Several talks covered systems and new technologies that clinicians, patients, and researchers can use to understand human genomic variation. Jessica Chong (University of Washington, USA) described MyGene2 (http://mygene2.org), a website that allows families to share their de-identified personal data and find other families with similar traits. Jennifer Harrow (Illumina, UK) discussed using BaseSpace (https://basespace.illumina.com/) for the analysis of clinical sequencing data. Deanna Church (10x Genomics, USA) presented Linked-Reads, a technology that makes it easier to find variants in less accessible genomic regions such as the HLA locus. Several presenters showed new methods to identify the functional effects of sequence variants. Konrad Karczewski (Massachusetts General Hospital, USA) presented the Loss Of Function Transcript Effect Estimator (LOFTEE, https://github.com/konradjk/loftee). LOFTEE uses a support vector machine to identify sequence variants that significantly disrupt a gene and potentially affect biological processes. Martin Kircher (University of Washington, USA) discussed a massively parallel reporter assay (MPRA) that uses a lentivirus for genomic integration, called lentiMPRA [3]. He used lentiMPRA to predict enhancer activity, and to more generally measure the functional effect of non-coding variants. William McLaren (European Bioinformatics Institute, UK) presented Haplosaurus, a variant effect predictor that uses haplotype-phased data (https://github.com/willmclaren/ensembl-vep).

Two presenters discussed genome informatics approaches to the analysis of cancer immunotherapy response. Meromit Singer (Broad Institute, USA) performed single-cell RNA profiling in dysfunctional CD8+ T cells. She identified metallothioneins as drivers of T cell dysfunction and revealed novel sub-populations of dysfunctional T cells [4]. Christopher Miller (Washington University, St Louis, USA) tracked the response to cancer immunotherapy in the genome of patients [5].

In a keynote lecture, Elaine Mardis (Washington University, St Louis, USA), described computational tools and databases created to collect and process cancer-specific mutation datasets. A substantive increase in the amount of clinical sequencing performed as part of cancer diagnosis and treatment necessitated the development of these tools. She emphasized the shift in categorization of cancers—previously oncologists classified cancers by tissue, but increasingly they classify cancers by which genes are mutated. Mardis suggested that we should instead describe cancers by the affected metabolic and regulatory pathways, which can provide insight even for previously unseen disruption. This disruption can be genetic mutations, but it can also manifest as other changes to cellular state, which must be measured with other techniques, such as RNA-seq. The tools Mardis described help interpret the mutations identified by sequencing. These include the Database of Curated Mutations (DoCM). She also described Personalized Variant Antigens by Cancer Sequencing (pVAC-seq), a tool for identifying tumor neoantigens from DNA-seq and RNA-seq data. She also described Clinical Interpretations of Variants in Cancer (CIViC), a platform for crowd-sourcing data on clinical consequences of genomic variants. CIViC has 1565 evidence items describing the interpretation of genetic variants, and Mardis announced a forthcoming Variant Curation Hackathon to identify more.