Related Experiment Video
Updated: Jun 18, 2025
Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
Published on: March 12, 2021
RFMix-reader: Accelerated reading and processing for local ancestry studies
1Lieber Institute for Brain Development; Department of Neurology, Johns Hopkins University School of Medicine · Funded by National Institute on Minority Health and Health Disparities (K99MD016964).
Motivation:
Local ancestry inference is a powerful technique in genetics, revealing population history and the genetic basis of diseases. It is particularly valuable for improving eQTL discovery and fine-mapping in admixed populations. Despite the widespread use of the RFMix software for local ancestry inference, large-scale genomic studies face challenges of high memory consumption and processing times when handling RFMix output files.
Results:
Here, I present RFMix-reader, a new Python-based parsing software, designed to streamline the analysis of large-scale local ancestry datasets. This software prioritizes computational efficiency and memory optimization, leveraging GPUs when available for additional speed boosts. By overcoming these data processing hurdles, RFMix-reader empowers researchers to unlock the full potential of local ancestry data for understanding human health and health disparities.
Availability:
RFMix-reader is freely available on PyPI at https://pypi.org/project/RFMix-reader/, implemented in Python 3, and supported on Linux, Windows, and Mac OS.
