix (digital gene expression), suitable for downstream normalization, differential expression (edgeR, DESeq2, limma-voom, etc.), and other transcriptomics analyses. ffli.dev +1

This dataset enables reproducible computational analyses — for example, detection of DEGs, immune cell composition estimation, pathway enrichment, classifier / signature building for COVID-19.

As such, it can serve as a resource for researchers interested in COVID-19 immunology, biomarker discovery, host response profiling, comparative viral transcriptomics, or meta-analysis with other publicly available datasets.

All required data files (metadata, counts or processed tables as uploaded) are made available to facilitate reanalysis and transparent computational workflows.

These files go with a short transcriptomics (RNA-Seq) tutorial that I am preparing for an undergraduate level tutorial. The data analysis will be on a Galaxy server. I'll update the description with a link to the tutorial text when its ready. These data are a subset of those published by O’Connell R, Thon M et al. 2012. Lifestyle transitions in plant pathogenic Colletotrichum fungi defined by genome and transcriptome analyses. Nature Genetics. 44:1060–1065.

Escaped vs. unescaped text import into excel.

The First Public Data Release (DR1) of Transient Host Exchange (THEx) Dataset Paper describing the dataset: “Linking Extragalactic Transients and their Host Galaxy Properties: Transient Sample, Multi-Wavelength Host Identification, and Database Construction” (Qin et al. 2021) The data release contains four compressed archives. “BSON export” is a binary export of the “host_summary” collection, which is the “full version” of the dataset. The schema was presented in the Appendix section of the paper. You need to set up a MongoDB server to use this version of the dataset. After setting up the server, you may import this BSON file into your local database as a collection using “mongorestore” command. You may find some useful tutorials for setting up the server and importing BSON files into your local database at: https://docs.mongodb.com/manual/installation/ https://www.mongodb.com/basics/bson You may run common operations like query and aggregation once you import this BSON snapshot into your local database. An official tutorial can be found at: https://docs.mongodb.com/manual/tutorial/query-documents/ There are other packages (e.g., pymongo for Python) and software to perform these database operations. “JSON export” is a compressed archive of JSON files. Each file, named by the unique id and the preferred name of the event, contains complete host data of a single event. The data schema and contents are identical to the “BSON” version. “NumPy export” contains a series of NumPy tables in “npy” format. There is a row-to-row correspondence across these files. Except for the “master table” (THEx-v8.0-release-assembled.npy), which contains all the columns, each file contains the host properties cross-matched in a single external catalog. The meta info and ancillary data are summarized in THEx-v8.0-release-assembled-index.npy. There is also a THEx-v8.0-release-typerowmask.npy file, which has rows co-indexed with other files and columns named after each transient type. The “rowmask” file allows you to select a subset of events under a specific transient type. Note that in this version, we only include cataloged properties of the confirmed hosts or primary candidates. If the confirmed host (or primary candidate) cross-matched multiple sources in a specific catalog, we only use the representative source for host properties. Properties of other cross-matched groups are not included. Finally, table THEx-v8.0-release-MWExt.npy contains the calculated foreground extinction (in magnitudes) at host positions. These extinction values have not been applied to magnitude columns in our dataset. You need to perform this correction by yourself if desired. “FITS export” includes the same individual tables as in “NumPy export”. However, the FITS standard limits the number of columns in a table. Therefore, we do not include the “master table” in “FITS export.” Finally, in BSON and JSON versions, cross-matched groups (under the “groups” key) are ordered by the default ranking function. Even if the first group in this list (namely, the confirmed host or primary host candidate) is a mismatched or misidentified one, we keep it in its original position. The result of visual inspection, including our manual reassignments, has been summarized under the “vis_insp” key. For NumPy and FITS versions, if we have manually reassigned the host of an event, the data presented in these tables are also updated accordingly. You may use the “case_code” column in the “index” file to find the result of visual inspection and manual reassignment, where the flags for this “case_code” column are summarized in case-code.txt. Generally, codes “A1” and “F1” are known and new hosts that passed our visual inspection, while codes “B1” and “G1” are mismatched known hosts and possibly misidentified new hosts that have been manually reassigned.

This page provides links to data files used in the experiment described in Photometric asymmetry between clockwise and counterclockwise spiral galaxies in SDSS The files can be uploaded to SDSS Catalog Archive Server (CAS) and then used to replicate the results of the experiment. For instance, comparing the r magnitude of the of the classes can be done with the following CAS query: select avg(g) from PhotoObjAll, MyDB.cw where Objid=ID and g>0 and ra>90 and ra<270 select stdev(g) from PhotoObjAll, MyDB.cw where Objid=ID and g>0 and ra>90 and ra<270 select count(g) from PhotoObjAll, MyDB.cw where Objid=ID and g>0 and ra>90 and ra<270 select avg(g) from PhotoObjAll, MyDB.ccw where Objid=ID and g>0 and ra>90 and ra<270 select stdev(g) from PhotoObjAll, MyDB.ccw where Objid=ID and g>0 and ra>90 and ra<270 select count(g) from PhotoObjAll, MyDB.ccw where Objid=ID and g>0 and ra>90 and ra<270 Then the t-test can be calculated using the mean, standard deviation, and number of samples of the two classes. The "g>0" is added to avoid possible flag values such as "-9999". Paper reference: Shamir, L., Photometric asymmetry between clockwise and counterclockwise spiral galaxies in SDSS, PASA, In Press, 2017.

Table of the three top terms of the three top annotation clusters from David. The gene IDs submitted to DAVID were selected based on different H3K4me1 occupancy between fetal and adult brain clusters, by using the Genomic HyperBrowser. Benjamini = Benjamini-Hochberg.Gene Ontology terms enriched by genes with different H3K4me1 occupancy in fetal and adult brain cell types.

Context

Data was obtained by running the following query at "http://skyserver.sdss.org/dr16/en/tools/search/sql.aspx">http://skyserver.sdss.org/dr16/en/tools/search/sql.aspx

SELECT TOP 500000 p.objid,p.ra,p.dec,p.u,p.g,p.r,p.i,p.z, p.run, p.rerun, p.camcol, p.field, s.specobjid, s.class, s.z as redshift, s.plate, s.mjd, s.fiberid FROM PhotoObj AS p JOIN SpecObj AS s ON s.bestobjid = p.objid WHERE p.u BETWEEN 0 AND 19.6 AND g BETWEEN 0 AND 20

Content

Contains merged data from between imaging (PhotoObj) and spectra (SpecObj) tables from the Science Archive Server.

Variables: objid = Unique Object Identifier ra = J2000 Right Ascension (r-band) dec = J2000 Declination (r-band) u = better of deV/Exp magnitude fit (u-band) g = better of deV/Exp magnitude fit (g-band) r = better of deV/Exp magnitude fit (r-band) i = better of deV/Exp magnitude fit (i-band) z = better of deV/Exp magnitude fit (z-band) run = Run Number rerun = Rerun Number camcol = Camera column field = Field number specobjid = Object Identifier class = object class (galaxy, star or quasar object) redshift = Final Redshift plate = plate number mjd = Median Julian Date of observation fiberid = fiberID

Acknowledgements

Data obtained from SDSS.

Inspiration

I'm using this for a classification project at Lambda School. Ad Astra!

Galaxy Blackhole Server Tenstorrent USA Inc., 78735 Austin US Auftraggeber: Forschungszentrum Jülich GmbH, Einkauf u.- Materialwirtschaft, 52428 Jülich, Telef. 02461-613041 Vergabeart: Verhandlungsvergabe ohne Teilnahmewettbewerb nach § 8 Abs. 4 Nr. 10 UVgO Lieferzeit: 16.02.2026 Betrieb und Wartung der Software Doktoranden Management System DokMS (JuDocs)

Downloaded workflows from Galaxy Servers

Phylogenetic information inferred from the study of homologous genes helps us to understand the evolution of genes and gene families, including the identification of ancestral gene duplication events as well as regions under positive or purifying selection within lineages. Gene family and orthogroup characterisation enables the identification of syntenic blocks, which can then be visualised with various tools. Unfortunately, currently available tools display only an overview of syntenic regions as a whole, limited to the gene level, and none provide further details about structural changes within genes, such as the conservation of ancestral exon boundaries amongst multiple genomes. We present Aequatus, a standalone web-based tool that provides an in-depth view of gene structure across gene families, with various options to render and filter visualisations. It relies on pre-calculated alignment and gene feature information typically held in, but not limited to, the Ensembl Compara and Core databases. We also offer Aequatus.js, a reusable JavaScript module that fulfils the visualisation aspects of Aequatus, available within the Galaxy web platform as a visualisation plugin, which can be used to visualise gene trees generated by the GeneSeqToFamily workflow. Aequatus is an open-source tool freely available to download under the MIT license at https://github.com/TGAC/Aequatus A demo server is available at http://aequatus.earlham.ac.uk/ A publicly available instance of the GeneSeqToFamily workflow to generate gene tree information and visualise it using Aequatus is available on the Galaxy EU server at https://usegalaxy.eu

Feedback: Mukharbek Organokov organokov.m@gmail.com

Context

Sloan Digital Sky Survey current DR16 Server Data release with Galaxies, Stars and Quasars.

License: Creative Commons Attribution license (CC-BY) More datailes here. Find more here.

Content

The table results from a query which joins two tables:
- "PhotoObj" which contains photometric data
- "SpecObj" which contains spectral data.

16 variables (double) and 1 additional variable (char) 'class'. A class object can be predicted from the other 16 variables.

Variables description:
objid = Object Identifier
ra = J2000 Right Ascension (r-band)
dec = J2000 Declination (r-band)
u = better of deV/Exp magnitude fit (u-band)
g = better of deV/Exp magnitude fit (g-band)
r = better of deV/Exp magnitude fit (r-band)
i = better of deV/Exp magnitude fit (i-band)
z = better of deV/Exp magnitude fit (z-band)
run = Run Number
rerun = Rerun Number
camcol = Camera column
field = Field number
specobjid = Object Identifier
class = object class (galaxy, star or quasar object)
redshift = Final Redshift
plate = plate number
mjd = MJD of observation
fiberid = fiberID

Comments

• A four-color UVGR intermediate-band photometric system (Thuan-Gunn astronomic magnitude system) is discussed in [1]. The Sloan Digital Sky Survey (SDSS) photometric system, a new five-color (u′ g′ r′ i′ z′) wide-band CCD system is described in [2]
• The variables 'run', 'rerun', 'camcol' and 'field' features which describe a field within an image taken by the SDSS. A field is basically a part of the entire image corresponding to 2048 by 1489 pixels. A field can be identified by: - run number, which identifies the specific scan, - the camera column, or "camcol," a number from 1 to 6, identifying the scanline within the run, and the field number. The field number typically starts at 11 (after an initial rampup time), and can be as large as 800 for particularly long runs. - An additional number, rerun, specifies how the image was processed.
• The variable 'class' identifies an object to be either a galaxy (GALAXY), star (STAR) or quasar (QSO).
  ####References:
  [1] Thuan & Gunn (1976, PASP, 88,543)
  [2] Fukugita, M. et al, Astronomical J. v.111, p.1748

Data server

Data can be obtained using SkyServer SQL Search with the command below:
-- This query does a table JOIN between the imaging (PhotoObj) and spectra
-- (SpecObj) tables and includes the necessary columns in the SELECT to upload
-- the results to the SAS (Science Archive Server) for FITS file retrieval.
SELECT TOP 100000
p.objid,p.ra,p.dec,p.u,p.g,p.r,p.i,p.z,
p.run, p.rerun, p.camcol, p.field,
s.specobjid, s.class, s.z as redshift,
s.plate, s.mjd, s.fiberid
FROM PhotoObj AS p
JOIN SpecObj AS s ON s.bestobjid = p.objid
WHERE
p.u BETWEEN 0 AND 19.6
AND g BETWEEN 0 AND 20

Learn how to. Some examples. Full SQL Tutorial.

Or perform a complicated, CPU-intensive query of SDSS catalog data using CasJobs, SQL-based interface to the CAS.

Acknowledgements

SDSS collaboration.

Inspiration

The Sloan Digital Sky Survey has created the most detailed three-dimensional maps of the Universe ever made, with deep multi-color images of one-third of the sky, and spectra for more than three million astronomical objects. It allows to learn and explore all phases and surveys - past, present, and future - of the SDSS.

Methods DNA libraries for Illumina NGS were prepared with a two-step PCR approach. For this, the Nucleosome 1 - Linker - Nucleosome 2 sequence was split into two amplicons, one comprising the Nucleosome 1 - Linker and the second comprising Linker - Nucleosome 2. For library preparation, 1 μL of bisulfite-converted DNA was amplified in a first PCR reaction using barcoded primers and HotStartTaq DNA Polymerase (QIAGEN). In the second PCR reaction, 1 µL of PCR1 product was amplified using i5 and i7 indexing primers and Q5 polymerase (New England Biolabs). Successful amplification was verified by agarose gel electrophoresis. Samples were pooled in equimolar amounts, purified with NucleoSpin® Gel and PCR Clean-up kit and used for Illumina paired end 2×250 bp sequencing conducted at Novogene. Bioinformatic analysis of NGS data was conducted using a local instance of a Galaxy server. Obtained sequence reads were trimmed with the Trim Galore! Tool, discarding tails with a quality score below 20. Afterwards reads were paired using PEAR. Reads were filtered according to the expected DNA length using the Galaxy Filter FASTQ tool. The de-multiplexing was done by the selection of the reads with specific combinations of barcodes and Illumina adapters which were then then mapped against a corresponding reference sequence using bwameth (github.com/brentp/bwa-meth). Finally, the DNA methylation of individual CpG sites was computed using MethylDackel (github.com/dpryan79/MethylDackel). Here, the final analysed sequences are provided. DNA sequencenes Dinucleosome Linker-70 CGAGGTCGACGGTATCGATAAGCTTCTGGAGAATCCCCCAGCCGAGGCCGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCAGGTGTCAGATATATACATCCTGTAACGCTATCCGCGCCACGTCTACGCTNNNTACGAGAACGCCGAGACGTGCGAGCAGCGAAAGCGGCCGaCCTGGAGAATCCAGGTGCTGAGGCAGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCACCACTCAGATATATACATCCTGTAAGGGCGAATTCCACATTG Dinucleosome Linker-58(1) CGAGGTCGACGGTATCGATAAGCTTCTGGAGAATCCCCCAGCCGAGGCCGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCAGGTGTCAGATATATACATCCTGTGCCACGTCTACGCTNNNTACGAGAACGCCGAGACGTGCGAGCAGCGAAAGCGGCCGaCCTGGAGAATCCAGGTGCTGAGGCAGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCACCACTCAGATATATACATCCTGTAAGGGCGAATTCCACATTG Dinucleosome Linker-58(2) CGAGGTCGACGGTATCGATAAGCTTCTGGAGAATCCCCCAGCCGAGGCCGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCAGGTGTCAGATATATACATCCTGTAACGCTATCCGCGCCACGTCTACGCTNNNGAGACGTGCGAGCAGCGAAAGCGGCCGaCCTGGAGAATCCAGGTGCTGAGGCAGCTCAATTGGTCGTAGCAAGCTCTAGCACCGCTTAAACGCACGTACGCGTTGTCCCCCGCGTTTTAACCGCCAAGGGGATTACTCCCTAGTCTCCAGGCACCACTCAGATATATACATCCTGTAAGGGCGAATTCCACATTG CpG rich DNA CTATGGAAACCCCTGTGGAGCTTCAGGGGCACGAGTGAGGCGGGCGCTGGCGGGCCAAGGTGACGAAGGCGCCTCCGGCTCTTGGGCCAGCGGACTGAGCGGTGGAGCAGAACTTGGGTGCCTCGGGGACCGCCAAAAAGTGGCCTTGTCCACTTCTCTGAG Further information The primers are provided in "NGS_Primers" A compilation of the sequencing reads provided here is given in "NGS_Table" References The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2022 update. Nucleic acids research 2022, 50, W345-W351, doi: 10.1093/nar/gkac247 Albrecht, C., Bashtrykov, P., and Jeltsch, A. (2024) Amplicon-Based Bisulfite Conversion-NGS DNA Methylation Analysis Protocol. Methods Mol Biol 2842, 405-418, doi: 10.1007/978-1-0716-4051-7_21 For all details regarding furhter analysis and interpretation of the data, refer to the corresponding manuscript (Gutekunst et al., 2026), which is connected with this data entry.

Expression of BISPR, BST2, and OASL in other arboviral diseases.

Method overview To achieve targeted ASM, transient transfection of the dCas9-10X SunTag-BFP, scFv-DNMT3A-3L-sfGFP, and sgRNA-DsRed plasmids was performed in HEK293 cells. Control experiments were conducted with a scrambled sgRNA that does not have a binding site in the human genome. Initial studies showed that cells positive for all three plasmids exhibited highest fluorescence of the corresponding reporter proteins on day 3 post-transfection. Hence FACS sorting was conducted at this time point. Genomic DNA was isolated from the FACS-sorted cells at day 3 after transfection and subjected to bisulfite treatment. Library preparation was performed using the bisulfite-converted samples, followed by NGS and data analysis. Most methylation experiments were conducted in three independent biological replicates. Method details Genomic DNA was extracted using QIAmp DNA Mini Kit (Qiagen). 500 ng of genomic DNA was subjected to overnight digestion with EcoRV which is not cutting in any of the target amplicons. Zymo EZ DNA Methylation-Lightning Kit (D5030-E) was used for bisulfite conversion. The library for NGS was prepared by two consecutive PCR reactions (Leitao et al, 2018). Firstly, bisulfite converted genomic DNA of each sample was amplified with target gene specific primers. The gene specific optimized amount of a product from the first PCR was used as a template for the second PCR to add the Illumina TruSeq sequencing adapters. Final products were quantified, pooled in equimolar amounts and purified using SPRIselect beads (Beckman Coulter). Ready-to-use pools of libraries were sequenced on NovaSeq 6000 using a PE250 flow cell (Novogene). NGS data were obtained in the form of FASTQ files. Data analysis FASTQ files were processed on the local instance of Galaxy server as described earlier (Bashtrykov & Jeltsch, 2018) with some modifications. Briefly, an adaptor and low-quality trimming was conducted using Trim Galore! (developed by Felix Krueger at the Babraham Institute). The two associated paired reads were merged using PEAR (Zhang et al, 2014). Experiment specific combinations of Illumina indices and barcodes were used to extract reads for the individual experiments from the pool of reads. This information is provided in the included Excel file. Reads corresponding to different alleles of the same target gene were identified based on the presence of the SNP. Two files of reads corresponding to alleles were generated and their methylation level was analyzed independently. First, reads were mapped against a reference sequence of the target region using bwameth (Pedersen et al, 2014) and then methylation of individual CpG sites was computed using MethylDackel (https://github.com/dpryan79/MethylDackel). Final visualization and statistics were prepared using Microsoft Excel. References Bashtrykov P, Jeltsch A (2018). DNA Methylation Analysis by Bisulfite Conversion Coupled to Double Multiplexed Amplicon-Based Next-Generation Sequencing (NGS). Methods Mol Biol 1767: 367-382 Leitao E, Beygo J, Zeschnigk M, Klein-Hitpass L, Bargull M, Rahmann S, Horsthemke B (2018). Locus-Specific DNA Methylation Analysis by Targeted Deep Bisulfite Sequencing. Methods Mol Biol 1767: 351-366 Pedersen JS, Valen E, Velazquez AM, Parker BJ, Rasmussen M, Lindgreen S, et al., Orlando L (2014). Genome-wide nucleosome map and cytosine methylation levels of an ancient human genome. Genome Res 24: 454-466 Zhang J, Kobert K, Flouri T, Stamatakis A (2014). PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30: 614-620

MicroRNAs (miRNAs) are important regulators of gene expression. The large-scale detection and profiling of miRNAs has accelerated with the development of high-throughput small RNA sequencing (sRNA-Seq) techniques and bioinformatics tools. However, generating high-quality comprehensive miRNA annotations remains challenging, due to the intrinsic complexity of sRNA-Seq data and inherent limitations of existing miRNA predictions. Here, we present iwa-miRNA, a Galaxy-based framework that can facilitate miRNA annotation in plant species by combining computational analysis and manual curation. iwa-miRNA is specifically designed to generate a comprehensive list of miRNA candidates, bridging the gap between already annotated miRNAs provided by public miRNA databases and new predictions from sRNA-Seq datasets. It can also assist users to select promising miRNA candidates in an interactive mode through the automated and manual steps, contributing to the accessibility and reproducibility of genome-wide miRNA annotation. iwa-miRNA is user-friendly and can be easily deployed as a web application for researchers without programming experience. With flexible, interactive, and easy-to-use features, iwa-miRNA is a valuable tool for annotation of miRNAs in plant species with reference genomes. We illustrated the application of iwa-miRNA for miRNA annotation of plant species with varying complexity. The sources codes and web server of iwa-miRNA is freely accessible at: http://iwa-miRNA.omicstudio.cloud/.

BackgroundMicrosoft Excel automatically converts certain gene symbols, database accessions, and other alphanumeric text into dates, scientific notation, and other numerical representations. These conversions lead to subsequent, irreversible, corruption of the imported text. A recent survey of popular genomic literature estimates that one-fifth of all papers with supplementary gene lists suffer from this issue.ResultsHere, we present an open-source tool, Escape Excel, which prevents these erroneous conversions by generating an escaped text file that can be safely imported into Excel. Escape Excel is implemented in a variety of formats (http://www.github.com/pstew/escape_excel), including a command line based Perl script, a Windows-only Excel Add-In, an OS X drag-and-drop application, a simple web-server, and as a Galaxy web environment interface. Test server implementations are accessible as a Galaxy interface (http://apostl.moffitt.org) and simple non-Galaxy web server (http://apostl.moffitt.org:8000/).ConclusionsEscape Excel detects and escapes a wide variety of problematic text strings so that they are not erroneously converted into other representations upon importation into Excel. Examples of problematic strings include date-like strings, time-like strings, leading zeroes in front of numbers, and long numeric and alphanumeric identifiers that should not be automatically converted into scientific notation. It is hoped that greater awareness of these potential data corruption issues, together with diligent escaping of text files prior to importation into Excel, will help to reduce the amount of Excel-corrupted data in scientific analyses and publications.

A scientific workflow describes a process for accomplishing a scientific objective, usually expressed in terms of tasks and their dependencies. We have collected publicly available workflows from Galaxy Main Server and tried to reuse them. This dataset contained our collected workflows.

Discover the booming market for commercial soup kettles and warmers! This comprehensive analysis reveals a projected $350 million market by 2033, driven by rising foodservice demand and technological advancements. Learn about key players, market trends, and growth opportunities.

Expression and purification of DNMT1 for biochemical work Full length murine DNMT1 (UniProtKB P13864) was overexpressed and purified as described (Adam, et al. 2020) using the Bac-to-Bac baculovirus expression system (Invitrogen). The expression construct of the DNMT1 with mutated CXXC domain was taken from Bashtrykov, et al. (2012). Synthesis long DNA substrate and methylation reactions with them The sequence of the 349 bp substrate with 44 CpG sites was taken from Adam et al. 2020. It was used in unmethylated and hemimethylated form. Generation of the substrates and the methylation reaction were conducted as described (Adam, et al. 2020). In brief, for the generation of hemimethylated substrates, the unmethylated DNA was methylated in vitro by M.SssI (purified as described in Adam, et al. 2020) to introduce methylation at all CpG sites, or by M.HhaI (NEB) together with M.MspI (NEB) to introduce methylation at GCGC and CCGG sites. For the synthesis of hemimethylated substrates, the upper strand of the methylated substrate was digested with lambda exonuclease, the ss-DNA purified and finally ds hemimethylated DNA was generated by by primer extension using Phusion® HF DNA Polymerase (Thermo). Methylation reaction were conducted using mixtures of UM, fully hemimethylated and patterned substrate (total DNA concentration 200 ng in 20 µL) in methylation buffer (100 mM HEPES, 1 mM EDTA, 0.5 mM DTT, 0.1 mg mL-1 BSA, pH 7.2 with KOH) containing 1 mM AdoMet. DNMT1 concentrations and incubation times are indicated in the text. Methylation was followed by bisulfite conversion using the EZ DNA Methylation-LightningTM Kit (ZYMO RESEARCH) followed by library generation and Illumina paired-end sequencing (Novogene). Flanking sequence preference analysis with randomized single-site substrates Methylation reactions of the randomized substrate with DNMT1 were performed similarly as described (Adam, et al. 2020; Gao, et al. 2020). Briefly, single-stranded oligonucleotides containing a methylated, hydroxymethylated or unmethylated CpG site embedded in a 10 nucleotide random context were obtained from IDT and used for generation of 67 bps long double-stranded DNA substrates by primer extension. Pools of these randomized substrates were then mixed in different combination, methylated by DNMT1 in methylation buffer (100 mM HEPES, 1 mM EDTA, 0.5 mM DTT, 0.1 mg mL-1 BSA, pH 7.2 with KOH) containing 1 mM AdoMet. DNMT1 concentrations and incubation times are indicated in the text. Methylation was followed by bisulfite conversion using the EZ DNA Methylation-LightningTM Kit (ZYMO RESEARCH) followed by library generation and Illumina paired-end sequencing (Novogene). Bioinformatics analysis NGS data sets were bioinformatically analyzed using a local instance of the Galaxy server as described (Adam, et al. 2020; Dukatz, et al. 2020; Dukatz, et al. 2022). In brief, for the long substrate, reads were trimmed, filtered by quality, mapped against the reference sequence and demultiplexed using substrate type and experiment specific barcodes. Afterwards, methylation information was assigned and retrieved by home-made skripts. For the randomized substrate, reads were trimmed and filtered according to the expected DNA size. The original DNA sequence was then reconstituted based on the bisulfite converted upper and lower strands to investigate the average methylation state of both CpG sites and the NNCGNN flanks using home-made skripts. Methylation rates of 256 NNCGNN sequence contexts in the competitive methylation experiments with the mixed single-site substrates were determined by fitting to monoexponential reaction progress curves with variable time points with MatLab skripts as described (Adam, et al. 2022). Pearson correlation factors were calculated with Excel using the correl function. Structure of the deposited data Methylation data of long substrates are placed in the “long DNA substrates” folder. Methylation data of short single-site substrates with randomized flanks are placed in the “single sites substrates” folder. In both folder an explanatory pdf file gives further information. Subfolders are arranged by enzyme (CXXC mutant or DNMT1 WT). Then, for each enzyme, the different substrates or substrate mixtures are provided in separate subfolders. References Adam S, Bräcker J, Klingel V, Osteresch B, Radde NE, Brockmeyer J, Bashtrykov P, Jeltsch A. Flanking sequences influence the activity of TET1 and TET2 methylcytosine dioxygenases and affect genomic 5hmC patterns. Communications Biology 5, 92 (2022) Adam S, Anteneh H, Hornisch M, Wagner V, Lu J, Radde NE, Bashtrykov P, Song J, Jeltsch A. DNA sequence-dependent activity and base flipping mechanisms of DNMT1 regulate genome-wide DNA methylation. Nature Commun 11, 3723 (2020) Bashtrykov P, et al. Specificity of Dnmt1 for methylation of hemimethylated CpG sites resides in its catalytic domain. Chem Biol 19, 572-578 (2012) Dukatz M, Dittrich M, Stahl E, Adam S, de Mendoza A,...

Instruction video to install and use GLASSgo on multiple resources (e.g., Docker, Galaxy, web server). GLASSgo is available at Github with instructions and application data and is distributed under the MIT license. Furthermore, GLASSgo can applied using the RNA Workbench Server or the GLASSgo Web Server