Akira Hasegawa

Genome-wide mapping of the Galleria mellonella larvae transcription start sites during fungal infection and treatment

This study maps transcription start sites and active enhancers in Galleria mellonella larvae during fungal infection and treatment using LQ-ssCAGE.

A single-cell atlas of transcribed cis-regulatory elements in the human genome

The paper constructs a single-cell atlas of transcribed cis-regulatory elements in the human genome, revealing gene regulation patterns and trait-gene relationships across cell populations.

Antisense oligonucleotide-mediated perturbation of long non-coding RNA reveals functional features in stem cells and across cell types

The paper performs knockdown of 200 lncRNAs in human iPS cells, identifies 36 with growth inhibition, and finds molecular phenotypes in 29.3% of cases, with higher agreement in molecular than cell growth phenotypes across cell types.

Discovery of widespread transcription initiation at microsatellites predictable by sequence-based deep neural network.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

Automatic identification of small molecules that promote cell conversion and reprogramming

DECCODE identifies small molecules that enhance cell reprogramming and conversion using transcriptional data and drug profiles.

Functional Annotation of Human Long Non-Coding RNAs via Molecular Phenotyping

The paper functionally annotates human long non-coding RNAs by systematically knocking down 285 lncRNAs and analyzing molecular phenotypes using CAGE sequencing.

Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

The paper introduces SkewC, a method to identify poor quality cells in scRNA-seq by analyzing gene coverage skewness, and validates it across 37,993 cells from 15 protocols.

Evidence of transcription at polyT short tandem repeats

The paper investigates transcription initiation at polyT short tandem repeats (STRs) using CAGE data and develops a deep learning model to predict STR-associated transcription with high accuracy.

C1 CAGE detects transcription start sites and enhancer activity at single-cell resolution

C1 CAGE detects transcription start sites and enhancer activity in lung cancer cells, revealing subpopulations with distinct responses to TGF-β and enhancer RNA dynamics.

Linking FANTOM5 CAGE Peaks To Annotations With CAGEscan

The paper enhances the FANTOM5 expression atlas by associating gene-orphan promoter regions with their transcripts using CAGEscan.