Alexandre P. Marand

The paper introduces methods to estimate and remove ambient contamination in scifi-ATAC data, improving the accuracy of chromatin profiles and biological discovery.

The paper studies dedifferentiation in Arabidopsis thaliana using single-cell RNA sequencing, revealing cell-type-specific transcriptional changes and the role of phytohormones and chromatin remodeling.

This study identifies cell-type-specific regulatory variation in maize hybrids, showing how chromatin accessibility changes contribute to heterosis by reducing cell-type specificity.

The paper introduces PEAgent, a deep-learning framework predicting single-cell chromatin accessibility from DNA sequence in plants, revealing cell-type-specific cis-regulatory patterns and their conservation.

The paper identifies a conserved cis-regulatory code across 589 grass species and finds that motif conservation declines rapidly but remains significant over evolutionary time, with adaptive changes linked to ecological transitions.

The paper identifies trans-regulatory variants associated with chromatin accessibility in maize hybrids, revealing their role in heterosis and gene expression.

The paper corrects distance bias in single-cell co-accessibility and deep-learning chromatin interaction predictions using multi-component power-law penalties informed by Gaussian mixture modeling and polymer-physics scaling.

The paper identifies cell-type-specific cis-regulatory variants in maize using single-cell genomics and population genetics, revealing their role in local adaptation and flowering.

The paper constructs a spatially resolved multiomic single-cell atlas of soybean development, identifying cell types, chromatin regions, and gene regulatory networks involved in growth and nitrogen fixation.

GenomicLinks predicts 3D chromatin loops in maize by identifying sequence features associated with transcription factor binding motifs, validated using single-cell co-accessibility data.

The paper introduces scifi-ATAC-seq, a method that increases single-cell ATAC-seq throughput by 20-fold using combinatorial fluidic indexing.

The paper describes a computational framework for analyzing ATAC-STARR-seq data from maize to assess cis-regulatory element activity.

The paper trains deep learning models on ATAC-seq and DNA methylation data from 12 and 10 angiosperm species to predict chromatin accessibility and methylation across species, finding strong conservation of chromatin mechanisms.

This study introduces a combinatorial indexing strategy for single-cell epigenomic profiling in plants, demonstrating its effectiveness in analyzing chromatin accessibility in Arabidopsis root cells.

A multi-genome analysis of maize reveals previously unknown variation in gene content, genome structure, and methylation.

The paper creates a cis-regulatory atlas in maize using scATAC-seq, identifying cell-type-specific CREs and their regulatory roles.

The paper uses DNA methylation profiles from a single tissue to identify unmethylated regions (UMRs) that mark expressed genes and cis-regulatory elements in plant genomes.