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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551768v1?rss=1 Authors: Breimann, S., Kamp, F., Steiner, H., Frishman, D. Abstract: Amino acid scales are crucial for protein prediction tasks, many of them being curated in the AAindex database. Despite various clustering attempts to organize them and to better understand their relationships, these approaches lack the fine-grained classification necessary for satisfactory interpretability in many protein prediction problems. To address this issue, we developed AAontology, a two-level classification for 586 amino acid scales (mainly from AAindex) together with an in-depth analysis of their relations, using bag-of-word-based classification, clustering, and manual refinement over multiple iterations. AAontology organizes physicochemical scales into 8 categories and 67 subcategories, enhancing the interpretability of scale-based machine learning methods in protein bioinformatics. Thereby it enables researchers to gain a deeper biological insight. We anticipate that AAontology will be a building block to link amino acid properties with protein function and dysfunctions as well as aid informed decision-making in mutation analysis or protein drug design. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551759v1?rss=1 Authors: Choudhery, S., DeJesus, M., Srinivasan, A., Rock, J., Schnappinger, D., Ioerger, T. Abstract: An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The premise is to look for CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. One thing that is unique about CRISPRi experiments is that sgRNAs for a given target can induce a wide range of protein depletion. The effect of sgRNA strength can be partially predicted based on sequence features or empirically quantified by a passaging experiment. sgRNA strength interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). sgRNA strength has not been explicitly accounted for in previous analytical methods for CRISPRi. We propose a novel method for statistical analysis of CRISPRi CGI data called CRISPRi-DR (for Dose-Response model). CRISPRi-DR incorporates data points from measurements of abundance at multiple inhibitor concentrations using a classic dose-response equation. Importantly, the effect of sgRNA strength can be incorporated into this model in a way that mimics the non-linear interaction between the two covariates on mutant abundance. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis and show that genes known to interact with various anti-tubercular drugs are ranked highly. We observe similar results in MAGeCK, a related analytical method, for datasets of low variance. However, for noisier datasets, MAGeCK is more susceptible to false positives whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data, due to CRISPRi-DRs integration of data over multiple concentrations and sgRNA strengths. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551686v1?rss=1 Authors: Li, Y., Yang, R. Abstract: Summary: We introduce PxBLAT, a Python library designed to enhance usability and efficiency in interacting with the BLAST-like alignment tool (BLAT). PxBLAT provides an intuitive application programming interface (API) design, allowing the incorporation of its functionality directly into Python-based bioinformatics workflows. Besides, it integrates seamlessly with Biopython and comes equipped with user-centric features like server readiness checks and port retry mechanisms. PxBLAT removes the necessity for system calls and intermediate files, as well as reducing latency and data conversion overhead. Benchmark tests reveal PxBLAT gains a ~20% performance boost compared to BLAT in the Python environment. Availability and Implementation: PxBLAT supports Python (version 3.8+), and pre-compiled packages are released via PyPI (https://pypi.org/project/ pxblat/) and Bioconda (https://anaconda.org/ bioconda/pxblat). The source code of PxBLAT is available under the terms of an open-source MIT license and hosted on GitHub (https:// github.com/ylab-hi/pxblat). Its documentation is available on ReadTheDocs (https://pxblat. readthedocs.io/en/latest/). Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551654v1?rss=1 Authors: Li, P., Wei, J., Zhu, Y. Abstract: Interpreting the function of genes and gene sets identified from omics experiments remains a challenge, as current pathway analysis tools often fail to account for complex interactions across genes and pathways under specific tissues and cell types. We introduce CellGO, a tool for cell type-specific gene functional analysis. CellGO employs a deep learning model to simulate signaling propagation within a cell, enabling the development of a heuristic pathway activity measuring system to identify cell type-specific active pathways given a single gene or a gene set. It is featured with additional functions to uncover pathway communities and the most active genes within pathways to facilitate mechanistic interpretation. This study demonstrated that CellGO can effectively capture cell type-specific pathways even when working with mixed cell-type markers. CellGO's performance was benchmarked using gene knockout datasets, and its implementation effectively infers the cell type-specific pathogenesis of risk genes associated with neurodevelopmental and neurodegenerative disorders, suggesting its potential in understanding complex polygenic diseases. CellGO is accessible through a python package and a four-mode web interface for interactive usage with pretrained models on 71 single-cell datasets from human and mouse fetal and postnatal brains. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551072v1?rss=1 Authors: Zeng, Y., You, Z., Guo, J., Zhao, J., Zhou, Y., Huang, J., Lyu, X., Chen, L., Li, Q. Abstract: The landscape of 3D-genome is crucial for transcription regulation. But capturing the dynamics of chromatin conformation is costly and technically challenging. Here we described Chrombus-XMBD, a graph generative model capable of predicting chromatin interactions ab inito based on available chromatin features. Chrombus employes dynamic edge convolution with QKV attention setup, which maps the relevant chromatin features to a learnable embedding space thereby generate genome-wide 3D-contactmap. We validated Chrombus predictions with published databases of topological associated domains (TAD), eQTLs and gene-enhancer interactions. Chrombus outperforms existing algorithms in efficiently predicting long-range chromatin interactions. Chrombus also exhibits strong generalizability across different cell lineage and species. Additionally, the parameter sets of Chrombus inform the biological processes underlying 3D-genome. Our model provides a new perspective towards interpretable AI-modeling of the dynamics of chromatin interactions and better understanding of cis-regulation of gene expression. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551653v1?rss=1 Authors: Vastrad, B. M., Vastrad, C. M. Abstract: Sepsis is the leading systemic inflammatory response syndrome in worldwide, yet relatively little is known about the genes and signaling pathways involved in sepsis progression. The current investigation aimed to elucidate potential key candidate genes and pathways in sepsis and its associated complications. Next generation sequencing (NGS) dataset (GSE185263) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 348 sepsis samples and 44 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. Next, we made use of the g:Profiler to analyze gene ontology (GO) and REACTOME pathway. Then protein-protein interaction (PPI) of these DEGs was visualized by Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING). Furthermore, we constructed miRNA-hub gene regulatory network and TF-hub gene regulatory network among hub genes utilizing miRNet and NetworkAnalyst online databases tool and Cytoscape software. Finally, we performed receiver operating characteristic (ROC) curve analysis of hub genes through the pROC package in R statistical software. In total, 958 DEGs were identified, of which 479 were up regulated and 479 were down regulated. GO and REACTOME results showed that DEGs mainly enriched in regulation of cellular process, response to stimulus, extracellular matrix organization and immune system. The hub genes of PRKN, KIT, FGFR2, GATA3, ERBB3, CDK1, PPARG, H2BC5, H4C4 and CDC20 might be associated with sepsis and its associated complications. Predicted miRNAs (e.g., hsa-mir-548ad-5p and hsa-mir-2113) and TFs (e.g., YAP1 and TBX5) were found to be significantly correlated with sepsis and its associated complications. In conclusion, the DEGs, relative pathways, hub genes, miRNA and TFs identified in the current investigation might help in understanding of the molecular mechanisms underlying sepsis and its associated complications progression and provide potential molecular targets and biomarkers for sepsis and its associated complications. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551637v1?rss=1 Authors: Lause, J., Ziegenhain, C., Hartmanis, L., Berens, P., Kobak, D. Abstract: Before downstream analysis can reveal biological signals in single-cell RNA sequencing data, normalization and variance stabilization are required to remove technical noise. Recently, Pearson residuals based on negative binomial models have been suggested as an efficient normalization approach. These methods were developed for UMI-based sequencing protocols, where unique molecular identifiers (UMIs) help to remove PCR amplification noise by keeping track of the original molecules. In contrast, full-length protocols such as Smart-seq2 lack UMIs and retain amplification noise, making negative binomial models inapplicable. Here, we extend Pearson residuals to such read count data by modeling them as a compound process: we assume that the captured RNA molecules follow the negative binomial distribution, but are replicated according to an amplification distribution. Based on this model, we introduce compound Pearson residuals and show that they can be analytically obtained without explicit knowledge of the amplification distribution. Further, we demonstrate that compound Pearson residuals lead to a biologically meaningful gene selection and low-dimensional embeddings of complex Smart-seq2 datasets. Finally, we empirically study amplification distributions across several sequencing protocols, and suggest that they can be described by a broken power law. We show that the resulting compound distribution captures overdispersion and zero-inflation patterns characteristic of read count data. In summary, compound Pearson residuals provide an efficient and effective way to normalize read count data based on simple mechanistic assumptions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.551620v1?rss=1 Authors: Sharma, A., Lopez, Y., JIA, S., Lysenko, A., Boroevich, K., Tsunoda, T. Abstract: Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods have been employed for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies within real-world datasets. In this paper, we present Multi-representation DeepInsight (abbreviated as MRep-DeepInsight), an innovative extension of the DeepInsight method, specifically designed to enhance the analysis of tabular data. By generating multiple representations of samples using diverse feature extraction techniques, our approach aims to capture a broader range of features and reveal deeper insights. We demonstrate the effectiveness of MRep-DeepInsight on single-cell datasets, Alzheimer's data, and artificial data, showcasing an improved accuracy over the original DeepInsight approach and machine learning methods like random forest and L2-regularized logistic regression. Our results highlight the value of incorporating multiple representations for robust and accurate tabular data analysis. By embracing the power of diverse representations, MRep-DeepInsight offers a promising avenue for advancing decision-making and scientific discovery across a wide range of fields. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.04.410498v1?rss=1 Authors: Dick, K., Green, J. R. Abstract: No. While our experiments ultimately failed, this work was motivated by the seemingly reasonable hypothesis that encoding protein sequences as a fractal-based image in combination with a binary mask identifying those pixels representative of the protein binding interface could effectively be used to fine-tune a semantic segmentation model. We were wrong. Despite the shortcomings of this work, a number of insights were drawn, inspiring discussion about how this fractal-based space may be exploited to generate effective protein binding site predictors in the future. Furthermore, these realizations promise to orient complimentary studies leveraging fractal-based representations, whether in the field of bioinformatics, or more broadly within disparate fields leveraging sequence-type data, such as Natural Language Processing. In a non-traditional way, this work presents the experimental design undertaken and interleaves various insights and limitations. It is the hope of this work that those interested in leveraging fractal-based representations and deep learning architectures as part of their work will benefit from the insights arising from this work. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551813v1?rss=1 Authors: Soto Miranda, M., Narvaez Romo, R., Moshiri, N. Abstract: Introduction: Public health faces the ongoing mission of safeguarding the population's health against various infectious diseases caused by a great number of pathogens. Epidemiology is an essential discipline in this field. With the rise of more advanced technologies, new tools are emerging to enhance the capability to intervene and control an epidemic. Among these approaches, molecular clustering comes forth as a promising option. However, appropriate genetic distance thresholds for defining clusters are poorly explored in contexts outside of Human Immunodeficiency Virus-1 (HIV-1). Methods: In this work, using the well-used pairwise Tamura-Nei 93 (TN93) distance threshold of 0.015 for HIV-1 as a point of reference for molecular cluster properties of interest, we perform molecular clustering on whole genome sequence datasets from HIV-1, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), Zaire ebolavirus, and Mpox virus, to explore potential pairwise distances thresholds for these other viruses. Results: We found the following pairwise TN93 distance thresholds as potential candidates for use in molecular clustering: 0.00014 (4 mutations) for SARS-CoV-2, 0.00016 (3 mutations) for Ebola, and 0.0000051 (1 mutation) for Mpox. Conclusion: This study provides valuable information for epidemic control strategies, and public health efforts in managing infectious diseases caused by these viruses. The identified pairwise distance thresholds for molecular clustering can serve as a foundation for future research and intervention to combat epidemics effectively. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.04.551958v1?rss=1 Authors: Courtney, E., Datta, A., Mathews, D. H., Ward, M. Abstract: Determining RNA secondary structure is a core problem in computational biology. Fast algorithms for predicting secondary structure are fundamental to this task. We describe a modified formulation of the Zuker-Stiegler algorithm with coaxial stacking, a stabilizing interaction in which the ends of multi-loops are stacked. In particular, optimal coaxial stacking is computed as part of the dynamic programming state, rather than inline. We introduce a new notion of sparsity, which we call replaceability. The modified formulation along with replaceability allows sparsification to be applied to coaxial stacking as well, which increases the speed of the algorithm. We implemented this algorithm in software we call memerna, which we show to have the fastest exact RNA folding implementation out of several popular RNA folding packages supporting coaxial stacking. We also introduce a new notation for secondary structure which includes coaxial stacking, terminal mismatches, and dangles (CTDs) information. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551876v1?rss=1 Authors: Ozturk, K., Panwala, R., Sheen, J., Ford, K., Payne, N., Zhang, D.-E., Hutter, S., Haferlach, T., Ideker, T., Mali, P., Carter, H. Abstract: Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. Perturb-seq provides a tool to investigate the effects of individual mutations on cellular programs. Here we deploy SEUSS, a Perturb-seq like approach, to generate and assay mutations at physical interfaces of the RUNX1 Runt domain. We measured the impact of 115 mutations on RNA profiles in single myelogenous leukemia cells and used the profiles to categorize mutations into three functionally distinct groups: wild-type (WT)-like, loss-of-function (LOF)-like and hypomorphic. Notably, the largest concentration of functional mutations (non-WT-like) clustered at the DNA binding site and contained many of the more frequently observed mutations in human cancers. Hypomorphic variants shared characteristics with loss of function variants but had gene expression profiles indicative of response to neural growth factor and cytokine recruitment of neutrophils. Additionally, DNA accessibility changes upon perturbations were enriched for RUNX1 binding motifs, particularly near differentially expressed genes. Overall, our work demonstrates the potential of targeting protein interaction interfaces to better define the landscape of prospective phenotypes reachable by amino acid substitutions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.03.551784v1?rss=1 Authors: Fiam, R. N., Csabai, I., Solymosi, N. Abstract: This study proposes a novel approach to studying SARS-CoV-2 virus mutations through sequencing data comparison. Traditional consensus-based methods, which focus on the most common nucleotide at each position, might overlook or obscure the presence of low-frequency variants. Our method, in contrast, retains all sequenced nucleotides at each position, forming a genomic matrix. Utilizing simulated short reads from genomes with specified mutations, we contrasted our genomic matrix approach with the consensus sequence method. Our matrix methodology accurately reflected the known mutations and true compositions, demonstrating its efficacy in understanding the sample variability and their interconnections. Further tests using real data from GISAID and NCBI-SRA confirmed its reliability and robustness. As we see, the genomic matrix approach offers a more accurate representation of the viral genomic diversity, thereby providing superior insights into virus evolution and epidemiology. Future application recommendations are provided based on our observed results. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.02.550128v1?rss=1 Authors: Liu, Q., Gao, Y., Gao, Y., Li, W., Wu, S. Abstract: The identification of T cell neo-epitopes is fundamental and computational challenging in tumor immunotherapy study. As the binding of pMHC - T cell receptor (TCR) is the essential condition for neo-epitopes to trigger the cytotoxic T cell reactivity, several computational studies have been proposed to predict neo-epitopes from the perspective of pMHC-TCR binding recognition. However, they often failed with the inaccurate binding prediction for a single pMHC -TCR pair due to the highly diverse TCR space. In this study, we proposed a novel weakly-supervised learning framework, i.e., TCRBagger, to facilitate the personalized neo-epitope identification with weakly-supervised peptide-TCR binding prediction by bagging a sample-specific TCR profile. TCRBagger integrates three carefully designed learning strategies, i.e. a self-supervised learning strategy, a denoising learning strategy and a Multi-Instance Learning (MIL) strategy in the modeling of peptide-TCR binding. Our comprehensive tests revealed that TCRBagger exhibited great advances over existing tools by modeling interactions between peptide and TCR profiles. We further applied TCRBagger in different clinical settings, including (1) facilitating the peptide-TCR binding prediction under MIL using single-cell TCR-seq data. (2) improving the patient-specific neoantigen prioritization compared to the existing neoantigen identification tools. Collectively, TCRBagger provides novel perspectives and contributions for identifying neo-epitopes as well as discovering potential pMHC-TCR interactions in personalized tumor immunotherapy. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551396v1?rss=1 Authors: Liu, S., Yu, J., Ni, N., Wang, Z., Chen, M., Li, Y., Xu, C., Bai, Q., Ding, Y., Wang, C., Zhang, J., Yao, X., Liu, H. Abstract: Predicting drug-target interaction (DTI) is a critical and rate-limiting step in drug discovery. Traditional wet-lab experiments are reliable but expensive and time-consuming. Recently, deep learning has revealed itself as a new and promising tool for accelerating the DTI prediction process because its powerful performance. Due to the vast chemical space, the DTI prediction models are typically expected to discover drugs or targets that are absent from the training set. However, generalizing prediction performance to novel drug-target pairs that belong to different distributions is a challenge for deep learning methods. In this work, we propose an Ensemble of models that capture both Domain-generIc and domain-Specific features (E-DIS) to learn diversity domain features and adapt to out-of-distribution (OOD) data. We employed Mixture-of-Experts (MOE) as a domain-specific feature extractor for the raw data to prevent the loss of any crucial features by the encoder during the learning process. Multiple experts are trained on different domains to capture and align domain-specific information from various distributions without accessing any data from unseen domains. We evaluate our approach using four benchmark datasets under both in-domain and cross-domain settings and compare it with advanced approaches for solving OOD generalization problems. The results demonstrate that E-DIS effectively improves the robustness and generalizability of DTI prediction models by incorporating diversity domain features. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551559v1?rss=1 Authors: Dadlani, E., Dash, T., Sahoo, D. Abstract: Tumor-associated Macrophages (or TAMs) are amongst the most common cells that play a significant role in the initiation and progression of colorectal cancer (CRC). Recently, Ghosh et al. proposed distinguishing signatures for identifying macrophage polarization states, namely, immuno-reactive and immuno-tolerant, using the concept of Boolean implications and Boolean networks. Their signature, called the Signature of Macrophage Reactivity and Tolerance (SMaRT), comprises of 338 human genes (equivalently, 298 mouse genes). However, SMaRT was constructed using datasets that were not specialized towards any particular disease. In this paper, (a) we perform a comprehensive analysis of the SMaRT signature on single-cell human and mouse colorectal cancer RNA-seq datasets; (b) we then adopt a technique akin to transfer learning to construct a "refined" SMaRT signature for investigating TAMs and their polarization in the CRC tumor microenvironment. Towards validation of our refined gene signature, we use (a) 5 pseudo-bulk RNA-seq datasets derived from single-cell human datasets; and (b) 5 large-cohort microarray datasets from humans. Furthermore, we investigate the translational potential of our refined gene signature in problems related to MSS/MSI (4 datasets) and CIMP+/CIMP- status (4 datasets). Overall, our refined gene signature and its extensive validation provide a path for its adoption in clinical practice in diagnosing colorectal cancer and associated attributes. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551497v1?rss=1 Authors: Tsishyn, M., Cia, G., Hermans, P., Kwasigroch, J., Rooman, M., Pucci, F. Abstract: Systematically predicting the effects of mutations on protein fitness is essential for the understanding of genetic diseases. Indeed, predictions complement experimental efforts in analyzing how variants lead to dysfunctional proteins that in turn can cause diseases. Here we present our new fitness predictor, FiTMuSiC, which leverages structural, evolutionary and coevolutionary information. We show that FiTMuSiC predicts fitness with high accuracy despite the simplicity of its underlying model: it was one of the top predictors on the hydroxymethylbilane synthase (HMBS) target of the sixth round of the Critical Assessment of Genome Interpretation challenge (CAGI6). To further demonstrate FiTMuSiC's robustness, we compared its predictions with in vitro activity data on HMBS, variant fitness data on human glucokinase (GCK), and variant deleteriousness data on HMBS and GCK. These analyses further confirm FiTMuSiC's qualities and accuracy, which compare favorably with those of other predictors. Additionally, FiTMuSiC returns two scores that separately describe the functional and structural effects of the variant, thus providing mechanistic insight into why the variant leads to fitness loss or gain. We also provide an easy-to-use webserver at babylone.ulb.ac.be/FiTMuSiC, which is freely available for academic use and does not require any bioinformatics expertise, which simplifies the accessibility of our tool for the entire scientific community. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551483v1?rss=1 Authors: Gorantla, R., Kubincova, A., Weisse, A. Y., Mey, A. S. J. S. Abstract: Accurate in silico prediction of protein-ligand binding affinity is important in the early stages of drug discovery. Deep learning-based methods exist but have yet to overtake more conventional methods such as giga-docking largely due to their lack of generalisability. To improve generalizability we need to understand what these models learn from input protein and ligand data. We systematically investigated a sequence-based deep learning framework to assess the impact of protein and ligand encodings on predicting binding affinities for commonly used kinase data sets. The role of proteins is studied using convolutional neural network-based encodings obtained from sequences and graph neural network-based encodings enriched with structural information from contact maps. Ligand-based encodings are generated from graph-neural networks. We test different ligand perturbations by randomizing node and edge properties. For proteins we make use of 3 different protein contact generation methods (AlphaFold2, Pconsc4, and ESM-1b) and compare these with a random control. Our investigation shows that protein encodings do not substantially impact the binding predictions, with no statistically significant difference in binding affinity for KIBA in the investigated metrics (concordance index, Pearson's R Spearman's Rank, and RMSE). Significant differences are seen for ligand encodings with random ligands and random ligand node properties, suggesting a much bigger reliance on ligand data for the learning tasks. Using different ways to combine protein and ligand encodings, did not show a significant change in performance. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551575v1?rss=1 Authors: Yuan, Q., Duren, Z. Abstract: Accurate context-specific Gene Regulatory Networks (GRNs) inference from genomics data is a crucial task in computational biology. However, existing methods face limitations, such as reliance on gene expression data alone, lower resolution from bulk data, and data scarcity for specific cellular systems. Despite recent technological advancements, including single-cell sequencing and the integration of ATAC-seq and RNA-seq data, learning such complex mechanisms from limited independent data points still presents a daunting challenge, impeding GRN inference accuracy. To overcome this challenge, we present LINGER (LIfelong neural Network for GEne Regulation), a novel deep learning-based method to infer GRNs from single-cell multiome data with paired gene expression and chromatin accessibility data from the same cell. LINGER incorporates both 1) atlas-scale external bulk data across diverse cellular contexts and 2) the knowledge of transcription factor (TF) motif matching to cis-regulatory elements as a manifold regularization to address the challenge of limited data and extensive parameter space in GRN inference. Our results demonstrate that LINGER achieves 2-3 fold higher accuracy over existing methods. LINGER reveals a complex regulatory landscape of genome-wide association studies, enabling enhanced interpretation of disease-associated variants and genes. Additionally, following the GRN inference from a reference sc-multiome data, LINGER allows for the estimation of TF activity solely from bulk or single-cell gene expression data, leveraging the abundance of available gene expression data to identify driver regulators from case-control studies. Overall, LINGER provides a comprehensive tool for robust gene regulation inference from genomics data, empowering deeper insights into cellular mechanisms. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551468v1?rss=1 Authors: Mukashyaka, P., Sheridan, T. B., Foroughi pour, A., Chuang, J. H. Abstract: Deep learning has revolutionized digital pathology, allowing for automatic analysis of hematoxylin and eosin (H&E) stained whole slide images (WSIs) for diverse tasks. In such analyses, WSIs are typically broken into smaller images called tiles, and a neural network backbone encodes each tile in a feature space. Many recent works have applied attention based deep learning models to aggregate tile-level features into a slide-level representation, which is then used for slide-level prediction tasks. However, training attention models is computationally intensive, necessitating hyperparameter optimization and specialized training procedures. Here, we propose SAMPLER, a fully statistical approach to generate efficient and informative WSI representations by encoding the empirical cumulative distribution functions (CDFs) of multiscale tile features. We demonstrate that SAMPLER-based classifiers are as accurate or better than state-of-the-art fully deep learning attention models for classification tasks including distinction of: subtypes of breast carcinoma (BRCA: AUC=0.911 {+/-} 0.029); subtypes of non-small cell lung carcinoma (NSCLC: AUC=0.940 {+/-} 0.018); and subtypes of renal cell carcinoma (RCC: AUC=0.987 {+/-} 0.006). A major advantage of the SAMPLER representation is that predictive models are greater than 100X faster compared to attention models. Histopathological review confirms that SAMPLER-identified high attention tiles contain tumor morphological features specific to the tumor type, while low attention tiles contain fibrous stroma, blood, or tissue folding artifacts. We further apply SAMPLER concepts to improve the design of attention-based neural networks, yielding a context aware multi-head attention model with increased accuracy for subtype classification within BRCA and RCC (BRCA: AUC=0.921 {+/-} 0.027, and RCC: AUC=0.988 {+/-} 0.010). Finally, we provide theoretical results identifying sufficient conditions for which SAMPLER is optimal. SAMPLER is a fast and effective approach for analyzing WSIs, with greatly improved scalability over attention methods to benefit digital pathology analysis. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551502v1?rss=1 Authors: Glascock, A. L., Waltari, E., Dudas, G., Wong, J., Ahyong, V. Abstract: Background: Genome streamlining, the process by which genomes become smaller and encode fewer genes over time, is a common phenomenon among pathogenic bacteria. This reduction is driven by selection for faster replication and minimized energy expenditure in a nutrient-rich environment. As pathogens evolve to become more reliant on the host, metabolic genes and resulting capabilities are lost in favor of siphoning metabolites from the host. Characterizing genome streamlining, gene loss, and pathway degradation can be useful in assessing pathogen metabolic dependency on host metabolism and identifying potential targets for host-directed therapeutics. Results: PoMeLo (Predictor of Metabolic Loss) is a novel evolutionary genomics-guided computational approach for identifying metabolic gaps in the genomes of pathogenic bacteria. PoMeLo leverages a centralized public database of high-quality genomes and annotations and allows the user to compare an unlimited number of genomes across individual genes and pathways. PoMeLo runs locally using user-friendly prompts in a matter of minutes and generates tabular and visual outputs for users to compare predicted metabolic capacity between groups of bacteria and individual species. Each pathway is assigned a Predicted Metabolic Loss (PML) score to assess the magnitude of genome streamlining. Optionally, PoMeLo places results in an evolutionary context by including phylogenetic relationships in visual outputs. It can also initially compute phylogenetically-weighted mean genome sizes to identify genome streamlining events. Here we describe PoMeLo and demonstrate its use in identifying metabolic gaps in genomes of pathogenic Treponema species. Conclusions: PoMeLo represents an advance over existing methods for identifying metabolic gaps in genomic data, allowing comparison across large numbers of genomes and placing the resulting data in a phylogenetic context. PoMeLo is freely available for academic and non-academic use at https://github.com/czbiohub-sf/pomelo. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.551496v1?rss=1 Authors: GUYON, F., Moroy, G. Abstract: Motivation: Peptides are molecules involved in many essential biological activities by interacting with proteins in the body. They can also be used as therapeutic molecules, particularly to disrupt protein-protein interactions involved in disease. However, it is difficult to identify potential therapeutic peptides if the structure of the protein-protein complex to be inhibited has not been solved. Results: The PepIT program was developed to propose peptides that can interact with a given protein. PepIT is based on a non-sequential alignment algorithm to identify peptide binding sites that share geometrical and physicochemical properties with a surface region of the target protein. PepIT compares the entire surface of the target protein with the peptide binding sites of the Propedia dataset, which contains more than 19,000 high-resolution protein-peptide structures. Once a peptide binding site similar to a portion of the protein surface is found, the peptide bound to the binding site is repositioned on the corresponding portion of the protein surface. Availability and implementation: The PepIT source code is freely available at https://github.com/DSIMB/pepit Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.08.01.550795v1?rss=1 Authors: Sahrhage, M., Paul, N. B., Beissbarth, T., Haubrock, M. Abstract: Nucleosome positioning is a key factor in the regulation of transcription, as nucleosomes regulate the dynamic accessibility of chromatin for transcription factor binding and interact with the transcription machinery at every stage. Influences to steer nucleosome positioning are diverse, and the importance of the DNA sequence for this process in contrast to active chromatin remodeling has been the subject of a long discussion. In this study, we evaluate the functional role of DNA sequence for all major elements along the process of transcription. For this purpose, we developed a random forest classifier based on local DNA structure that assesses the sequence-intrinsic support for nucleosome positioning. On this basis, we created an easy-to-use data resource that can be applied in a genome-wide manner to the human genome. In our comprehensive analysis, we found a special role of DNA in mediating the competition of nucleosomes with cis-regulatory elements, in enabling steady transcription, for positioning of stable nucleosomes in exons and for nucleosome depletion during transcription termination. In contrast, we relate these findings to concurrent processes that generate strongly positioned nucleosomes in vivo that are not mediated by sequence, such as energy-dependent remodeling of chromatin. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551395v1?rss=1 Authors: Xu, Z. Abstract: Background: Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease characterized by excessive scarring of lung tissue. Recent studies have indicated a potential link between IPF and type 2 diabetes (T2D), suggesting that T2D may contribute to the pathogenesis of IPF or vice versa. In this study, we aim to investigate the underlying molecular mechanisms and pathways involved in the development of IPF complicated with T2D using microarray data analysis. Methods: The datasets for Type 2 Diabetes (T2D) (GSE25724) and Idiopathic Pulmonary Fibrosis (IPF) (GSE110147) were obtained from the Gene Expression Omnibus (GEO) database. Differential expression analysis was conducted using the 'limma' package in R to identify genes that were significantly differentially expressed between T2D and IPF samples. Functional enrichment analysis of these differentially expressed genes was performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG). To explore protein-protein interactions, protein-protein interaction networks were constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized using Cytoscape. CytoHubba, a plugin in Cytoscape, was utilized to identify hub genes in the network. The identified hub genes were further validated in independent datasets: GSE166467 for T2D and GSE24206 for IPF. To assess the predictive value of the hub genes, receiver operating characteristic (ROC) curves were generated. Additionally, gene set enrichment analysis was performed to uncover potential biological pathways associated with the hub genes. Finally, an analysis of immune infiltration within the hub gene network was conducted. Results: A total of 255 differentially expressed genes (DEGs) were found to be commonly dysregulated in both Type 2 Diabetes (T2D) and Idiopathic Pulmonary Fibrosis (IPF). Pathways related to metabolic processes were significantly enriched in the analysis of both T2D and IPF. Through the validation process, RPL30 was identified as a hub gene, exhibiting an area under the curve (AUC) value greater than 0.65 for both T2D and IPF. Additionally, we identified 92 transcription factors (TFs) and 54 microRNAs (miRNAs) that may potentially regulate the expression of RPL30. Conclusions: This study provides novel insights by highlighting the role of RPL30 in the shared pathogenesis of pulmonary fibrosis and Type 2 diabetes (T2D). The findings suggest that RPL30 has the potential to serve as a biomarker and therapeutic target for these conditions. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551386v1?rss=1 Authors: Zhang, S., Heil, B. J., Mao, W., Greene, C. S., Heller, E. A. Abstract: High throughput gene expression profiling is a powerful approach to generate hypotheses on the underlying causes of biological function and disease. Yet this approach is limited by its ability to infer underlying biological pathways and burden of testing tens of thousands of individual genes. Machine learning models that incorporate prior biological knowledge are necessary to extract meaningful pathways and generate rational hypothesis from the vast amount of gene expression data generated to date. We adopted an unsupervised machine learning method, Pathway-level information extractor (PLIER), to train the first mouse PLIER model on 190,111 mouse brain RNA-sequencing samples, the greatest amount of training data ever used by PLIER. mousiPLER converted gene expression data into a latent variables that align to known pathway or cell maker gene sets, substantially reducing data dimensionality and improving interpretability. To determine the utility of mousiPLIER, we applied it to a mouse brain aging study of microglia and astrocyte transcriptomic profiling. We found a specific set of latent variables that are significantly associated with aging, including one latent variable (LV41) corresponding to striatal signal. We next performed k-means clustering on the training data to identify studies that respond strongly to LV41, finding that the variable is relevant to striatum and aging across the scientific literature. Finally, we built a web server (http://mousiplier.greenelab.com/) for users to easily explore the learned latent variables. Taken together this study provides proof of concept that mousiPLIER can uncover meaningful biological processes in mouse transcriptomic studies. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551238v1?rss=1 Authors: Ruiz-Arenas, C., Marin-Goni, I., Wang, L., Ochoa, I., Perez-Jurado, L. A., Hernaez, M. Abstract: Background/Objectives: Grouping gene expression into gene set activity scores (GSAS) provides better biological insights than studying individual genes. However, existing gene set projection methods cannot return representative, robust, and interpretable GSAS. Methods: We developed NetActivity, a machine learning framework that generates GSAS based on a sparsely-connected autoencoder, where each neuron in the inner layer represents a gene set. We proposed a three-tier training that yielded representative, robust, and interpretable GSAS. NetActivity model was trained with 1,518 GO biological processes terms and KEGG pathways and all GTEx samples. Results: NetActivity generates GSAS robust to the initialization parameters and representative of the original transcriptome, and assigned higher importance to more biologically relevant genes. Moreover, NetActivity returns GSAS with a more consistent definition and higher interpretability than GSVA and hipathia, state-of-the-art gene set projection methods. Finally, NetActivity enables combining bulk RNA-seq and microarray datasets in a meta-analysis of prostate cancer progression, highlighting gene sets related to cell division, key for disease progression. When applied to metastatic prostate cancer, gene sets associated with cancer progression were also altered due to drug resistance, while a classical enrichment analysis identified gene sets irrelevant to the phenotype. Conclusion: NetActivity encodes gene expression into representative, robust, and interpretable GSAS, which can improve differential analyses and studies' replicability. NetActivity is publicly available in Bioconductor and GitHub. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551228v1?rss=1 Authors: ZHAO, K., SO, H.-C., Lin, Z. Abstract: Single-cell RNA-sequencing (scRNA-seq) technology has been widely used to measure the transcriptome of cells in complex and heterogeneous systems. Integrative analysis of multiple scRNA-seq data can transform our understanding of various aspects of biology at the single-cell level. Many computational methods are proposed for data integration. However, few methods for scRNA-seq data integration explicitly model variation from heterogeneous biological conditions for interpretation. Modeling the variation helps understand the effect of biological conditions on complex biological systems. Our study proposes SR2 to capture gene expression patterns from heterogeneous biological conditions and discover cell identity simultaneously. Therefore, it can uncover the effect of biological conditions on the gene expression of cells and simultaneously achieve state-of-the-performance in cell identity discovery in our comprehensive comparison. Notably, SR2 is extended to model the effects of biological conditions on gene expression for cell populations, thus uncovering the effect of biological conditions on gene expression for cell populations and identifying putative condition-associated cell populations. To improve its scalability, we incorporate a batch-fitting strategy to ensure it is scalable to scRNA-seq data with arbitrary sample sizes. Moreover, the broad applicability of SR2 in biomedical studies has been demonstrated via applications. The complete package of SR2 is available at https://github.com/kai0511/SR2. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551202v1?rss=1 Authors: Gao, J., Guo, S., Zhang, Y. Abstract: The rapid accumulation of public single-cell RNA-seq (scRNA-seq) data and advances in integration algorithms make it possible to build the organ and body-scale cell atlases after overcoming the batch effect originating from different studies, donors, and sequence platforms. However, projecting the query cells onto the carefully constructed reference atlas to rapidly interpret cell states remains challenging. Here, we present ProjectSVR (https://github.com/jarninggau/ProjectSVR), a machine learning-based algorithm for mapping the query cells onto well-constructed reference embeddings. We demonstrate the effectiveness of ProjectSVR in multiple case studies of different tissue and experimental design, including (1) interpreting the decidual immune microenvironment from recurrent pregnancy loss (RPL) patients, (2) resolving the tumor-infiltrated T cell states from patients receiving anti-PD1 treatment, (3) mapping the genetic-perturbed mouse germ cells to integrated mouse testicular cell atlas (mTCA) for inferring their arrested developmental stage, and (4) evaluating in vitro induced meiosis by via mapping the induced germ cells onto mTCA. Taken together, our results show that ProjectSVR is a useful tool for reference mapping and make the scRNA-seq data analysis more convenient when well-annotated atlases emerge. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551358v1?rss=1 Authors: de Farias Filho, E. D., Stransky, B., Terrematte, P. C. A. Abstract: Renal carcinoma is a pathology of silent and multifactorial development characterized by a high rate of metastases in patients. After several studies have elucidated the activity of coding genes in the metastatic progression of renal carcinoma, new studies seek to evaluate the association of non-coding genes, such as competitive endogenous RNA (ceRNA). Thus, this study aims to build a gene signature for clear cell renal cell carcinoma (ccRCC) associated with metastatic development from a ceRNA network and to analyze the probable biological functions performed by the participants of the signature. Using ccRCC data from The Cancer Genome Atlas (TCGA), we constructed the ceRNA network with the differentially expressed genes, assembled nine gene signatures from eight feature selection techniques, and analyzed the evaluation metrics of the classification models in the benchmarking process. With the signature, we performed somatic and copy number alteration analysis, survival and metastatic progression risk analysis, and functional annotation analysis. In this study, we present an 11-gene signature (SNHG15, AF117829.1, hsa-miR-130a-3p, hsa-mir-381-3p, BTBD11, INSR, HECW2, RFLNB, PTTG1, HMMR, RASD1). Validation using the external dataset of the International Cancer Genome Consortium (ICGC-RECA) made it possible to assess the generalization of the signature, which showed an Area Under Curve of 81.5%. The genomic analysis identified the signature participants on chromosomes with highly mutated regions (G-index greater than 2). The hsa-miR-130a-3p, AF117829.1, hsa-miR-381-3p, and PTTG1 had a significant relationship between expression and patient survival, and the first two had a significant association with metastatic development. In addition, functional annotation resulted in relevant pathways for tumor development, such as PI3K/AKT, TNF, FoxO, RNA polymerase two transcription regulation, and cell control. Finally, by analyzing the connections of the signature genes within the ceRNA network in conjunction with studies in the literature, it was possible to obtain an overview of their activities within the ccRCC. Therefore, this gene signature identified new coding and non-coding genes and could act as potential biomarkers for a better understanding of renal carcinoma and in the development of future treatments in the clinical area. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551246v1?rss=1 Authors: Dodin, Y. Abstract: Background: Globally, lung cancer is the leading cause of cancer-related deaths, primarily non-small cell lung cancer (NSCLC). Kirsten Rat Sarcoma Oncogene Homolog (KRAS) mutations are common in NSCLC and linked to a poor prognosis. Covalent inhibitors targeting KRAS-G12C mutation have improved treatment for some patients, but most KRAS-mutant lung adenocarcinoma (KRAS-MT LUAD) cases lack targeted therapies. More research is required to identify prognostic genes in KRAS-MT LUAD. Objective: We aimed to identify hub genes within key co-expression gene network modules specifically associated with KRAS-MT LUAD. These hub genes hold the potential to serve as therapeutic targets or biomarkers, providing insights into the pathogenesis and prognosis of lung cancer. Methods: We performed a comprehensive analysis on KRAS-MT LUAD using diverse data sources. This included TCGA project data for RNA-seq, clinical information, and somatic mutations, along with RNA-seq data for adjacent normal tissues. DESeq2 identified differentially expressed genes (DEGs), while weighted gene co-expression network analysis (WGCNA) revealed co-expression modules. Overlapping genes between DEGs and co-expression module with the highest significance were analyzed using gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) network analysis. Hub genes were identified with the Maximal Clique Centrality (MCC) algorithm in Cytoscape. Prognostic significance was assessed through survival analysis and validated using the GSE72094 dataset from Gene Expression Omnibus (GEO) database. Results: In KRAS-MT LUAD, 3,122 DEGs were found (2,131 up-regulated, 985 down-regulated). The blue module, among 25 co-expression modules from WGCNA, had the strongest correlation. 804 genes overlapped between DEGs and the blue module. Among twenty hub genes in the blue module, leucine-rich repeats containing G protein-coupled receptor 4 (LGR4) overexpression correlated with worse overall survival (OS) in KRAS-MT LUAD patients (P=0.012). The prognostic significance of LGR4 was confirmed using GSE72094, but surprisingly, the direction of the association was opposite to what was expected. Conclusion: LGR4 is a potential prognostic biomarker in KRAS-MT LUAD. Contrasting associations in TCGA and GSE72094 datasets reveal the complexity of KRAS-MT LUAD. Further investigations are required to understand LGR4's role in lung adenocarcinoma prognosis, especially in the context of KRAS mutations. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.28.551039v1?rss=1 Authors: Kernfeld, E. M., Yang, Y., Weinstock, J. S., Battle, A., Cahan, P. M. Abstract: Due to the abundance of single cell RNA-seq data, a number of methods for predicting expression after perturbation have recently been published. Expression prediction methods are enticing because they promise to answer pressing questions in fields ranging from developmental genetics to cell fate engineering and because they are faster, cheaper, and higher-throughput than their experimental counterparts. However, the absolute and relative accuracy of these methods is poorly characterized, limiting their informed use, their improvement, and the interpretation of their predictions. To address these issues, we created a benchmarking platform that combines a panel of large-scale perturbation datasets with an expression forecasting software engine that encompasses or interfaces to current methods. We used our platform to systematically assess methods, parameters, and sources of auxiliary data. We found that uninformed baseline predictions, which were not always included in prior evaluations, yielded the same or better mean absolute error than benchmarked methods in all test cases. These results cast doubt on the ability of current expression forecasting methods to provide mechanistic insights or to rank hypotheses for experimental follow-up. However, given the rapid pace of innovation in the field, new approaches may yield more accurate expression predictions. Our platform will serve as a neutral benchmark to improve methods and to identify contexts in which expression prediction can succeed. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551200v1?rss=1 Authors: Kumar, S., Pauline, G., Vindal, V. Abstract: In biological network analysis, identifying key molecules plays a decisive role in the development of potential diagnostic and therapeutic candidates. Among various approaches of network analysis, network vulnerability analysis is quite important, as it assesses significant associations between topological properties and the functional essentiality of a network. Further, some node centralities are also used to screen out key molecules. Among these node centralities, escape velocity centrality (EVC), and its extended version (EVC+) outperform others, viz., Degree, Betweenness, and Clustering coefficient. Keeping this in mind, we aimed to develop a first-of-its-kind R package named NetVA, which analyzes networks to identify key molecular players through network vulnerability and EVC+-based approaches. To demonstrate the application and relevance of our package in network analysis, previously published and publicly available protein-protein interactions (PPIs) data of human breast cancer were analyzed. This resulted in identifying some most important proteins. These included essential proteins, non-essential proteins, hubs, and bottlenecks, which play vital roles in breast cancer development. Thus, the NetVA package, available at https://github.com/kr-swapnil/NetVA with a detailed tutorial to download and use, assists in predicting potential candidates for therapeutic and diagnostic purposes by exploring various topological features of a disease-specific PPIs network. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.30.551108v1?rss=1 Authors: Miao, Z., Wang, J., Park, K., Kuang, D., Kim, J. Abstract: Single nucleus ATAC-seq (snATAC-seq) experimental designs have become increasingly complex with multiple factors that might affect chromatin accessibility, including cell type, tissue of origin, sample location, batch, etc., whose compound effects are difficult to test by existing methods. In addition, current snATAC-seq data present statistical difficulties due to their sparsity and variations in individual sequence capture. To address these problems, we present a zero-adjusted statistical model, PACS, that can allow complex hypothesis testing of factors that affect accessibility while accounting for sparse and incomplete data. For differential accessibility analysis, PACS controls the false positive rate and achieves on average a 17% to 122% higher power than existing tools. We demonstrate the effectiveness of PACS through several analysis tasks including supervised cell type annotation, compound hypothesis testing, batch effect correction, and spatiotemporal modeling. We apply PACS to several datasets from a variety of tissues and show its ability to reveal previously undiscovered insights in snATAC-seq data. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.30.551115v1?rss=1 Authors: Smith, C. C., Kern, A. D. Abstract: Summary: We present a deep learning tool, disperseNN2, for estimating the mean per-generation dispersal distance from georeferenced polymorphism data. Our neural network performs feature extraction on pairs of genotypes, and uses the geographic information that comes with each sample. These attributes led disperseNN2 to outperform a state-of-the-art deep learning method that does not use explicit spatial information: error was reduced by 33% and 42% using sample sizes of 10 and 100 individuals, respectively. disperseNN2 is particularly useful for non-model organisms or systems with sparse genomic resources, as it uses unphased, single nucleotide polymorphisms as its input. Availability and Implementation: https://github.com/kr-colab/disperseNN2. Contact: chriscs@uoregon.edu Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.551370v1?rss=1 Authors: Kountz, D. J., Balskus, E. P. Abstract: Analyses of microbial genome sequencing data have revealed unexpectedly wide distributions of enzymes from specialized metabolic pathways, including enzymes from methanogens, providing exciting opportunities for discovery. Here, we identify a family of gene clusters (the type 1 mlp gene clusters (MGCs)) that encodes homologs of the soluble coenzyme M methyltransferases (SCMTs) involved in methylotrophic methanogenesis and is widespread in bacteria and archaea. Type 1 MGCs are expressed and regulated in a number of medically, environmentally, and industrially important organisms, making them likely to be physiologically relevant. Enzyme annotation and analysis of genomic context suggests these gene clusters are likely to play a role in methyl-sulfur and/or methyl-selenide metabolism in numerous anoxic environments, including the human gut microbiome. Notably, we propose that type 1 MGCs could participate in selenium and methionine salvage pathways that could impact sulfur and selenium cycling in diverse, anoxic environments. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.31.550344v1?rss=1 Authors: Van Norden, M. C., Falls, Z., Mandloi, S., Segal, B., Baysal, B., Samudrala, R., Elkin, P. L. Abstract: Intra-organism biodiversity is thought to arise from epigenetic modification of our constituent genes and post-translational modifications after mRNA is translated into proteins. We have found that post-transcriptional modification, also known as RNA editing, is also responsible for a significant amount of our biodiversity, substantively expanding this story. The APOBEC (apolipoprotein B mRNA editing catalytic polypeptide-like) family RNA editing enzymes APOBEC3A and APOBEC3G catalyze the deamination of cytosines to uracils (C greater than U) in specific stem-loop structures.1,2 We used RNAsee (RNA site editing evaluation), a tool developed to predict the locations of APOBEC3A/G RNA editing sites, to determine whether known single nucleotide polymorphisms (SNPs) in DNA could be replicated in RNA via RNA editing. About 4.5% of non-synonymous SNPs which result in C greater than U changes in RNA, and about 5.4% of such SNPs labelled as pathogenic, were identified as probable sites for APOBEC3A/G editing. This suggests that the variant proteins created by these DNA mutations may also be created by transient RNA editing, with the potential to affect human health. Those SNPs identified as potential APOBEC3A/G-mediated RNA editing sites were disproportionately associated with cardiovascular diseases, digestive system diseases, and musculoskeletal diseases. Future work should focus on common sites of RNA editing, any variant proteins created by these RNA editing sites, and the effects of these variants on protein diversity and human health. Classically, our biodiversity is thought to come from our constitutive genetics, epigenetic phenomenon, transcriptional differences, and post-translational modification of proteins. Here, we have shown evidence that RNA editing, often stimulated by environmental factors, could account for a significant degree of the protein biodiversity leading to human disease. In an era where worries about our changing environment are ever increasing, from the warming of our climate to the emergence of new diseases to the infiltration of microplastics and pollutants into our bodies, understanding how environmentally sensitive mechanisms like RNA editing affect our own cells is essential. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.28.550985v1?rss=1 Authors: Li, H., Wang, Y., Lai, Y., Zeng, F., Yang, F. Abstract: Various studies have linked several diseases, including cancer and Covid-19, to single nucleotide variations (SNV). Although scRNA-seq technology can provide SNV and gene expression data, few studies have integrated and analyzed these multimodal data. To address this issue, this paper introduces Interpretable Single-cell Multimodal Data Integration Based on Variational Autoencoder (ISMI-VAE). ISMI-VAE leverages latent variable models that utilize the characteristics of SNV and gene expression data to overcome high noise levels, and uses deep learning techniques to integrate multimodal information, map them to a low-dimensional space, and classify disease cells. Moreover, ISMI-VAE introduces an attention mechanism to reflect feature importance and analyze genetic features that could potentially cause disease. Experimental results on three cancer data sets and one Covid-19 data set demonstrate that ISMI-VAE surpasses the baseline method in terms of both effectiveness and interpretability, and can effectively identify disease-causing gene features. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.28.551024v1?rss=1 Authors: Kumler, W. E., Hazelton, B. J., Ingalls, A. E. Abstract: Chromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality. Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone. We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.28.551057v1?rss=1 Authors: Flores-Bautista, E., Thomson, M. Abstract: Understanding the circuits that control cell differentiation is a fundamental problem in developmental biology. Single-cell RNA sequencing has emerged as a powerful tool for investigating this problem. However, the reconstruction of developmental trajectories is based on the assumption that cell states traverse a tree-like structure, which may bias our understanding of critical developmental mechanisms. To address this limitation, we propose a topological approach that enables identifying signatures of functional biological circuits as persistent homology groups in transcriptome space. In this work, we applied our approach to more than ten single-cell developmental atlases and found that topological transcriptome spaces are predominantly path-connected and only sometimes simply connected. We developed a framework, TopGen, that identifies transiently expressed genes along topological motifs using homology generators. We show that TopGen can identify genetic drivers of topological structures in simulated datasets. Finally, we applied TopGen to analyze topological loops representing stem-like, transdifferentiation, and convergent cell circuits, found in C. elegans, H. vulgaris, and N. vectensis, respectively. Our results show that some essential differentiation mechanisms use non-trivial topological motifs, and that these motifs can be conserved in a cell-type--specific manner. Thus, our approach to studying the topological properties of developmental datasets opens new possibilities for understanding cell development and differentiation. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550881v1?rss=1 Authors: Dieckhaus, H., Brocidiacono, M., Randolph, N., Kuhlman, B. Abstract: Amino acid mutations that lower a protein's thermodynamic stability are implicated in numerous diseases, and engineered proteins with enhanced stability are important in research and medicine. Computational methods for predicting how mutations perturb protein stability are therefore of great interest. Despite recent advancements in protein design using deep learning, in silico prediction of stability changes has remained challenging, in part due to a lack of large, high-quality training datasets for model development. Here we introduce ThermoMPNN, a deep neural network trained to predict stability changes for protein point mutations given an initial structure. In doing so, we demonstrate the utility of a newly released mega-scale stability dataset for training a robust stability model. We also employ transfer learning to leverage a second, larger dataset by using learned features extracted from a deep neural network trained to predict a protein's amino acid sequence given its three-dimensional structure. We show that our method achieves competitive performance on established benchmark datasets using a lightweight model architecture that allows for rapid, scalable predictions. Finally, we make ThermoMPNN readily available as a tool for stability prediction and design. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550838v1?rss=1 Authors: Ellin, N. R., Miranda Quintana, R. A., Prentice, B. Abstract: Imaging mass spectrometry is a label-free imaging modality that allows for the spatial mapping of many compounds directly in tissues. In an imaging mass spectrometry experiment, a raster of the tissue surface produces a mass spectrum at each sampled x, y position, resulting in thousands of individual mass spectra, each comprising a pixel in the resulting ion images. However, efficient analysis of imaging mass spectrometry datasets can be challenging due to the hyperspectral characteristics of the data. Each spectrum contains several thousand unique compounds at discrete m/z values that result in unique ion images, which demands robust and efficient algorithms for searching, statistical analysis, and visualization. Some traditional post-processing techniques are fundamentally ill-equipped to dissect these types of data. For example, while principal component analysis (PCA) has long served as a useful tool for mining imaging mass spectrometry datasets to identify correlated analytes and biological regions of interest, the interpretation of the PCA scores and loadings can be non-trivial. The loadings often containing negative peaks in the PCA-derived pseudo-spectra, which are difficult to ascribe to underlying tissue biology. Herein, we have utilized extended similarity indices to streamline the interpretation of imaging mass spectrometry data. This novel workflow uses PCA as a pixel-selection method to parse out the most and least correlated pixels, which are then compared using the extended similarity indices. The extended similarity indices complement PCA by removing all non-physical artifacts and streamlining the interpretation of large volumes of IMS spectra simultaneously. The linear complexity, O(N), of these indices suggests that large imaging mass spectrometry datasets can be analyzed in a 1:1 scale of time and space with respect to the size of the input data. The extended similarity indices algorithmic workflow is exemplified here by identifying discrete biological regions of mouse brain tissue. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550528v1?rss=1 Authors: Bradford, L. M., Carrillo, C., Wong, A. Abstract: Background: Culture-independent diagnostic tests (CIDTs) are gaining popularity as tools for detecting pathogens in food. Shotgun sequencing holds substantial promise for food testing as it provides abundant information on microbial communities, but the challenge is in analyzing large and complex sequencing datasets with a high degree of both sensitivity and specificity. Falsely classifying sequencing reads as originating from pathogens can lead to unnecessary food recalls or production shutdowns, while low sensitivity resulting in false negatives could lead to preventable illness. Results: We have developed a bioinformatic pipeline for identifying Salmonella as a model pathogen in metagenomic datasets with very high sensitivity and specificity. We tested this pipeline on mock communities of closely related bacteria and with simulated Salmonella reads added to published metagenomic datasets. Salmonella-derived reads could be found at very low abundances (high sensitivity) without false positives (high specificity). Carefully considering software parameters and database choices is essential to avoiding false positive sample calls. With well-chosen parameters plus additional steps to confirm the taxonomic origin of reads, it is possible to detect pathogens with very high specificity and sensitivity. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550821v1?rss=1 Authors: Soneson, C., Iesmantavicius, V., Hess, D., Stadler, M. B., Seebacher, J. Abstract: We describe einprot, an R package providing easy-to-use reproducible workflows for quality control, statistical analysis and visualization of quantitative proteomics data. einprot is applicable to tabular output from MaxQuant, Proteome Discoverer and FragPipe, and a single function call generates an html report that describes the full analysis pipeline applied to the data and contains static and interactive figures and tables for further exploration. This has the potential to facilitate routine analyses as well as to provide a standardized, yet comprehensive way to communicate results to collaborators and the broader community. The source file underlying the report is also returned, giving the user full flexibility to further modify the workflow according to their needs. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.28.550005v1?rss=1 Authors: Fang, T., Szklarczyk, D., Hachilif, R., von Mering, C. Abstract: Background: Protein-protein interactions play essential roles in almost all biological processes. The binding interfaces between interacting proteins impose evolutionary constraints, leading to co-evolutionary signals that have successfully been employed to predict protein interactions from multiple sequence alignments (MSAs). During the construction of MSAs for this purpose, critical choices have to be made: how to ensure the reliable identification of orthologs, how to deal with paralogs, and how to optimally balance the need for large alignments versus sufficient alignment quality. Results: Here, we propose a divide-and-conquer strategy for MSA generation: instead of building a single, large alignment for each protein, multiple distinct alignments are constructed, each covering only a single clade in the tree of life. Co-evolutionary signals are searched separately within these clades, and are only subsequently integrated into a final interaction prediction using machine learning. We find that this strategy markedly improves overall prediction performance, concomitant with better alignment quality. Using the popular DCA algorithm to systematically search pairs of such alignments, a genome-wide all-against-all interaction scan in a bacterial genome is demonstrated. Conclusions: Given the recent successes of AlphaFold in predicting protein-protein interactions at atomic detail, a discover-and-refine approach is proposed: our method could provide a fast and accurate strategy for pre-screening the entire genome, submitting to AlphaFold only promising interaction candidates - thus reducing false positives as well as computation time. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550799v1?rss=1 Authors: Zimmerman, L., Alon, N., Levin, I., Koganitsky, A., Brestel, C., Lapidoth, G. D. Abstract: The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters. To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes. Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme. Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.27.550791v1?rss=1 Authors: Balaramane, D., Spill, Y. G., Weber, M., Bardet, A. F. Abstract: DNA methylation is an epigenetic mark involved in the regulation of gene expression and patterns of DNA methylation anticorrelates with chromatin accessibility and transcription factor binding. DNA methylation can be profiled at the single cytosine resolution in the whole genome and has been performed in many cell types and conditions. Computational approaches are then essential to study DNA methylation patterns in a single condition or capture dynamic changes of DNA methylation levels across conditions. Towards this goal, we developed MethyLasso, a new approach based on the segmentation of DNA methylation data, that enables the identification of low-methylated regions (LMRs), unmethylated regions (UMRs), DNA methylation valleys (DMVs) and partially methylated domains (PMDs) in a single condition as well as differentially methylated regions (DMRs) between two conditions. We performed a rigorous benchmarking comparing existing approaches by evaluating the number, size, level of DNA methylation, boundaries, CpG content and coverage of the regions using several real datasets as well as the sensitivity and precision of the approaches using simulated data and show that MethyLasso performs best overall. MethyLasso is freely available at https://github.com/abardet/methylasso. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.26.550623v1?rss=1 Authors: Abecassis, N., Gomez Luna, J., Mutlu, O., Ginosar, R., Moisson-Franckhauser, A., Yavits, L. Abstract: Variant calling is a fundamental stage in genome analysis that identifies mutations (variations) in a sequenced genome relative to a known reference genome. Pair-HMM is a key part of the variant calling algorithm and its most compute-intensive part. In recent years, Processing-in-Memory (PiM) solutions, which consist of placing compute capabilities near/inside memory, have been proposed to speed up the genome analysis pipeline. We implement the Pair-HMM algorithm on a commercial PiM platform developed by UPMEM. We modify the Pair-HMM algorithm to make it more suitable for PiM execution with acceptable loss of accuracy. We evaluate our implementation on single chromosomes and whole genome sequencing datasets, demonstrating up to 2x speedup compared to existing CPU accelerations and up to 3x speedup compared to FPGA accelerations. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.26.550670v1?rss=1 Authors: Chen, Z., Wang, C., Huang, S., Shi, Y., Xi, R. Abstract: In single-cell RNA sequencing (scRNA-seq) studies, cell-types and their associated marker genes are often identified by clustering and differential expression gene (DEG) analysis. scRNA-seq data contain many genes not relevant to cell-types and gene selection procedures are needed for more accurate clustering. An ideal gene selection procedure should select all DEGs between cell-types for best cell-type identification. However, because cell-types are unknown, gene selection and DEG analysis are performed separately using different methods. Genes are selected using surrogate criteria not directly related with clustering, which often miss important genes or select unimportant genes. Clustering accuracy could be seriously influenced because of the inferior gene selection. DEGs are often detected by comparing different clusters, leading to many false DEGs due to the selection bias problem. In this paper, we present Festem, a unified method for gene selection and DEG analysis in scRNA-seq studies. Festem investigates gene's clustering information based on the observation that marginal distributions of DEGs are mixtures of their different cell-type-conditional distributions, and can directly select the clustering-informative DEGs and avoid the selection bias problem. Extensive simulation and real data analyses show that Festem achieves high precision and recall for DEG detection, and enables more accurate clustering and cell-type identification. Applications to several scRNA-seq datasets demonstrate that Festem can identify cell-types that are often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify CD8+ T cell-types and find that their marker genes are novel prognostic biomarkers. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.26.550653v1?rss=1 Authors: Bailey, M., Moayedpour, S., Li, R., Corrochano-Navarro, A., Kötter, A., Kogler-Anele, L., Riahi, S., Grebner, C., Hessler, G., Matter, H., Bianciotto, M., Mas, P., Bar-Joseph, Z., Jager, S. Abstract: A key challenge in drug discovery is to optimize, in silico, various absorption and affinity properties of small molecules. One strategy that was proposed for such optimization process is active learning. In active learning molecules are selected for testing based on their likelihood of improving model performance. To enable the use of active learning with advanced neural network models we developed two novel active learning batch selection methods. These methods were tested on several public datasets for different optimization goals and with different sizes. We have also curated new affinity datasets that provide chronological information on state-of-the-art experimental strategy. As we show, for all datasets the new active learning methods greatly improved on existing and current batch selection methods leading to significant potential saving in the number of experiments needed to reach the same model performance. Our methods are general and can be used with any package including the popular DeepChem library. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC

Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.07.26.550714v1?rss=1 Authors: Black, M. B., Efremenko, A. Y., McMullen, P. D., Barutcu, A. R., Nong, A. Abstract: In vitro toxicology has used immortalized cancer cell lines as model human systems for decades. However, these cell lines pose problems in designing toxicity testing programs as they inherently do not represent normal human biology. There is also a huge number of such cell lines to choose from, derived from human cancer cells from nearly every tissue. We explored the idea of using available basal gene expression data (NCI-60 cell line panel and Human GTEx tissue data) to determine if there was sufficient variability in cell line gene expression to group cell lines by relevance to specific human tissues. The transcriptomic analysis suggests that the variability in gene expression in cancer cell lines and in normal human tissue is minimal. The overall basal gene expression of cancer cells lines even overlapped normal human tissue gene expression. While some human tissues (e.g., lung) have basal expression profiles that do not appear to be like any cancer cell line, including cancers that may be derived from the same tissue, most human tissues show basal expression profiles comparable to several cancer cell lines. These results are important to address the genomic baseline and variability of cancer cell lines used for new approach methods of toxicity testing. Copy rights belong to original authors. Visit the link for more info Podcast created by Paper Player, LLC