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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Optimizing inference of segmentation on high-resolution images in MLExchange

MLExchange is a machine learning (ML) operations platform providing web user-interfaces (UIs) for data visualization and analysis pipelines at synchrotron facilities. Among these UIs is the segmentation app which helps synchrotron users utilize ML algorithms to automatically segment high-resolution scientific images with minimal manual annotation effort. In this work, we share code optimizations that significantly speed up the segmentation inference workflow of large data in short time. By optimizing the sequence of CPU-GPU data transfers and introducing CPU parallelization to key operations, we improve the per-device, per-image frame computational efficiency and observe close to 3×$$\times$$ speedup over the original segmentation inference workflow run time when utilizing a single GPU. Further adaptations enabling multi-GPU inference yield more than 40×$$\times$$ speedup with 100 GPUs compared to the optimized single GPU inference workflow. This acceleration of the segmentation inference workflow will provide MLExchange users with easy access to segmentation results with little wait time.

Lu, Shizhao↗
DOE OSTI2024

Missing microbial eukaryotes and misleading meta-omic conclusions

Meta-omics is commonly used for large-scale analyses of microbial eukaryotes, including species or taxonomic group distribution mapping, gene catalog construction, and inference on the functional roles and activities of microbial eukaryotes in situ. Here, we explore the potential pitfalls of common approaches to taxonomic annotation of protistan meta-omic datasets. We re-analyze three environmental datasets at three levels of taxonomic hierarchy in order to illustrate the crucial importance of database completeness and curation in enabling accurate environmental interpretation. We show that taxonomic membership of sequence clusters estimates community composition more accurately than returning exact sequence labels, and overlap between clusters can address database shortcomings. Clustering approaches can be applied to diverse environments while continuing to exploit the wealth of annotation data collated in databases, and selecting and evaluating these databases is a critical part of correctly annotating protistan taxonomy in environmental datasets. We argue that ongoing curation of genetic resources is crucial in accurately annotating protists in in situ meta-omic datasets. Moreover, we propose that precise taxonomic annotation of meta-omic data is a clustering problem rather than a feasible alignment problem.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

SvAnna: efficient and accurate pathogenicity prediction of coding and regulatory structural variants in long-read genome sequencing

Structural variants (SVs) are implicated in the etiology of Mendelian diseases but have been systematically underascertained owing to sequencing technology limitations. Long-read sequencing enables comprehensive detection of SVs, but approaches for prioritization of candidate SVs are needed. Structural variant Annotation and analysis (SvAnna) assesses all classes of SVs and their intersection with transcripts and regulatory sequences, relating predicted effects on gene function with clinical phenotype data. SvAnna places 87% of deleterious SVs in the top ten ranks. The interpretable prioritizations offered by SvAnna will facilitate the widespread adoption of long-read sequencing in diagnostic genomics. SvAnna is available at https://github.com/TheJacksonLaboratory/SvAnna.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

CYPminer: an automated cytochrome P450 identification, classification, and data analysis tool for genome data sets across kingdoms

Background: Cytochrome P450 monooxygenases (termed CYPs or P450s) are hemoproteins ubiquitously found across all kingdoms, playing a central role in intracellular metabolism, especially in metabolism of drugs and xenobiotics. The explosive growth of genome sequencing brings a new set of challenges and issues for researchers, such as a systematic investigation of CYPs across all kingdoms in terms of identification, classification, and pan-CYPome analyses. Such investigation requires an automated tool that can handle an enormous amount of sequencing data in a timely manner. Results: CYPminer was developed in the Python language to facilitate rapid, comprehensive analysis of CYPs from genomes of all kingdoms. CYPminer consists of two procedures i) to generate the Genome-CYP Matrix (GCM) that lists all occurrences of CYPs across the genomes, and ii) to perform analyses and visualization of the GCM, including pan-CYPomes (pan- and core-CYPome), CYP co-occurrence networks, CYP clouds, and genome clustering data. The performance of CYPminer was evaluated with three datasets from fungal and bacterial genome sequences. Conclusions: CYPminer completes CYP analyses for large-scale genomes from all kingdoms, which allows systematic genome annotation and comparative insights for CYPs. CYPminer also can be extended and adapted easily for broader usage.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

DNA methylation and gene expression regulation associated with vascularization in Sorghum bicolor

Plant secondary cell walls constitute the majority of plant biomass. They are predominantly found in xylem cells, which are derived from vascular initials during vascularization. Little is known about these processes in grass species despite their emerging importance as biomass feedstocks. The targeted biofuel crop Sorghum bicolor has a sequenced and well-annotated genome, making it an ideal monocot model for addressing vascularization and biomass deposition. Here we generated tissue-specific transcriptome and DNA methylome data from sorghum shoots, roots and developing root vascular and nonvascular tissues. Many genes associated with vascular development in other species show enriched expression in developing vasculature. However, several transcription factor families varied in vascular expression in sorghum compared with Arabidopsis and maize. Furthermore, differential expression of genes associated with DNA methylation were identified between vascular and nonvascular tissues, implying that changes in DNA methylation are a feature of sorghum root vascularization, which we confirmed using tissue-specific DNA methylome data. Roots treated with a DNA methylation inhibitor also showed a significant decrease in root length. Tissues and organs can be discriminated based on their genomic methylation patterns and methylation context. Consequently, tissue-specific changes in DNA methylation are part of the normal developmental process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The first two chromosome‐scale genome assemblies of American hazelnut enable comparative genomic analysis of the genus Corylus

Summary The native, perennial shrub American hazelnut ( Corylus americana ) is cultivated in the Midwestern United States for its significant ecological benefits, as well as its high‐value nut crop. Implementation of modern breeding methods and quantitative genetic analyses of C. americana requires high‐quality reference genomes, a resource that is currently lacking. We therefore developed the first chromosome‐scale assemblies for this species using the accessions ‘Rush’ and ‘Winkler’. Genomes were assembled using HiFi PacBio reads and Arima Hi‐C data, and Oxford Nanopore reads and a high‐density genetic map were used to perform error correction. N50 scores are 31.9 Mb and 35.3 Mb, with 90.2% and 97.1% of the total genome assembled into the 11 pseudomolecules, for ‘Rush’ and ‘Winkler’, respectively. Gene prediction was performed using custom RNAseq libraries and protein homology data. ‘Rush’ has a BUSCO score of 99.0 for its assembly and 99.0 for its annotation, while ‘Winkler’ had corresponding scores of 96.9 and 96.5, indicating high‐quality assemblies. These two independent assemblies enable unbiased assessment of structural variation within C. americana , as well as patterns of syntenic relationships across the Corylus genus. Furthermore, we identified high‐density SNP marker sets from genotyping‐by‐sequencing data using 1343 C. americana , C. avellana and C. americana × C. avellana hybrids, in order to assess population structure in natural and breeding populations. Finally, the transcriptomes of these assemblies, as well as several other recently published Corylus genomes, were utilized to perform phylogenetic analysis of sporophytic self‐incompatibility (SSI) in hazelnut, providing evidence of unique molecular pathways governing self‐incompatibility in Corylus .

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

ABSTRACT There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules (“functional modules”), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida . Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets. IMPORTANCE This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

09 BIOMASS FUELS↗
DOE OSTI2020

Distributed Many-to-Many Protein Sequence Alignment using Sparse Matrices

Identifying similar protein sequences is a core step in many computational biology pipelines such as detection of homologous protein sequences, generation of similarity protein graphs for downstream analysis, functional annotation, and gene location. Performance and scalability of protein similarity search have proven to be a bottleneck in many bioinformatics pipelines due to increase in cheap and abundant sequencing data. This work presents a new distributed-memory software PASTIS. PASTIS relies on sparse matrix computations for efficient identification of possibly similar proteins. We use distributed sparse matrices for scalability and show that the sparse matrix infrastructure is a great fit for protein similarity search when coupled with a fully-distributed dictionary of sequences that allow remote sequence requests to be fulfilled. Our algorithm incorporates the unique bias in amino acid sequence substitution in search without altering basic sparse matrix model, and in turn, achieves ideal scaling up to millions of protein sequences.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Addressing the dynamic nature of reference data: a new nucleotide database for robust metagenomic classification

Accurate metagenomic classification relies on comprehensive, up-to-date, and validated reference databases. While the NCBI BLAST Nucleotide (nt) database, encompassing a vast collection of sequences from all domains of life, represents an invaluable resource, its massive size—currently exceeding 10 12 nucleotides—and exponential growth pose significant challenges for researchers seeking to maintain current nt-based indices for metagenomic classification. Recognizing that no current nt-based indices exist for the widely used Centrifuge classifier, and the last public version currently available was released in 2018, we addressed this critical gap by leveraging advanced high-performance computing resources. We present new Centrifuge-compatible nt databases, meticulously constructed using a novel pipeline incorporating different quality control measures, including reference decontamination and filtering. These measures demonstrably reduce spurious classifications, as shown through our reanalysis of published metagenomic data where Plasmodium annotations were dramatically reduced using our decontaminated database, highlighting how database quality can significantly impact research conclusions. Through temporal comparisons, we also reveal how our approach minimizes inconsistencies in taxonomic assignments stemming from asynchronous updates between public sequence and taxonomy databases. These discrepancies are particularly evident in taxa such as Listeria monocytogenes and Naegleria fowleri, where classification accuracy varied significantly across database versions. These new databases, made available as pre-built Centrifuge indexes, respond to the need for an open, robust, nt-based pipeline for taxonomic classification in metagenomics. Applications such as environmental metagenomics, forensics, and clinical metagenomics, which require comprehensive taxonomic coverage, will benefit from this resource. Our work highlights the importance of treating reference databases as dynamic entities, subject to ongoing quality control and validation akin to software development best practices. This approach is crucial for ensuring accuracy and reliability of metagenomic analysis, especially as databases continue to expand in size and complexity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

RCSB Protein Data Bank 1D3D module: displaying positional features on macromolecular assemblies

Mapping positional features from one-dimensional (1D) sequences onto three-dimensional (3D) structures of biological macromolecules is a powerful tool to show geometric patterns of biochemical annotations and provide a better understanding of the mechanisms underpinning protein and nucleic acid function at the atomic level. We present a new library designed to display fully customizable interactive views between 1D positional features of protein and/or nucleic acid sequences and their 3D structures as isolated chains or components of macromolecular assemblies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

efam: an e xpanded, metaproteome-supported HMM profile database of viral protein fam ilies

Viruses infect, reprogram and kill microbes, leading to profound ecosystem consequences, from elemental cycling in oceans and soils to microbiome-modulated diseases in plants and animals. Although metagenomic datasets are increasingly available, identifying viruses in them is challenging due to poor representation and annotation of viral sequences in databases. Here, we establish efam, an expanded collection of Hidden Markov Model (HMM) profiles that represent viral protein families conservatively identified from the Global Ocean Virome 2.0 dataset. This resulted in 240 311 HMM profiles, each with at least 2 protein sequences, making efam >7-fold larger than the next largest, pan-ecosystem viral HMM profile database. Adjusting the criteria for viral contig confidence from ‘conservative’ to ‘eXtremely Conservative’ resulted in 37 841 HMM profiles in our efam-XC database. To assess the value of this resource, we integrated efam-XC into VirSorter viral discovery software to discover viruses from less-studied, ecologically distinct oxygen minimum zone (OMZ) marine habitats. This expanded database led to an increase in viruses recovered from every tested OMZ virome by ~24% on average (up to ~42%) and especially improved the recovery of often-missed shorter contigs (<5 kb). Additionally, to help elucidate lesser-known viral protein functions, we annotated the profiles using multiple databases from the DRAM pipeline and virion-associated metaproteomic data, which doubled the number of annotations obtainable by standard, single-database annotation approaches. Together, these marine resources (efam and efam-XC) are provided as searchable, compressed HMM databases that will be updated bi-annually to help maximize viral sequence discovery and study from any ecosystem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Human Host Cellular Response to HCoV-229E Infection Proteomics (ACS-JM-DP2)

The purpose of this experiment was to evaluate the human host cellular response to wild-type Human coronavirus strain 229E (HCoV-229E) infection. Sample data was obtained for mock and infected immortalized human lung epithelial cells (A549) (MOI 5) nuclear extracts, immortalized human lung fibroblasts cells (MRC5) (MOI5) nuclear extracts, and primary human airway epithelial (HAE) (MOI 3) cells from lung tissue and processed for proteome analysis. Processed datasets are openly accessible from the download button and contain secondary processed proteomic results files and supporting metadata materials. Experimental proteomics samples were prepared using Limited Proteolysis (LiP) methods for Label-free quantification (LFQ) and global proteomic evaluation. Sample data was acquired using a Q-Exactive HF-X mass spectrometer and was processed and compiled using MaxQuant software (v.1.6.17.0). Processed proteomic data downloads include a sample naming key, processed MaxQuant results/parameters, and protein annotated relative abundance files. See corresponding primary data accessions below and Viral Experiment LiP Analysis source code supporting data transparency and reuse. Experimental transcriptomics samples were collected in parallel and processed for RNA sequencing (RNA-Seq) as summarized under ACS-DP1 (https://data.pnnl.gov/group/nodes/dataset/34069).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Comprehensive SNP Data for 1,323 GWAS Population in Populus trichocarpa and Combined Annotation Files for P. trichocarpa v3.0 and v3.1

The VCF dataset includes genetic variations found in 1,323 Populus trichocarpa genotypes, providing valuable information for scientists studying plant genetics. Researchers have generated this dataset using whole-genome DNA short-read sequencing on the Illumina Genome Analyzer, HiSeq 2000, and HiSeq 2500 platforms. This sequencing effort ensured a minimum expected sequencing depth of 15×. The dataset comprises more than 9.7 million single nucleotide polymorphisms (SNPs) and indel variants. The combined annotation files are derived from P. trichocarpa v3.0 and v3.1. We merged these files to create a comprehensive annotation file used for GWAS analysis. In total, 38,830 genes overlapped between the two versions. For overlapping genes, we defined the start as the smaller and the end as the larger among the two versions to increase the likelihood of locating candidate genetic loci. Additionally, we included 2,505 unique genes from v3.0 and 4,120 unique genes from v3.1, resulting in a total of 45,455 genes in the updated annotation file.

09 BIOMASS FUELS↗
DOE OSTI2024

An FPGA-based hardware accelerator supporting sensitive sequence homology filtering with profile hidden Markov models

Abstract Background Sequence alignment lies at the heart of genome sequence annotation. While the BLAST suite of alignment tools has long held an important role in alignment-based sequence database search, greater sensitivity is achieved through the use of profile hidden Markov models (pHMMs). Here, we describe an FPGA hardware accelerator, called HAVAC, that targets a key bottleneck step (SSV) in the analysis pipeline of the popular pHMM alignment tool, HMMER. Results The HAVAC kernel calculates the SSV matrix at 1739 GCUPS on a $$\sim$$ ∼ $3000 Xilinx Alveo U50 FPGA accelerator card, $$\sim$$ ∼ 227× faster than the optimized SSV implementation in nhmmer . Accounting for PCI-e data transfer data processing, HAVAC is 65× faster than nhmmer’s SSV with one thread and 35× faster than nhmmer with four threads, and uses $$\sim$$ ∼ 31% the energy of a traditional high end Intel CPU. Conclusions HAVAC demonstrates the potential offered by FPGA hardware accelerators to produce dramatic speed gains in sequence annotation and related bioinformatics applications. Because these computations are performed on a co-processor, the host CPU remains free to simultaneously compute other aspects of the analysis pipeline.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Temporal Cyber Attack Detection.

Rigorous characterization of the performance and generalization ability of cyber defense systems is extremely difficult, making it hard to gauge uncertainty, and thus, confidence. This difficulty largely stems from a lack of labeled attack data that fully explores the potential adversarial space. Currently, performance of cyber defense systems is typically evaluated in a qualitative manner by manually inspecting the results of the system on live data and adjusting as needed. Additionally, machine learning has shown promise in deriving models that automatically learn indicators of compromise that are more robust than analyst-derived detectors. However, to generate these models, most algorithms require large amounts of labeled data (i.e., examples of attacks). Algorithms that do not require annotated data to derive models are similarly at a disadvantage, because labeled data is still necessary when evaluating performance. In this work, we explore the use of temporal generative models to learn cyber attack graph representations and automatically generate data for experimentation and evaluation. Training and evaluating cyber systems and machine learning models requires significant, annotated data, which is typically collected and labeled by hand for one-off experiments. Automatically generating such data helps derive/evaluate detection models and ensures reproducibility of results. Experimentally, we demonstrate the efficacy of generative sequence analysis techniques on learning the structure of attack graphs, based on a realistic example. These derived models can then be used to generate more data. Additionally, we provide a roadmap for future research efforts in this area.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Finding the missing pieces: filling gaps that impede the translation of omics data into models

High-throughput omics technologies such as DNA sequencing have made the sequencing and computational assembly of microbial genomes recovered from the environment relatively routine. Computational inference of the protein products encoded by these genomes, and the associated biochemical functions, should enable the accurate prediction and modeling of microbial metabolism, organismal interactions, and ecosystem processes. However, a lack of scalable, probabilistic protein annotation tools limits the full potential of modeling for understanding the metabolism and biogeochemical cycles of microbial communities. Our approach to improve inference of protein annotations and metabolic models relied on learning from and emulating expert manual curation, leveraging software engineering and data science best practices to scale up the throughput and accuracy of annotations and metabolic model construction, building software to objectively evaluate different annotation strategies, and more closely linking the protein annotation and metabolic model inference process. Outcomes of this research include several improved or new computational tools, including DRAM (Distilled and Refined Annotation of Metabolism) for annotating microbial genomes with protein function and metabolic traits, CAMPER (Curated Annotations for Microbial Polyphenol Enzymes and Reactions) for annotating key polyphenol metabolisms, EC-Bench for comprehensive and unbiased benchmarking of annotation tools, and several apps available via the DOE Systems Biology Knowledgebase (KBase) for building genome-scale metabolic models. We demonstrate that these tools allow us to scalably annotate and understand thousands of genomes for microbial communities from a variety of systems and test cases, including rivers, thawing permafrost, and gut microbiomes. All of these computational tools are available as open-source software, with most broadly and easily accessible to the scientific community via KBase apps.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Genomes_SET2_178 - Shared

The dataset consists of 178 bacterial genomes in total. Of these, 145 genomes were drawn from published literature linked to Biolog Gen III growth data, while 33 genomes came from soil isolates collected at the Cedar Creek Ecosystem Science Research Reserve (CCESR) in Minnesota. The isolates were sequenced on an Illumina HiSeq platform (~50× coverage), assembled with Megahit, and annotated using RAST at PATRIC, with strict quality filtering (<90% completeness or >5% contamination).

59 BASIC BIOLOGICAL SCIENCES↗