Ethics review as a component of institutional approval for a multicentre continuous quality improvement project: the investigator's perspective

BACKGROUND: For ethical approval of a multicentre study in Canada, investigators must apply separately to individual Research Ethics Boards (REBs). In principle, the protection of human research subjects is of utmost importance. However, in practice, the process of multicentre ethics review can be time consuming and costly, requiring duplication of effort for researchers and REBs. We used our experience with ethical review of The Canadian Perinatal Network (CPN), to gain insight into the Canadian system. METHODS: The applications forms of 16 different REBs were abstracted for a list of standardized items. The application process across sites was compared. Correspondence between the REB and the investigators was documented in order to construct a timeline to approval, identify the specific issues raised by each board, and describe how they were resolved. RESULTS: Each REB had a different application form. Most (n = 9) had a two or three step application process. Overall, it took a median of 31 days (range 2-174 days) to receive an initial response from the REB. Approval took a median of 42 days (range 4-443 days). Privacy and consent were the two major issues raised. Several additional minor or administrative issues were raised which delayed approval. CONCLUSIONS: For CPN, the Canadian REB process of ethical review proved challenging. REBs acted independently and without unified application forms or submission procedures. We call for a critical examination of the ethical, privacy and institutional review processes in Canada, to determine the best way to undertake multicentre review

In silico discovery of repetitive elements as key sequence determinants of 3D genome folding

Natural and experimental genetic variants can modify DNA loops and insulating boundaries to tune transcription, but it is unknown how sequence perturbations affect chromatin organization genome-wide. We developed an in silico deep-learning strategy to quantify the effect of any insertion, deletion, inversion, or substitution on chromatin contacts and systematically scored millions of synthetic variants. While most genetic manipulations have little impact, regions with CTCF motifs and active transcription are highly sensitive, as expected. However, our analysis also points to noncoding RNA genes and several families of repetitive elements as CTCF motif-free DNA sequences with particularly large effects on nearby chromatin interactions, sometimes exceeding the effects of CTCF sites and explaining interactions that lack CTCF. We anticipate that our available disruption tracks may be of broad interest and utility as a measure of 3D genome sensitivity and our computational strategies may serve as a template for biological inquiry with deep learning

ChromaFactor: deconvolution of single-molecule chromatin organization with non-negative matrix factorization

Abstract: The investigation of chromatin organization in single cells holds great promise for identifying causal relationships between genome structure and function. However, analysis of single-molecule data is hampered by extreme yet inherent heterogeneity, making it challenging to determine the contributions of individual chromatin fibers to bulk trends. To address this challenge, we propose ChromaFactor, a novel computational approach based on non-negative matrix factorization that deconvolves single-molecule chromatin organization datasets into their most salient primary components. ChromaFactor provides the ability to identify trends accounting for the maximum variance in the dataset while simultaneously describing the contribution of individual molecules to each component. Applying our approach to two single-molecule imaging datasets across different genomic scales, we find that these primary components demonstrate significant correlation with key functional phenotypes, including active transcription, enhancer-promoter distance, and genomic compartment. ChromaFactor offers a robust tool for understanding the complex interplay between chromatin structure and function on individual DNA molecules, pinpointing which subpopulations drive functional changes and fostering new insights into cellular heterogeneity and its implications for bulk genomic phenomena

Learning Molecular Representations for Medicinal Chemistry

The accurate modeling and prediction of small molecule properties and bioactivities depend on the critical choice of molecular representation. Decades of informatics-driven research have relied on expert-designed molecular descriptors to establish quantitative structure-activity and structure-property relationships for drug discovery. Now, advances in deep learning make it possible to efficiently and compactly learn molecular representations directly from data. In this review, we discuss how active research in molecular deep learning can address limitations of current descriptors and fingerprints while creating new opportunities in cheminformatics and virtual screening. We provide a concise overview of the role of representations in cheminformatics, key concepts in deep learning, and argue that learning representations provides a way forward to improve the predictive modeling of small molecule bioactivities and properties

EcoCyc: a comprehensive view of Escherichia coli biology.

EcoCyc (http://EcoCyc.org) provides a comprehensive encyclopedia of Escherichia coli biology. EcoCyc integrates information about the genome, genes and gene products; the metabolic network; and the regulatory network of E. coli. Recent EcoCyc developments include a new initiative to represent and curate all types of E. coli regulatory processes such as attenuation and regulation by small RNAs. EcoCyc has started to curate Gene Ontology (GO) terms for E. coli and has made a dataset of E. coli GO terms available through the GO Web site. The curation and visualization of electron transfer processes has been significantly improved. Other software and Web site enhancements include the addition of tracks to the EcoCyc genome browser, in particular a type of track designed for the display of ChIP-chip datasets, and the development of a comparative genome browser. A new Genome Omics Viewer enables users to paint omics datasets onto the full E. coli genome for analysis. A new advanced query page guides users in interactively constructing complex database queries against EcoCyc. A Macintosh version of EcoCyc is now available. A series of Webinars is available to instruct users in the use of EcoCyc