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Machine learning-guided channelrhodopsin engineering enables minimally invasive optogenetics

Machine learning-guided channelrhodopsin engineering enables...

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作者： Claire N. Bedbrook Kevin K. Yang J. Elliott Robinson Elisha D. Mackey Viviana Gradinaru Frances H. Arnold

We engineered light-gated channelrhodopsins (ChRs) whose current strength and light sensitivity enable minimally invasive neuronal circuit interrogation. Current ChR tools applied to the mammalian brain require intracranial surgery for transgene delivery and implantation of fiber-optic cables to produce light-dependent activation of a small volume of tissue. To facilitate expansive optogenetics without the need for invasive implants, our engineering approach leverages the substantial literature of ChR variants to train statistical models for the design of high-performance ChRs. With Gaussian process models trained on a limited experimental set of 102 functionally characterized ChRs, we designed high-photocurrent ChRs with high light sensitivity. Three of these, ChRger1–3, enable optogenetic activation of the nervous system via systemic transgene delivery. ChRger2 enables light-induced neuronal excitation without fiber-optic implantation; that is, this opsin enables transcranial optogenetics. less

关键词： Machine Learning Optogenetics Statistical Calculation Neuronal Culture Electrophysiology Surgery Cell And Tissue Culture Microscopy Transfection Real-time PCR MATLAB Sequence Comparison Data analysis Python programming language GraphPad Prism GenBank Mus musculus Neuroscience Protein function predictions

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Statistical Methods for Identifying Biomarkers from miRNA Profiles of Cancers

Statistical Methods for Identifying Biomarkers from miRNA Pr...

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作者： Junghyun Namkung

Biomarkers play important roles in early diagnosis and treatment plan for cancer patients and the importance is growing. With advances in high-throughput molecular profiling technology for various types of molecules such as DNA, RNA, proteins, or metabolites, it is now possible to perform massive profiling analysis that allows accelerating discovery of novel biomolecules. Because no single marker is sufficiently accurate for clinical use, the cancer biomarker is developed in the form of multiple biomarker panels. No single marker is sufficiently accurate for clinical use, and thus cancer biomarkers are developed in the form of multiple biomarker panels. Of various types of molecular biomarkers, microRNA (miRNA) has emerged as a class of promising cancer biomarker recently. MiRNAs are small noncoding RNAs that regulate gene expression. The chapter overviews the process of identification of biomarker panels from miRNA profiles focusing on statistical methods. Introduction to molecular cancer biomarkers is touched first. From sample design to miRNA profiling process is reviewed in the method section. Statistical methods for biomarker development are introduced according to three typical purposes of molecular biomarkers: tumor subtype classification, early detection, and prediction of treatment response or prognosis of patients. Example codes for R program are provided as well for selected methods. less

关键词： Gene Expression Profiling Neural Network Model Decision Tree Regression Analysis Quantitative Reverse Transcription PCR Microarray Statistical Calculation RNA-seq Diagnostic Techniques Fractionation PCR Data analysis MicroRNA Sequencing programming language Biomarker identification Penalized regression Molecular subtype Cancer biomarker Cox proportional hazard model MiRNA profile

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Codon Resolution Analysis of Ribosome Profiling Data

Codon Resolution Analysis of Ribosome Profiling Data

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作者： Alexander Bartholomäus Zoya Ignatova

Translation is a central biological process in living cells. Ribosome profiling approach enables assessing translation on a global, cell-wide level. Extracting versatile information from the ribosome profiling data usually requires specialized expertise for handling the sequencing data that is not available to the broad community of experimentalists. Here, we provide an easy-to-use and modifiable workflow that uses a small set of commands and enables full data analysis in a standardized way, including precise positioning of the ribosome-protected fragments, for determining codon-specific translation features. The workflow is complemented with simple step-by-step explanations and is accessible to scientists with no computational background. less

关键词： Ribosomal Profiling Deep Sequencing Statistical Calculation Bowtie Illumina Sequencing Frameshift Detection Codon Usage Analysis Data analysis programming language bedtools Cutadapt Escherichia coli Escherichia coli str. K-12 substr. MG1655 Calibration Nucleotide resolution Codon Translation and regulation Analysis pipeline

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An Overview of the Computational Analyses and Discovery of Transcription Factor Binding Sites

An Overview of the Computational Analyses and Discovery of T...

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作者： Istvan Ladunga

Here we provide a pragmatic, high-level overview of the computational approaches and tools for the discovery of transcription factor binding sites. Unraveling transcription regulatory networks and their malfunctions such as cancer became feasible due to recent stellar progress in experimental techniques and computational analyses. While predictions of isolated sites still pose notorious challenges, cis-regulatory modules (clusters) of binding sites can now be identified with high accuracy. Further support comes from conserved DNA segments, co-regulation, transposable elements, nucleosomes, and three-dimensional chromosomal structures. We introduce computational tools for the analysis and interpretation of chromatin immunoprecipitation, next-generation sequencing, SELEX, and protein-binding microarray results. Because immunoprecipitation produces overly large DNA segments and well over half of the sequencing reads from constitute background noise, methods are presented for background correction, sequence read mapping, peak calling, false discovery rate estimation, and co-localization analyses. To discover short binding site motifs from extensive immunoprecipitation segments, we recommend algorithms and software based on expectation maximization and Gibbs sampling. Data integration using several databases further improves performance. Binding sites can be visualized in genomic and chromatin context using genome browsers. Binding site information, integrated with co-expression in large compendia of gene expression experiments, allows us to reveal complex transcriptional regulatory networks. less

关键词： Peak Calling ChIP Assay ChIP-seq SELEX Microarray Mutagenesis RNA Interference Cross-linking Sonication Base-calling Bowtie Whole-genome Sequencing Statistical Calculation Monte Carlo Simulations Homology Modeling Molecular Dynamics Simulation ChIP-PCR Statistical Inference Genome Visualization Text Mining programming language computational prediction Transcription factor data integration next-generation sequencing transcriptional regulation transcription factor binding site background correction Anti-GST

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Using hyperLOPIT to perform high-resolution mapping of the spatial proteome

Using hyperLOPIT to perform high-resolution mapping of the s...

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作者： Claire M Mulvey Lisa M Breckels Aikaterini Geladaki Nina Kočevar Britovšek Daniel J H Nightingale Andy Christoforou Mohamed Elzek Michael J Deery Laurent Gatto Kathryn S Lilley

The organization of eukaryotic cells into distinct subcompartments is vital for all functional processes, and aberrant protein localization is a hallmark of many diseases. Microscopy methods, although powerful, are usually low-throughput and dependent on the availability of fluorescent fusion proteins or highly specific and sensitive antibodies. One method that provides a global picture of the cell is localization of organelle proteins by isotope tagging (LOPIT), which combines biochemical cell fractionation using density gradient ultracentrifugation with multiplexed quantitative proteomics mass spectrometry, allowing simultaneous determination of the steady-state distribution of hundreds of proteins within organelles. Proteins are assigned to organelles based on the similarity of their gradient distribution to those of well-annotated organelle marker proteins. We have substantially re-developed our original LOPIT protocol (published by Nature Protocols in 2006) to enable the subcellular localization of thousands of proteins per experiment (hyperLOPIT), including spatial resolution at the suborganelle and large protein complex level. This Protocol Extension article integrates all elements of the hyperLOPIT pipeline, including an additional enrichment strategy for chromatin, extended multiplexing capacity of isobaric mass tags, state-of-the-art mass spectrometry methods and multivariate machine-learning approaches for analysis of spatial proteomics data. We have also created an open-source infrastructure to support analysis of quantitative mass-spectrometry-based spatial proteomics data (http://***/packages/pRoloc ) and an accompanying interactive visualization framework (http://***/packages/pRolocGUI ). The procedure we outline here is applicable to any cell culture system and requires ∼1 week to complete sample preparation steps, ∼2 d for mass spectrometry data acquisition and 1–2 d for data analysis and d

关键词： Proteomics Harvesting Cells Cell Lysis Density-gradient Ultracentrifugation Solid-phase Extraction (SPE) Fractionation Microscopy Localization Of Organelle Proteins By Isotope Tagging Cell Fractionation Mass Spectrometry Isobaric Mass Labeling Cell And Tissue Culture HPLC BCA assay Ultra-performance Liquid Chromatography Collision-induced Dissociation Subcellular Fractionation Trypsination Electron Microscopy (EM) Light Microscopy Homogenization Western Blot SRM Tandem Mass Spectrometry Peptide Identification Machine Learning Immunofluorescence Microscopy hyperLOPIT Data analysis Spatial proteomics programming language Mus musculus Mycoplasma Lens ES-E14TG2a Computational platforms and environments Organelles

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geneExpressionFromGEO: An R Package to Facilitate Data Reading from Gene Expression Omnibus (GEO)

geneExpressionFromGEO: An R Package to Facilitate Data Readi...

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作者： Davide Chicco

Gene expression profiling is a useful way to measure the activity of genes in molecular biology and, because of its effectiveness, researchers have released thousands of gene expression datasets publicly in online databases and repositories, such as Gene Expression Omnibus (GEO). To read and analyze gene expression data, the computational biology community has developed several tools and platforms, including Bioconductor, an R open-source platform of software packages that can be used to analyze these data. Despite the usefulness of Bioconductor and of its packages, it is still difficult to read gene expression data from GEO, and to assign gene symbols to the probesets of datasets. To alleviate this problem, we introduce here a new R software package, geneExpressionFromGEO, which provides to the users the possibility to easily download gene expression data from GEO and to easily associate gene symbols to probesets. In this short chapter, we describe the assets of our software package, and we report an example of its usage. We believe that geneExpressionFromGEO can be very useful for the R community of bioinformaticians working on gene expression data. less

关键词： Microarray Gene Expression Profiling programming language Gene expression Bioconductor Gene Expression Omnibus

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BLASTmap: A Shiny-Based Application to Visualize BLAST Results as Interactive Heat Maps and a Tool to Design Gene-Specific Baits for Bespoke Target Enrichment Sequencing

BLASTmap: A Shiny-Based Application to Visualize BLAST Resul...

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作者： Katie Baker Gordon Stephen Shona Strachan Miles Armstrong Ingo Hein

Numerous genes that determine the outcome of plant–pathogen interactions are currently being discovered and include, for example, immune receptors, susceptibility factors and pathogen effectors and their host targets. Target enrichment sequencing provides a means to preferentially resequence these genes of interest without the need to first generate a genotype-specific genome assembly. The Basic Local Alignment Search Tool (BLAST), in combination with the here developed BLASTmap, can be used to design probes that specifically target such gene(s), either by using the target species or the closest related genus as a reference. BLAST is a ubiquitous tool in biological sequence analysis and a multitude of programs are available for the visualization of BLAST alignments. However, there are currently no dedicated programs for visual comparison of large-scale BLAST output attributes such as bit score. The need to quickly and efficiently compare many thousands of BLAST results led to the development of BLASTmap, an interactive web application created using the Shiny R package, customized for clustering and viewing BLAST results as an interactive heat map. Here we show an example of how BLASTmap was successfully applied to analyze custom DNA/RNA probe sequences and to visually determine that four probes are sufficient for the specific yet inclusive enrichment of the potato R2 disease resistance gene family. less

关键词： Genome Assembly Sequence Analysis programming language Enrichment Protein sequence BLAST DNA sequence Interactive map Bait design

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Identification and Validation of miRNA Target Sites Within Nontraditional miRNA Targets

Identification and Validation of miRNA Target Sites Within N...

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作者： Scott T. Younger David R. Corey

miRNAs are endogenous small RNAs that regulate gene expression through recognition of complementary RNA sequences. While miRNAs have generally been understood to repress gene expression posttranscriptionally through recognition of 3′-untranslated regions (3′-UTRs) of mRNA transcripts, they have the potential to target additional classes of RNAs. Understanding the expanding pool of potential miRNA targets has been hindered by the lack of tools for predicting target sites within these RNAs. Here, the principles for developing computational algorithms for identifying putative miRNA target sites outside of mRNA are discussed. Laboratory techniques for validating computational miRNA target predictions are described. less

关键词： Transfection Genome Visualization Gene Prediction Cell And Tissue Culture RNA Extraction Real-time PCR Quantitative Reverse Transcription PCR programming language microRNA Noncoding RNA Duplex RNA

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Small-Molecule Library Screening by Docking with PyRx

Small-Molecule Library Screening by Docking with PyRx

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作者： Sargis Dallakyan Arthur J. Olson

Virtual molecular screening is used to dock small-molecule libraries to a macromolecule in order to find lead compounds with desired biological function. This in silico method is well known for its application in computer-aided drug design. This chapter describes how to perform small-molecule virtual screening by docking with PyRx, which is open-source software with an intuitive user interface that runs on all major operating systems (Linux, Windows, and Mac OS). Specific steps for using PyRx, as well as considerations for data preparation, docking, and data analysis, are also described. less

关键词： Molecular Docking AutoDock Docking Method Virtual Screening Data analysis Python programming language PubChem Computer-aided drug design Virtual molecular screening Vina Open Babel

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Natural language Processing in Biomedicine: A Unified System Architecture Overview

Natural Language Processing in Biomedicine: A Unified System...

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作者： Son Doan Mike Conway Tu Minh Phuong Lucila Ohno-Machado

In contemporary electronic medical records much of the clinically important data—signs and symptoms, symptom severity, disease status, etc.—are not provided in structured data fields but rather are encoded in clinician-generated narrative text. Natural language processing (NLP) provides a means of unlocking this important data source for applications in clinical decision support, quality assurance, and public health. This chapter provides an overview of representative NLP systems in biomedicine based on a unified architectural view. A general architecture in an NLP system consists of two main components: background knowledge that includes biomedical knowledge resources and a framework that integrates NLP tools to process text. Systems differ in both components, which we review briefly. Additionally, the challenge facing current research efforts in biomedical NLP includes the paucity of large, publicly available annotated corpora, although initiatives that facilitate data sharing, system evaluation, and collaborative work between researchers in clinical NLP are starting to emerge. less

关键词： Natural language Processing Machine Learning Mammography programming language Homo sapiens Biomedicine Electronic medical record Rule-based learning method System architecture Unified Medical language System

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