MOTIVATION: Massively parallel reporter assays (MPRA) enable systematic screening of DNA sequence variants for effects on transcriptional activity. However, convenient analysis tools are still needed. RESULTS: We introduce MPRAscore, a novel tool to infer allele-specific effects on transcription from MPRA data. MPRAscore uses a weighted, variance-regularized method to calculate variant effect sizes robustly, and a permutation approach to test for significance without assuming normality or independence. AVAILABILITY AND IMPLEMENTATION: Source code (C++), precompiled binaries and data used in the paper at https://github.com/abhisheknrl/MPRAscore and https://www.ncbi.nlm.nih.gov/bioproject/PRJNA554195. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: Graphical Gaussian models (GGMs) are a promising approach to identify gene regulatory networks. Such models can be robustly inferred by solving the sparse inverse covariance selection (SICS) problem. With the high dimensionality of genomics data, fast methods capable of solving large instances of SICS are needed. We developed a novel network modeling tool, Ultranet, that solves the SICS problem with significantly improved efficiency. Ultranet combines a range of mathematical and programmatical techniques, exploits the structure of the SICS problem and enables computation of genome-scale GGMs without compromising analytic accuracy. Availability and implementation: Ultranet is implemented in C++ and available at www.broadinstitute.org/ultranet. Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract Motivation: Multimillion-probe microarrays allow detection of gains and losses of chromosomal material at unprecedented resolution. However, the data generated by these arrays are several-fold larger than data from earlier platforms, creating a need for efficient analysis tools that scale robustly with data size. Results: We developed a new aberration caller, Ultrasome, that delineates genomic changes-of-interest with dramatically improved efficiency. Ultrasome shows near-linear computational complexity and processes latest generation copy number arrays about 10 000 times faster than standard methods with preserved analytic accuracy. Availability: www.broad.mit.edu/ultrasome. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.