---
title: "Recomb-Mix Cloud: cost-efficient, scalable pipeline for local ancestry inference"
id: "biorxiv-0-recomb-mix-cloud-a-cost-efficient-and-scalable-cloud-friendly-pipeline-for"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-0-recomb-mix-cloud-a-cost-efficient-and-scalable-cloud-friendly-pipeline-for"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1?rss=1"
published_at: "2026-10-07T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Recomb-Mix Cloud: cost-efficient, scalable pipeline for local ancestry inference
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-0-recomb-mix-cloud-a-cost-efficient-and-scalable-cloud-friendly-pipeline-for
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1?rss=1)
- **Published At:** 2026-10-07T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- Recomb-Mix Cloud is a cloud-ready pipeline developed to perform **local ancestry inference (LAI)** on large biobank-scale datasets that are often accessible only via cloud platforms. - The pipeline is based on **Recomb-Mix**, an LAI method that operates at the variant level and is designed to require less computation than window-based LAI methods. - Recomb-Mix Cloud provides end-to-end functionality: panel sample selection, marker alignment, construction of compact reference panels, Recomb-Mix threading, and exporting LAI calls in common data formats. - The authors provide pre-computed compact population reference panels built from the Human Genome Diversity Project and the 1000 Genomes Project for immediate use. - On the All of Us Researcher Workbench, Recomb-Mix was reported to be substantially cheaper and faster than two common LAI tools, **FLARE** and **RFMix**, while producing largely consistent LAI calls. - Reported estimated compute cost per All of Us whole-genome sample using a pre-computed reference panel: **$0.01** for Recomb-Mix Cloud, compared with **$0.43** for FLARE and **$4.58** for RFMix. - The pipeline aims to lower the resource barrier for LAI, enabling more equitable access for resource-constrained researchers working with cloud-hosted genomic data. - Recomb-Mix Cloud pipeline code and compact reference panels are available at the authors’ GitHub repository: https://github.com/ucfcbb/Recomb-Mix. - The work is presented as a preprint and has not been peer reviewed; the authors declared no competing interests and listed NIH funding sources.
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Yuan Wei 1Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Yuan%2BWei%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Wei%20Y&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AYuan%2BWei%2B) Degui Zhi 2McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Degui%2BZhi%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zhi%20D&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ADegui%2BZhi%2B) * [ORCID record for Degui Zhi](http://orcid.org/0000-0001-7754-1890 "Open in new tab") Shaojie Zhang 1Department of Computer Science, University of Central Florida, Orlando, FL, 32816, USA * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Shaojie%2BZhang%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Zhang%20S&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AShaojie%2BZhang%2B) * [ORCID record for Shaojie Zhang](http://orcid.org/0000-0002-4051-5549 "Open in new tab") * [Abstract](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5834163/1) * [Full Text](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1.full-text)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_full_text/node:5834163/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5834163/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5834163/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5834163/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5834163/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Local ancestry inference (LAI) reveals population compositions of an individual’s chromo-somes, which empowers population genetics and genetic association studies of diverse populations. The recent availability of large genomic datasets from biobanks offers unprecedented opportunities for LAI. However, such datasets are typically only accessible from cloud-based platforms, and the lack of efficient and cost-effective tooling makes LAI prohibitive for resource-constrained researchers. Here, we develop Recomb-Mix Cloud, a cloud-ready pipeline for LAI based on Recomb-Mix, a recently introduced efficient LAI method. Recomb-Mix focuses on individual variants while maintaining a lower computational burden than other LAI methods focusing on genomic windows. Recomb-Mix Cloud offers a cloud-friendly complete pipeline from panel sample selection, marker alignment, compact reference panel construction, Recomb-Mix threading, and generating LAI calls in common data formats. Recomb-Mix Cloud also offers a set of pre-computed compact population reference panels constructed based on data from the Human Genome Diversity Project and the 1000 Genomes Project. Our results showed Recomb-Mix is about one and two orders of magnitude cheaper and faster than FLARE and RFMix, two popular LAI methods, while offering largely consistent LAI calls on the All of Us Researcher Workbench. The estimated overall compute cost per All of Us sample for whole-genome LAI analysis using Recomb-Mix Cloud with a pre-computed reference panel is just $0.01, compared with $0.43 for FLARE and $4.58 for RFMix. Therefore, Recomb-Mix Cloud is well-positioned to democratize local ancestry-based genetics research on modern biobank-sized data. The Recomb-Mix Cloud pipeline and the compact reference panels are available at . ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * {Yuan.Wei{at}ucf.edu, Shaojie.Zhang{at}ucf.edu} * Degui.Zhi{at}uth.tmc.edu ## Funder Information Declared National Institutes of Health, https://ror.org/01cwqze88, R01HG010086, R01AG081398 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/02/2026.09.28.755192.full.pdf)Full Text & In-line Figures[XML](https://www.biorxiv.org/content/biorxiv/early/2026/10/02/2026.09.28.755192.source.xml) [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [Supplementary Material ](https://www.biorxiv.org/content/10.64898/2026.09.28.755192v1.supplementary-material) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Recomb-Mix Cloud: a cost-efficient and scalable cloud-friendly pipeline for local ancestry inference Yuan Wei, Degui Zhi, Shaojie Zhang bioRxiv 2026.09.28.755192; doi: https://doi.org/10.64898/2026.09.28.755192 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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