RNA-Seq Blog Transcriptome Research & Industry News
Single-cell RNA sequencing (scRNA-seq) strives to capture cellular diversity with higher resolution than bulk RNA sequencing. Clustering analysis is critical...
Read More »scSemiAAE – a semi-supervised clustering model for single-cell RNA-seq data
RNAincoder – a deep learning-based encoder for RNA and RNA-associated interaction
Ribonucleic acids (RNAs) involve in various physiological/pathological processes by interacting with proteins, compounds, and other RNAs. A variety of powerful computational methods have been developed to predict such valuable interactions. However, all these methods rely heavily on the ‘digitalization’ (also ...
Read More »New in silico benchmarking tool to evaluate and validate single-cell RNA-Seq and spatial transcriptomics computational methods
UCLA researchers have developed an “all-in-one,” next-generation statistical simulator capable of assimilating a wide range of information to generate realistic synthetic data and provide a benchmarking tool for medical and biological researchers who use advanced...
Read More »scSELpy – manual cell selection in single cell transcriptomics for analysis of immune cell subsets
Single cell RNA sequencing plays an increasing and indispensable role in immunological research such as in the field of inflammatory bowel diseases (IBD)...
Read More »An upgraded computational tool gets more out of RNA-Seq data
An upgraded computational tool from St. Jude Children’s Research Hospital can find potentially druggable hidden drivers of cancer and other biological processes using multi-omics data...
Read More »GuiltyTargets-COVID-19 – a machine learning method for the identification and characterization of novel COVID-19 drug targets from bulk and scRNA-Seq data
In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic...
Read More »scSpace – reconstruction of the cell pseudo-space from single-cell RNA sequencing data
Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Zhejiang University researchers have developed a single-cell spatial position ...
Read More »Nano-DMS-MaP – isoform-specific RNA structure determination
Genome-wide measurements of RNA structure can be obtained using reagents that react with unpaired bases, leading to adducts that can be identified by mutational profiling on next-generation sequencing...
Read More »CloneTracer – clonally resolved single-cell multi-omics
Researchers have developed a new method to distinguish between cancerous and healthy stem cells and progenitor cells from samples of patients with acute myeloid leukaemia (AML), a disease...
Read More »Read2Tree – inference of phylogenetic trees directly from raw sequencing reads
Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional constraints in sequencing coverage, assembly and annotation quality, especially for large datasets...
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