Zhengchang Su

The paper compares genome sequences and genetic variations of three eared pheasant species at different altitudes to identify adaptation mechanisms.

This study uses single-cell RNA-seq to analyze transcriptomic changes during the maternal to zygote transition in mice and rats, revealing similar but distinct transcriptional programs in both species.

This study performs single-cell RNA-seq on C. elegans germline sections to analyze gene expression and splicing during oogenesis and fertilization, revealing novel insights into germline-somatic interactions.

HRannot provides an accurate and user-friendly gene annotation pipeline for vertebrate genomes, outperforming existing tools and enabling annotation of both known and novel genes.

The paper identifies widespread loss-of-function mutations in chicken genomes during evolution and domestication, showing non-random patterns and selection-driven gene loss.

The paper predicts functional states and types of cis-regulatory modules (CRMs) across 107 cell/tissue types, revealing that dual-functional CRMs and silencers are more prevalent than previously thought.

The paper annotates four high-quality chicken genomes, identifying over 1,400 missing genes in sub-telomeric regions with high G/C content that were previously absent from reference annotations.

The paper presents a high-quality genome assembly for the white-eared pheasant and related transcriptome and population genetics data.

The paper identifies prevalent false positives and false negatives in STARR-seq data and explores their underlying causes using predicted cis-regulatory modules and cell line data.

This study identifies 660 antisense RNAs in E. coli K12 under various culture conditions and shows their role in regulating gene expression during environmental stress.

This study identifies numerous selective sweeps and genes under artificial selection in indigenous and commercial chickens using re-sequencing and statistical analysis.

The paper creates a comprehensive map of cis-regulatory modules and transcription factor binding sites in 80% of the mouse genome using ChIP-seq data.

The paper predicts functional states of cis-regulatory modules in humans and mice using epigenetic marks and machine learning, finding common epigenetic rules across species.

PCRMS provides a database of predicted cis-regulatory modules and transcription factor binding sites in human and mouse genomes, enabling exploration and analysis of regulatory sequences.

This paper introduces a clustering algorithm for scRNA-seq data that improves accuracy by combining local and global similarity measures, outperforming existing methods on benchmark datasets.

The paper predicts cis-regulatory modules in the human genome, finding they are more prevalent than previously thought.

The paper uses deep learning to identify sequence determinants of histone modification patterns in CD4+ T cell differentiation.

BicGO is a biclustering algorithm that accurately identifies complex biclusters in large datasets using global optimization, outperforming existing tools on both simulated and real data.

ProSampler efficiently identifies transcription factor binding motifs in large ChIP-seq datasets, revealing complex motif patterns and cooperative binding mechanisms.