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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Annotation of DOM metabolomes with an ultrahigh resolution mass spectrometry molecular formula library

Current approaches to analyzing metabolomic data often rely on matching MS/MS fragmentation data to sparse libraries or databases. This approach results in limited identification of features, often with less than 10% of the dataset being annotated. A complementary approach is to assign molecular formula to features based on accurate mass measurements, but the platforms commonly used for metabolomics do not have the needed accuracy or resolving power to do this robustly, particularly for larger molecules. Using our newly modified analysis tool, CoreMS, we generated a library of molecular formula from pooled samples analyzed with LC-21T FT-ICR MS. This library successfully annotated approximately 53.2% of features identified from the exometabolome of marine diatom Phaeodactylum tricornutum – a nearly ten-fold increase over the 5.9% annotation rate achieved using a conventional MS/MS library matching approach. Using this FT-ICR MS library approach, we were able to differentiate differences in the exometabolome of P. tricornutum in iron replete and iron limited conditions, with 668 metabolites being differentially expressed (p < 0.05, 2 x intensity difference) under these conditions. The traditional MS/MS fragmentation-based annotation approach only annotated 61 of these metabolites, while our novel pipeline annotated 450 metabolites and revealed 12 metabolites that were significantly more abundant under low iron conditions. Our results demonstrate the utility of ultrahigh resolution mass spectrometry for generating more comprehensive and confident molecular annotations.

21T-FTICR-MS, CoreMS↗
DOE OSTI2020

Term Matrix: a novel Gene Ontology annotation quality control system based on ontology term co-annotation patterns

Biological processes are accomplished by the coordinated action of gene products. Gene products often participate in multiple processes, and can therefore be annotated to multiple Gene Ontology (GO) terms. Nevertheless, processes that are functionally, temporally and/or spatially distant may have few gene products in common, and co-annotation to unrelated processes probably reflects errors in literature curation, ontology structure or automated annotation pipelines. We have developed an annotation quality control workflow that uses rules based on mutually exclusive processes to detect annotation errors, based on and validated by case studies including the three we present here: fission yeast protein-coding gene annotations over time; annotations for cohesin complex subunits in human and model species; and annotations using a selected set of GO biological process terms in human and five model species. For each case study, we reviewed available GO annotations, identified pairs of biological processes which are unlikely to be correctly co-annotated to the same gene products (e.g. amino acid metabolism and cytokinesis), and traced erroneous annotations to their sources. To date we have generated 107 quality control rules, and corrected 289 manual annotations in eukaryotes and over 52 700 automatically propagated annotations across all taxa.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Improved Annotation of Untargeted Metabolomics Data through Buffer Modifications That Shift Adduct Mass and Intensity

Annotation of untargeted high-resolution full-scan LC-MS metabolomics data remains challenging due to individual metabolites generating multiple LC-MS peaks arising from isotopes, adducts and fragments. Adduct annotation is a particular challenge, as the same mass difference between peaks can arise from adduct formation, fragmentation, or different biological species. To address this, here we describe a Buffer Modification Workflow (BMW), in which the same sample is run by LC-MS in both liquid chromatography solvent with 14 NH 3 -acetate buffer, and in solvent with the buffer modified with 15 NH 3 -formate. Buffer switching results in characteristic mass and signal intensity changes for adduct peaks, facilitating their annotation. This relatively simple and convenient chromatography modification annotated yeast metabolomics data with similar effectiveness to growing the yeast in isotope-labeled media. Application to mouse liver data annotated both known metabolite and known adduct peaks with 95% accuracy. Altogether, it identified 26% of ~ 27,000 liver LC-MS features as putative metabolites, of which ~ 2600 showed HMDB or KEGG database formula match. This workflow is well-suited to biological samples that cannot be readily isotope labeled, including plants, mammalian tissues, and tumors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Annotation of images based on a 3D model of objects

An annotation system for providing annotations for original images is provided. In some embodiments, the annotation system accesses an annotation associated with an object of a 3D model. The annotation system also accesses and displays an original image. The annotation system renders a model image of the 3D model based on the view from which the original image was collected. When the model image contains the annotated object, the annotation system provides an indication that an annotation is associated with the object of the original image. The annotation system can provide indications of the annotation for other original images that include the annotated object irrespective of the view from which the other original images are collected.

42 ENGINEERING↗
DOE OSTI2025

PDF Entity Annotation Tool (PEAT)

While different text mining approaches – including the use of Artificial Intelligence (AI) and other machine based methods - continue to expand at a rapid pace, the tools used by researchers to create the labeled datasets required for training, modeling, and evaluation remain rudimentary. Labeled datasets contain the target attributes the machine is going to learn; for example, training an algorithm to delineate between images of a car or truck would generally require a set of images with a quantitative description of the underlying features of each vehicle type. Development of labeled textual data that can be used to build natural language machine learning models for scientific literature is not currently integrated into existing manual workflows used by domain experts. Published literature is rich with important information, such as different types of embedded text, plots, and tables that can all be used as inputs to train ML/natural language processing (NLP) models, when extracted and prepared in machine readable formats. Currently, both normalized data extraction of use to domain experts and extraction to support development of ML/NLP models are labor intensive and cumbersome manual processes. Automatic extraction of data and information from formats such as PDFs that are optimized for layout and human readability, not machine readability. The PDF (Portable Document Format) Entity Annotation Tool (PEAT) was developed with the goal of allowing users to annotate publications within their current print format, while also allowing those annotations to be captured in a machine-readable format. One of the main issues with traditional annotation tools is that they require transforming the PDF into plain text to facilitate the annotation process. While doing so lessens the technical challenges of annotating data, the user loses all structure and provenance that was inherent in the underlying PDF. Also, textual data extraction from PDFs can be an error prone process. Challenges include identifying sequential blocks of text and a multitude of document formats (multiple columns, font encodings, etc.). As a result of these challenges, using existing tools for development of NLP/ML models directly from PDFs is difficult because the generated outputs are not interoperable. We created a system that allows annotations to be completed on the original PDF document structure, with no plain text extraction. The result is an application that allows for easier and more accurate annotations. In addition, by including a feature that grants the user the ability to easily create a schema, we have developed a system that can be used to annotate text for different domain-centric schemas of relevance to subject matter experts. Different knowledge domains require distinct schemas and annotation tags to support machine learning.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Variation in forest root image annotation by experts, novices, and AI

Abstract Background The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest. Results Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation ( p = 0.01). The CNN net root length change results were closer to manual ( p = 0.08) but there remained substantial variation. Conclusions Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.

Handy, Grace↗
DOE OSTI2024

Semi-automatic image annotation using 3D LiDAR projections and depth camera data

Efficient image annotation is necessary to utilize deep learning object recognition neural networks in nuclear safeguards, such as for the detection and localization of target objects like nuclear material containers (NMCs). This capability can help automate the inventory accounting of different types of NMCs within nuclear storage facilities. The conventional manual annotation process is labor-intensive and time-consuming, hindering the rapid deployment of deep learning models for NMC identifications. This paper introduces a novel semi-automatic method for annotating 2D images of nuclear material containers (NMCs) by combining 3D light detection and ranging (LiDAR) data with color and depth camera images collected from a handheld scan system. The annotation pipeline involves an operator manually marking new target objects on a LiDAR-generated map, and projecting these 3D locations to images, thereby automatically creating annotations from the projections. The semi-automatic approach significantly reduces manual efforts and the expertise in image annotation that is required to perform the task, allowing deep learning models to be trained on-site within a few hours. The paper compares the performance of models trained on datasets annotated through various methods, including semi-automatic, manual, and commercial annotation services. The evaluation demonstrates that the semi-automatic annotation method achieves comparable or superior results, with a mean average precision (mAP) above 0.9, showcasing its efficiency in training object recognition models. Additionally, the paper explores the application of the proposed method to instance segmentation, achieving promising results in detecting multiple types of NMCs in various formations.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2020

*-DCC: A platform to collect, annotate, and explore a large variety of sequencing experiments

Background: Over the past few years the variety of experimental designs and protocols for sequencing experiments increased greatly. To ensure the wide usability of the produced data beyond an individual project, rich and systematic annotation of the underlying experiments is crucial. Findings: We first developed an annotation structure that captures the overall experimental design as well as the relevant details of the steps from the biological sample to the library preparation, the sequencing procedure, and the sequencing and processed files. Through various design features, such as controlled vocabularies and different field requirements, we ensured a high annotation quality, comparability, and ease of annotation. The structure can be easily adapted to a large variety of species. We then implemented the annotation strategy in a user-hosted web platform with data import, query, and export functionality. Conclusions: We present here an annotation structure and user-hosted platform for sequencing experiment data, suitable for lab-internal documentation, collaborations, and large-scale annotation efforts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

kb_DRAM: annotation and metabolic profiling of genomes with DRAM in KBase

Microbial genome annotation is the process of identifying structural and functional elements in DNA sequences and subsequently attaching biological information to those elements. DRAM is a tool developed to annotate bacterial, archaeal, and viral genomes derived from pure cultures or metagenomes. DRAM goes beyond traditional annotation tools by distilling multiple gene annotations to genome level summaries of functional potential. Despite these benefits, a downside of DRAM is the requirement of large computational resources, which limits its accessibility. Further, it did not integrate with downstream metabolic modeling tools that require genome annotation. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated with the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point-and-click interface, as well as generate genome-scale metabolic models from DRAM annotations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Comparison of automated chemical-guided segmentation and human annotation of soil organic matter in X-ray microcomputed tomography imaging in contrasted soil types

Soil organic matter (OM) formation and persistence is strongly influenced by the spatial distribution of organic substrates and microscale soil heterogeneity by dictating OM accessibility to microorganisms. However, traditional size and/or density fractionation techniques disrupt aggregate architecture, eliminating spatial information needed to fully understand intra-aggregate OM distribution. To quantify three-dimensional OM spatial distribution and automate segmentation in X-ray microcomputed tomography (µCT) imaging without human annotation bias, we developed an iodine gas vapor (I2) based staining workflow that eliminates labor-intensive manual annotation while maintaining segmentation accuracy, using aggregates from four taxonomically diverse soils (Xerofluvent, Haploxeroll Sphagnofibrist, Palehumult) with an 8-fold range of soil organic carbon. Human annotation of 10 µCT slices by the experienced and inexperienced annotators resulted in variations up to 3% in the Dice similarity coefficient (DSC), reflecting a degree of inherent subjectivity of manual labeling. Such inconsistencies are expected to compound as the number of manually annotated slices increases. Dual-energy µCT imaging at 33.1 keV (below the iodine (I) K-edge) and 33.2 keV (above the I K-edge) was used to resolve aggregate microstructure following I2 staining. The automated image subtraction pipeline identified OM regions by the I Kedge induced brightness increases, achieving DSC values of 0.58–0.83 relative to an experienced annotator. Sensitivity analyses revealed that the reconstruction alpha value—optimized via the open-source tool TomocuPy—and the 3D registration slice count were the primary determinants of accuracy, providing a novel benchmark for dual-energy soil imaging. The pipeline without GPU acceleration achieved 9.6 to 43.2 times faster than manual annotation. Using GPU-accelerated image post-processing and affine transformation matrices, the pipeline successfully segmented OM elements for large-scale datasets (3232×3232 pixel, 2048 slices) within ~5200 s from raw file acquisition to segmented output. The high-throughput approach enables the quantification of OM spatial distribution across diverse and heterogeneous soil.

Soil microbial biomass↗
DOE OSTI2021

Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO)

Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying gene function, especially within model organisms. Unprecedented expansion of the scientific literature and validation of the predicted proteins have increased both data value and the challenges of keeping pace. Capturing literature-based functional annotations is limited by the ability of biocurators to handle the massive and rapidly growing scientific literature. Within the community-oriented wiki framework for GO annotation called the Gene Ontology Normal Usage Tracking System (GONUTS), we describe an approach to expand biocuration through crowdsourcing with undergraduates. This multiplies the number of high-quality annotations in international databases, enriches our coverage of the literature on normal gene function, and pushes the field in new directions. From an intercollegiate competition judged by experienced biocurators, Community Assessment of Community Annotation with Ontologies (CACAO), we have contributed nearly 5,000 literature-based annotations. Many of those annotations are to organisms not currently well-represented within GO. Over a 10-year history, our community contributors have spurred changes to the ontology not traditionally covered by professional biocurators. The CACAO principle of relying on community members to participate in and shape the future of biocuration in GO is a powerful and scalable model used to promote the scientific enterprise. It also provides undergraduate students with a unique and enriching introduction to critical reading of primary literature and acquisition of marketable skills.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Sensitive and error-tolerant annotation of protein-coding DNA with BATH

We present BATH, a tool for highly sensitive annotation of protein-coding DNA based on direct alignment of that DNA to a database of protein sequences or profile hidden Markov models (pHMMs). BATH is built on top of the HMMER3 code base, and simplifies the annotation workflow for pHMM-based translated sequence annotation by providing a straightforward input interface and easy-to-interpret output. BATH also introduces novel frameshift-aware algorithms to detect frameshift-inducing nucleotide insertions and deletions (indels). BATH matches the accuracy of HMMER3 for annotation of sequences containing no errors, and produces superior accuracy to all tested tools for annotation of sequences containing nucleotide indels. These results suggest that BATH should be used when high annotation sensitivity is required, particularly when frameshift errors are expected to interrupt protein-coding regions, as is true with long-read sequencing data and in the context of pseudogenes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Accuracy-Based Annotation Quality Score (ABAQS) v1.0

Assessing genome annotation quality is crucial for downstream analyses, but current methods are inadequate for eukaryotes. We present Accuracy-Based Annotation Quality Score (ABAQS), a novel, minimal-data-driven method that comprehensively assesses annotation quality. ABAQS evaluates multiple factors, including genome completeness, gene model validity, and protein profile accuracy, outperforming other metrics like BUSCO and PSAURON. We applied ABAQS to over 2500 eukaryotic genomes and showed its robustness and effectiveness in evaluating genome annotation quality, making it a valuable tool for researchers working with genomic data. ABAQS reveals significant variation in annotation quality and highlights the importance of filtering in improving annotation quality and accuracy.

Haridas, Sajeet [Lawrence Berkeley National Labora↗
DOE OSTI2020

P finder: genomic and metagenomic annotation of RNase P RNA gene (rnpB)

Abstract Background The rnpB gene encodes for an essential catalytic RNA (RNase P). Like other essential RNAs, RNase P’s sequence is highly variable. However, unlike other essential RNAs (i.e. tRNA, 16 S, 6 S,...) its structure is also variable with at least 5 distinct structure types observed in prokaryotes. This structural variability makes it labor intensive and challenging to create and maintain covariance models for the detection of RNase P RNA in genomic and metagenomic sequences. The lack of a facile and rapid annotation algorithm has led to the rnpB gene being the most grossly under annotated essential gene in completed prokaryotic genomes with only a 24% annotation rate. Here we describe the coupling of the largest RNase P RNA database with the local alignment scoring algorithm to create the most sensitive and rapid prokaryote rnpB gene identification and annotation algorithm to date. Results Of the 2772 completed microbial genomes downloaded from GenBank only 665 genomes had an annotated rnpB gene. We applied P Finder to these genomes and were able to identify 2733 or nearly 99% of the 2772 microbial genomes examined. From these results four new rnpB genes that encode the minimal T-type P RNase P RNAs were identified computationally for the first time. In addition, only the second C-type RNase P RNA was identified in Sphaerobacter thermophilus . Of special note, no RNase P RNAs were detected in several obligate endosymbionts of sap sucking insects suggesting a novel evolutionary adaptation. Conclusions The coupling of the largest RNase P RNA database and associated structure class identification with the P Finder algorithm is both sensitive and rapid, yielding high quality results to aid researchers annotating either genomic or metagenomic data. It is the only algorithm to date that can identify challenging RNAse P classes such as C-type and the minimal T-type RNase P RNAs. P Finder is written in C# and has a user-friendly GUI that can run on multiple 64-bit windows platforms (Windows Vista/7/8/10). P Finder is free available for download at https://github.com/JChristopherEllis/P-Finder as well as a small sample RNase P RNA file for testing.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Structure-aware annotation of leucine-rich repeat domains

Protein domain annotation is typically done by predictive models such as HMMs trained on sequence motifs. However, sequence-based annotation methods are prone to error, particularly in calling domain boundaries and motifs within them. These methods are limited by a lack of structural information accessible to the model. With the advent of deep learning-based protein structure prediction, existing sequenced-based domain annotation methods can be improved by taking into account the geometry of protein structures. We develop dimensionality reduction methods to annotate repeat units of the Leucine Rich Repeat solenoid domain. The methods are able to correct mistakes made by existing machine learning-based annotation tools and enable the automated detection of hairpin loops and structural anomalies in the solenoid. The methods are applied to 127 predicted structures of LRR-containing intracellular innate immune proteins in the model plant Arabidopsis thaliana and validated against a benchmark dataset of 172 manually-annotated LRR domains.

Xu, Boyan↗
DOE OSTI2020

Draft genome of multiple resistance donor plant Sinapis alba: An insight into SSRs, annotations and phylogenetics

Sinapis alba is a wild member of the Brassicaceae family reported to possess genetic resistance against major biotic and abiotic stresses of oilseed brassicas. However, the resistance nature of S. alba was not exploited generously due to the unavailability of usable genome sequences in public databases. Therefore, the present study was conducted to assemble the first draft genome from raw whole genome shotgun sequences with annotation and develop simple sequence repeat markers for molecular genetics and marker-assisted breeding. Results The raw genome sequences had 96x coverage on the Illumina platform with 170 Gbp data. The developed assembly by SOAPdenovo2 has ~459 Mbp genome size covered in 403,423 contigs with an average size of 1138.04 bp. The assembly was BLASTX with Arabidopsis thaliana which showed 32.9% positive hits between both plants. The top hit species distribution analysis showed the highest similarity with A. thaliana. A total of 809,597 GO level annotations were recorded after BLASTX results, and 34,012 sequences were annotated with different enzyme codes grouped under seven classes. The gene prediction tool AUGUSTUS identified 113,107 probable genes with an average size of 684 bp. The biochemical pathway annotation assigned 16,119 potential genes to 152 KEGG maps and 1751 enzyme codes. The development of potential SSRs from the de-novo assembly yielded 70731 unique primer pairs. Out of 159 randomly selected SSR markers for validation, 149 successfully amplified in S. alba. However, 10 SSR markers did not amplify during the validation experiment. Conclusion The annotated genome assembly with a large number of SSRs was developed in the present study. To the best of our knowledge, this is the first report of S. alba genome assembly development, annotation, and SSRs mining to date. The data presented here will be a very important resource for future crop improvement programs, especially for resistant breeding.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A Mixed-Method Design Approach for Empirically Based Selection of Unbiased Data Annotators

Implicit bias embedded in the annotated data is by far the greatest impediment in the effectual use of supervised machine learning models in tasks involving race, ethics, and geopolitical polarization. For societal good and demonstrable positive impact on wider society, it is paramount to carefully select data annotators and rigorously validate the annotation process. Current approaches to selecting annotators are not sufficiently grounded in scientific principles and are limited at the policy-guidance level, thereby rendering them unusable for machine learning practitioners. This work proposes a new approach based on the mixed-methods design that is functional, adaptable, and simpler to implement in selecting unbiased annotators for any machine learning problem. By demonstrating it on a real-world geopolitical problem, we also identified and ranked key inane profile characteristics towards an empirically-based selection of unbiased data annotators.

Thakur, Gautam Malviya↗
DOE OSTI2025

Livewire: Automatic Annotations

Diogenes processes datasets to provide data quality metrics for the Livewire platform and creates standardized data dictionaries from data annotations. Diogenes needs data annotations that clearly outline thenformat and organization of the data. It also relies on the type, class, and unit of each data piece for comprehensive analysis, which it cannot determine independently. The Annotation Tool significantly reduces the time needed to create annotations for Diogenes by generating data annotations with the correct formatting and content. It also employs machine learning and hard-coded models to automatically annotate data class, quality type, and data units.

33 - ADVANCED PROPULSION SYSTEMS↗