Increasing interest in determining the effects of genetic variation for bioengineering, human health and basic biological research has propelled the development of technologies for high-throughput mutagenesis and selection. However, since designing functional assays is challenging and systematic testing of combinations of mutations is intractable, there is a parallel need to develop more accurate computational predictions.. Most computational methods have relied significantly on the signal of evolutionary conservation, but do not account for dependencies between positions in a sequence. We present an unsupervised method for predicting the effects of mutations (EVmutation) that explicitly captures residue dependencies between positions. We find that it improves the prediction accuracies of a comprehensive collection of recent high-throughput experimental fitness landscapes, biochemical measurements and human disease mutations. We suggest EVmutation may be useful to assess the quantitative effects of mutations in genes of any organism and provide precomputed predictions for ~ 7000 human proteins.

BioNumbers (http://www.bionumbers.hms.harvard.edu) is a database of key numbers in molecular and cell biology—the quantitative properties of biological systems of interest to computational, systems and molecular cell biologists. Contents of the database range from cell sizes to metabolite concentrations, from reaction rates to generation times, from genome sizes to the number of mitochondria in a cell. While always of importance to biologists, having numbers in hand is becoming increasingly critical for experimenting, modeling, and analyzing biological systems. BioNumbers was motivated by an appreciation of how long it can take to find even the simplest number in the vast biological literature. All numbers are taken directly from a literature source and that reference is provided with the number. BioNumbers is designed to be highly searchable and queries can be performed by keywords or browsed by menus. BioNumbers is a collaborative community platform where registered users can add content and make comments on existing data. All new entries and commentary are curated to maintain high quality. Here we describe the database characteristics and implementation, demonstrate its use, and discuss future directions for its development.