7205 results for "({!join from=dataset_studies to=id v=$q1}) OR ("transcriptome" OR "RNA-seq" OR "rna sequencing" OR "rna-sequencing")"

in 17.57 milliseconds.

• DAC Human Skeletal Muscles Transcriptome

  Dac EGAC00001002603

• MSI-Exome-Transcriptome-DAC

  Dac EGAC00001001845

• Reconstruction of human terminal erythroid differentiation cell at single cell level

  Terminal erythroid differentiation cells(GYPA+ cells) were isolated from neonate cord blood(CB) and adult bowe marrow(BM). single cell transcriptome sequencing library was constructed by 10X genomics system and sequenced by Illumina X ten platform. Sequencing data was analyzed to profile dynamic expression genes and centre regulators during terminal erythroid differentiation stage within CB and BM.

  Study EGAS00001003114

• CTD-ILD_BALF_and_blood_scRNA-seq_data

  Study EGAS00001007539

• Efficient prediction of a spatial transcriptomics profile better characterizes breast cancer tissue sections without costly experimentation

  Spatial transcriptomics is an emerging technology requiring costly reagents and considerable skills, limiting the identification of transcriptional markers related to histology. Here, we show that predicted spatial gene-expressions in unmeasured regions and tissues can enhance biologists��� histological interpretations. We developed the Deep learning model for Spatial gene Clusters and Expression, DeepSpaCE and confirmed its performance using the spatial-transcriptome profiles and immunohistochemistry images of consecutive human breast cancer tissue sections. For example, the predicted expression patterns of SPARC, an invasion marker, highlighted a small tumor-invasion region that is difficult to identify using raw data of spatial transcriptome alone because of a lack of measurements. We further developed semi-supervised DeepSpaCE using unlabeled histology images and increased the imputation accuracy of consecutive sections, enhancing applicability for a small sample size. Our method enables users to derive hidden histological characters via spatial transcriptome and gene annotations, leading to accelerated biological discoveries without additional experiments.

  Study JGAS000290

• Etiology of congenital thyroid dysfunction in Down Syndrome: a multi-omics investigation

  The goal of this study was to investigate dfiferences in the thyroid DNA methylome and transcriptome between fetuses with Down syndrome and fetuses with no genetic/developmental abnormality.

  Study EGAS50000000264

• Spatial and temporal transcriptome analysis on human skeletal muscle regeneration

  Skeletal muscle carries a unique ability for adaptation as well as regeneration that highly depends on a supportive cellular microenvironment. Here we show that spatial and single-cell transcriptomic analysis in human skeletal muscle is a unique approach to illuminate cellular interactions in the microenvironment.

  Dataset EGAD50000000263

• Effect_of_FAM50_knockout_on_the_transcriptome

  A knockout clone has been generated for both FAM50A and FAM50B; knockout of the other gene is then performed and the transcriptome is analysed to look at the effect of dual gene loss.

  Study EGAS00001004836

• Whole transcriptome sequencing of the dMMR colorectal cancer cohort

  We leveraged the ImmunoID NeXT platform, an Immune-enhanced multiomics platform to profile a cohort of patients with dMMR CRC. This sub-study includes the whole transriptome sequencing data

  Study EGAS50000001742

• STAT1 AM RNAseq

  The transcriptome of peripheral blood cells (PBMCs) from control or patients with an activation mutation on the STAT1 gene was analyzed. This analysis aimed to identify the major changes in the circulating immune cells of patients with STAT1 mutation and compare this with the result of the perturbation prediction tool huva (human variation, R).

  Dataset EGAD00001006962