Interpreting and Integrating Big Data in the Life Sciences
New advances in extracting and learning from protein–protein interactions within unstructured biomedical text data
Current whole-genome sequencing data requires a multitude of processing steps to account for technical noise and accurately identify genetic variation for downstream analyses, such as association testing with complex traits and diseases. In the latest issue of Emerging Topics in Life Sciences: Interpreting and Integrating Big Data in the Life Sciences, Jew and Sul review the widely-used approaches for variant calling and quality control. This cover features a scheme from their review, highlighting the conversion of a biological sample, such as blood or saliva, into a digital representation of genetic variation found in an individual.