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6,225 results for “expression analysis”
Figure 1 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 1. (a) TcXPC protein model. (b) TevXPC protein model. (c) TbXPC protein model (d) Model of TevXPC protein bound to a mismatch DNA. (e) TcXPC (red), TevXPC (blue) and TbXPC (green) models overlay. (f) Crystal structure of Rad4-Rad23 bound to a mismatched DNA performed by Min and Pavletich (2007).
Figure 3 in Bioinformatics and expression analysis of the Xeroderma Pigmentosum complementation group C (XPC) of Trypanosoma evansi in Trypanosoma cruzi cells
Figure 3. (a) TcXPB protein model. (b) TevXPB protein model. (c) TcXPB (green) and TevXPB (red) models overlay.
Pelagomonas calceolata gene expression levels in different nitrogen conditions and differential expression analysis.
<p>These files contains the expression levels and DESeq2 results of <em>Pelagomonas calceolata</em> genes cultivated with different nitrate conditions. Two strains of <em>P. calceolata </em>(RCC100 and RCC697) were cultivated and their RNAs reads were aligned on the predicted genes of <em>P. calceolata</em> RCC100 genome: <a href="https://www.ncbi.nlm.nih.gov/Traces/wgs/CAKKNE01?display=download" rel="nofollow">https://www.ncbi.nlm.nih.gov/Traces/wgs/CAKKNE01?display=download</a></p> <p>The following culture conditions were analysed :</p> <p>882 µM of Nitrate (RCC100 and RCC697) </p> <p>441 µM of Nitrate (RCC100)</p> <p>220 µM of Nitrate (RCC100 and RCC697)</p> <p>50 µM of Nitrate (RCC697)</p> <p>882 µM Cyanate (RCC100)</p> <p>882 µM Ammonia (RCC100)</p> <p>441 µM Urea (RCC100)</p> <p><a href="../api/records/12582059/draft/files/20230427_RCC100-Nitrate_transcriptomes_rawcounts.tsv/content" target="_blank" rel="noopener noreferrer">20230427_RCC100-Nitrate_transcriptomes_rawcounts.tsv</a> : the file contains the raw read counts of RCC100 in 6 culture conditions in triplicate + the gene names = 19 columns.</p> <p><a href="../api/records/12582059/draft/files/20230427_RCC100-Nitrate_transcriptomes_TPM.tsv/content" target="_blank" rel="noopener noreferrer">20230427_RCC100-Nitrate_transcriptomes_TPM.tsv</a> : same data normalized in transcript per kb per million mapped reads (TPM).</p> <p><a href="../api/records/12582059/draft/files/20230427_RCC100-Nitrate_transcriptomes_rawcounts.tsv/content" target="_blank" rel="noopener noreferrer">20230427_RCC697-Nitrate_transcriptomes_rawcounts.tsv</a> : the file contains the raw read counts of RCC697 of 3 culture conditions in triplicate + the gene names = 10 columns.</p> <p><a href="../api/records/12582059/draft/files/20230427_RCC100-Nitrate_transcriptomes_TPM.tsv/content" target="_blank" rel="noopener noreferrer">20230427_RCC697-Nitrate_transcriptomes_TPM.tsv</a> : same data normalized in transcript per kb per million mapped reads (TPM).</p> <p><span>Differential expression analysis (DESeq2) was performed by pairwise comparisons between the standard condition (882 µM nitrate) and low-nitrate conditions (50, 220 or 441 µM nitrate) or changing nitrogen sources (882 µM ammonium, 882 µM cyanate and 441 µM urea). Each DESeq-results_RCCxxx_xxx.tsv file contains 6 columns : <em>P.calceolata </em>gene name, base Mean, log2 Fold Change, standard error value (lfcSE), pvalue and adjusted pvalue (padj).<br></span></p>
Figure 2 in Characteristic analysis of prolactin and its receptor genes from Rana chensinensis and expression pattern during metamorphosis
Figure 2. Nucleotide and deduced aa sequences of rcPRLR. The predicted site of signal peptide cleavage is marked by a triangle. The conserved cysteine residues are circled. Three potential N-glycosylation sites are underlined by dotted lines. The WS motif is boxed and TMD is underlined with a solid line. Within the ICD, boxes 1 and 2 are underlined with a double line and a wavy line, respectively. The stop codon is marked by an asterisk.
Figure 7 in Characteristic analysis of prolactin and its receptor genes from Rana chensinensis and expression pattern during metamorphosis
Figure 7. Expression analysis of rcPRLR by qRT-PCR in liver (A), skin (B), tail (C), and kidneys (D) of different stages. The data for rcPRLR mRNA levels were normalized by those for rpl8 mRNA levels and expressed relative to the value for stage 43 tissue, respectively. The experiments were repeated 3 times using 3 independent biological samples. Each bar represents mean ± SD. Those with different letters were regarded as statistically significant at the 5% level (Duncan test).
Figure 6 in Characteristic analysis of prolactin and its receptor genes from Rana chensinensis and expression pattern during metamorphosis
Figure 6. Detection of rcPRL and rcPRLR in tadpoles by RT-PCR. (A) and (B): Upper panel, RT-PCR using rcPRL and rcPRLR gene-specific primers, respectively; lower panel, control RT-PCR using rpl8 gene-specific primers.
Figure 1 in Characteristic analysis of prolactin and its receptor genes from Rana chensinensis and expression pattern during metamorphosis
Figure 1. Nucleotide and deduced aa sequences of rcPRL. The predicted site of signal peptide cleavage is marked by a triangle. The conserved cysteine residues are circled and the potential phosphorylation site is boxed, while the four α-helical domains are boxed in shaded rectangles. The stop codon is marked by an asterisk.
Figure 5 in Characteristic analysis of prolactin and its receptor genes from Rana chensinensis and expression pattern during metamorphosis
Figure 5. Construction of neighbor-joining tree based on aa sequences of PRL (A) and PRLR (B). The sequences used above are: Rana catesbeiana (CAA34199.1, BAD14941.1), Xenopus laevis (AAH75216.1, AAI70439.1), Chelonia mydas (XP_007059983.1, EMP31801.1), Anser anser (ADG03649.1, ABW74516.1), Sus scrofa (NP_999091.1, ABA41035.1), Mus musculus (P06879.1, CAA51789.1), Homo sapiens (NP_001157030.1, NP_000940.1), Oncorhynchus mykiss (NP_001118205.1, NP_001118071.1), Danio rerio (AAH92358.1, AAI63012.1), Bufo japonicus (BAF75354.1), and Cynops pyrrhogaster (BAB61107.1). Bootstrap percentage values are indicated for each node.
Figure 4 in Characteristic analysis of prolactin and its receptor genes from Rana chensinensis and expression pattern during metamorphosis
Figure 4. Alignment of aa sequences of various PRLRs. Conserved cysteines are circled; 3 potential N-glycosylation sites (His 212; WS motif, Box 1; and DSGRGS motif) are marked by boxes; Trp165 and Tyr609 are marked by triangles. Dots (·) represent spaces inserted to maximize similarity, and small letters represent the conserved amino acids in that position.
Figure 3 in Characteristic analysis of prolactin and its receptor genes from Rana chensinensis and expression pattern during metamorphosis
Figure 3. Alignment of aa sequences of various PRLs. Conserved cysteines are boxed, while motifs 1 and 2 are marked by arrows.
Figure 3 in Validation of reference genes for quantitative expression analysis by qPCR in various tissues of date mussel (Lithophaga lithophaga)
Figure 3. Pairwise variation (V-value) of candidate reference genes in date mussel (L. lithophaga) using geNorm.
Figure 2 in Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency
Figure 2. Phylogenetic analysis based on nucleotide sequences of plant phosphate transporters. Plant phosphate transporters were assembled using ClustalX, and NJ-plot was used to develop the tree. Abbreviations are shown for respective transporters: ZmPTs: Zea mays phosphate transporters;AtPT: Arabidopsis thaliana phosphate transporters;LePT: Lycopersicon esculentum phosphate transporters; OsPT: Oryza sativa phosphate transporters; HvPT: Hordeum vulgare phosphate transporters; SbPT: Sorghum bicolor phosphate transporters.
Figure 1. A – P in Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency
Figure 1. A – P-efficient and P-inefficient maize plants grown in the Cerrado under low Pi conditions.B – Dry weight of maize genotypes. C – Root/shoot ratio of maize plants. D and E – Phosphorus content. F – Anthocyanin concentration. G and H – Units of APA activity. B to H, The maize plants were grown in hydroponics culture in the presence (250 µM Pi - gray bar) or absence (0 µM Pi – black bar) of phosphate harvested after 15 days in treatment. Each bar is the mean of three replicates with a standard deviation.
Figure 3. A in Expression analysis of phosphate induced genes in contrasting maize genotypes for phosphorus use efficiency
Figure 3. A – Northern blot analysis of phosphate starvation-induced ZmPTs genes in maize genotypes. B – Expression of ZmPTs in a plant grown in different phosphorus concentrations. C – Suppression of the ZmPTs expression by Pi resupply. D – Expression of ZmPT genes using RNA isolated from different root parts. E – Expression of ZmPTs in different maize plants under Pi starvation. F – Effect of duration of phosphate starvation on ZmPTs genes expression in maize genotypes. G – Expression of ZmPT homologs in roots of two sorghum genotypes. Total RNA isolated from different times of hydroponically grown plants supplied with half-strength modified Hoagland's solution containing 250 µM phosphate (+) or no phosphate (-) for different days or different concentrations as indicated. All the blots were probed with 32P labeled ZmPTs. The panel below the Northern blots is the ethidium bromide-stained gel prior to blotting showing the RNA integrity and uniformity of loading.
Extended data tables to Haering and Habermann, F1000Res, RNfuzzyApp: an R shiny RNA-seq data analysis app for visualisation, differential expression analysis, time-series clustering and enrichment analysis
<p><b>Background</b> </p> <p>RNA-seq is a widely adopted affordable method for large scale gene expression profiling. However, user-friendly and versatile tools for wet-lab biologists to analyse RNA-seq data beyond standard analyses such as differential expression, are rare. Especially, the analysis of time-series data is difficult for wet-lab biologists lacking advanced computational training. Furthermore, most meta-analysis tools are tailored for model organisms and not easily adaptable to other species.</p> <p><b>Results</b></p> <p>With RNfuzzyApp, we provide a user-friendly, web-based R-shiny app for differential expression analysis, as well as time-series analysis of RNA-seq data. RNfuzzyApp offers several methods for normalization and differential expression analysis of RNA-seq data, providing easy-to-use toolboxes, interactive plots and downloadable results. For time-series analysis, RNfuzzyApp presents the first web-based, automated pipeline for soft clustering with the Mfuzz R package, including methods to aid in cluster number selection, Mfuzz loop computations, cluster overlap analysis, as well as cluster enrichments.</p> <p><b>Conclusion</b></p> <p>RNfuzzyApp is an intuitive, easy to use and interactive R shiny app for RNA-seq differential expression and time-series analysis, offering a rich selection of interactive plots, providing a quick overview of raw data and generating rapid analysis results. Furthermore, its orthology assignment, enrichment analysis, as well as ID conversion functions are accessible to non-model organisms.</p>
Figure 1 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 1 Mite growth curve of Dermatophagoides pteronyssinus (a) andD. farinae (b) in the semisynthetic culture medium. Data showing the kinetics of the mites' growth in semi-synthetic culture medium are expressed as arithmetic mean ± standard error of triplicates, at each time of growth.
Figure 3 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 3 Kinetics of Der 1 and Der 2 major allergen levels during the growth ofDermatophagoides pteronyssinus (a) andD. farinae (b) in the semi-synthetic culture medium. Data showing the kinetics
Figure 2 in Kinetics of Dermatophagoides pteronyssinus and Dermatophagoides farinae growth and an analysis of the allergen expression in semi-synthetic culture medium
Figure 2 SDS-PAGE IgE-immunoblotting of extracts fromDermatophagoides pteronyssinus (a) and D. farinae (b) mites along their growth. Mw: Molecular weight marker. Numbers on lines indicate
Performance analysis of micro-expression recognition over different sample image sizes.
<p>Performance of micro-expression recognition accuracy analyzed using different sample sizes with motion and geometric features. The sample sizes analyzed are: 140x170, 280x340, 560x680 and 1120x1360 using SMIC, CASMEII, CAS(ME)^2 and SAMM. The experiments were conducted using three different feature extraction setups: (i) optimized BiWOOF, (ii) full-face graph, and (iii) full-face graph with amplitude-based emotion magnification method (A-EMM). The results were compared with the results of the original BiWOOF presented in [1] as the baseline study. These files are published under CC0 license.</p> <p> </p> <p>References</p> <p>[1] Liong, S. T., See, J., Wong, K., & Phan, R. C. W. (2018). Less is more: Micro-expression recognition from video using apex frame. <em>Signal Processing: Image Communication</em>, <em>62</em>, 82-92.</p>
RNAseq data: Analysis of circRNA expression in human neuronal differentiation
<p>This dataset contains sequencing read count data related to samples from differentiating human neuroepithelial stem cells (NES) collected at days zero (NES), five (D5) and 28 (D28) of differentiation. Details on how samples were collected and how data was generated and processed are described below.</p> <p> </p> <p><em>Sample preparation</em></p> <p>NES were seeded on tissue culture flasks coated with 20 μg/ml poly-L-ornithine (Sigma-Aldrich P3655), and 1 μg/ml laminin (Sigma-Aldrich L2020). Cells were grown in DMEM/F12+GlutaMAX medium (ThermoFisher 31331093) supplemented with 0.05X B27 (ThermoFisher 17504044), 1X N2 (ThermoFisher 17502001), 10 ng/ml bFGF (fisher scientific CTP0261), 10 ng/ml EGF (PeproTech AF-100-15) and 10 U/ml penicillin/streptomycin (ThermoFisher 15140122). Medium was exchanged 50% daily and cells maintained in 5% CO2 at 37ºC, passaging once 100% confluent and seeding at a density of 5x104 cells/cm2. Neural differentiation was induced by growth factor withdrawal the day after plating with media B27 concentration increased to 0.5X. Media was exchanged 50% every second day up until D15, after which media was supplemented with 0.4 ug/ml laminin and exchanged 50% every three days. </p> <p> </p> <p><em>RNA extraction and sequencing</em></p> <p>Cells were lysed in TRIzol reagent (ThermoFischer 15596026) before separating with chloroform and mixing the aqueous phase with isopropanol as per manufacturer directions. RNA was then isolated from the isopropanol/chloroform solution using the ReliaPrep RNA Cell Miniprep kit (Promega Z6010). Libraries were prepared with Illumina Truseq Stranded total RNA RiboZero GOLD kit and sequenced on the NovaSeq6000 platform with a 2x151 setup using NovaSeqXp workflow in S4 mode flowcell.</p> <p> </p> <p><em>Data generation</em></p> <p>Raw reads were processed using cutadapt v3.2 to trim adaptor sequences and low-quality base pairs and discard short reads (options: -m 20 -e 0.1 -q 20 -O 1). The GRCh37 genome assembly was used for all alignment, annotation, and downstream analysis steps. Trimmed read weres alignment to the GRCh37 genome assembly using TopHat v2.0.9 tophat_fusion (with Bowtie v1.1.2 and Samtools v0.1.19) with –fusion-min-dist 200. BAM files have been anonymised by removal of potentially identifiable genetic variant information using BAMboozle v0.5.0 (Ziegenhain & Sandberg, 2021) with default settings. This BAM files and corresponding index (.bai) files are provided here with naming convention "<em>label.</em>bam" Information on sample labels and corresponding conditions is provided in the file 'metadata.txt'.</p> <p><br> </p>
ScienceDex guides
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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.