Search
Search Results
-
RNA-Seq Data Analysis
RNA-Seq data analysis stands as a vital part of genomics research, turning vast and complex datasets into meaningful biological insights. It is a...
-
Short-Read RNA-Seq
RNA sequencing (RNA-Seq) has emerged as a powerful and versatile tool for the comprehensive analysis of transcriptomes and has been widely used to...
-
RNA-clique: a method for computing genetic distances from RNA-seq data
BackgroundAlthough RNA-seq data are traditionally used for quantifying gene expression levels, the same data could be useful in an integrated...
-
Metatranscriptomic RNA-Seq Data Analysis of Virus-Infected Host Cells
RNA sequencing (RNA-seq)RNA sequencing (RNA-seq) analysis of virus-infected host cells enables researchers to study a wide range of phenomena...
-
A comprehensive workflow for optimizing RNA-seq data analysis
BackgroundCurrent RNA-seq analysis software for RNA-seq data tends to use similar parameters across different species without considering...
-
Computational Analysis of Single-Cell RNA-Seq Data
Single-cell RNA sequencing (scRNA-seq) is gaining popularity as this allows you to profile a large number of individual cells. However, as the volume...
-
Benchmarking algorithms for joint integration of unpaired and paired single-cell RNA-seq and ATAC-seq data
BackgroundSingle-cell RNA-sequencing (scRNA-seq) measures gene expression in single cells, while single-nucleus ATAC-sequencing (snATAC-seq)...
-
Targeting nucleotide metabolic pathways in colorectal cancer by integrating scRNA-seq, spatial transcriptome, and bulk RNA-seq data
BackgroundColorectal cancer is a malignant tumor of the digestive system originating from abnormal cell proliferation in the colon or rectum, often...
-
Curare and GenExVis: a versatile toolkit for analyzing and visualizing RNA-Seq data
Even though high-throughput transcriptome sequencing is routinely performed in many laboratories, computational analysis of such data remains a...
-
RNA Sequencing (RNA-seq)
RNA sequencing (or popularly known as RNA-seq) or whole transcriptome shortgun sequencing (WTSS) is a useful next-generation sequencing (NGS)...
-
KARR-seq reveals cellular higher-order RNA structures and RNA–RNA interactions
RNA fate and function are affected by their structures and interactomes. However, how RNA and RNA-binding proteins (RBPs) assemble into higher-order...
-
RNA-Seq Experiment and Data Analysis
With the ability to obtain several millions of reads per sample, high-throughput RNA sequencing (RNA-Seq) enables investigation of any transcriptome...
-
Integrative analysis of Iso-Seq and RNA-seq data reveals transcriptome complexity and differential isoform in skin tissues of different hair length Yak
BackgroundThe hair follicle development process is regulated by sophisticated genes and signaling networks, and the hair grows from the hair...
-
RNA Preparation and RNA-Seq Bioinformatics for Comparative Transcriptomics
The principal transcriptome analysis is the determination of differentially expressed genes across experimental conditions. For this, the...
-
A sco** review on deep learning for next-generation RNA-Seq. data analysis
In the last decade, transcriptome research adopting next-generation sequencing (NGS) technologies has gathered incredible momentum amongst functional...
-
Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data
Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially...
-
Effective methods for bulk RNA-seq deconvolution using scnRNA-seq transcriptomes
BackgroundRNA profiling technologies at single-cell resolutions, including single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq,...
-
rMATS-turbo: an efficient and flexible computational tool for alternative splicing analysis of large-scale RNA-seq data
Pre-mRNA alternative splicing is a prevalent mechanism for diversifying eukaryotic transcriptomes and proteomes. Regulated alternative splicing plays...
-
Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously
Large compendia of gene expression data have proven valuable for the discovery of novel biological relationships. Historically, most available RNA...
-
scFSNN: a feature selection method based on neural network for single-cell RNA-seq data
While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like...
