Comment

The release of the first telomere-to-telomere (T2T) human genome sequence marks a milestone for human genomics research and holds promise of complete genomes for evolutionary genomic studies. Here we describe the advances that this new human genome assembly represents and explore the potential insights that the complete genome sequence could bring to evolutionary genomics. We also discuss the potential challenges to be faced in applying this new sequencing strategy to a broad spectrum of extant species.

Most research aiming at understanding the molecular foundations of life and disease has focused on a limited set of increasingly well-known proteins while the biological functions of many others remain poorly understood. We propose to form the Understudied Protein Initiative with the objective of reducing the annotation gap by systematically associating uncharacterized proteins with proteins of known function, thereby laying the groundwork for future detailed mechanistic studies.

Here we discuss barriers to reproducibility in regard to microscopes and related hardware, along with best practices for sharing novel designs created using computer-aided design (CAD). We hope to start a fruitful community discussion on how instrument development, especially in microscopy, can become more open and reproducible, ultimately leading to better, more trustworthy science.

Interactions between carbohydrates and the proteins that bind them (lectins) are often some of the first between a host cell and a viral invader. With its highly glycosylated spike protein, SARS-CoV-2 is no exception. Interrogating glycosylation is vital to understand viral infection, yet it has been a challenge. Improvement in methods ranging from mass spectrometry to glycan arrays and modeling simulations are yielding atomic-level information about the glycans that decorate viruses and host cells alike.

This Comment discusses the main animal models that have had a key role in our understanding of the immune and viral dynamics of SARS-CoV-2.

Critical technological advances have enabled the rapid investigations into the immune responses elicited by SARS-CoV-2, the pathogen responsible for the COVID-19 pandemic. We discuss the cutting-edge methods used to deconvolve the B-cell responses against this virus and the impact they have had in the ongoing public health crisis.

High-resolution structural information is critical for rapid development of vaccines and therapeutics against emerging human pathogens. Structural biology methods have been at the forefront of research on SARS-CoV-2 since the beginning of the COVID-19 pandemic. These technologies will continue to be powerful tools to fend off future public health threats.

During the COVID-19 pandemic, genomics and bioinformatics have emerged as essential public health tools. The genomic data acquired using these methods have supported the global health response, facilitated the development of testing methods and allowed the timely tracking of novel SARS-CoV-2 variants. Yet the virtually unlimited potential for rapid generation and analysis of genomic data is also coupled with unique technical, scientific and organizational challenges. Here, we discuss the application of genomic and computational methods for efficient data-driven COVID-19 response, the advantages of the democratization of viral sequencing around the world and the challenges associated with viral genome data collection and processing.

The imminent release of tissue atlases combining multichannel microscopy with single-cell sequencing and other omics data from normal and diseased specimens creates an urgent need for data and metadata standards to guide data deposition, curation and release. We describe a Minimum Information about Highly Multiplexed Tissue Imaging (MITI) standard that applies best practices developed for genomics and for other microscopy data to highly multiplexed tissue images and traditional histology.

The splendid computational success of AlphaFold and RoseTTAFold in solving the 60-year-old problem of protein folding raises an obvious question: what new avenues should structural biology explore? We propose a strong pivot toward the goal of reading mechanism and function directly from the amino acid sequence. This ambitious goal will require new data analytical tools and an extensive database of the atomic-level structural trajectories traced out on energy landscapes as proteins perform their function.

AlphaFold is a neural-network-based approach to predicting protein structures with high accuracy. We describe how it works in general terms and discuss some anticipated impacts on the field of structural biology.

Deep learning has transformed protein structure modeling. Here we relate AlphaFold and RoseTTAFold to classical physically based approaches to protein structure prediction, and discuss the many areas of structural biology that are likely to be affected by further advances in deep learning.

The release of protein structure predictions from AlphaFold will increase the number of protein structural models by almost three orders of magnitude. Structural biology and bioinformatics will never be the same, and the need for incisive experimental approaches will be greater than ever. Combining these advances in structure prediction with recent advances in cryo-electron microscopy suggests a new paradigm for structural biology.

The greatly improved prediction of protein 3D structure from sequence achieved by the second version of AlphaFold in 2020 has already had a huge impact on biological research, but challenges remain; the protein folding problem cannot be considered solved. We expect fierce competition to improve the method even further and new applications of machine learning to help illuminate proteomes and their many interactions.

Rigorous record-keeping and quality control are required to ensure the quality, reproducibility and value of imaging data. The 4DN Initiative and BINA here propose light Microscopy Metadata Specifications that extend the OME Data Model, scale with experimental intent and complexity, and make it possible for scientists to create comprehensive records of imaging experiments.

Deep learning algorithms are powerful tools for analyzing, restoring and transforming bioimaging data. One promise of deep learning is parameter-free one-click image analysis with expert-level performance in a fraction of the time previously required. However, as with most emerging technologies, the potential for inappropriate use is raising concerns among the research community. In this Comment, we discuss key concepts that we believe are important for researchers to consider when using deep learning for their microscopy studies. We describe how results obtained using deep learning can be validated and propose what should, in our view, be considered when choosing a suitable tool. We also suggest what aspects of a deep learning analysis should be reported in publications to ensure reproducibility. We hope this perspective will foster further discussion among developers, image analysis specialists, users and journal editors to define adequate guidelines and ensure the appropriate use of this transformative technology.

To make machine-learning analyses in the life sciences more computationally reproducible, we propose standards based on data, model and code publication, programming best practices and workflow automation. By meeting these standards, the community of researchers applying machine-learning methods in the life sciences can ensure that their analyses are worthy of trust.

We present the AIMe registry, a community-driven reporting platform for AI in biomedicine. It aims to enhance the accessibility, reproducibility and usability of biomedical AI models, and allows future revisions by the community.

Life scientists in Africa have had limited opportunity to participate in international advanced scientific training programs and workshops, which largely benefit researchers in North America, Europe and the Asia–Pacific region. Here, we chronicle the strategies adopted and challenges encountered in organizing Imaging Africa, an all-expenses-paid, continent-wide practical workshop in optical microscopy hosted in South Africa from 13 to 17 January 2020. Our experience can help steer other groups who similarly seek to organize impactful and sustainable training initiatives in Africa.

DOME is a set of community-wide recommendations for reporting supervised machine learning–based analyses applied to biological studies. Broad adoption of these recommendations will help improve machine learning assessment and reproducibility.