Determining the full complement of protein-coding genes is a key goal of genome annotation. The most powerful approach for confirming protein-coding potential is the detection of cellular protein expression through peptide mass spectrometry (MS) experiments. Here, we mapped peptides detected in seven large-scale proteomics studies to almost 60% of the protein-coding genes in the GENCODE annotation of the human genome. We found a strong relationship between detection in proteomics experiments and both gene family age and cross-species conservation. Most of the genes for which we detected peptides were highly conserved. We found peptides for >96% of genes that evolved before bilateria. At the opposite end of the scale, we identified almost no peptides for genes that have appeared since primates, for genes that did not have any protein-like features or for genes with poor cross-species conservation. These results motivated us to describe a set of 2001 potential non-coding genes based on features such as weak conservation, a lack of protein features, or ambiguous annotations from major databases, all of which correlated with low peptide detection across the seven experiments. We identified peptides for just 3% of these genes. We show that many of these genes behave more like non-coding genes than protein-coding genes and suggest that most are unlikely to code for proteins under normal circumstances. We believe that their inclusion in the human protein-coding gene catalogue should be revised as part of the ongoing human genome annotation effort.

Pathogenic variations in the sodium voltage-gated channel alpha subunit 1 (SCN1A) gene are responsible for multiple epilepsy phenotypes, including Dravet syndrome, febrile seizures (FS) and genetic epilepsy with FS plus. Phenotypic heterogeneity is a hallmark of SCN1A-related epilepsies, the causes of which are yet to be clarified. Genetic variation in the non-coding regulatory regions of SCN1A could be one potential causal factor. However, a comprehensive understanding of the SCN1A regulatory landscape is currently lacking. Here, we summarized the current state of knowledge of SCN1A regulation, providing details on its promoter and enhancer regions. We then integrated currently available data on SCN1A promoters by extracting information related to the SCN1A locus from genome-wide repositories and clearly defined the promoter and enhancer regions of SCN1A. Further, we explored the cellular specificity of differential SCN1A promoter usage. We also reviewed and integrated the available human brain-derived enhancer databases and mouse-derived data to provide a comprehensive computationally developed summary of SCN1A brain-active enhancers. By querying genome-wide data repositories, extracting SCN1A-specific data and integrating the different types of independent evidence, we created a comprehensive catalogue that better defines the regulatory landscape of SCN1A, which could be used to explore the role of SCN1A regulatory regions in disease.