GigaScience最新文献

{"title":"Habitat suitability maps for Australian flora and fauna under CMIP6 climate scenarios.","authors":"Carla L Archibald, David M Summers, Erin M Graham, Brett A Bryan","doi":"10.1093/gigascience/giae002","DOIUrl":"10.1093/gigascience/giae002","url":null,"abstract":"<p><strong>Background: </strong>Spatial information about the location and suitability of areas for native plant and animal species under different climate futures is an important input to land use and conservation planning and management. Australia, renowned for its abundant species diversity and endemism, often relies on modeled data to assess species distributions due to the country's vast size and the challenges associated with conducting on-ground surveys on such a large scale. The objective of this article is to develop habitat suitability maps for Australian flora and fauna under different climate futures.</p><p><strong>Results: </strong>Using MaxEnt, we produced Australia-wide habitat suitability maps under RCP2.6-SSP1, RCP4.5-SSP2, RCP7.0-SSP3, and RCP8.5-SSP5 climate futures for 1,382 terrestrial vertebrates and 9,251 vascular plants vascular plants at 5 km2 for open access. This represents 60% of all Australian mammal species, 77% of amphibian species, 50% of reptile species, 71% of bird species, and 44% of vascular plant species. We also include tabular data, which include summaries of total quality-weighted habitat area of species under different climate scenarios and time periods.</p><p><strong>Conclusions: </strong>The spatial data supplied can help identify important and sensitive locations for species under various climate futures. Additionally, the supplied tabular data can provide insights into the impacts of climate change on biodiversity in Australia. These habitat suitability maps can be used as input data for landscape and conservation planning or species management, particularly under different climate change scenarios in Australia.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10939329/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140039094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"\"UDE DIATOMS in the Wild 2024\": a new image dataset of freshwater diatoms for training deep learning models.","authors":"Aishwarya Venkataramanan, Michael Kloster, Andrea Burfeid-Castellanos, Mimoza Dani, Ntambwe A S Mayombo, Danijela Vidakovic, Daniel Langenkämper, Mingkun Tan, Cedric Pradalier, Tim Nattkemper, Martin Laviale, Bánk Beszteri","doi":"10.1093/gigascience/giae087","DOIUrl":"10.1093/gigascience/giae087","url":null,"abstract":"<p><strong>Background: </strong>Diatoms are microalgae with finely ornamented microscopic silica shells. Their taxonomic identification by light microscopy is routinely used as part of community ecological research as well as ecological status assessment of aquatic ecosystems, and a need for digitalization of these methods has long been recognized. Alongside their high taxonomic and morphological diversity, several other factors make diatoms highly challenging for deep learning-based identification using light microscopy images. These include (i) an unusually high intraclass variability combined with small between-class differences, (ii) a rather different visual appearance of specimens depending on their orientation on the microscope slide, and (iii) the limited availability of diatom experts for accurate taxonomic annotation.</p><p><strong>Findings: </strong>We present the largest diatom image dataset thus far, aimed at facilitating the application and benchmarking of innovative deep learning methods to the diatom identification problem on realistic research data, \"UDE DIATOMS in the Wild 2024.\" The dataset contains 83,570 images of 611 diatom taxa, 101 of which are represented by at least 100 examples and 144 by at least 50 examples each. We showcase this dataset in 2 innovative analyses that address individual aspects of the above challenges using subclustering to deal with visually heterogeneous classes, out-of-distribution sample detection, and semi-supervised learning.</p><p><strong>Conclusions: </strong>The problem of image-based identification of diatoms is both important for environmental research and challenging from the machine learning perspective. By making available the so far largest image dataset, accompanied by innovative analyses, this contribution will facilitate addressing these points by the scientific community.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11604061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142750299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chromosome-level genome of the poultry shaft louse Menopon gallinae provides insight into the host-switching and adaptive evolution of parasitic lice.","authors":"Ye Xu, Ling Ma, Shanlin Liu, Yanxin Liang, Qiaoqiao Liu, Zhixin He, Li Tian, Yuange Duan, Wanzhi Cai, Hu Li, Fan Song","doi":"10.1093/gigascience/giae004","DOIUrl":"10.1093/gigascience/giae004","url":null,"abstract":"<p><strong>Background: </strong>Lice (Psocodea: Phthiraptera) are one important group of parasites that infects birds and mammals. It is believed that the ancestor of parasitic lice originated on the ancient avian host, and ancient mammals acquired these parasites via host-switching from birds. Here we present the first chromosome-level genome of Menopon gallinae in Amblycera (earliest diverging lineage of parasitic lice). We explore the transition of louse host-switching from birds to mammals at the genomic level by identifying numerous idiosyncratic genomic variations.</p><p><strong>Results: </strong>The assembled genome is 155 Mb in length, with a contig N50 of 27.42 Mb. Hi-C scaffolding assigned 97% of the bases to 5 chromosomes. The genome of M. gallinae retains a basal insect repertoire of 11,950 protein-coding genes. By comparing the genomes of lice to those of multiple representative insects in other orders, we discovered that gene families of digestion, detoxification, and immunity-related are generally conserved between bird lice and mammal lice, while mammal lice have undergone a significant reduction in genes related to chemosensory systems and temperature. This suggests that mammal lice have lost some of these genes through the adaption to environment and temperatures after host-switching. Furthermore, 7 genes related to hematophagy were positively selected in mammal lice, suggesting their involvement in the hematophagous behavior.</p><p><strong>Conclusions: </strong>Our high-quality genome of M. gallinae provides a valuable resource for comparative genomic research in Phthiraptera and facilitates further studies on adaptive evolution of host-switching within parasitic lice.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 1","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10904027/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139899653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging citizen science for monitoring urban forageable plants.","authors":"Filipi Miranda Soares, Luís Ferreira Pires, Maria Carolina Garcia, Yamine Bouzembrak, Lidio Coradin, Natalia Pirani Ghilardi-Lopes, Rubens Rangel Silva, Aline Martins de Carvalho, Benildes Coura Moreira Dos Santos Maculan, Sheina Koffler, Uiara Bandineli Montedo, Debora Pignatari Drucker, Raquel Santiago, Anand Gavai, Maria Clara Peres de Carvalho, Ana Carolina da Silva Lima, Hillary Dandara Elias Gabriel, Stephanie Gabriele Mendonça de França, Karoline Reis de Almeida, Bárbara Junqueira Dos Santos, Antonio Mauro Saraiva","doi":"10.1093/gigascience/giae007","DOIUrl":"10.1093/gigascience/giae007","url":null,"abstract":"<p><p>Urbanization brings forth social challenges in emerging countries such as Brazil, encompassing food scarcity, health deterioration, air pollution, and biodiversity loss. Despite this, urban areas like the city of São Paulo still boast ample green spaces, offering opportunities for nature appreciation and conservation, enhancing city resilience and livability. Citizen science is a collaborative endeavor between professional scientists and nonprofessional scientists in scientific research that may help to understand the dynamics of urban ecosystems. We believe citizen science has the potential to promote human and nature connection in urban areas and provide useful data on urban biodiversity.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10914215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140039095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci.","authors":"Anish M S Shrestha, Mark Edward M Gonzales, Phoebe Clare L Ong, Pierre Larmande, Hyun-Sook Lee, Ji-Ung Jeung, Ajay Kohli, Dmytro Chebotarov, Ramil P Mauleon, Jae-Sung Lee, Kenneth L McNally","doi":"10.1093/gigascience/giae013","DOIUrl":"10.1093/gigascience/giae013","url":null,"abstract":"<p><strong>Background: </strong>As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.</p><p><strong>Results: </strong>We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.</p><p><strong>Conclusions: </strong>RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11148593/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141237423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.","authors":"Teresa Müller, Stefan Mautner, Pavankumar Videm, Florian Eggenhofer, Martin Raden, Rolf Backofen","doi":"10.1093/gigascience/giae022","DOIUrl":"10.1093/gigascience/giae022","url":null,"abstract":"<p><strong>Background: </strong>RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throughput methods can lack precision because of a high false-positive rate.</p><p><strong>Results: </strong>We present CheRRI, the first tool to evaluate the biological relevance of putative RNA-RNA interaction sites. CheRRI filters candidates via a machine learning-based model trained on experimental RNA-RNA interactome data. Its unique setup combines interactome data and an established thermodynamic prediction tool to integrate experimental data with state-of-the-art computational models. Applying these data to an automated machine learning approach provides the opportunity to not only filter data for potential false positives but also tailor the underlying interaction site model to specific needs.</p><p><strong>Conclusions: </strong>CheRRI is a stand-alone postprocessing tool to filter either predicted or experimentally identified potential RNA-RNA interactions on a genomic level to enhance the quality of interaction candidates. It is easy to install (via conda, pip packages), use (via Galaxy), and integrate into existing RNA-RNA interaction pipelines.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11152173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141261603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PhageGE: an interactive web platform for exploratory analysis and visualization of bacteriophage genomes.","authors":"Jinxin Zhao, Jiru Han, Yu-Wei Lin, Yan Zhu, Michael Aichem, Dimitar Garkov, Phillip J Bergen, Sue C Nang, Jian-Zhong Ye, Tieli Zhou, Tony Velkov, Jiangning Song, Falk Schreiber, Jian Li","doi":"10.1093/gigascience/giae074","DOIUrl":"10.1093/gigascience/giae074","url":null,"abstract":"<p><strong>Background: </strong>Antimicrobial resistance is a serious threat to global health. Due to the stagnant antibiotic discovery pipeline, bacteriophages (phages) have been proposed as an alternative therapy for the treatment of infections caused by multidrug-resistant pathogens. Genomic features play an important role in phage pharmacology. However, our knowledge of phage genomics is sparse, and the use of existing bioinformatic pipelines and tools requires considerable bioinformatic expertise. These challenges have substantially limited the clinical translation of phage therapy.</p><p><strong>Findings: </strong>We have developed PhageGE (Phage Genome Explorer), a user-friendly graphical interface application for the interactive analysis of phage genomes. PhageGE enables users to perform key analyses, including phylogenetic analysis, visualization of phylogenetic trees, prediction of phage life cycle, and comparative analysis of phage genome annotations. The new R Shiny web server, PhageGE, integrates existing R packages and combines them with several newly developed functions to facilitate these analyses. Additionally, the web server provides interactive visualization capabilities and allows users to directly export publication-quality images.</p><p><strong>Conclusions: </strong>PhageGE is a valuable tool that simplifies the analysis of phage genome data and may expedite the development and clinical translation of phage therapy. PhageGE is publicly available at https://jason-zhao.shinyapps.io/PhageGE_Update/.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":3.5,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11423353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142344887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AltaiR: a C toolkit for alignment-free and temporal analysis of multi-FASTA data.","authors":"Jorge M Silva, Armando J Pinho, Diogo Pratas","doi":"10.1093/gigascience/giae086","DOIUrl":"10.1093/gigascience/giae086","url":null,"abstract":"<p><strong>Background: </strong>Most viral genome sequences generated during the latest pandemic have presented new challenges for computational analysis. Analyzing millions of viral genomes in multi-FASTA format is computationally demanding, especially when using alignment-based methods. Most existing methods are not designed to handle such large datasets, often requiring the analysis to be divided into smaller parts to obtain results using available computational resources.</p><p><strong>Findings: </strong>We introduce AltaiR, a toolkit for analyzing multiple sequences in multi-FASTA format using exclusively alignment-free methodologies. AltaiR enables the identification of singularity and similarity patterns within sequences and computes static and temporal dynamics without restrictions on the number or size of input sequences. It automatically filters low-quality, biased, or deviant data. We demonstrate AltaiR's capabilities by analyzing more than 1.5 million full severe acute respiratory virus coronavirus 2 sequences, revealing interesting observations regarding viral genome characteristics over time, such as shifts in nucleotide composition, decreases in average Kolmogorov sequence complexity, and the evolution of the smallest sequences not found in the human host.</p><p><strong>Conclusions: </strong>AltaiR can identify temporal characteristics and trends in large numbers of sequences, making it ideal for scenarios involving endemic or epidemic outbreaks with vast amounts of available sequence data. Implemented in C with multithreading and methodological optimizations, AltaiR is computationally efficient, flexible, and dependency-free. It accepts any sequence in FASTA format, including amino acid sequences. The complete toolkit is freely available at https://github.com/cobilab/altair.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction and analysis of telomere-to-telomere genomes for 2 sweet oranges: Longhuihong and Newhall (Citrus sinensis).","authors":"Lin Hong, Xin-Dong Xu, Lei Yang, Min Wang, Shuang Li, Haijian Yang, Si-Ying Ye, Ling-Ling Chen, Jia-Ming Song","doi":"10.1093/gigascience/giae084","DOIUrl":"10.1093/gigascience/giae084","url":null,"abstract":"<p><strong>Background: </strong>Sweet orange (Citrus sinensis Osbeck) is a fruit crop of high nutritional value that is widely consumed around the world. However, its susceptibility to low-temperature stress limits its cultivation and production in regions prone to frost damage, severely impacting the sustainable development of the sweet orange industry. Therefore, developing cold-resistant sweet orange varieties is of great necessity. Traditional hybrid breeding methods are not feasible due to the polyembryonic phenomenon in sweet oranges, necessitating the enhancement of its germplasm through molecular breeding. High-quality reference genomes are valuable for studying crop resistance to biotic and abiotic stresses. However, the lack of genomic resources for cold-resistant sweet orange varieties has hindered the progress in developing such varieties and researching their molecular mechanisms of cold resistance.</p><p><strong>Findings: </strong>This study integrated PacBio HiFi, ONT, Hi-C, and Illumina sequencing data to assemble telomere-to-telomere (T2T) reference genomes for the cold-resistant sweet orange mutant \"Longhuihong\" (Citrus sinensis [L.] Osb. cv. LHH) and its wild-type counterpart \"Newhall\" (C. sinensis [L.] Osb. cv. Newhall). Comprehensive evaluations based on multiple criteria revealed that both genomes exhibit high continuity, completeness, and accuracy. The genome sizes were 340.28 Mb and 346.33 Mb, with contig N50 of 39.31 Mb and 36.77 Mb, respectively. In total, 31,456 and 30,021 gene models were annotated in the respective genomes. Leveraging these assembled genomes, comparative genomics analyses were performed, elucidating the evolutionary history of the sweet orange genome. Moreover, the study identified 2,886 structural variants between the 2 genomes, with several SVs located in the upstream, downstream, or intronic regions of homologous genes known to be associated with cold resistance.</p><p><strong>Conclusions: </strong>The study de novo assembled 2 T2T reference genomes of sweet orange varieties exhibiting different levels of cold tolerance. These genomes serve as valuable foundational resources for genomic research and molecular breeding aimed at enhancing cold tolerance in sweet oranges. Additionally, they expand the existing repository of reference genomes and sequencing data resources for C. sinensis. Moreover, these genomes provide a critical data foundation for comparative genomics analyses across different plant species.</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11590112/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142715757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"demuxSNP: supervised demultiplexing single-cell RNA sequencing using cell hashing and SNPs.","authors":"Michael P Lynch, Yufei Wang, Shannan Ho Sui, Laurent Gatto, Aedin C Culhane","doi":"10.1093/gigascience/giae090","DOIUrl":"10.1093/gigascience/giae090","url":null,"abstract":"<p><strong>Background: </strong>Multiplexing single-cell RNA sequencing experiments reduces sequencing cost and facilitates larger-scale studies. However, factors such as cell hashing quality and class size imbalance impact demultiplexing algorithm performance, reducing cost-effectiveness.</p><p><strong>Findings: </strong>We propose a supervised algorithm, demuxSNP, which leverages both cell hashing and genetic variation between individuals (single-nucletotide polymorphisms [SNPs]). demuxSNP addresses fundamental limitations in demultiplexing methods that use only one data modality. Some cells may be confidently demultiplexed using probabilistic hashing methods. demuxSNP uses these data to infer the genotype of singlet and doublet clusters and predict on cells assigned as negative, uncertain, or doublet using a nearest-neighbor approach adapted for missing data.We benchmarked demuxSNP against hashing, genotype-free SNP and hybrid methods on simulated and real data from renal cell cancer. demuxSNP outperformed standalone hashing methods on low-quality hashing data benchmark, improved overall classification accuracy, and allowed more high RNA quality cells to be recovered. Through varying simulated doublet rates, we showed that genotype-free SNP and hybrid methods that leverage them were impacted by class size imbalance and doublet rate. demuxSNP's supervised approach was more robust to doublet rate in experiments with class size imbalance.</p><p><strong>Conclusions: </strong>demuxSNP uses hashing and SNP data to demultiplex datasets with low hashing quality where biological samples are genetically distinct. Unassigned or negative cells with high RNA quality are recovered, making more cells available for analysis. Data simulation and benchmarking pipelines as well as processed benchmarking data for 5-50% doublets are publicly available. demuxSNP is available as an R/Bioconductor package (https://doi.org/doi:10.18129/B9.bioc.demuxSNP).</p>","PeriodicalId":12581,"journal":{"name":"GigaScience","volume":"13 ","pages":""},"PeriodicalIF":11.8,"publicationDate":"2024-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11604057/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142750345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}