A new algorithm increases the accuracy of techniques that detect rare genetic variants among populations, according to a study published 27 July in Bioinformatics.

A new tool called Gene2FANs creates gene-association networks based on 14 databases that list gene function as well as protein-protein interactions, according to a study published 2 July in BMC Informatics.

Researchers have launched an effort to yoke together disparate gene sequencing projects in the U.S., Canada and the U.K., aiming to double the number of known autism-related genes in the next three to four years.

Identifying genetic variants that affect the expression of other genes can enhance traditional gene association analyses and highlight candidate risk factors for autism, according to a study published 16 May in Molecular Autism.

A new computational analysis can predict whether a gene is implicated in autism or intellectual disability with up to 98 percent accuracy, according to a study published 15 May in the American Journal of Medical Genetics.

A ten-year initiative announced last month by the Allen Institute for Brain Science aims to catalog the development, structure and function of neural circuits in the brain at an unprecedented level of detail.

Researchers have mapped networks of genes expressed at the same time and place in the brain and shown that rare and common autism-linked mutations are likely to function in the same pathways. The results were published 8 March in PLoS Genetics.

Researchers have developed a tool that can predict whether DNA mutations that change a single base pair are likely to alter RNA splicing, a process that modifies the sequence of mRNA, the genetic message that codes for protein. The results were published 10 February in Bioinformatics.

As babies are learning to talk, they shift their focus from speakers’ eyes to their lips, according to a new study that could inform efforts to find an early predictor of autism.

A mathematical approach called ‘NEW biology,’ or network-enabled wisdom biology, aims to solve one of the biggest problems in disease research: isolating the key factors that drive diseases from a glut of information.