In Brazil, training capable bioinformaticians is done, mostly, in graduate programs, sometimes with experiences during the undergraduate period. However, this formation tends to be inefficient in attracting students to the area and mainly in attracting professionals to support research projects in research groups. To solve these issues, participation in short courses is important for training students and professionals in the usage of tools for specific areas that use bioinformatics, as well as in ways to develop solutions tailored to the local needs of academic institutions or research groups. In this aim, the project “Bioinformática na Estrada” (Bioinformatics on the Road) proposed improving bioinformaticians’ skills in undergraduate and graduate courses, primarily in the countryside of the State of Pará, in the Amazon region of Brazil. The project scope is practical courses focused on the areas of interest of the place where the courses are occurring to train and encourage students and researchers to work in this field, reducing the existing gap due to the lack of qualified bioinformatics professionals. Theoretical and practical workshops took place, such as Introduction to Bioinformatics, Computer Science Basics, Applications of Computational Intelligence applied to Bioinformatics and Biotechnology, Computational Tools for Bioinformatics, Soil Genomics and Research Perspectives and Horizons in the Amazon Region. In the end, 444 undergraduate and graduate students from higher education institutions in the state of Pará and other Brazilian states attended the events of the Bioinformatics on the Road project.

Data for sequence comparison of commamox genomes and genes identified. This dataset is associated with the following publication: Camejo, P., J. Santodomingo, K. McMahon, and D. Noguera. Genome-enabled insights into the ecophysiology of the comammox bacterium Ca. Nitrospira nitrosa. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 2(5): 1-16, (2017).

The COVID-19 pandemic has shown that bioinformatics--a multidisciplinary field that combines biological knowledge with computer programming concerned with the acquisition, storage, analysis, and dissemination of biological data--has a fundamental role in scientific research strategies in all disciplines involved in fighting the virus and its variants. It aids in sequencing and annotating genomes and their observed mutations; analyzing gene and protein expression; simulation and modeling of DNA, RNA, proteins and biomolecular interactions; and mining of biological literature, among many other critical areas of research. Studies suggest that bioinformatics skills in the Latin American and Caribbean region are relatively incipient, and thus its scientific systems cannot take full advantage of the increasing availability of bioinformatic tools and data. This dataset is a catalog of bioinformatics software for researchers and professionals working in life sciences. It includes more than 300 different tools for varied uses, such as data analysis, visualization, repositories and databases, data storage services, scientific communication, marketplace and collaboration, and lab resource management. Most tools are available as web-based or desktop applications, while others are programming libraries. It also includes 10 suggested entries for other third-party repositories that could be of use.

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

RNA expression analysis was performed on the corpus luteum tissue at five time points after prostaglandin F2 alpha treatment of midcycle cows using an Affymetrix Bovine Gene v1 Array. The normalized linear microarray data was uploaded to the NCBI GEO repository (GSE94069). Subsequent statistical analysis determined differentially expressed transcripts ± 1.5-fold change from saline control with P ≤ 0.05. Gene ontology of differentially expressed transcripts was annotated by DAVID and Panther. Physiological characteristics of the study animals are presented in a figure. Bioinformatic analysis by Ingenuity Pathway Analysis was curated, compiled, and presented in tables. A dataset comparison with similar microarray analyses was performed and bioinformatics analysis by Ingenuity Pathway Analysis, DAVID, Panther, and String of differentially expressed genes from each dataset as well as the differentially expressed genes common to all three datasets were curated, compiled, and presented in tables. Finally, a table comparing four bioinformatics tools' predictions of functions associated with genes common to all three datasets is presented. These data have been further analyzed and interpreted in the companion article "Early transcriptome responses of the bovine mid-cycle corpus luteum to prostaglandin F2 alpha includes cytokine signaling". Resources in this dataset:Resource Title: Supporting information as Excel spreadsheets and tables. File Name: Web Page, url: http://www.sciencedirect.com/science/article/pii/S2352340917304031?via=ihub#s0070

The size of the Bioinformatics Platforms Market market was valued at USD 16.36 Million in 2023 and is projected to reach USD 27.93 Million by 2032, with an expected CAGR of 7.94% during the forecast period. The Bioinformatics Platforms Market includes the software and tools required to understand biological data that contain genomic, proteomic, or metabolic data. These platforms include support for various applications like drug discovery, individualized medicine, and clinically related diagnostics through helps of data integration, statistical analysis and visualization. Some of the emerging trends that are driving the bioinformatics market are cloud-based bioinformatics solutions to support scalability and collaboration, advanced machine learning and artificial intelligence (AI) technologies to accurately analyze raised significance of multi-omics data integration for profound tumor bioinformatics analysis. Such factors pulling the market ahead include increasing volume of biological data in facets like research and clinical trials, evolving sequencing technologies, along with the increasing requirement for enhanced data management and analysis in genomics and proteomics. Further, the rising usage of bioinformatics for customized treatment and the growing number of research studies in genomics complement the market’s growth. Recent developments include: In June 2022, California's biotechnology research startup LatchBio launched an end-to-end bioinformatics platform for handling big biotech data to accelerate scientific discovery., In March 2022, ARUP launched Rio, a bioinformatics pipeline and analytics platform for better, faster next-generation sequencing test results.. Key drivers for this market are: Increasing Demand for Nucleic Acid and Protein Sequencing, Increasing Initiatives from Governments and Private Organizations; Accelerating Growth of Proteomics and Genomics; Increasing Research on Molecular Biology and Drug Discovery. Potential restraints include: Lack of Well-defined Standards and Common Data Formats for Integration of Data, Data Complexity Concerns and Lack of User-friendly Tools. Notable trends are: Sequence Analysis Platform Segment is Expected Hold a Significant Share Over the Forecast Period.

Over the past year, biology educators and staff at the U.S. Department of Energy Systems Biology Knowledgebase (KBase) initiated a collaborative effort to develop a curriculum for bioinformatics education. KBase is a free web-based platform where anyone can conduct sophisticated and reproducible bioinformatic analyses via a graphical user interface. Here, we demonstrate the utility of KBase as a platform for bioinformatics education, and present a set of modular, adaptable, and customizable instructional units for teaching concepts in Genomics, Metagenomics, Pangenomics, and Phylogenetics. Each module contains teaching resources, publicly available data, analysis tools, and Markdown capability, enabling instructors to modify the lesson as appropriate for their specific course. We present initial student survey data on the effectiveness of using KBase for teaching bioinformatic concepts, provide an example case study, and detail the utility of the platform from an instructor’s perspective. Even as in-person teaching returns, KBase will continue to work with instructors, supporting the development of new active learning curriculum modules. For anyone utilizing the platform, the growing KBase Educators Organization provides an educators network, accompanied by community-sourced guidelines, instructional templates, and peer support, for instructors wishing to use KBase within a classroom at any educational level–whether virtual or in-person.

This dataset tracks annual science proficiency from 2021 to 2022 for Research Laboratory High School-bioinformatic vs. New York and Buffalo City School District

Cumulative number of data packages in the Knowledge Network for Biocomplexity until 2007-06-21This data set records the cumulative number of data packages in the Knowledge Network for Biocomplexity (KNB) data repository through 2007-06-21. A data package represents a set of data files and metadata files that together make a coherent, citable unit for some particular scientific activity. Each data package in the KNB is described by a scientific metadata document and can be composed of one or more data files that contain various segments of the data in question.cumdatasets-20070622.csv

This record includes training materials associated with the Australian BioCommons workshop ‘Make your bioinformatics workflows findable and citable’. This workshop took place on 21 March 2023. Event description Computational workflows are invaluable resources for research communities. They help us standardise common analyses, collaborate with other researchers, and support reproducibility. Bioinformatics workflow developers invest significant time and expertise to create, share, and maintain these resources for the benefit of the wider community and being able to easily find and access workflows is an essential factor in their uptake by the community. Increasingly, the research community is turning to workflow registries to find and access public workflows that can be applied to their research. Workflow registries support workflow findability and citation by providing a central repository and allowing users to search for and discover them easily. This workshop will introduce you to workflow registries and support attendees to register their workflows on the popular workflow registry, WorkflowHub. We’ll kick off the workshop with an introduction to the concepts underlying workflow findability, how it can benefit workflow developers, and how you can make the most of workflow registries to share your computational workflows with the research community. You will then have the opportunity to register your own workflows in WorkflowHub with support from our trainers. Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Index of training materials (PDF): List and description of all materials associated with this event including the name, format, location and a brief description of each file. 2023-03-21_Workflows_slides (PDF): A copy of the slides presented during the workshop Materials shared elsewhere: A recording of the first part of this workshop is available on the Australian BioCommons YouTube Channel: https://youtu.be/2kGKxaPuQN8

The dataset was collected through whole-transcriptome RNA-Sequencing technologies. The processing method was described in the manuscript.

The Bioinformatics Data Analysis Services market is experiencing robust growth, driven by the exponential increase in biological data generated through next-generation sequencing (NGS) and other high-throughput technologies. The market's expansion is fueled by the rising demand for personalized medicine, precision oncology, drug discovery, and agricultural biotechnology. Advancements in cloud computing and artificial intelligence (AI) are further accelerating the adoption of these services, enabling faster and more efficient analysis of complex datasets. Key players like Illumina, Thermo Fisher Scientific, and QIAGEN are strategically investing in R&D and acquisitions to strengthen their market positions and offer comprehensive solutions. The market is segmented based on service type (e.g., genomics, transcriptomics, proteomics), application (e.g., drug discovery, diagnostics), and deployment mode (cloud-based, on-premise). Competitive landscape is characterized by both large established players and smaller specialized companies focusing on niche applications. While the market faces challenges such as data security concerns and the need for skilled bioinformaticians, the overall growth trajectory remains positive. Looking ahead to 2033, the market is projected to maintain a significant Compound Annual Growth Rate (CAGR), fueled by continuous technological innovation and expanding applications. The increasing accessibility of bioinformatics tools and services, coupled with government initiatives promoting genomic research, will further propel market expansion. The integration of big data analytics and AI will play a critical role in unlocking valuable insights from complex biological datasets, leading to breakthroughs in various healthcare and research domains. Furthermore, strategic partnerships and collaborations between bioinformatics companies and research institutions will contribute to the market's continued growth. Despite potential restraints like regulatory hurdles and the high cost of advanced analytical tools, the long-term outlook for the Bioinformatics Data Analysis Services market remains highly promising.

The size of the Computational Biology Industry market was valued at USD XX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 13.33% during the forecast period. The computational biology industry is booming, driven by the growth in volumes of biological data generated by advancing genomics, proteomics, and systems biology. It involves an interdisciplinary approach that links biology, computer science, and mathematics to analyze complicated biological systems and processes-deemed indispensable for drug discovery, personalized medicine, and agricultural biotechnology. The rising incidence of chronic diseases necessitates targeted therapies and precise diagnostics, thereby becoming a key driver for market growth. The tools of computational biology, which include bioinformatics software, machine learning algorithms, and modeling simulations, enable the extraction of meaningful insights from vast datasets, accelerating the pace of scientific discovery. Technological advancements are further enhancing the functionality of computational biology. The way biological data is interpreted in terms of analysis is undergoing a fundamental shift with AI and machine learning being increasingly integrated in data analysis. Moreover, cloud computing makes it easy for researchers to share data as well as collaborate, making innovation in this field flourish. Geographical center, North America, strong existence of research institutions, biotechnology firms, and investments by funding in life sciences research. Asia-Pacific is emerging, with increased investments in the healthcare and biotechnology sectors and growing importance of personalized medicine. Essentially, the overall industry of computational biology would seem to have excellent chances for sustained expansion based on the further advancing nature of technology, be it a need to gain a clearer sense of incredible data sizes or the overall emphasis to expand focus around precision health solutions. Biological science continually advancing, through computation will unlock new sights, it will be driving an innovation engine across every single domain of healthcare delivery services. Recent developments include: February 2023: The Centre for Development of Advanced Computing (C-DAC) launched two software tools critical for research in life sciences. Integrated Computing Environment, one of the products, is an indigenous cloud-based genomics computational facility for bioinformatics that integrates ICE-cube, a hardware infrastructure, and ICE flakes. This software will help securely store and analyze petascale to exascale genomics data., January 2023: Insilico Medicine, a clinical-stage, end-to-end artificial intelligence (AI)-driven drug discovery company, launched the 6th generation Intelligent Robotics Lab to accelerate its AI-driven drug discovery. The fully automated AI-powered robotics laboratory performs target discovery, compound screening, precision medicine development, and translational research.. Key drivers for this market are: Increase in Bioinformatics Research, Increasing Number of Clinical Studies in Pharmacogenomics and Pharmacokinetics; Growth of Drug Designing and Disease Modeling. Potential restraints include: Lack of Trained Professionals. Notable trends are: Industry and Commercials Sub-segment is Expected to hold its Highest Market Share in the End User Segment.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for bioinformatics (computer science program) in the U.S.

The global life science tools and reagents market is experiencing robust growth, projected to reach $89,940 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 3.0% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the burgeoning field of biomedical research fuels demand for advanced tools and reagents across diverse applications, including proteomics, cell biology, epigenetics, metabolomics, and bioinformatics. The increasing prevalence of chronic diseases globally necessitates extensive research and development efforts, further stimulating market growth. Secondly, technological advancements are leading to the development of sophisticated, high-throughput tools that enhance research efficiency and accuracy, thereby attracting significant investments. Thirdly, government funding and initiatives supporting scientific research in various countries are contributing to the expansion of this market. Finally, the growing adoption of personalized medicine and advanced therapies relies heavily on innovative tools and reagents, ensuring continued market growth throughout the forecast period. The market's segmentation reveals promising opportunities within specific application areas. Proteomics and cell biology research consistently hold significant market shares due to their pivotal roles in understanding biological processes and developing new therapies. The emergence of epigenetics and metabolomics as significant research areas also contributes to the growth of the reagents segment. The competitive landscape features numerous established players, including Abbott Laboratories, Thermo Fisher Scientific, and Bio-Rad Laboratories, alongside emerging companies specializing in niche technologies. Geographic segmentation indicates that North America and Europe currently dominate the market, although rapidly developing economies in Asia-Pacific are expected to show significant growth in the coming years, presenting substantial future market potential. The market's growth trajectory remains positive, influenced by ongoing scientific advancements and the continued global focus on life science research and development. This comprehensive report provides an in-depth analysis of the global Life Science Tools and Reagents market, projected to be worth $150 billion by 2028. We delve into market segmentation, key trends, competitive landscape, and future growth projections, equipping stakeholders with actionable insights for strategic decision-making. The report leverages extensive primary and secondary research, encompassing detailed financial data and expert interviews. Keywords: Life Science, Tools, Reagents, Proteomics, Genomics, Cell Biology, Bioinformatics, Market Analysis, Market Research, Industry Trends, Market Size, Market Share, Competitive Landscape.

Bioinformatics Market Outlook

The global bioinformatics market size was projected at $10.4 billion in 2023 and is anticipated to grow to $24.8 billion by 2032, with a compound annual growth rate (CAGR) of 10.2%. This rapid growth is primarily attributed to the increasing demand for bioinformatics tools in genomics and proteomics research, thereby enhancing data interpretation and analysis capabilities. Additionally, the surge in the adoption of cloud-based solutions and the increasing volume of biological data generated through research activities are key factors driving the market growth. Furthermore, the rising emphasis on precision medicine and personalized healthcare approaches plays a significant role in the expansion of this market.

One of the major growth factors driving the bioinformatics market is the vast amount of biological data being generated, necessitating advanced data analysis and management tools. The advent of next-generation sequencing technologies has revolutionized genetic research, leading to exponential data generation. Bioinformatics provides the necessary computational solutions to manage, analyze, and interpret this data efficiently. Moreover, the increasing collaboration between biological scientists and computer experts is further accelerating the development of novel bioinformatics tools, enhancing their application across various domains. This interdisciplinary approach is not only improving research outcomes but also facilitating the discovery of new biological insights.

Another significant growth driver is the rising investment in research and development in the field of genomics and proteomics. Governments and private organizations across the globe are investing heavily in life sciences research to understand complex biological processes and diseases better. These investments are expected to increase the demand for sophisticated bioinformatics tools and services. Additionally, the integration of artificial intelligence and machine learning with bioinformatics is opening new avenues for research, enabling more precise data analysis and prediction models. This technological convergence is expected to provide significant growth opportunities for the bioinformatics market during the forecast period.

The increasing prevalence of chronic diseases and the growing need for personalized medicine are also contributing to the expansion of the bioinformatics market. Personalized medicine, which tailors healthcare to individual patients, relies heavily on bioinformatics to analyze genetic information and develop targeted therapies. As healthcare systems worldwide shift towards more personalized approaches, the demand for bioinformatics solutions is expected to rise significantly. Moreover, bioinformatics plays a crucial role in drug discovery and development processes, providing insights that accelerate the identification of potential drug targets and biomarkers.

The role of Life Sciences Software in the bioinformatics market is becoming increasingly prominent as researchers and healthcare providers seek more sophisticated tools to manage and analyze complex biological data. These software solutions are essential for processing the vast amounts of data generated by modern research techniques, such as next-generation sequencing and mass spectrometry. By providing robust data management and analysis capabilities, Life Sciences Software enables researchers to gain deeper insights into genetic and proteomic information, facilitating the discovery of new therapeutic targets and the development of personalized medicine approaches. As the demand for precision medicine continues to grow, the importance of Life Sciences Software in bioinformatics is expected to rise, driving innovation and market expansion.

Regionally, North America holds the largest share of the bioinformatics market due to the presence of a well-established healthcare infrastructure and significant investments in biotechnological research. The region is home to several leading bioinformatics companies and research institutions, which are at the forefront of innovation and technological advancements. Additionally, the Asia Pacific region is expected to witness the fastest growth during the forecast period, driven by increasing government funding for genomics research and the growing adoption of bioinformatics in emerging economies like China and India. The expansion of biopharmaceutical industries and a rising focus on precision medicine in these regions are further contributing to market growth.

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This record includes training materials associated with the Australian BioCommons workshop ‘Machine Learning in the Life Sciences’. This on 11 June 2024. Event description Machine learning promises to revolutionise life science research by speeding up data analysis, enabling prediction of biological patterns and modelling complex biological systems. But what exactly is machine learning and when should you use it? This hands-on online workshop provides a high-level introduction to machine learning: what it is, its advantages and disadvantages compared to traditional modelling approaches and the types of scenarios where it may be the right tool for the job. Using example datasets and basic machine learning pipelines we contrast a few commonly used algorithms for constructing predictive models and explore some of their trade-offs. We discuss common pitfalls in how machine learning is applied and evaluated, with a focus on its application in the life sciences, to help you recognise overly optimistic results. We discuss how and why such errors arise and strategies to avoid them. Lead trainer: Dr Benjamin Goudey, Research Fellow, Florey Department of Neuroscience and Mental Health Facilitators: Dr Erin Graham, Queensland Cyber Infrastructure Foundation (QCIF) / James Cook University William Pinzon Perez, Queensland Cyber Infrastructure Foundation (QCIF) Dr Giorgia Mori, Sydney Informatics Hub, University of Sydney Joseph McConnell, University of Adelaide Jessica Chung, Melbourne Bioinformatics 0000-0002-0627-0955 Host: Dr Melissa Burke, Australian BioCommons. Training materials Materials are shared under a Creative Commons Attribution 4.0 International agreement unless otherwise specified and were current at the time of the event. Files and materials included in this record: Event metadata (PDF): Information about the event including, description, event URL, learning objectives, prerequisites, technical requirements etc. Schedule (PDF): Schedule describing the timing of sessions for the in person and online events Materials shared elsewhere: This workshop follows the Google Colab Notebook developed by Dr Benjamin Goudey: https://github.com/bwgoudey/IntroMLforLifeScienceWorkshopR

Big data and computational tools have transformed the way we address biological questions. To prepare undergraduates for tomorrow’s challenges, life science curricula should integrate the understanding and use of these tools at all levels.

The life science tools and reagents market, valued at $95650 million in 2025, is projected to experience robust growth, driven by several key factors. The increasing prevalence of chronic diseases globally fuels demand for advanced diagnostic tools and reagents for research and development in pharmaceuticals and biotechnology. Furthermore, the rising adoption of personalized medicine and the burgeoning field of genomics are significantly boosting market expansion. Technological advancements, such as the development of high-throughput screening platforms and automation in laboratory processes, are enhancing efficiency and driving market growth. The proteomics, cell biology, epigenetics, and metabolomics application segments are key contributors to market expansion due to ongoing research initiatives in these areas. Within the types segment, tools like next-generation sequencing platforms and advanced microscopy equipment represent a significant market share, complemented by the consistent demand for a wide range of reagents including antibodies, enzymes, and assay kits. Competition among established players like Thermo Fisher Scientific, Illumina, and Bio-Rad, alongside the emergence of innovative smaller companies, ensures a dynamic and rapidly evolving market landscape. However, market growth is not without its challenges. High research and development costs for innovative tools and reagents can limit market accessibility, especially for smaller research institutions and developing nations. Stringent regulatory approvals and the complexity of intellectual property rights can further hinder market penetration for new entrants. Despite these restraints, the long-term outlook for the life science tools and reagents market remains positive, fuelled by continuous investment in research and development, increasing funding for scientific research from both public and private sources, and the ongoing push for advancements in healthcare technologies. The market is expected to experience a compound annual growth rate (CAGR) exceeding 3.1% through 2033, solidifying its position as a vital sector within the broader life sciences industry.

Leveraging prior viral genome sequencing data to make predictions on whether an unknown, emergent virus harbors a 'phenotype-of-concern' has been a long-sought goal of genomic epidemiology. A predictive phenotype model built from nucleotide-level information alone is challenging with respect to RNA viruses due to the ultra-high intra-sequence variance of their genomes, even within closely related clades. We developed a degenerate k-mer method to accommodate this high intra-sequence variation of RNA virus genomes for modeling frameworks. By leveraging a taxonomy-guided 'group-shuffle-split' cross validation paradigm on complete coronavirus assemblies from prior to October 2018, we trained multiple regularized logistic regression classifiers at the nucleotide k-mer level. We demonstrate the feasibility of this method by finding models accurately predicting withheld SARS-CoV-2 genome sequences as human pathogens and accurately predicting withheld Swine Acute Diarrhea Syndrome coronavirus (SADS-CoV) genome sequences as non-human pathogens. Feature selection using L1 regularization identified several degenerate nucleotide predictor motifs with high model coefficients for the human pathogen class that were present across widely disparate clades of coronaviruses. However, these motifs differed in which genes they were present in, what specific codons were used to encode them, and what the translated amino acid motif was. This emphasizes the importance of a phenetic view of emerging pathogenic RNA viruses, as opposed to the canonical phylogenetic interpretations most commonly used to track and manage viral zoonoses. Applying our model to more recent Orthocoronavirinae genomes deposited since October 2018 yields a novel contextual view of pathogen potential across bat-related, canine-related, porcine-related, and rodent-related coronaviruses and critical adaptations which may have contributed to the emergence of the pandemic SARS-CoV-2 virus. Finally, we discuss the next steps to achieve robust predictive ensembles and the utility of these models (and their associated predictor motifs) to novel biosurveillance protocols that substantially increase the 'pound-for-pound' information content of field-collected sequencing data and make a strong argument for the necessity of routine collection and sequencing of zoonotic viruses.