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Biomedical scientists are increasingly using deconvolution methods, those used to computationally analyze the composition of complex mixtures of cells. One of their challenges is to...

A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level...

A key challenge in single-cell RNA-sequencing (scRNA-seq) data analysis is batch effects that can obscure the biological signal of interest. Although...

Ribosome Profiling (Ribo-seq) has revolutionized the study of RNA translation by providing information on ribosome positions across all translated RNAs with...

The pandemic has shown that scientists across the globe are willing to join forces to better understand the virus and to develop new treatments and vaccines. Key scientific achievements in this respect – some of them already in the early ...

Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional...

New software tool allows researchers to rapidly search datasets from single-cell sequencing A new software tool allows researchers to quickly query datasets generated from single-cell sequencing...

“Mixscape” tool helps researchers understand gene function and regulation A team of researchers from New York University and the New York Genome Center has developed a new computational tool to help understand the function and regulation of human genes. The ...

Long noncoding RNAs represent a large class of transcripts with two common features: they exceed an arbitrary length threshold of 200 nt and are...

Recent development of spatial transcriptomic technologies has made it possible to systematically characterize cellular heterogeneity while preserving spatial information, which greatly enables the investigation of structural organization of a tissue and its impact on modulating cellular behavior. On the other ...