The P10K database: a data portal for the protist 10 000 genomes project

Xinxin Gao 1,2,4, Kai Chen 3,4, Jie Xiong 4,5,4, Dong Zou 6,7,4, Fangdian Yang 8, Yingke Ma 9,10, Chuanqi Jiang 11, Xiaoxuan Gao 12, Guangying Wang 13, Siyu Gu 14,15, Peng Zhang 16, Shuai Luo 17, Kaiyao Huang 18,19, Yiming Bao 20,21,22, Zhang Zhang 23,24,25,✉, Lina Ma 26,27,28,✉, Wei Miao 29,30,31,✉

PMCID: PMC10767852  PMID: 37930867

This article has been corrected. See Nucleic Acids Res. 2023 Nov 30;52(D1):D1699.

Abstract

Protists, a highly diverse group of microscopic eukaryotic organisms distinct from fungi, animals and plants, exert crucial roles within the earth's biosphere. However, the genomes of only a small fraction of known protist species have been published and made publicly accessible. To address this constraint, the Protist 10 000 Genomes Project (P10K) was initiated, implementing a specialized pipeline for single-cell genome/transcriptome assembly, decontamination and annotation of protists. The resultant P10K database (https://ngdc.cncb.ac.cn/p10k/) serves as a comprehensive platform, collating and disseminating genome sequences and annotations from diverse protist groups. Currently, the P10K database has incorporated 2959 genomes and transcriptomes, including 1101 newly sequenced datasets by P10K and 1858 publicly available datasets. Notably, it covers 45% of the protist orders, with a significant representation (53% coverage) of ciliates, featuring nearly a thousand genomes/transcriptomes. Intriguingly, analysis of the unique codon table usage among ciliates has revealed differences compared to the NCBI taxonomy system, suggesting a need to revise the codon tables used for these species. Collectively, the P10K database serves as a valuable repository of genetic resources for protist research and aims to expand its collection by incorporating more sequenced data and advanced analysis tools to benefit protist studies worldwide.

Graphical Abstract

Graphical Abstract.

Graphical Abstract

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Introduction

Protists, a highly diverse group of single-celled eukaryotic organisms, comprise the kingdom Protista, which was proposed by Robert Whittaker in 1969 as one of the five kingdoms of life (1). Since the 17th century, microbiologists have been describing and cataloguing these diverse eukaryotes (2). With their crucial role in maintaining ecological balance, environmental health and energy transportation in the earth's biosphere, especially in aquatic ecosystems like marine and freshwater habitats, protists are of fundamental importance to serve as significant models for research in cytology, genetics, developmental biology and evolutionary biology.

In recent years, substantial genome sequencing efforts have been made across various life kingdoms, such as plants (3), fungi (4), different clades of animals (5–8) and prokaryotes (9,10). Consequently, genome databases have been established for these kingdoms. However, the distribution of sequenced eukaryotic genomes across different kingdoms is notably uneven, with a significant scarcity of genomes and transcriptomes among protists. Currently, the NCBI taxonomy system (https://www.ncbi.nlm.nih.gov/taxonomy) (11) has documented over 60 000 recorded protist species. Researchers in the field of protist genomics have made noteworthy contributions in the study of various protist groups and have built a variety of resources to support their research. Over 30 protist genomics databases have been developed, with notable examples including the ciliate genome database TGD (Tetrahymena Genome Database, https://tet.ciliate.org) (12), the algae genome database FWAlgaeDB (FreshWater Algae DataBase, http://www.fwalagedb.com), the parasitic protozoa genome database VEuPathDB (Eukaryotic Pathogen, Vector and Host Informatics Resource, https://veupathdb.org) (13) and the MMETSP (Marine Microbial Eukaryote Transcriptome Sequencing Project, http://marinemicroeukaryotes.org) (14). Despite these dedicated efforts, the availability of genome and transcriptome datasets for protists remains relatively limited, with no more than 2000 datasets accessible as of 2022. This indicates that many protist genomes remain unexplored, despite their biological and ecological importance. Moreover, the taxonomic diversity of protists, spanning across dozens of phyla, poses a formidable challenge when it comes to integrating additional genomes. As a result, there is a clear need for a dedicated genome data repository specifically tailored for protists.

To address this gap, the Protist 10 000 Genomes Project (P10K) was launched in 2019 (15). Its primary goal is to sequence over 10 000 protist genomes, with a specific emphasis on employing single-cell genome/transcriptome sequencing technologies to overcome the challenges associated with culturing protists. To facilitate achieving this goal and enable efficient use of the generated data, the P10K database (https://ngdc.cncb.ac.cn/p10k/) was established as a comprehensive data portal. Serving as a centralized repository, the P10K database stores the sequencing data, assembled genome sequences and annotations generated by the P10K initiative. Its main purpose is to provide a curated collection of high-quality protist genomes and offer user-friendly features, enabling easy access, intuitive browsing and online analysis.

Materials and methods

Sample collection and sequencing

Large-scale sampling has been continuously carried out in various regions of China, such as the Qinghai-Tibet Plateau area, Qinghai Lake area and the central and eastern regions, to gather a diverse range of protist specimens. These samples were collected from a variety of habitats, including lakes, rivers, wetlands, oceans and hot springs. Based on the morphological characteristics, protist cells were isolated from water and moss samples. A total of 1101 protist samples were obtained. Among them, 1078 samples are single-cell isolates due to difficulties in culturing or enrichment in the laboratory. Detailed meta-information regarding these samples is accessible through the sample page of the P10K database. In addition, DNA/RNA extraction for sequencing has been carried out for protists that could be successfully cultivated or enriched in laboratory.

Multiple displacement amplification (MDA) has been carried out on single cells for genome sequencing using the MGISEQ-2000 sequencing platform. Smart-seq2 was used to generate cDNA from single cells for transcriptome sequencing using the Illumina NovaSeq 6000 and Illumina HiSeq 4000 platforms. In the case of cultured samples, bulk DNA/RNA was extracted for sequencing using platforms such as Illumina, Oxford Nanopore, or PacBio Sequel Ⅱ.

Additional details regarding sample collection and sequencing are contained in the Supplementary Methods.

Data processing and annotation with standardized pipeline

To analyze the single-cell genome/transcriptome data, a standardized pipeline was developed to streamline the processes of assembly, decontamination and annotation tailored for protists (Supplementary Figure S1). The outlined procedure is as follows, and more details can be found in the Supplementary Methods.

Fsatp (16) was used to filter out low-quality reads and remove the adapters for next generation sequencing reads. For genome/transcriptome assembly, MEGAHIT (17) was used to single-cell genome assembly, Trinity (18) was used to single-cell transcriptome assembly, SOAPdenovo (19) and Canu (20) were used to assembling bulk genome sequencing in short reads and long reads, respectively.

The iGDP (https://github.com/GWang2022/iGDP) (21) was used to decontaminate the ciliate genomes, while MMseqs2 (22) was used to search against NR (NCBI non-redundant protein sequence database) to remove bacterial, archaeal and viral contamination for non-ciliate genomes and all transcriptomes. Additionally, contigs shorter than 1Kb were removed from genome assemblies.

For species annotations, nhmmer (23) was used to align the sequences with the rDNA hmm profile to extract SSU sequences. Then, the extracted SSU sequences was searched with NT (NCBI nucleotide sequence database) (24), SILVA (25) and PR2 (26) by BLASTN (27,28) to determine the optimal taxonomy annotation. For gene prediction, the genetic codes (codon tables) were estimated using codetta (29) and FACIL (30). The gene prediction process for the genomes in our pipeline used a combination of both homology-based (GenomeThreader (31) and AAT (32)) and de novo prediction methods (PASA (33), Augustus (34), GlimmerHMM (35) and SNAP (36)). Finally, an integrated set of gene models was created using EVidenceModeler (37) by merging all predicted gene models and RNA-seq transcripts if available. For transcriptome assemblies, the prediction of ORFs was performed using getorf (http://emboss.bioinformatics.nl/cgi-bin/emboss/help/getorf).

Gene functions were annotated using InterProScan (38). Orthologs and paralogs were identified using Diamond (39) with cutoff identities more than 30% and E-values lower than 1e–5.

The decontamination and gene prediction processes of this pipeline remain applicable when analyzing genome sequencing data from cultured protists such as long-read sequencing data (40–42).

For genome/transcriptome quality assessment, genome completeness estimated by BUSCO (43) and CDS completeness (ratio of gene models with start and stop codons) were integrity into Genome Quality (GQ). The calculation methodology is formulated as follows.

graphic file with name M0001.gif

According to the range of GQ values, genome integrity was divided into three levels: high (GQ ≥ 80%), medium (50–80%) and low (GQ < 50%).

Public data retrieval and curation

In addition to newly sequenced protist genomes and transcriptomes, we have also curated publicly available sequencing data for protists. In total, the P10K database has incorporated 1858 publicly available protist genomes/transcriptomes sourced from several public databases. Of these, 1193 genomes were downloaded from the NCBI (https://www.ncbi.nlm.nih.gov/assembly/) and 1 genome was downloaded from Ciliate.org (https://ciliates.org/), while 659 transcriptomes were generated by the MMETSP (14) and 5 transcriptomes of Euplotes were derived from the study of Gaydukova et al. (44). Samples from this public data were collected from various countries and regions worldwide. Out of these public genomes, both assembly sequences and gene annotations were obtained for 428 genomes, while only assembly sequences were obtained for the remaining 766 genomes. For the 659 transcriptomes with only assembled sequences from MMETSP (14), a re-annotation process was performed using our analysis pipeline (described above), which included decontamination and gene annotation steps.

Database implementation

The P10K Database was developed using Spring Boot (https://spring.io/projects/spring-boot) as the backend framework. All data were stored and managed using MySQL (https://dev.mysql.com). To ensure a seamless user experience and highly interactive web applications, the web pages were constructed using HTML5 and rendered using JSP (https://jakarta.ee/specifications/pages/3.0/). The front-end interfaces were built using Semantic UI (https://semantic-ui.com) and JQuery (https://jquery.com). Additionally, genome browser was implemented by igv.js (https://github.com/igvteam/igv.js#igvjs), and data visualization was achieved using HighCharts (https://www.highcharts.com) and DataTables (https://datatables.net). BLAST search was powered by the National Genomics Data Center (NGDC) BLAST online service (https://ngdc.cncb.ac.cn/blast/).

Database contents and features

Extensive coverage in terms of geography, habitat and taxonomy

The P10K database has successfully incorporated 2959 protist genomes/transcriptomes, including 1601 genomes and 1358 transcriptomes. These data were sourced from a wide variety of habitats, with 1101 generated by the P10K team distributed in China and 1858 publicly available from various locations worldwide (Figure 1A). Notably, a portion of the P10K dataset was collected from regions characterized by extreme conditions such as high altitudes, elevated temperatures in hot springs and environments tainted with heavy metals like cadmium and mercury (Figure 1B). These specific data acquisitions hold significant implications for advancing our understanding of protists thriving in such challenging circumstances. According to the current NCBI taxonomy (11), protists are categorized into 10 supergroups and 27 phyla (Figure 1C). Based on this taxonomic framework, the P10K database exhibits comprehensive inclusiveness, covering 9 supergroups and 24 phyla, with the exceptions of Blastocladiomycota, Hemimastigophora and Picozoa phyla. Regarding the quantity of accessible genome/transcriptome data, Ciliophora, Ochrophytes, Apicomplexa, and Oomycetes phyla stand out as the most plentiful, each surpassing 200 records (Figure 1C). In contrast, Imbricatea, Preaxostyla, Parabasalia and Foraminifera phyla have relatively few records, with each containing fewer than ten (Figure 1C). In terms of coverage of orders or classes, the P10K database encompasses 45% (174/385) of orders and 75% (64/85) of classes within the protist classification (Figure 1C). The statistical analysis reveals that the Alveolata (47/84), Stramenopiles (47/100), Amoebozoa (13/19), Discoba (8/13) and Haptista (8/11) supergroups exhibit coverage exceeding or nearly reaching 50% at the order level. In contrast, Rhizaria (7/37) only achieves a coverage of approximately 19% (Figure 1C).

Figure 1.

Figure 1.

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Geographic, habitat and taxonomic coverage of samples and data within the P10K database. (A) Sample collection locations. Newly sequenced genomes and transcriptomes as well as publicly available counterparts were labeled by different colors and shapes. Sampling sites were mapped using ArcMap 10.8 for visual representation. (B) Habitat statistics. Note that habitats with fewer than ten genome/transcriptome datasets were grouped together as ‘others’. (C) Taxonomic coverage. Protist groups were determined based on the NCBI taxonomy system, with the relationships among different groups based on previous reports. For the 10 supergroups (indicated by curves with different colors), the numbers of covered classes and the total numbers of classes (separated by slashes), as well as the numbers of covered orders and the total numbers of orders (separated by slashes), are listed in parentheses. Within the inner circle, the different groups/phyla are indicated. The number of covered orders and the number of datasets are listed for each group/phylum, separated by comma. Samples from the 10 existing supergroups that were not assigned to any phyla/groups are labeled as ‘others’ and enclosed in dashed boxes. (D) Distribution of 1101 newly sequenced genomes and transcriptomes across different groups/phyla. Blue numbers represent the count of newly sequenced datasets, while black numbers represent the total count of datasets within the phyla/groups. (E) Distribution of genomes/transcriptomes within ciliates. Classes and orders are listed in different circles from inner to outer. The number displayed in the outer circle denotes the total counts of integrated datasets for each order. The 14 orders newly included in the P10K project are indicated with a red star, while unclassified orders that not assigned to any orders within existing classes are named as ‘others’ and are represented by blue triangle.

The inclusion of 1101 newly sequenced genomes/ transcriptomes by the P10K team represents a noteworthy expansion of 37% within the dataset, encompassing species from 12 groups/phyla (Figure 1D). The dataset prominently features a substantial quantity of genomes/transcriptomes for Ciliophora, Tubulinea and Discosea phyla, with particular emphasis on Ciliophora (ciliates).

The Ciliophora phylum or ciliates stands as the most extensively represented organism group within the P10K database, comprising 985 genomes/transcriptomes, out of which 90% (884) are newly sequenced (Figure 1D). Notably, the newly sequenced data by P10K have introduced coverage for 14 new orders (Figure 1E), resulting in a substantial increase when compared to the currently public data. The genomes/transcriptomes were distributed across 10 classes and 30 orders, covering approximately 71% (10/14) of all classes and 53% (30/57) of all orders within the Ciliophora phylum (Figure 1E). Among the covered classes, Oligohymenophorea, Colpodea and Litostomatea feature the most extensive datasets. Notably, all the orders of Nassophorea and Litostomatea classes were covered. Among the covered orders, Sessilida and Colpodida each contain >100 genomes/transcriptomes, signifying the highest data representation. Following closely were Haptorida, Prorodontida, Heterotrichida and Hymenostomatida, each possessing >40 genomes/transcriptomes (Figure 1E).

High-quality curation and annotation based on tailored methodology

98% of newly sequenced datasets including 385 genomes and 693 transcriptomes were generated using advanced single-cell sequencing technologies, and we have dedicatedly developed a standardized pipeline tailored for these genome/transcriptome assembly, decontamination, species identification, gene annotation and evaluation (see details in Materials and Methods).

The decontamination process within the pipeline proved to be highly effective, as demonstrated by the analysis of GC content distribution. The final assembly displayed a single GC peak, indicating the absence of contaminants. In contrast, the raw assemblies often exhibited multiple GC peaks, suggesting the presence of contaminants (Figure 2A).

Figure 2.

Figure 2.

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Genome quality assessment. (A) Genome purity. GC content distribution is used to assess the purity of the genomes. The left panel displays the GC content distribution for raw genome assemblies, while the right panel showcases the distribution for final genome assemblies after decontamination. It is worth noting that the GC content of individual contigs was adjusted to subtract the average GC content of each respective assembly. (B) Genome completeness. Completeness levels, determined based on BUSCO estimation, are shown for genome (G) and transcriptome (T) data, and the newly sequenced data (P10K) and publicly available data (Public). (C) Genome integrity. Genome quality (GQ) metric is employed to estimate the overall genome integrity by integrating both genome and CDS completeness. The annotation levels are shown for genome (G) and transcriptome (T) data, and the newly sequenced data (P10K) and publicly available data (Public). The qualities in (B) and (C) are categorized into high, medium and low. The quantities used in the statistics are listed below the bars.

In terms of BUSCO completeness, the newly sequenced data and the publicly available data exhibited a comparable proportion of medium and high level of genome assembly completeness. It revealed that around 23% (94/406) of the newly sequenced genome and 30% (364/1194) of publicly available genome exhibited a completeness level ranging from 80% to 100% (high level) (Figure 2B). Additionally, 48% (193/406) of the newly sequenced genome and 39% (461/1194) of publicly available genome had a completeness level ranging from 50% to 80% (medium level) (Figure 2B).

The overall genome integrity assessment revealed that the quality of newly sequenced data is also comparable to publicly available data. To assess genome integrity, the genome quality (GQ) metric was employed by considering both genome/transcriptome assembly and CDS completeness (see details in Materials and methods). It was observed that 35% (140/400) of the newly sequenced genome and 16% (47/289) of publicly available genome exhibited GQ varying from 80% to 100% (high level) (Figure 2C). Additionally, 41.5% (166/400) of the newly sequenced genome and 78% (225/289) of publicly available genome showed GQ in the range from 50% to 80% (medium level) (Figure 2C). For transcriptomes, a comparable level of integrity was observed between the newly sequenced data and publicly available data. For genomes, although the newly sequenced data displayed slightly fewer proportions of medium and high level when compared to the publicly available data, it is noted that most of the publicly available genomes were generated from bulk DNA samples.

Genetic code variation among ciliates

One striking characteristic observed in ciliates (Ciliophora phylum) is the utilization of various alternative genetic codes. Precise estimation of the codon table holds significant implications for improving the accuracy of ciliate genome structure annotation. Through an analysis of codon tables in ciliate genomes/transcriptomes within the P10K database, a higher degree of genetic code variation among ciliates has been revealed, exceeding our previous understanding (45). Figure 3 showcases the inferred pattern of ciliate evolutionary trees, listing the types of genetic codes estimated within each genus during P10K genome annotation.

Figure 3.

Figure 3.

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Genetic codes of ciliates. The evolutionary relationships among different ciliates have been established according to one previous study (49). All genera with genome or transcriptome data available are included. The numerical label assigned to each genus represents the numbering of codon table (translation table) employed by species within that genus. In case where alternative codon usages exist, multiple numerical labels are separated by commas. The blue asterisk indicates genus that has been reported to include species lacking stop codons. Instances where there are discrepancies in codon tables between P10K estimations and the NCBI taxonomy system are highlighted with red arrows.

A total of seven codon tables (except the species without stop codon (46)) were discovered to be utilized for ciliates, numbering 1, 4, 6, 10, 12, 27 and 30 (Figure 3). Among these, the Spirotrichea class displayed considerable variability in codon usage, employing four codon tables (1,6,10,12). Notably, the majority of genera within Spirotrichea class predominantly utilized codon table 6. In classes such as Heterotrichea, Litostomatea, Colpodea, Prostomatea and Phyllopharyngea, the standard codon table (codon table 1) appeared to be the most popular. Within the Oligohymenophorea class, species primarily employed either the codon table 6 or 30. It is worth noting that only a small number of genera exhibited variations in codon table among different species, and this trend was consistent among ciliates as a whole (Figure 3). According to our data, a specific genus of ciliates is usually predominantly characterized by a single codon table, indicating that alternative codon table is rare within species belonging to the same genus. Overall, our dataset offers valuable insights into the genetic codes of numerous ciliates, particularly with the inclusion of 14 new orders. Upon comparing the P10K estimated codon tables to those in the NCBI taxonomy system (11), we discovered discrepancies for 31 ciliates species, primarily within the Heterotrichea class (Figure 3). To address this issue, we have engaged in discussions with the NCBI Taxonomy Group (11) and the NGDC Genome Warehouse (GWH) Group (https://ngdc.cncb.ac.cn/gwh/) (47,48). We hope to collectively explore and develop efficient methods that will enable us to accurately correct and validate the assignment of codon table, ensuring accurate representation of the genetic codes in these ciliate species as well as other protist species.

User-friendly web interface

The P10K database provides a user-friendly web interface that consists of four main modules: Browse, Genome visualization, Statistics and BLAST tool, enabling users to effortlessly browse and retrieve sequences and perform online analysis (Figure 4). To help users maximize the utility of the database, we have provided a step-by-step tutorial and instructional videos on the help page.

Figure 4.

Figure 4.

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Functional modules and features of the P10K database. The figure provides a concise overview of the modules, database organizations and features of the P10K database.

Browse

The Browse module features web pages for Sample, Genome and Gene that are meticulously designed to ensure easy navigation and intuitive accessibility. These pages present well-organized featured items and informative tables, allowing users to effortlessly browse and retrieve the desired information. The Sample page presents 22 subjects, such as biotic relationship, habitat, extreme environments, in a tabulated manner. Users have the option to select desired columns to display, sort column contents, and download the metadata. Customized filters can be applied by entering keywords in ‘Search’ box or exploring featured items listed above the table. Similarly, the Genome page, with 10 subjects displayed in the table, allows browsing by preset groups like ‘New Species Sequencing’, ‘Genome Re-annotation’, ‘Single-cell Genome’. The Gene page enables users to select interested species or sample to view all related genes in a tabulated format. Gene locations and structures can be explored through the Genome Browser.

Furthermore, the P10K database provides individual pages for samples and genes, accessible through searches using Sample ID or Gene ID in the homepage or the related hyperlinks in tables. The individual sample page comprises 44 subjects of seven sections, including summary, biological feature, geographic information, contact information, sequencing details, genome assembly and annotation information, as well as visualization. The individual gene page displays gene summaries, such as genomic position, genetic code used, CDS and protein sequences, and also provides GO annotations, orthologous and paralogous genes.

Data download

To enhance user convenience in accessing various levels of data for each sample, we offer a one-click download feature in individual sample page. This feature allows users to easily download genome assembly, genome annotation, CDS sequences, protein sequences, orthologous and paralogous genes, as well as gene annotation details. Additionally, users can connect to Genome Warehouse (GWH) (47,48) using Assembly ID and access external links such as NCBI Sequence Read Archive (SRA) (https://www.ncbi.nlm.nih.gov/sra) to obtain related data. For each gene, it is also possible to conveniently download CDS and protein sequences locally from the individual gene page.

Genome visualization

The genome visualization module allows users to expand the species taxonomy tree for viewing samples associated with each taxon. Numbers of related samples are listed within the brackets for each taxon and its subcategories. As users click on taxon or subcategory, the table in the bottom-right corner synchronizes and displays the genome assembly and annotation information for the related samples. Additionally, users can search for specific taxonomy using the search bar at the top of the tree. Clicking the view button allows users to visualize the genomic locations of gene, mRNA, CDS and exons for the selected sample.

BLAST tool

It also incorporates a BLAST tool for sequence searches and advanced features like distance trees and multiple sequence alignment (MSA viewer) for thorough analysis and comparison of obtained sequences. Currently, this platform offers four programs: BLASTN, BLASTP, BLASTX and TBLASTN, along with P10K collections of genome, gene, CDS and protein databases. It is noted that the results can be further filtered by organism, identity, E-value and query coverage. After selecting specific sequences from the BLAST results, users can generate distance trees and view multiple sequence alignment through MSA viewer.

Discussion and future directions

With its wealth of information and user-friendly design, the P10K database stands at the forefront of protist studies and bears great potential to play a crucial role in advancing protist genomics research. It serves as a valuable resource for the global scientific community, empowering researchers to explore the genetic intricacies of protists and accelerating discoveries in this dynamic field. Looking ahead, the next phase of the P10K project will concentrate on protists from other taxa, with the goal of achieving broad genomic coverage of representative species within each taxonomic group. These vast protist genome resources will be continuously updated and integrated in the P10K database. To achieve this, we will continuously collect data from databases and relevant literature, establish an annual batch search system, and employ manual screening to ensure the reliability of the comprehensive literature data. At the same time, the data presentation and interactive visualization of the P10K database will be further improved, and more bioinformatics tools such as multiple sequence alignment, conserved domain prediction and phylogeny analysis will be developed to provide users with convenient online services. In case of discrepancies in codon table assignment between the P10K database and NCBI taxonomy system (11), we hope to establish close collaborations with NCBI Taxonomy Group (11) and other relevant organizations, to collectively address this issue and ensure the precise representation of genetic codes in protist species. Furthermore, we will develop a system on the P10K platform that facilitates seamless access to other protist genome platforms, allowing users to access a wider range of data relevant to protist research. Collectively, the P10K database, as a dedicated data portal for protists, is of great significance and utility to decipher the genome sequences of protists, reconstruct the eukaryotic tree, and accordingly address fundamental scientific questions of broad interest.

Supplementary Material

gkad992_supplemental_file

Click here for additional data file. (461.6KB, pdf)

Acknowledgements

We thank the members of Protist 10 000 Genomes Project (P10K) consortium for helpful suggestions. The bioinformatics analysis was supported by the Wuhan Branch, Supercomputing Center, Chinese Academy of Sciences, China. The culture and maintenance of protist cells were supported by the National Aquatic Biological Resource Center (NABRC). We would like to extend our sincere appreciation to NGDC GSA and GWH Groups, particularly Meili Chen, Xuetong Zhao, Yanling Sun, Yanqing Wang and Lili Dong, for their assistance in depositing raw sequencing, genome assembly and annotation data, and to Zhuojing Fan for her contributions in web interface design. Additionally, we would like to express our thanks to NCBI Taxonomy Group for their assistance in genetic code assignment.

Contributor Information

Xinxin Gao, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; University of Chinese Academy of Sciences, Beijing 100049, China.

Kai Chen, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.

Jie Xiong, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; Key Laboratory of Breeding Biotechnology and Sustainable Aquaculture, Chinese Academy of Sciences, Wuhan 430072, China.

Dong Zou, China National Center for Bioinformation, Beijing 100101, China; National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.

Fangdian Yang, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.

Yingke Ma, China National Center for Bioinformation, Beijing 100101, China; National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.

Chuanqi Jiang, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.

Xiaoxuan Gao, Shandong University of Technology, Zibo 255000, China.

Guangying Wang, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.

Siyu Gu, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; University of Chinese Academy of Sciences, Beijing 100049, China.

Peng Zhang, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.

Shuai Luo, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China.

Kaiyao Huang, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; Key laboratory of Lake and Watershed Science for Water Security, Chinese Academy of Sciences, Nanjing 210008, China.

Yiming Bao, University of Chinese Academy of Sciences, Beijing 100049, China; China National Center for Bioinformation, Beijing 100101, China; National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.

Zhang Zhang, University of Chinese Academy of Sciences, Beijing 100049, China; China National Center for Bioinformation, Beijing 100101, China; National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.

Lina Ma, University of Chinese Academy of Sciences, Beijing 100049, China; China National Center for Bioinformation, Beijing 100101, China; National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.

Wei Miao, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan 430072, China; Key laboratory of Lake and Watershed Science for Water Security, Chinese Academy of Sciences, Nanjing 210008, China; Hubei Hongshan Laboratory, Wuhan 430070, China.

Data availability

The assemblies of P10K Database can be accessed at https://ngdc.cncb.ac.cn/p10k/. The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (50) in National Genomics Data Center (51), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (under the project of PRJCA017400) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa.

Supplementary data

Supplementary Data are available at NAR Online.

Funding

National Key R&D Program of China [2020YFA0907400]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDPB18, XDB38030400]; Youth Innovation Promotion Association of Chinese Academy of Sciences [2019104]; International Partnership Program of the Chinese Academy of Sciences [153F11KYSB20160008]; Natural Science Foundation of China [32122015, 32030021, 31872221, 31900339]; Open Biodiversity and Health Big Data Programme of IUBS. Funding for open access charge: Ministry of Science and Technology of the People's Republic of China; National Key Research and Development Program of China.

Conflict of interest statement. None declared.

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