rna-seq

Documents about rna-seq

• The Effect of Oncogenic Alterations on Phenotype Profiles in Colorectal Cancer Cells – a Pooled Single-Cell Screen#322212edoc-Server
• Image 12_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411644Figshare
• Image 13_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411645Figshare
• Image 10_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411646Figshare
• Image 11_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411647Figshare
• Image 15_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.png#411648Figshare
• Image 2_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411649Figshare
• Image 13_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411651Figshare
• Image 11_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411652Figshare
• Image 12_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411653Figshare
• Image 2_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411654Figshare
• Image 10_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.jpeg#411655Figshare
• Image 7_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.tif#411656Figshare
• Image 15_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.png#411657Figshare
• Image 5_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.tif#411658Figshare
• Image 6_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.tif#411659Figshare
• Image 3_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.tif#411660Figshare
• Image 4_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.tif#411661Figshare
• Image 1_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.tif#411662Figshare
• Table 1_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.xlsx#411663Figshare
• Image 9_Immunogenetics and TCR expression patterns together predict future T1D onset from multi-omic datasets.tif#411664Figshare
• GROQ-seq Datasets Across Transcription Factors (LacI, RamR, VanR), T7 RNA Polymerase and TEV Protease#192336bioRxiv / medRxiv
• USADAE: a deep learning approach to disentangle hidden covariates in RNA-seq data.#247547NCBI PubMed Central
• Key molecular mechanisms of mitochondrial metabolic pathways in specific cell subpopulations of pancreatic cancer based on scRNA-seq and bulk RNA-seq#335925PLOS
• Integrative analysis of scRNA-seq and bulk RNA-seq with machine learning develops a nucleotide metabolism-based prognostic model for ccRCC and reveals the function of IFI30.#613391NCBI PubMed Central
• Biochemistry, Structure, and Function of SMARCB1-mutant mSWI/SNF Chromatin Remodeling Complexes in Human Disease#643586Harvard DASH
• Dynamic Quantum Clustering of Gliomas RNA-seq Identifies Diagnostic Separation and Survival Gradients#162301bioRxiv / medRxiv
• cellGeometry: ultra-fast single-cell deconvolution of bulk RNA-Seq using a geometric solution#273237Europe PMC
• The promise of long-read RNA-seq: reducing bias in analyses of allele imbalance.#430871NCBI PubMed Central
• scVGAE: A ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation#623180arXiv (OAI Expanded)
• scVGAE: A ZINB-Based Variational Graph Autoencoder for Single-Cell RNA-Seq Imputation#625453arXiv (All)
• Integration of RNA-seq and scRNA-seq to investigate the role of cell cycle-related biomarkers in sepsis#446582PLOS
• Benchmarking immunoinformatic repertoire assemblies from bulk RNA-seq and PCR-based V(D)J sequencing using PCR-free SMRT RNA sequencing#1000637HAL (France)
• Publisher Correction: A general and flexible method for signal extraction from single-cell RNA-seq data#20090DOAJ
• CanID: A Robust and Accurate RNA-seq Expression-based Diagnostic Classification Scheme for Pediatric Malignancies.#247585NCBI PubMed Central
• Unique molecular identifiers don't need to be unique: a collision-aware estimator for RNA-seq quantification#293959bioRxiv / medRxiv
• Long-read RNA-seq resolves isoform-level and context-specific regulatory architecture of complex traits in cattle#339549bioRxiv / medRxiv
• Omega-seq: ultra-low-background RNA sequencing with faithful molecular counting and precise transcript-end capture#619179bioRxiv / medRxiv
• Whole-animal multiplexed single-cell RNA-seq reveals transcriptional shifts across Clytia medusa cell types#677164HAL (France)
• Bioinformatics analysis and single-cell RNA sequencing: elucidating the ubiquitination pathways and key enzymes in lung adenocarcinoma#935980Repositorio de Universidad Autónoma de Chile.
• Longitudinal Single-Cell RNA-seq Profiling of Lung Cell Phenotypes, Signaling, and Cross-talk During Fibrosis Resolution#158252bioRxiv / medRxiv
• NEXT-scASV: a Nextflow pipeline for allele-specific variant calling from single-cell RNA-seq data.#159864NCBI PubMed Central
• A dual approach to evaluate the performance of RNA-Seq data analysis pipelines with weak signals.#266928NCBI PubMed Central
• GxP-Ready Single-Cell RNA-seq and Spatial Transcriptomics End-to-End Pipeline for Clinical Research.#371402NCBI PubMed Central
• Bulk and single-cell RNA-seq analyses characterize a DBP-related circadian signature in ischemic stroke.#442885NCBI PubMed Central
• Using transcriptomics to study relapse in acute myeloid leukaemia#1005462The University of Melbourne Library Digital Repository
• Benchmarking long-read RNA-seq across modalities, methods, and sequencing depth in iNeurons#154139Unpaywall
• Comparison between a conventional tool and deep learning models for RNA velocity analysis of scRNA-Seq data.#185899NCBI PubMed Central
• Profiling neoadjuvant therapy response in rectal cancer using meta-analysis of publicly available transcriptomic RNA-seq datasets.#670988NCBI PubMed Central
• scEVE: a single-cell RNA-seq ensemble clustering algorithm capitalizing on the differences of predictions between multiple clustering methods#796386HAL (France)