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Bennett, K. P.

Publications and source records attributed to Bennett, K. P..

3 recordsLinked to original sources

The PAICE Suite Reveals Circadian Post-Transcriptional Timing of Non-Coding RNAs and Spliceosome Components in Mus musculus Macrophages

Circadian rhythms broadly regulate physiological functions by tuning oscillations in the levels of mRNAs and proteins to the 24-hour day/night cycle. Globally assessing which mRNAs and proteins are timed by the clock necessitates accurate recognition of oscillations in RNA and protein data, particularly in large omics data sets. Tools that employ fixed-amplitude models have previously been used to positive effect. However, the recognition of amplitude-change in circadian oscillations required a new generation of analytical software to enhance the identification of these oscillations. To address this gap, we created the Pipeline for Amplitude Integration of Circadian Exploration (PAICE) suite. Here, we demonstrate the PAICE suites increased detection of circadian trends through the joint modeling of the Mus musculus macrophage transcriptome and proteome. Our enhanced detection confirmed extensive circadian post-transcriptional regulation in macrophages, but highlighted that some of the reported discrepancy between mRNA and protein oscillations was due to noise in data. We further applied the PAICE suite to investigate the circadian timing of non-coding RNAs, documenting extensive circadian timing of long non-coding RNAs and small nuclear RNAs, which control the recognition of mRNA in the spliceosome complex. By tracking oscillating spliceosome complex proteins using the PAICE suite, we noted that the clock broadly regulates the spliceosome, particularly the major spliceosome complex. As most of the above-noted rhythms had damped amplitude changes in their oscillations, this work highlights the importance of the PAICE suite in the thorough enumeration of oscillations in omics-scale datasets.

systems biology↗

MOSAIC: A Joint Modeling Methodology for Combined Circadian and Non-Circadian Analysis of Multi-Omics Data

MotivationCircadian rhythms are approximately 24 hour endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation. ResultsTo determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the MOSAIC (Multi-Omics Selection with Amplitude Independent Criteria) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation. AvailabilityMOSAICs full interface is available at https://github.com/delosh653/MOSAIC. Contacthurlej2@rpi.edu Supplementary informationSupplementary data are available.at Bioinformatics online.

bioinformatics↗

ECHO: an Application for Detection and Analysis of Oscillators Identifies Metabolic Regulation on Genome-Wide Circadian Output

MotivationTime courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems.\n\nResultsWe have created a program to efficaciously identify oscillations in large-scale datasets, called the Extended Circadian Harmonic Oscillator application, or ECHO. ECHO utilizes an extended solution of the fixed amplitude mass-spring oscillator that incorporates the amplitude change coefficient. Employing synthetic datasets, we determined that ECHO outperforms existing methods in detecting rhythms with decreasing oscillation amplitudes and recovering phase shift. Rhythms with changing amplitudes identified from published biological datasets revealed distinct functions from those oscillations that were harmonic, suggesting purposeful biologic regulation to create this subtype of circadian rhythms.\n\nAvailabilityECHOs full interface is available at https://github.com/delosh653/ECHO. An R package for this functionality, echo.find, can be downloaded at https://CRAN.R-project.org/package=echo.find.\n\nContacthurleyj2@rpi.edu\n\nSupplementary informationSupplementary data are available

bioinformatics↗