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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

AlgaeOrtho, a bioinformatics tool for processing ortholog inference results in algae

Introduction: Microalgae constitute a prominent feedstock for producing biofuels and biochemicals by virtue of their prolific reproduction, high bioproduct accumulation, and the ability to grow in brackish and saline water. However, naturally occurring wild type algal strains are rarely optimal for industrial use; therefore, bioengineering of algae is necessary to generate superior performing strains that can address production challenges in industrial settings, particularly the bioenergy and bioproduct sectors. One of the crucial steps in this process is deciding on a bioengineering target: namely, which gene/protein to differentially express. These targets are often orthologs which are defined as genes/proteins originating from a common ancestor in divergent species. Although bioinformatics tools for the identification of protein orthologs already exist, processing the output from such tools is nontrivial, especially for a researcher with little or no bioinformatics experience. Methods: The present study introduces AlgaeOrtho, a user-friendly tool that builds upon the SonicParanoid orthology inference tool (based on an algorithm that identifies potential protein orthologs based on amino acid sequences) and the PhycoCosm database from JGI (Joint Genome Institute) to help researchers identify orthologs of their proteins of interest in multiple diverse algal species. Results: The output of this application includes a table of the putative orthologs of their protein of interest, a heatmap showing sequence similarity (%), and an unrooted tree of the putative protein orthologs. Notably, the tool would be instrumental in identifying novel bioengineering targets in different algal strains, including targets in not-fully annotated algal species, since it does not depend on existing protein annotations. We tested AlgaeOrtho using three case studies, for which orthologs of proteins relevant to bioengineering targets, were identified from diverse algal species, demonstrating its ease of use and utility for bioengineering researchers. Discussion: This tool is unique in the protein ortholog identification space as it can visualize putative orthologs, as desired by the user, across several algal species.

09 BIOMASS FUELS↗
NASA NTRS2017

Comparison of Two Bioinformatics Tools Used to Characterize the Microbial Diversity and Predictive Functional Attributes of Microbial Mats from Lake Obersee, Antarctica

In this study, using NextGen sequencing of the collective 16S rRNA genes obtained from two sets of samples collected from Lake Obersee, Antarctica, we compared and contrasted two bioinformatics tools, PICRUSt and Tax4Fun. We then developed an R script to assess the taxonomic and predictive functional profiles of the microbial communities within the samples. Taxa such as Pseudoxanthomonas, Planctomycetaceae, Cyanobacteria Subsection III, Nitrosomonadaceae, Leptothrix, and Rhodobacter were exclusively identified by Tax4Fun that uses SILVA database; whereas PICRUSt that uses Greengenes database uniquely identified Pirellulaceae, Gemmatimonadetes A1-B1, Pseudanabaena, Salinibacterium and Sinibacteraceae. Predictive functional profiling of the microbial communities using Tax4Fun and PICRUSt separately revealed common metabolic capabilities, while also showing specific functional IDs not shared between the two approaches. Combining these functional predictions using a customized R script revealed a more inclusive metabolic profile, such as hydrolases, oxidoreductases, transferases; enzymes involved in carbohydrate and amino acid metabolisms; and membrane transport proteins known for nutrient uptake from the surrounding environment. Our results present the first molecular-phylogenetic characterization and predictive functional profiles of the microbial mat communities in Lake Obersee, while demonstrating the efficacy of combining both the taxonomic assignment information and functional IDs using the R script created in this study for a more streamlined evaluation of predictive functional profiles of microbial communities.

Hyunmin Koo↗
DOE OSTI2022

Benchmark datasets for SARS-CoV-2 surveillance bioinformatics

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in cancer genomic studies. Here, we present SigProfilerExtractor, an automated tool for de novo extraction of mutational signatures, and benchmark it against another 13 bioinformatics tools by using 34 scenarios encompassing 2,500 simulated signatures found in 60,000 synthetic genomes and 20,000 synthetic exomes. For simulations with 5% noise, reflecting high-quality datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true-positive signatures while yielding 5-fold less false-positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome- and 19,184 whole-exome-sequenced cancers reveals four novel signatures. Two of the signatures are confirmed in independent cohorts, and one of these signatures is associated with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting signatures, and several novel mutational signatures, including one putatively attributed to direct tobacco smoking mutagenesis in bladder tissues.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

CyanoCyc cyanobacterial web portal

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists’ work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Comprehensive characterization of N- and O- glycosylation of SARS-CoV-2 human receptor angiotensin converting enzyme 2

The emergence of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has created the need for development of new therapeutic strategies. Understanding the mode of viral attachment, entry and replication has become a key aspect of such interventions. The coronavirus surface features a trimeric spike (S) protein that is essential for viral attachment, entry and membrane fusion. The S protein of SARS-CoV-2 binds to human angiotensin converting enzyme 2 (hACE2) for entry. Herein, we describe glycomic and glycoproteomic analysis of hACE2 expressed in HEK293 cells. We observed high glycan occupancy (73.2 to 100%) at all seven possible N-glycosylation sites and surprisingly detected one novel O-glycosylation site. To deduce the detailed structure of glycan epitopes on hACE2 that may be involved in viral binding, we have characterized the terminal sialic acid linkages, the presence of bisecting GlcNAc and the pattern of N-glycan fucosylation. We have conducted extensive manual interpretation of each glycopeptide and glycan spectrum, in addition to using bioinformatics tools to validate the hACE2 glycosylation. Our elucidation of the site-specific glycosylation and its terminal orientations on the hACE2 receptor, along with the modeling of hACE2 glycosylation sites can aid in understanding the intriguing virus-receptor interactions and assist in the development of novel therapeutics to prevent viral entry. Here, the relevance of studying the role of ACE2 is further increased due to some recent reports about the varying ACE2 dependent complications with regard to age, sex, race and pre-existing conditions of COVID-19 patients.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Integrated Phage-Host Prediction tool (iPHoP) v1.0.0

iPHoP is a bioinformatic tools that uses a set of approaches to predict the potential host of novel bacteriophages (viruses infecting bacteria) that are only known by their genome sequence, and not cultivated in the laboratory. Existing technologies typically rely on a single method, and the main advantage of iPHoP is its ability to integrate the results from multiple methods into a single prediction. This is of interest for microbial ecology researchers, as they often analyze novel bacteriophage genomes that they were able to assemble from metagenomes, but they don't know which bacteria these phages infect.

Roux, Simon↗
DOE OSTI2023

KBase Narrative - SOMATA - Motivating Example

A systems view in the biological context is a useful approach to study complex and potentially interacting systems. In this narrative (accompanying an associated manuscript), we take a systems view of software tools themselves. Instead of using software as a single tool, we demonstrate how users can approach software with the same mindset as the organisms they study; as a complex system that can be optimized based on one’s environment and goals. We demonstrate this idea with an example KBase narrative showing how application parameters in KBase tools can change how a researcher perceives and interacts with bioinformatics tools while conducting a common experimental scenario. In this scenario we are trying to understand how different chemical compounds in a growth media change the metabolic pathways utilized in Escherichia coli.

Cashman, Mikaela↗
DOE OSTI2021

Expanding the Scope of Genomic Security: Targeted Genome Editing within Microbiomes through Designer Bacteriophage Vectors

The ability to engineer the genome of a bacterial strain, not as an isolate, but while present among other microbes in a microbiome, would open new technological possibilities in the areas of medicine, energy and biomanufacturing. Our approach is to develop sets of phages (bacterial viruses) active on the target strain and themselves engineered to act not as killers but as vectors for gene delivery. This approach is rooted in our bioinformatic tools that map prophages accurately within bacterial genomes. We present new bioinformatic results in cross-contig search, design of phage genome assemblies, satellites that embed within prophages, alignment of large numbers of biological sequences, and improvement of reference databases for prophage discovery. We targeted a Pseudomonas putida strain within a lignin-degrading microbiome, but were unable to obtain active phages, and turned toward a defined microbiome of the mouse gut.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Introducing the Bacterial and Viral Bioinformatics Resource Center (BV-BRC): a resource combining PATRIC, IRD and ViPR

The National Institute of Allergy and Infectious Diseases (NIAID) established the Bioinformatics Resource Center (BRC) program to assist researchers with analyzing the growing body of genome sequence and other omics-related data. In this report, we describe the merger of the PAThosystems Resource Integration Center (PATRIC), the Influenza Research Database (IRD) and the Virus Pathogen Database and Analysis Resource (ViPR) BRCs to form the Bacterial and Viral Bioinformatics Resource Center (BV-BRC) https://www.bv-brc.org/. The combined BV-BRC leverages the functionality of the bacterial and viral resources to provide a unified data model, enhanced web-based visualization and analysis tools, bioinformatics services, and a powerful suite of command line tools that benefit the bacterial and viral research communities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

HIResist: a database of HIV-1 resistance to broadly neutralizing antibodies

Changing the course of the human immunodeficiency virus type I (HIV-1) pandemic is a high public health priority with approximately 39 million people currently living with HIV-1 (PLWH) and about 1.5 million new infections annually worldwide. Broadly neutralizing antibodies (bnAbs) typically target highly conserved sites on the HIV-1 envelope glycoproteins (Envs), which mediate viral entry, and block the infection of diverse HIV-1 strains. But different mechanisms of HIV-1 resistance to bnAbs prevent robust application of bnAbs for therapeutic and preventive interventions. Here we report the development of a new database that provides data and computational tools to aid the discovery of resistant features and may assist in analysis of HIV-1 resistance to bnAbs. Bioinformatic tools allow identification of specific patterns in Env sequences of resistant strains and development of strategies to elucidate the mechanisms of HIV-1 escape; comparison of resistant and sensitive HIV-1 strains for each bnAb; identification of resistance and sensitivity signatures associated with specific bnAbs or groups of bnAbs; and visualization of antibody pairs on cross-sensitivity plots. The database has been designed with a particular focus on user-friendly and interactive interface. Our database is a valuable resource for the scientific community and provides opportunities to investigate patterns of HIV-1 resistance and to develop new approaches aimed to overcome HIV-1 resistance to bnAbs.

60 APPLIED LIFE SCIENCES↗
NASA NTRSDate not supplied

AstroAmpSeq: Microbial Bioinformatics Education with NASA GeneLab’s Amplicon Pipeline

The prevalence and importance of large sequencing datasets in microbiology has led to a movement to share microbial ecology experimental data through open-access databases. This is particularly true of experiments that are difficult to replicate, such as those conducted in the spaceflight environment and shared via NASA GeneLab. It is now possible and indeed valuable for students to access and re-analyze these shared datasets for educational and research purposes. To provide students with experience utilizing microbial bioinformatics tools, GeneLab for Colleges and Universities (GL4U) has designed AstroAmpSeq, a week-long, virtually implemented project-based learning (PBL) minicourse to instruct undergraduate students on 16S amplicon sequencing. AstroAmpSeq was created to be accessible to students without prior bioinformatics or microbial ecology experience. During the minicourse students work in teams to process, analyze, and visualize a subsample of GeneLab dataset GLDS-280 using GeneLab’s standard amplicon processing pipeline, which is based in R. Students develop a hypothesis related to the dataset then generate and analyze figures to evaluate their hypothesis. Formative assessment of student learning is determined via pre- and post-evaluations, peer feedback, and self-reflection. Project and presentation rubrics serve as a summative assessment of student learning. GL4U AstroAmpSeq not only meets American Society for Microbiology Curriculum Guidelines, but also incites student interest in research by an inquiry-based approach and can be made part of a larger semester-long curriculum. GL4U AstroAmpSeq raises awareness of space microbiology and bioinformatics as a field and career path among undergraduates. Further, by using a GeneLab dataset and nesting microbiology techniques into the real-world application of space biology, AstroAmpSeq enforces deeper and longer-lasting student learning.

microbiology↗
NASA NTRSDate not supplied

Understanding amyloids to prevent biofilm formation in space

There is a pressing need to search for novel approaches to combat biofilm formation, both in space and in medical applications. Many proteins have the ability to form ordered aggregates called amyloids. Amyloids are known to be an important part of biofilms. The use of anti-amyloid drugs is a novel venue for the development of antimicrobial agents. The ultrastructure of the amyloid aggregate shows a high packing of proteins, the second-order structure of which is dominated by β-sheets. The ability to form an amyloid aggregate is especially typical for proteins containing domains (protein fragments) with sufficient lability to arrange themselves in a tight β-sheet structure. Bioinformatics tools allow the prediction of such behavior of proteins in genomic data. We use GeneLab data of microbial populations identified aboard the International Space Station and other spacecraft to look for bacterial species that utilize amyloid aggregation in biofilm formation. We use a combined bioinformatic approach with a relatively high throughput molecular biology assay and biophysical assays to evaluate the anti-amyloid anti-biofilm approach. The significance of the research extends from understanding basic microbial community responses to spaceflight, to biofouling of the built environments in space as well as the long-term health of astronauts. Bioinformatics shows that onboard the ISS, bacterial species produce far more amyloid and prion proteins than are currently verified, hence their role in bacterial ecosystems is largely unknown. As we propose there is a link between amyloid formation in space and biofilm production, this research should lead to new paths for biofilm remediation in space.

Tomasz Zajkowski↗
DOE OSTI2026

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools

Large language models (LLMs) show promise in supporting differential diagnosis, but their performance is challenging to evaluate due to the unstructured nature of their responses, and their accuracy compared to existing diagnostic tools is not well characterized. To assess the current capabilities of LLMs to diagnose genetic diseases, we benchmarked these models on 5213 previously published case reports using the Phenopacket Schema, the Human Phenotype Ontology and Mondo disease ontology. Prompts generated from each phenopacket were sent to seven LLMs, including four generalist models and three LLMs specialized for medical applications. The same phenopackets were used as input to a widely used diagnostic tool, Exomiser, in phenotype-only mode. The best LLM ranked the correct diagnosis first in 23.6% of cases, whereas Exomiser did so in 35.5% of cases. While the performance of LLMs for supporting differential diagnosis has been improving, it has not reached the level of commonly used traditional bioinformatics tools. Future research is needed to determine the best approach to incorporate LLMs into diagnostic pipelines.

Reese, Justin T. [Lawrence Berkeley National Labor↗
DOE OSTI2021

Interpreting the Lipidome: Bioinformatic Approaches to Embrace the Complexity

Background Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. Aim of review To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. Key scientific concepts of review Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Kyle, Jennifer E.↗
NASA NTRSDate not supplied

Benchmarking Computational Tools for Calling SNPs and Indels in Complex Microbial Populations

The NASA BioNutrients missions seek to understand the suitability of microorganisms for bioproduction during space flight. One topic of interest is the stability of microbial genomes during long-term ambient storage and subsequent rehydration and growth. To address these questions, samples from 8 species were flown to ISS for 5 years of desiccated storage at ambient temperature (Stasis Packs) and 2 species were packaged along with powdered media inside a bioreactor system to allow hydration and growth in microgravity (Production Packs). For both systems, Whole Genome Sequencing (WGS) of the DNA extracted from the returned samples and paired ground controls will be conducted to identify changes in genome stability due to time, storage conditions and growth in space. Across the technical replicates, ground controls, 10 timepoints, and multiple experimental conditions, ~300 samples have been selected for initial analysis with WGS sequencing to 100x coverage. A flexible and resource efficient mutation calling pipeline is needed to process this large dataset and allow for comparisons between species. Many bioinformatics tools for calling Indels and Single Nucleotide Variants (SNVs) are designed for use with pure isolates, where true variations from the reference genome are expected to dominate the reads aligning to the location of mutation. In contrast, DNA from the Stasis Pack (SP) samples was collected directly after recovery from desiccated storage and the Production Pack (PP) samples were collected after fermentation. In this context, reads with mutations are expected to be less frequent than reads that align with the reference genome, as each sample will include multiple lines of cells. Thus, BioNutrients samples are expected to be similar to samples from cancer cell or “pooled” sequencing approaches. In preparation for the analysis of the BioNutrients samples, we have tested three mutation calling tools (GATK for Microbes, BreSeq and DiscoSNP) designed for complex samples. A challenge of validating mutation identification pipelines is a lack of “Ground Truth” datasets, especially for complex samples. To compare these three tools, we sought to identify mutations in pre-existing WGS data collected from populations of Chlamydomonas reinhardtii that were exposed to UV mutagenesis and growth in LEO as part of the Space Algae-1 mission. Here we present a summary of these tools against the analysis originally conducted using the CRISP tool. Critical metrics are compared such as runtime, the number of SNPs, the number and size of Indels, and patterns of transversion and transitions identified by each tool are reported. By sharing these benchmarking results collected in support of the BioNutrients mission, we aim to guide others seeking to identify SNVs in similarly complex microbial samples.

Biology↗
DOE OSTI2025

Challenges and Opportunities in State‐of‐the‐Art Proteomics Analysis for Biomarker Development From Plasma Extracellular Vesicles

Extracellular vesicles (EVs) are membrane-bound particles secreted by cells, playing crucial roles in intercellular communication. The composition of EVs can undergo changes in response to stress and disease conditions, making them excellent biomarker candidates. However, extracting protein information from EVs can be challenging due to their low abundance in complex biofluids and copurification with contaminant proteins and particles. Techniques to enrich EVs have their strengths and limitations, without one being able to purify EVs to complete homogeneity. This can lead to compromised recovery rates and increased complexity, making data interpretation difficult. In this viewpoint article, we explore the concept that better characterization of EV composition, followed by quantification of EV proteins in complex samples, might be a more viable route for biomarker development. Mass spectrometers can provide reproducible deep coverage of the EV proteome, despite sample impurities. This paradigm shift presents opportunities to integrate advanced bioinformatics tools to refine the EV proteome landscape, identify novel biomarkers, and streamline validation processes in biomarker development. By focusing on leveraging technology rather than achieving absolute purity, this approach can transform current practices and open opportunities for robust biomarker discovery. Herein, we highlight not only such opportunities but also challenges to implement this concept.

Dakup, Panshak P. [Pacific Northwest National Labo↗
DOE OSTI2023

Interpreting omics data with pathway enrichment analysis

Pathway enrichment analysis is indispensable for interpreting omics datasets and generating hypotheses. However, the foundations of enrichment analysis remain elusive to many biologists. Here, in this study, we discuss best practices in interpreting different types of omics data using pathway enrichment analysis and highlight the importance of considering intrinsic features of various types of omics data. We further explain major components that influence the outcomes of a pathway enrichment analysis, including defining background sets and choosing reference annotation databases. To improve reproducibility, we describe how to standardize reporting methodological details in publications. This article aims to serve as a primer for biologists to leverage the wealth of omics resources and motivate bioinformatics tool developers to enhance the power of pathway enrichment analysis.

60 APPLIED LIFE SCIENCES↗