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A Practical Guide to Inferring Multi-Omics Networks in Plant Systems

A Practical Guide to Inferring Multi-Omics Networks in Plant...

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作者： Natalie M. Clark Bhavna Hurgobin Dior R. Kelley Mathew G. Lewsey Justin W. Walley

The inference of gene regulatory networks can reveal molecular connections underlying biological processes and improve our understanding of complex biological phenomena in plants. Many previous network studies have inferred networks using only one type of omics data, such as transcriptomics. However, given more recent work applying multi-omics integration in plant biology, such as combining (phospho)proteomics with transcriptomics, it may be advantageous to integrate multiple omics data types into a comprehensive network prediction. Here, we describe a state-of-the-art approach for integrating multi-omics data with gene regulatory network inference to describe signaling pathways and uncover novel regulators. We detail how to download and process transcriptomics and (phospho)proteomics data for network inference, using an example dataset from the plant hormone signaling field. We provide a step-by-step protocol for inference, visualization, and analysis of an integrative multi-omics network using currently available methods. This chapter serves as an accessible guide for novice and intermediate bioinformaticians to analyze their own datasets and reanalyze published *** inference of gene regulatory networks can reveal molecular connections underlying biological processes and improve our understanding of complex biological phenomena in plants. Many previous network studies have inferred networks using only one type of omics data, such as transcriptomics. However, given more recent work applying multi-omics integration in plant biology, such as combining (phospho)proteomics with transcriptomics, it may be advantageous to integrate multiple omics data types into a comprehensive network prediction. Here, we describe a state-of-the-art approach for integrating multi-omics data with gene regulatory network inference to describe signaling pathways and uncover novel regulators. We detail how to download and process transcriptomics and (phospho)proteomics data for network

关键词： Transcriptomics Proteomics Pathway Or Network Visualization Cytoscape Genome Assembly HISAT SAMtools StringTie programming language Gene regulatory networks Network inference Multi-omics Plant signaling

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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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Integrating Single-Cell Methylome and Transcriptome Data with MAPLE

Integrating Single-Cell Methylome and Transcriptome Data wit...

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作者： Yasin Uzun Hao Wu Kai Tan

As a mechanism of epigenetic gene regulation, DNA methylation has crucial roles in developmental and differentiation programs. Thanks to the recently introduced bisulfite-sequencing-based methods, it is possible to profile the entire methylome at single-cell resolution. However, analysis of single-cell methylome data is challenging due to data sparsity and moderate correlation with transcript level. Our recently developed computational framework, MAPLE, addresses these challenges using supervised learning models. Using both genomic sequence and methylation information as the input, MAPLE predicts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data. less

关键词： Data analysis Deep Sequencing Principal Component Analysis programming language Seurat Harmony Ensembl Homo sapiens Mus musculus Epigenomics DNA methylation Data integration Multi-omics Single-cell

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

BigNeuron: a resource to benchmark and predict performance o...

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作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E. Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-yan He Jochen F. Staiger Manuel Peter Daniel N. Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T. Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A. Ascoli Hanchuan Peng

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings. less

关键词： Light Microscopy Statistical Calculation Principal Component Analysis Microscopy Data analysis programming language Computational platforms and environments Computational neuroscience

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Measurement of Antibody Binding Affinity on Bacterial Surfaces Using Flow Cytometry

Measurement of Antibody Binding Affinity on Bacterial Surfac...

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作者： Vibha Kumra Ahnlide Pontus Nordenfelt

Antibody binding to bacterial surfaces plays a crucial role in immunity, and a key characteristic of this protein–protein interaction is the binding affinity. Determining the affinity of an antibody binding to its antigen is the first step in predicting the function in a physiological environment where other competing protein interactions may be present. Antibody–antigen affinity is often evaluated with isolated proteins. It is informative to also be able to assess antibody binding to a bacterial surface where many antigens might be present, including multiple copies of the specific antigen the antibody recognizes, and in a context where the antigen might be in a more natural conformation. In this chapter, we present a flow cytometry-based assay to measure and calculate the cell surface binding affinity or avidity of any mono- or polyclonal antibody solution. less

关键词： Flow Cytometry programming language MATLAB GraphPad Prism Enzymatic Cleavage Streptococcus pyogenes Bacteria Antibodies Affinity Avidity Anti-human

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Mapping of RNA Modifications by Direct Nanopore Sequencing and JACUSA2

Mapping of RNA Modifications by Direct Nanopore Sequencing a...

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作者： Amina Lemsara Christoph Dieterich Isabel S. Naarmann-de Vries

RNA modifications exist in all kingdom of life. Several different types of base or ribose modifications are now summarized under the term “epitranscriptome.” With the advent of high-throughput sequencing technologies, much progress has been made in understanding RNA modification biology and how these modifications can influence many aspects of RNA life. The most widespread internal modification on mRNA is m6A, which has been implicated in physiological processes as well as disease pathogenesis. Here, we provide a workflow for the mapping of m6A sites using Nanopore direct RNA sequencing data. Our strategy employs pairwise comparison of basecalling error profiles with JACUSA2. We outline a general strategy for RNA modification detection on mRNA and describe two specific use cases on m6A detection in detail. Use case 1: a sample of interest with modifications (e.g., “wild-type” sample) is compared to a sample lacking a specific modification type (e.g., “knockout” sample, here METTL3-KO) or Use case 2: a sample of interest with modifications is compared to a sample lacking all modifications (e.g., in vitro transcribed cDNA). We provide a detailed protocol on experimental and computational aspects. Extensive online material provides a snakemake pipeline to identify m6A positions in mRNA and to validate the results against a miCLIP-derived m6A reference set. The general strategy is flexible and can be easily adapted by users in different application scenarios. less

关键词： RNA-seq Nanopore Sequencing High-Throughput Sequencing SAMtools DNA Ligation PCR In Vitro Transcription Ensembl Variant Calling Base-calling miCLIP programming language bedtools Malus domestica RNA modification Prediction Epitranscriptome Nanopore Snakemake

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Creating De Novo Overlapped Genes

Creating De Novo Overlapped Genes

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作者： Dominic Y. Logel Paul R. Jaschke

Future applications of synthetic biology will rely on deploying engineered cells outside of lab environments for long periods of time. Currently, a significant roadblock to this application is the potential for deactivating mutations in engineered genes. A recently developed method to protect engineered coding sequences from mutation is called Constraining Adaptive Mutations using Engineered Overlapping Sequences (CAMEOS). In this chapter we provide a workflow for utilizing CAMEOS to create synthetic overlaps between two genes, one essential (infA) and one non-essential (aroB), to protect the non-essential gene from mutation and loss of protein function. In this workflow we detail the methods to collect large numbers of related protein sequences, produce multiple sequence alignments (MSAs), use the MSAs to generate hidden Markov models and Markov random field models, and finally generate a library of overlapping coding sequences through CAMEOS scripts. To assist practitioners with basic coding skills to try out the CAMEOS method, we have created a virtual machine containing all the required packages already installed that can be downloaded and run locally. less

关键词： Deep learning Machine Learning Multiple Sequence Alignment Database Search Protein Structure Analysis Python programming language Synthetic biology Protein design Multiple sequence alignments Generative model Markov random field Synthetic genomes Overlapping genes Genome compression

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Fast and robust metagenomic sequence comparison through sparse chaining with skani

Fast and robust metagenomic sequence comparison through spar...

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作者： Jim Shaw Yun William Yu

Sequence comparison tools for metagenome-assembled genomes (MAGs) struggle with high-volume or low-quality data. We present skani (https://***/bluenote-1577/skani ), a method for determining average nucleotide identity (ANI) via sparse approximate alignments. skani outperforms FastANI in accuracy and speed (>20× faster) for fragmented, incomplete MAGs. skani can query genomes against >65,000 prokaryotic genomes in seconds and 6 GB memory. skani unlocks higher-resolution insights for extensive, noisy metagenomic *** comparison tools for metagenome-assembled genomes (MAGs) struggle with high-volume or low-quality data. We present skani (https://***/bluenote-1577/skani ), a method for determining average nucleotide identity (ANI) via sparse approximate alignments. skani outperforms FastANI in accuracy and speed (>20× faster) for fragmented, incomplete MAGs. skani can query genomes against >65,000 prokaryotic genomes in seconds and 6 GB memory. skani unlocks higher-resolution insights for extensive, noisy metagenomic datasets.

关键词： Metagenomics Sequence Comparison Statistical Calculation Genome Comparison programming language Software Genome informatics

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Proof-oriented programming for high-assurance systems 23

Proof-oriented programming for high-assurance systems

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Proceedings of the 16th Innovations in Software Engineering Conference

作者： Aseem Rastogi Microsoft Research India India

ISBN: (纸本)9798400700644

Proof-oriented programming is a paradigm where programs are designed and developed along with mathematical proofs of their correctness and security. In recent years, proof-oriented programming has reached a point where not only several high-assurance software components have been developed using it, but these components have also been deployed in production systems. In this talk, I will provide an overview of this paradigm in the context of F*, a proof-oriented programming language developed at Microsoft Research. I will briefly discuss several critical software components, developed from scratch in F*. These components are already deployed in production systems such as Windows Hyper-V, the Linux kernel, Firefox, and mbedTLS. As a complementary methodology for retrofitting strong, formal guarantees on existing legacy services, I will present the Zeta framework. Zeta works by running a small, proven correct monitor in a trusted execution environment validating responses from the service. The key idea is that we only need to develop the monitor using proof-oriented programming once-and-for-all, while the large legacy service remains untrusted. I will illustrate Zeta by sketching an example of how we can make a concurrent, high-performance, key-value store "zero trust" and argue that this step incurs modest software engineering effort and modest runtime overheads.

关键词： programming language Program Analysis

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Centrality Analysis of Protein–Protein Interaction Networks Using R

Centrality Analysis of Protein–Protein Interaction Networks...

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作者： Vijaykumar Yogesh Muley

Proteins are structural and functional components of cells. They interact with each other to drive specific cellular functions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologically important proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein’s strategic positions in the network using an R programming *** are structural and functional components of cells. They interact with each other to drive specific cellular functions. The physical and functional protein interactions are an important feature of cellular organization and regulation. Protein interactions are represented as a network or a graph in which proteins are nodes, and interactions between them are edges. Perturbations in the network affecting essential or central proteins can have pathological consequences. Network or graph theory is a branch of mathematics that provides a conceptual framework to decipher topologically important proteins in the network. These concepts are known as centrality measures. This chapter introduces various centrality metrics and provides a stepwise protocol to quantify protein’s strategic positions in the network using an R programming language.

关键词： programming language Principal Component Analysis Graph theory Protein–protein interactions Network biology Network theory Protein interaction networks Network centrality Centrality indices Betweenness

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