A dose-response model for statistical analysis of chemical genetic interactions in CRISPRi screens
The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that ...
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: Sanjeevani Choudhery Source Type: research
< i > Where ’s Whaledo < /i > : A software toolkit for array localization of animal vocalizations
by Eric R. Snyder, Alba Solsona-Berga, Simone Baumann-Pickering, Kait E. Frasier, Sean M. Wiggins, John A. HildebrandWhere ’s Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is usefu...
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: Eric R. Snyder Source Type: research
Interpretable metric learning in comparative metagenomics: The adaptive Haar-like distance
by Evan D. Gorman, Manuel E. Lladser
Random forests have emerged as a promising tool in comparative metagenomics because they can predict environmental characteristics based on microbial composition in datasets whereβ-diversity metrics fall short of revealing meaningful relationships between samples. Nevertheless, despite this efficacy, they lack biological insight in tandem with their predictions, potentially hindering scientific advancement. To overcome this limitation, we leverage a geometric characterization of random forests to introduce a data-driven phylogeneticβ-diversity metric, the adaptive Haar-like distance....
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: Evan D. Gorman Source Type: research
Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration
by James Ryu, Amin Nejatbakhsh, Mahdi Torkashvand, Sahana Gangadharan, Maedeh Seyedolmohadesin, Jinmahn Kim, Liam Paninski, Vivek Venkatachalam
Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed f...
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: James Ryu Source Type: research
A multimodal Transformer Network for protein-small molecule interactions enhances predictions of kinase inhibition and enzyme-substrate relationships
by Alexander Kroll, Sahasra Ranjan, Martin J. Lercher
The activities of most enzymes and drugs depend on interactions between proteins and small molecules. Accurate prediction of these interactions could greatly accelerate pharmaceutical and biotechnological research. Current machine learning models designed for this task have a limited ability to generalize beyond the proteins used for training. This limitation is likely due to a lack of information exchange between the protein and the small molecule during the generation of the required numerical representations. Here, we introduce ProSmith, a machine learning framework...
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: Alexander Kroll Source Type: research
Krisp: A Python package to aid in the design of CRISPR and amplification-based diagnostic assays from whole genome sequencing data
by Zachary S. L. Foster, Andrew S. Tupper, Caroline M. Press, Niklaus J. Gr ünwald
Recent pandemics like COVID-19 highlighted the importance of rapidly developing diagnostics to detect evolving pathogens. CRISPR-Cas technology has recently been used to develop diagnostic assays for sequence-specific recognition of DNA or RNA. These assays have similar sensitivity to the gold standard qPCR but can be deployed as easy to use and inexpensive test strips. However, the discovery of diagnostic regions of a genome flanked by conserved regions where primers can be designed requires extensive bioinformatic analyses of genome sequ...
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: Zachary S. L. Foster Source Type: research
A computational analysis of the role of integrins and Rho-GTPases in the emergence and disruption of apical-basal polarization in renal epithelial cells
by Maria J. Hagelaars, Milica Nikolic, Maud Vermeulen, Sylvia Dekker, Carlijn V. C. Bouten, Sandra Loerakker
Apical-basal polarization in renal epithelial cells is crucial to renal function and an important trigger for tubule formation in kidney development. Loss of polarity can induce epithelial-to-mesenchymal transition (EMT), which can lead to kidney pathologies. Understanding the relative and combined roles of the involved proteins and their interactions that govern epithelial polarity may provide insights for controlling the process of polarization via chemical or mechanical manipulations in anin vitro orin vivo sett...
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: Maria J. Hagelaars Source Type: research
Competition between physical search and a weak-to-strong transition rate-limits kinesin binding times
by Trini Nguyen, Babu Reddy Janakaloti Narayanareddy, Steven P. Gross, Christopher E. Miles
The self-organization of cells relies on the profound complexity of protein-protein interactions. Challenges in directly observing these events have hindered progress toward understanding their diverse behaviors. One notable example is the interaction between molecular motors and cytoskeletal systems that combine to perform a variety of cellular functions. In this work, we leverage theory and experiments to identify and quantify the rate-limiting mechanism of the initial association between a cargo-bound kinesin motor and a microtu...
Source: PLoS Computational Biology - May 20, 2024 Category: Biology Authors: Trini Nguyen Source Type: research
Real-time forecasting of COVID-19-related hospital strain in France using a non-Markovian mechanistic model
We present a sub-national French framework for forecasting hospital strain based on a non-Markovian compartmental model, its associated online visualisation tool and a retrospective evaluation of the real-time forecasts it provided from January to December 2021 by comparing to three baselines derived from standard statistical forecasting methods (a naive model, auto-regression, and an ensemble of exponential smoothing and ARIMA). In terms of median absolute error for forecasting critical care unit occupancy at the two-week horizon, our model only outperformed the naive baseline for 4 out of 14 geographical units and underp...
Source: PLoS Computational Biology - May 17, 2024 Category: Biology Authors: Alexander Massey Source Type: research
Learning Micro-C from Hi-C with diffusion models
by Tong Liu, Hao Zhu, Zheng Wang
In the last few years, Micro-C has shown itself as an improved alternative to Hi-C. It replaced the restriction enzymes in Hi-C assays with micrococcal nuclease (MNase), resulting in capturing nucleosome resolution chromatin interactions. The signal-to-noise improvement of Micro-C allows it to detect more chromatin loops than high-resolution Hi-C. However, compared with massive Hi-C datasets available in the literature, there are only a limited number of Micro-C datasets. To take full advantage of these Hi-C datasets, we present HiC2MicroC, a computational method learning and then predicti...
Source: PLoS Computational Biology - May 17, 2024 Category: Biology Authors: Tong Liu Source Type: research
Ten simple rules for teaching an introduction to R
by Ava M. Hoffman, Carrie Wright (Source: PLoS Computational Biology)
Source: PLoS Computational Biology - May 16, 2024 Category: Biology Authors: Ava M. Hoffman Source Type: research
Nuclear export is a limiting factor in eukaryotic mRNA metabolism
by Jason M. M üller, Katharina Moos, Till Baar, Kerstin C. Maier, Kristina Zumer, Achim Tresch
The eukaryotic mRNA life cycle includes transcription, nuclear mRNA export and degradation. To quantify all these processes simultaneously, we perform thiol-linked alkylation after metabolic labeling of RNA with 4-thiouridine (4sU), followed by sequencing of RNA (SLAM-seq) in the nuclear and cytosolic compartments of human cancer cells. We develop a model that reliably quantifies mRNA-specific synthesis, nuclear export, and nuclear and cytosolic degradation rates on a genome-wide scale. We find that nuclear degradation of polya...
Source: PLoS Computational Biology - May 16, 2024 Category: Biology Authors: Jason M. M üller Source Type: research
Liebig ’s law of the minimum in the TGF-β/SMAD pathway
by Yuchao Li, Difan Deng, Chris Tina H öfer, Jihye Kim, Won Do Heo, Quanbin Xu, Xuedong Liu, Zhike Zi
Cells use signaling pathways to sense and respond to their environments. The transforming growth factor- β (TGF-β) pathway produces context-specific responses. Here, we combined modeling and experimental analysis to study the dependence of the output of the TGF-β pathway on the abundance of signaling molecules in the pathway. We showed that the TGF-β pathway processes the variation of TGF-β recept or abundance using Liebig’s law of the minimum, meaning that the output-modifying factor is the signaling protein that...
Source: PLoS Computational Biology - May 16, 2024 Category: Biology Authors: Yuchao Li Source Type: research
Interpretable online network dictionary learning for inferring long-range chromatin interactions
by Vishal Rana, Jianhao Peng, Chao Pan, Hanbaek Lyu, Albert Cheng, Minji Kim, Olgica Milenkovic
Dictionary learning (DL), implemented via matrix factorization (MF), is commonly used in computational biology to tackle ubiquitous clustering problems. The method is favored due to its conceptual simplicity and relatively low computational complexity. However, DL algorithms produce results that lack interpretability in terms of real biological data. Additionally, they are not optimized for graph-structured data and hence often fail to handle them in a scalable manner.
In order to address these limitations, we propose a novel D...
Source: PLoS Computational Biology - May 16, 2024 Category: Biology Authors: Vishal Rana Source Type: research
Inferring fungal growth rates from optical density data
by Tara Hameed, Natasha Motsi, Elaine Bignell, Reiko J. Tanaka
Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data ofAspergillus fumigatus growth (filamentous fungus). Our met...
Source: PLoS Computational Biology - May 16, 2024 Category: Biology Authors: Tara Hameed Source Type: research
