scholarly journals rehh 2.0: a reimplementation of the R package rehh to detect positive selection from haplotype structure

2016 ◽  
Author(s):  
Mathieu Gautier ◽  
Alexander Klassmann ◽  
Renaud Vitalis
Keyword(s):  
Positive Selection ◽  
Computation Time ◽  
Large Data ◽  
R Package ◽  
Large Data Sets ◽  
Data Sets ◽  
Genome Wide ◽  
Haplotype Data ◽  
Order Of Magnitude ◽  
Genomic Regions

AbstractIdentifying genomic regions with unusually high local haplotype homozygosity represents a powerful strategy to characterize candidate genes responding to natural or artificial positive selection. To that end, statistics measuring the extent of haplotype homozygosity within (e.g., EHH, iHS) and between (Rsb or XP-EHH) populations have been proposed in the literature. The rehh package for R was previously developed to facilitate genome-wide scans of selection, based on the analysis of long-range haplotypes. However, its performance wasn’t sufficient to cope with the growing size of available data sets. Here we propose a major upgrade of the rehh package, which includes an improved processing of the input files, a faster algorithm to enumerate haplotypes, as well as multi-threading. As illustrated with the analysis of large human haplotype data sets, these improvements decrease the computation time by more than an order of magnitude. This new version of rehh will thus allow performing iHS-, Rsb- or XP-EHH-based scans on large data sets. The package rehh 2.0 is available from the CRAN repository (http://cran.r-project.org/web/packages/rehh/index.html) together with help files and a detailed manual.

scholarly journals GenomegaMap: Within-Species Genome-Wide dN/dS Estimation from over 10,000 Genomes

2020 ◽  
Vol 37 (8) ◽  
pp. 2450-2460 ◽  
Author(s):  
Daniel J Wilson ◽  
Derrick W Crook ◽  
Timothy E A Peto ◽  
A Sarah Walker ◽  
Sarah J Hoosdally ◽  
...  
Keyword(s):  
Protein A ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Protein Coding ◽  
Dead Box ◽  
Coalescent Simulations ◽  
Genome Wide ◽  
Signature Of Selection ◽  
Signatures Of Selection

Abstract The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap’s simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.

scholarly journals Taxa: An R package implementing data standards and methods for taxonomic data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 272
Author(s):  
Zachary S.L. Foster ◽  
Scott Chamberlain ◽  
Niklaus J. Grünwald
Keyword(s):  
Large Data ◽  
R Package ◽  
Large Data Sets ◽  
Specific Information ◽  
Data Sets ◽  
Widespread Application ◽  
Taxonomic Information ◽  
Hierarchical Nature ◽  
Application Specific ◽  
Associated Data

The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.

A Fast Algorithm on Minimum-Time Scheduling of an Autonomous Ground Vehicle Using a Traveling Salesman Framework

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Soovadeep Bakshi ◽  
Zeyu Yan ◽  
Dongmei Chen ◽  
Qiang Qian ◽  
Yinan Chen
Keyword(s):  
Computation Time ◽  
Large Data ◽  
Exhaustive Search ◽  
Large Data Sets ◽  
Data Sets ◽  
Ground Vehicle ◽  
Manufacturing Automation ◽  
Time Scheduling ◽  
Step Algorithm ◽  
Starting Location

Manufacturing automation, especially through implementation of autonomous ground vehicle (AGV) technology, has been under intensive study due to increased productivity and reduced variations. The objective of this paper is to present an algorithm on scheduling of an AGV that traverses desired locations on a manufacturing floor. Although many algorithms have been developed to achieve this objective, most of them rely on exhaustive search, which is time-consuming. A novel two-step algorithm that generates “good,” but not necessarily optimal, solutions for relatively large data sets (≈1000 points) is proposed, taking into account time constraints. A tradeoff analysis of computational expense versus algorithm performance is discussed. The algorithm enables the AGV to find a tour, which is as good as possible within the time constraint, using which it can travel through all given coordinates before returning to the starting location or a specified end point. Compared to exhaustive search methods, this algorithm generates results within a stipulated computation time of 30 s on a laptop personal computer.

scholarly journals plantR: An R package and workflow for managing species records from biological collections

2021 ◽  
Author(s):  
Renato Augusto Ferreira Lima ◽  
Andrea Sanchez-Tapia ◽  
Sara R. Mortara ◽  
Hans Steege ◽  
Marinez F. Siqueira
Keyword(s):  
Biodiversity Conservation ◽  
Large Data ◽  
Data Retrieval ◽  
R Package ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Repositories ◽  
Biological Collections ◽  
Data Editing ◽  
User Friendly

Species records from biological collections are becoming increasingly available online. This unprecedented availability of records has largely supported recent studies in taxonomy, bio-geography, macro-ecology, and biodiversity conservation. Biological collections vary in their documentation and notation standards, which have changed through time. For different reasons, neither collections nor data repositories perform the editing, formatting and standardization of the data, leaving these tasks to the final users of the species records (e.g. taxonomists, ecologists and conservationists). These tasks are challenging, particularly when working with millions of records from hundreds of biological collections. To help collection curators and final users to perform those tasks, we introduce plantR an open-source package that provides a comprehensive toolbox to manage species records from biological collections. The package is accompanied by the proposal of a reproducible workflow to manage this type of data in taxonomy, ecology and biodiversity conservation. It is implemented in R and designed to handle relatively large data sets as fast as possible. Initially designed to handle plant species records, many of the plantR features also apply to other groups of organisms, given that the data structure is similar. The plantR workflow includes tools to (1) download records from different data repositories, (2) standardize typical fields associated with species records, (3) validate the locality, geographical coordinates, taxonomic nomenclature and species identifications, including the retrieval of duplicates across collections, and (4) summarize and export records, including the construction of species checklists with vouchers. Other R packages provide tools to tackle some of the workflow steps described above. But in addition to the new features and resources related to the data editing and validation, the greatest strength of plantR is to provide a comprehensive and user-friendly workflow in one single environment, performing all tasks from data retrieval to export. Thus, plantR can help researchers to better assess data quality and avoid data leakage in a wide variety of studies using species records.

scholarly journals TIN: An R Package for Transcriptome Instability Analysis

2015 ◽  
Vol 14 ◽  
pp. CIN.S31363 ◽  
Author(s):  
Bjarne Johannessen ◽  
Anita Sveen ◽  
Rolf I. Skotheim
Keyword(s):  
Expression Profiles ◽  
Large Data ◽  
R Package ◽  
Data Sets ◽  
Mrna Isoforms ◽  
Genome Wide ◽  
A Genome ◽  
Exon Level ◽  
Microarray Expression ◽  
Genome Scale

Alternative splicing is a key regulatory mechanism for gene expression, vital for the proper functioning of eukaryotic cells. Disruption of normal pre-mRNA splicing has the potential to cause and reinforce human disease. Owing to rapid advances in high-throughput technologies, it is now possible to identify novel mRNA isoforms and detect aberrant splicing patterns on a genome scale, across large data sets. Analogous to the genomic types of instability describing cancer genomes (eg, chromosomal instability and microsatellite instability), transcriptome instability (TIN) has recently been proposed as a splicing-related genome-wide characteristic of certain solid cancers. We present the R package TIN, available from Bioconductor, which implements a set of methods for TIN analysis based on exon-level microarray expression profiles. TIN provides tools for estimating aberrant exon usage across samples and for analyzing correlation patterns between TIN and splicing factor expression levels.

scholarly journals Taxa: An R package implementing data standards and methods for taxonomic data

F1000Research ◽  
2018 ◽  
Vol 7 ◽  
pp. 272 ◽  
Author(s):  
Zachary S.L. Foster ◽  
Scott Chamberlain ◽  
Niklaus J. Grünwald
Keyword(s):  
Large Data ◽  
R Package ◽  
Large Data Sets ◽  
Specific Information ◽  
Data Sets ◽  
Widespread Application ◽  
Taxonomic Information ◽  
Hierarchical Nature ◽  
Application Specific ◽  
Associated Data

The taxa R package provides a set of tools for defining and manipulating taxonomic data. The recent and widespread application of DNA sequencing to community composition studies is making large data sets with taxonomic information commonplace. However, compared to typical tabular data, this information is encoded in many different ways and the hierarchical nature of taxonomic classifications makes it difficult to work with. There are many R packages that use taxonomic data to varying degrees but there is currently no cross-package standard for how this information is encoded and manipulated. We developed the R package taxa to provide a robust and flexible solution to storing and manipulating taxonomic data in R and any application-specific information associated with it. Taxa provides parsers that can read common sources of taxonomic information (taxon IDs, sequence IDs, taxon names, and classifications) from nearly any format while preserving associated data. Once parsed, the taxonomic data and any associated data can be manipulated using a cohesive set of functions modeled after the popular R package dplyr. These functions take into account the hierarchical nature of taxa and can modify the taxonomy or associated data in such a way that both are kept in sync. Taxa is currently being used by the metacoder and taxize packages, which provide broadly useful functionality that we hope will speed adoption by users and developers.

scholarly journals Fast ordered sampling of DNA sequence variants

2017 ◽  
Author(s):  
Anthony J. Greenberg
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Hard Drives ◽  
Genome Wide ◽  
On Line ◽  
Dna Sequence Variants ◽  
Simple Sampling ◽  
Representative Samples ◽  
Memory Efficient

AbstractExplosive growth in the amount of genomic data is matched by increasing power of consumer-grade computers. Even applications that require powerful servers can be quickly tested on desktop or laptop machines if we can generate representative samples from large data sets. I describe a fast and memory-efficient implementation of an on-line sampling method developed for tape drives 30 years ago. Focusing on genotype files, I test the performance of this technique on modern solid-state and spinning hard drives, and show that it performs well compared to a simple sampling scheme. I illustrate its utility by developing a method to quickly estimate genome-wide patterns of linkage disequilibrium (LD) decay with distance. I provide open-source software that samples loci from several variant format files, a separate program that performs LD decay estimates, and a C++ library that lets developers incorporate these methods into their own projects.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

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