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bioRxiv2022

Enhanced Viral Metagenomics with Lazypipe 2

Viruses are the main agents causing emerging and re-emerging infectious diseases. It is therefore important to screen for and detect them and uncover the evolutionary processes that support their ability to jump species boundaries and establish themselves in new hosts. Metagenomic next-generation sequencing (mNGS) is a high-throughput, impartial technology that has enabled virologists to detect either known or novel, divergent viruses from clinical, animal, wildlife and environmental samples, with little a priori assumptions. mNGS is heavily dependent on bioinformatic analysis, with an emerging demand for integrated bioinformatic workflows. Here, we present Lazypipe2, an updated mNGS pipeline with, as compared to Lazypipe1, significant improvements in code stability, transparency, with added functionality and support for new software components. We also present extensive benchmarking results, including evaluation on a novel canine simulated metagenome, and precision and recall of virus detection at varying sequencing depth and low to extremely low proportion of viral genetic material. Additionally we report accuracy of virus detection with two strategies: homology searches using nucleotide or amino acid sequences. We show that Lazypipe2 with nucleotide-based annotation approaches near perfect detection of eukaryotic viruses and, in terms of accuracy, outperforms the compared pipelines. We also discuss the importance of homology searches with amino acid sequences for the detection of highly divergent novel viruses.

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bioRxiv2022

voyAGEr: free web interface for the analysis of age-related gene expression alterations in human tissues

We herein introduce voyAGEr, an online graphical interface to explore age-related gene expression alterations in 49 human tissues. voyAGEr offers a visualisation and statistical toolkit for the finding and functional exploration of sex- and tissue-specific transcriptomic changes with age. In its conception, we developed a novel bioinformatics pipeline leveraging RNA sequencing data, from the GTEx project, encompassing more than 900 individuals. voyAGEr reveals transcriptomic signatures of the known asynchronous ageing between tissues, allowing the observation of tissue-specific age-periods of major transcriptional changes, associated with alterations in different biological pathways, cellular composition, and disease conditions. Notably, voyAGEr was created to assist researchers with no expertise in bioinformatics, providing a supportive framework for elaborating, testing and refining their hypotheses on the molecular nature of human ageing and its association with pathologies, thereby also aiding in the discovery of novel therapeutic targets. voyAGEr is freely available at https://compbio.imm.medicina.ulisboa.pt/app/voyAGEr.

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bioRxiv2022

Prop3D: A flexible, Python-based platform for machine learning with protein structural properties and biophysical data

BackgroundMachine learning (ML) has a rich history in structural bioinformatics, and modern approaches, such as deep learning, are revolutionizing our knowledge of the subtle relationships between biomolecular sequence, structure, function, dynamics and evolution. As with any advance that rests upon statistical learning approaches, the recent progress in biomolecular sciences is enabled by the availability of vast volumes of sufficiently-variable data. To be useful, such data must be well-structured, machine-readable, intelligible and manipulable. These and related requirements pose challenges that become especially acute at the computational scales typical in ML. Furthermore, in structural bioinformatics such data generally relate to protein three-dimensional (3D) structures, which are inherently more complex than sequence-based data. A significant and recurring challenge concerns the creation of large, high-quality, openly-accessible datasets that can be used for specific training and benchmarking tasks in ML pipelines for predictive modeling projects, along with reproducible splits for training and testing. ResultsHere, we report Prop3D, a platform that allows for the creation, sharing and extensible reuse of libraries of protein domains, featurized with biophysical and evolutionary properties that can range from detailed, atomically-resolved physicochemical quantities (e.g., electrostatics) to coarser, residue-level features (e.g., phylogenetic conservation). As a community resource, we also supply a Prop3D-20sf protein dataset, obtained by applying our approach to CATH. We have developed and deployed the Prop3D framework, both in the cloud and on local HPC resources, to systematically and reproducibly create comprehensive datasets via the Highly Scalable Data Service (HSDS). Our datasets are freely accessible via a public HSDS instance, or they can be used with accompanying Python wrappers for popular ML frameworks. ConclusionProp3D and its associated Prop3D-20sf dataset can be of broad utility in at least three ways. Firstly, the Prop3D workflow code can be customized and deployed on various cloud-based compute platforms, with scalability achieved largely by saving the results to distributed HDF5 files via HSDS. Secondly, the linked Prop3D-20sf dataset provides a hand-crafted, already-featurized dataset of protein domains for 20 highly-populated CATH families; importantly, provision of this pre-computed resource can aid the more efficient development (and reproducible deployment) of ML pipelines. Thirdly, Prop3D-20sfs construction explicitly takes into account (in creating datasets and data-splits) the enigma of data leakage, stemming from the evolutionary relationships between proteins.

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bioRxiv2023

IsoAnalytics: A Single-cell Proteomics Web Server

MotivationSingle-cell proteomics provide unprecedented resolution to examine biological processes. Customized data analysis and facile data visualization are crucial for scientific discovery. Further, userfriendly data analysis and visualization software that is easily accessible for the general scientific community is essential. ResultsWe have created a web server, IsoAnalytics, that gives users without computational or bioinformatics background the ability to directly analyze and interactively visualize data obtained from the Isoplexis single cell technology platform. We envision this open-sourced web server will increase research productivity and serve as a free, competitive alternative for single-cell proteomics research. ContactAndrew.Koh@utsouthwestern.edu and Xiaowei.Zhan@utsouthwestern.edu AvailabilityIsoAnalytics is free and available at: https://cdc.biohpc.swmed.edu/isoplexis/ and is implemented in Python, with all major browsers supported. Code for IsoAnalytics is free and available at: https://github.com/zhanxw/Isoplexis_Data_Analysis. Supplementary InformationSupplementary data are available at Bioinformatics online.

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bioRxiv2023

Visualization and review of reads alignment on the graphical pan-genome with VAG

Recent advances in pangenomics have led to the rapid development of graph-based pangenomes that code genetic variant as nodes and edges preserve the contiguity of the sequence and structural variation between individuals. Data visualization is an essential component of genomic data analysis. However, the further application of the graph-based pangenome is still suffered from lacking bioinformatics tools to visualize graph format pangenomes and understand the reads alignment on graph pangenomes. In this research, we developed a novel bioinformatics platform, VAG (Visualizing read alignments in graph genomes), to overcome these challenges. VAG includes multifunctional modules integrated into a single command line and an online visualization platform supported through a web server. This tool can extract specific sequence regions from a graph pangenome and display read alignments on different paths of a graph pangenome. In addition, VAG provides population-level presence/absence variations frequency analysis and sequence path navigation to identify the population differentiation regions. To demonstrate the usage, we investigated genetic variations using a rice graph pangenome with population-level sequencing data to identify important genes and gene clusters underlying the indica-japonica differentiation with VAG. After investigating read alignments on the graph pangenome, we identified many false-positive alignments due to TE insertions. To reduce the impact of these misleading alignments, we developed a navigation module to determine and filter those false-positive alignments based on the pair-end alignment information. The utilization of mate-pair information in VAG provides a reliable reference for variation identification. Additionally, we developed a VAG web server to provide a user-friendly and interactive platform to visualize the read alignment data. VAG was also applied to SV discovery in the cucumber and soybean graph-based pangenome and details of VAG can be accessed by the following website (https://ricegenomichjx.xiaomy.net/VAG/sequenceextraction.php).

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bioRxiv2023

Visualizing and exploring patterns of large mutational events with SigProfilerMatrixGenerator

BackgroundAll cancers harbor somatic mutations in their genomes. In principle, mutations affecting between one and fifty base pairs are generally classified as small mutational events. Conversely, large mutational events affect more than fifty base pairs, and, in most cases, they encompass copy-number and structural variants affecting many thousands of base pairs. Prior studies have demonstrated that examining patterns of somatic mutations can be leveraged to provide both biological and clinical insights, thus, resulting in an extensive repertoire of tools for evaluating small mutational events. Recently, classification schemas for examining large-scale mutational events have emerged and shown their utility across the spectrum of human cancers. However, there has been no standard bioinformatics tool that allows visualizing and exploring these large-scale mutational events ResultsHere, we present a new version of SigProfilerMatrixGenerator that now delivers integrated capabilities for examining large mutational events. The tool provides support for examining copy-number variants and structural variants under two previously developed classification schemas and it supports data from numerous algorithms and data modalities. SigProfilerMatrixGenerator is written in Python with an R wrapper package provided for users that prefer working in an R environment. ConclusionsThe new version of SigProfilerMatrixGenerator provides the first standardized bioinformatics tool for optimized exploration and visualization of two previously developed classification schemas for copy number and structural variants. The tool is freely available at https://github.com/AlexandrovLab/SigProfilerMatrixGenerator with an extensive documentation at https://osf.io/s93d5/wiki/home/.

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bioRxiv2023

Metaphor - A workflow for streamlined assembly and binning of metagenomes

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyse genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customisable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly - combining the short input reads into longer, contiguous fragments (contigs), and binning - clustering these contigs into individual genome bins. Both processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully-automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data, and by combining multiple binning algorithms with a bin refinement step to achieve high quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets, and the impact of available assembly and binning strategies on the final results. The workflow is freely available at https://github.com/vinisalazar/metaphor. Author summaryWe present Metaphor, a user-friendly, automated workflow for the recovery of genomes from metagenomes. Our tool offers flexible options for assembling and binning metagenomic contigs, that may be adjusted according to the characteristics of the input data and available computational resources, and a combination of binning algorithms, which improves the quantity and quality of resulting genome bins. We showcase the performance of Metaphor on synthetic benchmarking datasets and discuss the implication of methodological decisions regarding the strategy for assembling and binning metagenomic contigs.

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bioRxiv2023

Integrating weighted correlation network analysis and machine learning identifies common trajectories of prostate cancer

BackgroundProstate cancer diagnosis and prognosis is currently limited by the availability of sensitive and specific biomarkers. There is an urgent need to develop molecular biomarkers that allow for the distinction of indolent from aggressive disease, the sensitive detection of heterogeneous tumors, or the evaluation of micro-metastases. The availability of multi-omics datasets in publicly accessible databases provides a valuable foundation to develop computational workflows for the identification of suitable biomarkers for clinical management of cancer patients. ResultsWe combined transcriptomic data of primary localized and advanced prostate cancer from two cancer databases. Transcriptomic analysis of metastatic tumors unveiled a distinct overexpression pattern of genes encoding cell surface proteins intricately associated with cell-matrix components and chemokine signaling pathways. Utilizing an integrated approach combining machine learning and weighted gene correlation network modules, we identified the EZH2-TROAP axis as the main trajectory from initial tumor development to lethal metastatic disease. In addition, we identified and independently validated 58 promising biomarkers that were specifically upregulated in primary localized or metastatic disease. Among those biomarkers, 22 were highly significant for predicting biochemical recurrence. Notably, we confirmed TPX2 upregulation at the protein level in an independent cohort of primary prostate cancer and matched lymph node metastases. ConclusionsThis study demonstrates the effectiveness of using advanced bioinformatics approaches to identify the biological factors that drive prostate cancer progression. Furthermore, the targets identified show promise as prognostic biomarkers in clinical settings. Thus, integrative bioinformatics methods provide both deeper understanding of disease dynamics and open the doors for future personalized interventions.

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bioRxiv2023

Crosstalkr: An open-source R package to facilitatedrug target identification.

In the last few decades, interest in graph-based analysis of biological networks has grown substantially. Protein-protein interaction networks are one of the most common biological networks, and represent the molecular relationships between every known protein and every other known protein. Integration of these interactomic data into bioinformatic pipelines may increase the translational potential of discoveries made through analysis of multi-omic datasets. Crosstalkr provides a unified toolkit for drug target and disease subnetwork identification, two of the most common uses of protein protein interaction networks. First, crosstalkr enables users to download and leverage high-quality protein-protein interaction networks from online repositories. Users can then filter these large networks into manageable subnetworks using a variety of methods. For example, network filtration can be done using random walks with restarts, starting at the user-provided seed proteins. Affinity scores from a given random walk with restarts are compared to a bootstrapped null distribution to assess statistical significance. Random walks are implemented using sparse matrix multiplication to facilitate fast execution. Next, users can perform in-silico repression experiments to assess the relative importance of nodes in their network. At this step, users can supply protein or gene expression data to make node rankings more meaningful. The default behavior evaluates the human interactome. However, users can evaluate more than 1000 non-human protein-protein interaction networks as a result of integration with StringDB. It is a free, open-source R package designed to allow users to integrate functional analysis using the protein-protein interaction network into existing bioinformatic pipelines. A beta version of crosstalkr available on CRAN (https://cran.rstudio.com/web/packages/crosstalkr/index.html).

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bioRxiv2023

PhyloSophos: a high-throughput scientific name mapping algorithm augmented with explicit consideration of taxonomic science

SummaryThe nature of taxonomic science and the scientific nomenclature system makes it difficult to use scientific names as identifiers without running into complications. To facilitate high-throughput analysis of biological data involving scientific names, we designed PhyloSophos, a Python package that takes into account the properties of scientific names and taxonomic systems to map name inputs to the entries within the reference database of choice. We would like to present three case-studies which demonstrates how our implementations, including rule-based pre-processing and recursive mapping could improve mapping performance and information availability. We expect PhyloSophos to help with the systematic processing of poorly digitized and curated biological data, such as biodiversity information and ethnopharmacological resources, thus enabling full-scale bioinformatics analysis using these data. Availability and implementationPhyloSophos is available at GitHub https://github.com/mhcho4096/phylosophos. Supplementary informationSupplementary data are available at Bioinformatics online.

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bioRxiv2023

Discovering motifs and genomic patterns with SMT: a high-performance data structure for counting kmers

MotivationThe search for conserved motifs in DNA sequences is an important problem in bioinformatics. The growing availability of large-scale genomic data poses significant challenges for computational biology, particularly in terms of efficiency in analysis, kmer identification, and noise presence. The detection of conserved motifs and patterns in DNA sequences is crucial for understanding gene functions and regulations. Therefore, it is essential to develop a data structure that can handle these large volumes of information and provide accurate and fast results. ResultsWe present SMT, an innovative tool designed to efficiently store and count kmers, optimizing memory usage and computation time. The application of SMT has also proven effective in discovering motifs in noisy datasets, allowing the identification of conserved regions in sequences. Furthermore, SMT enables exact searches in constant time and recovers the most abundant k-mers, as well as performs approximate searches in linear time to find fragments with up to d mutations. This approach facilitates large-scale data analysis and provides important insights into the conserved properties of biological sequences. The application of SMT in motif discovery demonstrates its potential to drive research in bioinformatics and genomics. Supplementary data and results are available to provide additional information and support the conclusions presented in this work. Availability and implementationThe source code of the presented method is publicly available at https://github.com/jadermcg/SMT.

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bioRxiv2023

GEGVIC: A workflow to analyze Gene Expression, Genetic Variations and Immune cell Composition of tumor samples using Next Generation Sequencing data

BackgroundThe application of next-generation sequencing techniques for genome and transcriptome profiling is to build the main source of data for cancer research. Hundreds of bioinformatic pipelines have been developed to handle the data generated by these technologies, but their use often requires specialized expertise in data wrangling and analysis that limit many biomedical researchers. Providing easy-to-use, yet comprehensive and integrative open-source tools is essential to help wet-lab and clinical scientists feel more autonomous in performing common omics data analysis in cancer research. ResultsHere, we present GEGVIC, an R tool to easily perform a set of frequently used analyses in cancer research, including differential gene expression, genomic mutations exploration and immune cell deconvolution using minimally processed human/mouse genomic and transcriptomic sequencing data. GEGVIC is designed as a modular pipeline that combines a variety of widely used available methods distributed in three principal modules (Gene Expression, Genomic Variation and Immune Composition), which run independently and include several visualization tools. This open-source software is also presented as a graphical user interface (GUI) using the Shiny framework (GEGVICShine) to eliminate the coding barrier for non-R users and enable comprehensive analyses of tumor samples via one-click features. ConclusionsIn summary, GEGVIC provides a simple, powerful and highly flexible workflow for researchers to process and interpret tumor transcriptomic and genomic data while decreasing or eliminating coding burden and facilitating efficiency for inexperienced bioinformatics users. GEGVIC R package instructions and source code are published on Github (https://github.com/oriolarques/GEGVIC), whereas GEGVICShine is hosted at https://gegvic.vhio.net/.

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bioRxiv2023

Benchmarking imputation methods for discrete biological data

Trait datasets are at the basis of a large share of ecology and evolutionary research, being used to infer ancestral morphologies, to quantify species extinction risks, or to evaluate the functional diversity of biological communities. These datasets, however, are often plagued by missing data, for instance due to incomplete sampling limited data and resource availabilities. Several imputation methods exist to predict missing values and have been successfully evaluated and used to fill the gaps in datasets of quantitative traits. Here we explore the performance of different imputation methods on discrete biological traits i.e. qualitative or categorical traits such as diet or habitat. We develop a bioinformatics pipeline to impute trait data combining phylogenetic, machine learning, and deep learning methods while integrating a simulation framework to evaluate their performance on synthetic datasets. Using this pipeline we run a wide range of simulations under different missing rates, mechanisms, and biases and different evolutionary models. Our results indicate that a new ensemble approach, where we combined the imputation results of a selection of imputation methods provides the most robust and accurate prediction of missing discrete traits. We apply our pipeline to an incomplete trait dataset of 1015 elasmobranch species (including sharks and rays) and found a high imputation accuracy of the predictions based on an expert-based assessment of the missing traits. Our bioinformatic pipeline, implemented in an open-source R package, facilitates the application and comparison of multiple imputation methods to make robust predictions of missing trait values in biological datasets.

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bioRxiv2023

Identification of Mirror Repeats in Viral Genomes using FPCB Analysis

The majority of living domains consist of DNA as genetic material with the minor exception of viruses. The unique nature of every species determines by its unique pattern of genome or gene products. The genomic features become an evident example of evolutionary study also. Different types of repeat patterns are observed in genomes of living domains including human beings whose two third portion of the genome is repetitive. Among the varied type of repeat sequences Mirror Repeats (MR) play crucial roles at the genetic level in every species. The major focus of our research is on identification & to check the distribution of mirror repeat. For this, we employed a bioinformatics-based approach refer as FASTA PARALLEL COMPLEMENT BLAST (FPCB) to identify unique mirror repeat (MR) sequences in some selected viral genomes from three different categories (Animal, Plant & Human). The identified repeats vary in their length as well as found to be distributed throughout the selected viral genomes. The maximum no of MR were reported in the case of Dengue virus (229) & minimum is in the case of TMV (97). In the remaining selected viruses - HCV, HPV, HTLV-1, PVY, Rabies virus 178, 156, 175, 203 & 204 MR sequences were reported. These sequences can be utilized in many ways like in molecular diagnosis, drug delivery target as well as evolutionary study, etc. The present research also helps in the development of novel tools of bioinformatics to study mirror repeats and their functional perspective in the context of their occurrence in all domains.

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bioRxiv2023

Building flexible and robust analysis frameworks for molecular subtyping of cancers

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray vs. RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification, and reporting and a use case of the framework in breast cancer.

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bioRxiv2023

Identification of pan-cancer/testis genes and validation of therapeutic targeting in triple-negative breast cancer: Lin28a- and Siglece-based vaccination induces anti-tumor immunity and inhibits metastasis

BackgroundCancer-testis (CT) genes are targets for tumor antigen-specific immunotherapy given that their expression is normally restricted to the immune-privileged testis in healthy individuals with aberrant expression in tumor tissues. While they represent targetable germ-tissue antigens and play important functional roles in tumorigenesis, there is currently no standardized approach for identifying clinically relevant CT genes. Optimized algorithms and validated methods for accurate prediction of reliable CT antigens with high immunogenicity are also lacking. MethodsSequencing data from the Genotype-Tissue Expression (GTEx) and The Genomic Data Commons (GDC) databases was utilized for the development of a bioinformatic pipeline to identify CT exclusive genes. A CT germness score was calculated based on the number of CT genes expressed within a tumor type and their degree of expression. The impact of tumor germness with clinical outcome was evaluated using healthy GTEx and GDC tumor samples. We then used a triple-negative breast cancer mouse model to develop and test an algorithm that predicts epitope immunogenicity based on the identification of germline sequences with strong MHCI and MHCII binding affinities. Germline sequences for CT genes were synthesized as long synthetic peptide vaccines and tested in the 4T1 triple-negative model of invasive breast cancer with Poly(I:C) adjuvant. Vaccine immunogenicity was determined by flow cytometric analysis of in vitro and in vivo T cell responses. Primary tumor growth and lung metastasis was evaluated by histopathology, flow cytometry and colony formation assay. ResultsWe developed a new bioinformatic pipeline to reliably identify CT exclusive genes as immunogenic targets for immunotherapy. We identified CT genes that are exclusively expressed within the testis, lack detectable thymic expression, and are significantly expressed in multiple tumor types. High tumor germness correlated with tumor progression but not with tumor mutation burden, supporting CT antigens as appealing targets in low mutation burden tumors. Importantly, tumor germness also correlated with markers of anti-tumor immunity. Vaccination of 4T1 tumor bearing mice with Siglece and Lin28a antigens resulted in increased T cell anti-tumor immunity and reduced primary tumor growth and lung metastases. ConclusionOur results present a novel strategy for the identification of highly immunogenic CT antigens for the development of targeted vaccines that induce anti-tumor immunity and inhibit metastasis.

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bioRxiv2023

Visualizing scRNA-Seq Data at Population Scale with GloScope

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organisms phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

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bioRxiv2023

Rocket-miR, a Translational Launchpad for miRNA-based Antimicrobial Drug Development

Developing software tools that leverage biological datasets to accelerate drug discovery is an important aspect of bioinformatics research. Here we present a novel example: a web application called Rocket-miR that applies an existing bioinformatics algorithm (IntaRNA) to predict cross-species miRNA-mRNA interactions and identify human miRNAs with potential antimicrobial activity against antibiotic resistant bacterial infections. Rocket-miR is the logical extension of our prior finding that human miRNA let-7b-5p impairs the ability of the ubiquitous opportunistic pathogen P. aeruginosa to form biofilms and resist the bactericidal effect of beta lactam antibiotics. Rocket-miRs point and click interface enables researchers without programming expertise to predict additional human-miRNA-pathogen interactions. Identified miRNAs can be developed into novel antimicrobials effective against the 24 clinically relevant pathogens, implicated in diseases of the lung, gut and other organs, that are included in the application. The manuscript incorporates three case studies contributed by microbiologists that study human pathogens to demonstrate the usefulness and usability of the application. Rocket-miR is accessible at the following link: http://scangeo.dartmouth.edu/RocketmiR/. ImportanceAntimicrobial resistant infections contribute to millions of deaths worldwide every year. In particular, the group of bacteria collectively known as ESKAPE (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa and Enterobacter sp.) pathogens are of considerable medical concern due to their virulence and exceptional ability to develop antibiotic resistance. New kinds of antimicrobial therapies are urgently needed to treat patients for whom existing antibiotics are ineffective. The Rocket-miR application predicts targets of human miRNAs in bacterial and fungal pathogens, rapidly identifying candidate miRNA-based antimicrobials. The applications target audience is microbiologists that have the laboratory resources to test the applications predictions. The Rocket-miR application currently supports 24 recognized human pathogens that are relevant to numerous diseases including CF, COPD, urinary tract infections and pneumonia. Furthermore, the application code was designed to be easily extendible to other human pathogens that commonly cause hospital-acquired infections.

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