Data analysis made easy. For biologists, especially.
Welcome to our new BioTuring Cellpedia series! We are excited to introduce a new blog series that provides you with an overview of some interesting datasets indexed in BioTuring Browser public repository, a platform for instant access and reanalysis of published single-cell RNA-seq data. Details on their experimental designs and […]
Single-cell RNA sequencing technologies have enabled many exciting discoveries of novel cell types and sub-types, such as the rosehip neurons (Boldog et al., 2018), disease-associated microglia (Keren-Shaul et al., 2017) and lipid-associated macrophages (Jaitin, Adlung, Thaiss, Weiner and Li et al., 2019). While sub-clustering cell populations is essential to find […]
Seurat (Butler et. al 2018) and Scanpy (Wolf et. al 2018) are two great analytics tools for single-cell RNA-seq data due to their straightforward and simple workflow. However, for those who want to interact with their data, and flexibly select a cell population outside a cluster for analysis, it is […]
We are glad to announce that we will upsize the current single-cell database in BioTuring Single-cell Browser to 5,500,000 cells this September. With this release, we will double the current number of publications indexed in BioTuring Single-cell Browser, and cross the number of cells hosted on available public single-cell data […]
In single-cell data analysis, it is critical to understand how gene expression varies among different cell types, tissue compartments, conditions, or different patients. Although many tools and methods have been developed to support single-cell differential expression analysis, they all require some basic coding skills. In this blog, we show you […]
In this blog, we introduce Venice a non-parametric approach for finding marker genes in single-cell RNA-seq data. Using a widely adopted benchmarking approach (Wang et al. 2019), Venice obtains the best accuracy among 14 other tools. Venice is also the fastest tool among all the benchmarked methods. Venice is open-source, […]
Single-cell sequencing technologies have brought up an unprecedented level of resolution to omics studies with extremely detailed views into individual cells’ expression patterns. With the emergence of drop-based sequencing methods, the resolution now even comes with scale and efficiency. In a single experiment, scientists can now profile hundreds of thousands […]
Tiny as they are, retinal cells are extremely abundant and complex. The total population of about 100 million rod photoreceptors is among the most numerous neuron populations in the human body, second to cerebellar granule cells (Masland et al., 2012). Forming a sheet of tissue that is only ~200 μm […]
1. An atlas of acute myeloid leukemia at single-cell resolution Adapted from Galan et. al., 2019 Acute myeloid leukemia (AML) is a cancer characterized by the accumulation of white blood cells in the bone marrow and blood. The disease has been notoriously challenging to study because of its extreme intratumoral […]
Ever questioned why the immune system does not attack our own double-stranded RNA? Speaking of this, we should not ignore the role of ADAR, the gene that encodes Double-stranded RNA-specific adenosine deaminase enzyme, responsible for converting adenosines to inosines (A -> I editing) in double-stranded RNA (dsRNA) substrates. ADAR was […]
Single-cell RNA-seq technologies have opened up a completely new era for transcriptomic studies. For the first time ever, scientists can look at individual transcriptomic profiles of millions of cells, and better understand how each cell functions in a tissue. Yet science is confronting bigger challenges analyzing these massive amounts of […]
Today we finished the first version of Hera-T, a new single-cell RNA-seq quantification algorithm. We developed Hera-T by improving challenging alignment errors that Cell Ranger has. As a result, Hera-T is more accurate than Cell Ranger. Hera-T is more than 10 times faster than Cell Ranger, while consuming just a small amount of […]
