Housing preferences during the pandemic: effect on home price, rent, and inflation measurement.

With shelter comprising a one-third weight in the Consumer Price Index, an accurate measure of rent change is essential for determining factors affecting inflation measurement, economic policy, and consumer and business decisions. The pandemic led to a shift in consumer demand for more residential space, both in the home's interior and exterior. With remote work severing the need to be located near the place of employment, some households opted for more space in lower-density areas, moving out of high-rise structures in the urban core to suburban and exurban single-family and low-rise homes, altering the price and rent-growth patterns among single-family detached, attached, and multifamily properties. While measures of rent change are available for multifamily residential properties, none exist for the single-family rental market, which makes up one-half of the residential rental market. The CoreLogic Single-Family Rent Index (SFRI) fills the gap in rent measurement. The SFRI is a repeat-transaction rent index for single-family homes and is available monthly for the U.S., by major metros, by rent price tier and by property type. The SFRI reveals that after 12 months of the pandemic annual rent growth for detached properties was more than 5 percentage points higher than for attached properties. Substituting the SFRI for Owners' Equivalent Rent, we find that Core CPI inflation would be nearly 2 percentage points higher by mid-2021. To the extent the rapid acceleration in single-family detached rent growth has yet to be reflected in the CPI, inflation measurement will be understated with delayed signals for economic policy makers.

Mango: Exploratory Data Analysis for Large-Scale Sequencing Datasets.

The decreasing cost of DNA sequencing over the past decade has led to an explosion of sequencing datasets, leaving us with petabytes of data to analyze. However, current sequencing visualization tools are designed to run on single machines, which limits their scalability and interactivity on modern genomic datasets. Here, we leverage the scalability of Apache Spark to provide Mango, consisting of a Jupyter notebook and genome browser, which removes scalability and interactivity constraints by leveraging multi-node compute clusters to allow interactive analysis over terabytes of sequencing data. We demonstrate scalability of the Mango tools by performing quality control analyses on 10 terabytes of 100 high-coverage sequencing samples from the Simons Genome Diversity Project, enabling capability for interactive genomic exploration of multi-sample datasets that surpass the computational limitations of single-node visualization tools. Mango is freely available for download with full documentation at https://bdg-mango.readthedocs.io/en/latest/.

DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark.

BACKGROUND: XHMM is a widely used tool for copy-number variant (CNV) discovery from whole exome sequencing data but can require hours to days to run for large cohorts. A more scalable implementation would reduce the need for specialized computational resources and enable increased exploration of the configuration parameter space to obtain the best possible results. RESULTS: DECA is a horizontally scalable implementation of the XHMM algorithm using the ADAM framework and Apache Spark that incorporates novel algorithmic optimizations to eliminate unneeded computation. DECA parallelizes XHMM on both multi-core shared memory computers and large shared-nothing Spark clusters. We performed CNV discovery from the read-depth matrix in 2535 exomes in 9.3 min on a 16-core workstation (35.3× speedup vs. XHMM), 12.7 min using 10 executor cores on a Spark cluster (18.8× speedup vs. XHMM), and 9.8 min using 32 executor cores on Amazon AWS' Elastic MapReduce. We performed CNV discovery from the original BAM files in 292 min using 640 executor cores on a Spark cluster. CONCLUSIONS: We describe DECA's performance, our algorithmic and implementation enhancements to XHMM to obtain that performance, and our lessons learned porting a complex genome analysis application to ADAM and Spark. ADAM and Apache Spark are a performant and productive platform for implementing large-scale genome analyses, but efficiently utilizing large clusters can require algorithmic optimizations and careful attention to Spark's configuration parameters.

The NIH BD2K center for big data in translational genomics.

The world's genomics data will never be stored in a single repository - rather, it will be distributed among many sites in many countries. No one site will have enough data to explain genotype to phenotype relationships in rare diseases; therefore, sites must share data. To accomplish this, the genetics community must forge common standards and protocols to make sharing and computing data among many sites a seamless activity. Through the Global Alliance for Genomics and Health, we are pioneering the development of shared application programming interfaces (APIs) to connect the world's genome repositories. In parallel, we are developing an open source software stack (ADAM) that uses these APIs. This combination will create a cohesive genome informatics ecosystem. Using containers, we are facilitating the deployment of this software in a diverse array of environments. Through benchmarking efforts and big data driver projects, we are ensuring ADAM's performance and utility.