The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies

Understanding the molecular machinery involved in transcriptional regulation is central to improving our knowledge of an organism’s development, disease, and evolution. The building blocks of this complex molecular machinery are an organism’s genomic DNA sequence and transcription factor proteins. Despite the vast amount of sequence data now available for many model organisms, predicting where transcription factors bind, often referred to as ‘motif detection’ is still incredibly challenging. In this study, we develop a novel bioinformatic approach to binding site prediction. We do this by extending pre-existing SVM approaches in an unbiased way to include all possible gapped k-mers, representing different combinations of complex nucleotide dependencies within binding sites. We show the advantages of this new approach when compared to existing SVM approaches, through a rigorous set of cross-validation experiments. We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early Drosophila embryos

Transcriptome profile in Drosophila Kc and S2 embryonic cell lines

Drosophila melanogaster cell lines are an important resource for a range of studies spanning genomics, molecular genetics, and cell biology. Amongst these valuable lines are Kc167 (Kc) and Schneider 2 (S2) cells, which were originally isolated in the late 1960s from embryonic sources and have been used extensively to investigate a broad spectrum of biological activities including cell-cell signaling and immune system function. Whole-genome tiling microarray analysis of total RNA from these two cell types was performed as part of the modENCODE project over a decade ago and revealed that they share a number of gene expression features. Here, we expand on these earlier studies by using deep-coverage RNA-sequencing approaches to investigate the transcriptional profile in Kc and S2 cells in detail. Comparison of the transcriptomes reveals that ∼75% of the 13,919 annotated genes are expressed at a detectable level in at least one of the cell lines, with the majority of these genes expressed at high levels in both cell lines. Despite the overall similarity of the transcriptional landscape in the two cell types, 2,588 differentially expressed genes are identified. Many of the genes with the largest fold change are known only by their CG designations, indicating that the molecular control of Kc and S2 cell identity may be regulated in part by a cohort of relatively uncharacterized genes. Our data also indicate that both cell lines have distinct hemocyte-like identities, but share active signaling pathways and express a number of genes in the network responsible for dorsal-ventral patterning of the early embryo. © The Author(s) 2023. Published by Oxford University Press on behalf of the Genetics Society of America

Thermodynamic modeling of transcription: sensitivity analysis differentiates biological mechanism from mathematical model-induced effects

<p>Abstract</p> <p>Background</p> <p>Quantitative models of gene expression generate parameter values that can shed light on biological features such as transcription factor activity, cooperativity, and local effects of repressors. An important element in such investigations is sensitivity analysis, which determines how strongly a model's output reacts to variations in parameter values. Parameters of low sensitivity may not be accurately estimated, leading to unwarranted conclusions. Low sensitivity may reflect the nature of the biological data, or it may be a result of the model structure. Here, we focus on the analysis of thermodynamic models, which have been used extensively to analyze gene transcription. Extracted parameter values have been interpreted biologically, but until now little attention has been given to parameter sensitivity in this context.</p> <p>Results</p> <p>We apply local and global sensitivity analyses to two recent transcriptional models to determine the sensitivity of individual parameters. We show that in one case, values for repressor efficiencies are very sensitive, while values for protein cooperativities are not, and provide insights on why these differential sensitivities stem from both biological effects and the structure of the applied models. In a second case, we demonstrate that parameters that were thought to prove the system's dependence on activator-activator cooperativity are relatively insensitive. We show that there are numerous parameter sets that do not satisfy the relationships proferred as the optimal solutions, indicating that structural differences between the two types of transcriptional enhancers analyzed may not be as simple as altered activator cooperativity.</p> <p>Conclusions</p> <p>Our results emphasize the need for sensitivity analysis to examine model construction and forms of biological data used for modeling transcriptional processes, in order to determine the significance of estimated parameter values for thermodynamic models. Knowledge of parameter sensitivities can provide the necessary context to determine how modeling results should be interpreted in biological systems.</p

Influence of the balanced scorecard on the science and innovation performance of Latin American universities

This is an Accepted Manuscript of an article published by Taylor & Francis in Knowledge Management Research & Practice on 2019, available online: http://www.tandfonline.com/10.1080/14778238.2019.1569488[EN] Pressure on the education system to meet society's needs has led some universities to adopt organisational performance measurement systems as strategic control tools. One of the most commonly used systems in business is the balanced scorecard (BSC). For Latin American universities, the urgent task of increasing the quantity and quality of research and innovation has led these universities to update their essential processes. A suitable control system is necessary to ensure the effectiveness of these new policies. Based on strategic management theory, this study focuses on the implementation of a BSC method in Latin American public universities. The aim of this study is to determine the influence of BSC implementation on universities? research and innovation performance. 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