Enhancing CRISPR deletion via pharmacological delay of DNA-PKcs.

CRISPR-Cas9 deletion (CRISPR-del) is the leading approach for eliminating DNA from mammalian cells and underpins a variety of genome-editing applications. Target DNA, defined by a pair of double-strand breaks (DSBs), is removed during nonhomologous end-joining (NHEJ). However, the low efficiency of CRISPR-del results in laborious experiments and false-negative results. By using an endogenous reporter system, we show that repression of the DNA-dependent protein kinase catalytic subunit (DNA-PKcs)-an early step in NHEJ-yields substantial increases in DNA deletion. This is observed across diverse cell lines, gene delivery methods, commercial inhibitors, and guide RNAs, including those that otherwise display negligible activity. We further show that DNA-PKcs inhibition can be used to boost the sensitivity of pooled functional screens and detect true-positive hits that would otherwise be overlooked. Thus, delaying the kinetics of NHEJ relative to DSB formation is a simple and effective means of enhancing CRISPR-deletion.

Graph-based data integration from bioactive peptide databases of pharmaceutical interest: toward an organized collection enabling visual network analysis.

MOTIVATION: Bioactive peptides have gained great attention in the academy and pharmaceutical industry since they play an important role in human health. However, the increasing number of bioactive peptide databases is causing the problem of data redundancy and duplicated efforts. Even worse is the fact that the available data is non-standardized and often dirty with data entry errors. Therefore, there is a need for a unified view that enables a more comprehensive analysis of the information on this topic residing at different sites. RESULTS: After collecting web pages from a large variety of bioactive peptide databases, we organized the web content into an integrated graph database (starPepDB) that holds a total of 71 310 nodes and 348 505 relationships. In this graph structure, there are 45 120 nodes representing peptides, and the rest of the nodes are connected to peptides for describing metadata. Additionally, to facilitate a better understanding of the integrated data, a software tool (starPep toolbox) has been developed for supporting visual network analysis in a user-friendly way; providing several functionalities such as peptide retrieval and filtering, network construction and visualization, interactive exploration and exporting data options. AVAILABILITY AND IMPLEMENTATION: Both starPepDB and starPep toolbox are freely available at http://mobiosd-hub.com/starpep/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Particle size distribution from extinction and absorption data of metallic nanoparticles.

We describe a method for inverting spectroscopic data of the absorption and extinction properties of colloidal samples of resonant particles. We show that, with some prior knowledge, the genetic algorithm employed is able to estimate the probability density function of particle sizes. Since the data are sensitive to the shape and material of the particles, some information about these properties can also be retrieved. The viability of the method is illustrated by inverting numerically generated data, as well as experimental data obtained with specially prepared samples of metallic nanoparticles in aqueous suspension.

Multi-hallmark long noncoding RNA maps reveal non-small cell lung cancer vulnerabilities.

Long noncoding RNAs (lncRNAs) are widely dysregulated in cancer, yet their functional roles in cancer hallmarks remain unclear. We employ pooled CRISPR deletion to perturb 831 lncRNAs detected in KRAS-mutant non-small cell lung cancer (NSCLC) and measure their contribution to proliferation, chemoresistance, and migration across two cell backgrounds. Integrative analysis of these data outperforms conventional "dropout" screens in identifying cancer genes while prioritizing disease-relevant lncRNAs with pleiotropic and background-independent roles. Altogether, 80 high-confidence oncogenic lncRNAs are active in NSCLC, which tend to be amplified and overexpressed in tumors. A follow-up antisense oligonucleotide (ASO) screen shortlisted two candidates, Cancer Hallmarks in Lung LncRNA 1 (CHiLL1) and GCAWKR, whose knockdown consistently suppressed cancer hallmarks in two- and three-dimension tumor models. Molecular phenotyping reveals that CHiLL1 and GCAWKR control cellular-level phenotypes via distinct transcriptional networks. This work reveals a multi-dimensional functional lncRNA landscape underlying NSCLC that contains potential therapeutic vulnerabilities.

Automatic construction of molecular similarity networks for visual graph mining in chemical space of bioactive peptides: an unsupervised learning approach.

The increasing interest in bioactive peptides with therapeutic potentials has been reflected in a large variety of biological databases published over the last years. However, the knowledge discovery process from these heterogeneous data sources is a nontrivial task, becoming the essence of our research endeavor. Therefore, we devise a unified data model based on molecular similarity networks for representing a chemical reference space of bioactive peptides, having an implicit knowledge that is currently not explicitly accessed in existing biological databases. Indeed, our main contribution is a novel workflow for the automatic construction of such similarity networks, enabling visual graph mining techniques to uncover new insights from the "ocean" of known bioactive peptides. The workflow presented here relies on the following sequential steps: (i) calculation of molecular descriptors by applying statistical and aggregation operators on amino acid property vectors; (ii) a two-stage unsupervised feature selection method to identify an optimized subset of descriptors using the concepts of entropy and mutual information; (iii) generation of sparse networks where nodes represent bioactive peptides, and edges between two nodes denote their pairwise similarity/distance relationships in the defined descriptor space; and (iv) exploratory analysis using visual inspection in combination with clustering and network science techniques. For practical purposes, the proposed workflow has been implemented in our visual analytics software tool ( http://mobiosd-hub.com/starpep/ ), to assist researchers in extracting useful information from an integrated collection of 45120 bioactive peptides, which is one of the largest and most diverse data in its field. Finally, we illustrate the applicability of the proposed workflow for discovering central nodes in molecular similarity networks that may represent a biologically relevant chemical space known to date.

Classification of riboswitch sequences using k-mer frequencies.

Riboswitches are non-coding RNAs that regulate gene expression by altering the structural conformation of mRNA transcripts. Their regulation mechanism might be exploited for interesting biomedical applications such as drug targets and biosensors. A major challenge consists in accurately identifying metabolite-binding RNA switches which are structurally complex and diverse. In this regard, we investigated the classification of 16 riboswitch families using supervised learning algorithms trained solely with sequence-based features. We generated a reduced feature set and proposed a visual representation to explore its components. We induced Support Vector Machine, Random Forest, Naive Bayes, J48, and HyperPipes classifiers with our proposed feature set and tested their performance over independent data. Our best multi-class classifier achieved F-measure values of 0.996 and 0.966 in the training and test phases, respectively, outperforming those of a previous approach. When compared against BLAST, our best classifiers yielded competitive results. This work shows that the classifiers trained with our sequence-based feature set accurately discriminate riboswitches.