Automated Prediction and Annotation of Small Open Reading Frames in Microbial Genomes

Targeted MYC2 stabilization confers citrus Huanglongbing resistance

Huanglongbing (HLB) is a devastating citrus disease. In this work, we report an HLB resistance regulatory circuit in Citrus composed of an E3 ubiquitin ligase, PUB21, and its substrate, the MYC2 transcription factor, which regulates jasmonate-mediated defense responses. A helitron insertion in the PUB21 promoter introduced multiple MYC2-binding cis-elements to create a regulatory circuit linking the PUB21 activity with MYC2 degradation. Ectopic expression of a natural dominant-negative PUB21 paralog discovered in distant Citrus relatives stabilized MYC2 and conferred resistance to HLB. Antiproteolysis peptides (APPs), identified by artificial intelligence, stabilized MYC2 by binding and inhibiting PUB21 activity. A 14-amino acid peptide, APP3-14, molecularly controlled HLB in greenhouse and field trials. This approach represents a strategy to combat uncultivable pathogens through targeted disease resistance protein stabilization.

Complementary Ribo-seq approaches map the translatome and provide a small protein census in the foodborne pathogen Campylobacter jejuni

In contrast to transcriptome maps, bacterial small protein (≤50-100 aa) coding landscapes, including overlapping genes, are poorly characterized. However, an emerging number of small proteins have crucial roles in bacterial physiology and virulence. Here, we present a Ribo-seq-based high-resolution translatome map for the major foodborne pathogen Campylobacter jejuni. Besides conventional Ribo-seq, we employed translation initiation site (TIS) profiling to map start codons and also developed a translation termination site (TTS) profiling approach, which revealed stop codons not apparent from the reference genome in virulence loci. Our integrated approach combined with independent validation expanded the small proteome by two-fold, including CioY, a new 34 aa component of the CioAB oxidase. Overall, our study generates a high-resolution annotation of the C. jejuni coding landscape, provided in an interactive browser, and showcases a strategy for applying integrated Ribo-seq to other species to enrich our understanding of small proteomes.

The hidden bacterial microproteome

Microproteins encoded by small open reading frames comprise the "dark matter" of proteomes. Although microproteins have been detected in diverse organisms from all three domains of life, many more remain to be identified, and only a few have been functionally characterized. In this comprehensive study of intergenic small open reading frames (ismORFs, 15-70 codons) in 5,668 bacterial genomes of the family Enterobacteriaceae, we identify 67,297 clusters of ismORFs subject to purifying selection. Expression of tagged Escherichia coli microproteins is detected for 11 of the 16 tested, validating the predictions. Although the ismORFs mainly code for hydrophobic, potentially transmembrane, unstructured, or minimally structured microproteins, some globular folds, oligomeric structures, and possible interactions with proteins encoded by neighboring genes are predicted. Complete information on the predicted microprotein families, including evidence of transcription and translation, and structure predictions are available as an easily searchable resource for investigation of microprotein functions.

Microproteins: emerging roles as antibiotics

Recent advances in computational prediction and experimental techniques have detected previously unknown microproteins, particularly in the human microbiome. These small proteins, produced by diverse microbial species, are emerging as promising candidates for new antibiotics.

The role of polypeptides encoded by ncRNAs in cancer

It was previously thought that ncRNA could not encode polypeptides, but recent reports have challenged this notion. As research into ncRNA progresses, it is increasingly clear that it serves roles beyond traditional mechanisms, playing significant regulatory roles in various diseases, notably cancer, which is responsible for 70% of human deaths. Numerous studies have highlighted the diverse regulatory mechanisms of ncRNA that are pivotal in cancer initiation and progression. The role of ncRNA-encoded polypeptides in cancer regulation has gained prominence. This article explores the newly identified regulatory functions of these polypeptides in three types of ncRNA-lncRNA, pri-miRNA, and circRNA. These polypeptides can interact with proteins, influence signaling pathways, enhance miRNA stability, and regulate cancer progression, malignancy, resistance, and other clinical challenges. Furthermore, we discuss the evolutionary significance of these polypeptides in the transition from RNA to protein, examining their emergence and conservation throughout evolution.

Mining human microbiomes reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally screened 444,054 previously reported putative small protein families from 1,773 human metagenomes for antimicrobial properties, identifying 323 candidates encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 70.5% displaying antimicrobial activity. As these compounds were different compared with previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergizing with each other, and modulating gut commensals, indicating a potential role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. Our report supports the existence of hundreds of antimicrobials in the human microbiome amenable to clinical translation.

Origins of Life: The Protein Folding Problem all over again?

How did specific useful protein sequences arise from simpler molecules at the origin of life? This seemingly needle-in-a-haystack problem has remarkably close resemblance to the old Protein Folding Problem, for which the solution is now known from statistical physics. Based on the logic that Origins must have come only after there was an operative evolution mechanism-which selects on phenotype, not genotype-we give a perspective that proteins and their folding processes are likely to have been the primary driver of the early stages of the origin of life.

Dual quorum-sensing control of purine biosynthesis drives pathogenic fitness of Enterococcus faecalis

Enterococcus faecalis is a resident of the human gut, though upon translocation to the blood or body tissues, it can be pathogenic. Here we discover and characterize two peptide-based quorum-sensing systems that transcriptionally modulate de novo purine biosynthesis in E. faecalis. Using a comparative genomic analysis, we find that most enterococcal species do not encode this system; E. moraviensis, E. haemoperoxidus and E. caccae, three species that are closely related to E. faecalis, encode one of the two systems, and only E. faecalis encodes both systems. We show that these systems are important for the intracellular survival of E. faecalis within macrophages and for the fitness of E. faecalis in a murine wound infection model. Taken together, we combine comparative genomics, microbiological, bacterial genetics, transcriptomics, targeted proteomics and animal model experiments to describe a paired quorum sensing mechanism that directly influences central metabolism and impacts the pathogenicity of E. faecalis.

PSPI: A deep learning approach for prokaryotic small protein identification

Small Proteins (SPs) are pivotal in various cellular functions such as immunity, defense, and communication. Despite their significance, identifying them is still in its infancy. Existing computational tools are tailored to specific eukaryotic species, leaving only a few options for SP identification in prokaryotes. In addition, these existing tools still have suboptimal performance in SP identification. To fill this gap, we introduce PSPI, a deep learning-based approach designed specifically for predicting prokaryotic SPs. We showed that PSPI had a high accuracy in predicting generalized sets of prokaryotic SPs and sets specific to the human metagenome. Compared with three existing tools, PSPI was faster and showed greater precision, sensitivity, and specificity not only for prokaryotic SPs but also for eukaryotic ones. We also observed that the incorporation of (n, k)-mers greatly enhances the performance of PSPI, suggesting that many SPs may contain short linear motifs. The PSPI tool, which is freely available at https://www.cs.ucf.edu/∼xiaoman/tools/PSPI/, will be useful for studying SPs as a tool for identifying prokaryotic SPs and it can be trained to identify other types of SPs as well.

Challenges in computational discovery of bioactive peptides in 'omics data

Peptides have a plethora of activities in biological systems that can potentially be exploited biotechnologically. Several peptides are used clinically, as well as in industry and agriculture. The increase in available 'omics data has recently provided a large opportunity for mining novel enzymes, biosynthetic gene clusters, and molecules. While these data primarily consist of DNA sequences, other types of data provide important complementary information. Due to their size, the approaches proven successful at discovering novel proteins of canonical size cannot be naïvely applied to the discovery of peptides. Peptides can be encoded directly in the genome as short open reading frames (smORFs), or they can be derived from larger proteins by proteolysis. Both of these peptide classes pose challenges as simple methods for their prediction result in large numbers of false positives. Similarly, functional annotation of larger proteins, traditionally based on sequence similarity to infer orthology and then transferring functions between characterized proteins and uncharacterized ones, cannot be applied for short sequences. The use of these techniques is much more limited and alternative approaches based on machine learning are used instead. Here, we review the limitations of traditional methods as well as the alternative methods that have recently been developed for discovering novel bioactive peptides with a focus on prokaryotic genomes and metagenomes.

A survey of experimental and computational identification of small proteins

Small proteins (SPs) are typically characterized as eukaryotic proteins shorter than 100 amino acids and prokaryotic proteins shorter than 50 amino acids. Historically, they were disregarded because of the arbitrary size thresholds to define proteins. However, recent research has revealed the existence of many SPs and their crucial roles. Despite this, the identification of SPs and the elucidation of their functions are still in their infancy. To pave the way for future SP studies, we briefly introduce the limitations and advancements in experimental techniques for SP identification. We then provide an overview of available computational tools for SP identification, their constraints, and their evaluation. Additionally, we highlight existing resources for SP research. This survey aims to initiate further exploration into SPs and encourage the development of more sophisticated computational tools for SP identification in prokaryotes and microbiomes.

ProTInSeq: transposon insertion tracking by ultra-deep DNA sequencing to identify translated large and small ORFs

Identifying open reading frames (ORFs) being translated is not a trivial task. ProTInSeq is a technique designed to characterize proteomes by sequencing transposon insertions engineered to express a selection marker when they occur in-frame within a protein-coding gene. In the bacterium Mycoplasma pneumoniae, ProTInSeq identifies 83% of its annotated proteins, along with 5 proteins and 153 small ORF-encoded proteins (SEPs; ≤100 aa) that were not previously annotated. Moreover, ProTInSeq can be utilized for detecting translational noise, as well as for relative quantification and transmembrane topology estimation of fitness and non-essential proteins. By integrating various identification approaches, the number of initially annotated SEPs in this bacterium increases from 27 to 329, with a quarter of them predicted to possess antimicrobial potential. Herein, we describe a methodology complementary to Ribo-Seq and mass spectroscopy that can identify SEPs while providing other insights in a proteome with a flexible and cost-effective DNA ultra-deep sequencing approach.

The Cryptic Bacterial Microproteome

Microproteins encoded by small open reading frames (smORFs) comprise the "dark matter" of proteomes. Although functional microproteins were identified in diverse organisms from all three domains of life, bacterial smORFs remain poorly characterized. In this comprehensive study of intergenic smORFs (ismORFs, 15-70 codons) in 5,668 bacterial genomes of the family Enterobacteriaceae, we identified 67,297 clusters of ismORFs subject to purifying selection. The ismORFs mainly code for hydrophobic, potentially transmembrane, unstructured, or minimally structured microproteins. Using AlphaFold Multimer, we predicted interactions of some of the predicted microproteins encoded by transcribed ismORFs with proteins encoded by neighboring genes, revealing the potential of microproteins to regulate the activity of various proteins, particularly, under stress. We compiled a catalog of predicted microprotein families with different levels of evidence from synteny analysis, structure prediction, and transcription and translation data. This study offers a resource for investigation of biological functions of microproteins.

Microproteins-Discovery, structure, and function

Advances in proteogenomic technologies have revealed hundreds to thousands of translated small open reading frames (sORFs) that encode microproteins in genomes across evolutionary space. While many microproteins have now been shown to play critical roles in biology and human disease, a majority of recently identified microproteins have little or no experimental evidence regarding their functionality. Computational tools have some limitations for analysis of short, poorly conserved microprotein sequences, so additional approaches are needed to determine the role of each member of this recently discovered polypeptide class. A currently underexplored avenue in the study of microproteins is structure prediction and determination, which delivers a depth of functional information. In this review, we provide a brief overview of microprotein discovery methods, then examine examples of microprotein structures (and, conversely, intrinsic disorder) that have been experimentally determined using crystallography, cryo-electron microscopy, and NMR, which provide insight into their molecular functions and mechanisms. Additionally, we discuss examples of predicted microprotein structures that have provided insight or context regarding their function. Analysis of microprotein structure at the angstrom level, and confirmation of predicted structures, therefore, has potential to identify translated microproteins that are of biological importance and to provide molecular mechanism for their in vivo roles.

Exploring microproteins from various model organisms using the mip-mining database

Microproteins, prevalent across all kingdoms of life, play a crucial role in cell physiology and human health. Although global gene transcription is widely explored and abundantly available, our understanding of microprotein functions using transcriptome data is still limited. To mitigate this problem, we present a database, Mip-mining ( https://weilab.sjtu.edu.cn/mipmining/ ), underpinned by high-quality RNA-sequencing data exclusively aimed at analyzing microprotein functions. The Mip-mining hosts 336 sets of high-quality transcriptome data from 8626 samples and nine representative living organisms, including microorganisms, plants, animals, and humans, in our Mip-mining database. Our database specifically provides a focus on a range of diseases and environmental stress conditions, taking into account chemical, physical, biological, and diseases-related stresses. Comparatively, our platform enables customized analysis by inputting desired data sets with self-determined cutoff values. The practicality of Mip-mining is demonstrated by identifying essential microproteins in different species and revealing the importance of ATP15 in the acetic acid stress tolerance of budding yeast. We believe that Mip-mining will facilitate a greater understanding and application of microproteins in biotechnology. Moreover, it will be beneficial for designing therapeutic strategies under various biological conditions.

Human gut metagenomic mining reveals an untapped source of peptide antibiotics

Drug-resistant bacteria are outpacing traditional antibiotic discovery efforts. Here, we computationally mined 444,054 families of putative small proteins from 1,773 human gut metagenomes, identifying 323 peptide antibiotics encoded in small open reading frames (smORFs). To test our computational predictions, 78 peptides were synthesized and screened for antimicrobial activity in vitro, with 59% displaying activity against either pathogens or commensals. Since these peptides were unique compared to previously reported antimicrobial peptides, we termed them smORF-encoded peptides (SEPs). SEPs killed bacteria by targeting their membrane, synergized with each other, and modulated gut commensals, indicating that they may play a role in reconfiguring microbiome communities in addition to counteracting pathogens. The lead candidates were anti-infective in both murine skin abscess and deep thigh infection models. Notably, prevotellin-2 from Prevotella copri presented activity comparable to the commonly used antibiotic polymyxin B. We report the discovery of hundreds of peptide sequences in the human gut.

Identification of small open reading frames in plant lncRNA using class-imbalance learning

Recently, small open reading frames (sORFs) in long noncoding RNA (lncRNA) have been demonstrated to encode small peptides that can help study the mechanisms of growth and development in organisms. Since machine learning-based computational methods are less costly compared with biological experiments, they can be used to identify sORFs and provide a basis for biological experiments. However, few computational methods and data resources have been exploited for identifying sORFs in plant lncRNA. Besides, machine learning models produce underperforming classifiers when faced with a class-imbalance problem. In this study, an alternative method called SMOTE based on weighted cosine distance (WCDSMOTE) which enables interaction with feature selection is put forward to synthesize minority class samples and weighted edited nearest neighbor (WENN) is applied to clean up majority class samples, thus, hybrid sampling WCDSMOTE-ENN is proposed to deal with imbalanced datasets with the multi-angle feature. A heterogeneous classifier ensemble is introduced to complete the classification task. Therefore, a novel computational method that is based on class-imbalance learning to identify the sORFs with coding potential in plant lncRNA (sORFplnc) is presented. Experimental results manifest that sORFplnc outperforms existing computational methods in identifying sORFs with coding potential. We anticipate that the proposed work can be a reference for relevant research and contribute to agriculture and biomedicine.

Characterization and Spatial Mapping of the Human Gut Metasecretome

Bacterially secreted proteins play an important role in microbial physiology and ecology in many environments, including the mammalian gut. While gut microbes have been extensively studied over the past decades, little is known about the proteins that they secrete into the gastrointestinal tract. In this study, we developed and applied a computational pipeline to a comprehensive catalog of human-associated metagenome-assembled genomes in order to predict and analyze the bacterial metasecretome of the human gut, i.e., the collection of proteins secreted out of the cytoplasm by human gut bacteria. We identified the presence of large and diverse families of secreted carbohydrate-active enzymes and assessed their phylogenetic distributions across different taxonomic groups, which revealed an enrichment in Bacteroidetes and Verrucomicrobia. By mapping secreted proteins to available metagenomic data from endoscopic sampling of the human gastrointestinal tract, we specifically pinpointed regions in the upper and lower intestinal tract along the lumen and mucosa where specific glycosidases are secreted by resident microbes. The metasecretome analyzed in this study constitutes the most comprehensive list of secreted proteins produced by human gut bacteria reported to date and serves as a useful resource for the microbiome research community. IMPORTANCE Bacterially secreted proteins are necessary for the proper functioning of bacterial cells and communities. Secreted proteins provide bacterial cells with the ability to harvest resources from the exterior, import these resources into the cell, and signal to other bacteria. In the human gut microbiome, these actions impact host health and allow the maintenance of a healthy gut bacterial community. We utilized computational tools to identify the major components of human gut bacterially secreted proteins and determined their spatial distribution in the gastrointestinal tract. Our analysis of human gut bacterial secreted proteins will allow a better understanding of the impact of gut bacteria on human health and represents a step toward identifying new protein functions with interesting applications in biomedicine and industry.

Novel insights into noncanonical open reading frames in cancer

With technological advances, previously neglected noncanonical open reading frames (nORFs) are drawing ever-increasing attention. However, the translation potential of numerous putative nORFs remains elusive, and the functions of noncanonical peptides have not been systemically summarized. Moreover, the relationship between noncanonical peptides and their counterpart protein or RNA products remains elusive and the clinical implementation of noncanonical peptides has not been explored. In this review, we highlight how recent technological advances such as ribosome profiling, bioinformatics approaches and CRISPR/Cas9 facilitate the research of noncanonical peptides. We delineate the features of each nORF category and the evolutionary process underneath the nORFs. Most importantly, we summarize the diversified functions of noncanonical peptides in cancer based on their subcellular location, which reflect their extensive participation in key pathways and essential cellular activities in cancer cells. Meanwhile, the equilibrium between noncanonical peptides and their corresponding transcripts or counterpart products may be dysregulated under pathological states, which is essential for their roles in cancer. Lastly, we explore their underestimated potential in clinical application as diagnostic biomarkers and treatment targets against cancer.

Thousands of small, novel genes predicted in global phage genomes

Small genes (<150 nucleotides) have been systematically overlooked in phage genomes. We employ a large-scale comparative genomics approach to predict >40,000 small-gene families in ∼2.3 million phage genome contigs. We find that small genes in phage genomes are approximately 3-fold more prevalent than in host prokaryotic genomes. Our approach enriches for small genes that are translated in microbiomes, suggesting the small genes identified are coding. More than 9,000 families encode potentially secreted or transmembrane proteins, more than 5,000 families encode predicted anti-CRISPR proteins, and more than 500 families encode predicted antimicrobial proteins. By combining homology and genomic-neighborhood analyses, we reveal substantial novelty and diversity within phage biology, including small phage genes found in multiple host phyla, small genes encoding proteins that play essential roles in host infection, and small genes that share genomic neighborhoods and whose encoded proteins may share related functions.

RiboReport - benchmarking tools for ribosome profiling-based identification of open reading frames in bacteria

Small proteins encoded by short open reading frames (ORFs) with 50 codons or fewer are emerging as an important class of cellular macromolecules in diverse organisms. However, they often evade detection by proteomics or in silico methods. Ribosome profiling (Ribo-seq) has revealed widespread translation in genomic regions previously thought to be non-coding, driving the development of ORF detection tools using Ribo-seq data. However, only a handful of tools have been designed for bacteria, and these have not yet been systematically compared. Here, we aimed to identify tools that use Ribo-seq data to correctly determine the translational status of annotated bacterial ORFs and also discover novel translated regions with high sensitivity. To this end, we generated a large set of annotated ORFs from four diverse bacterial organisms, manually labeled for their translation status based on Ribo-seq data, which are available for future benchmarking studies. This set was used to investigate the predictive performance of seven Ribo-seq-based ORF detection tools (REPARATION_blast, DeepRibo, Ribo-TISH, PRICE, smORFer, ribotricer and SPECtre), as well as IRSOM, which uses coding potential and RNA-seq coverage only. DeepRibo and REPARATION_blast robustly predicted translated ORFs, including sORFs, with no significant difference for ORFs in close proximity to other genes versus stand-alone genes. However, no tool predicted a set of novel, experimentally verified sORFs with high sensitivity. Start codon predictions with smORFer show the value of initiation site profiling data to further improve the sensitivity of ORF prediction tools in bacteria. Overall, we find that bacterial tools perform well for sORF detection, although there is potential for improving their performance, applicability, usability and reproducibility.

Engineering, decoding and systems-level characterization of chimpanzee cytomegalovirus

The chimpanzee cytomegalovirus (CCMV) is the closest relative of human CMV (HCMV). Because of the high conservation between these two species and the ability of human cells to fully support CCMV replication, CCMV holds great potential as a model system for HCMV. To make the CCMV genome available for precise and rapid gene manipulation techniques, we captured the genomic DNA of CCMV strain Heberling as a bacterial artificial chromosome (BAC). Selected BAC clones were reconstituted to infectious viruses, growing to similar high titers as parental CCMV. DNA sequencing confirmed the integrity of our clones and led to the identification of two polymorphic loci and a deletion-prone region within the CCMV genome. To re-evaluate the CCMV coding potential, we analyzed the viral transcriptome and proteome and identified several novel ORFs, splice variants, and regulatory RNAs. We further characterized the dynamics of CCMV gene expression and found that viral proteins cluster into five distinct temporal classes. In addition, our datasets revealed that the host response to CCMV infection and the de-regulation of cellular pathways are in line with known hallmarks of HCMV infection. In a first functional experiment, we investigated a proposed frameshift mutation in UL128 that was suspected to restrict CCMV's cell tropism. In fact, repair of this frameshift re-established productive CCMV infection in endothelial and epithelial cells, expanding the options of CCMV as an infection model. Thus, BAC-cloned CCMV can serve as a powerful tool for systematic approaches in comparative functional genomics, exploiting the close phylogenetic relationship between CCMV and HCMV.

Bakta: rapid and standardized annotation of bacterial genomes via alignment-free sequence identification

Command-line annotation software tools have continuously gained popularity compared to centralized online services due to the worldwide increase of sequenced bacterial genomes. However, results of existing command-line software pipelines heavily depend on taxon-specific databases or sufficiently well annotated reference genomes. Here, we introduce Bakta, a new command-line software tool for the robust, taxon-independent, thorough and, nonetheless, fast annotation of bacterial genomes. Bakta conducts a comprehensive annotation workflow including the detection of small proteins taking into account replicon metadata. The annotation of coding sequences is accelerated via an alignment-free sequence identification approach that in addition facilitates the precise assignment of public database cross-references. Annotation results are exported in GFF3 and International Nucleotide Sequence Database Collaboration (INSDC)-compliant flat files, as well as comprehensive JSON files, facilitating automated downstream analysis. We compared Bakta to other rapid contemporary command-line annotation software tools in both targeted and taxonomically broad benchmarks including isolates and metagenomic-assembled genomes. We demonstrated that Bakta outperforms other tools in terms of functional annotations, the assignment of functional categories and database cross-references, whilst providing comparable wall-clock runtimes. Bakta is implemented in Python 3 and runs on MacOS and Linux systems. It is freely available under a GPLv3 license at https://github.com/oschwengers/bakta. An accompanying web version is available at https://bakta.computational.bio.

Simultaneous ribosome profiling of hundreds of microbes from the human microbiome

Ribosome profiling enables sequencing of ribosome-bound fragments of RNA, revealing which transcripts are being translated as well as the position of ribosomes along mRNAs. Although ribosome profiling has been applied to cultured bacterial isolates, its application to uncultured, mixed communities has been challenging. We present MetaRibo-Seq, a protocol that enables the application of ribosome profiling directly to the human fecal microbiome. MetaRibo-Seq is a benchmarked method that includes several modifications to existing ribosome profiling protocols, specifically addressing challenges involving fecal sample storage, purity and input requirements. We also provide a computational workflow to quality control and trim reads, de novo assemble a reference metagenome with metagenomic reads, align MetaRibo-Seq reads to the reference, and assess MetaRibo-Seq library quality ( https://github.com/bhattlab/bhattlab_workflows/tree/master/metariboseq ). This MetaRibo-Seq protocol enables researchers in standard molecular biology laboratories to study translation in the fecal microbiome in ~5 d.

Comparative genomics identifies thousands of candidate structured RNAs in human microbiomes

BACKGROUND

Structured RNAs play varied bioregulatory roles within microbes. To date, hundreds of candidate structured RNAs have been predicted using informatic approaches that search for motif structures in genomic sequence data. The human microbiome contains thousands of species and strains of microbes. Yet, much of the metagenomic data from the human microbiome remains unmined for structured RNA motifs primarily due to computational limitations.

RESULTS

We sought to apply a large-scale, comparative genomics approach to these organisms to identify candidate structured RNAs. With a carefully constructed, though computationally intensive automated analysis, we identify 3161 conserved candidate structured RNAs in intergenic regions, as well as 2022 additional candidate structured RNAs that may overlap coding regions. We validate the RNA expression of 177 of these candidate structures by analyzing small fragment RNA-seq data from four human fecal samples.

CONCLUSIONS

This approach identifies a wide variety of candidate structured RNAs, including tmRNAs, antitoxins, and likely ribosome protein leaders, from a wide variety of taxa. Overall, our pipeline enables conservative predictions of thousands of novel candidate structured RNAs from human microbiomes.