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NASA NTRSDate not supplied

NASA GeneLab Multi-study Visualization Portal

NASA GeneLab has helped advance the field of Space Biology by providing a public repository where researchers can store, share, analyze and visualize the results of space flight related omics experiments. The GeneLab data visualization portal allows any user, regardless of bioinformatics knowledge or access to computational resources, to interact with the experimental data, draw their own conclusions, and gain insights about the effects of space on living systems. These tools help democratize scientific research and foster the NASA Open Science initiative. The new multi-study feature of the GeneLab visualization platform allows users to mine study metadata from RNA sequencing (RNA-seq) experiments to identify samples of interest by filtering datasets based on organism, tissue, assay technology type, and/or factor. Once samples are selected from multiple datasets, users can combine and normalize the sample data, then utilize the visualization displays, including Principal Component Analysis (PCA) plots, to assess sample distributions. Finally, users can perform differential gene expression analysis on the combined data and visualize the results through PCA plots, Volcano plots, Pair plots, Heatmap, Ideogram and Gene Set Enrichment Analysis. All user-generated results and visualizations will be available for download. Here, we present a biological study using samples from multiple GeneLab RNA-seq datasets and analyzed using the multi-study visualization platform to demonstrate inter- and intra-study variability, as well as commonly differentially expressed genes between spaceflight and ground control conditions across datasets. This new feature opens a wide range of possibilities and opportunities for further development including combining other assay technology types and integration with batch effect correction techniques and machine learning applications. Overall, this tool allows users to increase the statistical power of individual experiments, validate hypothesis, identify patterns, and opens the door to new and exciting research.

space biology↗
NASA NTRSDate not supplied

GL4U: GeneLab for Colleges and Universities

GeneLab for Colleges and Universities (GL4U) will provide space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab (GL) team will host two annual data processing bootcamps, one for college-level students (direct) and one for college educators (indirect – Training of Trainers), in which participants learn to analyze space-relevant omics data hosted on GL. The first bootcamp took place in early June 2021 with about 30 SJSU undergraduate students and covered space biology-specific lectures and hands-on instruction using Jupyter Notebooks (JNs) for RNA sequence (RNAseq) data analysis. All training materials including the enclosed files listed below will be made publicly available on GitHub. RNAseq Bootcamp Lectures (attached in combined file): Introduction to NASA, Space Biology, GeneLab, and the Command Line: NASA_GL_CL_Intro_FINAL.pdf - DRAFT from initial submission NASA_SB_GL_CL_Intro_FULL.pdf - FINAL version presented during the bootcamp - only minor edits from the draft version RNAseq and Data Processing Overview: RNAseq_Overview_FINAL.pdf - DRAFT from initial submission RNAseq_Overview_FULL.pdf - FINAL version presented during the bootcamp - only minor edits from the draft version Overview of the Statistics Used for RNAseq Data Analysis: SJSU_Statistics_Intro_Lecture_FINAL.pdf - DRAFT from initial submission Statistics_Overview_FULL.pdf - FINAL version presented during the bootcamp - only minor edits from the draft version Completed JNs in HTML format (attached in combined file): Unix_Intro_JN_06-2021_completed.html R_Intro_JN_06-2021_completed.html RNAseq_fastq_to_counts_JN_06-2021_completed.html RNAseq_DGE_JN_06-2021_completed.html RNAseq Bootcamp Recordings (attached): GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day1_Part_1_of_5.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day1_Part_2_of_5.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day1_Part_3_of_5.mp4 *There were issues with the part 4 recording so that is not available GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day1_Part_5_of_5.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day2_Part_1_of_3.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day2_Part_2_of_3.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day2_Part_3_of_3.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day3_Part_1_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day3_Part_2_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day3_Part_3_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day3_Part_4_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day4_Part_1_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day4_Part_2_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day4_Part_3_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day4_Part_4_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day5_Part_1_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day5_Part_2_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day5_Part_3_of_4.mp4 GL4U_RNAseq_Bootcamp_June_2021_Pilot_Day5_Part_4_of_4.mp4

GeneLab↗
NASA NTRSDate not supplied

Optimizing a Small RNAseq Analysis Pipeline for NASA GeneLab Using Open-Source Tools and Libraries

Small RNA sequencing (small RNAseq) is a powerful tool for studying the regulation of gene expression in various organisms. Small RNAseq has been leveraged in space biology research to study how expression of small RNAs, e.g. micro RNAs (miRNAs), small interfering RNAs (siRNAs), and piwi-interacting RNAs (piRNAs), change upon exposure to the space environment. NASA GeneLab currently hosts small RNAseq raw data derived from space-relevant experiments on the Open Science Data Repository (OSDR). To maximize the accessibility of these data to the scientific community, in addition to hosting raw data, which is only interpretable by bioinformaticians, GeneLab plans to process all small RNAseq datasets and make those processed data available to the scientific community via the OSDR. In this study, we present the development of the GeneLab standardized pipeline for processing small RNAseq datasets. Using human, plant, and synthetic small RNAseq datasets, we interrogate various open-source software and publicly available databases to evaluate their accuracy and reproducibility in each step of the pipeline. For quality control and adapter detection and trimming, we evaluated TrimGalore!, FASTX, SeqKit, and DNApi methods to optimize alignment to reference genomes. We compared BWA, Bowtie, and Bowtie2 to determine the optimal alignment tool. For each alignment tool we also assessed various reference databases, including Ensembl reference genomes and different types of small RNA reference databases, including genome, hairpin, and miRNA references from the miRbase and MirGeneDB databases. To quantify the aligned data, we compared SAMtools, HTSeq, and RSEM for counting alignment events from each alignment tool used. Finally, we evaluated various tools, including DESeq2 and EdgeR, for data normalization and subsequent differential expression analysis. We will present the results from our comparative analyses for each pipeline step and propose a consensus pipeline for processing small RNAseq data derived from various organisms exposed to the space environment.

SmallRNAseq, NASA GeneLab, quality control, adapte↗
NASA NTRSDate not supplied

The Nasa SRA Process as It Relates to Open-Source Workflows Developed for GeneLab Data Processing

To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.

Software Release Authorization↗
NASA NTRSDate not supplied

The NASA SRA Process as it Relates to Open-Source Workflows Developed for GeneLab Data Processing

To release open, standards-compliant processed data sets in the Open Science Data Repository (OSDR), the GeneLab Data Processing team works with the scientific community through the OSDR Analysis Working Groups to design and build open-source data processing pipelines. Once baselined internally, these pipelines are wrapped into workflows and published on the NASA GeneLab Data Processing public GitHub repository along with detailed instructions for installation and use. Each workflow must be approved through NASA's Software Release Authorization (SRA) process prior to publishing. However, the SRA process lacks sufficient documentation and clarity regarding which forms are applicable for new open-source software that utilizes publicly available 3rd party tools, and the SRA process can take several months to complete, making sharing software outside of NASA cumbersome and in contradiction with the concept of Open Science. Furthermore, the SRA process was designed as a one-size fits all approach and thus many of the questions asked are not applicable to our open-source workflows. Here we describe the software provided on the NASA GeneLab Data Processing GitHub repository, summarize our experiences with the SRA process to release these software, and propose a more stream-lined approach for review of open-source projects.

Software Release Authorization↗
NASA NTRS2019

Cross Kingdom Analysis of Data Within the GeneLab Repository Identifies a Potential Conserved Response of Life to the Stress Associated with Spaceflight

It is important to determine the health risks and potential survival for astronauts associated with long-term space missions. This entails not only understanding the impact the space environment will have on humans, but also how it will affect other organisms needed for humans to survive in space such as plants. In addition, it has been reported in the literature that hundreds of genes seem to be conserved and/or transferred between different organisms from bacteria, archaea, fungi, microorganisms, and plants to animals. Since space travel involves humans in a closed environment over a long period of time, we hypothesize that potential conserved biological factors will occur between the different organisms in that environment possibly due to transfer of genes. Determining the conserved factors that are commonly being regulated in space can shed insight into possible universal master regulators and also determine the symbiotic relationship between the organisms in space. Utilizing NASA's GeneLab Data Repository (a rapidly expanding, curated clustering of spaceflight-related ‘omics-level datasets for all organisms), we were able to uncover a novel pathway and factors that were commonly shared between humans, mice, plants, C. Elegans, and drosophilas. Through ChIP-Seq enrichment analysis techniques utilizing various GeneLab datasets from each species that were flown in space, we found the following factors to be conserved across all species: oxidative stress, DNA damage (through GABPA/NRFs and NFY), SIX5, GTF2B and glutamine synthetase. Such commonalities would likely reflect the effects of factors such as microgravity and the increased radiation exposure inherent in spaceflight on basic physical processes shared by all biological systems at the cellular level. Differences between organismal responses revealed by GeneLab's data should also help understand the unique reactions to life in space that arise from the very different lifestyles of microbes, animals and plants.

Barker, Richard↗
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 NTRSDate not supplied

NASA GeneLab: Open Science for Life in Space

The NASA GeneLab project (genelab.nasa.gov) seeks to get the most from space-relevant biology experiments by providing and maintaining a public database consisting of DNA, RNA, protein, and metabolite data from spaceflight experiments. Since these types of data, referred to as omics data, are difficult to understand for non-bioinformaticians, the GeneLab data processing team works with the scientific community to develop methods to process these data. The processed data found on GeneLab reveals information about which genes are turned on and turned off in the space environment, which helps us understand how space changes our biology and how we can best mitigate these effects to travel deeper into space.

Jonathan Oribello↗
NASA NTRS2016

GeneLab: Multi-Omics Investigation of Rodent Research-1 Bio-Banked Tissues

NASAs Rodent Research (RR) project is playing a critical role in advancing biomedical research on the physiological effects of space environments. Due to the limited resources for conducting biological experiments aboard the International Space Station (ISS), it is imperative to use crew time efficiently while maximizing high-quality science return. NASAs GeneLab project has as its primary objectives to 1) further increase the value of these experiments using a multi-omics, systems biology-based approach, and 2) disseminate these data without restrictions to the scientific community. The current investigation assessed viability of RNA, DNA, and protein extracted from archived RR-1 tissue samples for epigenomic, transcriptomic, and proteomic assays. During the first RR spaceflight experiment, a variety of tissue types were harvested from subjects, snap-frozen or RNAlater-preserved, and then stored at least a year at -80OC after return to Earth. They were then prioritized for this investigation based on likelihood of significant scientific value for spaceflight research. All tissues were made available to GeneLab through the bio-specimen sharing program managed by the Ames Life Science Data Archive and included mouse adrenal glands, quadriceps, gastrocnemius, tibialis anterior, extensor digitorum longus, soleus, eye, and kidney. We report here protocols for and results of these tissue extractions, and thus, the feasibility and value of these kinds of omics analyses. In addition to providing additional opportunities for investigation of spaceflight effects on the mouse transcriptome and proteome in new kinds of tissues, our results may also be of value to program managers for the prioritization of ISS crew time for rodent research activities. Support from the NASA Space Life and Physical Sciences Division and the International Space Station Program is gratefully acknowledged.

GeneLab↗
NASA NTRS2017

Systemic Response to Microgravity: Utilizing GeneLab Datasets to Identify Molecular Targets for Future Hypotheses-Driven Spaceflight Studies

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF(beta)1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF(beta)1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗
NASA NTRS2017

Systemic Microgravity Response: Utilizing GeneLab to Develop Hypotheses for Spaceflight Risks

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗
NASA NTRS2017

Systemic Microgravity Response: Utilizing GeneLab to Develop Hypotheses for Spaceflight Risks

Biological risks associated with microgravity is a major concern for space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. The overall goal of our work is to identify potential master regulators responsible for such responses to microgravity conditions. To do this we utilized the NASA GeneLab database which contains a wide array of omics experiments, including data from: 1) different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); 2) different tissues; and 3) different types of assays that measure epigenetic, transcriptional, and protein expression changes. We have performed meta-analysis identifying potential master regulators involved with systemic responses to microgravity. The analysis used 7 different murine and rat data sets, examining the following tissues: liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius). Using a systems biology approach, we were able to determine that p53 and immune related pathways appear central to pan-tissue microgravity responses. Evidence for a universal response in the form of consistency of change across tissues in regulatory pathways was observed in both STS and ISS experiments with varying durations; while degree of change in expression of these master regulators varied across species and strain, some change in these master regulators was universally observed. Interestingly, certain skeletal muscle (gastrocnemius and soleus) show an overall down-regulation in these genes, while in other types (extensor digitorum longus, tibialis anterior and quadriceps) they are up-regulated, suggesting certain muscle tissues may be compensating for atrophy responses caused by microgravity. Studying these organtissue-specific perturbations in molecular signaling networks, we demonstrate the value of GeneLab in characterizing potential master regulators associated with biological risks for spaceflight.

Microgravity↗
NASA NTRS2018

GeneLab Analysis Working Group Kick-Off Meeting

Goals to achieve for GeneLab AWG - GL vision - Review of GeneLab AWG charter Timeline and milestones for 2018 Logistics - Monthly Meeting - Workshop - Internship - ASGSR Introduction of team leads and goals of each group Introduction of all members Q/A Three-tier Client Strategy to Democratize Data Physiological changes, pathway enrichment, differential expression, normalization, processing metadata, reproducibility, Data federation/integration with heterogeneous bioinformatics external databases The GLDS currently serves over 100 omics investigations to the biomedical community via open access. In order to expand the scope of metadata record searches via the GLDS, we designed a metadata warehouse that collects and updates metadata records from external systems housing similar data. To demonstrate the capabilities of federated search and retrieval of these data, we imported metadata records from three open-access data systems into the GLDS metadata warehouse: NCBI's Gene Expression Omnibus (GEO), EBI's PRoteomics IDEntifications (PRIDE) repository, and the Metagenomics Analysis server (MG-RAST). Each of these systems defines metadata for omics data sets differently. One solution to bridge such differences is to employ a common object model (COM) to which each systems' representation of metadata can be mapped. Warehoused metadata records are then transformed at ETL to this single, common representation. Queries generated via the GLDS are then executed against the warehouse, and matching records are shown in the COM representation (Fig. 1). While this approach is relatively straightforward to implement, the volume of the data in the omics domain presents challenges in dealing with latency and currency of records. Furthermore, the lack of a coordinated has been federated data search for and retrieval of these kinds of data across other open-access systems, so that users are able to conduct biological meta-investigations using data from a variety of sources. Such meta-investigations are key to corroborating findings from many kinds of assays and translating them into systems biology knowledge and, eventually, therapeutics.

GeneLab↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets such as sex or age of the model organism used. In the present study, NASA GeneLab-hosted RNAseq datasets from rodent liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC, to determine statistical differences between datasets before and after correction, Principal Component Analysis, to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the standard approach. Thus, the most robust standard correction will be implemented in the GeneLab Visualization 2.0 platform when datasets are combined.

GeneLab, RNA-seq, Batch Correction↗
NASA NTRSDate not supplied

Evaluation of Correction Methods for NASA GeneLab Transcriptomic Datasets

Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. The results showed that the reference-based approach introduced several additional (and likely artificial) DEGs when compared with the respective standard approach. Of the methods tested, standard ComBat and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

GeneLab↗