Search NASA⌕ Search

SEARCH · Search NASA

Results for “sequencing data annotation”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Data from: Candidate variants for additive and interactive effects on bioenergy traits in switchgrass (Panicum virgatum L.) identified by genome-wide association analyses

Switchgrass is a promising herbaceous energy crop, but further gains in biomass yield and quality must be achieved to enable a viable bioenergy industry. Developing DNA markers can contribute to such progress, but depiction of genetic bases should be reliable, involving not only simple additive marker effects but also interactions with genetic backgrounds, e.g., ecotypes, or synergies with other markers. We analyzed plant height, carbon content, nitrogen content, and mineral concentration in a diverse panel consisting of 512 genotypes of upland and lowland ecotype. We performed association analyses based on exome capture sequencing and tested 439,170 markers for marginal effects, but also 83,290 markers for marker-by-ecotype interactions and up to 311,445 marker pairs for pairwise interactions. Analyses of pairwise interactions focused on subsets of marker pairs preselected based on marginal marker effects, gene ontology annotation, and pairwise marker associations. Our tests identified 12 significant effects. Homology and gene expression information corroborated seven effects and indicated plausible causal pathways: flowering time and lignin synthesis for plant height; plant growth and senescence for carbon content and mineral concentration. Four pairwise interactions were detected, including three interactions preselected based on pairwise marker correlations. Furthermore, one marker-by-ecotype interaction and one pairwise interaction were confirmed in an independent switchgrass panel. Our analyses identified reliable candidate variants for important bioenergy traits in switchgrass. Moreover, they exemplified the importance of interactive effects for the depiction of genetic bases, and illustrated the usefulness of preselection of marker pairs for identifying pairwise marker interactions in association testing.

09 BIOMASS FUELS↗
DOE OSTI2022

Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer

Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now approaching the accuracy of crystallographic experiments, and with accelerators like GPUs and TPUs making inference using large models rapid, genome-level structure prediction becomes an obvious aim. Leadership-class computing resources can be used to perform genome-scale protein structure prediction using state-of-the-art deep learning models, providing a wealth of new data for systems biology applications. Here we describe our efforts to efficiently deploy the AlphaFold v.2 program, for full-proteome structure prediction, at scale on the Oak Ridge Leadership Computing Facility's resources, including the Summit supercomputer. We performed inference to produce the predicted structures for 40,526 protein sequences, corresponding to four prokaryotic proteomes and one plant proteome, using under 4,400 total Summit node hours, equivalent to using the majority of the supercomputer for a little over one hour. We also designed an optimized structure refinement that reduced the time for the relaxation stage of the AlphaFold pipeline by over 10X for longer sequences. We demonstrate the types of analyses that can be performed on proteome-scale collections of sequences, including a search for novel quaternary structures and implications for functional annotation.

Gao, Mu↗
DOE OSTI2014

Single-cell genomics reveals features of a Colwellia species that was dominant during the Deepwater Horizon oil spill

During the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico a deep-sea hydrocarbon plume developed resulting in a rapid succession of bacteria. Colwellia eventually supplanted Oceanospirillales, which dominated the plume early in the spill. These successional changes may have resulted, in part, from the changing composition and abundance of hydrocarbons over time. Colwellia abundance peaked when gaseous and simple aromatic hydrocarbons increased, yet the metabolic pathway used by Colwellia in hydrocarbon disposition is unknown. Here we used single-cell genomics to gain insights into the genome properties of a Colwellia enriched during the DWH deep-sea plume. A single amplified genome (SAG) of a Colwellia cell isolated from a DWH plume, closely related (avg. 98% 16S rRNA gene similarity) to other plume Colwellia, was sequenced and annotated. The SAG was similar to the sequenced isolate Colwellia psychrerythraea 34H (84% avg. nucleotide identity). Both had genes for denitrification, chemotaxis, and motility, adaptations to cold environments and a suite of nutrient acquisition genes. The Colwellia SAG may be capable of gaseous and aromatic hydrocarbon degradation, which contrasts with a DWH plume Oceanospirillales SAG which encoded non-gaseous n-alkane and cycloalkane degradation pathways. The disparate hydrocarbon degradation pathways are consistent with hydrocarbons that were abundant at different times in the deep-sea plume; first, non-gaseous n-alkanes and cycloalkanes that could be degraded by Oceanospirillales, followed by gaseous, and simple aromatic hydrocarbons that may have been degraded by Colwellia. These insights into the genomic properties of a Colwellia species, which were supported by existing metagenomic sequence data from the plume and DWH contaminated sediments, help further our understanding of the successional changes in the dominant microbial players in the plume over the course of the DWH spill.

Colwellia↗
DOE OSTI2014

A Sorghum bicolor expression atlas reveals dynamic genotype-specific expression profiles for vegetative tissues of grain, sweet and bioenergy sorghums

Background: Effective improvement in sorghum crop development necessitates a genomics-based approach to identify functional genes and QTLs. Sequenced in 2009, a comprehensive annotation of the sorghum genome and the development of functional genomics resources is key to enable the discovery and deployment of regulatory and metabolic genes and gene networks for crop improvement. Results: This study utilizes the first commercially available whole-transcriptome sorghum microarray (Sorgh-WTa520972F) to identify tissue and genotype-specific expression patterns for all identified Sorghum bicolor exons and UTRs. The genechip contains 1,026,373 probes covering 149,182 exons (27,577 genes) across the Sorghum bicolor nuclear, chloroplast, and mitochondrial genomes. Specific probesets were also included for putative non-coding RNAs that may play a role in gene regulation (e. g., microRNAs), and confirmed functional small RNAs in related species (maize and sugarcane) were also included in our array design. We generated expression data for 78 samples with a combination of four different tissue types (shoot, root, leaf and stem), two dissected stem tissues (pith and rind) and six diverse genotypes, which included 6 public sorghum lines (R159, Atlas, Fremont, PI152611, AR2400 and PI455230) representing grain, sweet, forage, and high biomass ideotypes. Conclusions: Here we present a summary of the microarray dataset, including analysis of tissue-specific gene expression profiles and associated expression profiles of relevant metabolic pathways. With an aim to enable identification and functional characterization of genes in sorghum, this expression atlas presents a new and valuable resource to the research community.

Shakoor, N↗
DOE OSTI2015

Genomic Patterns of Positive Selection at the Origin of Rust Fungi

Understanding the origin and evolution of pathogenicity and biotrophic life-style of rust fungi has remained a conundrum for decades. Research on the molecular mechanisms responsible for rust fungi evolution has been hampered by their biotrophic life-style until the sequencing of some rust fungi genomes. With the availability of multiple whole genomes and EST data for this group, it is now possible to employ genome-wide surveys and investigate how natural selection shaped their evolution. In this work, we employed a phylogenomics approach to search for positive selection and genes undergoing accelerated evolution at the origin of rust fungi on an assembly of single copy genes conserved across a broad range of basidiomycetes. Up to 985 genes were screened for positive selection on the phylogenetic branch leading to rusts, revealing a pervasive signal of positive selection throughout the data set with the proportion of positively selected genes ranging between 19.6–33.3%. Additionally, 30 genes were found to be under accelerated evolution at the origin of rust fungi, probably due to a mixture of positive selection and relaxation of purifying selection. Functional annotation of the positively selected genes revealed an enrichment in genes involved in the biosynthesis of secondary metabolites and several metabolism and transporter classes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Compilation and utilization of a sorghum transcriptome compendium for gene regulatory network analysis and crop trait engineering

Sorghum bicolor (Sorghum) is a drought and heat tolerant C4 grass crop used to produce grain, forage, biofuels, and other bioproducts. Genetic improvement of sorghum hybrid crops is aided by a large and diverse germplasm, sorghum's diploid inbreeding genetics, and a relatively small genome that has facilitated genomic research. Over the past 20 years, the sorghum research community characterized the cytogenetic and recombinant landscapes of sorghum's 10 chromosomes, sequenced and annotated the sorghum genome, and used that information to identify genes/alleles that modulate flowering time, plant height, seed shattering, and other important traits. More recently, >1000 RNA-seq transcriptome profiles were collected from 15 sorghum genotypes to help understand the genetic basis of variation in growth and development of sorghum stems, tillers, roots, and leaves, and the regulation of biosynthetic pathways that produce epicuticular wax, dhurrin, and RFOs, compounds that contribute to sorghum's resilience. Transcriptome studies were designed to identify differentially expressed genes that are co-expressed during development or in response to a treatment to enable construction of gene regulatory networks. Co-expression and network analysis identified transcription factors and their cognate binding sites in target gene promoters and signaling pathways that modulate gene regulatory networks providing gene editing targets for further trait optimization. RNA-seq data from >20 experiments targeting sorghum organs, tissues, cell types, developmental stages, and responses to environmental conditions (i.e., diel, day-length, shading, water-deficit, temperature) has been compiled in a sorghum transcriptome compendium. The goal of this resource paper is to describe compendium content, accessibility, and a compendium data analysis pipeline and to illustrate the types of information that can be derived from the compendium with a focus on the elucidation of gene regulatory networks useful for guiding the improvement of sorghum traits through gene editing.

RNA-seq↗
DOE OSTI2016

Modeling biochemical pathways in the gene ontology

The concept of a biological pathway, an ordered sequence of molecular transformations, is used to collect and represent molecular knowledge for a broad span of organismal biology. Representations of biomedical pathways typically are rich but idiosyncratic presentations of organized knowledge about individual pathways. Meanwhile, biomedical ontologies and associated annotation files are powerful tools that organize molecular information in a logically rigorous form to support computational analysis. The Gene Ontology (GO), representing Molecular Functions, Biological Processes and Cellular Components, incorporates many aspects of biological pathways within its ontological representations. Here we present a methodology for extending and refining the classes in the GO for more comprehensive, consistent and integrated representation of pathways, leveraging knowledge embedded in current pathway representations such as those in the Reactome Knowledgebase and MetaCyc. With carbohydrate metabolic pathways as a use case, we discuss how our representation supports the integration of variant pathway classes into a unified ontological structure that can be used for data comparison and analysis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Evolution and comparative genomics of the most common Trichoderma species

Background: The growing importance of the ubiquitous fungal genus Trichoderma (Hypocreales, Ascomycota) requires understanding of its biology and evolution. Many Trichoderma species are used as biofertilizers and biofungicides and T. reesei is the model organism for industrial production of cellulolytic enzymes. In addition, some highly opportunistic species devastate mushroom farms and can become pathogens of humans. A comparative analysis of the first three whole genomes revealed mycoparasitism as the innate feature of Trichoderma. However, the evolution of these traits is not yet understood. Results: We selected 12 most commonly occurring Trichoderma species and studied the evolution of their genome sequences. Trichoderma evolved in the time of the Cretaceous-Palaeogene extinction event 66 (±15) mya, but the formation of extant sections (Longibrachiatum, Trichoderma) or clades (Harzianum/Virens) happened in Oligocene. The evolution of the Harzianum clade and section Trichoderma was accompanied by significant gene gain, but the ancestor of section Longibrachiatum experienced rapid gene loss. The highest number of genes gained encoded ankyrins, HET domain proteins and transcription factors. We also identified the Trichoderma core genome, completely curated its annotation, investigated several gene families in detail and compared the results to those of other fungi. Eighty percent of those genes for which a function could be predicted were also found in other fungi, but only 67% of those without a predictable function. Conclusions: Our study presents a time scaled pattern of genome evolution in 12 Trichoderma species from three phylogenetically distant clades/sections and a comprehensive analysis of their genes. The data offer insights in the evolution of a mycoparasite towards a generalist.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Annotation and sequence diversity of transposable elements in common bean (Phaseolus vulgaris)

Common bean (Phaseolus vulgaris) is an important legume crop grown and consumed worldwide.With the availability of the common bean genome sequence, the next challenge is to annotate the genome and characterize functional DNA elements. Transposable elements (TEs) are the most abundant component of plant genomes and can dramatically affect genome evolution and genetic variation. Thus, it is pivotal to identify TEs in the common bean genome. In this study, we performed a genome-wide transposon annotation in common bean using a combination of homology and sequence structure-based methods.We developed a 2.12-Mb transposon database which includes 791 representative transposon sequences and is available upon request or from www.phytozome.org. Of note, nearly all transposons in the database are previously unrecognized TEs. More than 5,000 transposon-related expressed sequence tags (ESTs) were detected which indicates that some transposons may be transcriptionally active. Two Ty1-copia retrotransposon families were found to encode the envelope-like protein which has rarely been identified in plant genomes. Also, we identified an extra open reading frame (ORF) termed ORF2 from 15 Ty3-gypsy families that was located between the ORF encoding the retrotransposase and the 3 LTR. The ORF2 was in opposite transcriptional orientation to retrotransposase. Sequence homology searches and phylogenetic analysis suggested that the ORF2 may have an ancient origin, but its function is not clear. These transposon data provide a useful resource for understanding the genome organization and evolution and may be used to identify active TEs for developing transposon-tagging system in common bean and other related genomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

IsoForma: An R Package for Quantifying and Visualizing Positional Isomers in Top-Down LC-MS/MS Data

Proteoforms, the different forms of a protein with sequence variations including post-translational modifications (PTMs), execute vital functions in biological systems such as cell signaling and epigenetic regulation. Precisely defining the stoichiometry of PTMs has been challenging because, in the widely used bottom-up proteomics methods, the detection occurs at the peptide level and thus the link between peptides and their specific modification site is lost, resulting in proteoform ambiguity. Advances in top-down mass spectrometry (MS) technology have permitted the direct characterization of intact proteoforms and their exact number of modification sites, allowing for the relative quantification of positional isomers (PI). Proteins with positional isomers refers to proteoforms with identical total mass and set of modifications but varying PTM site combinations. The relative abundance of PI can be estimated by matching proteoform-specific fragment ions to top-down tandem MS (MS2) data to localize and quantify modifications. However, current approaches heavily rely on manual annotation. Here, we present IsoForma, an open-source R package for relative quantification of PI within a single tool. We benchmarked IsoForma’s performance against two existing workflows and highlight the similarity of the results and improvements in speed. Overall, IsoForma provides a streamlined process, reduces the time of conducting isoform-based analyses, and offers an essential framework for developing customized proteoform analysis workflows. Finally, the software is open source and available at https://github.com/EMSL-Computing/isoforma-lib.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Comparative validation of the D. melanogaster modENCODE transcriptome annotation

Accurate gene model annotation of reference genomes is critical for making them useful. The modENCODE project has improved the D. melanogaster genome annotation by using deep and diverse high-throughput data. Since transcriptional activity that has been evolutionarily conserved is likely to have an advantageous function, we have performed large-scale interspecific comparisons to increase confidence in predicted annotations. To support comparative genomics, we filled in divergence gaps in the Drosophila phylogeny by generating draft genomes for eight new species. For comparative transcriptome analysis, we generated mRNA expression profiles on 81 samples from multiple tissues and developmental stages of 15 Drosophila species, and we performed cap analysis of gene expression in D. melanogaster and D. pseudoobscura. We also describe conservation of four distinct core promoter structures composed of combinations of elements at three positions. Overall, each type of genomic feature shows a characteristic divergence rate relative to neutral models, highlighting the value of multispecies alignment in annotating a target genome that should prove useful in the annotation of other high priority genomes, especially human and other mammalian genomes that are rich in noncoding sequences. We report that the vast majority of elements in the annotation are evolutionarily conserved, indicating that the annotation will be an important springboard for functional genetic testing by the Drosophila community.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2014

Alignathon: a competitive assessment of whole-genome alignment methods

Multiple sequence alignments (MSAs) are a prerequisite for a wide variety of evolutionary analyses. Published assessments and benchmark data sets for protein and, to a lesser extent, global nucleotide MSAs are available, but less effort has been made to establish benchmarks in the more general problem of whole-genome alignment (WGA). Using the same model as the successful Assemblathon competitions, we organized a competitive evaluation in which teams submitted their alignments and then assessments were performed collectively after all the submissions were received. Three data sets were used: Two were simulated and based on primate and mammalian phylogenies, and one was comprised of 20 real fly genomes. In total, 35 submissions were assessed, submitted by 10 teams using 12 different alignment pipelines. We found agreement between independent simulation based and statistical assessments, indicating that there are substantial accuracy differences between contemporary alignment tools. We saw considerable differences in the alignment quality of differently annotated regions and found that few tools aligned the duplications analyzed. We found that many tools worked well at shorter evolutionary distances, but fewer performed competitively at longer distances. We provide all data sets, submissions, and assessment programs for further study and provide, as a resource for future benchmarking, a convenient repository of code and data for reproducing the simulation assessments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Development of a Knowledgebase (MetRxn) of Metabolites, Reactions and Atom Mappings to Accelerate Discovery and Redesign

With advances in DNA sequencing and genome annotation techniques, the breadth of metabolic knowledge across all kingdoms of life is increasing. The construction of genome-scale models (GSMs) facilitates this distillation of knowledge by systematically accounting for reaction stoichiometry and directionality, gene to protein to reaction relationships, reaction localization among cellular organelles, metabolite transport costs and routes, transcriptional regulation, and biomass composition. Genome-scale reconstructions available now span across all kingdoms of life, from microbes to whole-plant models, and have become indispensable for driving informed metabolic designs and interventions. A key barrier to the pace of this development is our inability to utilize metabolite/reaction information from databases such as BRENDA [1], KEGG [2], MetaCyc [3], etc. due to incompatibilities of representation, duplications, and errors. Duplicate entries constitute a major impediment, where the same metabolite is found with multiple names across databases and models, which significantly slows downs the collating of information from multiple data sources. This can also lead to serious modeling errors such as charge/mass imbalances [4,5] which can thwart model predictive abilities such as identifying synthetic lethal gene pairs and quantifying metabolic flows. Hence, we created the MetRxn database [6] that takes the next step in integrating data from multiple sources and formats to automatically create a standardized knowledgebase. We subsequently deployed this resource to bring about new paradigms in genome-scale metabolic model reconstruction, metabolic flux elucidation through MFA, modeling of microbial communities, and pathway prospecting. This research has enabled the PI’s group to continue building upon research milestones and reach new ones (see list of MetRxn-related publications below).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Physiological, genomic, and sulfur isotopic characterization of methanol metabolism by Desulfovibrio carbinolicus

Methanol is often considered as a non-competitive substrate for methanogenic archaea, but an increasing number of sulfate-reducing microorganisms (SRMs) have been reported to be capable of respiring with methanol as an electron donor. A better understanding of the fate of methanol in natural or artificial anaerobic systems thus requires knowledge of the methanol dissimilation by SRMs. In this study, we describe the growth kinetics and sulfur isotope effects of Desulfovibrio carbinolicus, a methanol-oxidizing sulfate-reducing deltaproteobacterium, together with its genome sequence and annotation. D. carbinolicus can grow with a series of alcohols from methanol to butanol. Compared to longer-chain alcohols, however, specific growth and respiration rates decrease by several fold with methanol as an electron donor. Larger sulfur isotope fractionation accompanies slowed growth kinetics, indicating low chemical potential at terminal reductive steps of respiration. In a medium containing both ethanol and methanol, D. carbinolicus does not consume methanol even after the cessation of growth on ethanol. Among the two known methanol dissimilatory systems, the genome of D. carbinolicus contains the genes coding for alcohol dehydrogenase but lacks enzymes analogous to methanol methyltransferase. We analyzed the genomes of 52 additional species of sulfate-reducing bacteria that have been tested for methanol oxidation. There is no apparent relationship between phylogeny and methanol metabolizing capacity, but most gram-negative methanol oxidizers grow poorly, and none carry homologs for methyltransferase (mtaB). Although the amount of available data is limited, it is notable that more than half of the known gram-positive methanol oxidizers have both enzymatic systems, showing enhanced growth relative to the SRMs containing only alcohol dehydrogenase genes. Thus, physiological, genomic, and sulfur isotopic results suggest that D. carbinolicus and close relatives have the ability to metabolize methanol but likely play a limited role in methanol degradation in most natural environments.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Identification of a Fusarium ear rot resistance gene in maize by QTL mapping and RNA sequencing

Fusarium ear rot (FER) caused by Fusarium verticillioides is a prevalent maize disease. To comprehensively characterize the genetic basis of the natural variation in FER resistance, a recombinant inbred line (RIL) population was used to map quantitative trait loci (QTL) for FER resistance. A total of 17 QTL were identified by linkage mapping in eight environments. These QTL were located on six chromosomes and explained 3.88–15.62% of the total phenotypic variation. Moreover, qFER1.03 had the strongest effect and accounted for 4.98–15.62% of the phenotypic variation according to analyses of multiple environments involving best linear unbiased predictions. The chromosome segment substitution lines (CSSLs) derived from a cross between Qi319 (donor parent) and Ye478 (recurrent parent) were used to verify the contribution of qFER1.03 to FER resistance. The line CL171, which harbored an introgressed qFER1.03 , was significantly resistant to FER. Further fine mapping of qFER1.03 revealed that the resistance QTL was linked to insertion/deletion markers InDel 8 and InDel 2, with physical distances of 43.55 Mb and 43.76 Mb, respectively. Additionally, qFER1.03 differed from the previous resistance QTL on chromosome 1. There were three annotated genes in this region. On the basis of the RNA-seq data, which revealed the genes differentially expressed between the FER-resistant Qi319 and susceptible Ye478, GRMZM2G017792 (MPK3) was preliminarily identified as a candidate gene in the qFER1.03 region. The Pr-CMV-VIGS system was used to decrease the GRMZM2G017792 expression level in CL171 by 34–57%, which led to a significant decrease in FER resistance. Using RIL and CSSL populations combined with RNA-seq and Pr-CMV-VIGS, the candidate gene can be dissected effectively, which provided important gene resource for breeding FER-resistant varieties.

Xia, Yusheng↗
DOE OSTI2016

(Meta)genomic insights into the pathogenome of Cellulosimicrobium cellulans

Despite having serious clinical manifestations, Cellulosimicrobium cellulans remain under-reported with only three genome sequences available at the time of writing. Genome sequences of C. cellulans LMG16121, C. cellulans J36 and Cellulosimicrobium sp. strain MM were used to determine distribution of pathogenicity islands (PAIs) across C. cellulans , which revealed 49 potential marker genes with known association to human infections, e.g. Fic and VbhA toxin-antitoxin system. Oligonucleotide composition-based analysis of orthologous proteins (n = 791) across three genomes revealed significant negative correlation (P < 0.05) between frequency of optimal codons ( F opt ) and gene G+C content, highlighting the G+C-biased gene conversion (gBGC) effect across Cellulosimicrobium strains. Bayesian molecular-clock analysis performed on three virulent PAI proteins (Fic; D-alanyl-D-alanine-carboxypeptidase; transposase) dated the divergence event at 300 million years ago from the most common recent ancestor. Synteny-based annotation of hypothetical proteins highlighted gene transfers from non-pathogenic bacteria as a key factor in the evolution of PAIs. Additonally, deciphering the metagenomic islands using strain MM's genome with environmental data from the site of isolation (hot-spring biofilm) revealed (an)aerobic respiration as population segregation factor across the in situ cohorts. Furthermore, using reference genomes and metagenomic data, our results highlight the emergence and evolution of PAIs in the genus Cellulosimicrobium .

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

The total microbiome functions in bacteria and fungi

Unveiling the relationship between phylogeny and function of the microbiome is crucial to determine its contribution to ecosystem functioning. However, while there is a considerable amount of information on microbial phylogenetic diversity, our understanding of its relationship to functional diversity is still scarce. Here we predicted the total microbiome functions of bacteria and fungi on Earth using the total known functions from level 3 of KEGG Orthology by modelling the increase of functions with increasing diversity of bacteria or fungi. For bacteria and fungi, the unsaturated model described the data significantly better (for both P?<2.2e-16), suggesting the presence of two types of functions. Widespread functions ubiquitous in every living organism that make up two thirds of our current knowledge of microbiome functions are separated from rare functions from specialised enzymes present in only a few species. Given previous estimates on species richness, we predicted a global total of 35.5 million functions in bacteria and 3.2 million in fungi; of which only 0.02% and 0.14% are known today. Our approach highlights the necessity of novel and more sophisticated methods to unveil the entirety of rare functions to fully understand the involvement of the microbiome in ecosystem functioning. SIGNIFICANCE: The functionality of and within a microbial community is generally inferred based on the taxonomic annotation of the organism. However, our understanding of functional diversity and how it relates to taxonomy is still limited. Here we predict the total microbiome functionality in bacteria and fungi on Earth using known and annotated protein-coding sequences in species accumulation curves. Our estimates reveal that the majority of functionality (>99%) could be assigned to yet unknown and rare functions, highlighting that our current knowledge is incomplete and functional inference is thus lackluster.

Bacteria↗
DOE OSTI2021

Analyses of transcriptomes and the first complete genome of Leucocalocybe mongolica provide new insights into phylogenetic relationships and conservation

In this study, we report a de novo assembly of the first high-quality genome for a wild mushroom species Leucocalocybe mongolica (LM). We performed high-throughput transcriptome sequencing to analyze the genetic basis for the life history of LM. Our results show that the genome size of LM is 46.0 Mb, including 26 contigs with a contig N50 size of 3.6 Mb. In total, we predicted 11,599 protein-coding genes, of which 65.7% (7630) could be aligned with high confidence to annotated homologous genes in other species. We performed phylogenetic analyses using genes form 3269 single-copy gene families and showed support for distinguishing LM from the genus Tricholoma (L.) P.Kumm., in which it is sometimes circumscribed. We believe that one reason for limited wild occurrences of LM may be the loss of key metabolic genes, especially carbohydrate-active enzymes (CAZymes), based on comparisons with other closely related species. The results of our transcriptome analyses between vegetative (mycelia) and reproductive (fruiting bodies) organs indicated that changes in gene expression among some key CAZyme genes may help to determine the switch from asexual to sexual reproduction. Taken together, our genomic and transcriptome data for LM comprise a valuable resource for both understanding the evolutionary and life history of this species.

54 ENVIRONMENTAL SCIENCES↗