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NASA NTRS2016

Proteomic Assessment of Fluid Shifts and Association with Visual Impairment and Intracranial Pressure in Twin Astronauts

BACKGROUND: Astronauts participating in long duration space missions are at an increased risk of physiological disruptions. The development of visual impairment and intracranial pressure (VIIP) syndrome is one of the leading health concerns for crew members on long-duration space missions; microgravity-induced fluid shifts and chronic elevated cabin CO2 may be contributing factors. By studying physiological and molecular changes in one identical twin during his 1-year ISS mission and his ground-based co-twin, this work extends a current NASA-funded investigation to assess space flight induced "Fluid Shifts" in association with the development of VIIP. This twin study uniquely integrates physiological and -omic signatures to further our understanding of the molecular mechanisms underlying space flight-induced VIIP. We are: (i) conducting longitudinal proteomic assessments of plasma to identify fluid regulation-related molecular pathways altered by long-term space flight; and (ii) integrating physiological and proteomic data with genomic data to understand the genomic mechanism by which these proteomic signatures are regulated. PURPOSE: We are exploring proteomic signatures and genomic mechanisms underlying space flight-induced VIIP symptoms with the future goal of developing early biomarkers to detect and monitor the progression of VIIP. This study is first to employ a male monozygous twin pair to systematically determine the impact of fluid distribution in microgravity, integrating a comprehensive set of structural and functional measures with proteomic, metabolomic and genomic data. This project has a broader impact on Earth-based clinical areas, such as traumatic brain injury-induced elevations of intracranial pressure, hydrocephalus, and glaucoma. HYPOTHESIS: We predict that the space-flown twin will experience a space flight-induced alteration in proteins and peptides related to fluid balance, fluid control and brain injury as compared to his pre-flight protein/peptide signatures. Conversely, the trajectory of these protein signatures will remain relatively constant in his ground based co-twin. METHODS: We are using proteomic and standard immunoelectrophoresis techniques to delineate the change in protein signatures throughout the course of a long duration space flight in relation to the development of VIIP. We are also applying a novel cell-based metaboloic organ system assay ("Organs on a Plate") to address how these circulating biomarkers affect physiological processes at the cellular and organ level which could result in VIIP symptoms. These molecular data will be correlated with physiological measures (eg. extra and intracellular fluid volume, vascular filling/flow patterns, MRI, and Optic Coherence Tomography. DISCUSSION: Pre- and in-flight data collection is in progress for the space-flown twin, and similar data have been obtained from the ground-based twin. Biosamples will be batch processed when received from ISS after the conclusion of the 1-year mission. Omic and Physiological measures from the twin astronauts will be compared to similar data being collected on twin subjects who participated in simulated microgravity study. bed rest study.

Rana, Brinda K.↗
NASA NTRS2015

Software Development: 3D Animations and Creating User Interfaces for Realistic Simulations

My fall 2015 semester was spent at the Lyndon B. Johnson Space Center working in the Integrated Graphics, Operations, and Analysis Laboratory (IGOAL). My first project was to create a video animation that could tell the story of OMICS. OMICS is a term being used in the field of biomedical science to describe the collective technologies that study biological systems, such as what makes up a cell and how it functions with other systems. In the IGOAL I used a large 23 inch Wacom monitor to draw storyboards, graphics, and line art animations. I used Blender as the 3D environment to sculpt, shape, cut or modify the several scenes and models for the video. A challenge creating this video was to take a term used in biomedical science and describe it in such a way that an 8th grade student can understand. I used a line art style because it would visually set the tone for what we thought was an educational style. In order to get a handle on the perspective and overall feel for the animation without overloading my workspace, I split up the 2 minute animation into several scenes. I used Blender's python scripting capabilities which allowed for the addition of plugins to add or modify tools. The scripts can also directly interact with the objects to create naturalistic patterns or movements. After collecting the rendered scenes, I used Blender's built-in video editing workspace to output the animation. My second project was to write software that emulates a physical system's interface. The interface was to simulate a boat, ROV, and winch system. Simulations are a time and cost effective way to test complicated data and provide training for operators without having to use expensive hardware. We created the virtual controls with 3-D Blender models and 2-D graphics, and then add functionality in C# using the Unity game engine. The Unity engine provides several essential behaviors of a simulator, such as the start and update functions. A framework for Unity, which was developed in the lab, provided a way to place the different widgets on the virtual console dock and have them resize correctly based on the window dimensions.. My task in this project was to create the controls and visualizations for the data coming in from the simulator for the boat portion of the project. I wrote a class for each control window to handle the functionality of that widget. I implemented 11 widgets that make up the ship portion of the simulator. The members of the lab were each masters of their craft and I'm glad I had the opportunity to learn from them. I learned to plan strategically so I could finish this project on time. I allotted time for storyboarding, development, and refinement. In regards to animating I learned to use modifiers like lattice, boolean and build deformers. I also learned how to animate with drivers, how to use the dope sheet, and how to use the graph editor. In coding I learned to limit the chances for bugs by privatizing functions that should be exclusive to their class. I learned how to use the GIT repository to commit, stash and pull the latest build. I learned a bit of everything because I had the chance to see the entire application development process from the artwork, to the implementation.

Gordillo, Orlando Enrique↗
NASA NTRS2015

GeneLab

NASA GeneLab is expected to capture and distribute omics data and experimental and process conditions most relevant to research community in their statistical and theoretical analysis of NASAs omics data.

genetics↗
NASA NTRS2016

Epigenetics Research on the International Space Station

The International Space Station (ISS) is a state-of-the orbiting laboratory focused on advancing science and technology research. Experiments being conducted on the ISS include investigations in the emerging field of Epigenetics. Epigenetics refers to stably heritable changes in gene expression or cellular phenotype (the transcriptional potential of a cell) resulting from changes in a chromosome without alterations to the underlying DNA nucleotide sequence (the genetic code), which are caused by external or environmental factors, such as spaceflight microgravity. Molecular mechanisms associated with epigenetic alterations regulating gene expression patterns include covalent chemical modifications of DNA (e.g., methylation) or histone proteins (e.g., acetylation, phorphorylation, or ubiquitination). For example, Epigenetics ("Epigenetics in Spaceflown C. elegans") is a recent JAXA investigation examining whether adaptations to microgravity transmit from one cell generation to another without changing the basic DNA of the organism. Mouse Epigenetics ("Transcriptome Analysis and Germ-Cell Development Analysis of Mice in Space") investigates molecular alterations in organ-specific gene expression patterns and epigenetic modifications, and analyzes murine germ cell development during long term spaceflight, as well as assessing changes in offspring DNA. NASA's first foray into human Omics research, the Twins Study ("Differential effects of homozygous twin astronauts associated with differences in exposure to spaceflight factors"), includes investigations evaluating differential epigenetic effects via comprehensive whole genome analysis, the landscape of DNA and RNA methylation, and biomolecular changes by means of longitudinal integrated multi-omics research. And the inaugural Genes in Space student challenge experiment (Genes in Space-1) is aimed at understanding how epigenetics plays a role in immune system dysregulation by assaying DNA methylation in immune cells directly in space using miniPCR technology. In addition, NASA's geneLAB campaign covers the epigenome as part of the "expressome", by employing an innovative open source science platform for multi-investigator high throughput utilization of the ISS. Earth benefits of Epigenetics research onboard the ISS range from contributions to the fundamental understanding of epigenetic phenomena with applications in countermeasure development for biomedical conditions, to the generation of integrated strategies for personalized medicine based on unique physiological responses.

Love, John↗
NASA NTRS2016

GeneLab Phase 2: Integrated Search Data Federation of Space Biology Experimental Data

The GeneLab project is a science initiative to maximize the scientific return of omics data collected from spaceflight and from ground simulations of microgravity and radiation experiments, supported by a data system for a public bioinformatics repository and collaborative analysis tools for these data. The mission of GeneLab is to maximize the utilization of the valuable biological research resources aboard the ISS by collecting genomic, transcriptomic, proteomic and metabolomic (so-called omics) data to enable the exploration of the molecular network responses of terrestrial biology to space environments using a systems biology approach. All GeneLab data are made available to a worldwide network of researchers through its open-access data system. GeneLab is currently being developed by NASA to support Open Science biomedical research in order to enable the human exploration of space and improve life on earth. Open access to Phase 1 of the GeneLab Data Systems (GLDS) was implemented in April 2015. Download volumes have grown steadily, mirroring the growth in curated space biology research data sets (61 as of June 2016), now exceeding 10 TB/month, with over 10,000 file downloads since the start of Phase 1. For the period April 2015 to May 2016, most frequently downloaded were data from studies of Mus musculus (39) followed closely by Arabidopsis thaliana (30), with the remaining downloads roughly equally split across 12 other organisms (each 10 of total downloads). GLDS Phase 2 is focusing on interoperability, supporting data federation, including integrated search capabilities, of GLDS-housed data sets with external data sources, such as gene expression data from NIHNCBIs Gene Expression Omnibus (GEO), proteomic data from EBIs PRIDE system, and metagenomic data from Argonne National Laboratory's MG-RAST. GEO and MG-RAST employ specifications for investigation metadata that are different from those used by the GLDS and PRIDE (e.g., ISA-Tab). The GLDS Phase 2 system will implement a Google-like, full-text search engine using a Service-Oriented Architecture by utilizing publicly available RESTful web services Application Programming Interfaces (e.g., GEO Entrez Programming Utilities) and a Common Metadata Model (CMM) in order to accommodate the different metadata formats between the heterogeneous bioinformatics databases. GLDS Phase 2 completion with fully implemented capabilities will be made available to the general public in September 2017.

Space Biology↗
NASA NTRS2016

GeneLab for High Schools: Data Mining for the Next Generation

Modern biological sciences have become increasingly based on molecular biology and high-throughput molecular techniques, such as genomics, transcriptomics, and proteomics. NASA Scientists and the NASA Space Biology Program have aimed to examine the fundamental building blocks of life (RNA, DNA and protein) in order to understand the response of living organisms to space and aid in fundamental research discoveries on Earth. In an effort to enable NASA funded science to be available to everyone, NASA has collected the data from omics studies and curated them in a data system called GeneLab. Whilst most college-level interns, academics and other scientists have had some interaction with omics data sets and analysis tools, high school students often have not. Therefore, the Space Biology Program is implementing a new Summer Program for high-school students that aims to inspire the next generation of scientists to learn about and get involved in space research using GeneLabs Data System. The program consists of three main components core learning modules, focused on developing students knowledge on the Space Biology Program and Space Biology research, Genelab and the data system, and previous research conducted on model organisms in space; networking and team work, enabling students to interact with guest lecturers from local universities and their fellow peers, and also enabling them to visit local universities and genomics centers around the Bay area; and finally an independent learning project, whereby students will be required to form small groups, analyze a dataset on the Genelab platform, generate a hypothesis and develop a research plan to test their hypothesis. This program will not only help inspire high-school students to become involved in space-based research but will also help them develop key critical thinking and bioinformatics skills required for most college degrees and furthermore, will enable them to establish networks with their peers and connections with university Professors that may help them achieve their educational goals.

genelab↗
NASA NTRS2016

Integrating Large Scale Data Sets to Develop Predictive Hypotheses of Low-Dose Radiation-Induced Health Effects

Over one hundred years of radiation biology research has revealed much about the DNA damages induced by the deposition of energy from exposure to ionizing radiation and the subsequent cellular responses. However, there are still significant gaps in our understanding of how these might lead to detrimental health effects, particularly at low doses (100 mGy (milligray)). Recent advances in high throughput omics technologies enable interrogation of induced radiation effects at the genomic, proteomic and metabolomic levels. These include changes in gene expression, protein modifications, e.g., phosphorylation, acetylation, and methylation, and metabolic changes. We will discuss the integration of data obtained from multiple omics platforms to understand radiation dose, and dose rate effects in a complex human tissue model as a function of time. We will use as an example our results on the low dose responses in a 3D human skin model.

ionizing radiation↗
NASA NTRSDate not supplied

NASA GeneLab Platform Utilized for Biological Response to Space Radiation in Animal Models

Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors that will impact the health of astronauts on extended missions outside the protective effects of Earth’s magnetic field. The NASA GeneLab project has detailed information on radiation exposure using animal models with curated dosimetry information for spaceflight experiments. We analyzed multiple GeneLab omics datasets associated with both ground-based and spaceflight radiation studies that included in vivo and in vitro approaches. A range of ions from protons to iron particles with doses from 0.1 Gy to 1.0 Gy for ground studies and samples flown in Low Earth Orbit (LEO) with total doses of 1.0 mGy to 30 mGy were utilized From this analysis we were able to identify distinct biological signatures associating specific ions with specific biological responses due to radiation exposure in space. For example, we discovered changes in mitochondrial function, ribosomal assembly, and immune pathways as a function of dose. We provided a summary of how the GeneLab’s rich database of omics experiments with animal models can be used to generate novel hypotheses to better understand human health risks from GCR exposures.

Afshin Beheshti↗
NASA NTRSDate not supplied

NASA GeneLab Platform Utilized for Space Radiation Dosimetry Biological Response Compared to Radiation Ground Studies

Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors that will impact the health of astronauts on extended missions outside the protective effects of Earth’s magnetic field. The NASA GeneLab project has detailed information on radiation exposure using animal models with curated dosimetry information for spaceflight experiments. We analyzed multiple GeneLab omics datasets associated with both ground-based and spaceflight radiation studies that included in vivo and in vitro approaches. A range of ions from protons to iron particles with doses from 0.1 Gy to 1.0 Gy for ground studies and samples flown in Low Earth Orbit (LEO) with total doses of 1.0 mGy to 30 mGy were utilized From this analysis we were able to identify distinct biological signatures associating specific ions with specific biological responses due to radiation exposure in space. For example, we discovered changes in mitochondrial function, ribosomal assembly, and immune pathways as a function of dose. We provided a summary of how the GeneLab’s rich database of omics experiments with animal models can be used to generate novel hypotheses to better understand human health risks from GCR exposures.

Afshin Beheshti↗
NASA NTRS2017

Microbial Characteristics of ISS Environmental Surfaces

The microbiome of environmental surfaces from the International Space Station were characterized in order to examine the relationship to crew and hardware maintenance. The Microbial Observatory (ISS-MO) experiment generated a microbial census of ISS environments using advanced molecular microbial community analyses along with traditional culture-based methods. Since the “omics” methodologies generated an extensive microbial census, significant insights into spaceflight-induced changes in the populations of beneficial and/or potentially harmful microbes were gained. Surface samples were collected from several ISS surface locations from three flight opportunities, and were returned to Earth via the Soyuz TMA-14M or the Space X Dragon capsule. In addition to cultivation methods, viable microbial burden, iTag-based sequencing, and metagenome analyses were carried out. The cultivable microbial bioburden differed by location and sampling event. Exploring the ISS environmental microbiome revealed presence of opportunistic pathogens and antibiotic resistant microbes. Genes involved in ATP binding cassette transporters, two component systems, and beta-lactam resistance were among a diverse set of metabolic and genetic information processing pathways. Whole genome sequencing (WGS) of 50 ISS strains exhibiting resistance to various antibiotics was carried out. The antibiotic resistant genes deduced from the WGS were compared with the resistomes generated directly from the gene pool of the environmental samples. Two unique Aspergillus fumigatus strains isolated from the ISS were characterized and compared to the experimentally established clinical isolates Af293 and CEA10. A virulence assessment in a neutrophil-deficient larval zebrafish model of invasive aspergillosis indicated that both ISSFT-021 and IF1SW-F4 were significantly more lethal compared to Af293 and CEA10. The findings from this Environmental “Omics” project should be exploited to enhance human health and well-being of a closed system. In other words, the ISS-MO research aims to "translate" findings in fundamental research into medical practice (pathogen detection) and meaningful health outcomes (countermeasure development).

Perry, Jay↗
NASA NTRSDate not supplied

Mitochondria Dysfunction Central in Driving Health Risks Associated with Spaceflight

Background: Determining the biological impact of spaceflight through novel approaches is essential to reduce the health risks to astronauts for long-term space missions. The current established health risks due to spaceflight are only reflecting known symptomatic and physiologic responses and do not reflect early onset of other potential diseases. There are many unknown variables which still need to be identified to fully understand the health impacts due to the environmental factors in space. Materials and Methods: One method to uncover potential novel biological mechanisms responsible for health risks in astronauts is by utilizing NASA’s GeneLab platform (genelab.nasa.gov). GeneLab is public repository that hosts multiple omics datasets generated from space biology experiments that include experiments flown in space, simulated cosmic radiation experiments, and simulated microgravity experiments. This presentation will provide an example of analysis and novel hypothesis generation that is being produced with GeneLab datasets. A comprehensive multi-omics approach was implemented correlating transcriptomics, proteomics, metabolomics, and methylation analysis. Results: We found that cells have stronger overall biological response than the tissues to spaceflight, with mitochondrial activity and innate immunity pathways being heavily impacted. NASA Twin Study results are consistent with a specific alteration in mitochondrial ATP production. Our results indicate that the space environment can directly induce mitochondrial damage, with mitochondrial dysfunctions being a cause for chronic inflammation and both being involved in the development of metabolic disorders that cause changes in lipid metabolism. We also found biological changes occurring during spaceflight with cell cycle, circadian rhythm and olfactory activity pathways can also influence and be influenced by alterations on mitochondrial activity

Afshin Beheshti↗
NASA NTRSDate not supplied

Working at NASA as a (Molecular) Biologist...

Trained as a biologist, I landed my first job at NASA as a mission scientist on the Rodent Research project. As a mission scientist, I led science activities associated with development, flight, and post-flight analysis of rodent research missions on the ISS in consultation with project engineers, operational specialists, mission PI(s), and mission Implementation Partner(s). I also assessed the feasibility of scientific experiments as a technical and scientific expert in rodent research on the ISS. Eager to learn more about the molecular changes that occur as a result of spaceflight exposure, I transitioned to the NASA GeneLab project where I eventually became the data processing lead. NASA’s GeneLab helps scientists understand how the fundamental building blocks of life – DNA, RNA, proteins, and metabolites – change from exposure to the space environment including microgravity and cosmic radiation exposure. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data (collectively known as omics data) alongside essential metadata describing each spaceflight and space-relevant experiment. In order to maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab has started processing and analyzing these datasets to generate differential gene expression data and identify biological and physiological pathways that are dysregulated as a result of spaceflight. The user interface was designed to be accessible to a broad variety of users, including high school and college students who can use it to learn about omics data analysis and space biology. During the CA Space Grant NASA Panel event, I will provide an overview of my work at NASA on both the Rodent Research and GeneLab projects and will conclude by providing resources for opportunities to work with GeneLab and NASA at large.

Amanda M Saravia-Butler↗
NASA NTRSDate not supplied

My Journey to NASA and role as a (Molecular) Biologist…

My journey to NASA started in undergrad, where I performed microbiology research and became hooked in the STEM fields. From there I earned a Ph.D. in Biochemistry and Molecular Biology from Mayo Graduate school where I performed Pancreatic Cancer research and then went on to perform postdoctoral research in Developmental Biology at the University of Miami. Trained as a biologist, I landed my first job at NASA as a mission scientist on the Rodent Research project. As a mission scientist, I led science activities associated with development, flight, and post-flight analysis of rodent research missions on the ISS in consultation with project engineers, operational specialists, mission PI(s), and mission Implementation Partner(s). I also assessed the feasibility of scientific experiments as a technical and scientific expert in rodent research on the ISS. Eager to learn more about the molecular changes that occur as a result of spaceflight exposure, I transitioned to the NASA GeneLab project where I eventually became the data processing lead. NASA’s GeneLab helps scientists understand how the fundamental building blocks of life – DNA, RNA, proteins, and metabolites – change from exposure to the space environment including microgravity and cosmic radiation exposure. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data (collectively known as omics data) alongside essential metadata describing each spaceflight and space-relevant experiment. To maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab has started processing and analyzing these datasets to generate differential gene expression data and identify biological and physiological pathways that are dysregulated as a result of spaceflight. The user interface was designed to be accessible to a broad variety of users, including high school and college students who can use it to learn about omics data analysis and space biology. During the CA Space Grant Women in STEM Panel event, I will provide an overview of my journey to NASA including my work at NASA on both the Rodent Research and GeneLab projects. I will conclude by providing resources for opportunities to work with GeneLab and NASA at large followed by links to programs designed specifically to engage women in STEM fields.

Amanda M Saravia-Butler↗
NASA NTRSDate not supplied

My Journey to NASA and Role as a (Molecular) Biologist…

My journey to NASA started in undergrad, where I performed microbiology research and became hooked in the STEM fields. From there I earned a Ph.D. in Biochemistry and Molecular Biology from Mayo Graduate school where I performed Pancreatic Cancer research and then went on to perform postdoctoral research in Developmental Biology at the University of Miami. Trained as a biologist, I landed my first job at NASA as a mission scientist on the Rodent Research project. As a mission scientist, I led science activities associated with development, flight, and post-flight analysis of rodent research missions on the ISS in consultation with project engineers, operational specialists, mission PI(s), and mission Implementation Partner(s). I also assessed the feasibility of scientific experiments as a technical and scientific expert in rodent research on the ISS. Eager to learn more about the molecular changes that occur as a result of spaceflight exposure, I transitioned to the NASA GeneLab project where I eventually became the data processing lead. NASA’s GeneLab helps scientists understand how the fundamental building blocks of life – DNA, RNA, proteins, and metabolites – change from exposure to the space environment including microgravity and cosmic radiation exposure. GeneLab does so by providing fully coordinated epigenomics, genomics, transcriptomics, proteomics, and metabolomics data (collectively known as omics data) alongside essential metadata describing each spaceflight and space-relevant experiment. To maximize the intelligibility of these data, particularly for users with limited bioinformatics knowledge, GeneLab has started processing and analyzing these datasets to generate differential gene expression data and identify biological and physiological pathways that are dysregulated as a result of spaceflight. The user interface was designed to be accessible to a broad variety of users, including high school and college students who can use it to learn about omics data analysis and space biology. During the NASA Ames ECN at Lincoln HS Virtual Panel event, I will provide an overview of my journey to NASA including my work at NASA on both the Rodent Research and GeneLab projects. I will conclude by providing resources for opportunities to work with GeneLab and NASA at large followed by links to programs designed specifically to engage women in STEM fields.

Amanda M Saravia-Butler↗
NASA NTRSDate not supplied

NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation

The NASA GeneLab project (genelab.nasa.gov) seeks to accelerate space biology research through cataloging and democratizing omics data. Since raw omics data is largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data has greater immediate value to a wide range of users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. Previously, GeneLab developed a standardized pipeline for processing RNAseq data, referred to as the ‘GeneLab RNAseq Consensus Pipeline (RCP)’, in collaboration with GeneLab’s Analysis Working Groups. The work presented here is a Nextflow implementation of GeneLab’s RCP that automates and accelerates data processing of RNASeq datasets hosted on GeneLab. In addition to the core data processing, the workflow also includes staging of GeneLab raw data and a robust verification and validation (V&V) program that runs after each processing step to identify errors in real-time, stop additional downstream computation, and preserve computational resources. The workflow, including the staging and V&V functionality, is open source for others to reuse and modify at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNAseq.

Jonathan Dejesus Oribello↗
NASA NTRSDate not supplied

Open Science for Life in Space: Data Sharing and Tools for Knowledge Discovery

The fast-growing array of space biological data, which in the past was simply archived after minimal analysis, holds great potential if it can be reorganized and formatted for Open Science. Organizing the data for such analysis is a challenge because of its diverse nature (molecular, cellular, tissue, whole organism, behavior; tabular, imagery). Open Science is the concept that the more people have access to scientifically curated data, the more knowledge will be gained. This led NASA to start the development of GeneLab in 2015. GeneLab houses spaceflight and space-analog multi-omics datasets from plant, rodent, small animal, and microbial experiments. The success and knowledge gained from GeneLab led to a new alliance of NASA “Open Science Data Repositories” (OSDR), which include the Ames Life Sciences Data Archive (ALSDA) and the NASA Biological Institutional Scientific Collection (NBISC). Both are adopting the GeneLab data system, so data are more findable, accessible, interoperable, and reusable (FAIR). OSDR systems provide users the ability to upload, download, search, share, analyze, and visualize. Open Science also needs strong confidence in the data, which is gained through building science communities. With ~400 current members, GeneLab and ALSDA formed Analysis Working Groups (AWGs) to provide feedback on processing pipelines, metadata curation standards (for ‘omics and phenotypic-physiological-behavioral assays), and to collaborate in effectively reusing data. The AWG also led to the development of the Radiation Biology Ontology (RBO), ensuring radiation metadata are efficiently captured, connected, and interoperable. Feedback from the AWG provided design input toward the new single point-of-entry data submission portal for all investigators to submit, curate, and share their research data. Space biological data is now maximally open access, collected-curated with rich metadata, and formatted for interoperability to enable systems biology, meta-analysis, knowledge graphs, machine learning, modeling, and other reuse approaches. With potential for further federation of OSDR for data mining with traditional biological and medical databases (NIH, NCI, EBI, etc.), a new era for space biology has begun to support the knowledge discovery necessary for Lunar and Martian missions.

Ryan T Scott↗
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 NTRSDate not supplied

NASA GeneLab RNASeq Consensus Pipeline: A Nextflow Implementation

The NASA GeneLab project (genelab.nasa.gov) seeks to accelerate space biology research through cataloging and democratizing omics data. Since raw omics data is largely inaccessible to non-bioinformaticians, GeneLab works with the scientific community to develop standard processing pipelines to generate and publish processed data. Unlike raw data, processed data has greater immediate value to a wide range of users with varying technical backgrounds and computational capabilities. Standardizing processing workflows is essential to match the pace of raw data generation, ensure reproducibility, and enable standardized processed data for comparison across datasets. Previously, GeneLab developed a standardized pipeline for processing RNAseq data, referred to as the ‘GeneLab RNAseq Consensus Pipeline (RCP)’, in collaboration with GeneLab’s Analysis Working Groups. The work presented here is a Nextflow implementation of GeneLab’s RCP that automates and accelerates data processing of RNASeq datasets hosted on GeneLab. In addition to the core data processing, the workflow also includes staging of GeneLab raw data and a robust verification and validation (V&V) program that runs after each processing step to identify errors in real-time, stop additional downstream computation, and preserve computational resources. The workflow, including the staging and V&V functionality, is open source for others to reuse and modify at https://github.com/nasa/GeneLab_Data_Processing/tree/master/RNAseq.

Jonathan D Oribello↗