Investigation of machine learning algorithms for taxonomic classification of marine metagenomes.

Microbial communities play key roles in ocean ecosystems through regulation of biogeochemical processes such as carbon and nutrient cycling, food web dynamics, and gut microbiomes of invertebrates, fish, reptiles, and mammals. Assessments of marine microbial diversity are therefore critical to understanding spatiotemporal variations in microbial community structure and function in ocean ecosystems. With recent advances in DNA shotgun sequencing for metagenome samples and computational analysis, it is now possible to access the taxonomic and genomic content of ocean microbial communities to study their structural patterns, diversity, and functional potential. However, existing taxonomic classification tools depend upon manually curated phylogenetic trees, which can create inaccuracies in metagenomes from less well-characterized communities, such as from ocean water. Herein, we explore the utility of deep learning tools-DeepMicrobes and a novel Residual Network architecture-that leverage natural language processing and convolutional neural network architectures to map input sequence data (k-mers) to output labels (taxonomic groups) without reliance on a curated taxonomic tree. We trained both models using metagenomic reads simulated from marine microbial genomes in the MarRef database. The performance of both models (accuracy, precision, and percent microbe predicted) was compared with the standard taxonomic classification tool Kraken2 using 10 complex metagenomic data sets simulated from MarRef. Our results demonstrate that time, compute power, and microbial genomic diversity still pose challenges for machine learning (ML). Moreover, our results suggest that high genome coverage and rectification of class imbalance are prerequisites for a well-trained model, and therefore should be a major consideration in future ML work. IMPORTANCE Taxonomic profiling of microbial communities is essential to model microbial interactions and inform habitat conservation. This work develops approaches in constructing training/testing data sets from publicly available marine metagenomes and evaluates the performance of machine learning (ML) approaches in read-based taxonomic classification of marine metagenomes. Predictions from two models are used to test accuracy in metagenomic classification and to guide improvements in ML approaches. Our study provides insights on the methods, results, and challenges of deep learning on marine microbial metagenomic data sets. Future machine learning approaches can be improved by rectifying genome coverage and class imbalance in the training data sets, developing alternative models, and increasing the accessibility of computational resources for model training and refinement.

Associations of Microbial Diversity with Age and Other Clinical Variables among Pediatric Chronic Rhinosinusitis (CRS) Patients.

Chronic rhinosinusitis (CRS) is a heterogenous disease that causes persistent paranasal sinus inflammation in children. Microorganisms are thought to contribute to the etiology and progression of CRS. Culture-independent microbiome analysis offers deeper insights into sinonasal microbial diversity and microbe-disease associations than culture-based methods. To date, CRS-related microbiome studies have mostly focused on the adult population, and only one study has characterized the pediatric CRS microbiome. In this study, we analyzed the bacterial diversity of adenoid tissue, adenoid swab, maxillary sinus, and sinus wash samples from 45 pediatric CRS patients recruited from the Johns Hopkins All Children's Hospital (JHACH) in St. Petersburg, FL, USA. The alpha diversity in these samples was associated with baseline nasal steroid use, leukotriene receptor antagonist (LTRA) use, and total serum immunoglobulin (Ig) E (IgE) level. Streptococcus, Moraxella, and Haemophilus spp. were most frequently identified from sinus cultures and the sequenced 16S rRNA gene content. Comparative analyses combining our samples with the samples from the previous microbiome study revealed differentially abundant genera between patients with pediatric CRS and healthy controls, including Cutibacterium and Moraxella. Additionally, the abundances of Streptobacillus and Staphylococcus were consistently correlated with age in both adenoid- and sinus-derived samples. Our study uncovers new associations of alpha diversity with clinical parameters, as well as associations of specific genera with disease status and age, that can be further investigated.

Tourmaline: A containerized workflow for rapid and iterable amplicon sequence analysis using QIIME 2 and Snakemake.

BACKGROUND: Amplicon sequencing (metabarcoding) is a common method to survey diversity of environmental communities whereby a single genetic locus is amplified and sequenced from the DNA of whole or partial organisms, organismal traces (e.g., skin, mucus, feces), or microbes in an environmental sample. Several software packages exist for analyzing amplicon data, among which QIIME 2 has emerged as a popular option because of its broad functionality, plugin architecture, provenance tracking, and interactive visualizations. However, each new analysis requires the user to keep track of input and output file names, parameters, and commands; this lack of automation and standardization is inefficient and creates barriers to meta-analysis and sharing of results. FINDINGS: We developed Tourmaline, a Python-based workflow that implements QIIME 2 and is built using the Snakemake workflow management system. Starting from a configuration file that defines parameters and input files-a reference database, a sample metadata file, and a manifest or archive of FASTQ sequences-it uses QIIME 2 to run either the DADA2 or Deblur denoising algorithm; assigns taxonomy to the resulting representative sequences; performs analyses of taxonomic, alpha, and beta diversity; and generates an HTML report summarizing and linking to the output files. Features include support for multiple cores, automatic determination of trimming parameters using quality scores, representative sequence filtering (taxonomy, length, abundance, prevalence, or ID), support for multiple taxonomic classification and sequence alignment methods, outlier detection, and automated initialization of a new analysis using previous settings. The workflow runs natively on Linux and macOS or via a Docker container. We ran Tourmaline on a 16S ribosomal RNA amplicon data set from Lake Erie surface water, showing its utility for parameter optimization and the ability to easily view interactive visualizations through the HTML report, QIIME 2 viewer, and R- and Python-based Jupyter notebooks. CONCLUSION: Automated workflows like Tourmaline enable rapid analysis of environmental amplicon data, decreasing the time from data generation to actionable results. Tourmaline is available for download at github.com/aomlomics/tourmaline.

Dominance of Sulfurospirillum in Metagenomes Associated with the Methane Ice Worm (Sirsoe methanicola).

Sirsoe methanicola, commonly known as the methane ice worm, is the only macrofaunal species known to inhabit the Gulf of Mexico methane hydrates. Little is known about this elusive marine polychaete that can colonize rich carbon and energy reserves. Metagenomic analysis of gut contents and worm fragments predicted diverse metabolic capabilities with the ability to utilize a range of nitrogen, sulfur, and organic carbon compounds through microbial taxa affiliated with Campylobacterales, Desulfobacterales, Enterobacterales, SAR324, Alphaproteobacteria, and Mycoplasmatales. Entomoplasmatales and Chitinivibrionales were additionally identified from extracted full-length 16S rRNA sequences, and read analysis identified 196 bacterial families. Overall, the microbial community appeared dominated by uncultured Sulfurospirillum, a taxon previously considered free-living rather than host-associated. Metagenome-assembled genomes (MAGs) classified as uncultured Sulfurospirillum predicted thiosulfate disproportionation and the reduction of tetrathionate, sulfate, sulfide/polysulfide, and nitrate. Microbial amino acid and vitamin B12 biosynthesis genes were identified in multiple MAGs, suggesting nutritional value to the host. Reads assigned to aerobic or anaerobic methanotrophic taxa were rare. IMPORTANCE Methane hydrates represent vast reserves of natural gas with roles in global carbon cycling and climate change. This study provided the first analysis of metagenomes associated with Sirsoe methanicola, the only polychaete species known to colonize methane hydrates. Previously unrecognized participation of Sulfurospirillum in a gut microbiome is provided, and the role of sulfur compound redox reactions within this community is highlighted. The comparative biology of S. methanicola is of general interest given research into the adverse effects of sulfide production in human gut microbiomes. In addition, taxonomic assignments are provided for nearly 200 bacterial families, expanding our knowledge of microbiomes in the deep sea.

Metagenomes, Metatranscriptomes, and Metagenome-Assembled Genomes from Chesapeake and Delaware Bay (USA) Water Samples.

Here, we present 36 metagenomes, 59 metatranscriptomes, and 373 metagenome-assembled genomes (MAGs) from Chesapeake and Delaware Bay water samples. This data set will be useful for studying microbial biogeochemical cycling in estuaries.

Gill microbiome structure and function in the chemosymbiotic coastal lucinid Stewartia floridana.

Lucinid bivalves harbor environmentally acquired, chemosynthetic, gammaproteobacterial gill endosymbionts. Lucinid gill microbiomes, which may contain other gammaproteobacterial and/or spirochete taxa, remain under-sampled. To understand inter-host variability of the lucinid gill microbiome, specifically in the bacterial communities, we analyzed the microbiome content of Stewartia floridana collected from Florida. Sampled gills contained a monospecific gammaproteobacterial endosymbiont expressing lithoautotrophic, mixotrophic, diazotrophic and C1 compound oxidation-related functions previously characterized in similar lucinid species. Another low-abundance Spirochaeta-like species in ∼72% of the sampled gills was most closely related to Spirochaeta-like species in another lucinid Phacoides pectinatus and formed a clade with known marine Spirochaeta symbionts. The spirochete expressed genes were involved in heterotrophy and the transport of sugars, amino acids, peptides and other substrates. Few muscular and neurofilament genes from the host and none from the gammaproteobacterial and spirochete symbionts were differentially expressed among quadrats predominantly covered with seagrass species or 80% bare sand. Our results suggest that spirochetes are facultatively associated with S. floridana, with potential scavenging and nutrient cycling roles. Expressed stress- and defense-related functions in the host and symbionts also suggest species-species communications, which highlight the need for further study of the interactions among lucinid hosts, their microbiomes and their environment.

The Effects of Genetic Relatedness on the Preterm Infant Gut Microbiota.

The preterm infant gut microbiota is influenced by environmental, endogenous, maternal, and genetic factors. Although siblings share similar gut microbial composition, it is not known how genetic relatedness affects alpha diversity and specific taxa abundances in preterm infants. We analyzed the 16S rRNA gene content of stool samples, ≤ and >3 weeks postnatal age, and clinical data from preterm multiplets and singletons at two Neonatal Intensive Care Units (NICUs), Tampa General Hospital (TGH; FL, USA) and Carle Hospital (IL, USA). Weeks on bovine milk-based fortifier (BMF) and weight gain velocity were significant predictors of alpha diversity. Alpha diversity between siblings were significantly correlated, particularly at ≤3 weeks postnatal age and in the TGH NICU, after controlling for clinical factors. Siblings shared higher gut microbial composition similarity compared to unrelated individuals. After residualizing against clinical covariates, 30 common operational taxonomic units were correlated between siblings across time points. These belonged to the bacterial classes Actinobacteria, Bacilli, Bacteroidia, Clostridia, Erysipelotrichia, and Negativicutes. Besides the influence of BMF and weight variables on the gut microbial diversity, our study identified gut microbial similarities between siblings that suggest genetic or shared maternal and environmental effects on the preterm infant gut microbiota.

Gene expression responses to Zika virus infection in peripheral blood mononuclear cells from pregnant and non-pregnant women.

Congenital Zika syndrome is caused by mother-to-fetus transmission of the Zika virus (ZIKV). Peripheral blood mononuclear cells (PBMCs) are permissive to ZIKV infection and may carry ZIKV to the placenta. To identify pregnancy-related differences in PBMC responses against ZIKV infection, we compared gene expression profiles of ZIKV-infected and non-infected PBMCs cultured from pregnant and non-pregnant women. ZIKV-infected pregnant conditions generally overexpressed M1-shifted pro-inflammatory responses and underexpressed M2-shifted anti-inflammatory responses. Additionally, transcripts involved in osteoclast differentiation and cardiac myopathies were upregulated following ZIKV infection. Our results suggest potential roles of pregnancy-induced immune dysregulation in shaping neonatal pathology associated with ZIKV infection.

An introduction to phylosymbiosis.

Phylosymbiosis was recently formulated to support a hypothesis-driven framework for the characterization of a new, cross-system trend in host-associated microbiomes. Defining phylosymbiosis as 'microbial community relationships that recapitulate the phylogeny of their host', we review the relevant literature and data in the last decade, emphasizing frequently used methods and regular patterns observed in analyses. Quantitative support for phylosymbiosis is provided by statistical methods evaluating higher microbiome variation between host species than within host species, topological similarities between the host phylogeny and microbiome dendrogram, and a positive association between host genetic relationships and microbiome beta diversity. Significant degrees of phylosymbiosis are prevalent, but not universal, in microbiomes of plants and animals from terrestrial and aquatic habitats. Consistent with natural selection shaping phylosymbiosis, microbiome transplant experiments demonstrate reduced host performance and/or fitness upon host-microbiome mismatches. Hybridization can also disrupt phylosymbiotic microbiomes and cause hybrid pathologies. The pervasiveness of phylosymbiosis carries several important implications for advancing knowledge of eco-evolutionary processes that impact host-microbiome interactions and future applications of precision microbiology. Important future steps will be to examine phylosymbiosis beyond bacterial communities, apply evolutionary modelling for an increasingly sophisticated understanding of phylosymbiosis, and unravel the host and microbial mechanisms that contribute to the pattern. This review serves as a gateway to experimental, conceptual and quantitative themes of phylosymbiosis and outlines opportunities ripe for investigation from a diversity of disciplines.

Correction: Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Extensive Thioautotrophic Gill Endosymbiont Diversity within a Single Ctena orbiculata (Bivalvia: Lucinidae) Population and Implications for Defining Host-Symbiont Specificity and Species Recognition.

Seagrass-dwelling members of the bivalve family Lucinidae harbor environmentally acquired gill endosymbionts. According to previous studies, lucinid symbionts potentially represent multiple strains from a single thioautotrophic gammaproteobacterium species. This study utilized genomic- and transcriptomic-level data to resolve symbiont taxonomic, genetic, and functional diversity from Ctena orbiculata endosymbiont populations inhabiting carbonate-rich sediment at Sugarloaf Key, FL (USA). The sediment had mixed seagrass and calcareous green alga coverage and also was colonized by at least five other lucinid species. Four coexisting, thioautotrophic endosymbiont operational taxonomic units (OTUs), likely representing four strains from two different bacterial species, were identified from C. orbiculata Three of these OTUs also occurred at high relative abundances in the other sympatric lucinid species. Interspecies genetic differences averaged about 5% lower at both pairwise average nucleotide identity and amino acid identity than interstrain differences. Despite these genetic differences, C. orbiculata endosymbionts shared a high number of metabolic functions, including highly expressed thioautotrophy-related genes and a moderately to weakly expressed conserved one-carbon (C1) oxidation gene cluster previously undescribed in lucinid symbionts. Few symbiont- and host-related genes, including those encoding symbiotic sulfurtransferase, host respiratory functions, and host sulfide oxidation functions, were differentially expressed between seagrass- and alga-covered sediment locations. In contrast to previous studies, the identification of multiple endosymbiont taxa within and across C. orbiculata individuals, which were also shared with other sympatric lucinid species, suggests that neither host nor endosymbiont displays strict taxonomic specificity. This necessitates further investigations into the nature and extent of specificity of lucinid hosts and their symbionts.IMPORTANCE Symbiont diversity and host/symbiont functions have been comprehensively profiled for only a few lucinid species. In this work, unprecedented thioautotrophic gill endosymbiont taxonomic diversity was characterized within a Ctena orbiculata population associated with both seagrass- and alga-covered sediments. Endosymbiont metabolisms included known chemosynthetic functions and an additional conserved, previously uncharacterized C1 oxidation pathway. Lucinid-symbiont associations were not species specific because this C. orbiculata population hosted multiple endosymbiont strains and species, and other sympatric lucinid species shared overlapping symbiont 16S rRNA gene diversity profiles with C. orbiculata Our results suggest that lucinid-symbiont association patterns within some host species could be more taxonomically diverse than previously thought. As such, this study highlights the importance of holistic analyses, at the population, community, and even ecosystem levels, in understanding host-microbe association patterns.

Taxonomic and functional heterogeneity of the gill microbiome in a symbiotic coastal mangrove lucinid species.

Lucinidae clams harbor gammaproteobacterial thioautotrophic gill endosymbionts that are environmentally acquired. Thioautotrophic lucinid symbionts are related to metabolically similar symbionts associated with diverse marine host taxa and fall into three distinct phylogenetic clades. Most studies on the lucinid-bacteria chemosymbiosis have been done with seagrass-dwelling hosts, whose symbionts belong to the largest phylogenetic clade. In this study, we examined the taxonomy and functional repertoire of bacterial endosymbionts at an unprecedented resolution from Phacoides pectinatus retrieved from mangrove-lined coastal sediments, which are underrepresented in chemosymbiosis studies. The P. pectinatus thioautotrophic endosymbiont expressed metabolic gene variants for thioautotrophy, respiration, and nitrogen assimilation distinct from previously characterized lucinid thioautotrophic symbionts and other marine symbionts. At least two other bacterial species with different metabolisms were also consistently identified in the P. pectinatus gill microbiome, including a Kistimonas-like species and a Spirochaeta-like species. Bacterial transcripts involved in adhesion, growth, and virulence and mixotrophy were highly expressed, as were host-related hemoglobin and lysozyme transcripts indicative of sulfide/oxygen/CO2 transport and bactericidal activity. This study suggests the potential roles of P. pectinatus and its gill microbiome species in mangrove sediment biogeochemistry and offers insights into host and microbe metabolisms in the habitat.

Simple re-instantiation of small databases using cloud computing.

BACKGROUND: Small bioinformatics databases, unlike institutionally funded large databases, are vulnerable to discontinuation and many reported in publications are no longer accessible. This leads to irreproducible scientific work and redundant effort, impeding the pace of scientific progress. RESULTS: We describe a Web-accessible system, available online at http://biodb100.apbionet.org, for archival and future on demand re-instantiation of small databases within minutes. Depositors can rebuild their databases by downloading a Linux live operating system (http://www.bioslax.com), preinstalled with bioinformatics and UNIX tools. The database and its dependencies can be compressed into an ".lzm" file for deposition. End-users can search for archived databases and activate them on dynamically re-instantiated BioSlax instances, run as virtual machines over the two popular full virtualization standard cloud-computing platforms, Xen Hypervisor or vSphere. The system is adaptable to increasing demand for disk storage or computational load and allows database developers to use the re-instantiated databases for integration and development of new databases. CONCLUSIONS: Herein, we demonstrate that a relatively inexpensive solution can be implemented for archival of bioinformatics databases and their rapid re-instantiation should the live databases disappear.

Immunoinformatics: how in silico methods are re-shaping the investigation of peptide immune specificity.

In the past decade, information technology has enabled synergistic advances in key domains of immunological research including the development of diagnostics and vaccines. Computational methods of epitope mapping now play instrumental roles in bench experiments, by facilitating the selection of immunogenic targets and the modeling of downstream cellular responses. In this article, we summarize the latest development and application of immune epitope prediction methods and discuss future directions in this field which could enhance our understanding of immune specificity.

Computational Epigenetics: the new scientific paradigm.

Epigenetics has recently emerged as a critical field for studying how non-gene factors can influence the traits and functions of an organism. At the core of this new wave of research is the use of computational tools that play critical roles not only in directing the selection of key experiments, but also in formulating new testable hypotheses through detailed analysis of complex genomic information that is not achievable using traditional approaches alone. Epigenomics, which combines traditional genomics with computer science, mathematics, chemistry, biochemistry and proteomics for the large-scale analysis of heritable changes in phenotype, gene function or gene expression that are not dependent on gene sequence, offers new opportunities to further our understanding of transcriptional regulation, nuclear organization, development and disease. This article examines existing computational strategies for the study of epigenetic factors. The most important databases and bioinformatic tools in this rapidly growing field have been reviewed.

A proposed minimum skill set for university graduates to meet the informatics needs and challenges of the "-omics" era.

BACKGROUND: The development of high throughput experimental technologies have given rise to the "-omics" era where terabyte-scale datasets for systems-level measurements of various cellular and molecular phenomena pose considerable challenges in data processing and extraction of biological meaning. Moreover, it has created an unmet need for the effective integration of these datasets to achieve insights into biological systems. While it has increased the demand for bioinformatics experts who can interface with biologists, it has also raised the requirement for biologists to possess a basic capability in bioinformatics and to communicate seamlessly with these experts. This may be achieved by embedding in their undergraduate and graduate life science education, basic training in bioinformatics geared towards acquiring a minimum skill set in computation and informatics. RESULTS: Based on previous attempts to define curricula suitable for addressing the bioinformatics capability gap, an initiative was taken during the Workshops on Education in Bioinformatics and Computational Biology (WEBCB) in 2008 and 2009 to identify a minimum skill set for the training of future bioinformaticians and molecular biologists with informatics capabilities. The minimum skill set proposed is cross-disciplinary in nature, involving a combination of knowledge and proficiency from the fields of biology, computer science, mathematics and statistics, and can be tailored to the needs of the "-omics". CONCLUSION: The proposed bioinformatics minimum skill set serves as a guideline for biology curriculum design and development in universities at both the undergraduate and graduate levels.

The implementation of e-learning tools to enhance undergraduate bioinformatics teaching and learning: a case study in the National University of Singapore.

BACKGROUND: The rapid advancement of computer and information technology in recent years has resulted in the rise of e-learning technologies to enhance and complement traditional classroom teaching in many fields, including bioinformatics. This paper records the experience of implementing e-learning technology to support problem-based learning (PBL) in the teaching of two undergraduate bioinformatics classes in the National University of Singapore. RESULTS: Survey results further established the efficiency and suitability of e-learning tools to supplement PBL in bioinformatics education. 63.16% of year three bioinformatics students showed a positive response regarding the usefulness of the Learning Activity Management System (LAMS) e-learning tool in guiding the learning and discussion process involved in PBL and in enhancing the learning experience by breaking down PBL activities into a sequential workflow. On the other hand, 89.81% of year two bioinformatics students indicated that their revision process was positively impacted with the use of LAMS for guiding the learning process, while 60.19% agreed that the breakdown of activities into a sequential step-by-step workflow by LAMS enhances the learning experience CONCLUSION: We show that e-learning tools are useful for supplementing PBL in bioinformatics education. The results suggest that it is feasible to develop and adopt e-learning tools to supplement a variety of instructional strategies in the future.

Recent advances in computer-aided drug design.

Modern drug discovery is characterized by the production of vast quantities of compounds and the need to examine these huge libraries in short periods of time. The need to store, manage and analyze these rapidly increasing resources has given rise to the field known as computer-aided drug design (CADD). CADD represents computational methods and resources that are used to facilitate the design and discovery of new therapeutic solutions. Digital repositories, containing detailed information on drugs and other useful compounds, are goldmines for the study of chemical reactions capabilities. Design libraries, with the potential to generate molecular variants in their entirety, allow the selection and sampling of chemical compounds with diverse characteristics. Fold recognition, for studying sequence-structure homology between protein sequences and structures, are helpful for inferring binding sites and molecular functions. Virtual screening, the in silico analog of high-throughput screening, offers great promise for systematic evaluation of huge chemical libraries to identify potential lead candidates that can be synthesized and tested. In this article, we present an overview of the most important data sources and computational methods for the discovery of new molecular entities. The workflow of the entire virtual screening campaign is discussed, from data collection through to post-screening analysis.

Allergen Atlas: a comprehensive knowledge center and analysis resource for allergen information.

SUMMARY: A variety of specialist databases have been developed to facilitate the study of allergens. However, these databases either contain different subsets of allergen data or are deficient in tools for assessing potential allergenicity of proteins. Here, we describe Allergen Atlas, a comprehensive repository of experimentally validated allergen sequences collected from in-house laboratory, online data submission, literature reports and all existing general-purpose and specialist databases. Each entry was manually verified, classified and hyperlinked to major databases including Swiss-Prot, Protein Data Bank (PDB), Gene Ontology (GO), Pfam and PubMed. The database is integrated with analysis tools that include: (i) keyword search, (ii) BLAST, (iii) position-specific iterative BLAST (PSI-BLAST), (iv) FAO/WHO criteria search, (v) graphical representation of allergen information network and (vi) online data submission. The latest version contains information of 1593 allergen sequences (496 IUIS allergens, 978 experimentally verified allergens and 119 new sequences), 56 IgE epitope sequences, 679 links to PDB structures and 155 links to Pfam domains. AVAILABILITY: Allergen Atlas is freely available at http://tiger.dbs.nus.edu.sg/ATLAS/.

The value of position-specific scoring matrices for assessment of protein allegenicity.

BACKGROUND: Bioinformatics tools are commonly used for assessing potential protein allergenicity. While these methods have achieved good accuracies for highly conserved sequences, they are less effective when the overall similarity is low. In this study, we assessed the feasibility of using position-specific scoring matrices as a basis for predicting potential allergenicity in proteins. RESULTS: Two simple methods for predicting potential allergenicity in proteins, based on general and group-specific allergen profiles, are presented. Testing results indicate that the performances of both methods are comparable to the best results of other methods. The group-specific profile approach, with a sensitivity of 84.04% and specificity of 96.52%, gives similar results as those obtained using the general profile approach (sensitivity = 82.45%, specificity = 96.92%). CONCLUSION: We show that position-specific scoring matrices are highly promising for constructing computational models suitable for allergenicity assessment. These data suggest it may be possible to apply a targeted approach for allergenicity assessment based on the profiles of allergens of interest.