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54,099 results for “Expression”
Videos accompanying article "Facial Expressions for Sign Language Synthesis using FACSHuman and AZee"
<p>Accompanying videos for the article "Facial Expressions for Sign Language Synthesis using FACSHuman and AZee".</p> <ul> <li>all_action_units.mp4 - Shows mesh deformations for all the action units used.</li> <li>all_expressions.mp4 - Shows all the expressions synthesized using the study.</li> <li>big_threatening_hot.mp4 - Shows comparison of a sign with and without facial expressions.</li> </ul>
Gene expression landscape of cutaneous squamous cell carcinoma progression
<p>Data files associated with "Gene expression landscape of cutaneous squamous cell carcinoma progression" by Bencomo and Lee. </p><p>The README.md file has descriptions for each file.</p><p><strong>Funding support</strong>: This work was supported by a Scholar-Innovator Award from the Harrington Discovery Institute and a Discovery Boost grant from the American Cancer Society (CSL).</p>
Data from: Self-amplifying RNA generated with the modified nucleotides 5-methylcytidine and 5-methyluridine mediate strong expression and immunogenicity in vivo
<p>When utilized in therapeutic applications, synthetic self-amplifying RNA can lead to higher and more sustained expression than standard messenger RNA. This feature is particularly important for gene replacement therapy applications where prolonged expression could reduce the dose and frequency of treatments. The inclusion of modified nucleotides in synthetic non-amplifying mRNA has been shown to increase RNA stability, reduce immune activation and enhance gene expression. Preclinical and clinical studies with self-amplifying RNA (saRNA) have so far exclusively relied on RNA containing the canonical nucleotides adenosine, cytidine, guanosine and uridine. For the first time, we show that non-canonical nucleotides, such as m5C and m5U, are sufficiently compatible with a replicon derived from Venezuelan equine encephalitis alphavirus mediating protein translation <em>in vitro</em>, while those containing m1ψ in place of uridine show no detectable expression. When administered <em>in vivo</em>, saRNA generated with m5C or m5U mediate sustained gene expression of the luciferase reporter gene with those incorporating m5U appearing to lead to more prolonged expression. Finally, distinct antigen-specific humoral and cellular immune responses were induced by modified saRNA encoding the model antigen ovalbumin. The use of modified nucleotides with saRNA-based platforms could enhance their potential to be used effectively in a variety of applications.</p>
Identification and expression analysis of transcription factors in the Carallia brachiata genome
<p>Rhizophoraceae has 2 terrestrial genera and 4 marine genera. The intertidal zone in which marine mangroves are located is known for its low oxygen and high salinity. Marine and terrestrial genera have evolved distinct adaptive characteristics, among which viviparous reproduction is the most unique. To investigate the genetic foundations difference underlying the adaptive mechanisms of marine–terrestrial genera, we selected two species from Rhizophoraceae. <em>Kandelia obovata</em> is marine and viviparous, and <em>Carallia brachiata</em> is terrestrial and non-viviparous.<em> </em>We compared their transcriptome of 8 tissues (root, stem, leaf, flower, ovule, fruit, seed, embryo) and found that the mature reproductive organs (fruit, seed, embryo) of <em>K. obovata </em>did not reduce metabolic activity compared to <em>C. brachiata</em>. The reproductive organs of <em>K. obovata</em> were regulated by the same gene set as vegetative organs. This contrasted with <em>C. brachiata</em>. Eight kinds of hormone transduction genes were up-regulated in the seed of <em>K. obovata</em>. Finally, and most importantly, the transcriptional factors AP2 and ARF families were significantly more expressed in the reproductive organs of <em>K. obovata</em> than in those of <em>C. brachiata</em>. At the same time, the ERF family was more expressed in its roots. The findings suggested that the hormone transduction may contribute to viviparous initiation. Transcriptional factors were quite crucial for mangroves' adaptation to wetlands.</p>
A library of reporters of the global regulators of gene expression of Escherichia coli
<p>The topology of the transcription factor network (TFN) of <em>E. coli</em> is far from uniform, with 22 global regulator (GR) proteins controlling one-third of all genes. So far, their production rates cannot be tracked by comparable fluorescent proteins. We developed a library of fluorescent reporters for 16 GRs. Each consists of a single-copy plasmid coding for GFP fused to the full-length copy of the native promoter. We tracked their activity in exponential and stationary growth, and under weak and strong stresses. We show that the reporters have high sensitivity and specificity to all stresses tested and detect single-cell variability in transcription rates. Given the influence of GRs on the TFN, we expect that the new library will contribute to dissecting global transcriptional stress-response programs of <em>E. coli</em>. Moreover, it can be invaluable in bio-industrial applications that tune those programs to, instead of cell growth, favor productivity while reducing energy consumption.</p>
A phased genome of the highly heterozygous 'Texas' almond uncovers patterns of allele-specific expression linked to heterozygous structural variants
<h2># Genomic datasets associated to the publication: </h2> <h3># Gene-ID conversion with previous genome version</h3> <p>Texasv3_vs_Texasv2_GeneID.txt -- gene ID conversion between Texasv3 and Texasv2 (https://www.rosaceae.org/analysis/295)</p> <p>pdulcis26_to_F1_liftoff_polished.gff3 -- Texasv2 gene annotation liftoff on Phase-1 assembly (Phase-1 coordinates)</p> <h3># Phase-1</h3> <p>Texas_F1_K80_chr.fasta -- genome asssembly, phase-1 <br>Texas_F1_gene_models.gff3 -- phase-1 gene annotation (de novo annotation)<br>Functional_annotation_TexasF1.csv -- phase-1 gene functions <br>Texas_F1_ref_SV.vcf --- Structural variations relative to phase-0 (This file uses Phase-1 as reference)</p> <h3># Phase-0</h3> <p>Texas_F0_K80_chr.fasta -- genome asssembly, phase-0 <br>Texas_F0_gene_models.gff3 -- phase-0 gene annotation (liftoff from Phase-1)<br>Functional_annotation_TexasF0.csv -- phase-0 gene functions <br>Texas_F0_ref_SV.vcf --- Structural variations relative to phase-1 (This file uses Phase-0 as reference)</p> <h3># Transposable element annotation</h3> <p>Texas_F0_HiConf_TE_v3.gff3 --- TE annotation in Phase-0<br>Texas_F1_HiConf_TE_v3.gff3 --- TE annotation in Phase-1<br>Texasv3_TElib.fa --- TE library of TexasV3 (non-redundant repeat consensuses taking the account the two genome phases)</p> <h3># Gene sequences in fasta</h3> <p>Transcript, CDS and protein sequences in fasta format for phase-0 (F0) and phase-1 (F1) </p>
Gene expression data from clones of FAO cells with different glucose production capability
<div> <div> <div> <p>In response to energy and nutrient shortage, the liver triggers several catabolic processes to promote survival. Despite recent progresses, the precise molecular mechanisms regulating the hepatic adaptation to fasting remain incompletely characterized. Here, we report the identification of Hydroxysteroid dehydrogenase-like 2 (HSDL2) as a mitochondrial protein highly induced by fasting. We show that activation of PGC1a-PPARa and inhibition of the PI3K- mTORC1 axis stimulate HSDL2 expression in hepatocytes. We found that HSDL2 depletion decreases cholesterol conversion to bile acids (BAs) and impairs FXR activity. HSDL2 knockdown also reduces mitochondrial respiration, fatty acid oxidation, and TCA cycle activity. Bioinformatics analyses revealed that hepatic Hsdl2 expression positively associates with the postprandial excursion of various BA species in mice. Here, we show that liver-specific HSDL2 depletion impacts BA metabolism and decreases circulating cholesterol levels upon refeeding. Overall, our report identifies HSDL2 as a fasting-induced mitochondrial protein that links nutritional signals to BAs and cholesterol homeostasis.</p> </div> </div> </div>
Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data
<p>This repository contains raw and processed data of the original datasets generated for Lee et al. "Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data".</p> <p>The files Dilution_3_ROI02.ome.tiff, Dilution_3_ROI02_mask.ome.tiff, st_reclustered_expr_data.h5ad contain raw data (as ome tiff), a mesmer-generated segmentation mask, and single-cell quantification in anndata format of the cell pellet data generated for the study.</p> <p>The file tonsil-for-zenodo.tar.gz contains all raw and processed data of the IMC data of the 16 human tonsil ROIs:</p> <ul> <li>imc contains raw tiff stacks of the acquisition</li> <li>mask contains mesmer-generated masks</li> <li>channel.yml contains channel info</li> <li>exp_mat/dc/ contains single cell quantifications using mesmer segmentation</li> </ul>
Pleiotropic Expression Quantitative Trait Loci Are Enriched in Enhancers and Transcription Factor Binding Sites and Impact More Genes
<p>This dataset comprises two files that accompany the article (link to be added upon publication).</p> <h2>1. gwas2eqtl_colocalization_full.tar.gz</h2> <p>This file contains the complete colocalization dataset generated using the code from the following GitHub repository: gwas2eqtl. This dataset is used as input for the pleiotropic eQTL analysis available at gwas2eqtl_pleiotropy, which produces the figures in the article.</p> <p><strong>File structure:</strong></p> <blockquote> <p>.<br>└── gwas417<br> └── coloc<br> ├── ebi-a-GCST000998<br> │ └── pval_5e-08<br> │ └── r2_0.1<br> │ └── kb_1000<br> │ └── window_1000000<br> │ ├── Alasoo_2018_ge_macrophage_IFNg+Salmonella.tsv<br> │ ├── Alasoo_2018_ge_macrophage_IFNg.tsv<br>...</p> </blockquote> <p>Each TSV file contains the following columns:</p> <blockquote> <p>chrom pos rsid ref alt eqtl_gene_id gwas_beta gwas_pval gwas_id eqtl_beta eqtl_pval eqtl_id PP.H4.abf SNP.PP.H4 nsnps PP.H3.abf PP.H2.abf PP.H1.abf PP.H0.abf coloc_variant_id coloc_region<br>1 109272258 rs4970834 C T ENSG00000168765 -0.12874.25001047052626e-09 ebi-a-GCST000998 -0.250697 0.0893351 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0520205502224409 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109274968 rs12740374 G T ENSG00000168765 -0.103341 1.63998546891446e-09 ebi-a-GCST000998 -0.197397 0.172673Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0857585178966856 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109275216 rs660240 T C ENSG00000168765 0.1044492.78997299740827e-09 ebi-a-GCST000998 0.214165 0.139318 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0557749486050279 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109275684 rs629301 G T ENSG00000168765 0.1054716.129993302249e-10 ebi-a-GCST000998 0.197397 0.172673 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.22229240584331 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>1 109278889 rs602633 T G ENSG00000168765 0.1034352.15998134341707e-09 ebi-a-GCST000998 0.226329 0.102673 Alasoo_2018_ge_macrophage_IFNg+Salmonella 0.108283426725895 0.0782482718431504 6 0.000552728832012655 2.09397277427793e-07 0.890881419183881 0.000282215860934432 1_109279544_G_A 1:108779544-109779543<br>...</p> </blockquote> <p> </p> <p>The dataset provides colocalization statistics for GWAS-eQTL pairs, including posterior probabilities and variant annotations.</p> <h2>2. gwas2eqtl0.1.3.tsv.gz</h2> <p>This file is a filtered version of the colocalization dataset, refined based on cutoffs of PP.H4.abf ≥ 0.75 and SNP.PP.H4 ≥ 0. This subset is utilized in the gwas2eqtl web application for data visualization.</p> <p>Sample Columns:</p> <blockquote> <p>chrom pos19 pos38 cytoband rsid ref alt gwas_trait gwas_class gwas_beta eqtl_gene_symbol eqtl_beta eqtl_id eqtl_gene_id gwas_id gwas_pval eqtl_pval pp_h4_abf snp_pp_h4 tophits_variant_id nsnps<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.175816 BrainSeq_ge_brain ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 0.000841046 0.978425254116226 6.18454060493069e-12 1_1312114_T_C 3<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.175816 BrainSeq_ge_brain ENSG00000235098 ieu-a-294 2.85292266979231e-10 0.000841046 0.974019788384412 7.52286530905187e-12 1_1312114_T_C 4<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.293529 CommonMind_ge_DLPFC_naive ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 2.30452e-06 0.953333690803618 6.9758581004380506e-15 1_1312114_T_C 6<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.293529 CommonMind_ge_DLPFC_naive ENSG00000235098 ieu-a-294 2.85292266979231e-10 2.30452e-06 0.951092499048109 7.0876793540547e-15 1_1312114_T_C 7<br>1 1163804 1228424 1p36.33 rs7515488 C T Inflammatory bowel disease Autoimmune dis. 0.0874308 ANKRD65 -0.510549 FUSION_ge_adipose_naive ENSG00000235098 ebi-a-GCST003043 2.85292266979231e-10 1.2212e-06 0.974412793352836 1.70246427912903e-11 1_1312114_T_C 6</p> </blockquote> <p> </p> <p>This filtered dataset focuses on high-confidence colocalization events for functional exploration of genetic associations and regulatory mechanisms.</p>
Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"
<p>Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"</p> <p>The results are sorted by the three species analysed: the two algae <em><span>Zygnema circumcarinatum</span></em><span> SAG 698-1b (<em>Zygnema</em>) and <em>Mesotaenium endlicherianum </em></span><span>SAG 12.97 (<em>Mesotaenium</em>); and the bryophyte <em>Physcomitrium patens</em></span><span><em> </em>strain Gransden 2004 (<em>Physcomitrium</em>).</span></p>
The yin and yang of hsa-miR-1244 expression levels during activation of the UPR control cell fate - Raw data for label-free holographic microscopy
<p>Regulation of endoplasmic reticulum (ER) homeostasis plays a critical role in maintaining cell survival. When ER stress occurs, a network of three pathways called the unfolded protein response (UPR) is activated to reestablish homeostasis. While it is known that there is cross-talk between these pathways, how this complex network is regulated is not entirely clear. Using human cancer and non-cancer cell lines, two different genome-wide approaches, and two different ER stress models, we searched for miRNAs that were decreased during the UPR and surprisingly found only one, miR-1244, that was found in all these conditions. The activation of both UPR adaptive and apoptotic signaling pathway was confirmed by parallel genome-wide mRNA expression arrays for Calu 3 cells and next generation sequencing for 16HBE14o- cells. We also verified that ER-stress related downregulation of miR-1244 expression occurred with 5 different ER stressors and was confirmed in another human cell line (HeLa S3). These analyses demonstrated that the outcome of this reduction during ER stress supported both IRE1 signaling and elevated BIP expression. Further analysis also revealed that this novel miRNA impacted all three pathways of the UPR using inhibitors specific for IRE1, ATF6, and PERK. This is the first example of a complex mechanism by which this miRNA serves as a regulatory check point for all 3 pathways that is switched off after UPR activation. In summary, the results indicate that ER stress reduction of <em>miR-1244 </em>expression contributes to the pro-survival arm of UPR.</p> <p>For real-time monitoring of cell viability, we applied real-time and label-free holographic microscopy-based monitoring of cell death and viability using HoloMonitor M4<strong>®</strong> time-lapse cytometer (Phase Holographic Imaging PHI AB, Lund, Sweden). Holographic microscopy was used to follow the optical thickness and irregularity of cells exposed for up to 24 h to Tm or Tg in the presence or absence of <em>miR-1244 </em>mimic or antagomiR. The images from up to 8 independent optical fields were collected and analyzed according to manufacture instructions with HoloMonitor® App Suite software. Healthy cells are irregular in shape and thin, whereas dying cells are round and thick . For all analysis, the same cells parameters qualification was applied.</p> <p> </p> <p> </p> <p><strong>Figure 4. Overexpression of </strong><strong>miR-1244 promotes cell death. </strong>The results of real-time monitoring of cell viability are shown with the real time and label free holographic microscopy using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later monitored up to 24 h. Images were collected every 15 min (from 8 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 5. </strong><strong>miR-1244 influences the fate of cells challenged with Tm induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tm (2.5 µg/ml) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. <a name="OLE_LINK3"></a>Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p><strong>Figure 6. Exogenous </strong><strong>miR-1244 influences the fate of cells challenged with Tg induced ER stress. </strong>The results of real-time monitoring of cell viability with the real time and label free holographic microscopy are shown using a HoloMonitor M4<strong>®</strong> time-lapse cytometer of HeLa cells transfected with <em>miR-1244</em> mimic or inhibitor or the scramble control and 48 h later treated with Tg (500 nM) up to 24 h. Images were collected every 15 min (from 5 independent optical fields), and the distribution of live (blue) and dying cells (red) based on their optical thickness (Y-axes) and irregularity (X-axes) is presented at the 0, 8, 16 and 24 h time points. The images from up to 5 independent optical fields were collected and analyzed according to manufacturer’s instructions with HoloMonitor® App Suite software. Representative samples are shown (<strong>A</strong>). For all analyses, the same cell parameter qualifications were applied. Experiments were performed in triplicate. Based on the cells irregularity and average optical thickness the percentages of healthy cells (<strong>B</strong>) and of dying cells (<strong>C</strong>) were calculated. Data represents the mean ± SE of three independent experiments. *<em>P</em>< 0.05, **<em>P</em>< 0.001, ***<em>P</em>< 0.0001 were considered significant.</p> <p> </p> <p> </p>
Processed datasets and codes for differential expression analysis on polulation-level RNA-seq data
<p>This version includes codes and data necessary to reproduce all results in our response to the correspondences ("Response to 'Neglecting normalization impact in semi‑synthetic RNA‑seq data simulation generates artificial false positives' and 'Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples'") (<a href="https://doi.org/10.1186/s13059-024-03232-8">https://doi.org/10.1186/s13059-024-03232-8</a>).</p> <p>It also includes a README file to guide the reproduction of the results in our original publication and resources for the goodness of fit test in the original publication, "Exaggerated False Positives by Popular Differential Expression Methods When Analyzing Human Population Samples" (<a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4">https://genomebiology.biomedcentral.com/articles/10.1186/s13059-022-02648-4</a>).</p>
The role of spatial terms in time expressions: A case study of Chinese temporal words
<p>This project provides the research data, namely all the Chinese temporal words collected from the BCC corpus for The role of spatial terms in time expressions: A case study of Chinese temporal words. There is one table here which includes all tokens taken from the BCC corpus. More specifically, it lists 692 unique Chinese temporal words along with their frequencies. All of these temporal words have been classified into three categories: explicitly spatialized temporal words, implicitly spatialized temporal words, and others. All explicitly spatialized temporal words have been annotated based on the type of spatial terms they are marked with, mainly including location/direction, motion, dimension, objects, deixis, and spatial mixtures.</p>
Multiple extracellular polymeric substances pathways expressed by Accumulibacter and the flanking community during aerobic granule formation and after influent modification
<p><span>Aerobic granular sludge is a biological wastewater treatment process in which a microbial community forms a granular biofilm. The role of <em>Candidatus</em> Accumulibacter in the production of a biofilm matrix, composed of extracellular polymeric substances, was studied in a sequencing batch reactor enriched with polyphosphate-accumulating organisms. The metabolisms of the microbial populations were investigated using <em>de novo</em> metatranscriptomics analysis. Finally, the effect of decreasing the influent phosphate concentration on the granule stability and microbial activity was investigated. </span></p> <p><span>A few weeks after the reactor start-up, the microbial community was dominated by <em>Accumulibacter</em> with up to nine species active in parallel. However, the most active species differed according to sampling time. Decreasing drastically the influent phosphate concentration led to a dominance of the glycogen-accumulating organism <em>Propionivibrio</em>, with some <em>Accumulibacter</em> species still abundant. <em>De novo</em> metatranscriptomics analysis indicated a high diversity of potential extracellular substances produced mainly by <em>Accumulibacter</em>, <em>Azonexus</em>, <em>Candidatus</em> Contendobacter, and <em>Propionivibrio</em>. Moreover, the results suggest that <em>Azonexus</em>, <em>Contendobacter</em> and <em>Propionivibrio</em> recycle the neuraminic acids produced by <em>Accumulibacter</em>. Changes in the microbial community did not cause the granules to disintegrate, indicating that a <em>Propionivibrio</em>-dominated community can maintain stable granules.</span></p>
ENCODE LR-RNA-seq models and expression values
<p>In this object are the following files:</p> <p> </p> <ul> <li>filt_ab_tpm_mouse.tsv / filt_ab_tpm_human.tsv: Expression levels in TPM for each transcript in human and mouse</li> <li>lr_mouse_library_data_summary.tsv / lr_human_library_data_summary.tsv: Metadata for each dataset in human and mouse</li> <li>cerberus.gtf / mouse_cerberus.gtf: Transcript models in GTF format</li> <li>human_ucsc_transcripts.gtf / mouse_ucsc_transcripts.gtf: Transcript models for transcripts that passed expression filtering (>= 1 TPM in at least one library; transcripts from known </li> <li>human_protein_summary.tsv / mouse_protein_summary.tsv: Summary of protein coding predictions, including ORF locations and NMD status</li> </ul>
Capturing hidden regulation based on noise change of gene expression level from single cell RNA-seq in yeast
<p> </p> <p><strong>Abstract</strong></p> <p><strong>Recent progress in high throughput single cell RNA-seq (scRNA-seq) has activated the development of data-driven inferring methods of gene regulatory networks. Most network estimations assume that perturbations produce downstream effects. However, the effects of gene perturbations are sometimes compensated by a gene with redundant functionality (functional compensation). In order to avoid functional compensation, previous studies constructed double gene deletions, but its vast nature of gene combinations was not suitable for comprehensive network estimation. We hypothesized that functional compensation may emerge as a noise change without mean change (noise-only change) due to varying physical properties and strong compensation effects. Here, we show compensated interactions, which are not detected by mean change, are captured by noise-only change quantified from scRNA-seq. We investigated whether noise-only change genes caused by a single deletion of STP1 and STP2, which have strong functional compensation, are enriched in redundantly regulated genes. As a result, noise-only change genes are enriched in their redundantly regulated genes. Furthermore, novel downstream genes detected from noise change are enriched in “transport”, which is related to known downstream genes. Herein, we suggest the noise difference comparison has the potential to be applied as a new strategy for network estimation that capture even compensated interaction.</strong></p> <p><em><strong>SourceCode</strong></em></p> <p><strong><em>Main.R</em></strong><br> Please execute code along with the guide in this script</p> <p><em><strong>CreateBASiCS.R</strong></em><br> Create BASiCS chain objects, which include mean and noise data, from the raw count matrix. This step takes ~48 hours. Use of HPC is recommended</p> <p><em><strong>DifferenceTestAll.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from all cells. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>DifferenceTestCluster.R</strong></em><br> Execute comparison with regard to the mean and noise of expression level quantified from cells belonging to the same cluster. Test enrichment to the redundantly regulated genes.</p> <p><em><strong>STP12_detail_analysis.Rmd</strong></em><br> Output results mentioned in the discussion. Code is placed within the chunk.</p> <p><em><strong>CreateSupFig2.R</strong></em><br> Generate supplement figure 2</p> <p><em><strong>CreateFigure4AndS6.R</strong></em><br> Generate figure 4 and supplement figure 6</p> <p><strong>YeastGeneNameList.csv</strong><br> Data that convert ORF id to the common name<br> This data includes 3 columns, Name, ORF, and SGD.<br> - Name: Common name (e.g., TSC3)<br> - ORF: ORF id with Yeastract format (e.g., YBR058C.A)<br> - SGD: ORF id with SGD format (e.g., YBR058C-A)</p> <p><br> <strong>GeneticInteraction(ORF).csv</strong><br> Genetic interaction data downloaded from Yeastract. This data includes two-column, V1 and V2.<br> The interaction is described as two matrices such as V1 output to V2.</p> <p><strong>NoiseChangeGeneList</strong><br> This folder contains data required for generating tables 1 and S2.</p>
Liver and Blood Expression Prediction Generated for Opportunities and challenges for transcriptome-wide association studies
<p>db files are in sqlite format and contain weights to calculate genetic predictors of gene expression</p> <p>Blood, Liver indicate the tissue where the RNA was extracted from</p> <p>Crohns and LDL indicate that prediction models were generated using SNPs available in the GWAS summary results used in the paper (Wainberg et al <a href="https://doi.org/10.1038/s41588-019-0385-z">https://doi.org/10.1038/s41588-019-0385-z</a>)</p> <p>*txt.gz filex contain covariances between SNPs needed to run the summary version of PrediXcan or other TWAS methods</p> <p>Information about the RNAseq data can be found here https://pubmed.ncbi.nlm.nih.gov/27540175/</p> <p>Sample size n=522</p>
Analysis of microRNA expression profiles in peri-miniscrew implant crevicular fluid in orthodontics
<p> This study systematically evaluated microRNA (miRNA) expression patterns in peri-miniscrew implant crevicular fluid (PMICF) in orthodontic patients.</p>
High rates of evolution preceded shifts to sex-biased gene expression in Leucadendron, the most sexually dimorphic angiosperms
<p>Differences between males and females are usually more subtle in dioecious plants than animals, but strong sexual dimorphism has evolved convergently in the South African Cape plant genus <i>Leucadendron</i>. Such sexual dimorphism in leaf size is expected largely to be due to differential gene expression between the sexes. We compared patterns of gene expression in leaves among ten <i>Leucadendron </i>species across the genus. Surprisingly, we found no positive association between sexual dimorphism in morphology and the number or the percentage of sex-biased genes. Sex bias in most sex-biased genes evolved recently and was species-specific. We compared rates of evolutionary change in expression for genes that were sex-biased in one species but unbiased in others and found that sex-biased genes evolved faster in expression than un-biased genes. This greater rate of expression evolution of sex-biased genes, also documented in animals, might suggest the possible role of sexual selection in the evolution of gene expression. However, our comparative analysis clearly indicates that the more rapid rate of expression evolution of sex-biased genes predated the origin of bias, and shifts towards bias were depleted in signatures of adaptation. Our results are thus more consistent with the view that sex bias is simply freer to evolve in genes less subject to constraints in expression level.</p>
Methylation and gene expression data from: Differential DNA methylation across environments has no effect on gene expression in the eastern oyster
<p>1. It has been hypothesized that environmentally induced changes to gene body methylation could facilitate adaptive transgenerational responses to changing environments.</p> <p>2. We compared patterns of global gene expression (Tag-seq) and gene body methylation (reduced representation bisulfite sequencing) in 80 eastern oysters (<i>Crassostrea virginica</i>) from six full-sib families, common gardened for 14 months at two sites in the northern Gulf of Mexico that differed in mean salinity.</p> <p>3. At the time of sampling, oysters from the two sites differed in mass by 60% and in parasite loads by nearly two orders of magnitude. They also differentially expressed 35% of measured transcripts. However, we observed differential methylation at only 1.4% of potentially methylated loci in comparisons between individuals from these different environments, and little correspondence between differential methylation and differential gene expression.</p> <p>4. Instead, methylation patterns were largely driven by genetic differences among families, with a PERMANOVA analysis indicating nearly a two orders of magnitude greater number of genes differentially methylated between families than between environments.</p> <p>5. An analysis of CpG observed/expected values (CpG O/E ) across the <i>C. virginica</i> genome showed a distinct bimodal distribution, with genes from the first cluster showing the lower CpG O/E values, greater methylation, and higher, and more stable gene expression, while genes from the second cluster showed lower methylation, and lower and more variable gene expression.</p> <p>6. Taken together, the differential methylation results suggest that only a small portion of the <i>C. virginica</i> genome is affected by environmentally induced changes in methylation. At this point, there is little evidence to suggest that environmentally induced methylation states would play a leading role in regulating gene expression responses to new environments.</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.