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54,099 results for “Expression”
SEESAW quantification data for temporal gene expression across osteoblastogenesis (B6xCAST), n=9
<p>Osteoblast cells mature from a mesenchymal stem cell pool to become cells capable of forming bone matrix and mineralizing this matrix. The goal of this study was to characterize temporal changes in the transcriptome across osteoblast maturation, starting with committed mesenchymal stem cell/ early pre-osteoblast stage through to mature osteoblasts capable of matrix mineralization. Methods: Enriched populations of pre-osteoblast-like cells were obtained from neonatal calvaria from B6xCAST mice expressing CFP under the control of the Col3.6 promoter. These cells were placed into culture for 4 days, removed from culture and subjected to FACS sorting based on the presence/absence of CFP expression. Cells expressing CFP were returned to culture, subjected to an osteoblast differentiation cocktail and RNA was collected at 2, 4, 6, 8, 10, 12, 14, 16 and 18 days post differentiation. Methods II: mRNA profiles for each time point were generated by next generation RNA sequencing, using an Illumina HiSeq 2000. Three technical replicates per sample were sequenced. Overall design: Gene expression in calvarial osteoblasts from neonatal B6xCAST-Col3.6 CFP mice at 9 time points post differentiation.</p> <p>File description: the R data files (.rda) provide outputs of the scripts in the mikelove/osteoblast-quant GitHub repo (July 2022, commit 01d96490), having run the fishpond package function importAllelicCounts() followed by minimal filtering. The `_counts.rda` files contain SummarizedExperiment objects with estimated count, TPM abundance, and effective length, but do not contain inferential replicates (bootstrap counts), although the transcript-level allelic counts object contains bootstrap mean and variances for every isoform, sample, and allele. The other two `.rda` files are summarized to gene level.</p> <p>The `_quant_dirs.tgz` files contain all the Salmon quantification data including bootstraps for the 9 time points. They are grouped into sets of three for convenience. The `CAST_EiJ.diploid.fa.gz` file provides the transcript sequences that were used for Salmon quantification.</p> <p>The `B6xCAST_discordant_global_AI.csv` file contains the same information as presented in Table S1 of Wu et al (2022). These are TSS-level results for 134 genes showing significant and discordant patterns within gene.</p> <p>The other 6 CSV files provide global and dynamic AI testing results at three levels of resolution: gene level, isoform level (txp), and TSS level where TSS within 50bp are combined into a single TSS-group. The significance cutoff is a q-value of 0.05. The code used for generating these results is provided in the GitHub repo: FennecFish/osteoblast-test.</p>
MELAN-A EXPRESSION RELATED TO APOPTOSIS OF MELANOCYTES IN SEGMENTAL AND NONSEGMENTAL VITILIGO
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Vitiligo is a progressive depigmentation of the skin with unclear etiology. Cell-mediated immunity has been suggested to play an important role in the pathogenesis of vitiligo’s progression. Melan-A has a high affinity for specific CD8+ T cells and is one of the critical markers for detecting damage to melanocytes. </p> <p><strong>Aim: </strong>Our study aims to demonstrate the differences of Melan-A expression associated with apoptosis of melanocytes in patients with segmental vitiligo (SV) and those with non-segmental vitiligo (NSV). </p> <p><strong>Methods:</strong> The subjects consisted of 64 patients diagnosed with vitiligo, of whom 33 were with NSV and 31 with SV. Skin biopsy and direct immunofluorescence were used to examine Melan-A, and the TUNEL method was performed to examine melanocyte apoptosis in both groups. Group comparisons were conducted using appropriate statistical methods. </p> <p><strong>Results: </strong>Melan-A expression was significantly higher in the NSV group than in the SV group, and there was a significant difference between two groups (p = 0.001). The median of melanocyte apoptosis in the NSV group was relatively higher than in the SV group, and a significant difference was found between the two groups (p = 0.001). The Spearman’s rank correlation test between Melan-A expression and melanocyte apoptosis in the NSV group was 0.767 (76.7%) and showed a significant relationship (p <0.05). The same test in the SV group was 0.583 (58.3%) and showed a significant relationship (p <0.05). In both groups, the higher the Melan-A expression, the higher the melanocyte apoptosis. </p> <p><strong>Conclusion:</strong> Melan-A expression and melanocyte apoptosis are correlated. The higher Melan-A expression and melanocyte apoptosis in NSV indicates more severe vitiligo disease compared to SV. </p>
Cytochrome P450 Genes Expressed in Phasmatodea Midguts
<p>In June 2021, representative sequences for insect cytochrome P450s were downloaded from NCBI, limiting the search to those in the UniProtKB database. The resulting 111 sequences were used as a query to mine the above transcriptomes using tblastn with an expect value threshold of e-10. These were manually annotated by removing truncated sequences, using the ExPASy online translation tool to obtain the complete amino acid sequences, removing duplicates using the sRNAtoolbox webserver, and confirming that the sequences were cytochrome P450s by identifying them using blastp against the NCBI database. The resulting sequences were combined with the representatives from NCBI, aligned using the Clustal W program built into the software MEGA version X. Any sequences missing the heme-binding domain FXXGXXXCXG/A, which is a signature motif for CYPs, were deleted. The sequences were also checked for the presence and absence of other four signature motifs from insect CYPs: helix C (WxxxR), helix I (GxE/DTT/S), helix K (ExLR), and PERF (PxxFxPE/DRE/F).</p>
Code for: MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells
<p>Contains source data file and code for publication "MHC Heterozygosity Prunes the Numbers of Different T Cell Receptors Expressed in CD4 T Cells".<br>Associated FASTQ files are deposited on the NIH SRA under accession: PRJNA1106276</p>
The expression level of microRNA in invasive and nonivasive gonadotrph pituitary tumors
<p>The data include the normalized read counts from smallRNA sequencing of 20 RNA samples from gonadotroph pituitary tumors<br>The quality of small RNA fractions was assessed using Agilent 2100 Bioanalyzer with Small RNA Kit Chip (Agilent) and measured with Qubit RNA HS Assay Kits (Thermo Fisher Scientific). One μg of total RNA was used for sequencing library construction with an Ion Total RNA-Seq Kit v2 (Thermo Fisher Scientific), according to the manufacturer’s protocol. Ion Xpress™ RNA-Seq Barcode Kit was used for hybridization and ligation of RNA adapters that allows for multiplexed sequencing. RNA reverse transcription and subsequent cDNA purification and library size selection were performed using Nucleic Acid Binding Beads. cDNA was PCR-amplified, followed by DNA purification and size selection. The amount and size distribution of the amplified DNA was determined using Bioanalyzer 2100 using a High Sensitivity DNA Kit (Agilent). The length of miRNA ligation products in barcoded libraries ranged between 94 and114 bp. Template preparation for clonal amplification of up to four<br>miRNA libraries at a concentration of 18pM and loading of the PI chip were performed using Ion Chef Instrument, with Ion PI™ Hi-Q™ Chef Kit (Thermo Fisher Scientific). Ion Proton Sequencer (Thermo Fisher Scientific) was used for sequencing. Unmapped bam files were converted into fastq files with a bamToFastq script from bedtools. Read mapping to known human miRNAs (according to miRBase v.22) and reads quantification were performed using miRDeep2.14. Data normalization was performed using DESeq2. </p>
The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons
<p>This project contains the annotated transcriptomes in GTF format used in the manuscript "The Human Developing Cerebral Cortex Is Characterized by an Elevated De Novo Expression of Long Noncoding RNAs in Excitatory Neurons" DOI: <a href="https://doi.org/10.1093/molbev/msae123">https://doi.org/10.1093/molbev/msae123</a></p>
Changes in gene expression during germination reveal pea genotypes with either 'quiescence' or 'escape' mechanisms of waterlogging tolerance
<p>Waterlogging causes germination failure in pea (<em>Pisum sativum</em> L.). Three genotypes (BM-3, NL-2 and Kaspa) contrasting in ability to germinate in waterlogged soil were exposed to different durations of waterlogging. Whole genome RNAseq was employed to capture differentially expressing genes. The ability to germinate in waterlogged soil was associated with testa colour and testa membrane integrity as confirmed by electrical conductivity measurements. Among the most differentially regulated genes, upregulated gene tyrosine protein kinase responsible for metabolic regulation and downregulated LOX5 involved in fat metabolism indicated energy preservation in tolerant Kaspa, while in the other tolerant NL-2 subtilase family protein and PNC2 involved in protein and fat metabolism respectively showed upregulated expression suggesting energy utilization during waterlogging. By contrast, in sensitive genotype BM-3 high upregulation was recorded for the kunitz-type trypsin/protease inhibitor whose role is blocking the activity of protein metabolism leading to excessive lipid metabolism causing membrane leakage and subsequent seed damage. Pathway analyses based on gene ontologies showed seed storage protein metabolism as upregulated in tolerant genotypes and downregulated in the sensitive genotype. Understanding the tolerance mechanism provides a platform to breed for adaptation to waterlogging stress at germination in pea. </p>
Changes in gene expression during germination reveal pea genotypes with either 'quiescence' or 'escape' mechanisms of waterlogging tolerance
<p>Waterlogging causes germination failure in pea (<em>Pisum sativum</em> L.). Three genotypes (BM-3, NL-2 and Kaspa) contrasting in ability to germinate in waterlogged soil were exposed to different durations of waterlogging. Whole genome RNAseq was employed to capture differentially expressing genes. The ability to germinate in waterlogged soil was associated with testa colour and testa membrane integrity as confirmed by electrical conductivity measurements. Among the most differentially regulated genes, upregulated gene tyrosine protein kinase responsible for metabolic regulation and downregulated LOX5 involved in fat metabolism indicated energy preservation in tolerant Kaspa, while in the other tolerant NL-2 subtilase family protein and PNC2 involved in protein and fat metabolism respectively showed upregulated expression suggesting energy utilization during waterlogging. By contrast, in sensitive genotype BM-3 high upregulation was recorded for the kunitz-type trypsin/protease inhibitor whose role is blocking the activity of protein metabolism leading to excessive lipid metabolism causing membrane leakage and subsequent seed damage. Pathway analyses based on gene ontologies showed seed storage protein metabolism as upregulated in tolerant genotypes and downregulated in the sensitive genotype. Understanding the tolerance mechanism provides a platform to breed for adaptation to waterlogging stress at germination in pea. </p>
Phaeocystis globosa colonial gene expression
<p>Data and analysis for the paper: </p> <p><strong>Differential gene expression supports a resource-intensive, defensive role for colony production in the bloom-forming haptophyte, <em>Phaeocystis globosa</em></strong></p> <p>by: Margaret Mars Brisbin and Satoshi Mitarai</p> <p>The <em>Phaeocystis globosa</em> CCMP1528 transcriptome used in the study (phaeocystisglobosa_euk_seqs.fasta or pg_euk_seqs_altnames.fasta) was assembled with trimmed sequencing reads from 8 biological replicates (4 colonial replicates and 4 solitary replicates) with the Trinity software (v2.3.2).</p> <p>Raw sequencing reads are available from the NCBI SRA with accession numbers: SRR7811979–SRR7811986.</p> <p>Before assembling the transcriptome, reads were quality filtered and trimmed with the Trimmomatic software (v3.36) using the command:</p> <pre><code>java -jar $TRIM/trimmomatic-0.36.jar PE -phred33 $DATA2/S${SLURM_ARRAY_TASK_ID}_S*_R1_001.fastq.gz \ $DATA2/S${SLURM_ARRAY_TASK_ID}_S*_R2_001.fastq.gz \ $OUT/S${SLURM_ARRAY_TASK_ID}_1_paired.fq $OUT/S${SLURM_ARRAY_TASK_ID}_1_unpaired.fq \ $OUT/S${SLURM_ARRAY_TASK_ID}_2_paired.fq $OUT/S${SLURM_ARRAY_TASK_ID}_2_unpaired.fq \ ILLUMINACLIP:$TRIM/adapters/NexteraPE-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36</code></pre> <p>Trimmed reads were mapped to the ERCC reference sequences for Mix1 and mapped reads were filtered using the following commands from bowtie2 (v2.2.6), samtools, and bedtools: </p> <pre><code>bowtie2 -t -x $REF \ -1 $DATA/S${SLURM_ARRAY_TASK_ID}_1_paired.fq \ -2 $DATA/S${SLURM_ARRAY_TASK_ID}_2_paired.fq \ -S $OUT/S${SLURM_ARRAY_TASK_ID}_ercc.sam samtools view -bS $DATA/S${SLURM_ARRAY_TASK_ID}_ercc.sam >$DATA/S${SLURM_ARRAY_TASK_ID}.bam samtools sort $DATA/S${SLURM_ARRAY_TASK_ID}.bam $DATA/S${SLURM_ARRAY_TASK_ID}_sorted samtools view -b -f 13 S${SLURM_ARRAY_TASK_ID}_sorted.bam > S${SLURM_ARRAY_TASK_ID}_unmapped.bam samtools sort -n $DATA/S${SLURM_ARRAY_TASK_ID}_unmapped.bam $DATA/S${SLURM_ARRAY_TASK_ID}.qsort bedtools bamtofastq -i $DATA/S${SLURM_ARRAY_TASK_ID}.qsort.bam -fq $DATA/S${SLURM_ARRAY_TASK_ID}_1_paired.fq -fq2 $DATA/S${SLURM_ARRAY_TASK_ID}_2_paired.fq</code></pre> <p>The resulting Trimmed reads without ERCC sequences were used to make the transcriptome assembly: </p> <pre><code>Trinity --seqType fq --max_memory 475G \ --left $DATA2/C1_1_paired.fq,$DATA2/C2_1_paired.fq,$DATA2/C3_1_paired.fq,$DATA2/C4_1_paired.fq,$DATA2/S1_1_paired.fq,$DATA2/S2_1_paired.fq,$DATA2/S3_1_paired.fq,$DATA2/S4_1_paired.fq \ --right $DATA2/C1_2_paired.fq,$DATA2/C2_1_paired.fq,$DATA2/C3_2_paired.fq,$DATA2/C4_2_paired.fq,$DATA2/S1_2_paired.fq,$DATA2/S2_2_paired.fq,$DATA2/S3_2_paired.fq,$DATA2/S4_2_paired.fq \ --CPU 12</code></pre> <p>The Trinity assembly was dereplicated with CD-HIT-EST (v2016-0304) at 95% : </p> <pre><code>cd-hit-est -i $DATA/Trinity.fasta -o Trinity_Pg_clustered_95 -c 0.95 -n 8 -p 1 -g 1 -M 200000 -T 8 -d 40</code></pre> <p>The Trinity assembly was filtered to remove bacterial contamination by first running a blastn(v2.6.0+) against the nr/nt NCBI database:</p> <pre><code>blastn -query $DATA/Trinity_Pg_clustered_95.fasta -task blastn -db $REF -num_threads 12 -max_target_seqs 1 -outfmt 5 > TrinityBlast.xml</code></pre> <p>and then removing bacterial reads with custom python scripts included here: TrinityBlastXML.ipynb and FIlterTrinityEukNotEuk.ipynb </p> <p>RSEM (v1.2.22) was run with the final transcriptome assembly (phaeocystisglobosa_euk_seqs.fasta or pg_euk_seqs_altnames.fasta): </p> <pre><code>rsem-calculate-expression --bowtie2 --paired-end \ $DATA/C${SLURM_ARRAY_TASK_ID}_1_paired.fq \ $DATA/C${SLURM_ARRAY_TASK_ID}_2_paired.fq \ $REF/rsemref_longISO/pg_euks_RSEMref \ $REF/rsemout_longISO/C${SLURM_ARRAY_TASK_ID} rsem-calculate-expression --bowtie2 --paired-end \ $DATA/S${SLURM_ARRAY_TASK_ID}_1_paired.fq \ $DATA/S${SLURM_ARRAY_TASK_ID}_2_paired.fq \ $REF/rsemref_longISO/pg_euks_RSEMref \ $REF/rsemout_longISO/S${SLURM_ARRAY_TASK_ID} </code></pre> <p>The resulting data files are: C*.genes.results and S*.genes.results which were used with DESeq2 in the R environment to analyze different gene expression. The code for these analyses is available in html and R markdown (PhaeoColSol_DE.html, PhaeoColSol_DE.Rmd). </p> <p>The transcriptome assembly was annotated with the Dammit software (v1.0rc2), which wraps Transdecoder, HMMER, and BUSCO, and by submitting the translated amino acid sequences to GhostKOALA. </p> <p>The raw pfam Dammit annotation results are included: pg_euk_seqs.fasta.x.pfam.gff3. These results were parsed with the script: Pfam_gffParsing.ipynb. The resulting file, pfam_parsed_annotation.csv, is used in the script PhaeoColSol_DE.Rmd with pfam2go4R.txt for GO enrichment analysis. The script shinycolsol.Rmd creates an interactive plot of GO enrichment results. </p> <p>The GhostKOALA results are user_ko.csv, and are used in the script PhaeoColSol_DE.Rmd for KEGG pathway enrichment analysis. </p>
The relationship between linguistic expression and symptoms of depression, anxiety, and suicidal thoughts: A longitudinal study of blog content
<p>To investigate the associations between linguistic features and symptoms of depression, generalised anxiety, and suicidal ideation, we extracted linguistic features from individuals’ blog content and correlated it with validated mental health data in a longitudinal study (n=38). Depressive symptoms were assessed using the self-report Patient Health Questionnaire (PHQ-9), anxiety symptoms using the self-report Generalised Anxiety Disorder Scale (GAD-7), and social media data was analysed using the Linguistic Inquiry and Word Count (LIWC) tool for linguistic features. Bivariate and multivariate analyses were performed to investigate the correlations between the linguistic features and mental health scores between subjects. We then used the multivariate regression model to predict longitudinal changes in mood within subjects.</p>
Processed data for the study on "Chromatin 3D interactions mediate genetic effects on gene expression"
<p>This repository contains the processed data that was generated as part of the following study:</p> <p>Delaneau et al. (2019) <strong>Chromatin 3D interactions mediate genetic effects on gene expression.</strong></p> <p><em>Abstract:</em> Studying the genetic basis of gene expression and chromatin organization is key to characterize the effect of genetic variability on the function and structure of the human genome. Here, we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and inter-chromosomal levels within 12,583 Cis Regulatory Domains and 30 Trans Regulatory Hubs that highly reflect the local (i.e. Topologically Associating Domains) and global (i.e. open/close chromatin compartments) nuclear chromatin organization. These structures delimit cell type specific regulatory networks that control gene expression/co-expression and mediate the genetic effects of <em>cis</em>- and <em>trans</em>-acting regulatory variants on genes.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Chromatin QTLs for H3K27ac, H3K4me1 and H3K4me3 discovered in 317 Lymphoblastoids Cell Lines (LCLs) and 78 Fibroblasts.</li> <li>Molecular QTLs affecting the activity and structure of Cis Regulatory Domains (CRDs) in LCLs.</li> <li>Basic information about the full set of genetic variants being analyzed in the study.</li> <li>The peak coordinates, their hierarchy based on inter-individual correlation and the CRD calls for both LCLs and Fibroblasts.</li> <li>The functional links discovered in LCLs between CRDs and genes.</li> <li>eQTLs for LCLs.</li> <li>A README file containing the description of the file format for each file.</li> </ol>
GTEx exon-level expression summaries for Snaptron
<p>GTEx exon-level expression summary from the Snaptron collection. Format is a tab-separated text file compressed and indexed using BGZip, along with supplementary files containing a Tabix index for the data (ending in tbi) as well as two files describing the samples represented in the columns of the data file (ending in tsv). Uses GENCODE v25 annotation for quantification. Source data for the quantification are the bigWig files produced as part of recount2. More information at http://snaptron.cs.jhu.edu.</p>
GTEx gene-level expression summary for Snaptron
<p>GTEx gene-level expression summary from the Snaptron collection. Format is a tab-separated text file compressed and indexed using BGZip, along with supplementary files containing a Tabix index for the data (ending in tbi) as well as two files describing the samples represented in the columns of the data file (ending in tsv). Uses GENCODE v25 annotation for quantification. Source data for the quantification are the bigWig files produced as part of recount2. More information at http://snaptron.cs.jhu.edu.</p>
GTEx exon-exon-junction-level expression summary for Snaptron
<p>GTEx exon-level expression summary from the Snaptron collection. Format is a tab-separated text file compressed and indexed using BGZip, along with supplementary files containing a Tabix index for the data (ending in tbi) as well as two files describing the samples represented in the columns of the data file (ending in tsv). Uses Rail-RNA for spliced alignment. More information at http://snaptron.cs.jhu.edu.</p>
Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis [Suppl Data]
<p>Alcoholic hepatitis (AH) is a life-threatening condition characterized by profound hepatocellular dysfunction for which targeted treatments are urgently needed. Identification of molecular drivers is hampered by the lack of suitable animal models. By performing RNA sequencing in livers from patients with different phenotypes of alcohol-related liver disease (ALD), we show that the development of AH is characterized by the defective activity of liver-enriched transcription factors (LETFs). TGFb1is a key upstream transcriptome regulator in AH and induces the use of HNF4aP2 promoter in hepatocytes, which results in defective metabolic and synthetic functions. Gene polymorphisms in LETFs including HNF4aare not associated with the development of AH. In contrast, epigenetic studies show that AH livers have profound changes in DNA methylation state and chromatin remodeling, affecting HNF4a-dependent gene expression. We conclude that targeting TGFb1and epigenetic drivers that modulate HNF4a-dependent gene expression could be beneficial to improve hepatocellular function in patients with AH.</p>
Multi-study reanalysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures
<p>Supplemental Information, Supplementary Tables, and Supplementary Files, as well as accompanying data for the manuscript "Stratified computational meta-analysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures" by Raeuf Roushangar and George I. Mias. </p>
Graph topological features extracted from expression profiles of neuroblastoma patients
<p><strong>Introduction</strong></p> <p>This dataset contains the data described in the paper titled "A deep neural network approach to predicting clinical outcomes of neuroblastoma patients." by Tranchevent, Azuaje and Rajapakse. More precisely, this dataset contains the topological features extracted from graphs built from publicly available expression data (see details below). This dataset does not contain the original expression data, which are available elsewhere. We thank the scientists who did generate and share these data (please see below the relevant links and publications).</p> <p> </p> <p><strong>Content</strong></p> <p>File names start with the name of the publicly available dataset they are built on (among "Fischer", "Maris" and "Versteeg"). This name is followed by a tag representing whether they contain raw data ("raw", which means, in this case, the raw topological features) or TF formatted data ("TF", which stands for TensorFlow). This tag is then followed by a unique identifier representing a unique configuration. The configuration file "Global_configuration.tsv" contains details about these configurations such as which topological features are present and which clinical outcome is considered.</p> <p>The code associated to the same manuscript that uses these data is at <a href="https://gitlab.com/biomodlih/SingalunDeep">https://gitlab.com/biomodlih/SingalunDeep</a>. The procedure by which the raw data are transformed into the TensorFlow ready data is described in the paper.</p> <p> </p> <p><strong>File format</strong></p> <p>All files are TSV files that correspond to matrices with samples as rows and features as columns (or clinical data as columns for clinical data files). The data files contain various sets of topological features that were extracted from the sample graphs (or Patient Similarity Networks - PSN). The clinical files contain relevant clinical outcomes.</p> <p>The raw data files only contain the topological data. For instance, the file "Fischer_raw_2d0000_data_tsv" contains 24 values for each sample corresponding to the 12 centralities computed for both the microarray (<em>Fischer-M</em>) and RNA-seq (<em>Fischer-R</em>) datasets. The TensorFlow ready files do not contain the sample identifiers in the first column. However, they contain two extra columns at the end. The first extra column is the sample weights (for the classifiers and because we very often have a dominant class). The second extra column is the class labels (binary), based on the clinical outcome of interest.</p> <p> </p> <p><strong>Dataset details</strong></p> <p>The <em>Fischer</em> dataset is used to train, evaluate and validate the models, so the dataset is split into train / eval / valid files, which contains respectively 249, 125 and 124 rows (samples) of the original 498 samples. In contrast, the other two datasets (<em>Maris</em> and <em>Versteeg</em>) are smaller and are only used for validation (and therefore have no training or evaluation file).</p> <p>The <em>Fischer</em> dataset also has more data files because various configurations were tested (see manuscript). In contrast, the validation, using the <em>Maris</em> and <em>Versteeg</em> datasets is only done for a single configuration and there are therefore less files.</p> <p>For <em>Fischer</em>, a few configurations are listed in the global configuration file but there is no corresponding raw data. This is because these items are derived from concatenations of the original raw data (see global configuration file and manuscript for details).</p> <p> </p> <p><strong>References</strong></p> <p>This dataset is associated with Tranchevent L., Azuaje F.. Rajapakse J.C., A deep neural network approach to predicting clinical outcomes of neuroblastoma patients.</p> <p>If you use these data in your research, please do not forget to also cite the researchers who have generated the original expression datasets.</p> <p><em>Fischer</em> dataset:</p> <ul> <li>Zhang W. et al., Comparison of RNA-seq and microarray-based models for clinical endpoint prediction. Genome Biology 16(1) (2015). doi:10.1186/s13059-015-0694-1</li> <li>Wang C. et al., The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance. Nat. Biotechnol. 32(9), 926–932. doi:10.1038/nbt.3001</li> </ul> <p><em>Versteeg</em> dataset:</p> <ul> <li>Molenaar J.J. et al., Sequencing of neuroblastoma identifies chromothripsis and defects in neuritogenesis genes. Nature 483(7391), 589–593. doi:10.1038/nature10910</li> </ul> <p><em>Maris</em> dataset:</p> <ul> <li>Wang Q. et al., Integrative genomics identifies distinct molecular classes of neuroblastoma and shows that multiple genes are targeted by regional alterations in DNA copy number. Cancer Res. 66(12), 6050–6062. doi:10.1158/0008-5472.CAN-05-4618</li> </ul>
Integration of expression datasets to identify biomarkers for accurate Gleason scoring in Prostate Cancer -- Supplementary data
<p>This dataset contains expression data from multiple sources used to identify biomarker candidates for prostate cancer aggressiveness. The data includes transcriptional expression levels, patient metadata, and other relevant features utilized in our machine-learning models. The training dataset was extracted from the repository described at Matos-Filipe, et al. (2022) [1].</p> <p>Raw ML metrics from models gaussian Naïve Bayes classifiers using this dataset are available in </p> <p> </p> <p>[1] <span><span><span>Matos-Filipe, P, et al. "</span></span></span>The usage of transcriptomics datasets as sources of Real-World Data for clinical trialling". <span>bioRxiv (</span><span>2022). </span><span><span>doi:</span> https://doi.org/10.1101/2022.11.10.515995</span></p>
Arena assay and gene expression of stingless bees
<p>The dataset includes behavioral data collected from arena experiments with workers of stingless bee species <em>Scaptotrigona postica</em>, <em>Frieseomelitta varia</em>, and <em>Melipona quadrifasciata</em>. Additionally, it contains gene expression data (CT values) for these species across different life stages and tissues.</p>
Association of GDF-15 expression with immune parameter in a pan-cancer analysis
<p>Immunotherapy with checkpoint blockers has significantly revolutionized the treatment landscape for many cancer patients. However, despite their success, checkpoint inhibitors have limitations that affect their effectiveness across a broader patient population. Soluble and cell-bound factors in the tumor microenvironment negatively impact cancer immunity. GDF15, a member of the TGF-β superfamily is associated with various physiological and pathological conditions, including cancer. Its overexpression in certain cancers has been linked to immune evasion. In this study we investigated the relationship between high GDF15 expression with various immune parameter in an in-silico analysis of 11,000 tumors from the TCGA database. Patients with non-small cell lung cancer and urothelial cancer was identified frequently GDF-15 immunosuppressed. Processed RNA sequencing data (TPM) obtained from firebrowse.org and the corresponding immune parameters were included in this dataset. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.