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opencc-by-4.0Oct 2018View details →

Human Hyperpolarization Activated Cyclic Nucleotide Gated Ion Channel 4 (HCN4); A Target Enabling Package

HCN4 is one of four hyperpolarisation activated cyclic nucleotide gated ion channels. It is responsible for the pacemaker or funny (If) current in the heart and is required for maintenance of a stable heartbeat. Mutations in HCN4 lead to a number of arrhythmias. HCN4 is the target for the angina drug ivabradine, which reduces HCN4 activity. However, ivabradine is non-selective, affecting all of the four HCN channels. HCN4 is a close homologue of HCN2, which is a target for neuropathic and inflammatory pain treatment. We have solved the structure of HCN4 both in complex with cyclic AMP and without nucleotide. Comparison of our HCN4 structure with that of the related HCN1 channel (86% identity) allows us to suggest ways to design selectivity for small molecule inhibitors between these closely related channels.

opencc-by-4.0Oct 2024View details →

Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite

Data necessary to reproduce figures in manuscript titled "Expanding and improving analyses of nucleotide recoding RNA-seq experiments with the EZbakR suite". Includes: <ol> <li>Compressed arrow dataset used to produce Figure S6 and S7 (subtlseq_data.tar.gz)</li> <li>Compressed arrow dataset used to produce Figure 7 (subtlseq_perturbations_data.tar.gz)</li> <li>eCLIP DDX3X peak calls from ENCODE used in Figure 7 (ENCFF901BYH_DDX3X_eCLIP.bed)</li> <li>Annotations used to process data (Hs_ensembl_lvl1_and_2.gtf and Hs_ensembl.gtf)</li> <li>Processed data to produce Figures 3C and S2 (cB_ensembl_totRNAsubtlseq.csv.gz and cB_ensemblLvl1and2_totRNAsubtlseq.csv.gz, respectively).</li> <li>Simulated data originally used in bakR publication (Vock and Simon, 2023) used to make Figure 6 of EZbakR suite paper.</li> <li>Processed data from the nanodynamo paper (Tarrerro et al. 2024) used to make Figure S9</li> <li>Table from Ietswaart et al. 2024 of kinetic parameter estimates and PUND calls from that study (mmc2.xlsx)</li> </ol> Also includes supplemental tables of: <ol> <li>List of genes producing transcripts predicting to undergo nuclear decay (PUNDs; Supplemental_Table_PUNDs.csv)</li> <li>Estimates for mature RNA synthesis, nuclear degradation, nuclear export, and cytoplasmic degradation rate constants from Ietswaart et al., 2024 total-cytoplasmic-nuclear TimeLapse-seq dataset (Supplemental_Table_NucCytoEsts.csv)</li> <li>Estimates for premature RNA synthesis, premature RNA processing, and mature RNA degreadation obtained from EZbakR analysis of Ietswaart et al., 2024 total RNA TimeLapse-seq dataset (Supplemental_Table_PtoMests.csv).</li> </ol> Scripts to reproduce figures can be found at: https://github.com/isaacvock/EZbakRsuite_paper_code Updated to include data necessary to reproduce new figures/panels in revisions.

opencc-by-4.0Mar 2024View details →

Dataset for the QPRTase (Nicotinate-nucleotide pyrophosphorylase [carboxylating]) antibody screening study

This project contains the following underlying data included in a study aimed at characterizing four commercial antibodies against Nicotinate-nucleotide pyrophosphorylase [carboxylating] (QPRTase) protein, encoded by the QPRT gene. The study is available on Zenodo (DOI: 10.5281/zenodo.7459387).

opencc-by-4.0Nov 2021View details →

LA1141 × OH8245 inbred backcross (IBC) single nucleotide polymorphism (SNP) markers for genetic studies

The LA1141 × OH8245 157 polymorphic SNP markers from an optimized tomato panel Sim et al., 2012 were used for linkage map construction in the BC2S3 IBC and composite interval mapping QTL analysis. Genetic map position and physical position corresponding to Sl4.0 (Hosmani et al., 2019), and flanking sequences are provided.

opencc-by-4.0Mar 2022View details →

Control T-cell receptor (TCR) alpha and beta chain nucleotide and amino acid sequences from human and mouse

A dataset of pooled T-cell receptor (TCR) sequences for TCR alpha and beta chains of human and mouse. Sequences are obtained from various samples of healthy individuals/mice using our conventional protocols: see for example [Britanova et al "Dynamics of individual T cell repertoires: from cord blood to centenarians" The Journal of Immunology 2016] and [Izraelson et al. "Comparative analysis of murine T‐cell receptor repertoires." Immunology 2018]. The sequences are stored as gzipped clonotype tables in VDJtools format, see [https://vdjtools-doc.readthedocs.io/en/master/input.html#vdjtools-format]. This control dataset can be used as a proxy for a generative VDJ rearrangement model to estimate the expected frequency distribution of TCRs and check for enrichment of rare TCR clonotypes and groups of similar TCR sequences. For the implementation of the enrichment analysis, please see CalcDegreeStats routine from VDJtools software, see [https://vdjtools-doc.readthedocs.io/en/master/annotate.html#calcdegreestats]. Files named "human.tra.strict.txt.gz", etc are pools of random/naive TCR clonotypes containing unique V/J/CDR3 nucleotide sequence combinations observed in data. The pools.zip file is used for TCR motif inference in VDJdb database [https://github.com/antigenomics/vdjdb-motifs], it contains human.tra.aa.txt, etc files that contain random/naive TCR clonotypes grouped by CDR3 amino acid sequence with the most frequent representative V and J.

opencc-by-4.0Sep 2022View details →

Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams

DNA were derived from fin tissue samples taken from individual trout captured from Little Jacks Creek, Big Jacks Creek , and Duncan Creek of the Owyhee mountains and Keithly Creek and Upper Mann Creek in the Hitt mountains of Western Idaho, United States. Fin tissues were collected from individual trout from each stream during monthly sampling events in June through October 2020.  DNA Extraction: Extraction of DNA from caudal fin tissues were performed using Quick-DNA Miniprep Plus purification kits (Zymo Research Inc.©). Small sections of fin tissue (≤ 25 mg) were collected from each sample. This was mixed with a digesting solution comprised of ultra-pure water, solid tissue buffer (Zymo Research Inc.©) and proteinase K. All tissues were digested in sealed microcentrifuge tubes for at minimum 3 h at 55°C in a water bath. We then aliquoted 100 µL of digestion supernatant and combined with 200 µL of genomic binding buffer (Zymo Research Inc.©). DNA was eluted in 50, 75, and 100 µL of elution buffer to determine which volume provided sufficient DNA concentration for genotyping. After it was determined all quantities produced suitable concentrations, going forward, 50 µL of elution buffer used. Genotyping: Following extraction, genotyping-in-thousands sequencing took place at the Hagerman National Fish Hatchery’s genetics research facility with the assistance of the Columbia River Intertribal Fish Commission (CRTFC). Genotyping protocols were as described in Campbell et al. (2015) and summarized below. First, samples were prepared for amplification via PCR by combining DNA extracts with a Qiagen Plus multiplex master mix and a species-specific pooled primer mix. This step added the Illumina sequencing primer sites to amplicons. Following the creation of the PCR cocktail, thermocycling was conducted for amplification. Amplified samples were then diluted 20-fold. Diluted samples were transferred to new 96-well PCR plates where two genetic indexes and barcodes provides a unique set of tagging primers to each well and plate. Tagged plates then underwent a second PCR step. After the second PCR, all DNA were transferred to Charm Biotech normalization plates where DNA was bound to wells, washed, and finally eluted. After normalization, all DNA was pooled together and a purification step using magnetized beads in two steps to selectively remove fragments of DNA that are both too large and too small for sequencing. Following purification, each plate was quantified via qPCR using Life Technologies QuantStudio 6 Flex Instrument (Life Technologies). Finally, sequencing was performed using an Illumina HiSeq 1500 instrument. Ancillary peer-reviewed manuscripts: Genotyping protocols Campbell NR, Harmon SA, Narum SR. 2015. Genotyping-in-Thousands by sequencing (GT-seq): A cost effective SNP genotyping method based on custom amplicon sequencing. Mol Ecol Resour, 15: 855-867. https://doi.org/10.1111/1755-0998.12357 SNP loci reference Collins EE, Hargrove JS, Delomas TA, Narum SR. 2020. Distribution of genetic variation underlying adult migration timing in steelhead of the Columbia River basin. Ecology and Evolution, 10(17): 9486-9502. https://doi.org/10.1002/ece3.6641   Data Use: License: <a href="https://creativecommons.org/licenses/by/4.0/">CC-BY 4.0</a>    Recommended Citation: Wooding AP, Narum SR, Pradhan DS. 2022. Data from: Development of Single Nucleotide Polymorphism (SNP) Panel for determination of environmental influence on genome for wild Columbia River redband trout (Oncorhynchus mykiss gairdnerii) in Southwest Idaho streams (0.1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7055582 Funding for this project is provided by US National Science Foundation and Idaho EPSCoR through award: OIA-1757324

opencc-by-4.0Jul 2024View details →

Alignments and ML trees of cassava brown streak virus and Ugandana cassava brown streak virus polyprotein nucleotide sequences

Alignments of full and nearly full polyprotein-length nucleotide sequences from GenBank for the two ipomoviruses that cause cassava brown streak disease, in fasta format.  Separate alignments for 67 cassava brown streak virus sequences and 81 Ugandan cassava brown streak virus sequences are provided, as well as a combined alignment of 148 sequences.  Alignments were created with MUSCLE and then modified by eye in AliView. Also, two tree files (in nexus) format are supplied, resulting from a maximum likelihood analysis with IQTree on each of the two single-species datasets. Support for nodes with aLRT and 100 actual bootstrap replicates are provided (aLRT/bootstrap).

opencc-by-4.0Jan 2019View details →

Data from: Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells

Data that was reported in "Selectivity of Guanine Nucleotide Exchange Factor-mediated Cdc42 activation in primary human endothelial cells" by  Nathalie R. Reinhard1, Sanne van der Niet1, Anna Chertkova1, Marten Postma1, Theodorus W.J. Gadella Jr.1, Peter L. Hordijk1,2, and Joachim Goedhart1*   Affiliations: 1 University of Amsterdam, Molecular Cytology, Swammerdam Institute for Life Sciences, van Leeuwenhoek Centre for Advanced Microscopy, Amsterdam, the Netherlands 2 Department of Physiology, Free University Medical Center, Amsterdam, The Netherlands   *Correspondence to: j.goedhart@uva.nl

opencc-by-4.0Sep 2024View details →

The North Pacific Eukaryotic Gene Catalog: clustered nucleotide metatranscripts and read counts

This data continues with the development of the NPEGC Trinity de novo metatranscriptome assemblies from the protein data repository of <a href="../doi/10.5281/zenodo.10472589">The North Pacific Eukaryotic Gene Catalog</a>. The nucleotide sequences corresponding to the NPEGC cluster representatives are collected together in these repository files: NPac.G1PA.bf100.id99.nt.fasta.gz NPac.G2PA.bf100.id99.nt.fasta.gz NPac.G3PA.bf100.id99.nt.fasta.gz NPac.G3PA_diel.bf100.id99.nt.fasta.gz NPac.D1PA.bf100.id99.nt.fasta.gz A full description of this data is published in Scientific Data, available here: <a href="https://www.nature.com/articles/s41597-024-04005-5" target="_blank" rel="noopener">The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations</a>. Please cite this publication if your research uses this data: Groussman, R. D., Coesel, S. N., Durham, B. P., Schatz, M. J., & Armbrust, E. V. (2024). The North Pacific Eukaryotic Gene Catalog of metatranscriptome assemblies and annotations. Scientific Data, 11(1), 1161. These nucleotide sequences have been sourced from the Zenodo repository for raw assemblies: <a href="../records/7332796">The North Pacific Eukaryotic Gene Catalog: Raw assemblies from Gradients 1, 2 and 3</a> Key processing steps are sampled below with links to the detailed code on the main github code repository: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog">https://github.com/armbrustlab/NPac_euk_gene_catalog</a> Code used to build the kallisto indices and map the short reads against indices with kallisto are online in the code repository here: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/NPEGC.nt_kallisto_counts.sh">NPEGC.nt_kallisto_counts.sh</a> There are two main steps: 1. Generate the kallisto index on the sets of clustered nucleotide metatranscripts 2. Map the short reads from environmental samples back to the assembly index As generated above, kallisto generates separate results files for each of the sample files. Even after compression, the total size of the tarballed kallisto output results directories are prohibitively large (>50GB). We use the code in this template R script to join together the 'est_count' estimated count values for the tens of millions of protein sequences in each project metatranscriptome, along with length. The code in this template script was used for each project: <a href="https://github.com/armbrustlab/NPac_euk_gene_catalog/blob/main/scripts/nt_data/aggregate_kallisto_counts.R">aggregate_kallisto_counts.R</a> The output count files for each project are Gzip-compressed and uploaded to the NPEGC nucleotide data repository here:  G1PA.raw.est_counts.csv.gz G2PA.raw.est_counts.csv.gz G3PA.raw.est_counts.csv.gz G3PA_diel.raw.est_counts.csv.gz D1PA.raw.est_counts.csv.gz

opencc-by-4.0Jun 2021View details →

Differential associations between nucleotide polymorphisms and physiological traits in Norway spruce (Picea abies Karst.) provenances under contrasting water regimes

Three datasets are provided here, yielded by a study on drought-stressed and control (well-watered) seedlings of Norway spruce (Picea abies Karst.), coming from 5 provenances distributed along a steep altitudinal gradient from 550 to 1,280 m a.s.l. in central Slovakia: 1. physiological traits 2. double-digest restriction-site associated sequencing data (ddRAD) 3. nuclear microsatellite (nSSR) genotypes

opencc-zeroFeb 2016View details →

Orthology guided transcriptome assembly of Italian ryegrass and meadow fescue for single nucleotide polymorphisms discovery (data set)

Transcriptome sequencing was performed on ten samples (corresponding to six genotypes) of Festuca pratensis and ten samples (corresponding to six genotypes) of Lolium multiflorum and fourteen samples of Lolium perenne (corresponding to fourteen genotypes). Using the OGA approach, 18,952 non-redundant F. pratensis transcripts were assembled by combining the contigs of all six genotypes based on orthology with the Brachypodium distachyon proteome. Similarly, 19,036 non-redundant L. multiflorum transcripts were assembled and annotated. In total, 17,455 orthologous transcripts were shared between the transcriptomes of the two species. Out of these, 16,613 orthologous transcripts overlap with the previously published L. perenne transcriptome containing 19,279 non-redundant transcripts(fasta files). We identified SNPs, the following criteria were used to classify it as one of following three classes (1) intraspecific SNPs (INTRA), (2) interspecific SNPs in two-way comparison (INTER-2W) and (3) interspecific SNPs in three-way comparison (INTER-3W) (GFF files).

opencc-by-4.0Aug 2011View details →

Fig. 3 in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data

Fig. 3. Bayesian consensus tree for the anabantoids, channids and catfishes (silurids, bagrids, clariids) obtained using partial Cytochrome b sequences with cyprinids as outgroup. The heteronchocleidids genera present on the anabantoids and channids are shown with their geographical areas. Values shown at each node refer to Bayesian posterior probabilities. (*refer to Table 3 for names used in GenBank).

opencc-by-4.0Aug 2011View details →

Fig. 2. Bayesian consensus tree generated from partial 28S in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data

Fig. 2. Bayesian consensus tree generated from partial 28S rDNA sequences (D1 domain) with Diplectanum spp. and Gyrodactylus spp. as outgroups. Values shown at each node refer to Bayesian (BI) posterior probabilities/maximum likelihood (ML) percentages of the bootstrap values with 100 replicates. Bootstrap values lower than 50 are given as dashes (-).

opencc-by-4.0Aug 2011View details →

Fig. 1 in Relationships Of The Heteronchocleidids (Heteronchocleidus, Eutrianchoratus And Trianchoratus) As Inferred From Ribosomal Dna Nucleotide Sequence Data

Fig. 1. Neighbour joining (NJ) tree constructed by PAUP* using partial 28S rDNA sequences (D1 domain) with Diplectanum spp. and Gyrodactylus spp. as outgroups. Percentages of the bootstrap values for neighbour joining (NJ)/maximum parsimony (MP) (NJ & MP=1,000 replicates) are shown along the branches. Bootstrap values lower than 50 are given as dashes (-).

F I G U R E 3 A in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 3 A priori discriminant analysis of principal components (DAPC) plot of Camel trout. Each point represents the genotype of an individual fish, with centroids for each site labelled. Discriminant function 1 (DF1) is represented by the x axis, and discriminant function 2 (DF2) by the y-axis

F I G U R E 1 in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 1 Map showing the location of rivers sampled for brown trout within the UK, France and Ireland. The left panel shows the rivers used to assess the performance of the single nucleotide polymorphisms (SNP) panel at characterising genetic parameters within and outside the target region. The top right (blue) panel shows the locations of the four sampled rivers in Mount's Bay, Cornwall (Case Study 1). The bottom right (red) panel shows the location of the sample locations in the Camel catchment (Case Study 2). The red box within the bottom right panel gives the position of the impassable De Lank quarry site

F I G U R E 2 A in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 2 A priori discriminant analysis of principal components (DAPC) of trout genotypes from rivers flowing into Mount's Bay, Cornwall. Individuals are represented by individual points, with centroids for each river labelled. Discriminant function 1 (DF1) is represented by the x axis, and discriminant function 2 (DF2) by the y-axis

F I G U R E 4 in A low-density single nucleotide polymorphism panel for brown trout (Salmo trutta L.) suitable for exploring genetic diversity at a range of spatial scales

F I G U R E 4 Correlation between geographic distance (km) against genetic distance (linear FST) for the trout samples from the River Camel. The red points represent those between the De Lank and all other sites, the black points for all pair-wise comparisons excluding the De Lank. Linear regression for all sites including the De Lank is given by the red line (r2 = 0.321, P = 0.231), and linear regression for all pair-wise sites excluding the De Lank is given by the black line (r2 = 0.658, P = 0.0671)