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Multiple Protein Sequence Alignment with MSAProbs

Multiple Protein Sequence Alignment with MSAProbs

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作者： Yongchao Liu Bertil Schmidt

Multiple sequence alignment (MSA) generally constitutes the foundation of many bioinformatics studies involving functional, structural, and evolutionary relationship analysis between sequences. As a result of the exponential computational complexity of the exact approach to producing optimal multiple alignments, the majority of state-of-the-art MSA algorithms are designed based on the progressive alignment heuristic. In this chapter, we outline MSAProbs, a parallelized MSA algorithm for protein sequences based on progressive alignment. To achieve high alignment accuracy, this algorithm employs a hybrid combination of a pair hidden Markov model and a partition function to calculate posterior probabilities. Furthermore, we provide some practical advice on the usage of the algorithm.

关键词： Multiple Sequence Alignment programming language Hidden Markov models Progressive alignment Partition function Consistency-based scheme

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Phylogenomic Analysis of EST Datasets

Phylogenomic Analysis of EST Datasets

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作者： José M. Peregrín-Alvarez John Parkinson

To date the genomes of over 600 organisms have been generated of which 100 are from eukaryotes. Together with partial genome data for an additional 700 eukaryotic organisms, these exceptional sequence resources offer new opportunities to explore phylogenetic relationships and species diversity. The identification of highly diverse sequences specific to an EST-based sequence dataset offers insights into the extent of genetic novelty within that dataset. Sequences that are only shared with other related species from the same taxon might represent genes associated with taxon-specific innovations. On the other hand, sequences that are highly conserved across many other species offer valuable resources for performing more in-depth phylogenetic analyses. In the following chapter, we guide the reader through the process of examining their sequence datasets in the context of phylogenetic relationships. Performed across large-scale datasets, such analyses are termed Phylogenomics. Two complementary approaches are described, both based on the use of BLAST similarity metrics. The first uses an established Java tool – SimiTri – to visualize sequence similarity relationships between the EST dataset and three user-defined datasets. The second focuses on the use of phylogenetic profiles to identify groups of taxonomically related sequences. less

关键词： Genomics Sequence Similarity Search Sequence Comparison programming language Echinococcus multilocularis bioinformatics clustering Expressed sequence tags ESTs phylogenetic profiles

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Large-Scale Sequencing of Plant Small RNAs

Large-Scale Sequencing of Plant Small RNAs

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作者： William P. Donovan Yuanji Zhang Miya D. Howell

Deep sequencing technologies have become very powerful tools in the identification and quantification of small RNAs involved in gene regulation. Small interfering RNA (siRNA) and miRNA are two classes of DCL-dependent small RNAs known to affect phenotype, developmental regulation, and various traits in plants. These small RNAs function by selectively repressing gene expression mainly by guiding cleavage, resulting in degradation of target transcripts. In this chapter, we describe a method for preparation of 5′-phosphate-dependent small RNA libraries, a hallmark of RNase III-like DCL products, for high-throughput sequencing, and recommendations for small RNA analysis. This method is useful for determining small RNA involvement in critical pathways in plants, identifying and quantifying novel small RNAs, and examining small RNA global expression patterns. less

关键词： DNA Sequencing High-Throughput Sequencing Library Preparation RNA Extraction PCR Deep Sequencing Size-exclusion Chromatography RNA Purification Fractionation DNA Sequencing By Synthesis Base-calling Data Mining DNA Ligation Sanger Sequencing Data analysis programming language siRNA miRNA Illumina small RNA reads

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TMT One-Stop Shop: From Reliable Sample Preparation to Computational Analysis Platform

TMT One-Stop Shop: From Reliable Sample Preparation to Compu...

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作者： Mehdi Mirzaei Dana Pascovici Jemma X. Wu Joel Chick Yunqi Wu Brett Cooke Paul Haynes Mark P. Molloy

In this chapter we describe the workflow we use for labeled quantitative proteomics analysis using tandem mass tags (TMT) starting with the sample preparation and ending with the multivariate analysis of the resulting data. We detail the step-by-step process from sample processing, labeling, fractionation, and data processing using Proteome Discoverer through to data analysis and interpretation in the context of a multi-run experiment. The final analysis and data interpretation rely on an R package we call TMTPrepPro, which are deployed on a local GenePattern server, and used for generating various outputs which are also outlined herein. less

关键词： Shotgun Proteomics Protein Extraction Liquid Chromatography-tandem Mass Spectrometry Liquid Chromatography (LC) Proteomics TMT Quantitation Fractionation Isobaric Mass Labeling BCA assay Solid-phase Extraction (SPE) HPLC Principal Component Analysis Data analysis programming language TMT Software workflow

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isoTar: Consensus Target Prediction with Enrichment Analysis for MicroRNAs Harboring Editing Sites and Other Variations

isoTar: Consensus Target Prediction with Enrichment Analysis...

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作者： Rosario Distefano Giovanni Nigita Dario Veneziano Giulia Romano Carlo M. Croce Mario Acunzo

MicroRNAs (miRNAs) are small noncoding RNA molecules (sncRNAs) involved in gene expression regulation. Having been widely studied during last two decades, they have been associated with several diseases, including cancer. Recent improvements in high throughput sequencing technologies have revealed a more complex miRNAome, due to miRNA sequence modification phenomena, such as RNA editing and isomiRs. As a result, a new class of tools is necessary in order to appropriately investigate this emerging complexity. To address such need, we developed isoTar, a high-performance Web-based containerized application designed for miRNA consensus targeting prediction and functional enrichment analyses. In the present chapter, we provide an overview of isoTar (https://***/isotar/ ), as well as benchmarks and a guide to its usage. less

关键词： High-Throughput Sequencing Functional Enrichment Python programming language Homo sapiens Malus domestica ADAR A-to-I RNA editing IsomiRs Editome

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Combination of Theoretical and Experimental Approaches for the Design and Study of Fibril-Forming Peptides

Combination of Theoretical and Experimental Approaches for t...

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作者： Phanourios Tamamis Emmanouil Kasotakis Georgios Archontis Anna Mitraki

Self-assembling peptides that can form supramolecular structures such as fibrils, ribbons, and nanotubes are of particular interest to modern bionanotechnology and materials science. Their ability to form biocompatible nanostructures under mild conditions through non-covalent interactions offers a big biofabrication advantage. Structural motifs extracted from natural proteins are an important source of inspiration for the rational design of such peptides. Examples include designer self-assembling peptides that correspond to natural coiled-coil motifs, amyloid-forming proteins, and natural fibrous proteins. In this chapter, we focus on the exploitation of structural information from beta-structured natural fibers. We review a case study of short peptides that correspond to sequences from the adenovirus fiber shaft. We describe both theoretical methods for the study of their self-assembly potential and basic experimental protocols for the assessment of fibril-forming assembly. less

关键词： Molecular Dynamics Simulation TEM Diffraction Method programming language Adenovirus Peptides Self-assembly Amyloid fibrils Beta-structure Implicit solvent Replica-exchange

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Protocol for Epistasis Detection with Machine Learning Using GenEpi Package

Protocol for Epistasis Detection with Machine Learning Using...

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作者： Olutomilayo Olayemi Petinrin Ka-Chun Wong

To develop medical treatments and prevention, the association between disease and genetic variants needs to be identified. The main goal of genome-wide association study (GWAS) is to discover the underlying reason for vulnerability to disease and utilize this knowledge for the development of prevention and treatment against these diseases. Given the methods available to address the scientific problems involved in the search for epistasis, there is not any standard for detecting epistasis, and this remains a problem due to limited statistical power. The GenEpi package is a Python package that uses a two-level workflow machine learning model to detect within-gene and cross-gene epistasis. This protocol chapter shows the usage of GenEpi with example data. The package uses a three-step procedure to reduce dimensionality, select the within-gene epistasis, and select the cross-gene epistasis. The package also provides a medium to build prediction models with the combination of genetic features and environmental influences.

关键词： Machine Learning Genome-Wide Association Study Gene Prediction Python programming language Homo sapiens Epistasis Genotype SNP Phenotype GenEpi

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A Practical Guide to Integrating Multimodal Machine Learning and Metabolic Modeling

A Practical Guide to Integrating Multimodal Machine Learning...

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作者： Supreeta Vijayakumar Giuseppe Magazzù Pradip Moon Annalisa Occhipinti Claudio Angione

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://***/Angione-Lab/Tutorials_Combining_ML_and_GSMM . less

关键词： Machine Learning Data Mining MATLAB programming language Python Data analysis Genomics Transcriptomics Deep learning Proteomics Lipidomics Metabolomics DNA Sequencing Microarray RNA-seq Mass Spectrometry Fluxomics Statistical Calculation Neural Network Model Saccharomyces cerevisiae Homo sapiens Data integration Flux balance analysis Multimodal Metabolic modeling Multi-omics Cancer survival prediction

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Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions

Optofluidic Raman-activated cell sorting for targeted genome...

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作者： Kang Soo Lee Fátima C. Pereira Márton Palatinszky Lars Behrendt Uria Alcolombri David Berry Michael Wagner Roman Stocker

Stable isotope labeling of microbial taxa of interest and their sorting provide an efficient and direct way to answer the question “who does what?” in complex microbial communities when coupled with fluorescence in situ hybridization or downstream ‘omics’ analyses. We have developed a platform for automated Raman-based sorting in which optical tweezers and microfluidics are used to sort individual cells of interest from microbial communities on the basis of their Raman spectra. This sorting of cells and their downstream DNA analysis, such as by mini-metagenomics or single-cell genomics, or cultivation permits a direct link to be made between the metabolic roles and the genomes of microbial cells within complex microbial communities, as well as targeted isolation of novel microbes with a specific physiology of interest. We describe a protocol from sample preparation through Raman-activated live cell sorting. Subsequent cultivation of sorted cells is described, whereas downstream DNA analysis involves well-established approaches with abundant methods available in the literature. Compared with manual sorting, this technique provides a substantially higher throughput (up to 500 cells per h). Furthermore, the platform has very high sorting accuracy (98.3 ± 1.7%) and is fully automated, thus avoiding user biases that might accompany manual sorting. We anticipate that this protocol will empower in particular environmental and host-associated microbiome research with a versatile tool to elucidate the metabolic contributions of microbial taxa within their complex communities. After a 1-d preparation of cells, sorting takes on the order of 4 h, depending on the number of cells required. less

关键词： Raman Spectroscopy Separation And Purification Cell Separation And Isolation Stable Isotope Labeling FISH Single-cell Genomics Microbial Culture Biopsy MATLAB PCR Genome Amplification FACS Sterilization programming language Mus musculus Vibrio cyclitrophicus 1F111 Escherichia coli Saccharomyces cerevisiae Vibrio alginolyticus Vibrio cyclitrophicus Lens Lab-on-a-chip Biodiversity Microbial ecology

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Molecular Descriptors for Structure–Activity Applications: A Hands-On Approach

Molecular Descriptors for Structure–Activity Applications: ...

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作者： Francesca Grisoni Davide Ballabio Roberto Todeschini Viviana Consonni

Molecular descriptors capture diverse parts of the structural information of molecules and they are the support of many contemporary computer-assisted toxicological and chemical applications. After briefly introducing some fundamental concepts of structure–activity applications (e.g., molecular descriptor dimensionality, classical vs. fingerprint description, and activity landscapes), this chapter guides the readers through a step-by-step explanation of molecular descriptors rationale and application. To this end, the chapter illustrates a case study of a recently published application of molecular descriptors for modeling the activity on cytochrome P450. less

关键词： Database Search Dissection Statistical Calculation MATLAB Principal Component Analysis Machine Learning Decision Tree programming language Python Molecular similarity QSAR Molecular descriptors Chemical space Mathematical chemistry

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