The advent of single-cell sequencing is providing unprecedented opportunities to disentangle tissue complexity and investigate cell identities and functions. However, the analysis of single cell data is a challenging, multi-step process that requires both...
Read More »Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data
Single-cell RNA-sequencing (scRNA-seq) enables researchers to quantify transcriptomes of thousands of cells simultaneously and study transcriptomic changes between cells. scRNA-seq datasets increasingly include multisubject, multicondition experiments to investigate cell-type-specific differential states (DS) between conditions. This can be performed by first ...
Read More »Benchmarking long-read RNA-sequencing analysis tools using in silico mixtures
The current lack of benchmark datasets with inbuilt ground-truth makes it challenging to compare the performance of existing long-read isoform...
Read More »Spacemake – processing and analysis of large-scale spatial transcriptomics data
Spatial sequencing methods increasingly gain popularity within RNA biology studies. State-of-the-art techniques quantify messenger RNA expression levels from tissue sections and at the same time register information about the original locations of the...
Read More »scTagger – a computational method to match cellular barcodes data from long-reads and short-reads
Single-cell RNA sequencing allows for characterizing the gene expression landscape at the cell type level. However, because of its use of short-reads, it is severely limited at detecting full-length features of transcripts such as alternative splicing. New library preparation techniques ...
Read More »Batch effect detection and correction in RNA-seq data using machine-learning-based automated assessment of quality
The constant evolving and development of next-generation sequencing techniques lead to high throughput data composed of datasets that include a large number of biological samples. Although a...
Read More »miRPipe – 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 networks and pathways, both under...
Read More »scISR – a novel method for single-cell data imputation using subspace regression
Recent advances in biochemistry and single-cell RNA sequencing (scRNA-seq) have allowed us to monitor the biological systems at the single-cell resolution...
Read More »Researchers produce a high-resolution single-molecule sequencing-based Arabidopsis transcriptome using novel methods of Iso-seq analysis
Accurate and comprehensive annotation of transcript sequences is essential for transcript quantification and differential gene and transcript expression analysis. Single-molecule long-read sequencing technologies provide improved integrity of transcript structures...
Read More »A comprehensive analytical protocol to interrogate multiple single-cell RNA-seq datasets with SingCellaR
Single-cell RNA sequencing has led to unprecedented levels of data complexity. Although several computational platforms are available, performing data analyses for multiple datasets remains a...
Read More »