AAontology: An ontology of amino acid scales for interpretable machine learning

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