scispace - formally typeset
H

Han Fang

Researcher at Cold Spring Harbor Laboratory

Publications -  43
Citations -  3939

Han Fang is an academic researcher from Cold Spring Harbor Laboratory. The author has contributed to research in topics: Genome & Indel. The author has an hindex of 14, co-authored 35 publications receiving 2348 citations. Previous affiliations of Han Fang include Stony Brook University.

Papers
More filters
Journal ArticleDOI

Accurate detection of complex structural variations using single-molecule sequencing.

TL;DR: NGMLR and Sniffles perform highly accurate alignment and structural variation detection from long-read sequencing data and can automatically filter false events and operate on low-coverage data, thereby reducing the high costs that have hindered the application of long reads in clinical and research settings.
Journal ArticleDOI

GenomeScope: fast reference-free genome profiling from short reads

TL;DR: GenomeScope is an open‐source web tool to rapidly estimate the overall characteristics of a genome, including genome size, heterozygosity rate and repeat content from unprocessed short reads, which are essential for studying genome evolution.
Posted Content

Linformer: Self-Attention with Linear Complexity

TL;DR: This paper demonstrates that the self-attention mechanism of the Transformer can be approximated by a low-rank matrix, and proposes a new self-Attention mechanism, which reduces the overall self-ATTention complexity from $O(n^2)$ to $O (n)$ in both time and space.
Journal ArticleDOI

Accurate de novo and transmitted indel detection in exome-capture data using microassembly

TL;DR: Scalpel's power to detect long (≥30 bp) transmitted events and enrichment for de novo likely gene-disrupting indels in autistic children and a detailed repeat analysis coupled with a self-tuning k-mer strategy allows Scalpel to outperform other state-of-the-art approaches for indel discovery.
Journal ArticleDOI

Reducing INDEL calling errors in whole genome and exome sequencing data

TL;DR: This work characterized whole genome sequencing, whole exome sequencing, and PCR-free sequencing data from the same samples to investigate the sources of INDEL errors and developed a classification scheme based on the coverage and composition to rank high and low quality INDEL calls.