RNA-Seq Blog Transcriptome Research & Industry News
Single-cell RNA-sequencing (scRNA-seq) technology enables researchers to investigate a genome at the cellular level with unprecedented resolution...
Read More »scAnnotate – an automated cell type annotation tool for single-cell RNA-sequencing data
DeepBIO – automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis.
Shandong University researchers have developed DeepBIO, the first-of-its-kind automated and interpretable deep-learning platform for high-throughput biological sequence functional analysis. DeepBIO is a one-stop-shop web service that enables...
Read More »annotate_my_genomes – an easy-to-use pipeline to improve genome annotation and uncover neglected genes by hybrid RNA sequencing
The advancement of hybrid sequencing technologies is increasingly expanding genome assemblies that are often annotated using hybrid sequencing transcriptomics, leading to improved genome characterization..
Read More »Vaeda – computational annotation of doublets in single-cell RNA sequencing data
Single-cell RNA sequencing (scRNA-seq) continues to expand our knowledge by facilitating the study of transcriptional heterogeneity at the level of single...
Read More »miRPipe – a unified computational framework for a robust, reliable, and reproducible identification of novel miRNAs from RNA sequencing data
In eukaryotic cells, miRNAs regulate a plethora of cellular functionalities ranging from cellular metabolisms, and development to the regulation of biological...
Read More »scPipeline – multi-level cellular and functional annotation of single-cell transcriptomes
Single-cell RNA-sequencing (scRNA-seq) offers functional insight into complex biology, allowing for the interrogation of cellular populations and gene expression programs at single-cell resolution. Researchers at the University of Toronto have developed...
Read More »Sparse group lasso outperforms other penalized regression methods for gene (feature) selection from scRNA-seq data
With the emergence of single-cell RNA sequencing (scRNA-seq) technology, scientists are able to examine gene expression at single-cell resolution. Analysis of scRNA-seq data has its own challenges...
Read More »SECANT – a biology-guided semi-supervised method for clustering, classification, and annotation of single-cell multi-omics
The recent advance of single cell sequencing (scRNA-seq) technology such as Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) allows researchers to quantify cell surface protein...
Read More »scWizard – a web-based automated tool for classifying and annotating single cells and downstream analysis of single-cell RNA-seq data in cancers
The emerging number of single-cell RNA-seq (scRNA-Seq) datasets allows the characterization of cell types across various cancer types. However, there is still lack of effective tools to integrate the various analysis of single-cells, especially for making...
Read More »devCellPy – a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data
A major informatic challenge in single cell RNA-sequencing analysis is the precise annotation of datasets where cells exhibit complex multilayered identities or transitory states. Researchers at the...
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