Heejung Shim

simPIC: flexible simulation of single-cell ATAC-seq paired-insertion counts from individuals to populations

simPIC simulates single-cell ATAC-seq count data across individuals and populations to enable controlled evaluation of chromatin accessibility QTL mapping methods.

Denoising image-based spatial transcriptomics data with DenoIST

DenoIST improves spatial transcriptomics data by removing contaminating transcripts, enhancing gene expression specificity and cell type annotation.

A paradoxical population structure of var DBLα types in Africa

The paper examines the population structure of DBLα types in Sub-Saharan Africa, finding varying conservation levels across geographical scales and frequencies.

Image-based spatial transcriptomics identifies molecular niche dysregulation associated with distal lung remodeling in pulmonary fibrosis

Xenium profiling of 343 genes in 1,126,820 nuclei from 19 human lungs maps fibrosis niches and orders alveolar remodeling from alveolar-capillary disruption to SPP1+ macrophage accumulation.

Going beyond cell clustering and feature aggregation: Is there single cell level information in single-cell ATAC-seq data?

This paper reviews challenges in analyzing scATAC-seq data and argues that true single-cell chromatin accessibility profiling has not yet been achieved.

Identification of cell barcodes from long-read single-cell RNA-seq with BLAZE

BLAZE identifies 10x cell barcodes from long-read nanopore scRNA-seq data without requiring matched short-read data, outperforming existing tools in accuracy and reducing false positives.

Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics

This paper explores the trade-off between preserving biological variation and removing batch effects in scRNA-seq data using Pareto multi-task learning and Mutual Information Neural Estimation.

sgcocaller and comapr: personalised haplotype assembly and comparative crossover map analysis using single-gamete sequencing data

This paper introduces sgcocaller and comapr, tools for haplotype assembly and crossover map analysis from single-gamete sequencing data, showing improved accuracy and efficiency over existing methods.

NanoSplicer: Accurate identification of splice junctions using Oxford Nanopore sequencing

NanoSplicer identifies splice junctions from Oxford Nanopore sequencing data without relying on short-read data or annotations, improving accuracy in high-error regions.

Robust differential composition and variability analysis for multisample cell omics

The paper introduces sccomp, a method for differential composition and variability analysis in cell omics data, showing improved performance over existing methods and identifying novel biological insights in breast cancer.

McSplicer: a probabilistic model for estimating splice site usage from RNA-seq data

McSplicer estimates splice site usage from RNA-seq data using a probabilistic model, achieving more accurate results than existing methods.

circuitSNPs: Predicting genetic effects using a Neural Network to model regulatory modules of DNase-seq footprints

circuitSNPs predicts the functional effects of genetic variants on chromatin accessibility using a neural network that models regulatory modules from DNase-seq footprints.