Time-Course Analysis of Cyanobacterium Transcriptome: Detecting Oscillatory Genes

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis

Analyzing circadian expression data by harmonic regression based on autoregressive spectral estimation

Motivation: Circadian rhythms are prevalent in most organisms. Identification of circadian-regulated genes is a crucial step in discovering underlying pathways and processes that are clock-controlled. Such genes are largely detected by searching periodic patterns in microarray data. However, temporal gene expression profiles usually have a short time-series with low sampling frequency and high levels of noise. This makes circadian rhythmic analysis of temporal microarray data very challenging

The Application and Challenges of RNA-Sequencing to the Study of Circadian Rhythms

The circadian clock drives daily rhythms in behavior and physiology, often in anticipation of the coming dusk or dawn. Almost all organisms possess an internal time-keeper, as it represents an adaptation to one of the most ancient selective pressures; the day-night cycle. Mounting evidence suggests the clock plays important roles in critical metabolic and signalling pathways, the sleep/wake cycle, immune function, as well as learning and memory. Perhaps more importantly, misregulation of the clock is associated with metabolic disorders, neurodegeneration, and incidence of cancer. In an effort to unlock the connections between the circadian clock and these downstream effects, researchers have searched for genes with rhytmic transcription driven by the clock. These so-called clock-controlled genes (CCGs) mediate these observed rhythms in important biological pathways. Over the past decade, researchers have searched for these CCGs using microarrays. However, with the growing popularity of high-throughput sequencing, and revelations about both the number and importance of non-coding RNAs (ncRNAs), investigators have begun to use RNA-seq for their circadian profiles. While RNA-seq has led to important findings about the circadian regulation of RNA editing, small RNAs, and epigenetic modifications, there is still much about its biases and limitations that we are still discovering. To this end, this thesis seeks to build upon this foundation and examine the use of RNA-seq for studying circadian transcription. I applied a hybrid RNA-seq, microarray approach to assay the circading transcriptome in liver, and eleven other mouse tissues. Notably, I saw that 1/3rd of ncRNAs conserved between human and mouse show rhythmic transcription. These rhythmic transcripts are strong candidates for future functional validation, and include important miRNA and snoRNA precursors. Additionaly, I found hundreds novel ncRNAs with rhythmic expression, which may provide novel CCGs. Lastly, I developped and applied a method for identifying the sources of bias in RNA-seq protocols. Taken together, this work extends our understanding of the circadian transcriptome, and the challenges associated with interpreting RNA-seq data

Predicting Audio Advertisement Quality

Online audio advertising is a particular form of advertising used abundantly in online music streaming services. In these platforms, which tend to host tens of thousands of unique audio advertisements (ads), providing high quality ads ensures a better user experience and results in longer user engagement. Therefore, the automatic assessment of these ads is an important step toward audio ads ranking and better audio ads creation. In this paper we propose one way to measure the quality of the audio ads using a proxy metric called Long Click Rate (LCR), which is defined by the amount of time a user engages with the follow-up display ad (that is shown while the audio ad is playing) divided by the impressions. We later focus on predicting the audio ad quality using only acoustic features such as harmony, rhythm, and timbre of the audio, extracted from the raw waveform. We discuss how the characteristics of the sound can be connected to concepts such as the clarity of the audio ad message, its trustworthiness, etc. Finally, we propose a new deep learning model for audio ad quality prediction, which outperforms the other discussed models trained on hand-crafted features. To the best of our knowledge, this is the first large-scale audio ad quality prediction study.Comment: WSDM '18 Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, 9 page

Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis.Facultad de Ciencias Exacta

Dynamical Modeling Techniques for Biological Time Series Data

The present thesis is articulated over two main topics which have in common the modeling of the dynamical properties of complex biological systems from large-scale time-series data. On one hand, this thesis analyzes the inverse problem of reconstructing Gene Regulatory Networks (GRN) from gene expression data. This first topic seeks to reverse-engineer the transcriptional regulatory mechanisms involved in few biological systems of interest, vital to understand the specificities of their different responses. In the light of recent mathematical developments, a novel, flexible and interpretable modeling strategy is proposed to reconstruct the dynamical dependencies between genes from short-time series data. In addition, experimental trade-offs and optimal modeling strategies are investigated for given data availability. Consistent literature on these topics was previously surprisingly lacking. The proposed methodology is applied to the study of circadian rhythms, which consists in complex GRN driving most of daily biological activity across many species. On the other hand, this manuscript covers the characterization of dynamically differentiable brain states in Zebrafish in the context of epilepsy and epileptogenesis. Zebrafish larvae represent a valuable animal model for the study of epilepsy due to both their genetic and dynamical resemblance with humans. The fundamental premise of this research is the early apparition of subtle functional changes preceding the clinical symptoms of seizures. More generally, this idea, based on bifurcation theory, can be described by a progressive loss of resilience of the brain and ultimately, its transition from a healthy state to another characterizing the disease. First, the morphological signatures of seizures generated by distinct pathological mechanisms are investigated. For this purpose, a range of mathematical biomarkers that characterizes relevant dynamical aspects of the neurophysiological signals are considered. Such mathematical markers are later used to address the subtle manifestations of early epileptogenic activity. Finally, the feasibility of a probabilistic prediction model that indicates the susceptibility of seizure emergence over time is investigated. The existence of alternative stable system states and their sudden and dramatic changes have notably been observed in a wide range of complex systems such as in ecosystems, climate or financial markets

Environmental entrainment demonstrates natural circadian rhythmicity in the cnidarian Nematostella vectensis

Author Posting. © Company of Biologists, 2019. This article is posted here by permission of Company of Biologists for personal use, not for redistribution. The definitive version was published in Journal of Experimental Biology 222(21), (2019): jeb.205393, doi:10.1242/jeb.205393.Considerable advances in chronobiology have been made through controlled laboratory studies, but distinct temporal rhythms can emerge under natural environmental conditions. Lab-reared Nematostella vectensis sea anemones exhibit circadian behavioral and physiological rhythms. Given that these anemones inhabit shallow estuarine environments subject to tidal inputs, it was unclear whether circadian rhythmicity would persist following entrainment in natural conditions, or whether circatidal periodicity would predominate. Nematostella were conditioned within a marsh environment, where they experienced strong daily temperature cycles as well as brief tidal flooding around the full and new moons. Upon retrieval, anemones exhibited strong circadian (∼24 h) activity rhythms under a light–dark cycle or continuous darkness, but reduced circadian rhythmicity under continuous light. However, some individuals in each light condition showed circadian rhythmicity, and a few individuals showed circatidal rhythmicity. Consistent with the behavioral studies, a large number of transcripts (1640) exhibited diurnal rhythmicity compared with very few (64) with semidiurnal rhythmicity. Diurnal transcripts included core circadian regulators, and 101 of 434 (23%) genes that were previously found to be upregulated by exposure to ultraviolet radiation. Together, these behavioral and transcriptional studies show that circadian rhythmicity predominates and suggest that solar radiation drives physiological cycles in this sediment-dwelling subtidal animal.A.M.T., R.R.H. and O.L. were supported by the Gordon and Betty Moore Foundation (grant number 4598 to A.M.T. and O.L.). H.E.R. was funded by a Martin Family Fellowship for Sustainability at Massachusetts Institute of Technology and an American dissertation grant from the American Association of University Women.2020-10-1