cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.8339791
title: Simulated Illumina metagenomic reads

description: We simulated metagenomic Illumina sequencing reads to a mixture ratio that approximates that found in patient sputa, albeit with a slightly higher mycobacterial component. In total, 0.9 gigabases were generated, at proportions: 46% each for bacteria and human, 6\% Mycobacterium tuberculosis complex (MTBC), and 1% each for virus and non-tuberculous mycobacteria (NTM).


The reference genomes that reads were simulated from for these groups were gathered as follows. The references for the virus group were obtained using kraken's (v2.1.2) --download-library functionality. The viral library was downloaded on June 15 2023. The human genome from which the reads were simulated was KOREF_S1v2.1 (RefSeq accession GCA_020497085.1), with contigs shorter than 10kbp removed. The bacterial references were obtained by first downloading the bacteria library through kraken, followed by a subsampling due to the size (166Gb) of the resulting FASTA file. We subsampled the file by first removing sequences with a length <50kbp. We then extracted each sequence into its own FASTA file under a directory for the genus of the sequence - excluding the Mycobacterium genus. Genera were randomly subsampled to contain a maximum of 1000 assemblies. Each genus was then reduced to a representative subset using Assembly Dereplicator (commit 2dfcb14; https://github.com/rrwick/Assembly-Dereplicator) by keeping only 10% of the assemblies for each genus (-f 0.1). The NTM references selected were M. abscessus (accession GCF_017190695.1), M. avium (GCF_020735285.1), M. kansasii (GCA_014701265.1), M. ulcerans (GCF_000013925.1), M. intracellulare (GCF_016756075.1), M. terrae (GCF_010727125.1), and M. fortuitum (GCF_001307545.1). The MTBC reference is a lineage 1 assembly (GCF_932530395.1).


Illumina reads were simulated with ART (v2016.06.05). We simulated paired reads from a MiSeq v3 system (-ss MSv3) with a read length of 150, a mean fragment length of 250 and fragment length standard deviation 10 (-l 150 -m 250 -s 10).


We removed simulated Illumina reads with any ambiguous base.
DOI: 10.5281/zenodo.8339788
title: Simulated Nanopore metagenomic reads

description: We simulated metagenomic Nanopore sequencing reads to a mixture ratio that approximates that found in patient sputa, albeit with a slightly higher mycobacterial component. In total, 4.5 gigabases were generated, at proportions: 46% each for bacteria and human, 6\% Mycobacterium tuberculosis complex (MTBC), and 1% each for virus and non-tuberculous mycobacteria (NTM).


The reference genomes that reads were simulated from for these groups were gathered as follows. The references for the virus group were obtained using kraken's (v2.1.2) --download-library functionality. The viral library was downloaded on June 15 2023. The human genome from which the reads were simulated was KOREF_S1v2.1 (RefSeq accession GCA_020497085.1), with contigs shorter than 10kbp removed. The bacterial references were obtained by first downloading the bacteria library through kraken, followed by a subsampling due to the size (166Gb) of the resulting FASTA file. We subsampled the file by first removing sequences with a length <50kbp. We then extracted each sequence into its own FASTA file under a directory for the genus of the sequence - excluding the Mycobacterium genus. Genera were randomly subsampled to contain a maximum of 1000 assemblies. Each genus was then reduced to a representative subset using Assembly Dereplicator (commit 2dfcb14; https://github.com/rrwick/Assembly-Dereplicator) by keeping only 10% of the assemblies for each genus (-f 0.1). The NTM references selected were M. abscessus (accession GCF_017190695.1), M. avium (GCF_020735285.1), M. kansasii (GCA_014701265.1), M. ulcerans (GCF_000013925.1), M. intracellulare (GCF_016756075.1), M. terrae (GCF_010727125.1), and M. fortuitum (GCF_001307545.1). The MTBC reference is a lineage 1 assembly (GCF_932530395.1).


We used Badreads (v0.4.0) to produce the simulated Nanopore reads for each group, specifying the number of bases in the appropriate proportions mentioned above. For all groups we specified no junk or random reads and 0.5% chimeric reads. In addition, for the MTBC, virus, and NTM groups we used a non-default length option --length 4000,3000 to produce reads with mean length 4000bp and a standard deviation of 3000. Defaults were used for all other options (the default error model is trained on real R10.4.1 Nanopore reads from 2023).


We filtered the simulated Nanopore reads to remove any read with a length <500bp or an ambiguous nucleotide (non-ACGT).
DOI: 10.5281/zenodo.8339789
title: Simulated Nanopore metagenomic reads

description: We simulated metagenomic Nanopore sequencing reads to a mixture ratio that approximates that found in patient sputa, albeit with a slightly higher mycobacterial component. In total, 4.5 gigabases were generated, at proportions: 46% each for bacteria and human, 6\% Mycobacterium tuberculosis complex (MTBC), and 1% each for virus and non-tuberculous mycobacteria (NTM).


The reference genomes that reads were simulated from for these groups were gathered as follows. The references for the virus group were obtained using kraken's (v2.1.2) --download-library functionality. The viral library was downloaded on June 15 2023. The human genome from which the reads were simulated was KOREF_S1v2.1 (RefSeq accession GCA_020497085.1), with contigs shorter than 10kbp removed. The bacterial references were obtained by first downloading the bacteria library through kraken, followed by a subsampling due to the size (166Gb) of the resulting FASTA file. We subsampled the file by first removing sequences with a length <50kbp. We then extracted each sequence into its own FASTA file under a directory for the genus of the sequence - excluding the Mycobacterium genus. Genera were randomly subsampled to contain a maximum of 1000 assemblies. Each genus was then reduced to a representative subset using Assembly Dereplicator (commit 2dfcb14; https://github.com/rrwick/Assembly-Dereplicator) by keeping only 10% of the assemblies for each genus (-f 0.1). The NTM references selected were M. abscessus (accession GCF_017190695.1), M. avium (GCF_020735285.1), M. kansasii (GCA_014701265.1), M. ulcerans (GCF_000013925.1), M. intracellulare (GCF_016756075.1), M. terrae (GCF_010727125.1), and M. fortuitum (GCF_001307545.1). The MTBC reference is a lineage 1 assembly (GCF_932530395.1).


We used Badreads (v0.4.0) to produce the simulated Nanopore reads for each group, specifying the number of bases in the appropriate proportions mentioned above. For all groups we specified no junk or random reads and 0.5% chimeric reads. In addition, for the MTBC, virus, and NTM groups we used a non-default length option --length 4000,3000 to produce reads with mean length 4000bp and a standard deviation of 3000. Defaults were used for all other options (the default error model is trained on real R10.4.1 Nanopore reads from 2023).


We filtered the simulated Nanopore reads to remove any read with a length <500bp or an ambiguous nucleotide (non-ACGT).
DOI: 10.5281/zenodo.8339731
title: Kraken2 Human Pangenome Reference Consortium database

description: A kraken2 database built from the genome assemblies used by the Human Pangenome Reference Consortium (https://projects.ensembl.org/hprc/). This archive contains the three files required by kraken2, hash.k2d, opts.k2d, and taxo.k2d, along with inspect.txt, which is obtained by running kraken2-inspect on the database, ktaxonomy.tsv, which contains the taxonomy information of the database (obtained by running https://github.com/jenniferlu717/KrakenTools#make_ktaxonomypy).


The genomes for this database were downloaded using the assembly summary text file included in this dataset and genome_updater.sh (v0.6.3; https://github.com/pirovc/genome_updater)


genome_updater.sh -m -a -f "genomic.fna.gz" -t 8 -e "hprc_assembly_summary.txt" -o HPRC_genomes/


The python script prepare_kraken_fasta.py was then used to prepare the assemblies for use in kraken with the following command


python prepare_kraken_fasta.py -r -T 9606 -o HPRC.fna HPRC_genomes/


The database was then built with kraken2 using the following commands


kraken2-build --download-taxonomy --db db/
kraken2-build --add-to-library HPRC.fna --db db/
kraken2-build --build --db db/ --threads 16
DOI: 10.5281/zenodo.8339732
title: Kraken2 Human Pangenome Reference Consortium database

description: A kraken2 database built from the genome assemblies used by the Human Pangenome Reference Consortium (https://projects.ensembl.org/hprc/). This archive contains the three files required by kraken2, hash.k2d, opts.k2d, and taxo.k2d, along with inspect.txt, which is obtained by running kraken2-inspect on the database, ktaxonomy.tsv, which contains the taxonomy information of the database (obtained by running https://github.com/jenniferlu717/KrakenTools#make_ktaxonomypy).


The genomes for this database were downloaded using the assembly summary text file included in this dataset and genome_updater.sh (v0.6.3; https://github.com/pirovc/genome_updater)


genome_updater.sh -m -a -f "genomic.fna.gz" -t 8 -e "hprc_assembly_summary.txt" -o HPRC_genomes/


The python script prepare_kraken_fasta.py was then used to prepare the assemblies for use in kraken with the following command


python prepare_kraken_fasta.py -r -T 9606 -o HPRC.fna HPRC_genomes/


The database was then built with kraken2 using the following commands


kraken2-build --download-taxonomy --db db/
kraken2-build --add-to-library HPRC.fna --db db/
kraken2-build --build --db db/ --threads 16
DOI: 10.5281/zenodo.10398850
title: Korea Coastal Debris observation 1

description: Debris detection using satellite data

Area 1 - Yellow Sea (Incheon, SkySat)
description: Created via Ocean Scan.Campaign ID: 944070e7-a9bd-4f68-8e72-615ba5042054.
DOI: 10.5281/zenodo.10398851
title: Korea Coastal Debris observation 1

description: Debris detection using satellite data

Area 1 - Yellow Sea (Incheon, SkySat)
description: Created via Ocean Scan.Campaign ID: 944070e7-a9bd-4f68-8e72-615ba5042054.
DOI: 10.11583/dtu.24612024
title: Gridded and timeseries data related to <b>Global and regional ocean mass budget closure since 2003</b>

description: Data related to Global and regional ocean mass budget closure since 2003 published in Nature Communications (doi: 10.1038/s41467-024-45726-w)<br><br>OMcontributions_2003_2022.nc<br>Contributions (Greenland, Glaciers, Antarctic, Land Water Storage driven by water management, and Land Water Storage due to natural change) to ocean mass change in a monthly 0.5x0.5 degree resolution.OMreconstruction_2003_2022.nc<br>Reconstruction of ocean mass change (OMrecon) and barystatic sea level change (OMrecon without atmosphere contribution = sum of contributions). Comparable with GRACE observations.stericAltimetry_2003_2022.nc<br>Altimetry sea surface heights corrected for steric sea level change with the default Microwave Radiometer (MWR)-derived wet troposphere correction, steALTmwr, and the hybrid version, where Climate Data Records (CDR) are used to compute wet troposphere correction after 2016 (steALTmwrcdr). The annual halosteric sea level change signal has been removed from the data, since it is attributed to a salinity drift in the ARGO observations system.GRACE_OM_2003_2022.nc:<br>GRACE-derived Ocean mass change based on GSFC Masons (Loomis et al, 2019). Missing GRACE months have been interpolated, but the GRACE-GRACE Follow On gap is indicated as NaN.masks.nc<br>Masks used to compute transform global grids to timeseries.source_data.xlsx:<br>Spreadsheet with global and regional timeseries data needed to compute the figures in 10.1038/s41467-024-45726-w. Numbers are in millimeter water equivalent. Also available as csv files in source_data.zip.
DOI: 10.6084/m9.figshare.25222577
title: <b>Fluoxetine and ketamine trigger the p75NTR proteolytic pathway and enhance extinction memory and brain plasticity through p75NTR</b>

description: <b>Pharmacologically diverse antidepressants were recently described to bind to TrkB and drive a positive allosteric modulation of endogenous BDNF. Although neurotrophins such as BDNF can bind to the p75 neurotrophin receptor (p75NTR), their precursors are the high affinity p75NTR ligands. While part of an unrelated receptor family capable of inducing completely opposite physiological changes, TrkB and p75NTR feature a cross-like conformation dimer and carry a cholesterol-recognition and alignment consensus in the transmembrane domain. Since such qualities were found crucial for antidepressants to bind to TrkB and drive behavioral and neuroplasticity effects, we hypothesized that their effects might also depend on p75NTR. Indeed, we found that antidepressants like fluoxetine and the fast-acting ketamine also bind to p75NTR, trigger its proteolytic pathway, and induce p75NTR-dependent behavioral/neuroplasticity changes. We suggest that antidepressants co-opt both BDNF/TrkB and proBDNF/p75NTR systems to induce a more efficient activity-dependent synaptic competition, thereby boosting the brain ability for remodeling.</b>
DOI: 10.6084/m9.figshare.25222577.v1
title: <b>Fluoxetine and ketamine trigger the p75NTR proteolytic pathway and enhance extinction memory and brain plasticity through p75NTR</b>

description: <b>Pharmacologically diverse antidepressants were recently described to bind to TrkB and drive a positive allosteric modulation of endogenous BDNF. Although neurotrophins such as BDNF can bind to the p75 neurotrophin receptor (p75NTR), their precursors are the high affinity p75NTR ligands. While part of an unrelated receptor family capable of inducing completely opposite physiological changes, TrkB and p75NTR feature a cross-like conformation dimer and carry a cholesterol-recognition and alignment consensus in the transmembrane domain. Since such qualities were found crucial for antidepressants to bind to TrkB and drive behavioral and neuroplasticity effects, we hypothesized that their effects might also depend on p75NTR. Indeed, we found that antidepressants like fluoxetine and the fast-acting ketamine also bind to p75NTR, trigger its proteolytic pathway, and induce p75NTR-dependent behavioral/neuroplasticity changes. We suggest that antidepressants co-opt both BDNF/TrkB and proBDNF/p75NTR systems to induce a more efficient activity-dependent synaptic competition, thereby boosting the brain ability for remodeling.</b>
DOI: 10.6084/m9.figshare.23480510.v1
title: Raw LC–MS/MS and Bulk RNA-Seq data

description: LC–MS/MS raw data
Raw files were searched against species-specific proteomes, using Proteome Discoverer Software 2.0 with SEQUEST HT and MS Amanda search engines.  Our search parameters identified fixed modifications, including cysteine carbamidomethylation, variable methionine oxidation, lysine carbamylation, and N-terminal acetylation and oxidation. The maximum number of missed cleavages permitted was two. Mass tolerance for precursor ions was set to 50 ppm and for fragment ions, 0.6 Da. Peptides with an Xcorr threshold ≤1% were subjected to validation through the MS Amanda search engine. A strict peptide false positive rate of 5% was used to accept proteins based on spectral match. Each distinct sample was analyzed in technical triplicates. Proteins identified in the extracellular spaces of rat or mouse crush-injured sciatic nerves are reported. Mass spectrometry interaction STatistics (MiST) scores were calculated, based on the abundance of the protein captured, reproducibility of capture, and specificity relative to the control bait (Free Fc) (Verschueren et al., 2016). CCR2/CCR4-binding interactions that generated MiST scores higher than 0.84 were reported
<br>
Bulk RNA-Seq data
Quality control of the raw fastq files was performed using the software tool FastQC (Andrew S., 2010) v0.11.8. Sequencing reads were trimmed with Trimmomatic (Bolger AM et al., 2014) v0.38 and aligned to the rat genome (mRatBN7.2 (The genomic reference consortium) using the STAR aligner (Dobin A et al., 2014) v2.5.1a. Read quantification was performed with RSEM (Li B. et al., 2011) v1.3.0 and the Ensembl release 107 annotation. The R BioConductor packages edgeR (Robinson MD et al., 2010) and limma (Ritchie ME et al., 2015) were used to implement the limma-voom (Law CW et al., 2014) method for differential expression analysis. The voom method was employed to model the mean-variance relationship in the log-cpm values weighted for inter-subject correlations in repeated measures of patients, after which lmFit was used to fit per-gene linear models and empirical Bayes moderation was applied with the eBayes function. Significance was defined by using an adjusted p-value cut-off of 0.4 after multiple testing correction (Benjamini Y et al., 1995) using a moderated t-statistic in limma and a minimum absolute log-fold-change threshold of 0
DOI: 10.6084/m9.figshare.23480510
title: Raw LC–MS/MS and Bulk RNA-Seq data

description: LC–MS/MS raw data
Raw files were searched against species-specific proteomes, using Proteome Discoverer Software 2.0 with SEQUEST HT and MS Amanda search engines.  Our search parameters identified fixed modifications, including cysteine carbamidomethylation, variable methionine oxidation, lysine carbamylation, and N-terminal acetylation and oxidation. The maximum number of missed cleavages permitted was two. Mass tolerance for precursor ions was set to 50 ppm and for fragment ions, 0.6 Da. Peptides with an Xcorr threshold ≤1% were subjected to validation through the MS Amanda search engine. A strict peptide false positive rate of 5% was used to accept proteins based on spectral match. Each distinct sample was analyzed in technical triplicates. Proteins identified in the extracellular spaces of rat or mouse crush-injured sciatic nerves are reported. Mass spectrometry interaction STatistics (MiST) scores were calculated, based on the abundance of the protein captured, reproducibility of capture, and specificity relative to the control bait (Free Fc) (Verschueren et al., 2016). CCR2/CCR4-binding interactions that generated MiST scores higher than 0.84 were reported
<br>
Bulk RNA-Seq data
Quality control of the raw fastq files was performed using the software tool FastQC (Andrew S., 2010) v0.11.8. Sequencing reads were trimmed with Trimmomatic (Bolger AM et al., 2014) v0.38 and aligned to the rat genome (mRatBN7.2 (The genomic reference consortium) using the STAR aligner (Dobin A et al., 2014) v2.5.1a. Read quantification was performed with RSEM (Li B. et al., 2011) v1.3.0 and the Ensembl release 107 annotation. The R BioConductor packages edgeR (Robinson MD et al., 2010) and limma (Ritchie ME et al., 2015) were used to implement the limma-voom (Law CW et al., 2014) method for differential expression analysis. The voom method was employed to model the mean-variance relationship in the log-cpm values weighted for inter-subject correlations in repeated measures of patients, after which lmFit was used to fit per-gene linear models and empirical Bayes moderation was applied with the eBayes function. Significance was defined by using an adjusted p-value cut-off of 0.4 after multiple testing correction (Benjamini Y et al., 1995) using a moderated t-statistic in limma and a minimum absolute log-fold-change threshold of 0
DOI: 10.6084/m9.figshare.25215947
title: Spatial-temporal variations in pollen load

description: We examined the variations in conspecific (CP) and heterospecific (HP) pollen loads on stigmas across 19 co-flowering species in six sub-alpine meadow communities over four consecutive years. Species identity significantly influences pollen deposition variations, accounting for most variance in both CP and HP. Species traits, independent of phylogenetic signals, are key in varying heterospecific pollen proportions (HP%). Species with higher heterospecific pollen proportions show more consistent exposure across individuals, unlike those with lower proportions, who experience greater variability. Supp.1-raw data.xlsx: Raw data of pollen deposition of the 19 co-flowering species in the six communities across four years.data.xlsx: Raw data for the linear mixed model, Table 2 and S3.data2.xlsx: Raw data for the hierarchical cluster analysis of 19 plant species in six communities for heterospecific pollen (HP) proportion of each year, Fig 3.data3.xlsx: Raw data for the relationship between the coefficient of variation (CV) of the proportion heterospecific pollen (HP) and the mean proportion across six communities and four years, Fig 6.R-code.txt documentation for the specific code.
DOI: 10.6084/m9.figshare.25215947.v1
title: Spatial-temporal variations in pollen load

description: We examined the variations in conspecific (CP) and heterospecific (HP) pollen loads on stigmas across 19 co-flowering species in six sub-alpine meadow communities over four consecutive years. Species identity significantly influences pollen deposition variations, accounting for most variance in both CP and HP. Species traits, independent of phylogenetic signals, are key in varying heterospecific pollen proportions (HP%). Species with higher heterospecific pollen proportions show more consistent exposure across individuals, unlike those with lower proportions, who experience greater variability. Supp.1-raw data.xlsx: Raw data of pollen deposition of the 19 co-flowering species in the six communities across four years.data.xlsx: Raw data for the linear mixed model, Table 2 and S3.data2.xlsx: Raw data for the hierarchical cluster analysis of 19 plant species in six communities for heterospecific pollen (HP) proportion of each year, Fig 3.data3.xlsx: Raw data for the relationship between the coefficient of variation (CV) of the proportion heterospecific pollen (HP) and the mean proportion across six communities and four years, Fig 6.R-code.txt documentation for the specific code.
DOI: 10.1594/pangaea.707970
title: Documentation of sediment core PS72/287-3