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Mapping Nucleosome Location Using FS-Seq

Mapping Nucleosome Location Using FS-Seq

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作者： Barry Milavetz Brenna Hanson Kincaid Rowbotham Jacob Haugen

The organization of nucleosomes in eukaryotic chromatin is thought to play a critical role in the regulation of the biological function of the chromatin. Because of this potential role in regulation, a number of techniques have been developed, which combine chromatin fragmentation around nucleosomes with next-generation sequencing to map the location of nucleosomes in chromatin. In this section, a procedure using a kit from New England Biolabs (NEB NEXT Ultra II FS DNA library prep Kit) to fragment chromatin in preparation for next-generation sequencing is described and compared to other available procedures for mapping nucleosome location. less

关键词： Real-time PCR Illumina Sequencing Library Preparation DNA Ligation DNA Sequencing Agarose Gel Electrophoresis Bowtie programming language Sequencing Nucleosomes Chromatin NGS Phasing

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Computational Methods and Deep Learning for Elucidating Protein Interaction Networks

Computational Methods and Deep Learning for Elucidating Prot...

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作者： Dhvani Sandip Vora Yogesh Kalakoti Durai Sundar

Protein interactions play a critical role in all biological processes, but experimental identification of protein interactions is a time- and resource-intensive process. The advances in next-generation sequencing and multi-omics technologies have greatly benefited large-scale predictions of protein interactions using machine learning methods. A wide range of tools have been developed to predict protein-protein, protein-nucleic acid, and protein-drug interactions. Here, we discuss the applications, methods, and challenges faced when employing the various prediction methods. We also briefly describe ways to overcome the challenges and prospective future developments in the field of protein interaction biology. less

关键词： Deep learning Machine Learning Protein Interaction Prediction Protein Structure Analysis Position-Specific Scoring Matrices Proteomics Homology Modeling PROSITE Natural language Processing Protein Analysis Microarray RNA-seq Data analysis Genomics Neural Network Model Protein Function Prediction Decision Tree Statistical Modeling Recombinant Protein Expression Python programming language Transcriptomics Lipidomics Interaction PPI Neural networks Protein networks

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Clustering and Annotation of T Cell Receptor Repertoires

Clustering and Annotation of T Cell Receptor Repertoires

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作者： Sebastiaan Valkiers Sofie Gielis Vincent M. Deuren Kris Laukens Pieter Meysman

Immunological protection against a wide variety of pathogens is largely mediated by the diverse and dynamic T cell receptor (TCR) repertoire, a crucial component of the adaptive immune system. An encounter with infectious agents stimulates specific T cells to initiate a direct immune response to combat intruders. Hence, the TCR repertoire may conceal crucial information regarding current and past infections and might assist in the development and monitoring of vaccines. To unlock its knowledge, we describe a computational workflow involving both supervised and unsupervised machine learning techniques to analyze and annotate full TCR repertoire data. The method is explained using data from a published yellow fever virus (YFV) vaccination study in healthy individuals. The TCR repertoire of one individual is studied before and 2 weeks after vaccination, using an efficient clustering method and identification of YFV-specific TCRs. less

关键词： Machine Learning Vaccination Python Statistical Calculation programming language T cell receptor Immunoinformatics TCR repertoire analysis Epitope-specific T cells TCR repertoire sequencing TCR clustering

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PRSafe: Primitive Recursive Function based Domain Specific language using LLVM

PRSafe: Primitive Recursive Function based Domain Specific L...

引用

20th International Conference on Electronics, Information, and Communication (ICEIC)

作者： Mahadevan, Sai Veerya Takano, Yuuki Miyaji, Atsuko Osaka Univ Dept Informat & Commun Technol Osaka Japan Osaka Univ Osaka Japan Japan Adv Inst Sci & Technol Nomi Japan

ISBN: (纸本)9781728191614

General Purpose programming languages such as C++, Python suffer from resource management and input errors because they are Turing-complete. The goal of this paper is to introduce a prototype functional Domain Specific language that is Non-Turing complete called PRSafe. The design of PRSafe is based on the memory and input properties of Primitive Recursive Functions (PRFs). Hence, all computations must terminate. For the language implementation, we use the Lex and Bison softwares for lexing and parsing respectively and LLVM compiler infrastructure for code generation and optimization. PRSafe is designed such that syntactically and semantically, it prohibits the creation of Turing-complete programs. It is eventually intended to be a mathematically verifiable compiler with better user-friendly error-diagnostics for eBPF programs than the eBPF verifier.

关键词： programming language primitive recursive function LLVM eBPF

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Defining Selective Neuronal Resilience and Identifying Targets of Neuroprotection and Axon Regeneration Using Single-Cell RNA Sequencing: Computational Approaches

Defining Selective Neuronal Resilience and Identifying Targe...

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作者： Salwan Butrus Srikant Sagireddy Wenjun Yan Karthik Shekhar

We describe a computational workflow to analyze single-cell RNA-sequencing (scRNA-seq) profiles of axotomized retinal ganglion cells (RGCs) in mice. Our goal is to identify differences in the dynamics of survival among 46 molecularly defined RGC types together with molecular signatures that correlate with these differences. The data consists of scRNA-seq profiles of RGCs collected at six time points following optic nerve crush (ONC) (see companion chapter by Jacobi and Tran). We use a supervised classification-based approach to map injured RGCs to type identities and quantify type-specific differences in survival at 2 weeks post crush. As injury-related changes in gene expression confound the inference of type identity in surviving cells, the approach deconvolves type-specific gene signatures from injury responses by using an iterative strategy that leverages measurements along the time course. We use these classifications to compare expression differences between resilient and susceptible subpopulations, identifying potential mediators of resilience. The conceptual framework underlying the method is sufficiently general for analysis of selective vulnerability in other neuronal systems. less

关键词： Single-cell RNA-seq Machine Learning programming language Python Scanpy Principal Component Analysis Harmony Differential Expression Analysis Statistical Inference Single-cell Gene Expression Analysis Retinal ganglion cells Supervised classification Optic nerve crush

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Rodent Models for Studying the Impact of Variation in Early Life Mother–Infant Interactions on Mood and Anxiety

Rodent Models for Studying the Impact of Variation in Early ...

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作者： Hannah E. Lapp Frances A. Champagne

The early life origins of risk for mood disorders and anxiety are supported by longitudinal studies in humans, particularly those focused on the experience of childhood adversity. Animal models have further supported the association between stress exposure during infancy and neurodevelopmental and physiological processes that shape behavioral indices of depression and anxiety. In rodents, these models have focused on disruption to mother–infant interactions that occur either naturally or through manipulation of the quantity and/or quality of maternal care. Though these models are challenging to implement, they serve as essential tools for establishing or characterizing the neurodevelopmental trajectories that lead to increased risk of psychopathology. In this chapter, we will describe methods that can be used to quantify maternal behavior in rats and mice, with particular emphasis on the use of newly developed video recording and machine learning approaches. We will also describe the premise and protocol for an established methodology to effectively manipulate mother–infant interactions within experimental designs that are exploring neurobiological and behavioral outcomes in offspring. This limited bedding and nesting material (LBN) manipulation is used broadly to study the impact of early life stress in rodents and when combined with high-resolution behavioral quantification approaches can yield novel hypotheses regarding the molecular, cellular, physiological, and behavioral mechanisms that contribute to altered brain function across the life span. less

关键词： Machine Learning Statistical Calculation Deep learning programming language Rattus norvegicus Rubus idaeus Rodent Maternal behavior Home-cage Bedding

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An Additive-Free Model for Tau Self-Assembly

An Additive-Free Model for Tau Self-Assembly

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作者： Youssra K. Al-Hilaly Karen E. Marshall Liisa Lutter Luca Biasetti Kurtis Mengham Charles R. Harrington Wei-Feng Xue Claude M. Wischik Louise C. Serpell

Tau is a natively unfolded protein that contributes to the stability of microtubules. Under pathological conditions such as Alzheimer’s disease (AD), tau protein misfolds and self-assembles to form paired helical filaments (PHFs) and straight filaments (SFs). Full-length tau protein assembles poorly and its self-assembly is enhanced with polyanions such as heparin and RNA in vitro, but a role for heparin or other polyanions in vivo remains unclear. Recently, a truncated form of tau (297–391) has been shown to self-assemble in the absence of additives which provides an alternative in vitro PHF model system. Here we describe methods to prepare in vitro PHFs and SFs from tau (297–391) named dGAE. We also discuss the range of biophysical/biochemical techniques used to monitor tau filament assembly and structure. less

关键词： Electron Microscopy (EM) Circular Dichroism Atomic Force Microscopy (AFM) TEM Immunogold Labeling SDS-PAGE Dot Blot X-ray Diffraction Diffraction Data Analysis Diffraction Method Image Processing programming language BCA assay Lens Oryctolagus cuniculus Self-assembly Tau X-ray fibre diffraction Cross-beta Paired helical filament Thioflavin S fluorescence Tyrosine fluorescence Anti-Tau Anti-rabbit Anti-mouse

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Machine Learning Methods for Survival Analysis with Clinical and Transcriptomics Data of Breast Cancer

Machine Learning Methods for Survival Analysis with Clinical...

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作者： Le Minh Doan Claudio Angione Annalisa Occhipinti

Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early-detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes. This tutorial presents a protocol for applying machine learning models in survival analysis for both clinical and transcriptomic data. We show that integrating clinical and mRNA expression data is essential to explain the multiple biological processes driving cancer progression. Our results reveal that machine-learning-based models such as random survival forests, gradient boosted survival model, and survival support vector machine can outperform the traditional statistical methods, i.e., Cox proportional hazard model. The highest C-index among the machine learning models was recorded when using survival support vector machine, with a value 0.688, whereas the C-index recorded using the Cox model was 0.677. Shapley Additive Explanation (SHAP) values were also applied to identify the feature importance of the models and their impact on the prediction outcomes. less

关键词： Machine Learning Transcriptomics Python programming language Data analysis Statistical Calculation Data integration Breast cancer Survival analysis Interpretability

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A Hands-On Guide to Generate Spatial Gene Expression Profiles by Integrating scRNA-seq and 3D-Reconstructed Microscope-Based Plant Structures

A Hands-On Guide to Generate Spatial Gene Expression Profile...

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作者： Manuel Neumann Jose M. Muino

Transcriptome profiles of individual cells in the plant are strongly dependent on their relative position. Cell differentiation is associated with tissue-specific transcriptomic changes. For that reason, it is important to study gene expression changes in a spatial context, and therefore to link those to potential morphological changes over developmental time. Even though great experimental advances have been made in recording spatial gene expression profiles, those attempts are limited in the plant field. New computational approaches attempt to solve this problem by integrating spatial expression profiles of few marker genes with single-cell/single-nuclei RNA-seq (scRNA-seq) methodologies. In this chapter, we provide a practical guide on how to predict gene expression patterns in a 3D plant structure by combining scRNA-seq data and 3D microscope-based reconstructed expression profiles of a small set of reference genes. We also show how to visualize these *** profiles of individual cells in the plant are strongly dependent on their relative position. Cell differentiation is associated with tissue-specific transcriptomic changes. For that reason, it is important to study gene expression changes in a spatial context, and therefore to link those to potential morphological changes over developmental time. Even though great experimental advances have been made in recording spatial gene expression profiles, those attempts are limited in the plant field. New computational approaches attempt to solve this problem by integrating spatial expression profiles of few marker genes with single-cell/single-nuclei RNA-seq (scRNA-seq) methodologies. In this chapter, we provide a practical guide on how to predict gene expression patterns in a 3D plant structure by combining scRNA-seq data and 3D microscope-based reconstructed expression profiles of a small set of reference genes. We also show how to visualize these results.

关键词： Single-cell RNA-seq Transcriptomics RNA-seq novoSpaRc programming language Python Seurat Arabidopsis thaliana Computational biology Flower meristem

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A Transferable Machine Learning Framework for Predicting Transcriptional Responses of Genes Across Species

A Transferable Machine Learning Framework for Predicting Tra...

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作者： Zhikai Liang Xiaoxi Meng James C. Schnable

Leveraging existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in other, less well-studied, species with assembled genomes. However, orthology is not a reliable predictor for the transcriptional responses of genes to stress. Machine learning methods can quantitatively estimate expression patterns and gene functions using known annotations and collections of features describing each gene. In this chapter, we describe a supervised machine learning framework to predict stress-responsive genes across species using only features derived from nucleotide sequences, using the example of cold stress-responsive genes in different Panicoid grass *** existing resources in studied species to predict gene functions has the potential to rapidly expand understanding of annotated genes in other, less well-studied, species with assembled genomes. However, orthology is not a reliable predictor for the transcriptional responses of genes to stress. Machine learning methods can quantitatively estimate expression patterns and gene functions using known annotations and collections of features describing each gene. In this chapter, we describe a supervised machine learning framework to predict stress-responsive genes across species using only features derived from nucleotide sequences, using the example of cold stress-responsive genes in different Panicoid grass species.

关键词： Machine Learning Gene Expression Profiling Trimmomatic Cufflinks Python Genome Assembly Genome Analysis Gene Prediction programming language Zea mays Setaria italica Random forest Gene annotation Grasses Transfer learning Dinucleotide frequency

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