Genetic counselling considerations with genetic/genomic testing in Neonatal and Pediatric Intensive Care Units: A scoping review.

Genetic and genomic technologies can effectively diagnose numerous genetic disorders. Patients benefit when genetic counselling accompanies genetic testing and international guidelines recommend pre- and post-test genetic counselling with genome-wide sequencing. However, there is a gap in knowledge regarding the unique genetic counselling considerations with different types of genetic testing in the Neonatal Intensive Care Unit (NICU) and the Pediatric Intensive Care Unit (PICU). This scoping review was conducted to identify the gaps in care with respect to genetic counselling for infants/pediatric patients undergoing genetic and genomic testing in NICUs and PICUs and understand areas in need of improvement in order to optimize clinical care for patients, caregivers, and healthcare providers. Five databases (MEDLINE [Ovid], Embase [Ovid], PsycINFO [Ebsco], CENTRAL [Ovid], and CINHAL [Ebsco]) and grey literature were searched. A total of 170 studies were included and used for data extraction and analysis. This scoping review includes descriptive analysis, followed by a narrative account of the extracted data. Results were divided into three groups: pre-test, post-test, and comprehensive (both pre- and post-test) genetic counselling considerations based on indication for testing. More studies were conducted in the NICU than the PICU. Comprehensive genetic counselling was discussed in only 31% of all the included studies demonstrating the need for both pre-test and post-test genetic counselling for different clinical indications in addition to the need to account for different cultural aspects based on ethnicity and geographic factors.

Efficient computation of contributional diversity metrics from microbiome data with FuncDiv.

MOTIVATION: Microbiome datasets with taxa linked to the functions (e.g. genes) they encode are becoming more common as metagenomics sequencing approaches improve. However, these data are challenging to analyze due to their complexity. Summary metrics, such as the alpha and beta diversity of taxa contributing to each function (i.e. contributional diversity), represent one approach to investigate these data, but currently there are no straightforward methods for doing so. RESULTS: We addressed this gap by developing FuncDiv, which efficiently performs these computations. Contributional diversity metrics can provide novel insights that would be impossible to identify without jointly considering taxa and functions. AVAILABILITY AND IMPLEMENTATION: FuncDiv is distributed under a GNU Affero General Public License v3.0 and is available at https://github.com/gavinmdouglas/FuncDiv. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.