Crowdsourced benchmarking of taxonomic metagenome profilers: lessons learned from the sbv IMPROVER Microbiomics challenge.

BACKGROUND: Selection of optimal computational strategies for analyzing metagenomics data is a decisive step in determining the microbial composition of a sample, and this procedure is complex because of the numerous tools currently available. The aim of this research was to summarize the results of crowdsourced sbv IMPROVER Microbiomics Challenge designed to evaluate the performance of off-the-shelf metagenomics software as well as to investigate the robustness of these results by the extended post-challenge analysis. In total 21 off-the-shelf taxonomic metagenome profiling pipelines were benchmarked for their capacity to identify the microbiome composition at various taxon levels across 104 shotgun metagenomics datasets of bacterial genomes (representative of various microbiome samples) from public databases. Performance was determined by comparing predicted taxonomy profiles with the gold standard. RESULTS: Most taxonomic profilers performed homogeneously well at the phylum level but generated intermediate and heterogeneous scores at the genus and species levels, respectively. kmer-based pipelines using Kraken with and without Bracken or using CLARK-S performed best overall, but they exhibited lower precision than the two marker-gene-based methods MetaPhlAn and mOTU. Filtering out the 1% least abundance species-which were not reliably predicted-helped increase the performance of most profilers by increasing precision but at the cost of recall. However, the use of adaptive filtering thresholds determined from the sample's Shannon index increased the performance of most kmer-based profilers while mitigating the tradeoff between precision and recall. CONCLUSIONS: kmer-based metagenomic pipelines using Kraken/Bracken or CLARK-S performed most robustly across a large variety of microbiome datasets. Removing non-reliably predicted low-abundance species by using diversity-dependent adaptive filtering thresholds further enhanced the performance of these tools. This work demonstrates the applicability of computational pipelines for accurately determining taxonomic profiles in clinical and environmental contexts and exemplifies the power of crowdsourcing for unbiased evaluation.

Shared data science infrastructure for genomics data.

BACKGROUND: Creating a scalable computational infrastructure to analyze the wealth of information contained in data repositories is difficult due to significant barriers in organizing, extracting and analyzing relevant data. Shared data science infrastructures like Boag is needed to efficiently process and parse data contained in large data repositories. The main features of Boag are inspired from existing languages for data intensive computing and can easily integrate data from biological data repositories. RESULTS: As a proof of concept, Boa for genomics, Boag, has been implemented to analyze RefSeq's 153,848 annotation (GFF) and assembly (FASTA) file metadata. Boag provides a massive improvement from existing solutions like Python and MongoDB, by utilizing a domain-specific language that uses Hadoop infrastructure for a smaller storage footprint that scales well and requires fewer lines of code. We execute scripts through Boag to answer questions about the genomes in RefSeq. We identify the largest and smallest genomes deposited, explore exon frequencies for assemblies after 2016, identify the most commonly used bacterial genome assembly program, and address how animal genome assemblies have improved since 2016. Boag databases provide a significant reduction in required storage of the raw data and a significant speed up in its ability to query large datasets due to automated parallelization and distribution of Hadoop infrastructure during computations. CONCLUSIONS: In order to keep pace with our ability to produce biological data, innovative methods are required. The Shared Data Science Infrastructure, Boag, provides researchers a greater access to researchers to efficiently explore data in new ways. We demonstrate the potential of a the domain specific language Boag using the RefSeq database to explore how deposited genome assemblies and annotations are changing over time. This is a small example of how Boag could be used with large biological datasets.

The genome of the soybean cyst nematode (Heterodera glycines) reveals complex patterns of duplications involved in the evolution of parasitism genes.

BACKGROUND: Heterodera glycines, commonly referred to as the soybean cyst nematode (SCN), is an obligatory and sedentary plant parasite that causes over a billion-dollar yield loss to soybean production annually. Although there are genetic determinants that render soybean plants resistant to certain nematode genotypes, resistant soybean cultivars are increasingly ineffective because their multi-year usage has selected for virulent H. glycines populations. The parasitic success of H. glycines relies on the comprehensive re-engineering of an infection site into a syncytium, as well as the long-term suppression of host defense to ensure syncytial viability. At the forefront of these complex molecular interactions are effectors, the proteins secreted by H. glycines into host root tissues. The mechanisms of effector acquisition, diversification, and selection need to be understood before effective control strategies can be developed, but the lack of an annotated genome has been a major roadblock. RESULTS: Here, we use PacBio long-read technology to assemble a H. glycines genome of 738 contigs into 123 Mb with annotations for 29,769 genes. The genome contains significant numbers of repeats (34%), tandem duplicates (18.7 Mb), and horizontal gene transfer events (151 genes). A large number of putative effectors (431 genes) were identified in the genome, many of which were found in transposons. CONCLUSIONS: This advance provides a glimpse into the host and parasite interplay by revealing a diversity of mechanisms that give rise to virulence genes in the soybean cyst nematode, including: tandem duplications containing over a fifth of the total gene count, virulence genes hitchhiking in transposons, and 107 horizontal gene transfers not reported in other plant parasitic nematodes thus far. Through extensive characterization of the H. glycines genome, we provide new insights into H. glycines biology and shed light onto the mystery underlying complex host-parasite interactions. This genome sequence is an important prerequisite to enable work towards generating new resistance or control measures against H. glycines.

Response to Persistent ER Stress in Plants: A Multiphasic Process That Transitions Cells from Prosurvival Activities to Cell Death.

The unfolded protein response (UPR) is a highly conserved response that protects plants from adverse environmental conditions. The UPR is elicited by endoplasmic reticulum (ER) stress, in which unfolded and misfolded proteins accumulate within the ER. Here, we induced the UPR in maize (Zea mays) seedlings to characterize the molecular events that occur over time during persistent ER stress. We found that a multiphasic program of gene expression was interwoven among other cellular events, including the induction of autophagy. One of the earliest phases involved the degradation by regulated IRE1-dependent RNA degradation (RIDD) of RNA transcripts derived from a family of peroxidase genes. RIDD resulted from the activation of the promiscuous ribonuclease activity of ZmIRE1 that attacks the mRNAs of secreted proteins. This was followed by an upsurge in expression of the canonical UPR genes indirectly driven by ZmIRE1 due to its splicing of Zmbzip60 mRNA to make an active transcription factor that directly upregulates many of the UPR genes. At the peak of UPR gene expression, a global wave of RNA processing led to the production of many aberrant UPR gene transcripts, likely tempering the ER stress response. During later stages of ER stress, ZmIRE1's activity declined, as did the expression of survival modulating genes, Bax inhibitor1 and Bcl-2-associated athanogene7, amid a rising tide of cell death. Thus, in response to persistent ER stress, maize seedlings embark on a course of gene expression and cellular events progressing from adaptive responses to cell death.

Computational Prediction of RNA-Protein Interactions.

Experimental methods for identifying protein(s) bound by a specific promoter-associated RNA (paRNA) of interest can be expensive, difficult, and time-consuming. This chapter describes a general computational framework for identifying potential binding partners in RNA-protein complexes or RNA-protein interaction networks. Protocols for using three web-based tools to predict RNA-protein interaction partners are outlined. Also, tables listing additional webservers and software tools for predicting RNA-protein interactions, as well as databases that contain valuable information about known RNA-protein complexes and recognition sites for RNA-binding proteins, are provided. Although only one of the tools described, lncPro, was designed expressly to identify proteins that bind long noncoding RNAs (including paRNAs), all three approaches can be applied to predict potential binding partners for both coding and noncoding RNAs (ncRNAs).

Methods for Genome-Wide Methylome Profiling of Campylobacter jejuni.

Methylation has a profound role in the regulation of numerous biological processes in bacteria including virulence. The study of methylation in bacteria has greatly advanced thanks to next-generation sequencing technologies. These technologies have expedited the process of uncovering unique features of many bacterial methylomes such as characterizing previously uncharacterized methyltransferases, cataloging genome-wide DNA methylations in bacteria, identifying the frequency of methylation at particular genomic loci, and revealing regulatory roles of methylation in the biology of various bacterial species. For instance, methylation has been cited as a potential source for the pathogenicity differences observed in C. jejuni strains with syntenic genomes as seen in recent publications. Here, we describe the methodology for the use of Pacific Biosciences' single molecule real-time (SMRT) sequencing for detecting methylation patterns in C. jejuni and bioinformatics tools to profile its methylome.

A Plasmodium-like virulence effector of the soybean cyst nematode suppresses plant innate immunity.

Heterodera glycines, the soybean cyst nematode, delivers effector proteins into soybean roots to initiate and maintain an obligate parasitic relationship. HgGLAND18 encodes a candidate H. glycines effector and is expressed throughout the infection process. We used a combination of molecular, genetic, bioinformatic and phylogenetic analyses to determine the role of HgGLAND18 during H. glycines infection. HgGLAND18 is necessary for pathogenicity in compatible interactions with soybean. The encoded effector strongly suppresses both basal and hypersensitive cell death innate immune responses, and immunosuppression requires the presence and coordination between multiple protein domains. The N-terminal domain in HgGLAND18 contains unique sequence similarity to domains of an immunosuppressive effector of Plasmodium spp., the malaria parasites. The Plasmodium effector domains functionally complement the loss of the N-terminal domain from HgGLAND18. In-depth sequence searches and phylogenetic analyses demonstrate convergent evolution between effectors from divergent parasites of plants and animals as the cause of sequence and functional similarity.

A MOTIF-BASED METHOD FOR PREDICTING INTERFACIAL RESIDUES IN BOTH THE RNA AND PROTEIN COMPONENTS OF PROTEIN-RNA COMPLEXES.

Efforts to predict interfacial residues in protein-RNA complexes have largely focused on predicting RNA-binding residues in proteins. Computational methods for predicting protein-binding residues in RNA sequences, however, are a problem that has received relatively little attention to date. Although the value of sequence motifs for classifying and annotating protein sequences is well established, sequence motifs have not been widely applied to predicting interfacial residues in macromolecular complexes. Here, we propose a novel sequence motif-based method for "partner-specific" interfacial residue prediction. Given a specific protein-RNA pair, the goal is to simultaneously predict RNA binding residues in the protein sequence and protein-binding residues in the RNA sequence. In 5-fold cross validation experiments, our method, PS-PRIP, achieved 92% Specificity and 61% Sensitivity, with a Matthews correlation coefficient (MCC) of 0.58 in predicting RNA-binding sites in proteins. The method achieved 69% Specificity and 75% Sensitivity, but with a low MCC of 0.13 in predicting protein binding sites in RNAs. Similar performance results were obtained when PS-PRIP was tested on two independent "blind" datasets of experimentally validated protein- RNA interactions, suggesting the method should be widely applicable and valuable for identifying potential interfacial residues in protein-RNA complexes for which structural information is not available. The PS-PRIP webserver and datasets are available at: http://pridb.gdcb.iastate.edu/PSPRIP/.

Transcriptome profiling of soybean (Glycine max) roots challenged with pathogenic and non-pathogenic isolates of Fusarium oxysporum.

BACKGROUND: Fusarium oxysporum is one of the most common fungal pathogens causing soybean root rot and seedling blight in U.S.A. In a recent study, significant variation in aggressiveness was observed among isolates of F. oxysporum collected from roots in Iowa, ranging from highly pathogenic to weakly or non-pathogenic isolates. RESULTS: We used RNA-seq analysis to investigate the molecular aspects of the interactions of a partially resistant soybean genotype with non-pathogenic/pathogenic isolates of F. oxysporum at 72 and 96 h post inoculation (hpi). Markedly different gene expression profiles were observed in response to the two isolates. A peak of highly differentially expressed genes (HDEGs) was triggered at 72 hpi in soybean roots and the number of HDEGs was about eight times higher in response to the pathogenic isolate compared to the non-pathogenic one (1,659 vs. 203 HDEGs, respectively). Furthermore, the magnitude of induction was much greater in response to the pathogenic isolate. This response included a stronger activation of defense-related genes, transcription factors, and genes involved in ethylene biosynthesis, secondary and sugar metabolism. CONCLUSIONS: The obtained data provide an important insight into the transcriptional responses of soybean-F. oxysporum interactions and illustrate the more drastic changes in the host transcriptome in response to the pathogenic isolate. These results may be useful in the developing new methods of broadening resistance of soybean to F. oxysporum, including the over-expression of key soybean genes.

Transcriptional analysis of phloem-associated cells of potato.

BACKGROUND: Numerous signal molecules, including proteins and mRNAs, are transported through the architecture of plants via the vascular system. As the connection between leaves and other organs, the petiole and stem are especially important in their transport function, which is carried out by the phloem and xylem, especially by the sieve elements in the phloem system. The phloem is an important conduit for transporting photosynthate and signal molecules like metabolites, proteins, small RNAs, and full-length mRNAs. Phloem sap has been used as an unadulterated source to profile phloem proteins and RNAs, but unfortunately, pure phloem sap cannot be obtained in most plant species. RESULTS: Here we make use of laser capture microdissection (LCM) and RNA-seq for an in-depth transcriptional profile of phloem-associated cells of both petioles and stems of potato. To expedite our analysis, we have taken advantage of the potato genome that has recently been fully sequenced and annotated. Out of the 27 k transcripts assembled that we identified, approximately 15 k were present in phloem-associated cells of petiole and stem with greater than ten reads. Among these genes, roughly 10 k are affected by photoperiod. Several RNAs from this day length-regulated group are also abundant in phloem cells of petioles and encode for proteins involved in signaling or transcriptional control. Approximately 22 % of the transcripts in phloem cells contained at least one binding motif for Pumilio, Nova, or polypyrimidine tract-binding proteins in their downstream sequences. Highlighting the predominance of binding processes identified in the gene ontology analysis of active genes from phloem cells, 78 % of the 464 RNA-binding proteins present in the potato genome were detected in our phloem transcriptome. CONCLUSIONS: As a reasonable alternative when phloem sap collection is not possible, LCM can be used to isolate RNA from specific cell types, and along with RNA-seq, provides practical access to expression profiles of phloem tissue. The combination of these techniques provides a useful approach to the study of phloem and a comprehensive picture of the mechanisms associated with long-distance signaling. The data presented here provide valuable insights into potentially novel phloem-mobile mRNAs and phloem-associated RNA-binding proteins.

A comparative analysis of methylome profiles of Campylobacter jejuni sheep abortion isolate and gastroenteric strains using PacBio data.

Campylobacter jejuni is a leading cause of human gastrointestinal disease and small ruminant abortions in the United States. The recent emergence of a highly virulent, tetracycline-resistant C. jejuni subsp. jejuni sheep abortion clone (clone SA) in the United States, and that strain's association with human disease, has resulted in a heightened awareness of the zoonotic potential of this organism. Pacific Biosciences' Single Molecule, Real-Time sequencing technology was used to explore the variation in the genome-wide methylation patterns of the abortifacient clone SA (IA3902) and phenotypically distinct gastrointestinal-specific C. jejuni strains (NCTC 11168 and 81-176). Several notable differences were discovered that distinguished the methylome of IA3902 from that of 11168 and 81-176: identification of motifs novel to IA3902, genome-specific hypo- and hypermethylated regions, strain level variability in genes methylated, and differences in the types of methylation motifs present in each strain. These observations suggest a possible role of methylation in the contrasting disease presentations of these three C. jejuni strains. In addition, the methylation profiles between IA3902 and a luxS mutant were explored to determine if variations in methylation patterns could be identified that might explain the role of LuxS-dependent methyl recycling in IA3902 abortifacient potential.

Predicting RNA-protein interactions using only sequence information.

BACKGROUND: RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions. RESULTS: We propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a Support Vector Machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens. CONCLUSIONS: Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using only sequence-derived information. RPISeq offers an inexpensive method for computational construction of RNA-protein interaction networks, and should provide useful insights into the function of non-coding RNAs. RPISeq is freely available as a web-based server at http://pridb.gdcb.iastate.edu/RPISeq/.

Molecular modeling of flexible arm-mediated interactions between bacterial chemoreceptors and their modification enzyme.

Sensory adaptation in bacterial chemotaxis is mediated by methylation and demethylation of specific glutamyl residues in the cytoplasmic domain of chemoreceptors. Methylation is catalyzed by methyltransferase CheR. In E. coli and related organisms, methylation sufficiently rapid to be physiologically effective requires a carboxyl terminal pentapeptide sequence on the receptor being modified or, via adaptational assistance, on a neighboring homodimer in a receptor cluster. Pentapeptide-enhanced methylation is thought to be mediated by a approximately 30 residue, potentially disordered sequence that serves as a flexible arm connecting the receptor body and pentapeptide-bound methyltransferase, thus allowing diffusionally restricted enzyme to reach methyl-accepting sites. However, it was not known how many or which sites on the same or neighboring receptors were accessible to the tethered enzyme. We investigated using molecular modeling and found that, in a hexagonal array of trimers of receptor dimers, CheR tethered to a dimer of chemoreceptor Tar by its native 30-residue flexible-arm sequence could reach all methyl-accepting sites on the dimer to which it was tethered plus 48 methyl-accepting sites distributed among nine neighboring dimers, equivalent to the total sites carried by six receptors. This modeling-determined methylation neighborhood of one enzyme-binding dimer and six neighbors corresponds precisely with the experimentally identified neighborhood of seven. Thus, the experimentally observed adaptational assistance can occur by docking of pentapeptide-bound, diffusionally restricted enzyme to methyl-accepting sites on neighboring receptors. Our analysis introduces the notion that physiologically relevant adaptational assistance could occur even if only a subset of sites on a particular receptor are within reach.

PlantGDB: a resource for comparative plant genomics.

PlantGDB (http://www.plantgdb.org/) is a genomics database encompassing sequence data for green plants (Viridiplantae). PlantGDB provides annotated transcript assemblies for >100 plant species, with transcripts mapped to their cognate genomic context where available, integrated with a variety of sequence analysis tools and web services. For 14 plant species with emerging or complete genome sequence, PlantGDB's genome browsers (xGDB) serve as a graphical interface for viewing, evaluating and annotating transcript and protein alignments to chromosome or bacterial artificial chromosome (BAC)-based genome assemblies. Annotation is facilitated by the integrated yrGATE module for community curation of gene models. Novel web services at PlantGDB include Tracembler, an iterative alignment tool that generates contigs from GenBank trace file data and BioExtract Server, a web-based server for executing custom sequence analysis workflows. PlantGDB also hosts a plant genomics research outreach portal (PGROP) that facilitates access to a large number of resources for research and training.

A simple approach for protein structure discrimination based on the network pattern of conserved hydrophobic residues.

Evolutionarily conserved hydrophobic residues at the core of protein structures are generally assumed to play a structural role in protein folding and stability. Recent studies have implicated that their importance to protein structures is uneven, with a few of them being crucial and the rest of them being secondary. In this work, we explored the possibility of employing this feature of native structures for discriminating non-native structures from native ones. First, we developed a network tool to quantitatively measure the structural contributions of individual amino acid residues. We systematically applied this method to diverse fold-type sets of native proteins. It was confirmed that this method could grasp the essential structural features of native proteins. Next, we applied it to a number of decoy sets of proteins. The results indicate that such an approach indeed identified non-native structures in most test cases. This finding should be of help for the investigation of the fundamental problem of protein structure prediction.