Tag Archives: bioinformatics

This position will collaborate with an enthusiastic and dynamic team of computational and experimental scientists to build a quantitative census of cell types in mouse brain, and understand the...

A recording of a live Zoom training for Bioinformatics for RNA Sequencing Analysis from the Tufts Data Lab, with Wenwen Hou, Rebecca Batorsky, and Albert Tai...

Nebion has recently launched their special COVID-19 support campaign. Academic researchers working from home are supported with online training and free access to the Professional Edition of GENEVESTIGATOR® for one month. GENEVESTIGATOR® enables analysis of deeply curated transcriptomic data from ...

Understanding the immune system in Chronic Immune-Mediated inflammatory diseases Our immune system is a complex network of cells and organs that together defend the body against harmful foreign invaders such as bacteria and viruses. Inflammation is a normal, controlled part ...

We invite applications from highly motivated candidates for a Quantitative Biologist/Bioinformatics position working within the Genomics group to design impactful studies and analyze single cell RNA-seq data across many therapeutic areas...

In recent years, high-throughput next-generation sequencing technology has allowed a rapid increase in diagnostic capacity and precision through different bioinformatics processing algorithms, tools, and...

Are you interested in analysing the expression dynamics in single cell RNAseq data, and estimating the RNA velocity of single cells? This talk (https://youtu.be/EPTgF4EA2zY) introduces the Velocyto package (http://velocyto.org/), winner of the SIB Bioinformatics Awards 2019 in the category Bioinformatics ...

Overview Yale University in Connecticut, USA, owns one of the world’s great medical historical libraries. It was opened in 1814 and by 1865 its collection numbered 1,200 books. Today it is known as the Yale Cushing/Whitney[1] Medical Library, and it ...

Identification of cell type subpopulations from complex cell mixtures using single-cell RNA-sequencing (scRNA-seq) data includes automated steps from normalization to cell clustering. However, assigning cell type labels to cell clusters is often conducted manually, resulting in limited documentation, low reproducibility ...

A comprehensive analysis of RNA-Seq data uses a wide range of different tools and algorithms, which are normally limited to R users only. While several tools and advanced analysis pipelines are available, some require programming skills and others lack the ...