Biology subjects

Lamoureux, C. R.

Publications and source records attributed to Lamoureux, C. R..

3 recordsLinked to original sources

bioRxiv2021

Mining all publicly available expression data to compute dynamic microbial transcriptional regulatory networks

We are firmly in the era of biological big data. Millions of omics datasets are publicly accessible and can be employed to support scientific research or build a holistic view of an organism. Here, we introduce a workflow that converts all public gene expression data for a microbe into a dynamic representation of the organisms transcriptional regulatory network. This five-step process walks researchers through the mining, processing, curation, analysis, and characterization of all available expression data, using Bacillus subtilis as an example. The resulting reconstruction of the B. subtilis regulatory network can be leveraged to predict new regulons and analyze datasets in the context of all published data. The results are hosted at https://imodulondb.org/, and additional analyses can be performed using the PyModulon Python package. As the number of publicly available datasets increases, this pipeline will be applicable to a wide range of microbial pathogens and cell factories.

bioinformatics↗

bioRxiv2021

Optimal dimensionality selection for independent component analysis of transcriptomic data

Independent Component Analysis (ICA) is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, ICA effectively reveals the source signals of the transcriptome as groups of co-regulated genes and their corresponding activities across diverse growth conditions. Two major variables that affect the output of ICA are the diversity and scope of the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. Here, we introduce a new method, called OptICA, for effectively finding the optimal dimensionality that consistently maximizes the number of biologically relevant components revealed while minimizing the potential for over-decomposition. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organisms underlying transcriptional regulatory network.

bioinformatics↗

bioRxiv2021

PRECISE 2.0: an expanded high-quality RNA-seq compendium for Escherichia coli K-12 reveals high-resolution transcriptional regulatory structure

Transcriptomic data is accumulating rapidly; thus, development of scalable methods for extracting knowledge from this data is critical. We assembled a top-down transcriptional regulatory network for Escherichia coli from a 1035-sample, single-protocol, high-quality RNA-seq compendium. The compendium contains diverse growth conditions, including: 4 temperatures; 9 media; 39 supplements, including antibiotics; and 76 unique gene knockouts. Using unsupervised machine learning, we extracted 117 regulatory modules that account for 86% of known regulatory network interactions. We also identified two novel regulons. After expanding the compendium with 1675 publicly available samples, we extracted similar modules, highlighting the methods scalability and stability. We provide workflows to enable analysis of new user data against this knowledge base, and demonstrate its utility for experimental design. This work provides a blueprint for top-down regulatory network elucidation across organisms using existing data, without any prior annotation and using existing data. Highlights- Single protocol, high quality RNA-seq dataset contains 1035 samples from Escherichia coli covering a wide range of growth conditions - Machine learning identifies 117 regulatory modules that capture the majority of known regulatory interactions - Resulting knowledge base combines expression levels and module activities to enable regulon discovery and empower novel experimental design - Standard workflows provided to enable application of knowledge base to new user data Graphical Abstract O_FIG_DISPLAY_L [Figure 1] M_FIG_DISPLAY C_FIG_DISPLAY

bioinformatics↗