Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
48,977
datasets available to search
ShareScore release 0.7.1
Dataset results
48,977 results for “Genes”
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>
Ontology based text mining of gene-phenotype associations: application to candidate gene prediction
<p>Gene-phenotype associations play an important role in understanding<br> the disease mechanisms which is a requirement for treatment<br> development. A portion of gene-phenotype associations are observed<br> mainly experimentally and made publicly available through several<br> standard resources such as MGI. However, there is still a vast<br> amount of gene--phenotype associations buried in the biomedical<br> literature. Given the large amount of literature data, we need<br> automated text mining tools to alleviate the burden in manual<br> curation of gene-phenotype associations and to develop<br> comprehensive resources. We developed an ontology based<br> approach in combination with statistical methods to text mine<br> gene-phenotype associations from literature. Our method achieved<br> AUC values of 0.90 and 0.75 in recovering known gene-phenotype<br> associations from HPO and MGI respectively. We posit that candidate<br> genes and their relevant diseases should be expressed with similar<br> phenotypes in publications. Thus, we demonstrate the utility of our<br> approach by predicting disease candidate genes based on the semantic<br> similarities of phenotypes associated with genes and diseases. We evaluated our disease candidate prediction model on<br> the gene-disease associations from MGI. Our model achieved AUC<br> values of 0.90 and 0.87 on OMIM (human) and MGI (mouse) datasets of<br> gene-disease associations respectively. Our manual analysis on the<br> text mined data revealed that, our method can accurately extract<br> gene-phenotype associations which are not currently covered by the<br> existing public gene-phenotype resources. Overall, results indicate<br> that our method can precisely extract known as well as new<br> gene-phenotype associations from literature. This released dataset at Zenodo covers our gene-phenotype extracts from the literature. All the methods used to extract the data are available at https://github.com/bio-ontology-research-group/genepheno.</p>
Supplementary data and scripts for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes"
<p>Supplementary data for Willemsen and Bravo 2019 "Origin and evolution of papillomavirus (onco)genes and genomes". The data set consists of two folders: “Bali-Phy” and “RandomPermutationTests”. The “Bali-Phy” folder contains the final results and convergence diagnostics of the Common Ancestry tests obtained by using the Bali-Phy software. The “RandomPermutationTests” folder contains all the data and scripts to repeat the random permutation tests described in the manuscript. Please see the corresponding README files for more information.</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 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>
Supplementary phylogenetic data for Manzano-Marín et. al. 2020 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems"
<p>Supplementary data for Manzano-Marín et. al. 2019 "Serial horizontal transfer of vitamin-biosynthetic genes enables the establishment of new nutritional symbionts in aphids' di-symbiotic systems".</p> <p>The data set consists of four folders:</p> <p>1) "Buchnera_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Buchnera</em> and resulting tree in NEWICK format.</p> <p>2) "Erwinia_phylo”: PHYLIP-formatted file used for phylogenetic reconstruction of <em>Erwinia</em> and resulting tree in NEWICK format.</p> <p>3) "Hamiltonella_phylo”: FASTA-formatted nucleotide alignment files of each gene and NEXUS-formatted files used for Bayesian phylogenetic reconstruction of <em>Hamiltonella</em> symbionts.</p> <p>4) "HGT_genes": FASTA-formatted nucleotide alignment files of each horizontally transferred gene and non-horizontally transferred genes nupC, and <em>gpmA</em>. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p> <p>5) "Tn3_pylo": FASTA-formatted amino acid alignment files of mobile elements related to the Tn3 family resolvase/invertase found in <em>Hamiltonella</em>-associated <em>Erwinia haradaeae</em> symbionts. Also, NEXUS-formatted files used for Bayesian phylogenetic reconstruction and of resulting trees.</p>
Heart Failure eQTLs companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure"
<p>These are the results of a QTL analysis companion to "Pathologic gene network rewiring implicates PPP1R3A as a central cardioprotective factor in pressure overload heart failure". We performed RNA expression measurements and obtained genotype information in genome-wide markers for 313 patients (177 failing hearts , 136 donor, non-failing [control] hearts) using Affymetrix expression and Affymetrix Human 6.0 respectively.<strong> </strong>Prior to eQTL discovery, we used PEER to find hidden covariates that could confound signals in our data as well as filtering any genotypes with major allele frequencies less than 5%. To test associations between gene expression in each cohort separately, we used QTLTools with an additive model accounting for gender, age, sample site, and the PEER factors as covariates. We corrected for eQTL multiple association testing using a 10000 permutations per locus in a 2 megabase window and a false discovery rate cutoff of 5%. To select the number of PEER factors, we performed the full analysis multiple times from 1 to 15 PEER factors and observed a saturation of new QTLs being discovered when using 10 factors.</p> <p>Four files are provided, two for each cohort (cases and controls):</p> <p>- peer_[cases|controls]_nominal.txt: Nominal associations with a p-value threshold of 0.001</p> <p>- peer_[cases|controls]_permutations_all.significant.txt: All significant associations detected after the QTLtools permutation test.</p> <p>The column names are those from QTLtools, in order:</p> <p><br> 1. The phenotype ID<br> 2. The chromosome ID of the phenotype<br> 3. The start position of the phenotype<br> 4. The end position of the phenotype<br> 5. The strand orientation of the phenotype<br> 6. The total number of variants tested in cis<br> 7. The distance between the phenotype and the tested variant (accounting for strand orientation)<br> 8. The ID of the tested variant ( in Affy 6.0 SNP ids)<br> 9. The chromosome ID of the variant<br> 10. The start position of the variant<br> 11. The end position of the variant<br> 12. The nominal P-value of association between the variant and the phenotype<br> 13. The corresponding regression slope<br> 14. A binary flag equal to 1 is the variant is the top variant in cis</p>
Gene-level counts according to their poly(A) length and additional uridine modifications in several stages of zebrafish, Xenopus, and mouse embryos
<p>This HDF5 file contains the processed data of primary poly(A) tail length analyses for the TAIL-seq runs used for Chang and Yeo et al. (2018; doi:10.1016/j.molcel.2018.03.004). The read count tables are stored under the two-level group structure of the run identifier as the first level and the sample identifier as the second level. A dataset at a leaf node is an unsigned integer array of the read count numbers by the length of poly(A) in rows and the length of U tails following after poly(A) in columns.</p> <p>Please refer to the <a href="https://data.mendeley.com/datasets/tzc5wwczyg/1">supplementary data page</a> of the original paper for more information about the experimental design.</p> <p> </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>
Dataset for genes and gene variants from familial gastroschisis
<p>The dataset consists of records from whole exome sequecing and bioinformatic analysis which includes genes and gene variants from a Mexican family with recurrence for gastroschisis (two affected half-sisters with gastroschisis, mother, and father of the proband).</p> <p>Release of this dataset was based on the Human Genome annotation, GRCh37/hg19.</p> <p>The full list of tables is described in the file READ ME and remain available in csv files.</p> <p> </p>
Bone metabolism gene variation and response to bisphosphonate treatment in women with postmenopausal osteoporosis
<p>This repository contains the raw and source data for the manuscript "Bone metabolism gene polymorphism and response to bisphosphonate treatment in women with postmenopausal osteoporosis" submitted to PLOS ONE.</p> <p><strong>Abstract: </strong></p> <p><em>Introduction:</em> Long-term treatment is used in patients with osteoporosis, and bisphosphonates (BPs) are the most commonly prescribed medications. However, in some patients this therapy is not effective, cause different side effects and complications. Unfortunately, at least one year is needed to identify and confirm an ineffectiveness of BPs therapy on bone mineral density (BMD). Among other factors, a response to BPs therapy may also be explained by genetic factors. The aim of this study was to analyze the influence of <em>SOST, PTH, FGF2, FDPS, GGPS1, </em>and<em> LRP5</em> gene polymorphisms on the response to treatment with BPs.</p> <p><em>Materials and methods: </em>Women with postmenopausal osteoporosis were included to this study if they used bisphosphonates for at least 12 months. Exclusion criteria were: persistence on BPs therapy less than 80%, bone metabolic diseases, diseases deemed to affect bone metabolism, malignant tumours, using of any medications influencing BMD. The study protocol was approved by the local ethics committee. The BMD at the lumbar spine and femoral neck were measured using dual x-ray absorptiometry (GE Lunar) before and at least 12 months after treatment with BPs. According to BMD change, patients were divided in two groups – responders and non-responders to BPs terapy. Polymorphic variants in <em>SOST, PTH, FGF2, FDPS, GGPS1, </em>and<em> LRP5</em> genes were determined using PCR analysis with TaqMan probes (Thermo Scientific).</p> <p><em>Results:</em> In total, 201 women with BPs therapy were included in the study. No statistically significant differences were observed in age, age at menopause, weight, height, BMI and baseline BMD levels between responders (122 subjects) and non-responders (79 subjects).</p> <p>As single markers, the <em>SOST </em>rs1234612 T/T (OR=2.3; P=0.02), <em>PTH</em> rs7125774 T/T (OR=2.8, P=0.0009), <em>FDPS</em> rs2297480 G/G (OR=29.3, P=2.2×10<sup>-7</sup>), and <em>GGPS1</em> rs10925503 C/C+C/T (OR=2.9; P=0.003) gene variants were over-represented in non-responders group. No significant association between <em>FGF2</em> rs6854081 and <em>LRP5</em> rs3736228 gene variants and response to BPs treatment was observed. The carriers of T-T-G-C allelic combination (constructed from rs1234612, rs7125774, rs2297480, and rs10925503) were predisposed to negative response to BPs treatment (OR = 4.9, 95% CI 1.7–14.6, P=0.005). The C-C-T-C combination was significantly over-represented in responders (OR = 0.1, 95% CI 0.1–0.5, P=0.006).</p> <p><em>Conclusions:</em> Our findings highlight the importance of identified single gene variants and their allelic combinations for pharmacogenetics of BPs therapy of osteoporosis. Complex screening of these genetic markers could be used as a new strategy for personalized antiresorptive therapy.</p>
The terrestrial carnivorous plant Utricularia reniformis sheds light on environmental and life-form genome plasticity: Annotation, Gene Ontology and raw data
<p><strong>Description:</strong> In this work, we deeply sequenced (genome and transcriptome of different organs), assembled, and analyzed the 311-Mbp genome of the terrestrial carnivorous plant <em>U. reniformis</em> (Lentibulariaceae). This project presents great importance to the understanding of genomic, evolutive and functional aspects of<em> U. reniformis</em>, which may, with the next-generation sequencing and computational biology approaches shed light to a better understanding not only for the biology and evolution of <em>Utricularia</em> genus, but also for other genera and lineages of the Lentibulariaceae family. Here we present all the raw data generated, including annotation and gene ontology files.</p> <p><strong>External Information</strong></p> <p><a href="https://genomevolution.org/coge/GenomeInfo.pl?gid=54799">Genome Browser</a> avaliable at CoGe Portal (https://genomevolution.org/coge/GenomeInfo.pl?gid=54799)</p> <p><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">GenBank </a><a href="http://https://www.ncbi.nlm.nih.gov/bioproject/290588">Bioproject</a> (https://www.ncbi.nlm.nih.gov/bioproject/290588) for raw genomic and transcriptomic reads</p> <p><a href="https://bv.fapesp.br/en/auxilios/84264/genomics-and-transcriptomics-of-utricularia-reniformis-lentibulariaceae-an-evolutive-and-function/">FAPESP grant website</a> contaning the project abstract and other information.</p> <p><strong>Papers published related to <em>Utricularia reniformis</em> genome</strong></p> <pre><strong>[1]</strong> Silva SR, Diaz YC, Penha HA, Pinheiro DG, Fernandes CC, Miranda VF, MichaelTP, Varani AM. <strong>The Chloroplast Genome of Utricularia reniformis Sheds Light on the Evolution of the ndh Gene Complex of Terrestrial Carnivorous Plants from the Lentibulariaceae Family</strong>. PLoS One. 2016 Oct 20;11(10):e0165176. doi:<strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/27764252">10.1371/journal.pone.0165176</a></strong>. </pre> <pre><strong>[2] </strong>Silva SR, Alvarenga DO, Aranguren Y, Penha HA, Fernandes CC, Pinheiro DG, Oliveira MT, Michael TP, Miranda VFO, Varani AM. <strong>The mitochondrial genome of the terrestrial carnivorous plant Utricularia reniformis (Lentibulariaceae): Structure, comparative analysis and evolutionary landmarks.</strong> PLoS One. 2017 Jul19;12(7):e0180484. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/28723946">10.1371/journal.pone.0180484</a></strong>.</pre> <pre><strong>[3] </strong>Silva SR, Moraes AP, Penha HA, Julião MHM, Domingues DS, Michael TP, Miranda VFO, Varani AM. <strong>The Terrestrial Carnivorous Plant Utricularia reniformis Sheds Light on Environmental and Life-Form Genome Plasticity.</strong> Int J Mol Sci. 2019 Dec 18;21(1). pii: E3. doi: <strong><a href="https://www.ncbi.nlm.nih.gov/pubmed/31861318">10.3390/ijms21010003</a></strong>.</pre> <p><strong>Acknowledgements</strong></p> <p>This work was supported by Sao Paulo Research Foundation FAPESP, Grant ID: [1325164-6]</p> <p> </p> <p><strong>---------------------------------------------------------</strong><br> <strong>FILES DESCRIPTION</strong><br> <strong>---------------------------------------------------------</strong><br> <br> ----------------<br> <strong>ANNOT-vFinal.sql: </strong>MySQL database containing all integrated annotation information of Urenif and Ugibba<br> ----------------<br> <strong>TABLE fields description</strong><br> gene_name gene name generated by EVidence Modeler + PASA<br> length gene lenght<br> status duplicate_gene_classifier status (0:singleton, 1:dispersed, 2:proximal, 3: tandem, 4:WGD)<br> product gene product <br> GOterms Blast2GO/OmicsBox GOterms<br> GO_mapping Blast2GO/OmicsBox GOterms derived from direct mapping (UniProt)<br> GO_annotation Blast2GO/OmicsBox annotated GOterms<br> GO_interpro Blast2GO/OmicsBox derived from InterProScan<br> EC Blast2GO/OmicsBox EC number<br> EC_name Blast2GO/OmicsBox enzyme name<br> NOG_annot EggNOG annotation description<br> NOG_EC EggNOG EC number<br> NOG_GO EggNOG GOterms<br> NOG_class EggNOG COG/KOG classfication<br> KEGG_Pathway EggNOG KEGG pathyways<br> KEGG_ko EggNOG KEGG ko<br> CAZy EggNOG CAZy enzymes<br> TAIR_gene Closest A. thaliana gene name (homologous) TAIR database lasted version<br> TAIR_annot Closest A. thaliana gene product (homologous) TAIR database lasted version <br> ortho MCL clustering among Vvinifera, Athaliana, and Slycopersicum (S:singleton, C: clustered, Y: shared)<br> ortho_two MCL clustering among Urenif and Ugibba (S:singleton, C: clustered, Y: shared)<br> -<br> -<br> ----------------<br> <strong>CEGs.zip </strong> 336 shared and concatenated CEGs from Urenif, U. gibba, Genlisea nigrocaulis, G. hispidula, G. aurea, G. pygmaea, and G. repens.<br> ----------------</p> <p><strong>ProcessRepeats_mod</strong> Modified version of RepeatMasker, ProcessRepeats script for detection of plant evolutionary lineages<br> ----------------</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia gibba</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Ugibba</strong><strong>-no-masked.fa </strong> Ugibba genome excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ugibba-softmasked.fa</strong> Ugibba genome RepeatMasker softmasked and excluding organellar genomes (provided by Lan et al., 2017)<br> <strong>Ug.collinearity </strong> MCScanX collinearity file<br> <strong>Ug-duplicates.txt</strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ug.gene_type </strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ug.tandem </strong> Ugibba tandem genes generated by MCScanX tool<br> <strong>Ugibba_annot.annot </strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim) <strong>Ugibba_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong><strong> </strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Ugibba</strong><strong>.cDNA</strong> Ugibba cDNAs fasta file<br> <strong>Ugibba</strong><strong>.CDS </strong> Ugibba CDSs fasta file<br> <strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Ugibba GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Ugibba</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Ugibba GFF3 file fully annotated (genes only)<br> <strong>Ugibba_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Ugibba_fasta.fasta</strong> Blast2GO/OmicsBox Ugibba fasta proteins containg annotation (product and GO terms)<br> <strong>ugibba_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>ugibba_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>ugibba_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)</p> <p><strong>Ugibba_GAF.txt</strong> GAF file<br> <strong>Ugibba</strong><strong>.gene</strong> Ugibba gene fasta file<br> <strong>Ugibba_GOstat.txt </strong> GOstat file<br> <strong>Ugibba</strong><strong>-PASA-assemblies.fasta </strong> Ugibba PASA assemblies<br> <strong>Ugibba</strong><strong>-PASA.stats </strong> Ugibba annotation STATS<br> <strong>Ugibba</strong><strong>.</strong><strong>prot</strong><strong> </strong> Ugibba protein fasta file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff </strong> Ugibba RepeatMasker gff file<br> <strong>Ugibba</strong><strong>-RepeatMasker.gff3 </strong> Ugibba RepeatMasker gff3 file<br> <strong>Ugibba</strong><strong>-RepeatMasker.tbl </strong> Ugibba RepeatMasker results<br> <strong>Ugibba</strong><strong>-RepeatMasker-v2.gff3</strong> Ugibba RepeatMasker gff3 second version file<br> <strong>Ugibba</strong><strong>-RNAseq-assembled.fasta </strong> Ugibba RNAseq assembled transcriptome (Trinity)<br> <strong>Ugibba_TEs_DANTE_2019.fa </strong> Ugibba TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Ugibba_WEGO.txt </strong> WEGO file</p> <p><strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> <em>Utricularia reniformis</em> files<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong><br> <strong>Urenif</strong><strong>-no-masked.fa </strong> Urenif genome excluding organellar genomes<br> <strong>Urenif</strong><strong>-</strong><strong>softmasked</strong><strong>.fa</strong> Urenif genome RepeatMasker softmasked and excluding organellar genomes<br> <strong>Ur.collinearity </strong> MCScanX collinearity file<br> <strong>Ur-duplicates.txt </strong> MCScanX duplicate_gene_classifier short report<br> <strong>Ur.gene_type</strong> MCScanX duplicate_gene_classifier full report<br> <strong>Ur.tandem</strong> Urenif tandem genes generated by MCScanX tool<br> <strong>Urenif_annot.annot</strong> Blast2GO/OmicsBox annotation file (eudicotyledons filtered and Viridiplantae GOSlim)<br> <strong>Urenif_annot-</strong><strong>noclean</strong><strong>.</strong><strong>annot</strong> Blast2GO/OmicsBox annotation file (not filtered)<br> <strong>Urenif</strong><strong>.cDNA</strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>.CDS </strong> Urenif cDNAs fasta file<br> <strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA-ANNOTATED.gff3</strong> Urenif GFF3 file fully annotated (including gene products and GO terms)</p> <p><strong>Urenif</strong><strong>-EVM.all-no-TEs-PASA.gff3</strong> Urenif GFF3 file fully annotated (genes only)<br> <strong>Urenif_export.txt</strong> Blast2GO/OmicsBox full exported table<br> <strong>Urenif_fasta.fasta</strong> Blast2GO/OmicsBox Urenif fasta proteins containg annotation (product and GO terms)<br> <strong>urenif_frozen_cleaned-validated.box</strong> Full Blast2GO/OmicsBox file</p> <p><strong>urenif_frozen.box</strong> Full Blast2GO/OmicsBox file (containing TEs genes annotation)</p> <p><strong>urenif_nogs_emapper_annotations.box</strong> Full Blast2GO/OmicsBox EggNOG file (containing TEs genes annotation)<br> <strong>Urenif_GAF.txt </strong> GAF file<br> <strong>Urenif</strong><strong>.gene</strong> Urenif gene fasta file<br> <strong>Urenif_GOStat.txt </strong> GOstat file<br> <strong>Urenif</strong><strong>-PASA-assemblies.fasta</strong> Urenif PASA assemblies<br> <strong>Urenif</strong><strong>-PASA.stats </strong> Urenif annotation STATS<br> <strong>Urenif</strong><strong>.</strong><strong>prot</strong><strong> </strong> Urenif protein fasta file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff </strong> Urenif RepeatMasker gff file<br> <strong>Urenif</strong><strong>-RepeatMasker.gff3 </strong> Urenif RepeatMasker gff3 file<br> <strong>Urenif</strong><strong>-RepeatMasker.tbl </strong> Urenif RepeatMasker results<br> <strong>Urenif</strong><strong>-RepeatMasker-v2.gff3 </strong> Urenif RepeatMasker gff3 second version file<br> <strong>Urenif</strong><strong>-RNAseq-assembled.fasta </strong> Urenif RNAseq assembled transcriptome (Trinity)<br> <strong>Urenif_TEs_DANTE_2019.fa </strong> Urenif TEs library, detected by REPET and annotated by PASTEC and DANTE<br> <strong>Urenif_WEGO.txt </strong> WEGO file<br> <strong>----------------------------------------------------------------------------------------------------------------------------------------------<br> ----------------------------------------------------------------------------------------------------------------------------------------------</strong></p>
Common biochemical and topological properties of metabolic genes recurrently dysregulated in tumors
<p>Although tumors exhibit numerous metabolic alterations, it’s unclear if common objectives and constraints underlie diverse metabolic changes. Here we interpret cancer gene expression, copy number variation, and survival data using a computational model, MetOncoFit. MetOncoFit evaluates142 metabolic features that can impact tumor fitness, including enzyme catalytic activity, pathway association, network topological attributes, and reaction flux. Meta-analysis of tumor databases using MetOncoFit revealed that metabolic enzymes with high catalytic activity were frequently up-regulated in many tumors and associated with poor survival. MetOncoFit also identified metabolites that were hot-spots of dysregulation. MetOncoFit illuminates how enzyme activity and metabolic network architecture influences tumorigenesis.</p>
UnientrezDB: Large-scale Gene Ontology Annotation Dataset and Evaluation Benchmarks with Unified Entrez Gene Identifiers
<p>Our work focuses on providing a comprehensive dataset and benchmarks for evaluating gene ontology annotations using a unified system of Entrez Gene Identifiers.</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>
Supplementary data: Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?
<p>Agronomic and quality data of organic wheat (<em>Triticum aestivum</em>), mean comparisons and supplementary figures related to the publication "Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?" by Grausgruber et al. (2024) published in the Journal of Cereal Science.</p>
The North Pacific Eukaryotic Gene Catalog: clustered nucleotide metatranscripts and read counts
<p>This data continues with the development of the NPEGC Trinity <em>de novo</em> 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:<br><br><em>NPac.G1PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G2PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA.bf100.id99.nt.fasta.gz</em><br><em>NPac.G3PA_diel.bf100.id99.nt.fasta.gz</em><br><em>NPac.D1PA.bf100.id99.nt.fasta.gz</em><br><br>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:<br><br>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. <em>Scientific Data</em>, <em>11</em>(1), 1161.<br><br>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></p> <p>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></p> <p><br>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><br><br>There are two main steps:<br>1. Generate the kallisto index on the sets of clustered nucleotide metatranscripts<br>2. Map the short reads from environmental samples back to the assembly index</p> <p>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.</p> <p>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><br>The output count files for each project are Gzip-compressed and uploaded to the NPEGC nucleotide data repository here: </p> <p><em>G1PA.raw.est_counts.csv.gz</em><br><em>G2PA.raw.est_counts.csv.gz</em><br><em>G3PA.raw.est_counts.csv.gz</em><br><em>G3PA_diel.raw.est_counts.csv.gz</em><br><em>D1PA.raw.est_counts.csv.gz</em></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.