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5,031 results for “mirnas”
Raw gel images accompanying the publication: Koralewska et al, NAR 2024, Short 2'-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production in vitro and in vivo, DOI 10.1093/nar/gkae284
<p>A set of raw gel images used in the article: Koralewska <em>et al</em>. Short 2’-O-methyl/LNA oligomers as highly-selective inhibitors of miRNA production <em>in vitro</em> and <em>in vivo, </em>NAR 2024, DOI 10.1093/nar/gkae284.</p>
Ultrasensitive and multiplexed miRNA detection system with DNA-PAINT
<p>This dataset contains the raw data that were used for the publication entitled, "Ultrasensitive and multiplexed miRNA detection system with DNA-PAINT" published in Biosensors and Bioelectronics on 30 December 2023.</p>
Test dataset for iwa-miRNA
<p>Available test data for iwa-miRNA. The description of the test data can be found in the README file.</p>
Investigating the role of miRNA in neural crest development and diseases
<p>This is a recording of the poster presentation of Mr Marco Antonaci (UEA) at the Swiss RNA Workshop 2022, January 21st</p>
Datasets from "Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data"
<p>A collection of datasets on miRNA and lung cancer used in</p> <p>Berg, O.F.B.: Circulating miRNA and Lung Cancer: - a More Comprehensive Analysis of Available Data.<br> NTNU Open (2022)</p> <p> </p> <p>The datasets in this collection are processed and normalized from available raw datasets. The processing code that was used can be found on <a href="https://github.com/OleFredrik1/masterthesis">https://github.com/OleFredrik1/masterthesis</a>. The raw datasets are:</p> <p><strong>Asakura2020:</strong></p> <p>Asakura, K., Kadota, T., Matsuzaki, J., Yoshida, Y., Yamamoto, Y., Nakagawa, K., Takizawa, S., Aoki, Y., Nakamura, E., Miura, J., Sakamoto, H., Kato, K., Watanabe, S.-i., and Ochiya, T. (2020). A miRNA-based diagnostic model predicts resectable lung cancer in humans with high accuracy. <em>Communications Biology</em>, 3(1):1–9.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Bianchi2011:</strong></p> <p>Bianchi, F., Nicassio, F., Marzi, M., Belloni, E., Dall’Olio, V., Bernard, L., Pelosi, G., Maisonneuve, P., Veronesi, G., and Di Fiore, P. P. (2011). A serum circulating miRNA diagnostic test to identify asymptomatic high-risk individuals with early stage lung cancer. <em>EMBO Molecular Medicine</em>, 3(8):495–503.</p> <p>Link: <a href="https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls">https://www.embopress.org/action/downloadSupplement?doi=10.1002%2Femmm.201100154&file=emmm_201100154_sm_suppdata2.xls</a></p> <p> </p> <p><strong>Chen2019:</strong></p> <p>Accession ID: GSE71661</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71661</a></p> <p> </p> <p><strong>Duan2021:</strong></p> <p>Duan, X., Qiao, S., Li, D., Li, S., Zheng, Z., Wang, Q., and Zhu, X. (2021). Circulating miRNAs in Serum as Biomarkers for Early Diagnosis of Non-small Cell Lung Cancer. <em>Frontiers in Genetics</em>, 12:987.</p> <p>Accession ID: GSE137140</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE137140</a></p> <p> </p> <p><strong>Fehlmann2020:</strong></p> <p>Fehlmann, T., Kahraman, M., Ludwig, N., Backes, C., Galata, V., Keller, V., Geffers, L., Mercaldo, N., Hornung, D., Weis, T., Kayvanpour, E., Abu-Halima, M., Deuschle, C., Schulte, C., Suenkel, U., von Thaler, A.-K., Maetzler, W., Herr, C., Fähndrich, S., Vogelmeier, C., Guimaraes, P., Hecksteden, A., Meyer, T., Metzger, F., Diener, C., Deutscher, S., Abdul-Khaliq, H., Stehle, I., Haeusler, S., Meiser, A., Groesdonk, H. V., Volk, T., Lenhof, H.-P., Katus, H., Balling, R., Meder, B., Kruger, R., Huwer, H., Bals, R., Meese, E., and Keller, A. (2020). Evaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients. <em>JAMA oncology</em>, 6(5):714–723.</p> <p>Accession ID: E-MTAB-8026</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-8026/</a></p> <p> </p> <p><strong>Halvorsen2016:</strong></p> <p>Halvorsen, A. R., Bjaanæs, M., LeBlanc, M., Holm, A. M., Bolstad, N., Rubio, L., Peñalver, J. C., Cervera, J., Mojarrieta, J. C., López-Guerrero, J. A., Brustugun, O. T., and Helland, Å. (2016). A unique set of 6 circulating microRNAs for early detection of non-small cell lung cancer. <em>Oncotarget</em>, 7(24):37250–37259.</p> <p>Accession ID: GSE70080</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70080</a></p> <p> </p> <p><strong>Jin2017:</strong></p> <p>Jin, X., Chen, Y., Chen, H., Fei, S., Chen, D., Cai, X., Liu, L., Lin, B., Su, H., Zhao, L., Su, M., Pan, H., Shen, L., Xie, D., and Xie, C. (2017). Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non–Small Cell Lung Cancer Using Next-Generation Sequencing. <em>Clinical Cancer Research</em>, 23(17):5311–5319.</p> <p>Link: <a href="https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as">https://aacrjournals.org/clincancerres/article/23/17/5311/123048/Evaluation-of-Tumor-Derived-Exosomal-miRNA-as</a> (table s1)</p> <p> </p> <p><strong>Keller2009:</strong></p> <p>Keller, A., Leidinger, P., Borries, A., Wendschlag, A., Wucherpfennig, F., Scheffler, M., Huwer, H., Lenhof, H.-P., and Meese, E. (2009). miRNAs in lung cancer - Studying complex fingerprints in patient’s blood cells by microarray experiments. <em>BMC Cancer</em>, 9(1):353.</p> <p>Accession ID: GSE17681</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17681</a></p> <p> </p> <p><strong>Keller2014:</strong></p> <p>Keller, A., Leidinger, P., Vogel, B., Backes, C., ElSharawy, A., Galata, V., Mueller, S. C., Marquart, S., Schrauder, M. G., Strick, R., Bauer, A., Wischhusen, J., Beier, M., Kohlhaas, J., Katus, H. A., Hoheisel, J., Franke, A., Meder, B., and Meese, E. (2014). miRNAs can be generally associated with human pathologies as exemplified for miR-144*. <em>BMC Medicine</em>, 12(1):224.</p> <p>Accession ID: GSE61741</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61741</a></p> <p> </p> <p><strong>Keller2020:</strong></p> <p>Keller, A., Fehlmann, T., Backes, C., Kern, F., Gislefoss, R., Langseth, H., Rounge, T. B., Ludwig, N., and Meese, E. (2020). Competitive learning suggests circulating miRNA profiles for cancers decades prior to diagnosis. <em>RNA Biology</em>, 17(10):1416–1426.</p> <p>Link: <a href="https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945">https://www.tandfonline.com/doi/full/10.1080/15476286.2020.1771945</a> (Supplemental Table 9)</p> <p> </p> <p><strong>Kryczka2021:</strong></p> <p>Kryczka, J., Migdalska-Sęk, M., Kordiak, J., Kiszałkiewicz, J. M., PastuszakLewandoska, D., Antczak, A., and Brzeziańska-Lasota, E. (2021). Serum Extracellular Vesicle-Derived miRNAs in Patients with Non-Small Cell Lung Cancer—Search for Non-Invasive Diagnostic Biomarkers. <em>Diagnostics</em>, 11(3):425.</p> <p>Link: <a href="https://www.mdpi.com/2075-4418/11/3/425/s1">https://www.mdpi.com/2075-4418/11/3/425/s1</a></p> <p> </p> <p><strong>Leidinger2011:</strong></p> <p>Leidinger, P., Keller, A., Borries, A., Huwer, H., Rohling, M., Huebers, J., Lenhof, H.-P., and Meese, E. (2011). Specific peripheral miRNA profiles for distinguishing lung cancer from COPD. <em>Lung Cancer</em>, 74(1):41–47.</p> <p>Accession ID: GSE24709</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE24709</a></p> <p> </p> <p><strong>Leidinger2014:</strong></p> <p>Leidinger, P., Backes, C., Dahmke, I. N., Galata, V., Huwer, H., Stehle, I., Bals, R., Keller, A., and Meese, E. (2014). What makes a blood cell based miRNA expression pattern disease specific? - A miRNome analysis of blood cell subsets in lung cancer patients and healthy controls. <em>Oncotarget</em>, 5(19):9484–9497.</p> <p>Accession ID: GSE55993</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55993</a></p> <p> </p> <p><strong>Leidinger2016:</strong></p> <p>Leidinger, P., Brefort, T., Backes, C., Krapp, M., Galata, V., Beier, M., Kohlhaas, J., Huwer, H., Meese, E., and Keller, A. (2016). High-throughput qRT-PCR validation of blood microRNAs in non-small cell lung cancer. <em>Oncotarget</em>, 7(4):4611–4623.</p> <p>Link: <a href="https://www.oncotarget.com/article/6566/text/">https://www.oncotarget.com/article/6566/text/</a> (supplementary files)</p> <p> </p> <p><strong>Li2017:</strong></p> <p>Li, L.-L., Qu, L.-L., Fu, H.-J., Zheng, X.-F., Tang, C.-H., Li, X.-Y., Chen, J., Wang, W.-X., Yang, S.-X., Wang, L., Zhao, G.-H., Lv, P.-P., Zhang, M., Lei, Y.-Y., Qin, H.-F., Wang, H., Gao, H.-J., and Liu, X.-Q. (2017). Circulating microRNAs as novel biomarkers of ALK-positive non-small cell lung cancer and predictors of response to crizotinib therapy. <em>Oncotarget</em>, 8(28):45399–45414.</p> <p>Accession ID: GSE94536</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE94536</a></p> <p> </p> <p><strong>Marzi2016:</strong></p> <p>Marzi, M. J., Montani, F., Carletti, R. M., Dezi, F., Dama, E., Bonizzi, G., Sandri, M. T., Rampinelli, C., Bellomi, M., Maisonneuve, P., Spaggiari, L., Veronesi, G., Bianchi, F., Di Fiore, P. P., and Nicassio, F. (2016). Optimization and Standardization of Circulating MicroRNA Detection for Clinical Application: The miR-Test Case. <em>Clinical Chemistry</em>, 62(5):743–754</p> <p>Accession ID: GSE76462</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76462</a></p> <p> </p> <p><strong>Nigita2018:</strong></p> <p>Nigita, G., Distefano, R., Veneziano, D., Romano, G., Rahman, M., Wang, K., Pass, H., Croce, C. M., Acunzo, M., and Nana-Sinkam, P. (2018). Tissue and exosomal miRNA editing in Non-Small Cell Lung Cancer. <em>Scientific Reports</em>, 8(1):10222.</p> <p>Accession ID: GSE114711</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE114711</a></p> <p> </p> <p><strong>Patnaik2012:</strong></p> <p>Patnaik, S. K., Yendamuri, S., Kannisto, E., Kucharczuk, J. C., Singhal, S., and Vachani, A. (2012). MicroRNA Expression Profiles of Whole Blood in Lung Adenocarcinoma. <em>PLOS ONE</em>, 7(9):e46045.</p> <p>Accession ID: GSE27486</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE27486</a></p> <p> </p> <p><strong>Patnaik2017:</strong></p> <p>Patnaik, S. K., Kannisto, E. D., Mallick, R., Vachani, A., and Yendamuri, S. (2017). Whole blood microRNA expression may not be useful for screening non-small cell lung cancer. <em>PLOS ONE</em>, 12(7):e0181926.</p> <p>Accession ID: GSE40738</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE40738</a></p> <p> </p> <p><strong>Qu2017:</strong></p> <p>Qu, L., Li, L., Zheng, X., Fu, H., Tang, C., Qin, H., Li, X., Wang, H., Li, J., Wang, W., Yang, S., Wang, L., Zhao, G., Lv, P., Lei, Y., Zhang, M., Gao, H., Song, S., and Liu, X. (2017). Circulating plasma microRNAs as potential markers to identify EGFR mutation status and to monitor epidermal growth factor receptor-tyrosine kinase inhibitor treatment in patients with advanced non-small cell lung cancer. <em>Oncotarget</em>, 8(28):45807–45824.</p> <p>Accession ID: GSE93300</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93300</a></p> <p> </p> <p><strong>Reis2020:</strong></p> <p>Reis, P. P., Drigo, S. A., Carvalho, R. F., Lopez Lapa, R. M., Felix, T. F., Patel, D., Cheng, D., Pintilie, M., Liu, G., and Tsao, M.-S. (2020). Circulating miR-16-5p, miR-92a-3p, and miR-451a in Plasma from Lung Cancer Patients: Potential Application in Early Detection and a Regulatory Role in Tumorigenesis Pathways. <em>Cancers</em>, 12(8):2071.</p> <p>Accession ID: GSE152702</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152702</a></p> <p> </p> <p><strong>Wozniak2015:</strong></p> <p>Wozniak, M. B., Scelo, G., Muller, D. C., Mukeria, A., Zaridze, D., and Brennan, P. (2015). Circulating MicroRNAs as Non-Invasive Biomarkers for Early Detection of Non-Small-Cell Lung Cancer. <em>PLOS ONE</em>, 10(5):e0125026.</p> <p>Accession ID: GSE64591</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64591</a></p> <p> </p> <p><strong>Yao2019:</strong></p> <p>Yao, B., Qu, S., Hu, R., Gao, W., Jin, S., Liu, M., and Zhao, Q. (2019). A panel of miRNAs derived from plasma extracellular vesicles as novel diagnostic biomarkers of lung adenocarcinoma. <em>FEBS Open Bio</em>, 9(12):2149–2158.</p> <p>Accession ID: GSE111803</p> <p>Link: <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE111803</a></p> <p> </p> <p><strong>Zaporozhchenko2018:</strong></p> <p>Zaporozhchenko, I. A., Morozkin, E. S., Ponomaryova, A. A., Rykova, E. Y., Cherdyntseva, N. V., Zheravin, A. A., Pashkovskaya, O. A., Pokushalov, E. A., Vlassov, V. V., and Laktionov, P. P. (2018). Profiling of 179 miRNA Expression in Blood Plasma of Lung Cancer Patients and Cancer-Free Individuals. <em>Scientific Reports</em>, 8(1):6348.</p> <p>Accession ID: E-MTAB-6304</p> <p>Link: <a href="https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/">https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-6304/</a></p> <p> </p> <p>The Abdollahi2019 and Boeri2011 datasets are not included as I recived them by email and I did not recieve conformation that they were OK with me publishing the datasets.</p>
Dyregulated miRNA isoforms across TCGA and TARGET cohorts
<p>The table reports the complete list of dysregulated miRNA isoform molecules across cohorts/cancer tissues, retained according to a |<em>linear fold change</em>| >1.5 and an <em>FDR adjusted p-value</em> <0.05.</p>
miRNAture v.1.1: updated dataset with curated metazoan miRBase v.22.1 and Rfam-14.4 miRNA families.
<p>This folder contains the updated data to annotate miRNAs using miRNAture v.1.1. As usual, it includes CMs, HMMs and required pre-calculated data to validate mature miRNAs (corrected hairpins, mature files and miRBase genomes). It contains a 1034 and 1124 metazoan miRNA families curated from miRBase v.22.1 and Rfam 14.4, respectively. To use this dataset, refer the path of this uncompressed folder with the -dataF/-datadir flag in miRNature v.1.1.</p>
Fig. 8 Urine miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 8 Urine miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs
Fig. 10 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 10 Venn diagrams showing the common and unique DE miRNAs (a) and DE piRNAs (b) in both serum and urine between the acutely and chronically infected rabbits versus uninfected rabbits
Fig. 5 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 5 Global view of temporal sRNA expression profiles in rabbit urine during T. gondii infection. a The sRNA correlation heatmap of sample clustering. b Principal component analysis of all identified urine sRNAs. c Unsupervised hierarchical clustering of sRNA profiling data. sRNA intensity is normalized so that blue represents low intensity and yellow represents high intensity. Columns were hierarchically clustered based on a complete linkage using Pearson correlation coefficients as the distance measure. Sample groups are acutely infected rabbits, chronically infected rabbits and uninfected control rabbits, which are labeled as AI, CI and Con, respectively
Fig. 4 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 4 First nucleotide bias of obtained small RNA in urine samples of rabbits. a First nucleotide bias of known miRNAs in rabbit urine. b First nucleotide bias of predicted miRNAs in rabbit urine. c First nucleotide bias of predicted piRNAs in rabbit urine
Fig. 1 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 1 Histological features of spleen section from healthy control rabbits and rabbits experimentally infected with Toxoplasma gondii. Images showing the H&E-stained spleen section at 100× (a, b). a Spleen section from healthy, uninfected rabbit. The structures of white pulp (WP) and red pulp (RP) were clearly identified with normal cell density. b Spleen section from a rabbit with acute T. gondii infection. The number and dimension of splenic nodule are increased, and more plasma cells are observed in the splenic cord (black triangle) of red pulp. Note that granulomas are present (big black arrow). Hemosiderin deposition (small black arrow) indicated red blood cell destruction. CA central arteriole. Scale bar = 100 μm
Fig. 6 Serum miRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 6 Serum miRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant miRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of miRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant miRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant miRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed miRNA among different comparison pairs. FDR represents false discovery rate
Fig. 7 Serum piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 7 Serum piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and control rabbits. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected rabbits and control rabbits. c The volcano plot shows the individual statistically significant piRNA between acutely infected rabbits and chronically infected rabbits. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs. FDR represents false discovery rate
Fig. 3 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 3 Global view of temporal sRNA expression profiles in rabbit serum during T. gondii infection. a The sRNA correlation heatmap of sample clustering. b Principal component analysis of all identified serum sRNA. c Unsupervised hierarchical clustering of sRNA profiling data. sRNA intensity is normalized so that blue represents low intensity and yellow represents high intensity. Columns are hierarchically clustered based on a complete linkage using Pearson correlation coefficients as the distance measure. Sample groups including acutely infected rabbits, chronically infected rabbits and uninfected control rabbits are labeled as AI, CI and Con, respectively
Fig. 2 in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 2 First nucleotide bias of obtained small RNA in serum samples of rabbits. a First nucleotide bias of known miRNAs in rabbit serum. b First nucleotide bias of predicted miRNAs in rabbit serum. c First nucleotide bias of predicted piRNAs in rabbit serum
Fig. 9 Urine piRNA profile analysis among different groups. a in A combined miRNA-piRNA signature in the serum and urine of rabbits infected with ToxoplaSMa gondii oocysts
Fig. 9 Urine piRNA profile analysis among different groups. a The volcano plot shows the individual statistically significant piRNA between acutely infected group and control group. In this plot, the x-axis is log2 fold-change, which shows the direction of the change (negative scale is decrease and positive scale is increase) in the levels of piRNA expression, while the y-axis is the –log10 FDR, which shows the significance of the change. b The volcano plot shows the individual statistically significant piRNA between chronically infected group and control group. c The volcano plot shows the individual statistically significant piRNA between acutely infected group and chronically infected group. d Venn diagram shows number of differentially expressed piRNA among different comparison pairs
Extended rat miRNA repertoire
<p>Generally, <em>Rattus norvegicus'</em> miRNA repertoire falls short compared to the other rodent model organism, <em>Mus musculus.</em></p> <p>To extend the miRNA catalogue in <em>Rattus norvegicus,</em> we utilized Infernal v1.1 (<a href="https://doi.org/10.1093/bioinformatics/btt509" target="_blank" rel="noopener">Nawrocki and Eddy, 2013</a>) to derive potential rat miRNA candidates starting from all available mammalian miRNA families in <a href="https://www.mirbase.org/" target="_blank" rel="noopener">miRBase</a>. We utilized MIRfix (<a href="https://doi.org/10.1093/bioinformatics/btz271" target="_blank" rel="noopener">Yazbeck et al., 2019</a>) to curate the extended miRNA datasets automatically. Subsequent manual inspection and curation of miRNA alignments resulted in a reliable and comprehensive update to the rat miRNA annotation.</p> <p>Key facts of the extended miRNA repertoire</p> <ul> <li>342 miRNA families (40 novel families)</li> <li>549 miRNA sequences (56 novel miRNAs)</li> <li> 11 corrected annotated miRNAs</li> </ul> <h3>European Nucleotide Archive</h3> <p>The 56 novel sequences not listed in miRBase before have been submitted to the European Nucleotide Archive at EMBL-EBI.<br>They are accessible with the accession numbers OZ078105 - OZ078160.<br>The sequences will be permanently available from the ENA browser at http://www.ebi.ac.uk/ena/data/view/<ACCESSION NUMBERS>.</p> <p>An overview of all sequences is given here: <a href="http://www.ebi.ac.uk/ena/data/view/OZ078105-OZ078160" target="_blank" rel="noopener">http://www.ebi.ac.uk/ena/data/view/OZ078105-OZ078160</a>.</p>
Fig. 30 in Taxonomic re-evaluation of Andrena cyanomicans PÉREZ, 1895, A. fratella WARNCKE, 1968, A. maderensis WARNCKE, 1969, A. mirna WARNCKE, 1969, A. notata WARNCKE, 1968, and A. portosanctana WARNCKE, 1969 (Hymenoptera, Anthophila)
Fig. 30: Morphometric analyses (males) of FL1/FL3 and FL3/Fl3 index, fovea length (FVL), fovea width (FVW), FVL/FVW index, head length (HL), head width (HW), HL/HW index, interocellar distance (IOD).
Fig. 23a-f in Taxonomic re-evaluation of Andrena cyanomicans PÉREZ, 1895, A. fratella WARNCKE, 1968, A. maderensis WARNCKE, 1969, A. mirna WARNCKE, 1969, A. notata WARNCKE, 1968, and A. portosanctana WARNCKE, 1969 (Hymenoptera, Anthophila)
Fig. 23a-f: Metasoma of the males: (a) Andrena cyanomicans (Photo: ZOBODAT, 2021/25/03); (b) A. fratella (Photo: ZOBODAT, 2021/25/03); (c) A. maderensis (Photo: L. Haitzinger, OLML); (d) A. mirna (Photo: M. Schwarz, OLML); (e) A. notata (Photo: ZOBODAT, 2021/25/03); (f) A. portosanctana (Photo: V. Smith, CAS).
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.