Experimental data exists for only a vanishingly small fraction of sequenced microbial genes. This community page discusses the progress made by the COMBREX project to address this important issue using both computational and experimental resources.

COMBREX (http://combrex.bu.edu) is a project to increase the speed of the functional annotation of new bacterial and archaeal genomes. It consists of a database of functional predictions produced by computational biologists and a mechanism for experimental biochemists to bid for the validation of those predictions. Small grants are available to support successful bids.

The constant improvement and falling prices of whole human genome Next Generation Sequencing (NGS) has resulted in rapid adoption of genomic information at both clinics and research institutions. Considered together, the complexity of genomics data, due to its large volume and diversity along with the need for genomic data sharing, has resulted in the creation of Application Programming Interface (API) for secure, modular, interoperable access to genomic data from different applications, platforms, and even organizations. The Genomics APIs are a set of special protocols that assist software developers in dealing with multiple genomic data sources for building seamless, interoperable applications leading to the advancement of both genomic and clinical research. These APIs help define a standard for retrieval of genomic data from multiple sources as well as to better package genomic information for integration with Electronic Health Records. This review covers three currently available Genomics APIs: a) Google Genomics, b) SMART Genomics, and c) 23andMe. The functionalities, reference implementations (if available) and authentication protocols of each API are reviewed. A comparative analysis of the different features across the three APIs is provided in the Discussion section. Though Genomics APIs are still under active development and have yet to reach widespread adoption, they hold the promise to make building of complicated genomics applications easier with downstream constructive effects on healthcare.

There are no known genetic variants with large effects on susceptibility to major depressive disorder (MDD). Although one proposed study approach is to increase sensitivity by increasing sample sizes, another is to focus on families with multiple affected individuals to identify genes with rare or novel variants with strong effects. Choosing the family-based approach, we performed whole-exome analysis on affected individuals (n = 12) across five MDD families, each with at least five affected individuals, early onset, and prepubertal diagnoses. We identified 67 genes where novel deleterious variants were shared among affected relatives. Gene ontology analysis shows that of these 67 genes, 18 encode transcriptional regulators, eight of which are expressed in the human brain, including four KRAB-A box-containing Zn2+ finger repressors. One of these, ZNF34, has been reported as being associated with bipolar disorder and as differentially expressed in bipolar disorder patients compared to healthy controls. We found a novel variant—encoding a non-conservative P17R substitution in the conserved repressor domain of ZNF34 protein—segregating completely with MDD in all available individuals in the family in which it was discovered. Further analysis showed a common ZNF34 coding indel segregating with MDD in a separate family, possibly indicating the presence of an unobserved, linked, rare variant in that particular family. Our results indicate that genes encoding transcription factors expressed in the brain might be an important group of MDD candidate genes and that rare variants in ZNF34 might contribute to susceptibility to MDD and perhaps other affective disorders.

The increased adoption of clinical whole exome sequencing (WES) has improved the diagnostic yield for patients with complex genetic conditions. However, the informatics practice for handling information contained in whole exome reports is still in its infancy, as evidenced by the lack of a common vocabulary within clinical sequencing reports generated across genetic laboratories. Genetic testing results are mostly transmitted using portable document format, which can make secondary analysis and data extraction challenging. This paper reviews a sample of clinical exome reports generated by Clinical Laboratory Improvement Amendments-certified genetic testing laboratories at tertiary-care facilities to assess and identify common data elements. Like structured radiology reports, which enable faster information retrieval and reuse, structuring genetic information within clinical WES reports would help facilitate integration of genetic information into electronic health records and enable retrospective research on the clinical utility of WES. We identify elements listed as mandatory according to practice guidelines but are currently missing from some of the clinical reports, which might help to organize the data when stored within structured databases. We also highlight elements, such as patient consent, that, although they do not appear within any of the current reports, may help in interpreting some of the information within the reports. Integrating genetic and clinical information would assist the adoption of personalized medicine for improved patient care and outcomes.