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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.

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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.

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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/).

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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.

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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.

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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.

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