---
title: "HapSolo2: GPU-accelerated ortholog-guided removal of secondary haplotigs from diploid assemblies"
id: "biorxiv-2-hapsolo2-gpu-accelerated-optimization-for-removing-secondary-haplotigs-from"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-2-hapsolo2-gpu-accelerated-optimization-for-removing-secondary-haplotigs-from"
content_type: "clinical_feed_article"
specialty: "General"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1?rss=1"
published_at: "2026-10-08T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# HapSolo2: GPU-accelerated ortholog-guided removal of secondary haplotigs from diploid assemblies
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-2-hapsolo2-gpu-accelerated-optimization-for-removing-secondary-haplotigs-from
- **Specialty:** [General](https://medichelpline.com/clinical-feed/general.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1?rss=1)
- **Published At:** 2026-10-08T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- HapSolo2 is a reimplementation of HapSolo designed to speed and modernize ortholog-guided removal of **secondary haplotigs** from heterozygous diploid genome assemblies. - The original HapSolo framed haplotig removal as an optimization across three pairwise alignment thresholds (percent identity, query coverage, ratio of aligned query to aligned reference length) to minimize a cost based on single-copy, duplicated, and missing conserved orthologs. - Limitations of the original included per-contig BUSCO v3 runs, Python 2.7/pandas dependence in inner loops, a single randomized forward-walk optimizer, and reliance on legacy OrthoDB v9 datasets that are no longer distributed. - HapSolo2 replaces the per-contig BUSCO step with miniprot-based ortholog classification against OrthoDB v9, v10, or v12.2 lineage datasets, eliminating the need for a BUSCO installation for each contig. - The package adds two optimizers—**steepest descent** and **simulated annealing**—to the original random forward-walk approach, and evaluates multiple walkers in batched CuPy array operations on a single **GPU**. - Performance: on an avocado assembly of 5,122 contigs scored against 1,440 orthologs, 100,000 iterations of random walk and steepest descent ran 5.6- and 7.6-fold faster on an NVIDIA RTX A6000 GPU than on 10 CPU processes. - Across 45 runs on four diploid assemblies (Anopheles funestus, Persea americana, Vitis vinifera, and Amblyraja radiata; sizes 212 Mb to 3.2 Gb) and three OrthoDB releases, the three optimizers reached best costs within 1.8% of one another. - Independent BUSCO v6.1 assessment showed large reductions in duplicated orthologs: from 52.0% to 5.6% in Persea americana, from 17.9% to 10.4% in Vitis vinifera, and from 7.8% to 2.8% in Anopheles funestus, with completeness decreasing by at most 1.3 percentage points. - On the avocado assembly, HapSolo2 reduced the cost achieved by the original HapSolo from 0.2200 to 0.1036 and increased contig N50 from 3,366 kb to 4,608 kb. - HapSolo2 is distributed as an installable Python package with a unified command-line pipeline, assembly statistics and plotting tools, an Apptainer container, and archived copies of legacy ortholog datasets; it makes ortholog-guided haplotig removal practical in minutes on a single workstation and reproducible across OrthoDB releases.
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Solares doi: https://doi.org/10.64898/2026.10.02.756147 Juan Yin 1University of California San Diego, La Jolla, CA, USA * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Juan%2BYin%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Yin%20J&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AJuan%2BYin%2B) Mansi Agrawal 2University of California, Davis, Davis, CA, USA * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Mansi%2BAgrawal%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Agrawal%20M&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AMansi%2BAgrawal%2B) Prashansa Goel 2University of California, Davis, Davis, CA, USA * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Prashansa%2BGoel%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Goel%20P&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3APrashansa%2BGoel%2B) Edwin A. Solares 3Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA 4ESB AI Lab Corporation, San Diego, CA, USA * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Edwin%2BA.%2BSolares%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Solares%20EA&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AEdwin%2BA.%2BSolares%2B) * [ORCID record for Edwin A. Solares](http://orcid.org/0000-0002-3220-4927 "Open in new tab") * For correspondence: edwin@esbailab.org * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5834278/1) * [Full Text](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1.full-text)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_full_text/node:5834278/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5834278/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5834278/1) * [Supplementary material](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1.supplementary-material)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_data/node:5834278/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5834278/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5834278/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract **Background** Secondary haplotigs retained in a primary assembly inflate genome size and duplicate gene content, and their removal remains a necessary curation step for heterozygous diploid genomes. HapSolo ([Solares et al. 2021](https://www.biorxiv.org/content/10.64898/2026.10.02.756147v1?rss=1#ref-21)) treats this step as an optimization problem: it searches three pairwise alignment thresholds (percent identity, query coverage, and the ratio of aligned query to aligned reference length) to minimize a cost defined over single-copy, duplicated, and missing conserved orthologs. The original implementation depended on a separate BUSCO v3 run for every contig, on Python 2.7 and pandas in its inner loop, on a single randomized forward-walk optimizer, and on OrthoDB v9 lineage datasets that are no longer distributed. **Results** Here we report HapSolo2, a reimplementation that replaces the per-contig BUSCO step with miniprot-based ortholog classification against OrthoDB v9, v10, or v12.2 lineage datasets, adds steepest descent and simulated annealing to the original random forward walk, and evaluates all walkers in batched CuPy array operations on a single GPU. On an avocado assembly of 5,122 contigs scored against 1,440 orthologs, 100,000 iterations of random walk and steepest descent ran 5.6- and 7.6-fold faster on one NVIDIA RTX A6000 than on 10 CPU processes. Across 45 runs on four diploid assemblies (_Anopheles funestus_ , _Persea americana_ , _Vitis vinifera_ , and _Amblyraja radi-ata_ ; 212 Mb to 3.2 Gb) and three OrthoDB releases, the three optimizers reached best costs within 1.8% of one another. Independent assessment with BUSCO v6.1 showed that HapSolo2 reduced duplicated orthologs from 52.0% to 5.6% in _P. americana_ , from 17.9% to 10.4% in _V. vinifera_ , and from 7.8% to 2.8% in _A. funestus_ , with completeness decreasing by at most 1.3 percentage points. On the avocado assembly, HapSolo2 reduced the cost reached by the original HapSolo from 0.2200 to 0.1036 and raised contig N50 from 3,366 kb to 4,608 kb. **Conclusions** HapSolo2 makes ortholog-guided haplotig removal practical in minutes on a single workstation, independent of a BUSCO installation, and reproducible across OrthoDB releases. It is distributed as an installable Python package with a unified command-line pipeline, an assembly statistics and plotting tool, an Apptainer container, and archived copies of the legacy ortholog datasets. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * ## Funder Information Declared University of California Office of the President, https://ror.org/00dmfq477, UC Presidential Postdoctoral Fellowship U.S. National Science Foundation, https://ror.org/021nxhr62, 21868-ACCESS-MCB180035 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY-ND 4.0 International license](http://creativecommons.org/licenses/by-nd/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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