Search

Motivation Gene regulatory networks define regulatory relationships between transcription factors and target genes within a biological system, and reconstructing them is essential for understanding cellular growth and function. Methods for inferring and reconstructing networks from genomics data have evolved rapidly over the last decade in response to advances in sequencing technology and machine learning. The scale of data collection has increased dramatically; the largest genome-wide gene expression datasets have grown from thousands of measurements to millions of single cells, and new technologies are on the horizon to increase to tens of millions of cells and above. Results In this work, we present the Inferelator 3.0, which has been significantly updated to integrate data from distinct cell types to learn context-specific regulatory networks and aggregate them into a shared regulatory network, while retaining the functionality of the previous versions. The Inferelator is able to integrate the largest single-cell datasets and learn cell-type-specific gene regulatory networks. Compared to other network inference methods, the Inferelator learns new and informative Saccharomyces cerevisiae networks from single-cell gene expression data, measured by recovery of a known gold standard. We demonstrate its scaling capabilities by learning networks for multiple distinct neuronal and glial cell types in the developing Mus musculus brain at E18 from a large (1.3 million) single-cell gene expression dataset with paired single-cell chromatin accessibility data. Availability and implementation The inferelator software is available on GitHub (https://github.com/flatironinstitute/inferelator) under the MIT license and has been released as python packages with associated documentation (https://inferelator.readthedocs.io/). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

The feedWebGL2 software package enables parallel computations using a feature of HTML5/WebGL2 called Transform/Feedback. The Transform/Feedback mechanism can be used within Jupyter widgets or in other web components and applications for a special class of parallel computations called shader-like calculations, which can solve many problems well, but are unsuitable for some classes of parallel algorithms. This article describes the characteristics of shader-like calculations and how they can be created using the feedWebGL2 software package. The example programs use shader-like calculations for special purpose graphical stages in combination with standard WebGL graphics pipelines, and also use them for general purposes, such as matrix computations. [ABSTRACT FROM AUTHOR]

Transcriptional programming of the innate immune response is pivotal for host protection. However, the transcriptional mechanisms that link pathogen sensing with innate activation remain poorly understood. During HIV-1 infection, human dendritic cells (DCs) can detect the virus through an innate sensing pathway, leading to antiviral interferon and DC maturation. Here, we develop an iterative experimental and computational approach to map the HIV-1 innate response circuitry in monocyte-derived DCs (MDDCs). By integrating genome-wide chromatin accessibility with expression kinetics, we infer a gene regulatory network that links 542 transcription factors with 21,862 target genes. We observe that an interferon response is required, yet insufficient, to drive MDDC maturation and identify PRDM1 and RARA as essential regulators of the interferon response and MDDC maturation, respectively. Our work provides a resource for interrogation of regulators of HIV replication and innate immunity, highlighting complexity and cooperativity in the regulatory circuit controlling the response to infection. • Network inference reveals a transcriptional map of the innate immune response to HIV • Integrating cell-specific chromatin accessibility data improves network inference • Immune responses coincide with network enrichment of IRF, STAT, and NF-κB factors • RARA and PRDM1 are validated as additional regulators of the innate response to HIV Pathogen sensing leads to host transcriptional reprogramming to protect against infection. However, it is unclear how transcription factor activity is coordinated across the genome. Johnson et al. integrate chromatin accessibility and gene expression data to infer and validate a gene regulatory network that directs the innate immune response to HIV. [ABSTRACT FROM AUTHOR]