464 results for "GitHub+primate+acoustics+baboon+Zenodo+10.5281+Gui*"

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• ST lobular manuscript dataset

  Spatial transcriptomics analysis for 43 breast cancer lobular samples. Note that there are 2 runs per sample Images and read counts will be made availalble on Zenodo upon publication.

  Dataset EGAD50000001467

• DAC-2023-07-05-Ritz (DAC-007)

  Full description of the dataset and all necessary information on metadata may be found under (https://zenodo.org/records/11237107): DAC-2023-07-05-Ritz (DAC-007), raw data in EGA, metadata in Zenodo (https://zenodo.org/records/11237107)

  Dataset EGAD50000001147

• Construction of Thousands of Single Cell Genome Sequencing Libraries Using Combinatorial Indexing

  Single-cell genome sequencing has proven valuable for the detection of somatic variation, particularly in the context of tumor evolution. Current technologies suffer from high library construction costs which restrict the number of cells that can be assessed and thus impose limitations on the ability to measure heterogeneity within a tissue. Here, we present Single cell Combinatorial Indexed Sequencing (SCI-seq) as a means of simultaneously generating thousands of low-pass single cell libraries for somatic copy number variant detection. We constructed libraries for 16,698 single cells from a combination of cultured cell lines, primate frontal cortex tissue, and two human adenocarcinomas, including a detailed assessment of subclonal variation within a pancreatic tumor. "Reprinted from Vitak, S.A., et. al. 2017 with permission from Nature Methods."

  Study phs001268

• Dataset Rdata segmentations

  This data set contains an .Rdata file for all the processed segmentation profiles from 81 lpWGS samples included used in downstream analyses from Github repository Evo_history_CACRC. Lastly, there is an .Rdata object with 50 segmentations for 50 total samples from 25 sporadic SNP adenomas used in the comparison with colitis samples.

  Dataset EGAD00010001586

• Human CD4+ T cells regulate peripheral immune responses in rheumatoid arthritis via IGFL2

  To elucidate the role of the immunoregulatory mechanisms of human CD4+ T cells, we analyzed CD4+ T cells in PBMC, synovium and synovial fluid using single-cell RNA sequencing (scRNA-seq). We identified insulin-like growth factor (IGF) like family member 2 (IGFL2), an evolutionary novel paralogous inflammatory factor found exclusively in primates. ScRNA-seq of RA synovium showed that IGFL2 is specifically expressed by CD4+ T cells, predominantly within peripheral helper T (Tph) cells, with upregulation correlating to disease severity. IGFL2 promotes TGF-��-induced CXCL13 production in CD4+ T cells. In addition, IGFL2 activates NF-��B signaling and induces monocyte gene signatures associated with pathogenic macrophages from RA synovial tissue, including CXCL10 and CXCL9. Notably, blood IGFL2 protein levels significantly correlate with RA disease severity and effectively discriminate RA patients. These findings highlight the substantial involvement of IGFL2 in RA pathogenesis, emphasizing how human CD4+ T cells exert pleiotropic regulation of local immune responses via this novel primate-specific soluble factor.

  Study JGAS000727

• Origins and functional consequence of somatic mitochondrial DNA mutations

  Recent sequencing studies have extensively explored the somatic alterations present in the nuclear genomes of cancers. Although mitochondria control energy metabolism and apoptosis, the origins and impact of cancer-associated mutations in mitochondrial DNA (mtDNA) are unclear. Here, we analysed somatic alterations in mtDNA from 1,675 tumors across 31 histologies. We identified 1,907 somatic substitutions, which exhibited dramatic replicative strand bias, predominantly C>T and A>G on the mitochondrial heavy strand. This strand-asymmetric signature differs from those found in nuclear cancer genomes but matches the inferred germline process shaping primate mtDNA sequence content. Numbers of mtDNA mutations showed considerable heterogeneity across tumor types. Missense mutations were selectively neutral and often gradually drifted towards homoplasmy over time. In contrast, mutations resulting in protein truncation undergo negative selection and were almost exclusively heteroplasmic. Our findings indicate that the endogenous mutational mechanism has far greater impact than any other external mutagens in mitochondria, and is fundamentally linked to mtDNA replication.

  Study EGAS00001000968

• Single-cell bam files and RNA sequencing of viral RNA stocks

  244 infected single-cell alveolar bam files, 48 empty well bam files, and 52 RNA sequencing of amplicons (4 SARS-CoV-2 variants with 12 batches and 4 viral variants pool samples). 244 alveolar single cells were captured over 12 experimental batches and experimental condition is written in metadata uploaded as "infected_cells_final_revision.csv". on github (https://github.com/twkim-0510/SARS-CoV-2_viral_competition). Each bam file name corresponds to the sample_name column of the metadata.

  Dataset EGAD00001009711

• Star2xml: metadata converter into XML

  Star2xml: metadata converter into XML Welcome to the landing page for star2xml - a tool developed by the European Genome-phenome Archive (EGA) that enables users to convert metadata into XML format that is compliant with EGA's programmatic submission system. For starters, if you are not sure what metadata is in this context, you may want to explore our brief metadata introduction. . Star2xml is useful for users who have already submitted their data to the EGA and want to also submit their metadata programmatically without having to create the XML files manually. The need for these XMLs containing metadata is explained in the EGA's metadata schemas and programmatic submission guidelines. In summary, star2xml allows for users to deposit metadata in a tabular format (e.g. excel spreadsheet) and transform it into a compliant XML format. This saves time in large submissions, where the sheer size of metadata makes it difficult to manage. For smaller submissions, it is recommended to use EGA's Submitter Portal (SP) instead. The installation of the tool is straightforward, and it contains detailed documentation on how to use it in its GitHub repository . While star2xml can help users convert metadata formats, it does not validate the resulting metadata against EGA's schemas. Therefore, after creating your XMLs, you should still validate your metadata before submitting it to EGA, as described in the documentation for programmatic metadata validation.

  Documentation tools/star2xml

• Spatial transcriptomics analysis of triple negative breast cancers

  While triple negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigated TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We performed an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures led to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identified nine TNBC ecotypes, further validated in external datasets. Several ecotypes were associated with disease outcome and characterized by potentially actionable features. In this work, we provideThis is the largest TNBC series providing a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy. All the processed data needed to replicate the results from the publication (counts, images, clinical data, etc) are available on Zenodo: https://zenodo.org/records/14204217

  Study EGAS50000000475

• PCA Atlas Visium spatial transcriptomics data (FASTQs, spaceranger BAMs and Visium mapping tables)

  This dataset contains Visium spatial transcriptomics data for the PCA Atlas study EGAS00001008332. It includes: - Raw Visium FASTQ runs (EGAR00004172272–EGAR00004172283) for all libraries. - spaceranger alignment BAM analyses for those libraries where BAM output was generated. - visium.csv: a slide-level mapping table linking Visium slide samples to EGA sample accessions, sequencing runs/experiments and spaceranger BAM analyses. - visium_tma.csv: a TMA mapping table describing how mini-TMA slides relate slide-level Visium libraries to tissue-level samples/cores. Together these objects provide the raw and aligned Visium data and the technical/biological mappings needed to understand and reuse the spatial transcriptomics component of the PCA Atlas. High resolution histology images are available from the following Zenodo repository, along with additional spaceranger outs for each slide: - https://zenodo.org/records/17411292

  Dataset EGAD00001015796