Detection of community structure has become a fundamental step in the analysis of biological networks with application to protein function annotation, disease gene prediction, and drug discovery. This recent impact creates a need to make these techniques and their accompanying visualization schemes available to a broad range of biologists. Here we present a service-oriented, end-to-end software framework, CDAPS (Community Detection APplication and Service), that integrates the identification, annotation, visualization, and interrogation of multiscale network communities, accessible within the popular Cytoscape network analysis platform. With novel design principles, CDAPS addresses unmet new challenges, such as identifying hierarchical community structures, comparison of outputs generated from diverse network resources, and easy deployment of new algorithms, to facilitate community-sourced science. We demonstrate that the CDAPS framework can be applied to high-throughput protein-protein interaction networks to gain novel insights, such as the identification of putative new members of known protein complexes.

Three-dimensional chromosome structure plays an integral role in gene expression and regulation, replication timing, and other cellular processes. Topologically associated domains (TADs), building blocks of chromosome structure, are genomic regions with higher contact frequencies within the region than outside the region. A central question is the degree to which TADs are conserved or vary between conditions. We analyze 137 Hi-C samples from 9 studies under 3 measures to quantify the effects of various sources of biological and experimental variation. We observe significant variation in TAD sets between both non-replicate and replicate samples, and provide initial evidence that this variability does not come from genetic sequence differences. The effects of experimental protocol differences are also measured, demonstrating that samples can have protocol-specific structural changes, but that TADs are generally robust to lab-specific differences. This study represents a systematic quantification of key factors influencing comparisons of chromosome structure, suggesting significant variability and the potential for cell-type-specific structural features, which has previously not been systematically explored. The lack of observed influence of heredity and genetic differences on chromosome structure suggests that factors other than the genetic sequence are driving this structure, which plays an important role in human disease and cellular functioning.

Three-dimensional chromosome structure plays an integral role in gene expression and regulation, replication timing, and other cellular processes. Topologically associating domains (TADs), one of the building blocks of chromosome structure, are genomic regions with higher contact frequencies within the region than outside the region. A central question is the degree to which TADs are conserved or vary between conditions. We analyze a set of 137 Hi-C samples from 9 di↵erent studies under 3 measures in order to quantify the e↵ects of various sources of biological and experimental variation. We observe significant variation in TAD sets between both non-replicate and replicate samples, and show that this variability does not seem to come from genetic sequence di↵erences. The e↵ects of experimental protocol di↵erences are also measured, demonstrating that samples can have protocol-specific structural changes, but that TADs are generally robust to lab-specific di↵erences. This study represents a systematic quantification of the key factors influencing comparisons of chromosome structure.

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Rapid proliferation is a hallmark of tumor cells, associated with sensitivity to chemicals that cause DNA replication stress (RS). Due to sustained proliferative signaling and/or defective DNA repair, cancer cells undergo persistent RS making them strongly dependent on the replication stress response (RSR). A consequence of this dependency is that RS becomes an exploitable therapeutic vulnerability in cancer treatment. Classical RS drugs work predominantly by interfering with DNA replication in dividing cells. Recently, an increasing number of drugs have been designed to specifically target RSR proteins. However, molecular pathways responsible for drug response are incompletely understood. Here we build an interpretable deep-learning model aimed at understanding mechanisms of susceptibility and resistance to replicative stress. Instead of associating genetic alterations with drug responses directly, our approach is to project individual mutations on a map of protein complexes and larger molecular assemblies with prior evidence for involvement in cancer. This approach is prompted and supported by the concept that cancer is a network-based disease arising from the action of hallmark cancer pathways. Through systematic interpretation, the model identifies 37 complexes that integrate rare alterations in hundreds of genes for accurate response prediction. The complexes, which cover roles in transcription, DNA repair, cell-cycle checkpoint, and immunity, are further investigated by directed genetic perturbations, validating 24 for which RS effects are phenocopied by CRISPR guide RNAs. For complexes with poorly characterized functions, further insights are obtained via their profiles of in-silico activation across RS agents. This study creates a library of system-level genetic vulnerabilities governing replication stress, with implications for drug selection and combination. Citation Format: Xiaoyu Zhao, Akshat Singhal, Trey Ideker. Identifying genetic dependencies of replication stress using interpretable artificial intelligence. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4306.