Streamlining universal single‐copy orthologue and ultraconserved element design: A case study in Collembola

Genomic data sets are increasingly central to ecological and evolutionary biology, but far fewer resources are available for invertebrates. Powerful new computational tools and the rapidly decreasing cost of Illumina sequencing are beginning to change this, enabling rapid genome assembly and reference marker extraction. We have developed and tested a practical workflow for developing genomic resources in nonmodel groups with real‐world data on Collembola (springtails), one of the most dominant soil animals on Earth. We designed universal molecular marker sets, single‐copy orthologues (BUSCO s) and ultraconserved elements (UCEs), using three existing and 11 newly generated genomes. Both marker types were tested in silico via marker capture success and phylogenetic performance. The new genomes were assembled with Illumina short reads and 9,585?14,743 protein‐coding genes were predicted with ab initio and protein homology evidence. We identified 1,997 benchmarking universal single‐copy orthologues (BUSCO s) across 14 genomes and created and assessed a custom BUSCO data set for extracting single‐copy genes. We also developed a new UCE probe set containing 46,087 baits targeting 1,885 loci. We successfully captured 1,437?1,865 BUSCO s and 975?1,186 UCEs across 14 genomes. Phylogenomic reconstructions using these markers proved robust, giving new insight on deep‐time collembolan relationships. Our study demonstrates the feasibility of generating thousands of universal markers from highly efficient whole‐genome sequencing, providing a valuable resource for genome‐scale investigations in evolutionary biology and ecology.     

Eu-Detect: An algorithm for detecting eukaryotic sequences in metagenomic data sets

Physical partitioning techniques are routinely employed (during sample preparation stage) for segregating the prokaryotic and eukaryotic fractions of metagenomic samples. In spite of these efforts, several metagenomic studies focusing on bacterial and archaeal populations have reported the presence of contaminating eukaryotic sequences in metagenomic data sets. Contaminating sequences originate not only from genomes of micro-eukaryotic species but also from genomes of (higher) eukaryotic host cells. The latter scenario usually occurs in the case of host-associated metagenomes. Identification and removal of contaminating sequences is important, since these sequences not only impact estimates of microbial diversity but also affect the accuracy of several downstream analyses. Currently, the computational techniques used for identifying contaminating eukaryotic sequences, being alignment based, are slow, inefficient, and require huge computing resources. In this article, we present Eu-Detect, an alignment-free algorithm that can rapidly identify eukaryotic sequences contaminating metagenomic data sets. Validation results indicate that on a desktop with modest hardware specifications, the Eu-Detect algorithm is able to rapidly segregate DNA sequence fragments of prokaryotic and eukaryotic origin, with high sensitivity. A Web server for the Eu-Detect algorithm is available at http://metagenomics.atc.tcs.com/Eu-Detect/.     

真菌基因组de novo测序组装的方法与实践

真菌基因组较其他真核生物基因组结构简单,长度短,易于测序、组装与注释,因此真菌基因组是研究真核生物基因组的模型。为研究真菌基因组组装策略,本研究基于Illumina HiSeq测序平台对烟曲霉菌株An16007基因组测序,分别使用5种de novo组装软件ABySS、SOAP-denovo、Velvet、MaSuRCA和IDBA-UD组装基因组,然后通过Augustus软件进行基因预测,BUSCO软件评估组装结果。研究发现,5种组装软件对基因组组装结果不同,ABySS组装的基因组较其他4种组装软件具有更高的完整性和准确性,且预测的基因数量较高,因此,ABySS更适合本研究基因组的组装。本研究提供了真菌de novo测序、组装及组装质量评估的技术流程,为基因组<100 Mb的真菌或其他生物基因组的研究提供参考。     

Gene identification in novel eukaryotic genomes by self-training algorithm

Finding new protein-coding genes is one of the most important goals of eukaryotic genome sequencing projects. However, genomic organization of novel eukaryotic genomes is diverse and ab initio gene finding tools tuned up for previously studied species are rarely suitable for efficacious gene hunting in DNA sequences of a new genome. Gene identification methods based on cDNA and expressed sequence tag (EST) mapping to genomic DNA or those using alignments to closely related genomes rely either on existence of abundant cDNA and EST data and/or availability on reference genomes. Conventional statistical ab initio methods require large training sets of validated genes for estimating gene model parameters. In practice, neither one of these types of data may be available in sufficient amount until rather late stages of the novel genome sequencing. Nevertheless, we have shown that gene finding in eukaryotic genomes could be carried out in parallel with statistical models estimation directly from yet anonymous genomic DNA. The suggested method of parallelization of gene prediction with the model parameters estimation follows the path of the iterative Viterbi training. Rounds of genomic sequence labeling into coding and non-coding regions are followed by the rounds of model parameters estimation. Several dynamically changing restrictions on the possible range of model parameters are added to filter out fluctuations in the initial steps of the algorithm that could redirect the iteration process away from the biologically relevant point in parameter space. Tests on well-studied eukaryotic genomes have shown that the new method performs comparably or better than conventional methods where the supervised model training precedes the gene prediction step. Several novel genomes have been analyzed and biologically interesting findings are discussed. Thus, a self-training algorithm that had been assumed feasible only for prokaryotic genomes has now been developed for ab initio eukaryotic gene identification.     

Shotgun metagenomics of soil invertebrate communities reflects taxonomy,biomass, and reference genome properties

• Metagenomics – shotgun sequencing of all DNA fragments from a community DNA extract – is routinely used to describe the composition, structure, and function of microorganism communities. Advances in DNA sequencing and the availability of genome databases increasingly allow the use of shotgun metagenomics on eukaryotic communities. Metagenomics offers major advances in the recovery of biomass relationships in a sample, in comparison to taxonomic marker gene‐based approaches (metabarcoding). However, little is known about the factors which influence metagenomics data from eukaryotic communities, such as differences among organism groups, the properties of reference genomes, and genome assemblies.
• We evaluated how shotgun metagenomics records composition and biomass in artificial soil invertebrate communities at different sequencing efforts. We generated mock communities of controlled biomass ratios from 28 species from all major soil mesofauna groups: mites, springtails, nematodes, tardigrades, and potworms. We shotgun sequenced these communities and taxonomically assigned them with a database of over 270 soil invertebrate genomes.
• We recovered over 95% of the species, and observed relatively high false‐positive detection rates. We found strong differences in reads assigned to different taxa, with some groups (e.g., springtails) consistently attracting more hits than others (e.g., enchytraeids). Original biomass could be predicted from read counts after considering these taxon‐specific differences. Species with larger genomes, and with more complete assemblies, consistently attracted more reads than species with smaller genomes. The GC content of the genome assemblies had no effect on the biomass–read relationships. Results were similar among different sequencing efforts.
• The results show considerable differences in taxon recovery and taxon specificity of biomass recovery from metagenomic sequence data. The properties of reference genomes and genome assemblies also influence biomass recovery, and they should be considered in metagenomic studies of eukaryotes. We show that low‐ and high‐sequencing efforts yield similar results, suggesting high cost‐efficiency of metagenomics for eukaryotic communities. We provide a brief roadmap for investigating factors which influence metagenomics‐based eukaryotic community reconstructions. Understanding these factors is timely as accessibility of DNA sequencing and momentum for reference genomes projects show a future where the taxonomic assignment of DNA from any community sample becomes a reality.

     

The genome reduction hypothesis and the phylogeny of eukaryotes

The first steps in eukaryotic evolution appear difficult to retrace despite the availability of an increasing amount of data. Current molecular phylogenies suggest that the eukaryotic tree would be better represented as a bush of major lineages whose order of emerge is poorly resolved. Such lack of resolution is often explained by a radiation event that would have left very little ancient signal in eukaryotic molecular markers. We suggest a complementary genomic approach that might help tackling this major issue. It rests on a hypothesis, the genome reduction hypothesis (GRH), suggesting that the divergence of major eukaryotic lineages might have been coupled with independent genomic reduction events, starting from a large and partially redundant chimerical genome. Thus, significant and coherent patterns of shared ancestral gene losses between major eukaryotic lineages might help polarizing the most basal nodes in the eukaryotic phylogeny. We propose a test for the GRH that exploits the increasing availability of complete eukaryotic genomes in public databases.     

Over 50,000 Metagenomically Assembled Draft Genomes for the Human Oral Microbiome Reveal New Taxa

The oral cavity of each person is home to hundreds of bacterial species. While taxa for oral diseases have been studied using culture-based characterization as well as amplicon sequencing,metagenomic and genomic information remains scarce compared to the fecal microbiome. Here,using metagenomic shotgun data for 3346 oral metagenomic samples together with 808 published samples, we obtain 56,213 metagenome-assembled genomes(MAGs), and more than 64% of the3589 species-level genome bins(SGBs) contai...     

A novel bioinformatics strategy for searching industrially useful genome resources from metagenomic sequence libraries

Although remarkable progress in metagenomic sequencing of various environmental samples has been made, large numbers of fragment sequences have been registered in the international DNA databanks, primarily without information on gene function and phylotype, and thus with limited usefulness. Industrial useful biological activity is often carried out by a set of genes, such as those constituting an operon. In this connection, metagenomic approaches have a weakness because sets of the genes are usually split up, since the sequences obtained by metagenome analyses are fragmented into 1-kb or much shorter segments. Therefore, even when a set of genes responsible for an industrially useful function is found in one metagenome library, it is usually difficult to know whether a single genome harbors the entire gene set or whether different genomes have individual genes. By modifying Self-Organizing Map (SOM), we previously developed BLSOM for oligonucleotide composition, which allowed classification (self-organization) of sequence fragments according to genomes. Because BLSOM could reassociate genomic fragments according to genomes, BLSOM may ameliorate the abovementioned weakness of metagenome analyses. Here, we have developed a strategy for clustering of metagenomic sequences according to phylotypes and genomes, by testing a gene set contributing to environment preservation.     

E value cutoff and eukaryotic genome content phylogenetics

Genome content analysis has been used as a source of phylogenetic information in large prokaryotic tree of life studies. Recently the sequencing of many eukaryotic genomes has allowed for the similar use of genome content analysis for these organisms too. In this communication we examine the utility of genome content analysis for recovering phylogenetic patterns in several eukaryotic groups. By constructing multiple matrices using different e value cutoffs we examine the dynamics of altering the e value cutoff on five eukaryotic genome data sets. Our analysis indicates that the e value cutoff that is used as a criterion in the construction of the genome content matrix is a critical factor in both the accuracy and information content of the analysis. Strikingly, genome content by itself is not a reliable or accurate source of characters for phylogenetic analysis of the taxa in the five data sets we analyzed. We discuss two problems--small genome attraction and genome duplications as being involved in the rather poor performance of genome content data in recovering eukaryotic phylogeny.     

Estimating the total genome length of a metagenomic sample using k-mers

Background

Metagenomic sequencing is a powerful technology for studying the mixture of microbes or the microbiomes on human and in the environment. One basic task of analyzing metagenomic data is to identify the component genomes in the community. This task is challenging due to the complexity of microbiome composition, limited availability of known reference genomes, and usually insufficient sequencing coverage.

Results

As an initial step toward understanding the complete composition of a metagenomic sample, we studied the problem of estimating the total length of all distinct component genomes in a metagenomic sample. We showed that this problem can be solved by estimating the total number of distinct k-mers in all the metagenomic sequencing data. We proposed a method for this estimation based on the sequencing coverage distribution of observed k-mers, and introduced a k-mer redundancy index (KRI) to fill in the gap between the count of distinct k-mers and the total genome length. We showed the effectiveness of the proposed method on a set of carefully designed simulation data corresponding to multiple situations of true metagenomic data. Results on real data indicate that the uncaptured genomic information can vary dramatically across metagenomic samples, with the potential to mislead downstream analyses.

Conclusions

We proposed the question of how long the total genome length of all different species in a microbial community is and introduced a method to answer it.

     

Assembly and RNA‐free annotation of highly heterozygous genomes: The case of the thick‐billed murre (Uria lomvia)

Thanks to a dramatic reduction in sequencing costs followed by a rapid development of bioinformatics tools, genome assembly and annotation have become accessible to many researchers in recent years. Among tetrapods, birds have genomes that display many features that facilitate their assembly and annotation, such as small genome size, low number of repeats and highly conserved genomic structure. However, we found that high genomic heterozygosity could have a great impact on the quality of the genome assembly of the thick‐billed murre (Uria lomvia), an arctic colonial seabird. In this study, we tested the performance of three genome assemblers, ray /sscape , soapdenovo 2 and platanus , in assembling the highly heterozygous genome of the thick‐billed murre. Our results show that platanus , an assembler specifically designed for heterozygous genomes, outperforms the other two approaches and produces a highly contiguous (N50 = 15.8 Mb) and complete genome assembly (93% presence of genes from the Benchmarking Universal Single Copy Ortholog [BUSCO] gene set). Additionally, we annotated the thick‐billed murre genome using a homology‐based approach that takes advantage of the genomic resources available for birds and other taxa. Our study will be useful for those researchers who are approaching assembly and annotation of highly heterozygous genomes, or genomes of species of conservation concern, and/or who have limited financial resources.     

i-ADHoRe 3.0--fast and sensitive detection of genomic homology in extremely large data sets

Comparative genomics is a powerful means to gain insight into the evolutionary processes that shape the genomes of related species. As the number of sequenced genomes increases, the development of software to perform accurate cross-species analyses becomes indispensable. However, many implementations that have the ability to compare multiple genomes exhibit unfavorable computational and memory requirements, limiting the number of genomes that can be analyzed in one run. Here, we present a software package to unveil genomic homology based on the identification of conservation of gene content and gene order (collinearity), i-ADHoRe 3.0, and its application to eukaryotic genomes. The use of efficient algorithms and support for parallel computing enable the analysis of large-scale data sets. Unlike other tools, i-ADHoRe can process the Ensembl data set, containing 49 species, in 1?h. Furthermore, the profile search is more sensitive to detect degenerate genomic homology than chaining pairwise collinearity information based on transitive homology. From ultra-conserved collinear regions between mammals and birds, by integrating coexpression information and protein-protein interactions, we identified more than 400 regions in the human genome showing significant functional coherence. The different algorithmical improvements ensure that i-ADHoRe 3.0 will remain a powerful tool to study genome evolution.     

Use of simulated data sets to evaluate the fidelity of metagenomic processing methods

Metagenomics is a rapidly emerging field of research for studying microbial communities. To evaluate methods presently used to process metagenomic sequences, we constructed three simulated data sets of varying complexity by combining sequencing reads randomly selected from 113 isolate genomes. These data sets were designed to model real metagenomes in terms of complexity and phylogenetic composition. We assembled sampled reads using three commonly used genome assemblers (Phrap, Arachne and JAZZ), and predicted genes using two popular gene-finding pipelines (fgenesb and CRITICA/GLIMMER). The phylogenetic origins of the assembled contigs were predicted using one sequence similarity-based (blast hit distribution) and two sequence composition-based (PhyloPythia, oligonucleotide frequencies) binning methods. We explored the effects of the simulated community structure and method combinations on the fidelity of each processing step by comparison to the corresponding isolate genomes. The simulated data sets are available online to facilitate standardized benchmarking of tools for metagenomic analysis.     

Ecogenomics of the SAR11 clade

Members of the SAR11 clade, despite their high abundance, are often poorly represented by metagenome-assembled genomes. This fact has hampered our knowledge about their ecology and genetic diversity. Here we examined 175 SAR11 genomes, including 47 new single-amplified genomes. The presence of the first genomes associated with subclade IV suggests that, in the same way as subclade V, they might be outside the proposed Pelagibacterales order. An expanded phylogenomic classification together with patterns of metagenomic recruitment at a global scale have allowed us to define new ecogenomic units of classification (genomospecies), appearing at different, and sometimes restricted, metagenomic data sets. We detected greater microdiversity across the water column at a single location than in samples collected from similar depth across the global ocean, suggesting little influence of biogeography. In addition, pangenome analysis revealed that the flexible genome was essential to shape genomospecies distribution. In one genomospecies preferentially found within the Mediterranean, a set of genes involved in phosphonate utilization was detected. While another, with a more cosmopolitan distribution, was unique in having an aerobic purine degradation pathway. Together, these results provide a glimpse of the enormous genomic diversity within this clade at a finer resolution than the currently defined clades.     

Individual genome assembly from complex community short-read metagenomic datasets

Assembling individual genomes from complex community metagenomic data remains a challenging issue for environmental studies. We evaluated the quality of genome assemblies from community short read data (Illumina 100 bp pair-ended sequences) using datasets recovered from freshwater and soil microbial communities as well as in silico simulations. Our analyses revealed that the genome of a single genotype (or species) can be accurately assembled from a complex metagenome when it shows at least about 20 × coverage. At lower coverage, however, the derived assemblies contained a substantial fraction of non-target sequences (chimeras), which explains, at least in part, the higher number of hypothetical genes recovered in metagenomic relative to genomic projects. We also provide examples of how to detect intrapopulation structure in metagenomic datasets and estimate the type and frequency of errors in assembled genes and contigs from datasets of varied species complexity.     

A comprehensive evolutionary classification of proteins encoded in complete eukaryotic genomes

Background

Sequencing the genomes of multiple, taxonomically diverse eukaryotes enables in-depth comparative-genomic analysis which is expected to help in reconstructing ancestral eukaryotic genomes and major events in eukaryotic evolution and in making functional predictions for currently uncharacterized conserved genes.

Results

We examined functional and evolutionary patterns in the recently constructed set of 5,873 clusters of predicted orthologs (eukaryotic orthologous groups or KOGs) from seven eukaryotic genomes: Caenorhabditis elegans, Drosophila melanogaster, Homo sapiens, Arabidopsis thaliana, Saccharomyces cerevisiae, Schizosaccharomyces pombe and Encephalitozoon cuniculi. Conservation of KOGs through the phyletic range of eukaryotes strongly correlates with their functions and with the effect of gene knockout on the organism's viability. The approximately 40% of KOGs that are represented in six or seven species are enriched in proteins responsible for housekeeping functions, particularly translation and RNA processing. These conserved KOGs are often essential for survival and might approximate the minimal set of essential eukaryotic genes. The 131 single-member, pan-eukaryotic KOGs we identified were examined in detail. For around 20 that remained uncharacterized, functions were predicted by in-depth sequence analysis and examination of genomic context. Nearly all these proteins are subunits of known or predicted multiprotein complexes, in agreement with the balance hypothesis of evolution of gene copy number. Other KOGs show a variety of phyletic patterns, which points to major contributions of lineage-specific gene loss and the 'invention' of genes new to eukaryotic evolution. Examination of the sets of KOGs lost in individual lineages reveals co-elimination of functionally connected genes. Parsimonious scenarios of eukaryotic genome evolution and gene sets for ancestral eukaryotic forms were reconstructed. The gene set of the last common ancestor of the crown group consists of 3,413 KOGs and largely includes proteins involved in genome replication and expression, and central metabolism. Only 44% of the KOGs, mostly from the reconstructed gene set of the last common ancestor of the crown group, have detectable homologs in prokaryotes; the remainder apparently evolved via duplication with divergence and invention of new genes.

Conclusions

The KOG analysis reveals a conserved core of largely essential eukaryotic genes as well as major diversification and innovation associated with evolution of eukaryotic genomes. The results provide quantitative support for major trends of eukaryotic evolution noticed previously at the qualitative level and a basis for detailed reconstruction of evolution of eukaryotic genomes and biology of ancestral forms.     

High-quality genome assembly of Huazhan and Tianfeng,the parents of an elite rice hybrid Tian-you-hua-zhan

High-quality rice reference genomes have accelerated the comprehensive identification of genome-wide variations and research on functional genomics and breeding. Tian-you-hua-zhan has been a leading hybrid in China over the past decade. Here, de novo genome assembly strategy optimization for the rice indica lines Huazhan (HZ) and Tianfeng (TF), including sequencing platforms, assembly pipelines and sequence depth, was carried out. The PacBio and Nanopore platforms for long-read sequencing were utilized, with the Canu, wtdbg2, SMARTdenovo, Flye, Canu-wtdbg2, Canu-SMARTdenovo and Canu-Flye assemblers. The combination of PacBio and Canu was optimal, considering the contig N50 length, contig number, assembled genome size and polishing process. The assembled contigs were scaffolded with Hi-C data, resulting in two “golden quality” rice reference genomes, and evaluated using the scaffold N50, BUSCO, and LTR assembly index. Furthermore, 42,625 and 41,815 non-transposable element genes were annotated for HZ and TF, respectively. Based on our assembly of HZ and TF, as well as Zhenshan97, Minghui63, Shuhui498 and 9311, comprehensive variations were identified using Nipponbare as a reference. The de novo assembly strategy for rice we optimized and the “golden quality” rice genomes we produced for HZ and TF will benefit rice genomics and breeding research, especially with respect to uncovering the genomic basis of the elite traits of HZ and TF.

     

eggNOG-mapper v2: Functional Annotation,Orthology Assignments,and Domain Prediction at the Metagenomic Scale

Even though automated functional annotation of genes represents a fundamental step in most genomic and metagenomic workflows, it remains challenging at large scales. Here, we describe a major upgrade to eggNOG-mapper, a tool for functional annotation based on precomputed orthology assignments, now optimized for vast (meta)genomic data sets. Improvements in version 2 include a full update of both the genomes and functional databases to those from eggNOG v5, as well as several efficiency enhancements and new features. Most notably, eggNOG-mapper v2 now allows for: 1) de novo gene prediction from raw contigs, 2) built-in pairwise orthology prediction, 3) fast protein domain discovery, and 4) automated GFF decoration. eggNOG-mapper v2 is available as a standalone tool or as an online service at http://eggnog-mapper.embl.de.     

Metagenomic Profiling of Known and Unknown Microbes with MicrobeGPS

Microbial community profiling identifies and quantifies organisms in metagenomic sequencing data using either reference based or unsupervised approaches. However, current reference based profiling methods only report the presence and abundance of single reference genomes that are available in databases. Since only a small fraction of environmental genomes is represented in genomic databases, these approaches entail the risk of false identifications and often suggest a higher precision than justified by the data. Therefore, we developed MicrobeGPS, a novel metagenomic profiling approach that overcomes these limitations. MicrobeGPS is the first method that identifies microbiota in the sample and estimates their genomic distances to known reference genomes. With this strategy, MicrobeGPS identifies organisms down to the strain level and highlights possibly inaccurate identifications when the correct reference genome is missing. We demonstrate on three metagenomic datasets with different origin that our approach successfully avoids misleading interpretation of results and additionally provides more accurate results than current profiling methods. Our results indicate that MicrobeGPS can enable reference based taxonomic profiling of complex and less characterized microbial communities. MicrobeGPS is open source and available from https://sourceforge.net/projects/microbegps/ as source code and binary distribution for Windows and Linux operating systems.