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3,584 results for “genomic analysis”
Genomic Analysis of Pediatric Low Grade Gliomas
Pediatric low-grade gliomas (PLGGs) are among the most common solid tumors in children but, apart from mutations or duplications in the BRAF kinase in specific subclasses, few genetic driver events are known. Diffuse PLGGs compose a set of uncommon subtypes that exhibit invasive growth and are therefore especially challenging clinically. These tumors are particularly poorly understood. We performed high-resolution copy-number analysis of 44 diffuse PLGGs to identify recurrent alterations. Diffuse PLGGs exhibited fewer such alterations than adult low-grade gliomas, but we identified several significantly recurrent events. The most significant event, 8q13.1 gains, was observed in 28% of diffuse astrocytoma WHO grade II (DA2) and resulted in partial duplication of the transcription factor MYBL1 with truncation of its C-terminal negative-regulatory domain. A similar recurrent deletion-truncation breakpoint was identified in two angiocentric gliomas in the related gene MYB on 6q23.3. Whole genome sequencing of a MYBL1-rearranged diffuse astrocytoma grade II demonstrated MYBL1 tandem duplication and few other events. Two truncated MYBL1 transcripts identified in this tumor induced anchorage-independent growth when expressed in 3T3 cells and tumor formation in nude mice. Truncated transcripts were also expressed in two additional tumors with MYBL1 partial duplication. Our results define clinically relevant molecular subclasses of diffuse PLGGs and highlight a potential role for the MYB family in the biology of low-grade gliomas.
Genomic Analysis of Fibrolamellar Hepatocellular Carcinoma
Fibrolamellar hepatocellular carcinoma (FL-HCC) is a very rare and distinct subtype of hepatocellular carcinoma that occurs in the previously healthy livers of adolescents and young adults. The low incidence rate has kept FL-HCC an understudied cancer, thus the list of FL-HCC markers is limited and the genetic alterations driving its growth have remained unknown. In order to gain a better understanding of the molecular mechanisms underlying FL-HCC initiation and progression, we performed an in-depth genomic analyses of one tumor followed by immunohistochemistry validation on seven other tumors. We showed the expression of neuroendocrine markers in FL-HCC. In addition, DNA/RNA sequencing revealed that common cancer pathways are not visibly altered in FL-HCC but identified two novel structural variants, both resulting in fusion transcripts. Through in vitro studies, we demonstrated the oncogenic properties of these fusion transcripts. These experiments further highlight the tumorigenic role of gene fusions in the etiology of pediatric solid tumors and identify both candidate biomarkers and possible therapeutic targets for this lethal pediatric disease.
Genomic Analysis in Pediatric Malignancies
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Genome-wide DNA methylation analysis one year post-COVID-19 infection
GEO Series GSE197152. Homo sapiens. 15 samples. Type: Methylation profiling by array.
Right-brain expression-biased wnt3 is vital in maintaining normal language function via combined functional genomic and genetic analysis
GEO Series GSE287961. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Genome-wide analysis of whole-lung and alveolar macrophages before and after gut microbiome depletion
GEO Series GSE53174. Mus musculus. 32 samples. Type: Expression profiling by array.
Dataset for Enhort: A Platform for Deep Analysis of Genomic Positions
<p>Tar-archive includes the following data:</p> <ul> <li>Sqlite3-database needed for Enhort</li> <li>All bed-Files referenced by database</li> </ul> <p>These files are only needed when you want to host your own Enhort-Server. If you only want to use the software, please visit <a href="https://enhort.mni.thm.de">https://enhort.mni.thm.de</a>.</p>
Data from: High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar
<p>The following dataset contains the processed data presented in the article<strong> "High-resolution methylome analysis uncovers stress-responsive genomic hotspots and drought-sensitive TE superfamilies in the clonal Lombardy poplar"</strong></p> <ul> <li><strong>Supplementary_methods.docx: </strong>contain detailed information for the experimental stress treatments, sequencing library preparation, sequencing and DMR calling.</li> </ul> <ul> <li><strong>BedGraph files (CpG.bed, CHG.bed, CHH.bed):</strong> contain methylation levels (%) for each cytosine in the Lombardy poplar genome, in the respective sequence context. The first three columns represent the genomic coordinates of the cytosine, the 56 following columns indicate the methylation levels for each of the samples. Missing values are represented with NA (when particular cytosines were not captured by the sequencing method). </li> <li><strong>DMR_annotation_Populus_nigra_Italica_after_biotic_and_abiotic_treatments.txt: </strong>contains all the identified regions that showed significant stress-induced differential methylation (DMR). The file include all annotation for each single DMR: genomic location, genomic feature, gene, TE, sequence context and stress treatment, besides other specific relevant information.</li> <li><strong>sample_IDs_basic_metadata.txt</strong>: contains the sample ID and the associated metadata (stress treatment and ortet location and ID) for all samples used in the analysis.</li> <li><strong>Supplementary_file_1_metadata_samples.xlsx: </strong>contains the metadata associated to each sample including: sequencing statistics before and after quality and adapter trimming, read mapping and coverage statistics, and number of interrogated cytosines on each sequence context (CpG, CHG, CHH).</li> <li><strong>Supplementary_file_2_GO_enrichments.xlsx: </strong>contains the complete results for the GO enrichment analysis for different gene datasets associated to: drought-CHH-DMRs, SINEs, MITEs, SINEs + MITEs.</li> <li><strong>italica_denovo_TE_280920.gff</strong>: contains the predicted TEs using the following methodology. First, TEs were de-novo annotated using the Extensive de-novo TE Annotator (EDTA) (version 1.8.3) (https://github.com/oushujun/EDTA) with default parameters, except for option --sensitive: 1, which uses RepeatModeler (version 2.0.1) to identify remaining TEs. All the steps in EDTA pipeline were selected (filter, final and anno) in order to perform whole-genome annotation/analysis after the TE library was constructed. Then, in the annotated library from EDTA, we merged overlapping fragments and fragments located at a close distance (<10bp) in a strand wise manner. The merged fragment was annotated as the family of longer merged fragment. Structural variants derived from nanopore data were used to redefine the boundaries of overlapping TE fragments to be more precise with actual predictions. LINE elements were identified independently by RepeatModeler in order to construct a more comprehensive de-novo TE library.</li> <li><strong>SaliS.fasta:</strong> contains the consensus sequences of <strong>Sali</strong>caceae <strong>S</strong>INE families (SaliS), the file was built by extracting information from the supplementary table 2 of the publication: "Divergence of 3′ ends as a driver of short interspersed nuclear element (SINE) evolution in the Salicaceae" (https://doi.org/10.1111/tpj.14721)</li> <li><strong>Pnigra_Italica_SaliS.bed: </strong>the file contains the annotated SaliS found by blastn over the P. nigra Italica reference genome (-qcov_hsp_perc 90 -perc_identity 70 -word_size 7). Column headers: chr, start, end, length, strand, perc_identity, SaliS family.</li> <li><strong>Pnigra_Italica_all_TEs_for_anno.bed: </strong>contains the merged information from <strong>italica_denovo_TE_280920.gff </strong>and<strong> Pnigra_Italica_SaliS.bed.</strong> Column headers: chr, start, end, length, strand, perc_identity (only for SaliS), TE superfamily.</li> <li><strong>CXX_ortet_DMRs_merged.bed</strong>: contains DMRs merged from all pairwise DMR callings between two ortets. One file per context. Column headers: chr, start, end, number of comparisons where the DMR occur, avg number of cytosines (when called in multiple DMR callings), avg differential methylation vs. control (when called in multiple DMR callings), avg adjusted p value (when called in multiple DMR callings), avg DMR length (when called in multiple DMR callings).</li> </ul> <p><strong>SCRIPTS</strong></p> <ul> <li><strong>cov_filtering.sh:</strong> to filter individual positions according to a custom threshold.</li> <li><strong>unionbedg_with_NAs.sh:</strong> to merge information from different samples in a single file taking into account the percentage of missing values per position across the given samples.</li> <li><strong>anovas_and_contrasts_boxplots_barplots_cld.r</strong>: to perform statistical tests for the effect of treatments and ortets on the average global methylation. Each sequence context was analyzed separately.</li> <li><strong>CHH_noise_filter.sh:</strong> to remove cytosines with invariable methylation values across 90% of the samples.</li> <li><strong>GlobalMethAvg_calculation.r</strong>: to calculate global average methylation given a methylation file (CpG.bed, CHG/bed or CHH.bed) and sample file.</li> <li><strong>Hclustering_and_PCAs_analysis.r:</strong> to perform hierarchical clustering, principal component analysis and plot the respective figures.</li> <li><strong>ICC_matrices_analysis.r:</strong> to calculate intraclass correlation coefficients among all pairwise combinations and plot colored grids</li> </ul> <p> </p> <p>Annotations are based on the de novo reference genome of the Populus nigra cv. Italica clone uploaded in the ENA project: PRJEB44889 (<a href="http://www.ebi.ac.uk/ena/browser/view/GCA_950102115">www.ebi.ac.uk/ena/browser/view/GCA_950102115</a>). Bisulfite sequencing data can be found under the ENA project: PRJEB51831</p>
Dataset related to article "Genomic instability analysis in DNA from Papanicolaou test provides proof-of-principle early diagnosis of high-grade serous ovarian cancer"
<p>This record contains data related to the article "Genomic instability analysis in DNA from Papanicolaou test provides proof of principle early diagnosis of high-grade serous ovarian cancer".</p><p>Abstract</p><p>Late diagnosis and the lack of screening methods for early detection define high-grade serous ovarian cancer (HGSOC) as the gynecological malignancy with the highest mortality rate. In the work presented here, we investigated a retrospective and multicentric cohort of 250 archival Papanicolaou (Pap) test smears collected during routine gynecological screening. Samples were taken at different time points (from 1 month to 13.5 years before diagnosis) from 113 presymptomatic women who were subsequently diagnosed with HGSOC (pre-HGSOC) and from 77 healthy women. Genome instability was detected through low-pass whole-genome sequencing of DNA derived from Pap test samples in terms of copy number profile abnormality (CPA). CPA values of DNA extracted from Pap test samples from pre-HGSOC women were substantially higher than those in samples from healthy women. Consistently with the longitudinal analysis of clonal pathogenic <i>TP53</i> mutations, this assay could detect HGSOC presence up to 9 years before diagnosis. This finding confirms the continual shedding of tumor cells from fimbriae toward the endocervical canal, suggesting a new path for the early diagnosis of HGSOC. We integrated the CPA score into the EVA (early ovarian cancer) test, the sensitivity of which was 75% (95% CI, 64.97 to 85.79), the specificity 96% (95% CI, 88.35 to 100.00), and the accuracy 81%. This proof-of-principle study indicates that the early diagnosis of HGSOC is feasible through the analysis of genomic alterations in DNA from endocervical smears.</p>
Genome-wide analysis of matched microRNA-mRNA time-course data after androgen injection to prostate cancer LNCaP cell line
GEO Series GSE21245. Homo sapiens. 20 samples. Type: Expression profiling by array; Non-coding RNA profiling by array.
Genome-wide analysis of gene expression in Krt1+/+ versus Krt1-/- epidermis and full-thickness skin.
GEO Series GSE32951. Mus musculus. 12 samples. Type: Expression profiling by array.
Genome-wide analysis of BRCA1 and PALB2
GEO Series GSE45715. Homo sapiens. 53 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by array.
Genome-wide expression analysis of xylogenesis -related genes in Eucalyptus tereticornis
GEO Series GSE73030. Eucalyptus; Eucalyptus tereticornis. 4 samples. Type: Expression profiling by array.
Comparative genomic hybridization analysis in glycine max fast neutron mutants - seed composition in southern U.S. varieties
GEO Series GSE125533. Glycine max. 4 samples. Type: Genome variation profiling by array.
Integrative genome-wide analysis in non-small cell lung cancer cells
GEO Series GSE63356. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Genome-wide analysis of gene expression by LSD1 overexpression or inhibiting by ORY1001 with or without irradiation in MDA-MB-231 breast cancer cells
GEO Series GSE120193. Homo sapiens. 6 samples. Type: Expression profiling by array.
Genome-wide analysis of distribution of RNA Helicase Maleless (MLE) in Drosophila Melanogaster early female embryo, before (0-2 Hr) and after (2-4 Hr) maternal to zygotic transition (MZT)
GEO Series GSE220981. Drosophila melanogaster. 5 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Genome-wide methylation analysis of T-cells using Infinium HumanMethylation BeadChip
GEO Series GSE136189. Homo sapiens. 40 samples. Type: Methylation profiling by genome tiling array.
Genome-wide expression analysis of self renewing hematopoietic stem cells (HSC) in control and CD70TG mice.
GEO Series GSE31574. Mus musculus. 16 samples. Type: Expression profiling by array.
Genome-wide microRNA analysis of NPC and non-NPC specimens gene expression
GEO Series GSE22587. Homo sapiens. 12 samples. Type: Non-coding RNA profiling by array.
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.