scholarly journals Philympics 2021: Prophage Predictions Perplex Programs

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 758
Author(s):  
Michael J. Roach ◽  
Katelyn McNair ◽  
Sarah K Giles ◽  
Laura K Inglis ◽  
Evan Pargin ◽  
...  
Keyword(s):  
Gold Standard ◽  
Large Scale ◽  
Bacterial Genome ◽  
Learning Approaches ◽  
Bacterial Genomes ◽  
Computational Framework ◽  
Computational Tools ◽  
Prediction Tools ◽  
Prediction Algorithms ◽  
Genome Annotations

Background Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Methods We present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f1 score from the analysis of seven different algorithms for the prediction of prophages. Results We identified different strengths and weaknesses between the prophage prediction tools. Several tools exhibit exceptional f1 scores, while others have better recall at the expense of more false positives. The tools vary greatly in runtime performance with few exhibiting all desirable qualities for large-scale analyses. Conclusions Our library of gold-standard prophage annotations and benchmarking framework provide a valuable resource for exploring strengths and weaknesses of current and future prophage annotation tools. We discuss caveats and concerns in this analysis, how those concerns may be mitigated, and avenues for future improvements. This framework will help developers identify opportunities for improvement and test updates. It will also help users in determining the tools that are best suited for their analysis.

scholarly journals Philympics 2021: Prophage Predictions Perplex Programs

2021 ◽  
Author(s):  
Michael J. Roach ◽  
Katelyn McNair ◽  
Sarah K Giles ◽  
Laura K Inglis ◽  
Evan Pargin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Gold Standard ◽  
Bacterial Genome ◽  
Active Area ◽  
Learning Approaches ◽  
Bacterial Genomes ◽  
Computational Framework ◽  
Computational Tools ◽  
Prediction Algorithms ◽  
Genome Annotations

Most bacterial genomes contain integrated bacteriophages—prophages—in various states of decay. Many are active and able to excise from the genome and replicate, while others are cryptic prophages, remnants of their former selves. Over the last two decades, many computational tools have been developed to identify the prophage components of bacterial genomes, and it is a particularly active area for the application of machine learning approaches. However, progress is hindered and comparisons thwarted because there are no manually curated bacterial genomes that can be used to test new prophage prediction algorithms. Here, we present a library of gold-standard bacterial genome annotations that include manually curated prophage annotations, and a computational framework to compare the predictions from different algorithms. We use this suite to compare all extant stand-alone prophage prediction algorithms to identify their strengths and weaknesses. We provide a FAIR dataset for prophage identification, and demonstrate the accuracy, precision, recall, and f1 score from the analysis of seven different algorithms for the prediction of prophages. We discuss caveats and concerns in this analysis and how those concerns may be mitigated.

scholarly journals Consistent Metagenome-Derived Metrics Verify and Delineate Bacterial Species Boundaries

mSystems ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Matthew R. Olm ◽  
Alexander Crits-Christoph ◽  
Spencer Diamond ◽  
Adi Lavy ◽  
Paula B. Matheus Carnevali ◽  
...  
Keyword(s):  
Bacterial Diversity ◽  
Ribosomal Proteins ◽  
Large Scale ◽  
Bacterial Species ◽  
Bacterial Genome ◽  
16S Rrna Genes ◽  
Rrna Genes ◽  
Species Discrimination ◽  
Bacterial Genomes ◽  
Discrimination Power

ABSTRACT Longstanding questions relate to the existence of naturally distinct bacterial species and genetic approaches to distinguish them. Bacterial genomes in public databases form distinct groups, but these databases are subject to isolation and deposition biases. To avoid these biases, we compared 5,203 bacterial genomes from 1,457 environmental metagenomic samples to test for distinct clouds of diversity and evaluated metrics that could be used to define the species boundary. Bacterial genomes from the human gut, soil, and the ocean all exhibited gaps in whole-genome average nucleotide identities (ANI) near the previously suggested species threshold of 95% ANI. While genome-wide ratios of nonsynonymous and synonymous nucleotide differences (dN/dS) decrease until ANI values approach ∼98%, two methods for estimating homologous recombination approached zero at ∼95% ANI, supporting breakdown of recombination due to sequence divergence as a species-forming force. We evaluated 107 genome-based metrics for their ability to distinguish species when full genomes are not recovered. Full-length 16S rRNA genes were least useful, in part because they were underrecovered from metagenomes. However, many ribosomal proteins displayed both high metagenomic recoverability and species discrimination power. Taken together, our results verify the existence of sequence-discrete microbial species in metagenome-derived genomes and highlight the usefulness of ribosomal genes for gene-level species discrimination. IMPORTANCE There is controversy about whether bacterial diversity is clustered into distinct species groups or exists as a continuum. To address this issue, we analyzed bacterial genome databases and reports from several previous large-scale environment studies and identified clear discrete groups of species-level bacterial diversity in all cases. Genetic analysis further revealed that quasi-sexual reproduction via horizontal gene transfer is likely a key evolutionary force that maintains bacterial species integrity. We next benchmarked over 100 metrics to distinguish these bacterial species from each other and identified several genes encoding ribosomal proteins with high species discrimination power. Overall, the results from this study provide best practices for bacterial species delineation based on genome content and insight into the nature of bacterial species population genetics.

scholarly journals A Universal, Genomewide GuideFinder for CRISPR/Cas9 Targeting in Microbial Genomes

mSphere ◽  
2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Michelle Spoto ◽  
Changhui Guan ◽  
Elizabeth Fleming ◽  
Julia Oh
Keyword(s):  
Gene Function ◽  
Large Scale ◽  
Essential Gene ◽  
Bacterial Species ◽  
Bacterial Genome ◽  
Model Organisms ◽  
Design Parameters ◽  
Bacterial Genomes ◽  
Wide Range ◽  
User Friendly

ABSTRACT The CRISPR/Cas system has significant potential to facilitate gene editing in a variety of bacterial species. CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) represent modifications of the CRISPR/Cas9 system utilizing a catalytically inactive Cas9 protein for transcription repression and activation, respectively. While CRISPRi and CRISPRa have tremendous potential to systematically investigate gene function in bacteria, few programs are specifically tailored to identify guides in draft bacterial genomes genomewide. Furthermore, few programs offer open-source code with flexible design parameters for bacterial targeting. To address these limitations, we created GuideFinder, a customizable, user-friendly program that can design guides for any annotated bacterial genome. GuideFinder designs guides from NGG protospacer-adjacent motif (PAM) sites for any number of genes by the use of an annotated genome and FASTA file input by the user. Guides are filtered according to user-defined design parameters and removed if they contain any off-target matches. Iteration with lowered parameter thresholds allows the program to design guides for genes that did not produce guides with the more stringent parameters, one of several features unique to GuideFinder. GuideFinder can also identify paired guides for targeting multiplicity, whose validity we tested experimentally. GuideFinder has been tested on a variety of diverse bacterial genomes, finding guides for 95% of genes on average. Moreover, guides designed by the program are functionally useful—focusing on CRISPRi as a potential application—as demonstrated by essential gene knockdown in two staphylococcal species. Through the large-scale generation of guides, this open-access software will improve accessibility to CRISPR/Cas studies of a variety of bacterial species. IMPORTANCE With the explosion in our understanding of human and environmental microbial diversity, corresponding efforts to understand gene function in these organisms are strongly needed. CRISPR/Cas9 technology has revolutionized interrogation of gene function in a wide variety of model organisms. Efficient CRISPR guide design is required for systematic gene targeting. However, existing tools are not adapted for the broad needs of microbial targeting, which include extraordinary species and subspecies genetic diversity, the overwhelming majority of which is characterized by draft genomes. In addition, flexibility in guide design parameters is important to consider the wide range of factors that can affect guide efficacy, many of which can be species and strain specific. We designed GuideFinder, a customizable, user-friendly program that addresses the limitations of existing software and that can design guides for any annotated bacterial genome with numerous features that facilitate guide design in a wide variety of microorganisms.

scholarly journals A universal, genome-wide guide finder for CRISPR/Cas9 targeting in microbial genomes

2017 ◽  
Author(s):  
Michelle Spoto ◽  
Elizabeth Fleming ◽  
Julia Oh
Keyword(s):  
Large Scale ◽  
Essential Gene ◽  
Bacterial Species ◽  
Bacterial Genome ◽  
Design Parameters ◽  
Bacterial Genomes ◽  
Microbial Genomes ◽  
Genome Wide ◽  
Cas9 Protein ◽  
User Friendly

AbstractBackgroundThe CRISPR/Cas system has significant potential to facilitate gene editing in a variety of bacterial species. CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) represent modifications of the CRISPR/Cas9 system utilizing a catalytically inactive Cas9 protein for transcription repression or activation, respectively. While CRISPRi and CRISPRa have tremendous potential to systematically investigate gene function in bacteria, no pan-bacterial, genome-wide tools exist for guide discovery. We have created Guide Finder: a customizable, user-friendly program that can design guides for any annotated bacterial genome.ResultsGuide Finder designs guides from NGG PAM sites for any number of genes using an annotated genome and fasta file input by the user. Guides are filtered according to user-defined design parameters and removed if they contain any off-target matches. Iteration with lowered parameter thresholds allows the program to design guides for genes that did not produce guides with the more stringent parameters, a feature unique to Guide Finder. Guide Finder has been tested on a variety of diverse bacterial genomes, on average finding guides for 95% of genes. Moreover, guides designed by the program are functionally useful—focusing on CRISPRi as a potential application—as demonstrated by essential gene knockdown in two staphylococcal species.ConclusionsThrough the large-scale generation of guides, this open-access software will improve accessibility to CRISPR/Cas studies for a variety of bacterial species.

Recent Progress in Machine Learning-based Prediction of Peptide Activity for Drug Discovery

2019 ◽  
Vol 19 (1) ◽  
pp. 4-16 ◽  
Author(s):  
Qihui Wu ◽  
Hanzhong Ke ◽  
Dongli Li ◽  
Qi Wang ◽  
Jiansong Fang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Large Scale ◽  
Recent Progress ◽  
High Specificity ◽  
Learning Approaches ◽  
Anticancer Peptides ◽  
The Past ◽  
Traditional Approaches ◽  
Large Scale Screening

Over the past decades, peptide as a therapeutic candidate has received increasing attention in drug discovery, especially for antimicrobial peptides (AMPs), anticancer peptides (ACPs) and antiinflammatory peptides (AIPs). It is considered that the peptides can regulate various complex diseases which are previously untouchable. In recent years, the critical problem of antimicrobial resistance drives the pharmaceutical industry to look for new therapeutic agents. Compared to organic small drugs, peptide- based therapy exhibits high specificity and minimal toxicity. Thus, peptides are widely recruited in the design and discovery of new potent drugs. Currently, large-scale screening of peptide activity with traditional approaches is costly, time-consuming and labor-intensive. Hence, in silico methods, mainly machine learning approaches, for their accuracy and effectiveness, have been introduced to predict the peptide activity. In this review, we document the recent progress in machine learning-based prediction of peptides which will be of great benefit to the discovery of potential active AMPs, ACPs and AIPs.

scholarly journals Serodiagnosis and Bacterial Genome of Helicobacter pylori Infection

Toxins ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 467
Author(s):  
Aina Ichihara ◽  
Hinako Ojima ◽  
Kazuyoshi Gotoh ◽  
Osamu Matsushita ◽  
Susumu Take ◽  
...  
Keyword(s):  
Helicobacter Pylori ◽  
Antibody Titer ◽  
Bacterial Genome ◽  
Serum Antibody ◽  
Gene Mutations ◽  
Bacterial Genomes ◽  
Western Blots ◽  
A Genome ◽  
Vaca Gene ◽  
H Pylori

The infection caused by Helicobacter pylori is associated with several diseases, including gastric cancer. Several methods for the diagnosis of H. pylori infection exist, including endoscopy, the urea breath test, and the fecal antigen test, which is the serum antibody titer test that is often used since it is a simple and highly sensitive test. In this context, this study aims to find the association between different antibody reactivities and the organization of bacterial genomes. Next-generation sequences were performed to determine the genome sequences of four strains of antigens with different reactivity. The search was performed on the common genes, with the homology analysis conducted using a genome ring and dot plot analysis. The two antigens of the highly reactive strains showed a high gene homology, and Western blots for CagA and VacA also showed high expression levels of proteins. In the poorly responsive antigen strains, it was found that the inversion occurred around the vacA gene in the genome. The structure of bacterial genomes might contribute to the poor reactivity exhibited by the antibodies of patients. In the future, an accurate serodiagnosis could be performed by using a strain with few gene mutations of the antigen used for the antibody titer test of H. pylori.

scholarly journals PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammadreza Yaghoobi ◽  
Krzysztof S. Stopka ◽  
Aaditya Lakshmanan ◽  
Veera Sundararaghavan ◽  
John E. Allison ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Metals And Alloys ◽  
Analysis Tool ◽  
Computational Framework ◽  
Crystal Plasticity Finite Element ◽  
Polycrystalline Metals ◽  
Simulation Based ◽  
Open Source Framework

AbstractThe PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

scholarly journals Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches

2021 ◽  
Author(s):  
Mehdi A. Beniddir ◽  
Kyo Bin Kang ◽  
Grégory Genta-Jouve ◽  
Florian Huber ◽  
Simon Rogers ◽  
...  
Keyword(s):  
Natural Product ◽  
Large Scale ◽  
Scale Analysis ◽  
Computational Tools ◽  
Natural Product Discovery ◽  
Large Scale Analysis ◽  
Metabolite Annotation

This review highlights the key computational tools and emerging strategies for metabolite annotation, and discusses how these advances will enable integrated large-scale analysis to accelerate natural product discovery.

scholarly journals FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

2021 ◽  
Author(s):  
Maxwell Adam Levinson ◽  
Justin Niestroy ◽  
Sadnan Al Manir ◽  
Karen Fairchild ◽  
Douglas E. Lake ◽  
...  
Keyword(s):  
Computational Result ◽  
Large Scale ◽  
Graph Model ◽  
Inferential Reasoning ◽  
Computational Framework ◽  
Textual Description ◽  
Evidence Graph ◽  
Computational Analyses ◽  
Processing Steps ◽  
Multiple Processing

AbstractResults of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

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