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This editorial provides a brief overview of the 12th International Society for Computational Biology (ISCB) Student Council Symposium and the 4th European Student Council Symposium held in Florida, USA and The Hague, Netherlands, respectively. Further, the role of the ISCB Student Council in promoting education and networking in the field of computational biology is also highlighted.

Uncovering how transcription factors regulate their targets at DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) and assign mechanisms in normal and diseased states. RNA-seq is a standard method measuring gene regulation using an established set of analysis stages. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time-series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods integrating ordered RNA-seq data with protein–DNA binding data to distinguish direct from indirect interactions are urgently needed. We present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time-series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to determine causal regulatory mechanism networks by leveraging time-series RNA-seq, motif analysis, protein–DNA binding data, and protein-protein interaction networks. TIMEOR’s user-catered approach helps non-coders generate new hypotheses and validate known mechanisms. We used TIMEOR to identify a novel link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git and http://timeor.brown.edu.

The advancement of high‐throughput genomic assays has led to enormous growth in the availability of large‐scale biological datasets. Over the last two decades, these increasingly complex data have required statistical approaches that are more sophisticated than traditional linear models. Machine learning methodologies such as neural networks have yielded state‐of‐the‐art performance for prediction‐based tasks in many biomedical applications. However, a notable downside of these machine learning models is that they typically do not reveal how or why accurate predictions are made. In many areas of biomedicine, this “black box” property can be less than desirable—particularly when there is a need to perform in silico hypothesis testing about a biological system, in addition to justifying model findings for downstream decision‐making, such as determining the best next experiment or treatment strategy. Explainable and interpretable machine learning approaches have emerged to overcome this issue. While explainable methods attempt to derive post hoc understanding of what a model has learned, interpretable models are designed to inherently provide an intelligible definition of their parameters and architecture. Here, we review the model transparency spectrum moving from black box and explainable, to interpretable machine learning methodology. Motivated by applications in genomics, we provide background on the advances across this spectrum, detailing specific approaches in both supervised and unsupervised learning. Importantly, we focus on the promise of incorporating existing biological knowledge when constructing interpretable machine learning methods for biomedical applications. We then close with considerations and opportunities for new development in this space.This article is categorized under: Statistical Models > Nonlinear Models Applications of Computational Statistics > Genomics/Proteomics/Genetics Applications of Computational Statistics > Computational and Molecular Biology

SummaryUncovering how transcription factors (TFs) regulate their targets at the DNA, RNA and protein levels over time is critical to define gene regulatory networks (GRNs) in normal and diseased states. RNA-seq has become a standard method to measure gene regulation using an established set of analysis steps. However, none of the currently available pipeline methods for interpreting ordered genomic data (in time or space) use time series models to assign cause and effect relationships within GRNs, are adaptive to diverse experimental designs, or enable user interpretation through a web-based platform. Furthermore, methods which integrate ordered RNA-seq data with transcription factor binding data are urgently needed. Here, we present TIMEOR (Trajectory Inference and Mechanism Exploration with Omics data in R), the first web-based and adaptive time series multi-omics pipeline method which infers the relationship between gene regulatory events across time. TIMEOR addresses the critical need for methods to predict causal regulatory mechanism networks between TFs from time series multi-omics data. We used TIMEOR to identify a new link between insulin stimulation and the circadian rhythm cycle. TIMEOR is available at https://github.com/ashleymaeconard/TIMEOR.git.

Sex-specific splicing is an essential process that regulates sex determination and drives sexual dimorphism. Yet, how early in development widespread sex-specific transcript diversity occurs was unknown because it had yet to be studied at the genome-wide level. We use the powerful Drosophila model to show that widespread sex-specific transcript diversity occurs early in development, concurrent with zygotic genome activation. We also present a new pipeline called time2splice to quantify changes in alternative splicing over time. Furthermore, we determine that one of the consequences of losing an essential maternally-deposited pioneer factor called CLAMP (Chromatin linked adapter for MSL proteins) is altered sex-specific splicing of genes involved in diverse biological processes that drive development. Overall, we show that sex-specific differences in transcript diversity exist even at the earliest stages of development.

Maternally deposited RNAs and proteins play a crucial role in initiating zygotic transcription during early embryonic development. However, the mechanisms by which maternal factors regulate zygotic transcript diversity early in development remain poorly understood. Furthermore, how early in development sex-specific transcript diversity occurs is not known genome-wide in any organism. Here, we determine that sex-specific transcript diversity occurs much earlier in development than previously thought in Drosophila, concurrent with Zygotic genome activation (ZGA). We use genetic, biochemical, and genomic approaches to demonstrate that the essential maternally-deposited pioneer factor CLAMP (Chromatin linked adapter for MSL proteins) is a key regulator of sex-specific transcript diversity in the early embryo via the following mechanisms: 1) In both sexes, CLAMP directly binds to the gene bodies of female and male sex-specifically spliced genes. 2) In females, CLAMP modulates chromatin accessibility of an alternatively-spliced exon within Sex-lethal, the master regulator of sex determination, to promote protein production. 3) In males, CLAMP regulates Maleless (MLE) distribution, a spliceosome component to prevent aberrant sex-specific splicing. Thus, we demonstrate for the first time how a maternal factor regulates early zygotic transcriptome diversity sex-specifically. We also developed a new tool to measure how splicing changes over time called time2splice.

Alcohol use disorder (AUD) is characterized by loss of control in limiting alcohol intake. This may involve intermittent periods of abstinence followed by alcohol seeking and, consequently, relapse. However, little is understood of the molecular mechanisms underlying the impact of alcohol deprivation on behavior. Using a new Drosophila melanogaster repeated intermittent alcohol exposure model, we sought to identify how ethanol deprivation alters spontaneous behavior, determine the associated neural structures, and reveal correlated changes in brain gene expression. We found that repeated intermittent ethanol-odor exposures followed by ethanol-deprivation dynamically induces behaviors associated with a negative affect state. Although behavioral states broadly mapped to many brain regions, persistent changes in social behaviors mapped to the mushroom body and surrounding neuropil. This occurred concurrently with changes in expression of genes associated with sensory responses, neural plasticity, and immunity. Like social behaviors, immune response genes were upregulated following three-day repeated intermittent ethanol-odor exposures and persisted with one or two days of ethanol-deprivation, suggesting an enduring change in molecular function. Our study provides a framework for identifying how ethanol deprivation alters behavior with correlated underlying circuit and molecular changes.