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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 OSTI2018

Navigating the maze: the impact of configurability in bioinformatics software

The bioinformatics software domain contains thousands of applications for automating tasks such as the pairwise alignment of DNA sequences, building and reasoning about metabolic models or simulating growth of an organism. Its end users range from sophisticated developers to those with little computational experience. In response to their needs, developers provide many options to customize the way their algorithms are tuned. Yet there is little or no automated help for the user in determining the consequences or impact of the options they choose. In this paper we describe our experience working with configurable bioinformatics tools. We find limited documentation and help for combining and selecting options along with variation in both functionality and performance. We also find previously undetected faults. We summarize our findings with a set of lessons learned, and present a roadmap for creating automated techniques to interact with bioinformatics software. We believe these will generalize to other types of scientific software.

McDevitt, Mikaela Cashman↗
DOE OSTI2024

Modularization of EDGE Workflows Using Nextflow: Improving the Efficiency and Maintainability of Bioinformatics Software

EDGE is a bioinformatics platform developed in 2016 by researchers at Los Alamos National Laboratory (LANL) to facilitate the analysis of next-generation sequencing data by researchers with varying levels of experience in bioinformatics (Li et al., 2017). Users with single-end, paired-end or long-read sequencing data can provide their reads as input to EDGE and select the combination of workflows to run that are most useful for their research (e.g., quality control of reads, genome assembly, or the taxonomic classification of input reads). Table 1 summarizes the modules available in EDGE. EDGE is available as a web platform at https://edgebioinformatics.org, as installable source code maintained on GitHub under a GPLv3 license, and as a publicly hosted Docker image.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A Tale from the Trenches: Applying Metamorphic and Differential Testing to Bioinformatics Software

Metamorphic and differential testing have been proposed as best practices for testing software that is difficult to test, such as for programs in scientific domains. An assumption is that these approaches can be easily customized and applied to almost any domain. However, scientific software is often data-driven, and metamorphic relations may require significant domain knowledge to develop. In addition, tools are often written for ad-hoc experimentation by the scientists and often embed many assumptions about the importance and representation of different natural phenomena. In this paper, we present our experience applying both metamorphic and differential testing to a set of four computational biology tools that predict the growth of an organism. While our original goal was to evaluate these techniques to improve our system-level testing, we encountered multiple roadblocks along the way. Although we did find faults (some confirmed by developers), we also uncovered a set of challenges, including the considerable manual effort required for (a) defining domain-specific tests, (b) validating correctness, and (c) distinguishing between issues stemming from poor data and those arising from incorrect software.

Marsh, Alexis L [Iowa State University/Ames Labora↗
DOE OSTI2017

bioboxes v510

Software is now both central and essential to modern biology, yet lack of availability, difficult installations, and complex user interfaces make software hard to obtain and use. Containerisation, as exemplified by the Docker platform, has the potential to solve the problems associated with sharing software. The developers propose bioboxes: containers with standardised interfaces to make bioinformatics software interchangeable.

Barton, Michael↗
DOE OSTI2019

EDGE Bioinformatics

This report gives an overview of Empowering the Development of Genomics Expertise (EDGE) Bioinformatics software.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Building international genomics collaboration for global health security

Genome science and technologies are transforming life sciences globally in many ways and becoming a highly desirable area for international collaboration to strengthen global health. The Genome Science Program at the Los Alamos National Laboratory is leveraging a long history of expertise in genomics research to assist multiple partner nations in advancing their genomics and bioinformatics capabilities. The capability development objectives focus on providing a molecular genomics-based scientific approach for pathogen detection, characterization, and biosurveillance applications. The general approaches include introduction of basic principles in genomics technologies, training on laboratory methodologies and bioinformatic analysis of resulting data, procurement, and installation of next-generation sequencing instruments, establishing bioinformatics software capabilities, and exploring collaborative applications of the genomics capabilities in public health. Genome centers have been established with public health and research institutions in the Republic of Georgia, Kingdom of Jordan, Uganda, and Gabon; broader collaborations in genomics applications have also been developed with research institutions in many other countries.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Using KBase to Assemble and Annotate Prokaryotic Genomes

The DOE Systems Biology Knowledgebase is an open–access bioinformatics software and data platform for analyzing plants, microbes, and their communities. KBase enables scientists to create, execute, collaborate on, and share reproducible analyses of their biological data in the context of public data and private collaborator data. For microbiologists researching prokaryotes, KBase offers analysis tools for performing quality control and assessment of Next–Generation Sequencing reads, de novo assembly, genome annotation, and tools for analyzing structural and functional features of genomes. As a result, this unit demonstrates an example workflow for taking a comparative and iterative approach to assembly and annotation of prokaryotic genomes using KBase that can be used by microbiologists seeking to perform isolate analysis in a rapid and reproducible fashion.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Long non-coding RNA CCAT1 promotes glioma cell proliferation via inhibiting microRNA-410

Background and aim: Long non-coding RNAs have been confirmed to play a critical role in various cancers. In the present study, the effect of long non-coding RNA (lncRNA) CCAT1 on glioma cell proliferation and its potential mechanism were investigated. Methods and results: Real-time PCR results showed that lncRNA-CCAT1 expression was significantly upregulated in glioma cancer tissues and cell lines compared with controls. After inhibiting CCAT1 expression in glioma cell line U251 with siRNA-CCAT1 (si-CCAT1), the cell viability and cell colony formation were decreased, the cell cycle was arrested in G1 phase, and the cell apoptosis was increased. As reported in bioinformatics software starbase2.0, a total of 22 microRNAs were potentially targeted by CCAT1. It was confirmed that miR-410 was altered most by si-CCAT1. After up-regulating CCAT1 expression in U251 cells, miR-410 level was decreased. Luciferase reporter assay confirmed that CCAT1 targeted miR-410. Correlation analysis showed that CCAT1 expression was negatively related to miR-410 expression in glioma cancer tissues. In addition, down-regulation of miR-410 reversed the inhibitory effect of si-CCAT1 on glioma proliferation. Conclusion: These data demonstrated that lncRNA-CCAT1 promoted glioma cell proliferation via inhibiting miR-410, providing a new insight about the pathogenesis of glioma proliferation. - Highlights: • lncRNA-CCAT1 expression was significantly upregulated in glioma cancer tissues and cell lines. • Correlation analysis showed that CCAT1 expression was negatively related to miR-410 expression in glioma cancer tissues. • Down-regulation of miR-410 reversed the inhibitory effect of si-CCAT1 on glioma proliferation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

MiR-429 suppresses neurotrophin-3 to alleviate perineural invasion of pancreatic cancer

Highlights: • MiR-429 is downregulated in pancreatic cancer tissues and cells. • MiR-429 functions to inhibit cancer cell proliferation and invasion. • MiR-429 directly targets to NT-3 mRNA. • MiR-429 suppress NT-3 in cancer cells to resist the attraction TrkC in PC12 cells. • NT-3-TrkC signaling promotes PC12 neurite outgrowth and neuropathic pain. Perineural invasion (PNI) potentially increases the risk of relapse and abdominal pain in patients with pancreatic ductal adenocarcinoma (PDAC). However, the underlying mechanisms of PNI of PDAC is incompletely revealed. Our study aimed to investigate roles of miR-429 in modulating PNI in PDAC. We found that miR-429 was downregulated in PDAC cancer tissues and was profoundly decreased in tissues with PNI. It was reduced in nine of the ten examined pancreatic cancer cell lines. MiR-429 mimics restored its cellular expressions in MIA PaCa-2 and BxCP3 cells and significantly suppressed cell viability and invasion of the cancer cells. The online bioinformatic software predicted that neurotrophin-3 (NT-3) was a potential target gene of miR-429. It was showed that NT-3 mRNA elevated in PC cancer tissues, especially in patients presenting PNI. MiR-429 upregulation substantially suppressed the NT-3 mRNA and secretion in cancer cells. Also, the dual luciferase reporter assays confirmed the interaction between miR-429 and NT-3. When co-culturing the two PDAC cells with PC-12 cells, the invaded cell counts significantly increased comparing with the sole culture of cancer cells. However, miR-429 mimic transfection or NT-3 blocking retarded the cancer invasion in the co-culture system. Besides, we found that cancer cells conditioned medium (CM) treatment significantly increased the neurite outgrowth percentage in PC-12 cells, which was suppressed by culturing with CM from miR-429 mimics-transfected cells. In the CM cultured PC-12 cells, NT-3 receptor TrkC as well as pain-related proteins TRPV1 and TRPV2 significantly elevated. Collectively, miR-429 potentially suppressed neurotrophin-3 to alleviate PNI of PDAC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Shotgun metagenome data of a defined mock community using Oxford Nanopore, PacBio and Illumina technologies

Metagenomic sequence data from defined mock communities is crucial for the assessment of sequencing platform performance and downstream analyses, including assembly, binning and taxonomic assignment. We report a comparison of shotgun metagenome sequencing and assembly metrics of a defined microbial mock community using the Oxford Nanopore Technologies (ONT) MinION, PacBio and Illumina sequencing platforms. Our synthetic microbial community BMock12 consists of 12 bacterial strains with genome sizes spanning 3.2-7.2 Mbp, 40-73% GC content, and 1.5-7.3% repeats. Size selection of both PacBio and ONT sequencing libraries prior to sequencing was essential to yield comparable relative abundances of organisms among all sequencing technologies. While the Illumina-based metagenome assembly yielded good coverage with few misassemblies, contiguity was greatly improved by both, Illumina + ONT and Illumina + PacBio hybrid assemblies but increased misassemblies, most notably in genomes with high sequence similarity to each other. Our resulting datasets allow evaluation and benchmarking of bioinformatics software on Illumina, PacBio and ONT platforms in parallel.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Benchmarking second and third-generation sequencing platforms for microbial metagenomics

Shotgun metagenomic sequencing is a common approach for studying the taxonomic diversity and metabolic potential of complex microbial communities. Current methods primarily use second generation short read sequencing, yet advances in third generation long read technologies provide opportunities to overcome some of the limitations of short read sequencing. Here, we compared seven platforms, encompassing second generation sequencers (Illumina HiSeq 300, MGI DNBSEQ-G400 and DNBSEQ-T7, ThermoFisher Ion GeneStudio S5 and Ion Proton P1) and third generation sequencers (Oxford Nanopore Technologies MinION R9 and Pacific Biosciences Sequel II). We constructed three uneven synthetic microbial communities composed of up to 87 genomic microbial strains DNAs per mock, spanning 29 bacterial and archaeal phyla, and representing the most complex and diverse synthetic communities used for sequencing technology comparisons. Our results demonstrate that third generation sequencing have advantages over second generation platforms in analyzing complex microbial communities, but require careful sequencing library preparation for optimal quantitative metagenomic analysis. Our sequencing data also provides a valuable resource for testing and benchmarking bioinformatics software for metagenomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

ULTRA-effective labeling of tandem repeats in genomic sequence

In the age of long read sequencing, genomics researchers now have access to accurate repetitive DNA sequence (including satellites) that, due to the limitations of short read-sequencing, could previously be observed only as unmappable fragments. Tools that annotate repetitive sequence are now more important than ever, so that we can better understand newly uncovered repetitive sequences, and also so that we can mitigate errors in bioinformatic software caused by those repetitive sequences. To that end, we introduce the 1.0 release of our tool for identifying and annotating locally repetitive sequence, ULTRA Locates Tandemly Repetitive Areas (ULTRA). ULTRA is fast enough to use as part of an efficient annotation pipeline, produces state-of-the-art reliable coverage of repetitive regions containing many mutations, and provides interpretable statistics and labels for repetitive regions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Kmer-SSR: a fast and exhaustive SSR search algorithm

Motivation: One of the main challenges with bioinformatics software is that the size and complexity of datasets necessitate trading speed for accuracy, or completeness. To combat this problem of computational complexity, a plethora of heuristic algorithms have arisen that report a ‘good enough’ solution to biological questions. However, in instances such as Simple Sequence Repeats (SSRs), a ‘good enough’ solution may not accurately portray results in population genetics, phylogenetics and forensics, which require accurate SSRs to calculate intra- and inter-species interactions. Results: We present Kmer-SSR, which finds all SSRs faster than most heuristic SSR identification algorithms in a parallelized, easy-to-use manner. The exhaustive Kmer-SSR option has 100% precision and 100% recall and accurately identifies every SSR of any specified length. To identify more biologically pertinent SSRs, we also developed several filters that allow users to easily view a subset of SSRs based on user input. Kmer-SSR, coupled with the filter options, accurately and intuitively identifies SSRs quickly and in a more user-friendly manner than any other SSR identification algorithm. Availability and implementation: The source code is freely available on GitHub at https://github. com/ridgelab/Kmer-SSR.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

BiG-SLiCE 2 v1.0.0

BiG-SLiCE was originally an open source Python-based command line bioinformatics software that offers a highly scalable clustering analysis on biosynthetic gene clusters (BGC) data. It allows a simultaneous analysis of millions of BGCs, exceeding the capability of other existing tools (around one hundred thousands). As a tradeoff, the clustering accuracy is relatively lower and sometimes fall short in corner cases and specific BGC classes such as the RiPPs (Ribosomally-translated, Post-translationally modified Peptides). In BiG-SLiCE V2 (developed in LBNL), the clustering algorithm has been significantly improved to deliver a much accurate result even for RiPPs and other previous corner case classes. Moreover, the speed of the overall pipeline has been improved by 50-100%. Finally, additional features were implemented to support downstream analyses of BiG-SLiCE results, such as customized tabular (TSV/CSV) and columnar (Parquet) outputs.

Kautsar, Satria↗
DOE OSTI2026

V-HAMSTeR v1.0.0

V-HAMSTeR is a bioinformatics software tool designed to predict the hosts of viruses directly from genomic sequences. It can be used by researchers to predict animal, prokaryotic, plant, protist or fungal viral hosts including viruses that may be fragmented or discovered in environmental metagenomic datasets. Features & Uses: The software employs a novel dual-stream deep learning architecture that dynamically fuses implicit sequence embeddings from a genomic foundation model with 13 explicit, handcrafted biological features (e.g., coding density and strand switch rates). To ensure maximum reliability, V=HAMSTeR deploys a 5-fold deep ensemble calibrated via Joint Temperature Scaling, providing users with statistically rigorous confidence probabilities. It also features an automated sequence chunking and mean-pooling module to seamlessly process variable-length contigs. Advantages Over Similar Technologies: Existing tools (e.g., IPEV, RNAVirHost) typically rely on either basic k-mers or isolated neural networks. V-HAMSTeR's hybrid architecture captures both broad genomic context and specific biological motifs that standalone foundation models often miss. Furthermore, unlike competitor tools that struggle with incomplete data or exhibit extreme overconfidence, V-HAMSTeR is explicitly benchmarked and mathematically calibrated for fragmented assemblies (1kb–10kb). This makes it uniquely robust, accurate, and trustworthy for the messy reality of real-world environmental viromics.

Grigson, Susie [Lawrence Berkeley National Laborat↗
DOE OSTI2021

An optimized FM-index library for nucleotide and amino acid search

Abstract Background Pattern matching is a key step in a variety of biological sequence analysis pipelines. The FM-index is a compressed data structure for pattern matching, with search run time that is independent of the length of the database text. Implementation of the FM-index is reasonably complicated, so that increased adoption will be aided by the availability of a fast and flexible FM-index library. Results We present AvxWindowedFMindex (AWFM-index), a lightweight, open-source, thread-parallel FM-index library written in C that is optimized for indexing nucleotide and amino acid sequences. AWFM-index introduces a new approach to storing FM-index data in a strided bit-vector format that enables extremely efficient computation of the FM-index occurrence function via AVX2 bitwise instructions, and combines this with optional on-disk storage of the index’s suffix array and a cache-efficient lookup table for partial k-mer searches. The AWFM-index performs exact match count and locate queries faster than SeqAn3’s FM-index implementation across a range of comparable memory footprints. When optimized for speed, AWFM-index is $$\sim $$ ∼ 2–4x faster than SeqAn3 for nucleotide search, and $$\sim $$ ∼ 2–6x faster for amino acid search; it is also $$\sim $$ ∼ 4x faster with similar memory footprint when storing the suffix array in on-disk SSD storage. Conclusions AWFM-index is easy to incorporate into bioinformatics software, offers run-time performance parameterization, and provides clients with FM-index functionality at both a high-level (count or locate all instances of a query string) and low-level (step-wise control of the FM-index backward-search process). The open-source library is available for download at https://github.com/TravisWheelerLab/AvxWindowFmIndex.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Qiita: rapid, web-enabled microbiome meta-analysis

Multi-omic insights into microbiome function and composition typically advance one study at a time. However, in order for relationships across studies to be fully understood, data must be aggregated into meta-analyses. This makes it possible to generate new hypotheses by finding features that are reproducible across biospecimens and data layers. Qiita dramatically accelerates such integration tasks in a web-based microbiome-comparison platform, which we demonstrate with Human Microbiome Project and Integrative Human Microbiome Project (iHMP) data.

microbiome, bioinformatics software, open source s↗