Benjamin Kynon J M
Lieber Institute for Brain Development; Department of Neurology, Johns Hopkins University School of Medicine · Funded by National Institute on Minority Health and Health Disparities (K99MD016964).
bioRxiv. 2024 Jul 18:2024.07.13.603370. doi: 10.1101/2024.07.13.603370.
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.
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.
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.
本地祖先推断是遗传学中的一项强大技术，可揭示群体历史和疾病的遗传基础。它对于改善混合群体中的eQTL发现和精细定位特别有价值。尽管RFMix软件在本地祖先推断中被广泛使用，但大规模基因组研究在处理RFMix输出文件时面临高内存消耗和处理时间的挑战。
在此，我介绍RFMix-reader，这是一种新的基于Python的解析软件，旨在简化大规模本地祖先数据集的分析。该软件优先考虑计算效率和内存优化，在可用时利用GPU来进一步提高速度。通过克服这些数据处理障碍，RFMix-reader使研究人员能够释放本地祖先数据的全部潜力，以了解人类健康和健康差异。
RFMix-reader可在PyPI上免费获取，网址为https://pypi.org/project/RFMix-reader/，用Python 3实现，并在Linux、Windows和Mac OS上得到支持。
