Understanding the impact of guide RNA (gRNA) and genomic locus on CRISPR-Cas9 activity is crucial to design effective gene editing assays. However, it is challenging to profile Cas9 activity in the endogenous cellular environment. Here we leverage our TRIP technology to integrate ~ 1k barcoded reporter genes in the genomes of mouse embryonic stem cells. We target the integrated reporters (IRs) using RNA-guided Cas9 and characterize induced mutations by sequencing. We report that gRNA-sequence and IR locus explain most variation in mutation efficiency. Predominant insertions of a gRNA-specific nucleotide are consistent with template-dependent repair of staggered DNA ends with 1-bp 5′ overhangs. We confirm that such staggered ends are induced by Cas9 in mouse pre-B cells. To explain observed insertions, we propose a model generating primarily blunt and occasionally staggered DNA ends. Mutation patterns indicate that gRNA-sequence controls the fraction of staggered ends, which could be used to optimize Cas9-based insertion efficiency.

Identifying patterns in temporal data supports complex analyses in several domains, including stock markets (finance) and social interactions (social science). Clinical and biological applications, such as monitoring patient response to treatment or characterizing activity at the molecular level, are also of interest. In particular, researchers seek to gain insight into the dynamics of biological processes, and potential perturbations of these leading to disease, through the discovery of patterns in time series gene expression data. For many years, clustering has remained the standard technique to group genes exhibiting similar response profiles. However, clustering defines similarity across all time points, focusing on global patterns which tend to characterize rather broad and unspecific responses. It is widely believed that local patterns offer additional insight into the underlying intricate events leading to the overall observed behavior. Efficient biclustering algorithms have been devised for the discovery of temporally aligned local patterns in gene expression time series, but the extraction of time-lagged patterns remains a challenge due to the combinatorial explosion of pattern occurrence combinations when delays are considered. We present heuristic approaches enabling polynomial rather than exponential time solutions for the problem.

Disease candidate gene prioritization addresses the association of novel genes with disease susceptibility or progression. Networkbased approaches explore the connectivity properties of biological networks to compute an association score between candidate and diseaserelated genes. Although several methods have been proposed to date, a number of concerns arise: (i) most networks used rely exclusively on curated physical interactions, resulting in poor coverage of the Human genome and leading to sparsity issues; (ii) most methods fail to incorporate interaction confidence weights; (iii) in some cases, relevance scores are computed as local measures based on the direct interactions with the disease-related genes, ignoring potentially relevant indirect interactions. In this study, we seek a robust network-based strategy by evaluating the performance of selected prioritization strategies using genes known to be involved in 29 different diseases.

Background. The ability to monitor changes in expression patterns over time, and to observe the emergence of coherent temporal responses using expression time series, is critical to advance our understanding of complex biological processes. Biclustering has been recognized as an effective method for discovering local temporal expression patterns and unraveling potential regulatory mechanisms. The general biclustering problem is NP-hard. In the case of time series this problem is tractable, and efficient algorithms can be used. However, there is still a need for specialized applications able to take advantage of the temporal properties inherent to expression time series, both from a computational and a biological perspective. Findings. BiGGEsTS makes available state-of-the-art biclustering algorithms for analyzing expression time series. Gene Ontology (GO) annotations are used to assess the biological relevance of the biclusters. Methods for preprocessing expression time series and post-processing results are also included. The analysis is additionally supported by a visualization module capable of displaying informative representations of the data, including heatmaps, dendrograms, expression charts and graphs of enriched GO terms. Conclusion. BiGGEsTS is a free open source graphical software tool for revealing local coexpression of genes in specific intervals of time, while integrating meaningful information on gene annotations. It is freely available at: http://kdbio.inesc-id.pt/software/biggests. We present a case study on the discovery of transcriptional regulatory modules in the response of Saccharomyces cerevisiae to heat stress.