AIMC Topic: RNA-Seq

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Dual-Organ Transcriptomic Analysis of Rainbow Trout Infected With Through Co-Expression and Machine Learning.

Frontiers in immunology Jul 8, 2021
is a major pathogen that causes a high mortality rate in trout farms. However, systemic responses to the pathogen and its interactions with multiple organs during the course of infection have not been well described. In this study, dual-organ transc...
Ciliophora Infections Hymenostomatida Host-Pathogen Interactions Gills Machine Learning Liver Gene Regulatory Networks Head Kidney Animals Virulence Factors Signal Transduction Oncorhynchus mykiss Transcriptome Virulence Immunity, Innate RNA-Seq Gene Expression Regulation Fish Diseases
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A Literature-Derived Knowledge Graph Augments the Interpretation of Single Cell RNA-seq Datasets.

Genes Jun 10, 2021
Technology to generate single cell RNA-sequencing (scRNA-seq) datasets and tools to annotate them have advanced rapidly in the past several years. Such tools generally rely on existing transcriptomic datasets or curated databases of cell type definin...
Molecular Sequence Annotation Humans Single-Cell Analysis RNA-Seq Databases, Genetic Natural Language Processing Organ Specificity
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coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data.

PLoS computational biology Jun 2, 2021
Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-seq data and...
Animals Dendritic Cells Humans Cerebral Cortex Single-Cell Analysis Mice Databases, Nucleic Acid Machine Learning Genomics RNA-Seq Information Theory Software Computational Biology RNA, Small Cytoplasmic Cluster Analysis
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MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks.

Genome biology May 20, 2021
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-c...
Single-Cell Analysis RNA-Seq Gene Expression Regulation Statistics, Nonparametric Humans Neural Networks, Computer Algorithms Computer Simulation Gene Expression Profiling
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Pseudocell Tracer-A method for inferring dynamic trajectories using scRNAseq and its application to B cells undergoing immunoglobulin class switch recombination.

PLoS computational biology May 3, 2021
Single cell RNA sequencing (scRNAseq) can be used to infer a temporal ordering of cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic or...
Neural Networks, Computer Interleukin-4 Immunoglobulin G Computer Simulation Computational Biology Signal Transduction Mice, Inbred C57BL Gene Expression Animals Single-Cell Analysis RNA-Seq Mice NF-kappa B p50 Subunit Receptors, Cytokine B-Lymphocytes STAT6 Transcription Factor Recombination, Genetic Supervised Machine Learning Basic-Leucine Zipper Transcription Factors Databases, Nucleic Acid Immunoglobulin Class Switching Models, Immunological
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Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters.

International journal of molecular sciences Mar 25, 2021
RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as bi...
Biomarkers Transcriptome Prefrontal Cortex Diagnosis, Computer-Assisted RNA-Seq Computational Biology RNA, Messenger RNA, Long Noncoding Humans Machine Learning Algorithms Factor Analysis, Statistical Schizophrenia Multigene Family RNA, Untranslated
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DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires.

Nature communications Mar 11, 2021
Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diver...
T-Lymphocytes Mice Humans Deep Learning Databases, Genetic Neural Networks, Computer RNA-Seq Amino Acid Sequence Algorithms Receptors, Antigen, T-Cell Animals High-Throughput Nucleotide Sequencing
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2passtools: two-pass alignment using machine-learning-filtered splice junctions increases the accuracy of intron detection in long-read RNA sequencing.

Genome biology Mar 1, 2021
Transcription of eukaryotic genomes involves complex alternative processing of RNAs. Sequencing of full-length RNAs using long reads reveals the true complexity of processing. However, the relatively high error rates of long-read sequencing technolog...
Sequence Alignment RNA Splicing RNA Splice Sites RNA-Seq Computational Biology Molecular Sequence Annotation Introns Machine Learning Software Algorithms Reproducibility of Results
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Single Cell RNA-Seq and Machine Learning Reveal Novel Subpopulations in Low-Grade Inflammatory Monocytes With Unique Regulatory Circuits.

Frontiers in immunology Feb 23, 2021
Subclinical doses of LPS (SD-LPS) are known to cause low-grade inflammatory activation of monocytes, which could lead to inflammatory diseases including atherosclerosis and metabolic syndrome. Sodium 4-phenylbutyrate is a potential therapeutic compou...
Male Single-Cell Analysis RNA-Seq Gene Regulatory Networks Machine Learning Animals Quality Control Phenylbutyrates Mice Monocytes Lipopolysaccharides Mice, Inbred C57BL
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Potential applications of deep learning in single-cell RNA sequencing analysis for cell therapy and regenerative medicine.

Stem cells (Dayton, Ohio) Feb 15, 2021
When used in cell therapy and regenerative medicine strategies, stem cells have potential to treat many previously incurable diseases. However, current application methods using stem cells are underdeveloped, as these cells are used directly regardle...
Cell- and Tissue-Based Therapy Deep Learning Single-Cell Analysis RNA-Seq Regenerative Medicine Humans Transcriptome
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