News and insights

Subscribe to our newsletter

Cellular protein levels are dictated by the net balance of mRNA expression (the type of RNA that provides genetic information for proteins), protein synthesis, and protein degradation. While changes in protein levels are commonly inferred from measuring changes in mRNA levels (due to the difficulties involved in measuring protein levels), it’s not often clear whether determining RNA levels is actually a good proxy for measuring protein levels.

There are many reasons why people gain different amounts of weight and why fat becomes stored in different parts of their bodies. Now researchers conducted the largest study of genetic variation to date to home in on genetic reasons. By analyzing genetic samples from more than 300,000 individuals to study obesity and body fat distribution, researchers in the international Genetic Investigation of Anthropometric Traits (GIANT) Consortium completed the largest study of genetic variation to date, and found over 140 locations across the genome that play roles in various obesity traits.

A team of ӳý researchers led by Jill Mesirov, Edo Airoldi, and Chris Burge recently published a paper describing their “” software for visualizing the abundance of mRNA isoforms in multiple RNA-Seq samples. Now integrated into the (IGV) browser, the plots display raw data that resembles small pieces of sashimi and include isoform abundance estimates from the quantitation program, MISO. Learn more about the software in the journal

How does a seemingly harmless member of the human microbiome become the fourth most common cause of infection in hospitals? A new paper from the lab of ӳý core member Aviv Regev reveals the molecular mechanisms that enable Candida albicans — a common fungus normally found in the human body — to evolve into a drug-resistant pathogen. Read the study in .

This week, Nature Genetics included papers on two new methods for leveraging large cohort studies. One paper — from the ӳý’s Program in (MPG) and , along with a team of collaborators — shares a powerful in genome-wide association studies (GWAS). The other — also with contributions from MPG — that vastly increases computation speed while simultaneously increasing the statistical power of large data sets.