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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
NASA NTRS1999

CRITICA: coding region identification tool invoking comparative analysis

Gene recognition is essential to understanding existing and future DNA sequence data. CRITICA (Coding Region Identification Tool Invoking Comparative Analysis) is a suite of programs for identifying likely protein-coding sequences in DNA by combining comparative analysis of DNA sequences with more common noncomparative methods. In the comparative component of the analysis, regions of DNA are aligned with related sequences from the DNA databases; if the translation of the aligned sequences has greater amino acid identity than expected for the observed percentage nucleotide identity, this is interpreted as evidence for coding. CRITICA also incorporates noncomparative information derived from the relative frequencies of hexanucleotides in coding frames versus other contexts (i.e., dicodon bias). The dicodon usage information is derived by iterative analysis of the data, such that CRITICA is not dependent on the existence or accuracy of coding sequence annotations in the databases. This independence makes the method particularly well suited for the analysis of novel genomes. CRITICA was tested by analyzing the available Salmonella typhimurium DNA sequences. Its predictions were compared with the DNA sequence annotations and with the predictions of GenMark. CRITICA proved to be more accurate than GenMark, and moreover, many of its predictions that would seem to be errors instead reflect problems in the sequence databases. The source code of CRITICA is freely available by anonymous FTP (rdp.life.uiuc.edu in/pub/critica) and on the World Wide Web (http:/(/)rdpwww.life.uiuc.edu).

Non-NASA Center↗
NASA NTRS2018

Monitoring Astronaut Health with DNA Sequencing

In recent years microbe a plethora of microbe populations have been identified onboard the ISS (International Space Station). Approaches for real-time tracking of microbes for routine housekeeping and food/water safety monitoring will be critical for mission safety and crew health on future longer duration missions to the Moon or Mars. This work is a proof-of-concept study demonstrating an end-to-end phylogenetic identification and full genome sequencing effort of multiple microbial populations. Our methodology utilized the ISS flight-certified WetLab-2 molecular toolbox and the Biomolecule Sequencer projects for real-time end-to-end on-orbit microbial biological samples processing and molecular analysis with real time results generated utilizing only field "offline" analytic software. For this experiment we colony-cultured several ISS isolated microorganisms before generation of the pre-sequencing library via the automated VolTRAX device which enabled high library turnover with little wet-bench activity or potential future costly astronaut time. The pre-sequencing library is diluted in loading buffer and injected into the MinION sample port, drawn into the nanopore window by capillary action, and sequenced using the MinKnown. 16S and full genome alignment, nucleotide matching, gene identification, and phylogenetic sorting was accomplished utilizing the Epi2me software and the offline NCBI Blast viral, microbiome, and human somatic databases. In short, the methodologies developed herein replace the myriad of specific, often highly targeted microbiological tests used in the clinical laboratory, which would be difficult if not impossible to currently implement aboard the ISS or in deep space, with a single metagenomics test.

genomics↗
NASA NTRS2012

Methods for determining the genetic affinity of microorganisms and viruses

Selecting which sub-sequences in a database of nucleic acid such as 16S rRNA are highly characteristic of particular groupings of bacteria, microorganisms, fungi, etc. on a substantially phylogenetic tree. Also applicable to viruses comprising viral genomic RNA or DNA. A catalogue of highly characteristic sequences identified by this method is assembled to establish the genetic identity of an unknown organism. The characteristic sequences are used to design nucleic acid hybridization probes that include the characteristic sequence or its complement, or are derived from one or more characteristic sequences. A plurality of these characteristic sequences is used in hybridization to determine the phylogenetic tree position of the organism(s) in a sample. Those target organisms represented in the original sequence database and sufficient characteristic sequences can identify to the species or subspecies level. Oligonucleotide arrays of many probes are especially preferred. A hybridization signal can comprise fluorescence, chemiluminescence, or isotopic labeling, etc.; or sequences in a sample can be detected by direct means, e.g. mass spectrometry. The method's characteristic sequences can also be used to design specific PCR primers. The method uniquely identifies the phylogenetic affinity of an unknown organism without requiring prior knowledge of what is present in the sample. Even if the organism has not been previously encountered, the method still provides useful information about which phylogenetic tree bifurcation nodes encompass the organism.

Fox, George E.↗
NASA NTRS1999

Transcription in archaea

Using the sequences of all the known transcription-associated proteins from Bacteria and Eucarya (a total of 4,147), we have identified their homologous counterparts in the four complete archaeal genomes. Through extensive sequence comparisons, we establish the presence of 280 predicted transcription factors or transcription-associated proteins in the four archaeal genomes, of which 168 have homologs only in Bacteria, 51 have homologs only in Eucarya, and the remaining 61 have homologs in both phylogenetic domains. Although bacterial and eukaryotic transcription have very few factors in common, each exclusively shares a significantly greater number with the Archaea, especially the Bacteria. This last fact contrasts with the obvious close relationship between the archaeal and eukaryotic transcription mechanisms per se, and in particular, basic transcription initiation. We interpret these results to mean that the archaeal transcription system has retained more ancestral characteristics than have the transcription mechanisms in either of the other two domains.

NASA Discipline Exobiology↗
NASA NTRS2020

Shedding Light on Microbial Dark Matter with A Universal Language of Life

The majority of microbial genomes have yet to be cultured, and most proteins predicted from microbial genomes or sequenced from the environment cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely on incomplete reference databases that cannot adequately capture the full functional diversity of the microbial tree of life, limiting our ability to model high-level features of biological sequences. The scientific community needs a means to capture the functionally and evolutionarily relevant features underlying biology, independent of our incomplete reference databases. Such a model can form the basis for transfer learning tasks, enabling downstream applications in environmental microbiology, medicine, and bioengineering. Here we present LookingGlass, a deep learning model capturing a “universal language of life”. LookingGlass encodes contextually-aware, functionally and evolutionarily relevant representations of short DNA reads, distinguishing reads of disparate function, homology, and environmental origin. We demonstrate the ability of LookingGlass to be fine-tuned to perform a range of diverse tasks: to identify novel oxidoreductases, to predict enzyme optimal temperature, and to recognize the reading frames of DNA sequence fragments. LookingGlass is the first contextually-aware, general purpose pre-trained “biological language” representation model for short-read DNA sequences. LookingGlass enables functionally relevant representations of otherwise unknown and unannotated sequences, shedding light on the microbial dark matter that dominates life on Earth.

A Hoarfrost↗
NASA NTRS2000

A calmodulin binding protein from Arabidopsis is induced by ethylene and contains a DNA-binding motif

Calmodulin (CaM), a key calcium sensor in all eukaryotes, regulates diverse cellular processes by interacting with other proteins. To isolate CaM binding proteins involved in ethylene signal transduction, we screened an expression library prepared from ethylene-treated Arabidopsis seedlings with 35S-labeled CaM. A cDNA clone, EICBP (Ethylene-Induced CaM Binding Protein), encoding a protein that interacts with activated CaM was isolated in this screening. The CaM binding domain in EICBP was mapped to the C-terminus of the protein. These results indicate that calcium, through CaM, could regulate the activity of EICBP. The EICBP is expressed in different tissues and its expression in seedlings is induced by ethylene. The EICBP contains, in addition to a CaM binding domain, several features that are typical of transcription factors. These include a DNA-binding domain at the N terminus, an acidic region at the C terminus, and nuclear localization signals. In database searches a partial cDNA (CG-1) encoding a DNA-binding motif from parsley and an ethylene up-regulated partial cDNA from tomato (ER66) showed significant similarity to EICBP. In addition, five hypothetical proteins in the Arabidopsis genome also showed a very high sequence similarity with EICBP, indicating that there are several EICBP-related proteins in Arabidopsis. The structural features of EICBP are conserved in all EICBP-related proteins in Arabidopsis, suggesting that they may constitute a new family of DNA binding proteins and are likely to be involved in modulating gene expression in the presence of ethylene.

Non-NASA Center↗
NASA NTRSDate not supplied

GeneLab for High Schools – Bioinformatic Training For Students And Educators

Modern biological sciences are increasingly based on high-throughput molecular techniques, including genomics, transcriptomics, and proteomics. NASA’s GeneLab program has collected extensive data from ‘omics’ studies, curated them into an accessible platform and provided data analysis/visualization tools to facilitate the generation of new hypotheses and research directions. GeneLab for High Schools (GL4HS), launched in 2017, has endeavored to utilize this database and provide tools for students to understand and analyze omics datasets whilst also learning about spaceflight research. The GL4HS program ran in person at Ames from 2017-2019 and has run virtually since 2020. Each year fifteen high school students are trained to analyze and interpret GeneLab transcriptomic data. Additionally, in the last several years we have expanded our “teacher training program” to include 10 teachers total in an effort to enable this program to be utilized in classrooms across the USA. Teachers also join the NASA GeneLab Education Working Group (EWG) enabling support as they implement custom GL4HS modules into their classrooms. The GL4HS program consists of three main components – (1) core learning modules, (2) networking and teamwork, and (3) an independent learning project. Students are also taught critical networking and science communication skills facilitating their ability to ‘sell their science’ in innovative and creative ways. This program has enabled students to learn about biology in space and to have a glimpse into the world of research for the first time. Many of the students in this program shared that the course was transformative to their perception about biological sciences and how it linked to other areas of STEM. The ultimate and long-term goal of GL4HS is to expand the program to multiple locations thereby facilitating the reach of NASA Space Biology beyond NASA-centric regions.

GeneLab↗
NASA NTRS2004

Characterization of Two Microbial Isolates from Andean Lakes in Bolivia

We are currently investigating the biological population present in the highest and least explored perennial lakes on earth in the Bolivian and Chilean Andes, including several volcanic crater lakes of more than 6000 m elevation, in combination of microbiological and molecular biological methods. Our samples were collected in saline lakes of the Laguna Blanca Laguna Verde area in the Bolivian Altiplano and in the Licancabur volcano crater (27 deg. 47 min S/67 deg. 47 min. W) in the ongoing project studying high altitude lakes. The main goal of the project is to look for analogies with Martian paleolakes. These Bolivian lakes can be described as Andean lakes following the classification of Chong. We have attempted to isolate pure cultures and phylogenetically characterize prokaryotes that grew under laboratory conditions. Sediment samples taken from the Licancabur crater lake (LC), Laguna Verde (LV), and Laguna Blanca (LB) were analyzed and cultured using enriched liquid media under both aerobic and anaerobic conditions. All cultures were incubated at room temperature (15 to 20 C) and under light exposure. For the reported isolates, 36 hours incubation were necessary for reaching optimal optical densities to consider them viable cultures. Ten serial dilutions starting from 1% inoculum were required to obtain a suitable enriched cell culture to transfer into solid media. Cultures on solid medium were necessary to verify the formation of colonies in order to isolate pure cultures. Different solid media were prepared using several combinations of both trace minerals and carbohydrates sources in order to fit their nutrient requirements. The microorganisms formed individual colonies on solid media enriched with tryptone, yeast extract and sodium chloride. Cells morphology was studied by optical and electronic microscopy. Rodshape morphologies were observed in most cases. Total bacterial genomic DNA was isolated from 50 ml late-exponential phase culture by using the CTAB miniprep protocol. The 16S rRNA genes were amplified by PCR using both Bacteria- and Archaeauniversal primer sets: 27f and 1492r, 21f and 1492r respectively. Sequences of 16S rRNA gene were determined and initially compared with reference sequences contained in the EMBL nucleotide sequence database by using the BLAST program and were subsequently aligned with 16S rRNA reference sequences in the ARB package (http://www.mikro.biologie.tu-muenchen.de). Aligned sequences were inserted within a stable phylogenetic tree by using the ARB parsimony tool. In this work we report the morphology and phylogenetic characterization of two isolates belonged to Laguna Blanca sediments.

Demergasso, C.↗
NASA NTRS2019

NASA GeneLab Space Omics Database: Expanding from Space to Ionizing Radiation Data on the Ground

NASA GeneLab is an open-access repository for omics datasets generated by biological experiments conducted in space or ground experiments relevant to spaceflight (e.g. simulated cosmic radiation, simulated microgravity, bed rest studies). The GeneLab Data Systems (GLDS) version 4.0 will be available on October 1st 2019, and will provide a state-of-the-art bioinformatics platform for the space biology and radiation communities to upload their data into an omics data commons, to process their data with vetted standard workflows and to compare with existing analyses. Started in 2015 as a repository designed to archive omics data from space experiments, GeneLab has expanded its scope to all ionizing radiation omics experiments conducted on the ground and has put considerable effort in providing carefully characterized radiation metadata on all datasets. GeneLab is also providing processed data derived from the raw data covering a large spectrum of omics (genome, epigenome, transcriptome, epitranscriptome, proteome, metabolome) to help users explore important questions: 1) Which genes or proteins are expressed differently in space for various living organisms? 2) What specific DNA mutations or epigenetic changes happen in space or after exposure to ionizing radiation? and 3) How does genetics affect these responses? Processed data available on GeneLab are derived by standard data analysis workflows vetted by hundreds of scientists who volunteered to join one of the four GeneLab Analysis Working Groups (Animal AWG, Plant AWG, Microbe AWG, Multi-Omics AWG). In this presentation, we will discuss how to bridge the gap between irradiation studies performed on earth and biological experiments conducted in space since the early 1990's. We will discuss how radiation dosimetry was estimated for datasets derived from samples collected during the Space Shuttle era on the International Space Station and on other orbiting platforms. Finally, we will address future strategies regarding dose monitoring in future missions into space, inter-agency efforts to unify data under one umbrella, and knowledge dissemination across the radiation research community and the space biology community.

open-science↗
NASA NTRS2016

Genelab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASA's premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

bioinformatics↗
NASA NTRS2016

GeneLab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASAs premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

spaceflight↗
NASA NTRS2002

Genes encoding calmodulin-binding proteins in the Arabidopsis genome

Analysis of the recently completed Arabidopsis genome sequence indicates that approximately 31% of the predicted genes could not be assigned to functional categories, as they do not show any sequence similarity with proteins of known function from other organisms. Calmodulin (CaM), a ubiquitous and multifunctional Ca(2+) sensor, interacts with a wide variety of cellular proteins and modulates their activity/function in regulating diverse cellular processes. However, the primary amino acid sequence of the CaM-binding domain in different CaM-binding proteins (CBPs) is not conserved. One way to identify most of the CBPs in the Arabidopsis genome is by protein-protein interaction-based screening of expression libraries with CaM. Here, using a mixture of radiolabeled CaM isoforms from Arabidopsis, we screened several expression libraries prepared from flower meristem, seedlings, or tissues treated with hormones, an elicitor, or a pathogen. Sequence analysis of 77 positive clones that interact with CaM in a Ca(2+)-dependent manner revealed 20 CBPs, including 14 previously unknown CBPs. In addition, by searching the Arabidopsis genome sequence with the newly identified and known plant or animal CBPs, we identified a total of 27 CBPs. Among these, 16 CBPs are represented by families with 2-20 members in each family. Gene expression analysis revealed that CBPs and CBP paralogs are expressed differentially. Our data suggest that Arabidopsis has a large number of CBPs including several plant-specific ones. Although CaM is highly conserved between plants and animals, only a few CBPs are common to both plants and animals. Analysis of Arabidopsis CBPs revealed the presence of a variety of interesting domains. Our analyses identified several hypothetical proteins in the Arabidopsis genome as CaM targets, suggesting their involvement in Ca(2+)-mediated signaling networks.

NASA Discipline Plant Biology↗
NASA NTRS2003

Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles

Genome-wide expression profiles contain global patterns that evade visual detection in current gene clustering analysis. Here, a Gene Expression Dynamics Inspector (GEDI) is described that uses self-organizing maps to translate high-dimensional expression profiles of time courses or sample classes into animated, coherent and robust mosaics images. GEDI facilitates identification of interesting patterns of molecular activity simultaneously across gene, time and sample space without prior assumption of any structure in the data, and then permits the user to retrieve genes of interest. Important changes in genome-wide activities may be quickly identified based on 'Gestalt' recognition and hence, GEDI may be especially useful for non-specialist end users, such as physicians. AVAILABILITY: GEDI v1.0 is written in Matlab, and binary Matlab.dll files which require Matlab to run can be downloaded for free by academic institutions at http://www.chip.org/~ge/gedihome.html Supplementary information: http://www.chip.org/~ge/gedihome.html.

Database Management Systems↗
NASA NTRS1991

Systolic array IC for genetic computation

Measuring similarities between large sequences of genetic information is a formidable task requiring enormous amounts of computer time. Geneticists claim that nearly two months of CRAY-2 time are required to run a single comparison of the known database against the new bases that will be found this year, and more than a CRAY-2 year for next year's genetic discoveries, and so on. The DNA IC, designed at HP-ICBD in cooperation with the California Institute of Technology and the Jet Propulsion Laboratory, is being implemented in order to move the task of genetic comparison onto workstations and personal computers, while vastly improving performance. The chip is a systolic (pumped) array comprised of 16 processors, control logic, and global RAM, totaling 400,000 FETS. At 12 MHz, each chip performs 2.7 billion 16 bit operations per second. Using 35 of these chips in series on one PC board (performing nearly 100 billion operations per second), a sequence of 560 bases can be compared against the eventual total genome of 3 billion bases, in minutes--on a personal computer. While the designed purpose of the DNA chip is for genetic research, other disciplines requiring similarity measurements between strings of 7 bit encoded data could make use of this chip as well. Cryptography and speech recognition are two examples. A mix of full custom design and standard cells, in CMOS34, were used to achieve these goals. Innovative test methods were developed to enhance controllability and observability in the array. This paper describes these techniques as well as the chip's functionality. This chip was designed in the 1989-90 timeframe.

Anderson, D.↗
NASA NTRS2001

Rapid evolution of cis-regulatory sequences via local point mutations

Although the evolution of protein-coding sequences within genomes is well understood, the same cannot be said of the cis-regulatory regions that control transcription. Yet, changes in gene expression are likely to constitute an important component of phenotypic evolution. We simulated the evolution of new transcription factor binding sites via local point mutations. The results indicate that new binding sites appear and become fixed within populations on microevolutionary timescales under an assumption of neutral evolution. Even combinations of two new binding sites evolve very quickly. We predict that local point mutations continually generate considerable genetic variation that is capable of altering gene expression.

Non-NASA Center↗
NASA NTRS2014

Robust Informatics Infrastructure Required For ICME: Combining Virtual and Experimental Data

With the increased emphasis on reducing the cost and time to market of new materials, the need for robust automated materials information management system(s) enabling sophisticated data mining tools is increasing, as evidenced by the emphasis on Integrated Computational Materials Engineering (ICME) and the recent establishment of the Materials Genome Initiative (MGI). This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Further, the use of increasingly sophisticated nonlinear, anisotropic and or multi-scale models requires both the processing of large volumes of test data and complex materials data necessary to establish processing-microstructure-property-performance relationships. Fortunately, material information management systems have kept pace with the growing user demands and evolved to enable: (i) the capture of both point wise data and full spectra of raw data curves, (ii) data management functions such as access, version, and quality controls;(iii) a wide range of data import, export and analysis capabilities; (iv) data pedigree traceability mechanisms; (v) data searching, reporting and viewing tools; and (vi) access to the information via a wide range of interfaces. This paper discusses key principles for the development of a robust materials information management system to enable the connections at various length scales to be made between experimental data and corresponding multiscale modeling toolsets to enable ICME. In particular, NASA Glenn's efforts towards establishing such a database for capturing constitutive modeling behavior for both monolithic and composites materials

Mutli-scale models↗
NASA NTRSDate not supplied

GeneLab: The NASA Systems Biology Platform for Space Omics Repository, Analysis and Visualization

The NASA GeneLab project capitalizes on multi-omic technologies to maximize the return on spaceflight experiments. To do this, GeneLab maintains a publicly accessible database (GLDS) that houses spaceflight and spaceflight relevant multi-omics dataand collaborates with NASA principal investigators and projects to generate additional omics data. GeneLab houses more than 220 transcriptomic, proteomic, metabolomic and epigenomic datasets from plant, animal and microbial experiments, with a growing number of these having been produced by the GeneLab sample processing lab. The GLDS contains rich metadata about each experiment and has recently integrated radiation dosimetery data from experiments flown on the Space Shuttle. GeneLab has also recently implemented an effort to present processed data in the GLDS in addition to the raw omics data. The processed data will enable interpretationof the data by a larger group of students, scientists and the general public. Standard pipelines for the transformation of raw data into visualizations were developed by four GeneLab Analysis Working Groups (animals, plants, microbes, multi-omics) comprised of over 120 scientists from NASA, industry, and academia. To explore the data, the GLDS provides users various tools for data analysis, collaborative workspace for file storage and sharing, and a visualization portal. The analysis platform built using the Galaxy toolshed provides access to a broad variety of users including those with limited bioinformatics experience and students to learn how to analyze spaceflight omics data. The visualization portal takes GeneLab one step closer to data democratization by removing all bioinformatics requisites to interpret transcriptomics data hosted in the repository. Discoveries made using GeneLabhave begunand will continue to deepen our understanding of biology, advance the field of genomics, and help to discover cures for diseases, create better diagnostic tools, and ultimately allow astronauts to better withstand the rigors of long-duration spaceflight.

Samrawit Getachew Gebre↗