Output Catalog
ASAP is committed to accelerating the pace of discovery and informing a path to a cure for Parkinson’s disease through collaboration, research-enabling resources, and data sharing. We’ve created this catalog to showcase the research outputs and tools developed by ASAP-funded programs.
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ASAP CRN Cloud Release Notes – Version 4.0.0
ASAP CRN Cloud released version 4.0.0 with expanded datasets including Human Postmortem-derived Brain Sequencing and Mouse datasets. New harmonized collections and individual datasets were added, enhancing research possibilities.
ASAP CRN Cloud Release Notes – Version 2.0.0
Version: 2.0.0 Release Date: December 11, 2024 This release notes document provides a concise overview of the updates and enhancements introduced in Version 2.0.0 of the CRN Cloud platform.
ASAP CRN Cloud Release Notes – Version 1.0.0
Version: 1.0.0 Release Date: June 25, 2024 This release notes document provides a concise overview of the updates and enhancements introduced in Version 1.0.0 of the CRN Cloud platform.
ASAP CRN Cloud Release Notes – Version 1.0.0-beta
Version: 1.0.0-beta Release Date: March 6, 2024 This release notes document provides a concise overview of the updates and enhancements introduced in Version 1.0.0-beta of the CRN Cloud platform.
ASAP CRN Cloud Release Notes – Version 0.0.1
Version: 0.0.1 Release Date: November 10, 2023 This release notes document provides a concise overview of Version 0.0.1 of the CRN Cloud platform.
ASAP CRN Cloud Release Notes – Version 3.0.0
Summary: Release notes for version 3.0.0 of CRN Cloud platform, released on September 30, 2025, detailing latest updates and enhancements.
A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex
A data-driven molecular map of the DLPFC reveals distinct spatial domains and cell populations, offering insights into neuropsychiatric disorders. The study provides a roadmap for implementing spatial clustering approaches in the human brain.
Regulation of mitophagy by the NSL complex underlies genetic risk for Parkinson’s disease: Bioinformatic Prioritisation and Hit Validation
This protocol describes the Bioinformatic Prioritization of PD GWAS candidates for High Content Screening, and Hit Validation by allele-specific expression (ASE) analysis.
Long-read RNA seq analysis using Talon
This is a pipeline that takes fastq data as input, generates fastq stats using nanostat, performs fastq processing and filtering using pychopper, maps the reads to the genome using minimap2, and uses talon to assemble and quantify transcripts.
Protein aggregation and calcium dysregulation are hallmarks of familial Parkinson’s disease in midbrain dopaminergic neurons
Mutations in SNCA gene cause PD by forming α-synuclein aggregates. Using hiPSCs, we traced pathophysiological events, revealing early oligomeric aggregate formation, calcium signaling impairments, and multiple cellular stresses leading to cell…
The annotation of GBA1 has been concealed by its protein-coding pseudogene GBAP1
The authors identify novel transcripts from both GBA1 and GBAP1, including protein-coding transcripts that are translated in vitro and detected in proteomic data, but that lack GCase activity.
SoRA microscopy protocol for imaging oligomers in human brain tissue
This protocol gives a step-by-step guide to imaging oligomers in human brain tissue using a spinning disk confocal microscope.
ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2
ggtranscript simplifies visualizing transcript structure with new geoms like range(), intron(), junction(), and junction_label_repel(). It extends ggplot2's flexibility to create informative plots for publication.
ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2
The authors present ggtranscript, an R package that provides a fast and flexible method to visualize and compare transcripts from long-read sequences. This tool is an extension of ggplot2.
Splicing accuracy varies across human introns, tissues and age
This in-depth characterization of mis-splicing can have important implications for our understanding of the role of splicing inaccuracies in human disease and the interpretation of long-read RNA-sequencing data.
