Michele Magrane

A major scientific drive is to characterize the protein-coding genome, which is a primary basis for studying human health. But the fundamental question remains of what has been missed in previous analyses. Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states^1,2,3, with major implications for biomedical science. However, a key gap in knowledge has been which ncORFs produce small microproteins or alternative protein molecules that contribute to the human proteome. Here we report the collaborative efforts of the TransCODE Consortium⁴ to produce a consensus landscape of protein-level evidence for ncORFs. We show that about 25% of a set of 7,264 ncORFs gives rise to detectable peptides in a large-scale analysis of 95,520 proteomics experiments. We develop an annotation framework for ncORF-encoded microproteins as human proteins and codify the new conceptual model of ‘peptideins’ as microproteins that have indeterminate potential as functional proteins. To probe the biological implications of peptideins, we create an evolutionary analysis approach, termed ORF relative branch length (ORBL), and determine that evolutionary constraint is common and associates with observation of ncORF-derived peptides. We then characterize a pan-essential cellular phenotype for one peptidein from the OLMALINC long non-coding RNA. Overall, we generate public research tools supported by GENCODE and PeptideAtlas and advance biomedical discovery for understudied components of the human proteome.

UniProt serves as a leading global data resource for protein sequence and functional information. Behind every UniProt entry lies a story that begins at the laboratory bench. This webinar will trace the journey of protein data from experimental discovery and literature curation to integration within the UniProt Knowledgebase. Participants will gain insight into how UniProt collects, evaluates, and organises functional information, ensuring its accuracy and accessibility. The session will also demonstrate how to explore this information effectively on the UniProt website, helping you to quickly locate the data you need.

One aim of the international Human Proteome Organization (HUPO) Human Proteome Project (HPP) is to obtain high-confidence translation evidence for every human protein-coding gene established in its target list of 19,433 entries based on the protein-coding genes from Ensembl-GENCODE. However, 76 are annotated in UniProtKB (as of release 2024_06) with PE5, indicating skepticism in the protein's existence from a manual curator, so it is unclear if these entries belong in the HPP target list. Here, we review these 76 entries by assembling evidence from the literature, reference databases, and genome alignments with other species to conclude whether these entries should be freed from their PE5 status to become annotated with PE1-4 in UniProtKB. We find that 17 of these have credible translation evidence and therefore should be upgraded to PE1. Another 15 lack translation evidence but have transcription evidence, the evolutionary hallmarks of protein-coding genes, and are presumed to produce functional proteins. 41 have no translational or transcriptional evidence, although they still bear the evolutionary hallmarks of protein-coding genes; currently, it remains unclear if these are protein-coding, so their representation becomes a matter of policy. Only 3 entries still seem best categorized as PE5 and excluded from the HUPO-HPP target list.

UniProt provides the scientific community with a freely accessible collection of protein sequence and functional information and is one of the most widely used protein resources in the world. This webinar will cover what functional information is available in UniProt and where that information comes from. It will also describe how the information is added to the resource and how to find it on the UniProt website.

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO—a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations—evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)—mechanistic models of molecular “pathways” (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project.

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational ‘vocabulary’ of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.