Simple, Robust Invertebrate DNA Barcoding: Chelex-Based DNA Extraction and Optimized COI Amplification.

Chelex-based DNA extractions are well suited for student DNA barcoding research because they are simple, safe, and inexpensive and can be performed without specialized laboratory equipment, allowing them to be performed in classrooms or at home. Extracted DNA is stable in Chelex solution for at least a week at ambient temperature, allowing collection of DNA samples from remote students. These extractions provide quality DNA for many taxa and are optimal for barcoding invertebrates, especially in combination with novel cytochrome c oxidase I (COI) primer cocktails and PCR cycling conditions.

A Rapid, Equipment-Free DNA Isolation Method for DNA Barcoding.

This rapid, equipment-free DNA isolation procedure using chromatography paper is a simple method that can be performed in less than 30 min and requires no wet lab experience. With minimal expense, it offers an affordable alternative for anyone wanting to explore biodiversity. It also provides an excellent option for use in classrooms or other activities that are time limited. The method works best for plants or lichens, producing stable DNA on Whatman® chromatography paper at room temperature, which can be eluted as needed.

Analysis of DNA Barcodes Using DNA Subway.

DNA Subway makes bioinformatic analysis of DNA barcodes classroom friendly, eliminating the need for software installations or command line tools. Subway bundles research-grade bioinformatics software into workflows with an easy-to-use interface. This chapter covers DNA Subway's DNA barcoding analysis workflow (Blue Line) starting with one or more Sanger sequence reads. During analysis, users can view trace files and sequence quality, pair and align forward and reverse reads, create and trim consensus sequences, perform BLAST searches, select reference data, align multiple sequences, and compute phylogenetic trees. High-quality sequences with the required metadata can also be submitted as barcode sequences to NCBI GenBank.

CyVerse: Cyberinfrastructure for open science.

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

Management, Analyses, and Distribution of the MaizeCODE Data on the Cloud.

MaizeCODE is a project aimed at identifying and analyzing functional elements in the maize genome. In its initial phase, MaizeCODE assayed up to five tissues from four maize strains (B73, NC350, W22, TIL11) by RNA-Seq, Chip-Seq, RAMPAGE, and small RNA sequencing. To facilitate reproducible science and provide both human and machine access to the MaizeCODE data, we enhanced SciApps, a cloud-based portal, for analysis and distribution of both raw data and analysis results. Based on the SciApps workflow platform, we generated new components to support the complete cycle of MaizeCODE data management. These include publicly accessible scientific workflows for the reproducible and shareable analysis of various functional data, a RESTful API for batch processing and distribution of data and metadata, a searchable data page that lists each MaizeCODE experiment as a reproducible workflow, and integrated JBrowse genome browser tracks linked with workflows and metadata. The SciApps portal is a flexible platform that allows the integration of new analysis tools, workflows, and genomic data from multiple projects. Through metadata and a ready-to-compute cloud-based platform, the portal experience improves access to the MaizeCODE data and facilitates its analysis.

Double triage to identify poorly annotated genes in maize: The missing link in community curation.

The sophistication of gene prediction algorithms and the abundance of RNA-based evidence for the maize genome may suggest that manual curation of gene models is no longer necessary. However, quality metrics generated by the MAKER-P gene annotation pipeline identified 17,225 of 130,330 (13%) protein-coding transcripts in the B73 Reference Genome V4 gene set with models of low concordance to available biological evidence. Working with eight graduate students, we used the Apollo annotation editor to curate 86 transcript models flagged by quality metrics and a complimentary method using the Gramene gene tree visualizer. All of the triaged models had significant errors-including missing or extra exons, non-canonical splice sites, and incorrect UTRs. A correct transcript model existed for about 60% of genes (or transcripts) flagged by quality metrics; we attribute this to the convention of elevating the transcript with the longest coding sequence (CDS) to the canonical, or first, position. The remaining 40% of flagged genes resulted in novel annotations and represent a manual curation space of about 10% of the maize genome (~4,000 protein-coding genes). MAKER-P metrics have a specificity of 100%, and a sensitivity of 85%; the gene tree visualizer has a specificity of 100%. Together with the Apollo graphical editor, our double triage provides an infrastructure to support the community curation of eukaryotic genomes by scientists, students, and potentially even citizen scientists.

DNA barcoding Brooklyn (New York): A first assessment of biodiversity in Marine Park by citizen scientists.

DNA barcoding is both an important research and science education tool. The technique allows for quick and accurate species identification using only minimal amounts of tissue samples taken from any organism at any developmental phase. DNA barcoding has many practical applications including furthering the study of taxonomy and monitoring biodiversity. In addition to these uses, DNA barcoding is a powerful tool to empower, engage, and educate students in the scientific method while conducting productive and creative research. The study presented here provides the first assessment of Marine Park (Brooklyn, New York, USA) biodiversity using DNA barcoding. New York City citizen scientists (high school students and their teachers) were trained to identify species using DNA barcoding during a two-week long institute. By performing NCBI GenBank BLAST searches, students taxonomically identified 187 samples (1 fungus, 70 animals and 116 plants) and also published 12 novel DNA barcodes on GenBank. Students also identified 7 ant species and demonstrated the potential of DNA barcoding for identification of this especially diverse group when coupled with traditional taxonomy using morphology. Here we outline how DNA barcoding allows citizen scientists to make preliminary taxonomic identifications and contribute to modern biodiversity research.

Unmet needs for analyzing biological big data: A survey of 704 NSF principal investigators.

In a 2016 survey of 704 National Science Foundation (NSF) Biological Sciences Directorate principal investigators (BIO PIs), nearly 90% indicated they are currently or will soon be analyzing large data sets. BIO PIs considered a range of computational needs important to their work, including high performance computing (HPC), bioinformatics support, multistep workflows, updated analysis software, and the ability to store, share, and publish data. Previous studies in the United States and Canada emphasized infrastructure needs. However, BIO PIs said the most pressing unmet needs are training in data integration, data management, and scaling analyses for HPC-acknowledging that data science skills will be required to build a deeper understanding of life. This portends a growing data knowledge gap in biology and challenges institutions and funding agencies to redouble their support for computational training in biology.

The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences.

The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identity management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning material, and best practice resources to help all researchers make the best use of their data, expand their computational skill set, and effectively manage their data and computation when working as distributed teams. iPlant's platform permits researchers to easily deposit and share their data and deploy new computational tools and analysis workflows, allowing the broader community to easily use and reuse those data and computational analyses.

The iPlant Collaborative: Cyberinfrastructure for Plant Biology.

The iPlant Collaborative (iPlant) is a United States National Science Foundation (NSF) funded project that aims to create an innovative, comprehensive, and foundational cyberinfrastructure in support of plant biology research (PSCIC, 2006). iPlant is developing cyberinfrastructure that uniquely enables scientists throughout the diverse fields that comprise plant biology to address Grand Challenges in new ways, to stimulate and facilitate cross-disciplinary research, to promote biology and computer science research interactions, and to train the next generation of scientists on the use of cyberinfrastructure in research and education. Meeting humanity's projected demands for agricultural and forest products and the expectation that natural ecosystems be managed sustainably will require synergies from the application of information technologies. The iPlant cyberinfrastructure design is based on an unprecedented period of research community input, and leverages developments in high-performance computing, data storage, and cyberinfrastructure for the physical sciences. iPlant is an open-source project with application programming interfaces that allow the community to extend the infrastructure to meet its needs. iPlant is sponsoring community-driven workshops addressing specific scientific questions via analysis tool integration and hypothesis testing. These workshops teach researchers how to add bioinformatics tools and/or datasets into the iPlant cyberinfrastructure enabling plant scientists to perform complex analyses on large datasets without the need to master the command-line or high-performance computational services.

An innovative plant genomics and gene annotation program for high school, community college, and university faculty.

Today's biology educators face the challenge of training their students in modern molecular biology techniques including genomics and bioinformatics. The Dolan DNA Learning Center (DNALC) of Cold Spring Harbor Laboratory has developed and disseminated a bench- and computer-based plant genomics curriculum for biology faculty. In 2007, a five-day "Plant Genomics and Gene Annotation" workshop was held at Florida A&M University in Tallahassee, FL, to enhance participants' knowledge and understanding of plant molecular genetics and assist them in developing and honing their laboratory and computer skills. Florida A&M University is a historically black university with over 95% African-American student enrollment. Sixteen participants, including high school (56%) and community college faculty (25%), attended the workshop. Participants carried out in vitro and in silico experiments with maize, Arabidopsis, soybean, and food products to determine the genotype of the samples. Benefits of the workshop included increased awareness of plant biology research for high school and college level students. Participants completed pre- and postworkshop evaluations for the measurement of effectiveness. Participants demonstrated an overall improvement in their postworkshop evaluation scores. This article provides a detailed description of workshop activities, as well as assessment and long-term support for broad classroom implementation.