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25,372 results for “Transcriptomics”

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dryad28/100

Data from: Sequencing of the needle transcriptome from Norway spruce (Picea abies Karst L.) reveals lower substitution rates, but similar selective constraints in gymnosperms and angiosperms

BACKGROUND: A detailed knowledge about spatial and temporal gene expression is important for understanding both the function of genes and their evolution. For the vast majority of species, transcriptomes are still largely uncharacterized and even in those where substantial information is available it is often in the form of partially sequenced transcriptomes. With the development of next generation sequencing, a single experiment can now simultaneously identify the transcribed part of a species genome and estimate levels of gene expression. RESULTS: mRNA from actively growing needles of Norway spruce (Picea abies) was sequenced using next generation sequencing technology. In total, close to 70 million fragments with a length of 76 bp were sequenced resulting in 5 Gbp of raw data. A de novo assembly of these reads, together with publicly available expressed sequence tag (EST) data from Norway spruce, was used to create a reference transcriptome. Of the 38,419 PUTs (putative unique transcripts) longer than 150 bp in this reference assembly, 83.5% show similarity to ESTs from other spruce species and of the remaining PUTs, 3,704 show similarity to protein sequences from other plant species, leaving 4,167 PUTs with limited similarity to currently available plant proteins. By predicting coding frames and comparing not only the Norway spruce PUTs, but also PUTs from the close relatives Picea glauca and Picea sitchensis to both Pinus taeda and Taxus mairei, we obtained estimates of synonymous and non-synonymous divergence among conifer species. In addition, we detected close to 15,000 SNPs of high quality and estimated gene expression differences between samples collected under dark and light conditions. CONCLUSIONS: Our study yielded a large number of single nucleotide polymorphisms as well as estimates of gene expression on transcriptome scale. In agreement with a recent study we find that the synonymous substitution rate per year (0.6 x 10-09 and 1.1 x 10-09) is an order of magnitude smaller than values reported for angiosperm herbs. However, if one takes generation time into account, most of this difference disappears. The estimates of the dN/dS ratio (non-synonymous over synonymous divergence) reported here are in general much lower than 1 and only a few genes showed a ratio larger than 1.

opencc-zeroDec 2011View details →
dryad28/100

Data from: Efficient detection of novel nuclear markers for Brassicaceae by transcriptome sequencing

The lack of DNA sequence information for most non-model organisms impairs the design of primers that are universally applicable for the study of molecular polymorphisms in nuclear markers. Next-generation sequencing (NGS) techniques nowadays provide a powerful approach to overcome this limitation. We present a flexible and inexpensive method to identify large numbers of nuclear primer pairs that amplify in most Brassicaceae species. We first obtained and mapped NGS transcriptome sequencing reads from two of the distantly related Brassicaceae species, Cardamine hirsuta and Arabis alpina, onto the Arabidopsis thaliana reference genome, and then identified short conserved sequence motifs among the three species bioinformatically. From these, primer pairs to amplify coding regions (nuclear protein coding loci, NPCL) and exon-primed intron-crossing sequences (EPIC) were developed. We identified 2,334 universally applicable primer pairs, targeting 1,164 genes, which provide a large pool of markers as readily usable genomic resource that will help addressing novel questions in the Brassicaceae family. Testing a subset of the newly designed nuclear primer pairs revealed that a great majority yielded a single amplicon in all of the 30 investigated Brassicaceae taxa. Sequence analysis and phylogenetic reconstruction with a subset of these markers on different levels of phylogenetic divergence in the mustard family were compared with previous studies. The results corroborate the usefulness of the newly developed primer pairs, e.g., for phylogenetic analyses or population genetic studies. Thus, our method provides a cost-effective approach for designing nuclear loci across a broad range of taxa and is compatible with current NGS technologies.

opencc-zeroDec 2014View details →
dryad28/100

Data from: Effects of 4-Hydroxy-2,3,3',4',5-pentachlorobiphenyl (4-OH-CB107) on liver transcriptome in rats: implication in the disruption of circadian rhythm and fatty acid metabolism

Polychlorinated biphenyls (PCBs) and their hydroxylated metabolites (OH-PCBs) have been detected in tissues of both wild animals and humans. Several previous studies have suggested adverse effects of OH-PCBs on the endocrine and nervous systems in mammals. However, there have been no studies on transcriptome analysis of the effects of OH-PCBs, and thus, the whole picture and mechanisms underlying the adverse effects induced by OH-PCBs are still poorly understood. We therefore investigated the mRNA expression profile in the liver of adult male Wistar rats treated with 4-hydroxy-2,3,3',4',5-pentachlorobiphenyl (4-OH-CB107) to explore the genes responsive to OH-PCBs and to understand the potential effects of the chemical. Next-generation RNA sequencing analysis revealed changes in the expression of genes involved in the circadian rhythm and fatty acid metabolism, such as nuclear receptor subfamily 1, group D, member 1 (Nr1d1), aryl hydrocarbon receptor nuclear translocator-like protein 1 (Arntl), cryptochrome circadian clock 1 (Cry1), and enoyl-CoA hydratase and 3-hydroxyacyl-CoA dehydrogenase (Ehhadh), in 4-OH-CB107-treated rats. In addition, biochemical analysis of the plasma revealed a dose-dependent increase in the leucine aminopeptidase (LAP), indicating the onset of liver damage. These results suggest that OH-PCB exposure may induce liver injury as well as disrupt the circadian rhythm and peroxisome proliferator-activated receptor (PPAR)-related fatty acid metabolism.

opencc-zeroDec 2017View details →
dryad28/100

Data from: Transcriptomic inspection revealed a possible pathway regulating the formation of the high-quality brush hair in Chinese Haimen goat (Capra hircus)

The high-quality brush hair, or Type III brush hair, is coarse hair but with a tip and little medulla, which uniquely grows in the cervical carina of Chinese Haimen goat (Capra hircus). To unveil the mechanism of the formation of Type III brush hair in Haimen goats, transcriptomic RNAseq technology was used for screening of differentially expressed genes (DEGs) in the skin samples of the Type III and the non-Type III hair goats, and these DEGs were analysed by KEGG pathway analysis. The results showed that a total of 295 DEGs were obtained, mainly from three main functional types: cellular component, molecular function and biological process. These DEGs were mainly enriched in three KEGG pathways, such as protein processing in endoplasmic reticulum, MAPK, and complement and coagulation cascades. These DEGs gave hints to a possible mechanism, under which heat stress possibly initiated the formation. The study provided some useful biological information, which could give a new view about the roles of certain factors in hair growth and give hints on the mechanism of the formation of the Type III brush hair in Chinese Haimen goat.

opencc-zeroDec 2016View details →
dryad28/100

Transcriptomics illuminate the phylogenetic backbone of tiger beetles

<p>Phylogenomics is progressing rapidly, allowing large strides forward into our understanding of the tree of life. In</p> <p>this study, we generated transcriptomes from ethanol-preserved specimens of 13 tiger beetle species (Coleoptera:</p> <p>Cicindelinae) and one Scaritinae outgroup. From these 14 transcriptomes and seven publicly available transcriptomes,</p> <p>we recovered an average of 2538 loci for phylogenetic analysis. We constructed an evolutionary tree of tiger beetles</p> <p>to examine deep-level relationships and examined the extent to which the composition of the dataset, missing data,</p> <p>gene tree inconsistency and codon position saturation impacted phylogenetic accuracy. Ethanol-preserved specimens</p> <p>yielded similar numbers of loci to specimens originally preserved in costly reagents, showcasing more flexibility in</p> <p>transcriptomics than anticipated. The number of loci and gene tree inconsistency had less impact on downstream</p> <p>results than third codon position saturation and missing data. Our results recovered tiger beetles as sister to</p> <p>Carabidae with strong support, confirming their taxonomic status as an independent family within Adephaga. Within</p> <p>tiger beetles, phylogenetic relationships were robust across all nodes. This new phylogenomic backbone represents a</p> <p>useful framework for future endeavours in tiger beetle systematics and serves as a starting point for the development</p> <p>of less costly target capture toolkits to expand the taxonomic breadth of the future tiger beetle tree of life.</p>

opencc-zeroJan 2020View details →
dryad28/100

Transcriptomic signatures of ageing vary in solitary and social forms of an orchid bee

<p>Eusocial insect queens are remarkable in their ability to maximise both fecundity and longevity, thus escaping the typical trade-off between these two traits. Several mechanisms have been proposed to underlie the remoulding of the trade-off, such as reshaping of the juvenile hormone pathway, or caste-specific susceptibility to oxidative stress. However, it remains a challenge to disentangle the molecular mechanisms underlying the remoulding of the trade-off in eusocial insects from caste-specific physiological attributes that have subsequently arisen. The socially polymorphic orchid bee <i>Euglossa viridissima</i> represents an excellent model to address the role of sociality <i>per se</i> in longevity as it allows direct comparisons of solitary and social individuals within a common genetic background. We investigated gene expression and juvenile hormone levels in young and old bees from both solitary and social nests. We found 902 genes to be differentially expressed with age in solitary females, including those involved in oxidative stress, <i>versus </i>only 100 genes in social dominant females, and 13 genes in subordinate females. A weighted gene co-expression network analysis further highlights pathways related to ageing in this species, including the target of rapamycin pathway. Eleven genes involved in translation, apoptosis and DNA repair show concurrent age-related expression changes in solitary but not in social females, representing potential differences based on social status. Juvenile hormone titres did not vary with age or social status. Our results represent an important step in understanding the proximate mechanisms underlying the remodelling of the fecundity/longevity trade-off that accompanies the evolutionary transition from solitary life to eusociality.</p>

opencc-zeroJun 2021View details →
dryad28/100

Data from: "Transcriptome sequence identity between Lyme disease tick vectors, Ixodes scapularis and Ixodes ricinus" in Genomic Resources Notes accepted 1 April 2014 to 31 May 2014

Ixodes scapularis and I. ricinus transmit the Lyme disease agent Borrelia burgdorferi in the U.S. and Europe, respectively. The only tick genome sequence available is that of I. scapularis, which constitutes a limitation for tick research. Recent evidences suggest that I. ricinus and I. scapularis transcriptomes share some degree of sequence identity. However, only the global transcriptome comparison reported here demonstrated that I. ricinus and I. scapularis share a 99.232±0.005 percent sequence identity with a very low frequency of INDELs. However, due to limitations of the current I. scapularis genome assembly, the number of aligned reads was only 26-27%. These results support the use of I. scapularis genome sequence as a reference for the analysis of I. ricinus transcriptomics and proteomics data, but addressing the limitations associated with the I. scapularis genome assembly.

opencc-zeroDec 2013View details →
dryad28/100

Proteomics and Transcriptomics of the Hippocampus and Cortex in SUDEP and High-Risk SUDEP Patients

<p class="AbstractSummary">To identify the molecular signaling pathways underlying sudden unexpected death in epilepsy (SUDEP) and high-risk SUDEP compared to control patients with epilepsy. For proteomics analyses, we evaluated the hippocampus and frontal cortex from microdissected postmortem brain tissue of 12 patients with SUDEP and 14 with non-SUDEP epilepsy. For transcriptomics analyses, we evaluated hippocampus and temporal cortex surgical brain tissue from patients with mesial temporal lobe epilepsy: 6 low-risk and 8 high-risk SUDEP as determined by a short (&lt;50 seconds) or prolonged (=50 seconds) postictal generalized EEG suppression (PGES) that may indicate severely depressed brain activity impairing respiration, arousal, and protective reflexes. In autopsy hippocampus and cortex, we observed no proteomic differences between patients with SUDEP and those with non-SUDEP epilepsy, contrasting with our previously reported robust differences between epilepsy and controls without epilepsy. Transcriptomics in hippocampus and cortex from patients with surgical epilepsy segregated by PGES identified 55 differentially expressed genes (37 protein-coding, 15 long noncoding RNAs, 3 pending) in hippocampus. The SUDEP proteome and high-risk SUDEP transcriptome were similar to those in other patients with epilepsy in hippocampus and cortex, consistent with diverse epilepsy syndromes and comorbid conditions associated with SUDEP. Studies with larger cohorts and different epilepsy syndromes, as well as additional anatomic regions, may identify molecular mechanisms of SUDEP.</p>

opencc-zeroJul 2021View details →
dryad28/100

Data from: Comparative developmental transcriptomics reveals rewiring of a highly conserved gene regulatory network during a major life history switch in the sea urchin genus Heliocidaris

The ecologically significant shift in developmental strategy from planktotrophic (feeding) to lecithotrophic (nonfeeding) development in the sea urchin genus Heliocidaris is one of the most comprehensively studied life history transitions in any animal. Although the evolution of lecithotrophy involved substantial changes to larval development and morphology, it is not known to what extent changes in gene expression underlie the developmental differences between species, nor do we understand how these changes evolved within the context of the well-defined gene regulatory network (GRN) underlying sea urchin development. To address these questions, we used RNA-seq to measure expression dynamics across development in three species: the lecithotroph Heliocidaris erythrogramma, the closely related planktotroph H. tuberculata, and an outgroup planktotroph Lytechinus variegatus. Using well-established statistical methods, we developed a novel framework for identifying, quantifying, and polarizing evolutionary changes in gene expression profiles across the transcriptome and within the GRN. We found that major changes in gene expression profiles were more numerous during the evolution of lecithotrophy than during the persistence of planktotrophy, and that genes with derived expression profiles in the lecithotroph displayed specific characteristics as a group that are consistent with the dramatically altered developmental program in this species. Compared to the transcriptome, changes in gene expression profiles within the GRN were even more pronounced in the lecithotroph. We found evidence for conservation and likely divergence of particular GRN regulatory interactions in the lecithotroph, as well as significant changes in the expression of genes with known roles in larval skeletogenesis. We further use coexpression analysis to identify genes of unknown function that may contribute to both conserved and derived developmental traits between species. Collectively, our results indicate that distinct evolutionary processes operate on gene expression during periods of life history conservation and periods of life history divergence, and that this contrast is even more pronounced within the GRN than across the transcriptome as a whole.

opencc-zeroDec 2015View details →
zenodo28/100

Analysis code and additional data associated with the manuscript entitled 'Interspecies transcriptome analyses identify genes that control the development and evolution of limb skeletal proportion'

<p>This dataset is associated with the research manuscript entitled &lsquo;<em>Interspecies transcriptome analyses identify genes that control the development and evolution of limb skeletal proportion</em>&rsquo; (https://www.biorxiv.org/content/10.1101/754002v2).</p> <p>The folder &lsquo;<strong>Zenodo_Saxena_etal_2021_AdditionalData_AnalysisCode</strong>&rsquo; contains:</p> <p>&gt; Analysis code used for jerboas-mouse differential RNASeq analysis (<strong>saxena_Interspecies_DESEQ2_analysis.R</strong>).</p> <p>&gt; A folder <strong>Mus_Jac_gtfDir</strong> with two gtf annotation files for 1:1 orthologs in Mouse (musAnno4_1To1Orthologs) and Jerboa (jerboaAnno4_1To1Orthologs) genomes generated with CESAR. This folder also contains two files with mouse or jerboa gene lengths in non-overlapping exons of each gene in the 1:1 orthologous GTF annotations (suffixed &quot;*_exon_lengths_per_gene.txt&quot;). A supporting R library required to run the analysis is provided in this folder (saxena_shared_library_v2.R).</p> <p>&gt;A <strong>STAR_countsDir</strong> folder with STAR generated GeneCounts for mouse and jerboa samples (suffixed &quot;*_SR50&quot;). Subfolders contain metatarsal (MT) and Radius/ulna (RU) STAR Genecounts used in the primary (&ldquo;*_n=3&quot;) and independent validation (&quot;*_n=2&quot;) analyses.</p> <p>&gt; A pdf file with <strong>Additional Figures 1 and 2</strong>. Fig 1 shows RNAScope <em>in situs </em>for <em>Galnt17</em> in jerboa and mouse postnatal day 5 cartilages. Fig 2 shows DESeq2 generated MA-plot for jerboa-mouse metatarsal comparisons (n=3).</p> <p>&gt; <strong>Additional Data Tables_1to3</strong> with DESeq2 differential expression results for all of the 17,464 mouse and jerboa orthologs in the primary (n=3, Table1) and independent validation (n=2, Table2) analyses. r-log&nbsp; transformed gene counts for 17,464 mouse and jerboa 1:1 orthologs in metatarsal samples (n=3, Table3).</p>

opencc-by-4.0Jul 2021View details →
zenodo28/100

perturbation-transcriptomics

<p>For perturbation transcriptomics project</p>

opencc-by-4.0Sep 2021View details →
zenodo28/100

WD40 RNAi, OE and WT fruit transcriptome at MG, Br and pink fruit stages-part2

<p>WD40 RNAi, OE and WT fruit transcriptome at MG, Br and pink fruit stages-part2</p>

opencc-by-4.0Sep 2021View details →
zenodo28/100

De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data

<p>This dataset contains gene expression values, i. e. transcript counts, of 278 intestinal epithelial cells.</p>

opencc-by-4.0Aug 2016View details →
zenodo28/100

Transcriptome profiling of derived-hepatocyte progenitors from human iPSCs with nanoCAGE - part1 - genomic alignments (hg19 + hg38)

<p>This repository contains genomic alignments (BED files) of paired-end nanoCAGE sequencing data (CAGEscan data) collected from Illumina MiSeq run IDs &quot;170630_M00528_0292_000000000-B9JY8&quot; (aka &quot;NC_LIMMS&quot;) and &quot;180221_M00528_0334_000000000-B6PJM&quot; (aka &quot;NC_LIMMS2&quot;). FASTQ files were processed with the MOIRAI pipeline OP-WORKFLOW-CAGEscan-short-reads-v2.1 (Hasegawa et al. BMC Bioinformatics&nbsp;2014 May 16;15:144. doi: 10.1186/1471-2105-15-144.). Filtered pairs of reads were aligned on the human genome assemblies hg19 and hg38. See tables below for a detailed description of the samples contained in each nanoCAGE library, including barcodes and index sequences used for the demultiplexing of sequencing reads. Corresponding raw sequencing data files (FASTQ files) were deposited at Zenodo under&nbsp;the following Digital Object Identifier: 10.5281/zenodo.1014009.</p> <p>&nbsp;</p> <p><em><strong>&quot;170630_M00528_0292_000000000-B9JY8&quot; (&quot;NC_LIMMS&quot;) :</strong></em></p> <p><strong>ID&nbsp;&nbsp; Sample_name&nbsp;&nbsp; Barcode_number&nbsp;&nbsp; Barcode_sequence &nbsp; Index_sequence</strong></p> <p>1&nbsp;&nbsp; iPSC_control_rep1&nbsp;&nbsp; 4&nbsp;&nbsp; ACAGAT&nbsp;&nbsp; NNNNNNNN</p> <p>2&nbsp;&nbsp; iPSC_control_rep2&nbsp;&nbsp; 24&nbsp;&nbsp; ATCGTG&nbsp;&nbsp; NNNNNNNN</p> <p>3&nbsp;&nbsp; iPSC_control_rep3&nbsp;&nbsp; 31&nbsp;&nbsp; CACGAT&nbsp;&nbsp; NNNNNNNN</p> <p>4&nbsp;&nbsp; S3P1_OK_rep1&nbsp;&nbsp; 36&nbsp;&nbsp; CACTGA&nbsp;&nbsp; NNNNNNNN</p> <p>5&nbsp;&nbsp; S3P1_OK_rep2&nbsp;&nbsp; 46&nbsp;&nbsp; CTGACG&nbsp;&nbsp; NNNNNNNN</p> <p>6&nbsp;&nbsp; S3P1_OK_rep3&nbsp;&nbsp; 63&nbsp;&nbsp; GAGTGA&nbsp;&nbsp; NNNNNNNN</p> <p>7&nbsp;&nbsp; S4P1_OK_rep1&nbsp;&nbsp; 79&nbsp;&nbsp; GTATAC&nbsp;&nbsp; NNNNNNNN</p> <p>8&nbsp;&nbsp; S4P1_OK_rep2&nbsp;&nbsp; 92&nbsp;&nbsp; TCGAGC&nbsp;&nbsp; NNNNNNNN</p> <p>9&nbsp;&nbsp; S4P1_OK_rep3&nbsp;&nbsp; 9&nbsp;&nbsp; ACATGA&nbsp;&nbsp; NNNNNNNN</p> <p>10&nbsp;&nbsp; S4P2_OK_rep1&nbsp;&nbsp; 21&nbsp;&nbsp; ATCATA&nbsp;&nbsp; NNNNNNNN</p> <p>11&nbsp;&nbsp; S4P2_OK_rep2&nbsp;&nbsp; 33&nbsp;&nbsp; CACGTG&nbsp;&nbsp; NNNNNNNN</p> <p>12&nbsp;&nbsp; S4P2_OK_rep3&nbsp;&nbsp; 45&nbsp;&nbsp; CGATGA&nbsp;&nbsp; NNNNNNNN</p> <p>13&nbsp;&nbsp; S1P1_rep1&nbsp;&nbsp; 57&nbsp;&nbsp; GAGATA&nbsp;&nbsp; NNNNNNNN</p> <p>14&nbsp;&nbsp; S1P1_rep2&nbsp;&nbsp; 69&nbsp;&nbsp; GCTCTC&nbsp;&nbsp; NNNNNNNN</p> <p>15&nbsp;&nbsp; S1P1_rep3&nbsp;&nbsp; 81&nbsp;&nbsp; GTATGA&nbsp;&nbsp; NNNNNNNN</p> <p>16&nbsp;&nbsp; S3P1_FAILED_rep1&nbsp;&nbsp; 93&nbsp;&nbsp; TCGATA&nbsp;&nbsp; NNNNNNNN</p> <p>17&nbsp;&nbsp; S3P1_FAILED_rep2&nbsp;&nbsp; 11&nbsp;&nbsp; AGTAGC&nbsp;&nbsp; NNNNNNNN</p> <p>18&nbsp;&nbsp; S3P1_FAILED_rep3&nbsp;&nbsp; 23&nbsp;&nbsp; ATCGCA&nbsp;&nbsp; NNNNNNNN</p> <p>19&nbsp;&nbsp; S4P1_FAILED_rep1&nbsp;&nbsp; 35&nbsp;&nbsp; CACTCT&nbsp;&nbsp; NNNNNNNN</p> <p>20&nbsp;&nbsp; S4P1_FAILED_rep2&nbsp;&nbsp; 47&nbsp;&nbsp; CTGAGC&nbsp;&nbsp; NNNNNNNN</p> <p>21&nbsp;&nbsp; S4P1_FAILED_rep3&nbsp;&nbsp; 59&nbsp;&nbsp; GAGCGT&nbsp;&nbsp; NNNNNNNN</p> <p>22&nbsp;&nbsp; S4P2_FAILED_rep1&nbsp;&nbsp; 71&nbsp;&nbsp; GCTGCA&nbsp;&nbsp; NNNNNNNN</p> <p>23&nbsp;&nbsp; S4P2_FAILED_rep2&nbsp;&nbsp; 83&nbsp;&nbsp; TATAGC&nbsp;&nbsp; NNNNNNNN</p> <p>24&nbsp;&nbsp; S4P2_FAILED_rep3&nbsp;&nbsp; 95&nbsp;&nbsp; TCGCGT&nbsp;&nbsp; NNNNNNNN</p> <p>&nbsp;</p> <p><em><strong>&quot;180221_M00528_0334_000000000-B6PJM&quot; (&quot;NC_LIMMS2&quot;):</strong></em></p> <p><strong>ID&nbsp;&nbsp; Sample_name&nbsp;&nbsp; Barcode_number&nbsp;&nbsp; Barcode_sequence &nbsp; Index_sequence</strong></p> <p>25&nbsp;&nbsp; PETRI_rep1&nbsp;&nbsp; 04&nbsp;&nbsp; ACAGAT&nbsp;&nbsp; NNNNNNNN</p> <p>26&nbsp;&nbsp; PETRI_rep2&nbsp;&nbsp; 24&nbsp;&nbsp; ATCGTG&nbsp;&nbsp; NNNNNNNN</p> <p>27&nbsp;&nbsp; PETRI_rep3&nbsp;&nbsp; 31&nbsp;&nbsp; CACGAT&nbsp;&nbsp; NNNNNNNN</p> <p>28&nbsp;&nbsp; BIOCHIP_E_rep1&nbsp;&nbsp; 6&nbsp;&nbsp; CACTGA&nbsp;&nbsp; NNNNNNNN</p> <p>29&nbsp;&nbsp; BIOCHIP_M_rep1&nbsp;&nbsp; 46&nbsp;&nbsp; CTGACG&nbsp;&nbsp; NNNNNNNN</p> <p>30&nbsp;&nbsp; BIOCHIP_S_rep1&nbsp;&nbsp; 63&nbsp;&nbsp; GAGTGA&nbsp;&nbsp; NNNNNNNN</p> <p>31&nbsp;&nbsp; BIOCHIP_E_rep2&nbsp;&nbsp; 79&nbsp;&nbsp; GTATAC&nbsp;&nbsp; NNNNNNNN</p> <p>32&nbsp;&nbsp; BIOCHIP_M_rep2&nbsp;&nbsp; 92&nbsp;&nbsp; TCGAGC&nbsp;&nbsp; NNNNNNNN</p> <p>33&nbsp;&nbsp; BIOCHIP_S_rep2&nbsp;&nbsp; 09&nbsp;&nbsp; ACATGA&nbsp;&nbsp; NNNNNNNN</p> <p>34&nbsp;&nbsp; BIOCHIP_E_rep3&nbsp;&nbsp; 21&nbsp;&nbsp; ATCATA&nbsp;&nbsp; NNNNNNNN</p> <p>35&nbsp;&nbsp; BIOCHIP_M_rep3&nbsp;&nbsp; 33&nbsp;&nbsp; CACGTG&nbsp;&nbsp; NNNNNNNN</p> <p>36&nbsp;&nbsp; BIOCHIP_S_rep3&nbsp;&nbsp; 45&nbsp;&nbsp; CGATGA&nbsp;&nbsp; NNNNNNNN</p> <p>37&nbsp;&nbsp; HEPATOCYTES_rep1&nbsp;&nbsp; 57&nbsp;&nbsp; GAGATA&nbsp;&nbsp; NNNNNNNN</p> <p>38&nbsp;&nbsp; HEPATOCYTES_rep2&nbsp;&nbsp; 69&nbsp;&nbsp; GCTCTC&nbsp;&nbsp; NNNNNNNN</p> <p>39&nbsp;&nbsp; iPSC_control_rep1-2&nbsp;&nbsp; 81&nbsp;&nbsp; GTATGA&nbsp;&nbsp; NNNNNNNN</p> <p>40&nbsp;&nbsp; BIOCHIP_E_rep2-2&nbsp;&nbsp; 93&nbsp;&nbsp; TCGATA&nbsp;&nbsp; NNNNNNNN</p> <p>41&nbsp;&nbsp; BIOCHIP_M_rep1-2&nbsp;&nbsp;&nbsp; 11&nbsp;&nbsp; AGTAGC&nbsp;&nbsp; NNNNNNNN</p> <p>42&nbsp;&nbsp; BIOCHIP_S_rep2-2&nbsp;&nbsp; 23&nbsp;&nbsp; ATCGCA&nbsp;&nbsp; NNNNNNNN</p> <p>&nbsp;</p>

openOct 2017View details →
zenodo28/100

Annotation of Sox family proteins of the snail Lymnaea stagnalis (Mollusca: Pulmonata) using transcriptomic data

<p>This annotation of Sox family proteins&nbsp; of Lymnaea stagnalis snail was performed using transcriptome data from&nbsp; the article:</p> <p>Sepp&auml;l&auml;, O., Walser, JC., Cereghetti, T. et al. Transcriptome profiling of Lymnaea stagnalis (Gastropoda) for ecoimmunological research. BMC Genomics 22, 144 (2021). https://doi.org/10.1186/s12864-021-07428-1</p> <p>Sox2, Sox14, Sox10 and Sox5/6 were annotated using tBLASTn searches against transcriptome from the article upper.&nbsp;</p>

opencc-by-4.0Dec 2022View details →
zenodo28/100

Rapid changes in transcriptomic profile and mitochondrial function in human soleus muscle after three-day dry immersion

<p>Supplemental Figures for a study <strong>Rapid changes in transcriptomic profile and mitochondrial function in human soleus muscle after three-day dry immersion</strong></p>

opencc-by-4.0Jan 2023View details →
zenodo28/100

Genomic and Transcriptomic Survey Provides Insights into Molecular Basis of Pathogenicity of the Sunflower Pathogen Phoma macdonaldii

<p>Supplementary Materials</p>

opencc-by-4.0Mar 2023View details →
zenodo28/100

Cellular and molecular heterogeneities and signatures, and pathological trajectories of fatal COVID-19 lungs defined by spatial single-cell transcriptome analysis

<p>Spatial in-situ data analysis.</p>

opencc-by-4.0Feb 2023View details →
zenodo28/100

EAGS: efficient and adaptive gaussian smoothing applied to high-resolved spatial transcriptomics

<p>This dataset is used to preserve the mouse brain and mouse olfactory bulb data (in h5ad format) involved in the EAGS study.</p> <p>You can get details of the different datasets from <strong>readme.txt</strong>.</p> <p>Abstract of the EAGS study:</p> <p>The emergence of high-resolved spatial transcriptomics (ST) technology has facilitated the research of novel methods to investigate biological development, growth and other complex biological processes. High-resolution and whole transcriptomics ST datasets require customized imputation methods to improve signal-to-noise ratio and the data quality. We propose an efficient and adaptive gaussian smoothing (EAGS) method for imputation on high-resolved ST. Its adaptive two-factor smoothing creates patterns based on the spatial and expression information of the cells, creates adaptive weights for the smoothing of cells in the same pattern, then utilizes the weights to restore the gene expression profiles. The performance and efficiency of EAGS are verified on high-resolved ST data of mouse brain and olfactory bulb. Compared with other competitive methods, EAGS shows higher clustering accuracy, better biological interpretation and a significant advantage in computational consumption.</p>

opencc-zeroJun 2023View details →
zenodo28/100

The response of the brood pouch transcriptome to synthetic estrogen exposure in the Gulf pipefish (Syngnathus scovelli)

<p>The raw data provided is for the manuscript &quot;The response of the brood pouch transcriptome to synthetic estrogen exposure in the Gulf pipefish (Syngnathus scovelli)&quot; (doi:&nbsp;10.3389/fmars.2023.1138597).&nbsp;Data in the csv file is presented for all male pipefish for which brood pouches were included in the RNA-Seq data set and morphological assessment of their banding pattern on their abdomens for all three independent reviewers. Body sizes were measured for total body length in mm. The data for each fish is provided for Treatment (Control or EE2), Replicate (number), status (P for pregnant or NP for non-pregnant), bodysize (total body length in mm), Image Letter ID (randomly assigned A thought P), and the stage of band developing from Figure 4 that each of the 3 reviewers assigned.&nbsp;<br> &nbsp;</p>

opencc-by-4.0May 2023View details →

ScienceDex guides

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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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record