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Single-cell RNA sequencing (scRNA-seq) has emerged as the key technique for studying transcriptomics at the single-cell level. In our previous work, we presented the DISCO database (https://www.immunesinglecell.org/) that integrates publicly available human scRNA-seq data. We now introduce an enhanced version of DISCO, which has expanded fourfold to include >100 million cells from >17 thousand samples. It provides uniformly realigned read count tables, curated metadata, integrated tissue and phenotype specific atlases, and harmonized cell type annotations. It also hosts a single-cell enhanced knowledgebase of cell type ontology and gene signatures relating to cell types and phenotypes. Lastly, it offers a suite of tools for data retrieval, integration, annotation, and mapping, allowing users to construct customized atlases and perform integrated analysis with their own data. These tools are also available in a standalone R package for offline analysis., (© The Author(s) 2024. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Many posttranslational modifications (N-myristoylation or glycosylphosphatidylinositol (GPI) lipid anchoring) and localization signals (the peroxisomal targeting signal PTS1) are encoded in short, partly compositionally biased regions at the N- or C-terminus of the protein sequence. These sequence signals are not well defined in terms of amino acid type preferences but they have significant interpositional correlations. Although the number of verified protein examples is small, the quantification of several physical conditions necessary for productive protein binding with the enzyme complexes executing the respective transformations can lead to predictors that recognize the signals from the amino acid sequence of queries alone. Taxon-specific prediction functions are required due to the divergent evolution of the active complexes. The big-Pi tool for the prediction of the C-terminal signal for GPI lipid anchor attachment is available for metazoan, protozoan and plant sequences. The myristoyl transferase (NMT) predictor recognizes glycine N-myristoylation sites (at the N-terminus and for fragments after processing) of higher eukaryotes (including their viruses) and fungi. The PTS1 signal predictor finds proteins with a C-terminus appropriate for peroxisomal import (for metazoa and fungi). Guidelines for application of the three WWW-based predictors (http://mendel.imp.univie.ac.at/) and for the interpretation of their output are described.