Whole Genome Shotgun Sequencing Data Research Articles

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome.

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles andFR profiles, and identified 14636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

K-mer genome-wide association study for anthracnose and BCMV resistance in a Phaseolus vulgaris Andean Diversity Panel.

Access to broad genomic resources and closely linked marker-trait associations for common beans (Phaseolus vulgaris L.) can facilitate development of improved varieties with increased yield, improved market quality traits, and enhanced disease resistance. The emergence of virulent races of anthracnose (caused by Colletotrichum lindemuthianum) and bean common mosaic virus (BCMV) highlight the need for improved methods to identify and incorporate pan-genomic variation in breeding for disease resistance. We sequenced the P. vulgaris Andean Diversity Panel (ADP) and performed a genome-wide association study (GWAS) to identify associations for resistance to BCMV and eight races of anthracnose. Historical single nucleotide polymorphism (SNP)-chip and phenotypic data enabled a three-way comparison between SNP-chip, reference-based whole genome shotgun sequence (WGS)-SNP, and reference-free k-mer (short nucleotide subsequence) GWAS. Across all traits, there was excellent concordance between SNP-chip, WGS-SNP, and k-mer GWAS results-albeit at a much higher marker resolution for the WGS data sets. Significant k-mer haplotype variation revealed selection of the linked I-gene and Co-u traits in North American breeding lines and cultivars. Due to structural variation, only 9.1 to 47.3% of the significantly associated k-mers could be mapped to the reference genome. Thus, to determine the genetic context of cis-associated k-mers, we generated draft whole genome assemblies of four ADP accessions and identified an expanded local repertoire of disease resistance genes associated with resistance to anthracnose and BCMV. With access to variant data in the context of a pan-genome, high resolution mapping of agronomic traits for common bean is now feasible.

Capturing the fusion of two ancestries and kinship structures in Merovingian Flanders

The Merovingian period (5th to 8th cc AD) was a time of demographic, socioeconomic, cultural, and political realignment in Western Europe. Here, we report the whole-genome shotgun sequence data of 30 human skeletal remains from a coastal Late Merovingian site of Koksijde (675 to 750 AD), alongside 18 remains from two Early to Late Medieval sites in present-day Flanders, Belgium. We find two distinct ancestries, one shared with Early Medieval England and the Netherlands, while the other, minor component, reflecting likely continental Gaulish ancestry. Kinship analyses identified no large pedigrees characteristic to elite burials revealing instead a high modularity of distant relationships among individuals of the main ancestry group. In contrast, individuals with >90% Gaulish ancestry had no kinship links among sampled individuals. Evidence for population structure and major differences in the extent of Gaulish ancestry in the main group, including in a mother-daughter pair, suggests ongoing admixture in the community at the time of their burial. The isotopic and genetic evidence combined supports a model by which the burials, representing an established coastal nonelite community, had incorporated migrants from inland populations. The main group of burials at Koksijde shows an abundance of >5 cM long shared allelic intervals with the High Medieval site nearby, implying long-term continuity and suggesting that similarly to Britain, the Early Medieval ancestry shifts left a significant and long-lasting impact on the genetic makeup of the Flemish population. We find substantial allele frequency differences between the two ancestry groups in pigmentation and diet-associated variants, including those linked with lactase persistence, likely reflecting ancestry change rather than local adaptation.

Accurate Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data.

The identification of sex-linked scaffolds and the genetic sex of individuals, i.e. their sex karyotype, is a fundamental step in population genomic studies. If sex-linked scaffolds are known, single individuals may be sexed based on read counts of next-generation sequencing data. If both sex-linked scaffolds as well as sex karyotypes are unknown, as is often the case for non-model organisms, they have to be jointly inferred. For both cases, current methods rely on arbitrary thresholds, which limits their power for low-depth data. In addition, most current methods are limited to euploid sex karyotypes (XX and XY). Here we develop BeXY, a fully Bayesian method to jointly infer the posterior probabilities for each scaffold to be autosomal, X- or Y-linked and for each individual to be any of the sex karyotypes XX, XY, X0, XXX, XXY, XYY and XXYY. If the sex-linked scaffolds are known, it also identifies autosomal trisomies and estimates the sex karyotype posterior probabilities for single individuals. As we show with downsampling experiments, BeXY has higher power than all existing methods. It accurately infers the sex karyotype of ancient human samples with as few as 20,000 reads and accurately infers sex-linked scaffolds from data sets of just a handful of samples or with highly imbalanced sex ratios, also in the case of low-quality reference assemblies. We illustrate the power of BeXY by applying it to both whole-genome shotgun and target enrichment sequencing data of ancient and modern humans, as well as several non-model organisms.

Phylogenomics reveals widespread hybridization and polyploidization in Henckelia (Gesneriaceae).

Hybridization has long been recognized as an important process for plant evolution and is often accompanied by polyploidization, another prominent force in generating biodiversity. Despite its pivotal importance in evolution, the actual prevalence and distribution of hybridization across the tree of life remain unclear. We used whole-genome shotgun (WGS) sequencing and cytological data to investigate the evolutionary history of Henckelia, a large genus in the family Gesneriaceae with a high frequency of suspected hybridization and polyploidization events. We generated WGS sequencing data at about 10× coverage for 26 Chinese Henckelia species plus one Sri Lankan species. To untangle the hybridization history, we separately extracted whole plastomes and thousands of single-copy nuclear genes from the sequencing data, and reconstructed phylogenies based on both nuclear and plastid data. We also explored sources of both genealogical and cytonuclear conflicts and identified signals of hybridization and introgression within our phylogenomic dataset using several statistical methods. Additionally, to test the polyploidization history, we evaluated chromosome counts for 45 populations of the 27 Henckelia species studied. We obtained well-supported phylogenetic relationships using both concatenation- and coalescent-based methods. However, the nuclear phylogenies were highly inconsistent with the plastid phylogeny, and we observed intensive discordance among nuclear gene trees. Further analyses suggested that both incomplete lineage sorting and gene flow contributed to the observed cytonuclear and genealogical discordance. Our analyses of introgression and phylogenetic networks revealed a complex history of hybridization within the genus Henckelia. In addition, based on chromosome counts for 27 Henckelia species, we found independent polyploidization events occurred within Henckelia after different hybridization events. Our findings demonstrated that hybridization and polyploidization are common in Henckelia. Furthermore, our results revealed that H. oblongifolia is not a member of the redefined Henckelia and they suggested several other taxonomic treatments in this genus.

Physlr: Next-Generation Physical Maps

While conventional physical maps helped build most of the reference genomes we use today, generating the maps was prohibitively expensive, and the technology was abandoned in favor of whole-genome shotgun sequencing (WGS). However, genome assemblies generated using WGS data are often less contiguous. We introduce Physlr, a tool that leverages long-range information provided by some WGS technologies to construct next-generation physical maps. These maps have many potential applications in genome assembly and analysis, including, but not limited to, scaffolding. In this study, using experimental linked-read datasets from two humans, we used Physlr to construct chromosome-scale physical maps (NGA50s of 52 Mbp and 70 Mbp). We also demonstrated how these physical maps can help scaffold human genome assemblies generated using various sequencing technologies and assembly tools. Across all experiments, Physlr substantially improved the contiguity of baseline assemblies over state-of-the-art linked-read scaffolders.

Long-Read Genome Assembly of Saccharomyces uvarum Strain CBS 7001.

ABSTRACTHere, we report a long-read genome assembly for Saccharomyces uvarum strain CBS 7001 based on PacBio whole-genome shotgun sequence data. Our assembly provides an improved reference genome for an important yeast in the Saccharomyces sensu stricto clade.

HolistIC: leveraging Hi-C and whole genome shotgun sequencing for double minute chromosome discovery.

Double minute (DM) chromosomes are acentric extrachromosomal DNA artifacts that are frequently observed in the cells of numerous cancers. They are highly amplified and contain oncogenes and drug-resistance genes, making their presence a challenge for effective cancer treatment. Algorithmic discovery of DM can potentially improve bench-derived therapies for cancer treatment. A hindrance to this task is that DMs evolve, yielding circular chromatin that shares segments from progenitor DMs. This creates DMs with overlapping amplicon coordinates. Existing DM discovery algorithms use whole genome shotgun sequencing (WGS) in isolation, which can potentially incorrectly classify DMs that share overlapping coordinates. In this study, we describe an algorithm called 'HolistIC' that can predict DMs in tumor genomes by integrating WGS and Hi-C sequencing data. The consolidation of these sources of information resolves ambiguity in DM amplicon prediction that exists in DM prediction with WGS data used in isolation. We implemented and tested our algorithm on the tandem Hi-C and WGS datasets of three cancer datasets and a simulated dataset. Results on the cancer datasets demonstrated HolistIC's ability to predict DMs from Hi-C and WGS data in tandem. The results on the simulated data showed the HolistIC can accurately distinguish DMs that have overlapping amplicon coordinates, an advance over methods that predict extrachromosomal amplification using WGS data in isolation. Our software, named 'HolistIC', is available at http://www.github.com/mhayes20/HolistIC. Supplementary data are available at Bioinformatics online.

Bacterial associations in the healthy human gut microbiome across populations

In a microbial community, associations between constituent members play an important role in determining the overall structure and function of the community. The human gut microbiome is believed to play an integral role in host health and disease. To understand the nature of bacterial associations at the species level in healthy human gut microbiomes, we analyzed previously published collections of whole-genome shotgun sequence data, totaling over 1.6 Tbp, generated from 606 fecal samples obtained from four different healthy human populations. Using a Random Forest Classifier, we identified 202 signature bacterial species that were prevalent in these populations and whose relative abundances could be used to accurately distinguish between the populations. Bacterial association networks were constructed with these signature species using an approach based on the graphical lasso. Network analysis revealed conserved bacterial associations across populations and a dominance of positive associations over negative associations, with this dominance being driven by associations between species that are closely related either taxonomically or functionally. Bacterial species that form network modules, and species that constitute hubs and bottlenecks, were also identified. Functional analysis using protein families suggests that much of the taxonomic variation across human populations does not foment substantial functional or structural differences.

The Nubeam reference-free approach to analyze metagenomic sequencing reads

We present Nubeam (nucleotide be a matrix) as a novel reference-free approach to analyze short sequencing reads. Nubeam represents nucleotides by matrices, transforms a read into a product of matrices, and assigns numbers to reads based on the product matrix. Nubeam capitalizes on the noncommutative property of matrix multiplication, such that different reads are assigned different numbers and similar reads similar numbers. A sample, which is a collection of reads, becomes a collection of numbers that form an empirical distribution. We demonstrate that the genetic difference between samples can be quantified by the distance between empirical distributions. Nubeam includes the k-mer method as a special case, but unlike the k-mer method, it is convenient for Nubeam to account for GC bias and nucleotide quality. As a reference-free approach, Nubeam avoids reference bias and mapping bias, and can work with organisms without reference genomes. Thus, Nubeam is ideal to analyze data sets from metagenomics whole genome shotgun (WGS) sequencing, where the amount of unmapped reads is substantial. When applied to a WGS sequencing data set to quantify distances between metagenomics samples from various human body habitats, Nubeam recapitulates findings made by mapping-based methods and sheds light on contributions of unmapped reads. Nubeam is also useful in analyzing 16S rRNA sequencing data, which is a more prevalent type of data set in metagenomics studies. In our analysis, Nubeam recapitulated the findings that natural microbiota in mouse gut are resilient under challenges, and Nubeam detected differences in vaginal microbiota between cases of polycystic ovary syndrome and healthy controls.

HolistIC: Leveraging Hi-C and Whole Genome Shotgun Sequencing for Double Minute Chromosome Discovery

Abstract Double minute chromosomes are acentric extrachromosomal DNA artifacts that are frequently observed in the cells of numerous cancers. They are highly amplified and contain oncogenes and drug resistance genes, making their presence a challenge for effective cancer treatment. Algorithmic discovery of double minutes (DM) can potentially improve bench-derived therapies for cancer treatment. A hindrance to this task is that DMs evolve, yielding circular chromatin that shares segments from progenitor double minutes. This creates double minutes with overlapping amplicon coordinates. Existing DM discovery algorithms use whole genome shotgun sequencing in isolation, which can potentially incorrectly classify DMs that share overlapping coordinates. In this study, we describe an algorithm called “ HolistIC” that can predict double minutes in tumor genomes by integrating whole genome shotgun sequencing (WGS) and Hi-C sequencing data. The consolidation of these sources of information resolves ambiguity in double minute amplicon prediction that exists in DM prediction with WGS data used in isolation. We implemented and tested our algorithm on the tandem Hi-C and WGS datasets of three cancer datasets and a simulated dataset. Results on the cancer datasets demonstrated HolistIC’s ability to predict DMs from Hi-C and WGS data in tandem. The results on the simulated data showed the HolistIC can accurately distinguish double minutes that have overlapping amplicon coordinates, an advance over methods that predict extrachromosomal amplification using WGS data in isolation. Availability Our software is available at http://www.github.com/mhayes20/HolistIC.

Ancient DNA reveals two paternal lineages C2a1a1b1a/F3830 and C2b1b/F845 in past nomadic peoples distributed on the Mongolian Plateau.

Since the third century CE, a series of nomadic tribes have been active on the eastern part of the Mongolian Plateau. Characterizing the genetic compositions of past nomadic people is significant for research on the nomadic cultures of the Eurasian Steppe region. Ancient DNA analysis facilitates a deeper understanding of the relationship between historical and modern nomadic populations. Whole-genome shotgun sequencing and capture sequencing of the nonrecombining region of the Y chromosome were performed for six ancient Hg C2/M217 individuals. The individuals were interred at six separate sites on the Mongolian Plateau and represent dates spanning the late Neolithic to Yuan Dynasty (~3,500-700 BP). After NRY capture sequencing, three of the six ancient samples were attributed to C2b1b/F845 and the other three ancient samples belonged to C2a1a1b1a/F3830. Analysis of whole-genome shotgun sequencing data shows that the ancient C2b1b/F845 individuals are closely related to She, Han and other East Asian populations, while the ancient C2a1a1b1a/F3830 individuals are more similar to modern Northeast Asian peoples, such as the Ulchi and Yakut. Hg C2/M217, widely distributed in the eastern part of the Eurasian continent, was discovered in the ancient Central Steppe and Baikal region. This study shows that there were two important subclades of Hg C2/M217 among the ancient nomadic peoples: C2a1a1b1a/F3830, which has made important genetic contributions to modern Mongolic- and Manchu-speaking populations, and C2b1b/F845, which probably originated in the farming populations of southern East Asia and made certain genetic contributions to past nomadic peoples on the Mongolian Plateau.

Gene copy number is associated with phytochemistry in Cannabis sativa.

Gene copy number (CN) variation is known to be important in nearly every species where it has been examined. Alterations in gene CN may provide a fast way of acquiring diversity, allowing rapid adaptation under strong selective pressures, and may also be a key component of standing genetic variation within species. Cannabis sativa plants produce a distinguishing set of secondary metabolites, the cannabinoids, many of which have medicinal utility. Two major cannabinoids—THCA (delta-9-tetrahydrocannabinolic acid) and CBDA (cannabidiolic acid)—are products of a three-step biochemical pathway. Using whole-genome shotgun sequence data for 69 Cannabis cultivars from diverse lineages within the species, we found that genes encoding the synthases in this pathway vary in CN. Transcriptome sequence data show that the cannabinoid paralogs are differentially expressed among lineages within the species. We also found that CN partially explains variation in cannabinoid content levels among Cannabis plants. Our results demonstrate that biosynthetic genes found at multiple points in the pathway could be useful for breeding purposes, and suggest that natural and artificial selection have shaped CN variation. Truncations in specific paralogs are associated with lack of production of particular cannabinoids, showing how phytochemical diversity can evolve through a complex combination of processes.

Characterization of Chloroplast Genomes, Nuclear Ribosomal DNAs, and Polymorphic SSR Markers Using Whole Genome Sequences of Two Euonymus hamiltonianus Phenotypes

Although genomics provides useful tools for crops, most wild resource plants still lack molecular data. To retrieve useful genomic data and thus provide fundamental information for a resource plant, we established a multi-directional approach using two low coverage whole-genome shotgun sequence (WGS) data of Euonymus hamiltonianus, which is a wild resource plant with potential as a medicinal and ornamental plant. We assembled complete chloroplast genome and nuclear ribosomal DNA (nrDNA) sequences and analyzed polymorphic simple sequence repeats (pSSRs) in the nuclear genome based on the comparison of WGS data between two different phenotypes. We developed a bioinformatics pipeline to identify pSSR motifs by systematic comparison of two WGS datasets. The pipeline is composed of multiple steps including end-joining of paired reads, isolation of joined reads harboring SSR motifs derived from unique non-repetitive regions, identification of pSSR via in silico comparison with counterpart WGS reads, design of pSSR primer sets, and validation. The pipeline was applied to WGS data of E. hamiltonianus and identified 161 contigs with pSSR motifs between the two different phenotypes. Based on the pSSR motifs, 20 primer pairs were designed, of which seven were successfully validated as real pSSR markers. We expect this information to be applicable to genomic resources of E. hamiltonianus.

Evaluation of genome scaffolding tools using pooled clone sequencing.

DNA sequencing technologies hold great promise in generating information that will guide scientists to understand how the genome effects human health and organismal evolution. The process of generating raw genome sequence data becomes cheaper and faster, but more error-prone. Assembly of such data into high-quality finished genome sequences remains challenging. Many genome assembly tools are available, but they differ in terms of their performance and their final output. More importantly, it remains largely unclear how to best assess the quality of assembled genome sequences. Here we evaluate the accuracies of several genome scaffolding algorithms using two different types of data generated from the genome of the same human individual: whole genome shotgun sequencing (WGS) and pooled clone sequencing (PCS). We observe that it is possible to obtain better assemblies if PCS data are used, compared to using only WGS data. However, the current scaffolding algorithms are developed only for WGS, and PCS-aware scaffolding algorithms remain an open problem.

Whole genome shotgun sequencing revealed highly polymorphic genome regions and genes in Escherichia coli O157:H7 isolates collected from a single feedlot.

Escherichia coli serotype O157:H7 continues to pose a serious health threat to human beings. Cattle, a major reservoir of the pathogen, harbor E. coli O157:H7 in their gastrointestinal tract and shed variable concentrations of E. coli O157:H7 into the environment. Genetic characterization of cattle-shed E. coli O157 strains is of interest to the livestock industry, food business, and public health community. The present study applied whole genome shotgun sequencing (WGS) and single nucleotide variant (SNV) calling to characterize 279 cattle-shed E. coli O157:H7 strains isolated from a single feedlot located in southwestern region of the US. More than 4,000 SNVs were identified among the strains and the resultant phylogenomic tree revealed three major groups. Using the Sakai strain genome as reference, more than 2,000 SNVs were annotated and a detailed SNV map generated. Results clearly revealed highly polymorphic loci along the E. coli O157:H7 genome that aligned with the prophage regions and highly variant genes involved in processing bacterial genetic information. The WGS data were further profiled against a comprehensive virulence factor database (VFDB) for virulence gene identification. Among the total 285 virulence genes identified, only 132 were present in all the strains. There were six virulence genes unique to single isolates. Our findings suggested that the genome variations of the E. coli O157:H7 were mainly attributable to dynamics of certain phages, and the bacterial strains have variable virulence gene profiles, even though they came from a single cattle population, which may explain the differences in pathogenicity, host prevalence, and transmissibility by E. coli O157:H7.

Comprehensive benchmarking and ensemble approaches for metagenomic classifiers

BackgroundOne of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole-genome shotgun sequencing data, comprehensive comparisons of these methods are limited.ResultsIn this study, we use the largest-to-date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of 11 metagenomic classifiers. Tools were characterized on the basis of their ability to identify taxa at the genus, species, and strain levels, quantify relative abundances of taxa, and classify individual reads to the species level. Strikingly, the number of species identified by the 11 tools can differ by over three orders of magnitude on the same datasets. Various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species. Overall, pairing tools with different classification strategies (k-mer, alignment, marker) can combine their respective advantages.ConclusionsThis study provides positive and negative controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision, accuracy, and recall. We show that proper experimental design and analysis parameters can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

McClintock: An Integrated Pipeline for Detecting Transposable Element Insertions in Whole-Genome Shotgun Sequencing Data.

Transposable element (TE) insertions are among the most challenging types of variants to detect in genomic data because of their repetitive nature and complex mechanisms of replication . Nevertheless, the recent availability of large resequencing data sets has spurred the development of many new methods to detect TE insertions in whole-genome shotgun sequences. Here we report an integrated bioinformatics pipeline for the detection of TE insertions in whole-genome shotgun data, called McClintock (https://github.com/bergmanlab/mcclintock), which automatically runs and standardizes output for multiple TE detection methods. We demonstrate the utility of McClintock by evaluating six TE detection methods using simulated and real genome data from the model microbial eukaryote, Saccharomyces cerevisiae. We find substantial variation among McClintock component methods in their ability to detect nonreference TEs in the yeast genome, but show that nonreference TEs at nearly all biologically realistic locations can be detected in simulated data by combining multiple methods that use split-read and read-pair evidence. In general, our results reveal that split-read methods detect fewer nonreference TE insertions than read-pair methods, but generally have much higher positional accuracy. Analysis of a large sample of real yeast genomes reveals that most McClintock component methods can recover known aspects of TE biology in yeast such as the transpositional activity status of families, target preferences, and target site duplication structure, albeit with varying levels of accuracy. Our work provides a general framework for integrating and analyzing results from multiple TE detection methods, as well as useful guidance for researchers studying TEs in yeast resequencing data.

Extensive Farming in Estonia Started through a Sex-Biased Migration from the Steppe

The transition from hunting and gathering to farming in Europe was brought upon by arrival of new people carrying novel material culture and genetic ancestry. The exact nature and scale of the transition-both material and genetic-varied in different parts of Europe [1-7]. Farming-based economies appear relatively late in Northeast Europe, and the extent to which they involve change in genetic ancestry is not fully understood due to the lack ofrelevant ancient DNA data. Here we present theresults from new low-coverage whole-genome shotgun sequence data from five hunter-gatherers and five first farmers of Estonia whose remains date to 4,500 to 6,300 years before present. We find evidence of significant differences between the two groups in the composition of autosomal as well as mtDNA, X chromosome, and Y chromosome ancestries. We find that Estonian hunter-gatherers of Comb Ceramic culture are closest to Eastern hunter-gatherers, which is in contrast to earlier hunter-gatherers from the Baltics, who are close to Western hunter-gatherers [8, 9]. The Estonian first farmers of Corded Ware culture show high similarity in their autosomes with European hunter-gatherers, Steppe Eneolithic and Bronze Age populations, and European Late Neolithic/Bronze Age populations, while their X chromosomes are in addition equally closely related to European and Anatolian and Levantine early farmers. These findings suggest that the shift to intensive cultivation and animal husbandry in Estonia was triggered by the arrival of new people with predominantly Steppe ancestry but whose ancestors had undergone sex-specificadmixture with early farmers with Anatolian ancestry.

Building two indica rice reference genomes with PacBio long-read and Illumina paired-end sequencing data.

Over the past 30 years, we have performed many fundamental studies on two Oryza sativa subsp. indica varieties, Zhenshan 97 (ZS97) and Minghui 63 (MH63). To improve the resolution of many of these investigations, we generated two reference-quality reference genome assemblies using the most advanced sequencing technologies. Using PacBio SMRT technology, we produced over 108 (ZS97) and 174 (MH63) Gb of raw sequence data from 166 (ZS97) and 209 (MH63) pools of BAC clones, and generated ~97 (ZS97) and ~74 (MH63) Gb of paired-end whole-genome shotgun (WGS) sequence data with Illumina sequencing technology. With these data, we successfully assembled two platinum standard reference genomes that have been publicly released. Here we provide the full sets of raw data used to generate these two reference genome assemblies. These data sets can be used to test new programs for better genome assembly and annotation, aid in the discovery of new insights into genome structure, function, and evolution, and help to provide essential support to biological research in general.