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Amanda M. Saravia-Butler

Publications and source records attributed to Amanda M. Saravia-Butler.

9 records
All sourcesNASA NTRSDOE OSTI
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

A Pipeline for Assessing the Quality of Rna-Seq Datasets in GeneLab

Transcriptome profiling by RNA sequencing (RNA-seq) is a powerful approach to identify gene expression changes in organisms exposed to unique environments such as spaceflight. One of the challenges of evaluating RNA-seq data both within and across different space-relevant studies is the ability to control for technical differences, including the use of different library preparation kits, sequencing platforms, RNA yield, and person-to-person variation. To help address this issue, the National Institute of Standards and Technology (NIST, nist.gov) initiated a consortium, at the request of industry and academia, to develop a set of controls for gene expression measurements. The result was a set of 92 unlabeled, polyadenylated transcripts that range from 250 – 2,000 nucleotides in length to mimic natural eukaryotic mRNAs. These External RNA Controls Consortium (ERCC) genes can be used in any RNA-seq experiment, by adding known concentrations of the ERCC genes to samples after RNA extraction, to offer a standard measurement for data comparison. At NASA GeneLab, we employ these controls as part of our standard operating procedures for every in-house RNA-seq study to assess the limit of detection, dynamic range, and power of differential expression analysis both within and across experiments. Here we will discuss the use, benefits, and limitations of ERCC genes and other types of controls, such as universal RNA references, to generate quality control information for RNA-seq studies conducted at GeneLab.

GeneLab↗
NASA NTRSDate not supplied

GL4U: Using Space Omics Data to Provide Bioinformatics Training for Students and Educators

NASA’s GeneLab project provides researchers open access to space-relevant experiment multi-omics data that can be mined to understand the effects of spaceflight on biological systems. To maximize the number of scientists who understand and utilize GeneLab data and data processing pipelines, GeneLab has created GeneLab for Colleges and Universities (GL4U). GL4U provides space biology-relevant training in bioinformatics to the next generation of scientists through direct and indirect approaches. The GeneLab team plans to 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 GeneLab’s space-relevant omics data. The GL4U direct training pilot program was conducted in June 2021. During the pilot, students participated in a week-long bootcamp consisting of space biology-specific lectures and hands-on instruction using Jupyter Notebooks to analyze RNA sequence data. This pilot demonstrated the capacity of GL4U for training young scientists and encouraging data re-use. In June 2022, GL4U partnered with Jet Propulsion Laboratory’s (JPL) Planetary Protection Center of Excellence to conduct the indirect training pilot program by training educators at historically black colleges and universities (HBCUs) and minority serving institutions (MSIs). During the educator pilot, participants received materials, training, and will be provided the necessary compute resources to enable them to run the bootcamp at their home institutions or alternatively to adapt the content to implement within existing courses, thereby extending the reach of this initiative. The GL4U training program provides undergraduate students from underrepresented groups the opportunity to learn about NASA and Space Biology, and to enhance their career prospects by gaining hands-on experience analyzing omics data, a skillset that is highly applicable and marketable in the life sciences. Pre- and post-bootcamp surveys were completed by all participants and show the overwhelming success of the bootcamps.

Amanda M. Saravia-Butler↗
NASA NTRSDate not supplied

Batch Effect Correction Methods for NASA GeneLab Transcriptomic Datasets

RNA sequencing (RNA-seq) data from space biology experiments promise to yield invaluable insights into the effects of spaceflight on terrestrial biology. However, sample numbers from each study are low due to limited crew availability, hardware, and space. To increase statistical power, spaceflight RNA-seq datasets from different missions are often aggregated together. However, this can introduce technical variation or "batch effects", often due to differences in sample handling, sample processing, and sequencing platforms. Several computational methods have been developed to correct for technical batch effects, thereby reducing their impact on true biological signals. In this study, we combined 7 mouse liver RNA-seq datasets from NASA GeneLab (part of the NASA Open Science Data Repository) to evaluate several common batch effect correction methods (ComBat and ComBat-seq from the sva R package, and Median Polish, Empirical Bayes, and ANOVA from the MBatch R package). We quantitatively evaluated the ability of these methods to correct for technical batch variables in space biology RNA-seq data using the following criteria: BatchQC, principal component analysis, dispersion separability criterion, log fold change correlation, and differential gene expression analysis. Each batch variable / correction method combination was then assessed using a custom scoring approach to identify the optimal correction method for the combined dataset, by geometrically probing the space of all allowable scoring functions to yield an aggregate volume-based scoring measure. Finally, we describe the way in which the GeneLab multi-study analysis and visualization portal will allow users to examine the presence or absence of batch effects using multiple metrics. If the user chooses to perform batch effect correction, the scoring approach described here can be implemented to identify the optimal correction method to use for their specific combined dataset prior to analysis.

Lauren M. Sanders↗
NASA NTRSDate not supplied

Elevating the Quality of Space Omics Sequencing Data: Innovations and Methodologies from NASA GeneLab Sample Processing Laboratory

NASA’s GeneLab, part of the NASA Open Science Data Repository, is a space-related database that hosts a diverse range of transcriptomics, proteomics, epigenomics and genomics data. The NASA GeneLab Sample Processing Laboratory (SPL) generates omics data from biological experiments conducted aboard the International Space Station, Space Shuttle and space related ground experiments, this omics data then hosted on the GeneLab repository. Samples generated such experiments pose numerous technical challenges such as small experimental sample size, variance in dissection times, limited tissue preservation methods, prolonged storage time, and more. GeneLab SPL team had developed specialized expertise in nucleic acid extraction, library preparation and sequencing of such biological samples via extensive training and years of experience. In order to ensure data accuracy and consistency across experiments, SPL has developed standardized protocols for each species and tissue type. These protocols in conjunction with quality control metrics and data standards are crucial in generating of high-quality data. SPL protocols and standards have been developed in collaboration with the scientific community and had been made publicly available on the GeneLab portal, guaranteeing comparability of datasets across spaceflight experiments. To ensure reliability of data generation, SPL leverages cutting-edge innovations in laboratory automation for sample processing. By leveraging these state-of-the-art platforms, SPL achieves high levels of data reproducibility while significantly minimizing sources of bias and variability, especially across experiments with large numbers of samples. Over the past few years, the space biology investigator community has accessed SPL-generated data from the Open Science Data Repository for a myriad of data re-analysis and re-use studies. We observe a trend that in-house SPL-generated data consistently outperforms outsourced sequencing data in terms of technical standards, quality control metrics, timeliness of data delivery, and sequencing and reagent efficiency. Superior data generation has and will continue to enable discoveries in disease, diagnostic tools, and the biological effects of long duration spaceflight.

GeneLab↗
NASA NTRSDate not supplied

Enabling Open and Interoperable Science: Multi-Omics Data Processing Platform with NASA GeneLab Standardized Bioinformatics Workflows for Space and Earth Research

Multi-omics biological data continues to be generated at an astounding pace. Genomics, transcriptomics, metabolomics, and proteomics, or collectively known as multi-omics data, are used to assess biological functions, and provide invaluable insights into human, animal, plant, and environmental health both on Earth and in Space. Despite the abundance of these valuable data, the need for bioinformatics expertise, particularly as it relates to the niche filed of space biology, and a lack of accessible resources for processing these data limit their usefulness in deriving biological insights. The NASA Open Science Data Repository (OSDR) provides access to omics data from various spaceflight and analog studies. To enhance the accessibility and reusability of these data, GeneLab (part of OSDR) designs and implements standardized, community-driven, open-source bioinformatics workflows to transform raw omics data into standardized processed data. Currently, GeneLab-processed data from hundreds of space studies have been reused for meta-analyses. This has led to new insights and scientific publications that extend beyond the initial research, thereby enriching our understanding of molecular-scale biological responses to the space environment. To make these bioinformatics workflows open and accessible, GeneLab teamed up with DOE-funded initiatives, including the National Microbiome Data Collaborative (NMDC), to create the NASA EDGE [Empowering the Development of Genomics Expertise] Bioinformatics web-based platform. NASA EDGE utilizes shared compute resources to run the GeneLab standardized bioinformatics workflows, which eliminates the need for researchers to have their own high performance computing cluster. The web-based platform makes complicated biological analyses incredibly easy to perform, thus expanding the reach of these analyses to bioinformatics novices, students, and even citizen scientists enabling them to contribute to scientific discoveries and progress. The authors will demonstrate how the NASA EDGE platform can be used to process microbial omics data hosted on OSDR as well as user-generated omics datasets using GeneLab’s standard workflows.

Amanda M. Saravia-Butler↗