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 constructs a single-cell chromatin accessibility atlas in rice and compares it across grass species to study the evolution of cell-type-specific cis-regulatory elements.

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.

This study uses single-cell ATAC-seq to analyze cis-regulatory elements in C4 photosynthesis across grass species, revealing cell-type-specific chromatin accessibility patterns and evolutionary dynamics.

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.