Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.

The biological interpretation of gene lists with interesting shared properties, such as up- or down-regulation in a particular experiment, is typically accomplished using gene ontology enrichment analysis tools. Given a list of genes, a gene ontology (GO) enrichment analysis may return hundreds of statistically significant GO results in a “flat” list, which can be challenging to summarize. It can also be difficult to keep pace with rapidly expanding biological knowledge, which often results in daily changes to any of the over 47,000 gene ontologies that describe biological knowledge. GOATOOLS, a Python-based library, makes it more efficient to stay current with the latest ontologies and annotations, perform gene ontology enrichment analyses to determine over- and under-represented terms, and organize results for greater clarity and easier interpretation using a novel GOATOOLS GO grouping method. We performed functional analyses on both stochastic simulation data and real data from a published RNA-seq study to compare the enrichment results from GOATOOLS to two other popular tools: DAVID and GOstats. GOATOOLS is freely available through GitHub: https://github.com/tanghaibao/goatools.

Alternative splicing (AS) generates isoform diversity for cellular identity and homeostasis in multicellular life. Although AS variation has been observed among single cells, little is known about the biological or evolutionary significance of such variation. We developed Expedition, a computational framework consisting of outrigger, a de novo splice graph transversal algorithm to detect AS; anchor, a Bayesian approach to assign modalities and bonvoyage, a visualization tool using non-negative matrix factorization to display modality changes. Applying Expedition to single pluripotent stem cells undergoing neuronal differentiation, we discover that up to 20% of AS exons exhibit bimodality. Bimodal exons are flanked by more conserved intronic sequences harboring distinct cis-regulatory motifs, constitute much of cell-type specific splicing, are highly dynamic during cellular transitions, preserve reading frame and reveal intricacy of cell states invisible to conventional gene expression analysis. Systematic AS characterization in single cells redefines our understanding of AS complexity in cell biology.