MOTIVATION: A crucial component of intuitive data visualization is presenting a hierarchical tree structure with interactive functions. For example, single-cell transcriptomics studies may generate gene expression values with developmental trajectories or cell lineage structures. Two common visualization methods, t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), require two separate figures to depict the distribution of cell types and gene expression data, with low-dimension projections that may not capture the hierarchical structures among cells. RESULTS: Here, we present a JavaScript framework and an interactive web app named Collapsible Tree, which presents values jointly with interactive, expandable, and collapsible lineage structures. For example, the Collapsible Tree presents cellular states and gene expression from single-cell transcriptomics within a single hierarchical plot, enabling comparisons of gene expression across lineages and subtle patterns between sub-lineages. Our framework can facilitate the exploration of complicated value patterns that are not evident in UMAP or t-SNE plots. AVAILABILITY AND IMPLEMENTATION: The Collapsible Tree web interface is available at https://collapsibletree.data2in.net. The JavaScript library source code is available at https://github.com/data2intelligence/collapsible_tree.

Motivation: Genome-wide clustered, regularly interspaced, short palindromic repeat (CRISPR)-Cas9 screen has been widely used to interrogate gene functions. However, the rules to design better libraries beg further refinement. Results: We found single guide RNA (sgRNA) outliers are characterized by higher G-nucleotide counts, especially in regions distal from the PAM motif and are associated with stronger off-target activities. Furthermore, using non-targeting sgRNAs as negative controls lead to strong bias, which can be mitigated by using sgRNAs targeting multiple 'safe harbor' regions. Custom-designed screens confirmed our findings and further revealed that 19 nt sgRNAs consistently gave the best signal-to-noise ratio. Collectively, our analysis motivated the design of a new genome-wide CRISPR/Cas9 screen library and uncovered some intriguing properties of the CRISPR-Cas9 system. Availability and implementation: The MAGeCK workflow is available open source at https://bitbucket.org/liulab/mageck_nest under the MIT license. Supplementary information: Supplementary data are available at Bioinformatics online.

Transcript degradation is a widespread and important mechanism for regulating protein abundance. Two major regulators of transcript degradation are RNA Binding Proteins (RBPs) and microRNAs (miRNAs). We computationally explored whether RBPs and miRNAs cooperate to promote transcript decay. We defined five RBP motifs based on the evolutionary conservation of their recognition sites in 3'UTRs as the binding motifs for Pumilio (PUM), U1A, Fox-1, Nova, and UAUUUAU. Recognition sites for some of these RBPs tended to localize at the end of long 3'UTRs. A specific group of miRNA recognition sites were enriched within 50 nts from the RBP recognition sites for PUM and UAUUUAU. The presence of both a PUM recognition site and a recognition site for preferentially co-occurring miRNAs was associated with faster decay of the associated transcripts. For PUM and its co-occurring miRNAs, binding of the RBP to its recognition sites was predicted to release nearby miRNA recognition sites from RNA secondary structures. The mammalian miRNAs that preferentially co-occur with PUM binding sites have recognition seeds that are reverse complements to the PUM recognition motif. Their binding sites have the potential to form hairpin secondary structures with proximal PUM binding sites that would normally limit RISC accessibility, but would be more accessible to miRNAs in response to the binding of PUM. In sum, our computational analyses suggest that a specific set of RBPs and miRNAs work together to affect transcript decay, with the rescue of miRNA recognition sites via RBP binding as one possible mechanism of cooperativity.

MOTIVATION: Clustering algorithms play an important role in the analysis of biological networks, and can be used to uncover functional modules and obtain hints about cellular organization. While most available clustering algorithms work well on biological networks of moderate size, such as the yeast protein physical interaction network, they either fail or are too slow in practice for larger networks, such as functional networks for higher eukaryotes. Since an increasing number of larger biological networks are being determined, the limitations of current clustering approaches curtail the types of biological network analyses that can be performed. RESULTS: We present a fast local network clustering algorithm SPICi. SPICi runs in time O(V log V+E) and space O(E), where V and E are the number of vertices and edges in the network, respectively. We evaluate SPICi's performance on several existing protein interaction networks of varying size, and compare SPICi to nine previous approaches for clustering biological networks. We show that SPICi is typically several orders of magnitude faster than previous approaches and is the only one that can successfully cluster all test networks within very short time. We demonstrate that SPICi has state-of-the-art performance with respect to the quality of the clusters it uncovers, as judged by its ability to recapitulate protein complexes and functional modules. Finally, we demonstrate the power of our fast network clustering algorithm by applying SPICi across hundreds of large context-specific human networks, and identifying modules specific for single conditions. AVAILABILITY: Source code is available under the GNU Public License at http://compbio.cs.princeton.edu/spici.