Lag penalized weighted correlation for time series clustering

A multi-step completion process model of cell plasticity

Plasticity is the potential for cells or cell populations to change their phenotypes and behaviors in response to internal or external cues. Plasticity is fundamental to many complex biological processes, yet to date there remains a lack of mathematical models that can elucidate and predict molecular behaviors in a plasticity program. Here, we report a new mathematical framework that models cell plasticity as a multi-step completion process, where the system moves from the initial state along a path guided by multiple intermediate attractors until the final state (i.e. a new homeostasis) is reached. Using omics time-series data as model input, we show that our method fits data well; identifies attractor states by their timing and molecular markers which are well-aligned with domain knowledge; and can make quantitative and time-resolved predictions such as the molecular outcomes of blocking a plasticity program from reaching completion, to an R2 of 0.53-0.63. We demonstrate that application of our model to primary patient-derived data can provide quantitative insights and predictions that may be useful in guiding further research and potential biomedical interventions.

Time series transcriptome analysis implicates the circadian clock in the Drosophila melanogaster female's response to sex peptide

Sex peptide (SP), a seminal fluid protein of Drosophila melanogaster males, has been described as driving a virgin-to-mated switch in females, through eliciting an array of responses including increased egg laying, activity, and food intake and a decreased remating rate. While it is known that SP achieves this, at least in part, by altering neuronal signaling in females, the genetic architecture and temporal dynamics of the female's response to SP remain elusive. We used a high-resolution time series RNA-sequencing dataset of female heads at 10 time points within the first 24 h after mating to learn about the genetic architecture, at the gene and exon levels, of the female's response to SP. We find that SP is not essential to trigger early aspects of a virgin-to-mated transcriptional switch, which includes changes in a metabolic gene regulatory network. However, SP is needed to maintain and diversify metabolic changes and to trigger changes in a neuronal gene regulatory network. We further find that SP alters rhythmic gene expression in females and suggests that SP's disruption of the female's circadian rhythm might be key to its widespread effects.

Multi-Objective Particle Swarm Optimization Based Preprocessing of Multi-Class Extremely Imbalanced Datasets

Today’s datasets are usually very large with many features and making analysis on such datasets is really a tedious task. Especially when performing classification, selecting attributes that are salient for the process is a brainstorming task. It is more difficult when there are many class labels for the target class attribute and hence many researchers have introduced methods to select features for performing classification on multi-class attributes. The process becomes more tedious when the attribute values are imbalanced for which researchers have contributed many methods. But, there is no sufficient research to handle extreme imbalance and feature selection together and hence this paper aims to bridge this gap. Here Particle Swarm Optimization (PSO), an efficient evolutionary algorithm is used to handle imbalanced dataset and feature selection process is also enhanced with the required functionalities. First, Multi-objective Particle Swarm Optimization is used to transform the imbalanced datasets into balanced one and then another version of Multi-objective Particle Swarm Optimization is used to select the significant features. The proposed methodology is applied on eight multi-class extremely imbalanced datasets and the experimental results are found to be better than other existing methods in terms of classification accuracy, G mean, F measure. The results validated by using Friedman test also confirm that the proposed methodology effectively balances the dataset with less number of features than other methods.

Secondary Metabolism Gene Clusters Exhibit Increasingly Dynamic and Differential Expression during Asexual Growth, Conidiation, and Sexual Development in Neurospora crassa

Secondary metabolite clusters (SMCs) encode the machinery for fungal toxin production. However, understanding their function and analyzing their products requires investigation of the developmental and environmental conditions in which they are expressed. Gene expression is often restricted to specific and unexamined stages of the life cycle. Therefore, we applied comparative genomics analyses to identify SMCs in Neurospora crassa and analyzed extensive transcriptomic data spanning nine independent experiments from diverse developmental and environmental conditions to reveal their life cycle-specific gene expression patterns. We reported 20 SMCs comprising 177 genes-a manageable set for investigation of the roles of SMCs across the life cycle of the fungal model N. crassa-as well as gene sets coordinately expressed in 18 predicted SMCs during asexual and sexual growth under three nutritional and two temperature conditions. Divergent activity of SMCs between asexual and sexual development was reported. Of 126 SMC genes that we examined for knockout phenotypes, al-2 and al-3 exhibited phenotypes in asexual growth and conidiation, whereas os-5, poi-2, and pmd-1 exhibited phenotypes in sexual development. SMCs with annotated function in mating and crossing were actively regulated during the switch between asexual and sexual growth. Our discoveries call for attention to roles that SMCs may play in the regulatory switches controlling mode of development, as well as the ecological associations of those developmental stages that may influence expression of SMCs. IMPORTANCE Secondary metabolites (SMs) are low-molecular-weight compounds that often mediate interactions between fungi and their environments. Fungi enriched with SMs are of significant research interest to agriculture and medicine, especially from the aspects of pathogen ecology and environmental epidemiology. However, SM clusters (SMCs) that have been predicted by comparative genomics alone have typically been poorly defined and insufficiently functionally annotated. Therefore, we have investigated coordinate expression in SMCs in the model system N. crassa, and our results suggest that SMCs respond to environmental signals and to stress that are associated with development. This study examined SMC regulation at the level of RNA to integrate observations and knowledge of these genes in various growth and development conditions, supporting combining comparative genomics and inclusive transcriptomics to improve computational annotation of SMCs. Our findings call for detailed study of the function of SMCs during the asexual-sexual switch, a key, often-overlooked developmental stage.

A Multimodal Approach to Discover Biomarkers for Taxane-Induced Peripheral Neuropathy (TIPN): A Study Protocol

Introduction: Taxanes are a class of chemotherapeutics commonly used to treat various solid tumors, including breast and ovarian cancers. Taxane-induced peripheral neuropathy (TIPN) occurs in up to 70% of patients, impacting quality of life both during and after treatment. TIPN typically manifests as tingling and numbness in the hands and feet and can cause irreversible loss of function of peripheral nerves. TIPN can be dose-limiting, potentially impacting clinical outcomes. The mechanisms underlying TIPN are poorly understood. As such, there are limited treatment options and no tools to provide early detection of those who will develop TIPN. Although some patients may have a genetic predisposition, genetic biomarkers have been inconsistent in predicting chemotherapy-induced peripheral neuropathy (CIPN). Moreover, other molecular markers (eg, metabolites, mRNA, miRNA, proteins) may be informative for predicting CIPN, but remain largely unexplored. We anticipate that combinations of multiple biomarkers will be required to consistently predict those who will develop TIPN. Methods: To address this clinical gap of identifying patients at risk of TIPN, we initiated the Genetics and Inflammatory Markers for CIPN (GENIE) study. This longitudinal multicenter observational study uses a novel, multimodal approach to evaluate genomic variation, metabolites, DNA methylation, gene expression, and circulating cytokines/chemokines prior to, during, and after taxane treatment in 400 patients with breast cancer. Molecular and patient reported data will be collected prior to, during, and after taxane therapy. Multi-modal data will be used to develop a set of comprehensive predictive biomarker signatures of TIPN. Conclusion: The goal of this study is to enable early detection of patients at risk of developing TIPN, provide a tool to modify taxane treatment to minimize morbidity from TIPN, and improved patient quality of life. Here we provide a brief review of the current state of research into CIPN and TIPN and introduce the GENIE study design.

Temporal Dynamic Methods for Bulk RNA-Seq Time Series Data

Dynamic studies in time course experimental designs and clinical approaches have been widely used by the biomedical community. These applications are particularly relevant in stimuli-response models under environmental conditions, characterization of gradient biological processes in developmental biology, identification of therapeutic effects in clinical trials, disease progressive models, cell-cycle, and circadian periodicity. Despite their feasibility and popularity, sophisticated dynamic methods that are well validated in large-scale comparative studies, in terms of statistical and computational rigor, are less benchmarked, comparing to their static counterparts. To date, a number of novel methods in bulk RNA-Seq data have been developed for the various time-dependent stimuli, circadian rhythms, cell-lineage in differentiation, and disease progression. Here, we comprehensively review a key set of representative dynamic strategies and discuss current issues associated with the detection of dynamically changing genes. We also provide recommendations for future directions for studying non-periodical, periodical time course data, and meta-dynamic datasets.

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.