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DOE OSTI2022

RCSB Protein Data Bank (RCSB.org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from artificial intelligence/machine learning

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center for the open-access PDB archive. As wwPDB-designated Archive Keeper, RCSB PDB is also responsible for PDB data security. Annually, RCSB PDB serves >10 000 depositors of three-dimensional (3D) biostructures working on all permanently inhabited continents. RCSB PDB delivers data from its research-focused RCSB.org web portal to many millions of PDB data consumers based in virtually every United Nations-recognized country, territory, etc. This Database Issue contribution describes upgrades to the research-focused RCSB.org web portal that created a one-stop-shop for open access to ∼200 000 experimentally-determined PDB structures of biological macromolecules alongside >1 000 000 incorporated Computed Structure Models (CSMs) predicted using artificial intelligence/machine learning methods. RCSB.org is a ‘living data resource.’ Every PDB structure and CSM is integrated weekly with related functional annotations from external biodata resources, providing up-to-date information for the entire corpus of 3D biostructure data freely available from RCSB.org with no usage limitations. Within RCSB.org, PDB structures and the CSMs are clearly identified as to their provenance and reliability. Both are fully searchable, and can be analyzed and visualized using the full complement of RCSB.org web portal capabilities.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Allosteric autoregulation of DNA binding via a DNA-mimicking protein domain: a biophysical study of ZNF410–DNA interaction using small angle X-ray scattering

ZNF410 is a highly-conserved transcription factor, remarkable in that it recognizes a 15-base pair DNA element but has just a single responsive target gene in mammalian erythroid cells. ZNF410 includes a tandem array of five zinc-fingers (ZFs), surrounded by uncharacterized N- and C-terminal regions. Unexpectedly, full-length ZNF410 has reduced DNA binding affinity, compared to that of the isolated DNA binding ZF array, both in vitro and in cells. AlphaFold predicts a partially-folded N-terminal subdomain that includes a 30-residue long helix, preceded by a hairpin loop rich in acidic (aspartate/glutamate) and serine/threonine residues. This hairpin loop is predicted by AlphaFold to lie against the DNA binding interface of the ZF array. In solution, ZNF410 is a monomer and binds to DNA with 1:1 stoichiometry. Surprisingly, the single best-fit model for the experimental small angle X-ray scattering profile, in the absence of DNA, is the original AlphaFold model with the N-terminal long-helix and the hairpin loop occupying the ZF DNA binding surface. For DNA binding, the hairpin loop presumably must be displaced. After combining biophysical, biochemical, bioinformatic and artificial intelligence-based AlphaFold analyses, we suggest that the hairpin loop mimics the structure and electrostatics of DNA, and provides an additional mechanism, supplementary to sequence specificity, of regulating ZNF410 DNA binding.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

TroR is the primary regulator of the iron homeostasis transcription network in the halophilic archaeon Haloferax volcanii

Abstract Maintaining the intracellular iron concentration within the homeostatic range is vital to meet cellular metabolic needs and reduce oxidative stress. Previous research revealed that the haloarchaeon Halobacterium salinarum encodes four diphtheria toxin repressor (DtxR) family transcription factors (TFs) that together regulate the iron response through an interconnected transcriptional regulatory network (TRN). However, the conservation of the TRN and the metal specificity of DtxR TFs remained poorly understood. Here we identified and characterized the TRN of Haloferax volcanii for comparison. Genetic analysis demonstrated that Hfx. volcanii relies on three DtxR transcriptional regulators (Idr, SirR, and TroR), with TroR as the primary regulator of iron homeostasis. Bioinformatics and molecular approaches revealed that TroR binds a conserved cis-regulatory motif located ∼100 nt upstream of the start codon of iron-related target genes. Transcriptomics analysis demonstrated that, under conditions of iron sufficiency, TroR repressed iron uptake and induced iron storage mechanisms. TroR repressed the expression of one other DtxR TF, Idr. This reduced DtxR TRN complexity relative to that of Hbt. salinarum appeared correlated with natural variations in iron availability. Based on these data, we hypothesize that variable environmental conditions such as iron availability appear to select for increasing TRN complexity.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Updated resources for exploring experimentally-determined PDB structures and Computed Structure Models at the RCSB Protein Data Bank

The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB, RCSB.org), the US Worldwide Protein Data Bank (wwPDB, wwPDB.org) data center for the global PDB archive, provides access to the PDB data via its RCSB.org research-focused web portal. We report substantial additions to the tools and visualization features available at RCSB.org, which now delivers more than 227000 experimentally determined atomic-level three-dimensional (3D) biostructures stored in the global PDB archive alongside more than 1 million Computed Structure Models (CSMs) of proteins (including models for human, model organisms, select human pathogens, crop plants and organisms important for addressing climate change). In addition to providing support for 3D structure motif searches with user-provided coordinates, new features highlighted herein include query results organized by redundancy-reduced Groups and summary pages that facilitate exploration of groups of similar proteins. Newly released programmatic tools are also described, as are enhanced training opportunities.

Burley, Stephen K.↗

DOE OSTI2026

EEPD1 evolved a unique DNA clamping dimer protecting reversed replication forks

Exonuclease/endonuclease/phosphatase (EEP)-fold hydrolases are canonically monomeric phosphodiesterases exemplified by APE1, DNase I, and TDP2 nucleases. While EEP family domain containing protein 1 (EEPD1) acts in DNA stress responses, its proposed nuclease activities are enigmatic. Here, we integrate hybrid structural methods, evolution, biochemistry, cancer genomics, plus molecular and cell biology to define EEPD1 structure, assembly, and function at stalled DNA replication forks. Results imply EEPD1 surprisingly requires both unique EEP domain dimer and distinctive tandem Helix-hairpin-Helix [(HhH) 2 ] domains to clamp double-stranded (ds) DNA at reversed DNA replication forks for fork protection. Small-angle X-ray Scattering (SAXS), crystal, and cryo-EM structures unveil an unprecedented tryptophan handshake dimer, conserved interface di-Trp-Pro pocket, and adjustable “wrist” enabling an open-closed conformational switch. EEPD1 dimer cooperatively binds complex dsDNA replication fork intermediates but alone lacks nuclease activity due to loss of key EEP catalytic residues during Metazoan evolution and atmospheric oxygen buildup. Instead, EEPD1 prevents nucleolytic degradation of reversed replication forks by MRE11. Furthermore, cancer bioinformatics support oxidative damage-dependent EEPD1 association as a significant modulator of overall patient survival. Collective findings uncover unexpected EEP dimer and fork protection function in clamping, not cleaving, reversed replication forks for metazoan oxidative stress responses controlling genome stability and cancer outcomes.

Shen, Runze [Univ. of Texas, Houston, TX (United S↗

DOE OSTI2013

The SEED and the Rapid Annotation of microbial genomes using Subsystems Technology (RAST)

In 2004, the SEED (http://pubseed.theseed.org/) was created to provide consistent and accurate genome annotations across thousands of genomes and as a platform for discovering and developing de novo annotations. The SEED is a constantly updated integration of genomic data with a genome database, web front end, API and server scripts. It is used by many scientists for predicting gene functions and discovering new pathways. In addition to being a powerful database for bioinformatics research, the SEED also houses subsystems (collections of functionally related protein families) and their derived FIGfams (protein families), which represent the core of the RAST annotation engine (http://rast. nmpdr.org/). When a new genome is submitted to RAST, genes are called and their annotations are made by comparison to the FIGfam collection. If the genome is made public, it is then housed within the SEED and its proteins populate the FIGfam collection. This annotation cycle has proven to be a robust and scalable solution to the problem of annotating the exponentially increasing number of genomes. To date, >12 000 users worldwide have annotated >60 000 distinct genomes using RAST. Here we describe the interconnectedness of the SEED database and RAST, the RAST annotation pipeline and updates to both resources.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Two RNA-binding motifs in eIF3 direct HCV IRES-dependent translation

The initiation of protein synthesis plays an essential regulatory role in human biology. At the center of the initiation pathway, the 13-subunit eukaryotic translation initiation factor 3 (eIF3) controls access of other initiation factors and mRNA to the ribosome by unknown mechanisms. Using electron microscopy (EM), bioinformatics and biochemical experiments, we identify two highly conserved RNA-binding motifs in eIF3 that direct translation initiation from the hepatitis C virus internal ribosome entry site (HCV IRES) RNA. Mutations in the RNA-binding motif of subunit eIF3a weaken eIF3 binding to the HCV IRES and the 40S ribosomal subunit, thereby suppressing eIF2-dependent recognition of the start codon. Mutations in the eIF3c RNA-binding motif also reduce 40S ribosomal subunit binding to eIF3, and inhibit eIF5B-dependent steps downstream of start codon recognition. These results provide the first connection between the structure of the central translation initiation factor eIF3 and recognition of the HCV genomic RNA start codon, molecular interactions that likely extend to the human transcriptome.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

Natural reassignment of CUU and CUA sense codons to alanine in Ashbya mitochondria

The discovery of diverse codon reassignment events has demonstrated that the canonical genetic code is not universal. Studying coding reassignment at the molecular level is critical for understanding genetic code evolution, and provides clues to genetic code manipulation in synthetic biology. Here we report a novel reassignment event in the mitochondria of Ashbya (Eremothecium) gossypii, a filamentous-growing plant pathogen related to yeast (Saccharomycetaceae). Bioinformatics studies of conserved positions in mitochondrial DNA-encoded proteins suggest that CUU and CUA codons correspond to alanine in A. gossypii, instead of leucine in the standard code or threonine in yeast mitochondria. Reassignment of CUA to Ala was confirmed at the protein level by mass spectrometry. We further demonstrate that a predicted tRNA$^{Ala}_{UAG}$ is transcribed and accurately processed in vivo, and is responsible for Ala reassignment. Enzymatic studies reveal that tRNA$^{Ala}_{UAG}$ is efficiently recognized by A. gossypii mitochondrial alanyl-tRNA synthetase (AgAlaRS). AlaRS typically recognizes the G3:U70 base pair of tRNA Ala ; a G3A change in AshbyatRNA$^{Ala}_{UAG}$ abolishes its recognition by AgAlaRS. Conversely, an A3G mutation in Saccharomyces cerevisiaetRNA$^{Ala}_{UAG}$ confers tRNA recognition by AgAlaRS. Our work highlights the dynamic feature of natural genetic codes in mitochondria, and the relative simplicity by which tRNA identity may be switched.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

MyTaxa: an advanced taxonomic classifier for genomic and metagenomic sequences

Determining the taxonomic affiliation of sequences assembled from metagenomes remains a major bottleneck that affects research across the fields of environmental, clinical and evolutionary microbiology. Here, we introduce MyTaxa, a homology-based bioinformatics framework to classify metagenomic and genomic sequences with unprecedented accuracy. The distinguishing aspect of MyTaxa is that it employs all genes present in an unknown sequence as classifiers, weighting each gene based on its (predetermined) classifying power at a given taxonomic level and frequency of horizontal gene transfer. MyTaxa also implements a novel classification scheme based on the genome-aggregate average amino acid identity concept to determine the degree of novelty of sequences representing uncharacterized taxa, i.e. whether they represent novel species, genera or phyla. Application of MyTaxa on in silico generated (mock) and real metagenomes of varied read length (100–2000 bp) revealed that it correctly classified at least 5% more sequences than any other tool. The analysis also showed that ~10% of the assembled sequences from human gut metagenomes represent novel species with no sequenced representatives, several of which were highly abundant in situ such as members of the Prevotella genus. Thus, MyTaxa can find several important applications in microbial identification and diversity studies.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

A glimpse into the fungal metabolomic abyss: Novel network analysis reveals relationships between exogenous compounds and their outputs

Fungal specialized metabolites are a major source of beneficial compounds that are routinely isolated, characterized, and manufactured as pharmaceuticals, agrochemical agents, and industrial chemicals. The production of these metabolites is encoded by biosynthetic gene clusters that are often silent under standard growth conditions. There are limited resources for characterizing the direct link between abiotic stimuli and metabolite production. Herein, we introduce a network analysis-based, data-driven algorithm comprising two routes to characterize the production of specialized fungal metabolites triggered by different exogenous compounds: the direct route and the auxiliary route. Both routes elucidate the influence of treatments on the production of specialized metabolites from experimental data. The direct route determines known and putative metabolites induced by treatments and provides additional insight over traditional comparison methods. The auxiliary route is specific for discovering unknown analytes, and further identification can be curated through online bioinformatic resources. We validated our algorithm by applying chitooligosaccharides and lipids at two different temperatures to the fungal pathogen Aspergillus fumigatus. After liquid chromatography–mass spectrometry quantification of significantly produced analytes, we used network centrality measures to rank the treatments’ ability to elucidate these analytes and confirmed their identity through fragmentation patterns or in silico spiking with commercially available standards. Later, we examined the transcriptional regulation of these metabolites through real-time quantitative polymerase chain reaction. Our data-driven techniques can complement existing metabolomic network analysis by providing an approach to track the influence of any exogenous stimuli on metabolite production. Our experimental-based algorithm can overcome the bottlenecks in elucidating novel fungal compounds used in drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Arabidopsis bZIP11 Is a Susceptibility Factor During Pseudomonas syringae Infection

The induction of plant nutrient secretion systems is critical for successful pathogen infection. Some bacterial pathogens (e.g., Xanthomonas spp.) use transcription activator-like (TAL) effectors to induce transcription of SWEET sucrose efflux transporters. Pseudomonas syringae pv. tomato strain DC3000 lacks TAL effectors yet is able to induce multiple SWEETs in Arabidopsis thaliana by unknown mechanisms. Because bacteria require other nutrients in addition to sugars for efficient reproduction, we hypothesized that Pseudomonas spp. may depend on host transcription factors involved in secretory programs to increase access to essential nutrients. Bioinformatic analyses identified the Arabidopsis basic-leucine zipper transcription factor bZIP11 as a potential regulator of nutrient transporters, including SWEETs and UmamiT amino acid transporters. Inducible downregulation of bZIP11 expression in Arabidopsis resulted in reduced growth of P. syringae pv. tomato strain DC3000, whereas inducible overexpression of bZIP11 resulted in increased bacterial growth, supporting the hypothesis that bZIP11-regulated transcription programs are essential for maximal pathogen titer in leaves. Our data are consistent with a model in which a pathogen alters host transcription factor expression upstream of secretory transcription networks to promote nutrient efflux from host cells. [Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .

Biochemistry & Molecular Biology↗

DOE OSTI2018

Correlating Enzymes to Antimicrobial Resistance in the Protein Data Bank

Antibiotic resistant bacteria are predicted to be the leading cause of death worldwide, with an expected death rate of 10 million people, annually, by 2050. It is vital that antimicrobial resistance (AMR) be understood at the molecular level. The Protein Data Bank (PDB) archive holds over 130,000 3D structures of macromolecular machines that can be analyzed using the RCSB PDB website at RCSB.org. Studying these 3D structures enables scientific research and education in a variety of fields, including drug discovery, nanotechnology, green energy, and disease. In this study, PDB enzymes related to antibiotic inhibitors were identified and further analyzed using additional bioinformatics including The Comprehensive Antibiotic Resistance Database (CARD). CARD holds up to date clinically relevant antibiotic related information. CARD identified 4,688 Genbank IDs (protein accession codes) that are related to AMR. These codes were then put into the Uniprot website which holds protein sequences. As a result, 696 macromolecules related to AMR were found in the PDB. I specifically researched Metallo Beta Lactamases (MBL) because have a large correlation to AMR and have been studied in great detail, with many examples in the PDB. Prominent enzymes in this category include the New Delhi Beta Lactamase which can cleave most if not all antibiotics. This particular “superbug” was found to cleave penicillin, cephalosporin, and carbapenem antibiotics. Using 3D visualizations of beta lactamase enzymes a correlation between zinc ions and spectrum of antibiotic resistance was found. For example, if an enzyme had two zinc ions instead of one it could inhibit broad spectrum antibiotics. In conclusion, the findings of this research presented here will be used to develop novel AMR tab at RCSB.org that will provide antimicrobial details of enzymes that were unavailable before. In the near future a phylogenetic tree of AMR related enzymes and classification details will be implemented. Support or Funding Information RCSB PDB is funded by a grant from the National Science Foundation (DBI‐1338415) , the National Institutes of Health , and the US Department of Energy . This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Salcedo, Priscilla M.↗

DOE OSTI2016

Comparative Genomics and Functional Analysis of Carbohydrate Utilization Networks in Unicyanobacterial Consortia Derived from Hypersaline Lake Microbial Mats

Hot Lake is a meromictic, hypersaline lake located in Washington state, USA. It harbors a phototrophic microbial mat community stable in dramatic seasonal environmental changes. The complexity of natural community hinders to analyze metabolic interactions between its members. To manage this obstacle, unicyanobacterial consortia (UCC) were sampled from the community and sequenced. UCC is a microbial community in which one cyanobacterium serves as the sole primary producer of carbon sources to several heterotrophic bacteria. Recently available metagenomes for two UCC revealed a shared set of 19 microbial operational taxonomic units (OTU), whose individual genomes were reconstructed by co‐assembly and abundance profile binning. We used this genomic data and a comparative genomics approach to infer carbohydrate utilization pathways and their transcription regulation. We scanned all UCC proteomes against the KEGG Orthology, Pfam, GO and SEED databases to reveal genes potentially involved in sugar utilization. Then we analyzed the genomic and functional contexts of gene loci encoding the obtained proteins and reconstructed the respective metabolic pathways and transcriptional regulons. As result, we observed catabolic pathways for 32 various carbohydrates and their derivatives. Distribution of the inferred catabolic pathways has highly mosaic structure across the UCC genomes. The largest number of pathways was observed in two representatives of the Halomonas genus (14–15 pathways) and one Rhodobacteriaceae sp. (15 pathways). In other UCC genomes, we found up to 10 carbohydrate utilization pathways per genome. However in 7 genomes we didn't observe any pathway. The most widely distributed pathways in UCC were the alpha‐glucosides and the DeLey‐Doudoroff galactose utilization pathways. Both were found in 7 genomes. Majority of pathways are distributed among 1–3 genomes. The reconstructed catabolic pathways were partially validated by phenotypic analyses of UCC community members that were isolated in a pure culture. In the Rhodobacteriaceae sp., we found a novel catabolic pathway that was proposed to be involved in mannoheptulose utilization. To our knowledge, the proposed mannoheptulose catabolic pathway is the first case of a pathway for heptose utilization and will require further experimental validation. The identified pathways are potentially regulated by local transcription factors (TFs) that are often co‐localized with the respective sugar catabolic gene loci. For 63 out of the 80 identified TFs we identified their cognate DNA binding motifs and reconstructed regulons using the bioinformatics approach. Support or Funding Information This research was supported by the Genomic Science Program (GSP), Office of Biological and Environmental Research (OBER), and U.S. Department of Energy (DOE) and is a contribution of the Pacific Northwest National Laboratory (PNNL) Foundational Scientific Focus Area.

Leyn, Semen A.↗

DOE OSTI2016

Multi‐omics of the Soil Microbiome

TheArctic is characterized by vast amounts of carbon stored in permafrost and isan important focal point for the study of climate change as increasing temperature may accelerate microbially‐mediated release of carbon stored inpermafrost into the atmosphere as CO 2 and CH 4 . Yet surprisingly, very little is known about the vulnerability of permafrost andresponse of microorganisms in the permafrost to their changing environment. To address these knowledge gaps we used omics technologiesto explore permafrost microbial communities, to reveal hitherto unknown soilmicrobial populations and to link them to specific soil biogeochemical processes. For these projects we employed an omics pipeline that includes metagenomics, metatranscriptomics and metaproteomics to determine the phylogenetic and functional gene compositions and their expression in complex, non‐sterile soil samples collected from the field at several locations in Alaska. These studies have presented several challenges due to the unprecedented large size of the datasets and the insufficiency of available bioinformatic tools & databases to analyze, correlate and integrate thedata. To address these challenges we developed a novel functional gene databaseand used different assembly and analysis algorithms to determine key featuresin the datasets. The data reveal the microbial diversity, composition andfunctional potential in permafrost and how the microbial communities respond toclimate change impacts. Support or Funding Information Microbiomes in Transition, LDRD, Pacific Northwest National Laboratories, Richland, Washington

Jansson, Janet K.↗

DOE OSTI2020

Precise mapping of new group I introns in tRNA genes

Bacterial tRNA have been found interrupted at various positions in the anticodon loop by group I introns, in four types. The primary bioinformatic tool for group I intron discovery is a covariance model that can identify conserved features in the catalytic core and can sometimes identify the typical uridine residue at the -1 position, preceding the 5-prime splice site, but cannot identify the typical guanidine residue at the omega position, preceding the 3-prime splice site, to achieve precise mapping. One approach to complete the automation of group I intron mapping is to focus instead on the exons, which is enabled by the regularity of tRNAs. We develop a software module, within a larger package (tFind) aimed at mapping bacterial tRNA and tmRNA genes precisely, that expands this list of four known classes of intron-interrupted tRNAs to 21 cases. A new covariance model for these introns is presented. The wobble base pair formed by the -1 uridine is considered a determinant of the 5-prime splice site, yet one reasonably large new type bears a cytidine nucleotide at that position.

Williams, Kelly P.↗

DOE OSTI2018

Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX)

ABSTRACT In this paper, we present peptide-pair encoding (PPE), a general-purpose probabilistic segmentation of protein sequences into commonly occurring variable-length sub-sequences. The idea of PPE segmentation is inspired by the byte-pair encoding (BPE) text compression algorithm, which has recently gained popularity in subword neural machine translation. We modify this algorithm by adding a sampling framework allowing for multiple ways of segmenting a sequence. PPE segmentation steps can be learned over a large set of protein sequences (Swiss-Prot) or even a domain-specific dataset and then applied to a set of unseen sequences. This representation can be widely used as the input to any downstream machine learning tasks in protein bioinformatics. In particular, here, we introduce this representation through protein motif discovery and protein sequence embedding. (i) DiMotif: we present DiMotif as an alignment-free discriminative motif discovery method and evaluate the method for finding protein motifs in three different settings: (1) comparison of DiMotif with two existing approaches on 20 distinct motif discovery problems which are experimentally verified, (2) classification-based approach for the motifs extracted for integrins, integrin-binding proteins, and biofilm formation, and (3) in sequence pattern searching for nuclear localization signal. The DiMotif, in general, obtained high recall scores, while having a comparable F1 score with other methods in the discovery of experimentally verified motifs. Having high recall suggests that the DiMotif can be used for short-list creation for further experimental investigations on motifs. In the classification-based evaluation, the extracted motifs could reliably detect the integrins, integrin-binding, and biofilm formation-related proteins on a reserved set of sequences with high F1 scores. (ii) ProtVecX: we extend k-mer based protein vector (ProtVec) embedding to variable-length protein embedding using PPE sub-sequences. We show that the new method of embedding can marginally outperform ProtVec in enzyme prediction as well as toxin prediction tasks. In addition, we conclude that the embeddings are beneficial in protein classification tasks when they are combined with raw k-mer features. Availability Implementations of our method will be available under the Apache 2 licence at http://llp.berkeley.edu/dimotif and http://llp.berkeley.edu/protvecx .

Asgari, Ehsaneddin↗

DOE OSTI2013

Improved exome prioritization of disease genes through cross-species phenotype comparison

Numerous new disease-gene associations have been identified by whole-exome sequencing studies in the last few years. However, many cases remain unsolved due to the sheer number of candidate variants remaining after common filtering strategies such as removing low quality and common variants and those deemed unlikely to be pathogenic (non-coding, not affecting splicing, synonymous or missense mutations annotated as non-pathogenic by prediction algorithms). The observation that each of our genomes contains about 100 genuine loss of function variants with ~20 genes completely inactivated makes identification of the causative mutation problematic when using these strategies alone. In some cases it may be possible to use multiple affected individuals, linkage data, identity-by-descent inference, de novo heterozygous mutations from trio analysis, or prior knowledge of affected pathways to narrow down to the causative variant. In cases where this is not possible or has proven unsuccessful we propose using the wealth of genotype to phenotype data that already exists from model organism studies to assess the potential impact of these exome variants. Here, we introduce PHenotypic Interpretation of Variants in Exomes (PHIVE), an algorithm that integrates the calculation of phenotype similarity between human diseases and genetically modified mouse models with evaluation of the variants according to allele frequency, pathogenicity and mode of inheritance approaches in our Exomiser tool. Large-scale validation of PHIVE analysis using 100,000 exomes containing known mutations demonstrated a substantial improvement (up to 54.1 fold) over purely variant-based (frequency and pathogenicity) methods with the correct gene recalled as the top hit in up to 83% of samples, corresponding to an area under the ROC curve of over 95%. We conclude that incorporation of phenotype data can play a vital role in translational bioinformatics and propose that exome sequencing projects should systematically capture clinical phenotypes to take advantage of the strategy presented here.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Accurate and complete genomes from metagenomes

Genomes are an integral component of the biological information about an organism; thus, the more complete the genome, the more informative it is. Historically, bacterial and archaeal genomes were reconstructed from pure (monoclonal) cultures, and the first reported sequences were manually curated to completion. However, the bottleneck imposed by the requirement for isolates precluded genomic insights for the vast majority of microbial life. Shotgun sequencing of microbial communities, referred to initially as community genomics and subsequently as genome-resolved metagenomics, can circumvent this limitation by obtaining metagenome-assembled genomes (MAGs); but gaps, local assembly errors, chimeras, and contamination by fragments from other genomes limit the value of these genomes. Here, we discuss genome curation to improve and, in some cases, achieve complete (circularized, no gaps) MAGs (CMAGs). To date, few CMAGs have been generated, although notably some are from very complex systems such as soil and sediment. Through analysis of about 7000 published complete bacterial isolate genomes, we verify the value of cumulative GC skew in combination with other metrics to establish bacterial genome sequence accuracy. The analysis of cumulative GC skew identified potential misassemblies in some reference genomes of isolated bacteria and the repeat sequences that likely gave rise to them. We discuss methods that could be implemented in bioinformatic approaches for curation to ensure that metabolic and evolutionary analyses can be based on very high-quality genomes.

59 BASIC BIOLOGICAL SCIENCES↗