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.
-
Output Type
-
Program
-
CRN Team Name
-
Theme
Hydrop enables droplet-based single-cell ATAC-seq and single-cell RNA-seq using dissolvable hydrogel beads
The data available in this repository can be used to replicate all the figures in the authors’ manuscript using their data analysis tutorial available at https://github.com/aertslab/hydrop_data_analysis.
Detection of mosaic and population-level structural variants with Sniffles2
Sniffles2 is a fast and accurate tool for identifying complex genomic alterations using long -read data.
Multiple genome alignment in the telomere-to-telomere assembly era
This review provides an overview of the algorithmic template that most multiple genome alignment methods follow.
Sex Distribution of GBA1 Variants Carriers with Dementia with Lewy Bodies and Parkinson’s Disease
Sex Distribution of GBA1 Variants Carriers with Dementia with Lewy Bodies and Parkinson’s Disease
Single-cell somatic copy number variants in brain using different amplification methods and reference genomes
Somatic mutations in the brain are well-known, requiring single-cell whole genome amplification before sequencing. PicoPLEX, MDA, and PTA whole genome amplification methods were compared on brain nuclei, showing different properties.
Response to: “Is Gauchian genotyping of GBA1 variants reliable?”
We recently described two methods for GBA1 analysis, which is hampered by the adjacent highly homologous pseudogene: Gauchian, a novel algorithm for analysis of short-read WGS, and targeted long-read sequencing 1. Tayebi et al have applied the…
Methods and applications for single-cell and spatial multi-omics
In this Review, we highlight advances in the developing field of single-cell and spatial multi-omics technologies (multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers.
Nova-ST: Nano-Patterned Ultra-Dense platform for spatial transcriptomics
Nova-ST, a dense nano-patterned spatial transcriptomics technique derived from randomly barcoded Illumina sequencing flow cells enables customized, low-cost, flexible, and high-resolution spatial profiling of large tissue sections.
Cell-type-directed design of synthetic enhancers
We show that deep learning models can be used to design synthetic, cell-type-specific enhancers, starting from random sequences, and that this optimization process allows detailed tracing of enhancer features at single-nucleotide resolution.
More of less: Novel multi-ome profiling of single human neurons
This review describes a novel single-cell multi-omic method, simultaneously profiling transcriptome, DNA methylome, and chromatin accessibility, to shed light on human neurons.
Behavioral screening defines three molecular Parkinsonism subgroups in Drosophila
We created a new collection of 24 genetically well-controlled Drosophila models of familial forms of parkinsonism. Using unbiased behavioral screening and machine learning we identified three clusters of mutants that converge.
Single cell long read whole genome sequencing reveals somatic transposon activity in human brain
Single cell long-read sequencing uncovers novel dynamics in three brains, shedding light on genomic variability. It reveals brain-specific transposable element activity, offering crucial insights into individual cell genomes.
CREsted: modeling genomic and synthetic cell type-specific enhancers across tissues and species
Deep learning models decode genomic regulatory codes well, especially enhancers. CREsted enables end-to-end enhancer modeling, design, and analysis, proving effective across datasets and species through comprehensive training and evaluation.
HyDrop v2: Scalable atlas construction for training sequence-to-function models
Single-cell chromatin accessibility data helps train deep learning models to decode enhancer logic. HyDrop v2 improves data generation across species, organs, and diseases, delivering results comparable to commercial platforms like 10x Genomics.
Reply to: Is Gauchian genotyping of GBA1 variants reliable?
Summarizing a study by N. Tayebi et al. in Communications Biology, published in 2025.
