scholarly journals chromPlot: visualization of genomic data in chromosomal context

2015 ◽  
Author(s):  
Karen Y. Oróstica ◽  
Ricardo A. Verdugo
Keyword(s):  
Genetic Variants ◽  
Genomic Data ◽  
Use Cases ◽  
Categorical Variables ◽  
Data Generation ◽  
Line Segments ◽  
Number Of Genes ◽  
Genomic Element ◽  
Genomic Regions ◽  
Simultaneous Visualization

ABSTRACTSummary: Visualizing genomic data in chromosomal context can help detecting errors in data generation or analysis and can suggest new hypotheses to be tested. Here we report a new tool for displaying large and diverse genomic data in idiograms of one or multiple chromosomes. The package is implemented in R so that visualization can be easily integrated with its numerous packages for processing genomic data. It supports simultaneous visualization of multiples tracks of data, each of potentially different nature. Large genomic regions such as QTLs or synteny tracts may be shown along histograms of number of genes, genetic variants, or any other type of genomic element. Tracks can also contain values for continuous or categorical variables and the user can choose among points, points connected by lines, line segments, barplots or histograms for representing data. chromPlot reads data from tables in BED format which are imported in R using its builtin functions. The information necessary to draw chromosomes for mouse and human is included with the package. Chromosomes for other organisms are downloaded automatically from the Ensembl website or can be provided by the user. We present common use cases here, and a full tutorial is included as the packages's vignette.Availability: chromPlot is distributed under a GLP2 licence at Genomed Lab: http://genomed.med.uchile.cl.Contact:[email protected]

scholarly journals Detection and evaluation of selection signatures in sheep

2018 ◽  
Vol 53 (5) ◽  
pp. 527-539 ◽  
Author(s):  
Tiago do Prado Paim ◽  
Patrícia Ianella ◽  
Samuel Rezende Paiva ◽  
Alexandre Rodrigues Caetano ◽  
Concepta Margaret McManus Pimentel
Keyword(s):  
Statistical Methods ◽  
Phenotypic Diversity ◽  
Selection Process ◽  
Genomic Data ◽  
Research Field ◽  
Economic Traits ◽  
Selection Signatures ◽  
Important Species ◽  
Challenges And Opportunities ◽  
Genomic Regions

Abstract: The recent development of genome-wide single nucleotide polymorphism (SNP) arrays made it possible to carry out several studies with different species. The selection process can increase or reduce allelic (or genic) frequencies at specific loci in the genome, besides dragging neighboring alleles in the chromosome. This way, genomic regions with increased frequencies of specific alleles are formed, caracterizing selection signatures or selective sweeps. The detection of these signatures is important to characterize genetic resources, as well as to identify genes or regions involved in the control and expression of important production and economic traits. Sheep are an important species for theses studies as they are dispersed worldwide and have great phenotypic diversity. Due to the large amounts of genomic data generated, specific statistical methods and softwares are necessary for the detection of selection signatures. Therefore, the objectives of this review are to address the main statistical methods and softwares currently used for the analysis of genomic data and the identification of selection signatures; to describe the results of recent works published on selection signatures in sheep; and to discuss some challenges and opportunities in this research field.

scholarly journals VariantKey: A Reversible Numerical Representation of Human Genetic Variants

2018 ◽  
Author(s):  
Nicola Asuni ◽  
Steven Wilder
Keyword(s):  
Open Source Software ◽  
Genetic Variants ◽  
Data Representation ◽  
Lookup Table ◽  
Memory Allocation ◽  
Computer Memory ◽  
Numerical Representation ◽  
Chromosome Position ◽  
The Individual ◽  
Genomic Regions

AbstractHuman genetic variants are usually represented by four values with variable length: chromosome, position, reference and alternate alleles. There is no guarantee that these components are represented in a consistent way across different data sources, and processing variant-based data can be inefficient because four different comparison operations are needed for each variant, three of which are string comparisons. Existing variant identifiers do not typically represent every possible variant we may be interested in, nor they are directly reversible. Similarly, genomic regions are typically represented inconsistently by three or four values. Working with strings, in contrast to numbers, poses extra challenges on computer memory allocation and data-representation. To overcome these limitations, a novel reversible numerical encoding schema for human genetic variants (VariantKey) and genomics regions (RegionKey), is presented here alongside a multi-language open-source software implementation (https://github.com/Genomicsplc/variantkey). VariantKey and RegionKey represents variants and regions as single 64 bit numeric entities, while preserving the ability to be searched and sorted by chromosome and position. The individual components of short variants can be directly read back from the VariantKey, while long variants are supported with a fast lookup table.

scholarly journals Spatially constrained tumour growth affects the patterns of clonal selection and neutral drift in cancer genomic data

2019 ◽  
Author(s):  
Kate Chkhaidze ◽  
Timon Heide ◽  
Benjamin Werner ◽  
Marc J. Williams ◽  
Weini Huang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Tumour Growth ◽  
Evolutionary Dynamics ◽  
Clonal Selection ◽  
Genomic Data ◽  
Confounding Factors ◽  
Data Generation ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

AbstractQuantification of the effect of spatial tumour sampling on the patterns of mutations detected in next-generation sequencing data is largely lacking. Here we use a spatial stochastic cellular automaton model of tumour growth that accounts for somatic mutations, selection, drift and spatial constrains, to simulate multi-region sequencing data derived from spatial sampling of a neoplasm. We show that the spatial structure of a solid cancer has a major impact on the detection of clonal selection and genetic drift from bulk sequencing data and single-cell sequencing data. Our results indicate that spatial constrains can introduce significant sampling biases when performing multi-region bulk sampling and that such bias becomes a major confounding factor for the measurement of the evolutionary dynamics of human tumours. We present a statistical inference framework that takes into account the spatial effects of a growing tumour and allows inferring the evolutionary dynamics from patient genomic data. Our analysis shows that measuring cancer evolution using next-generation sequencing while accounting for the numerous confounding factors requires a mechanistic model-based approach that captures the sources of noise in the data.SummarySequencing the DNA of cancer cells from human tumours has become one of the main tools to study cancer biology. However, sequencing data are complex and often difficult to interpret. In particular, the way in which the tissue is sampled and the data are collected, impact the interpretation of the results significantly. We argue that understanding cancer genomic data requires mathematical models and computer simulations that tell us what we expect the data to look like, with the aim of understanding the impact of confounding factors and biases in the data generation step. In this study, we develop a spatial simulation of tumour growth that also simulates the data generation process, and demonstrate that biases in the sampling step and current technological limitations severely impact the interpretation of the results. We then provide a statistical framework that can be used to overcome these biases and more robustly measure aspects of the biology of tumours from the data.

Prevalence and Adaptive Impact of Introgression

2021 ◽  
Vol 55 (1) ◽  
Author(s):  
Nathaniel B. Edelman ◽  
James Mallet
Keyword(s):  
Genetic Variation ◽  
Adaptive Evolution ◽  
Genetic Variants ◽  
Genomic Data ◽  
Annual Review ◽  
Publication Date ◽  
Adaptive Introgression ◽  
Changing Environments ◽  
Potential Benefits

Alleles that introgressed between species can influence the evolutionary and ecological fate of species exposed to novel environments. Hybrid offspring of different species are often unfit, and yet it has long been argued that introgression can be a potent force in evolution, especially in plants. Over the last two decades, genomic data have increasingly provided evidence that introgression is a critically important source of genetic variation and that this additional variation can be useful in adaptive evolution of both animals and plants. Here, we review factors that influence the probability that foreign genetic variants provide long-term benefits (so-called adaptive introgression) and discuss their potential benefits. We find that introgression plays an important role in adaptive evolution, particularly when a species is far from its fitness optimum, such as when they expand their range or are subject to changing environments. Expected final online publication date for the Annual Review of Genetics, Volume 55 is November 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Genetic epidemiology: Overview

2020 ◽  
pp. 327-342
Author(s):  
Frühling Rijsdijk ◽  
Paul F. O’Reilly
Keyword(s):  
Gene Mapping ◽  
Genetic Epidemiology ◽  
Genetic Variants ◽  
Theoretical Basis ◽  
Random Mating ◽  
Behavioural Genetics ◽  
Human Disorders ◽  
Behavioural Phenotypes ◽  
Genomic Regions ◽  
Study Designs

This chapter demonstrates the principles behind some of the major genetic study designs used in psychiatry research. The first part focuses on behavioural genetic designs, while the second part describes designs for ‘gene mapping’. Behavioural genetics examines the genetic basis of behavioural phenotypes, including both disorders and ‘normal’ dimensional traits. The theoretical basis is derived from population genetics, including properties such as segregation ratios, random mating, genetic variance, and genetic correlation between relatives. The second part of the chapter deals with gene mapping designs, in which specific genetic variants or genomic regions associated with a disorder or trait are identified. A brief outline of the most popular current approaches to the analysis of the genetics of complex human disorders is also provided.

scholarly journals Next generation tools for genomic data generation, distribution, and visualization

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
David A Nix ◽  
Tonya L Di Sera ◽  
Brian K Dalley ◽  
Brett A Milash ◽  
Robert M Cundick ◽  
...  
Keyword(s):  
Genomic Data ◽  
Data Generation ◽  
Next Generation

scholarly journals Characterization of Genomic Variants Associated with Resistance to Bedaquiline and Delamanid in Naive Mycobacterium tuberculosis Clinical Strains

2020 ◽  
Vol 58 (11) ◽  
Author(s):  
S. Battaglia ◽  
A. Spitaleri ◽  
A. M. Cabibbe ◽  
C. J. Meehan ◽  
C. Utpatel ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Genetic Variants ◽  
Protein Structures ◽  
Molecular Diagnostic ◽  
Diagnostic Tools ◽  
Clear Understanding ◽  
Data Set ◽  
Gene Target ◽  
Genomic Regions

ABSTRACT The role of mutations in genes associated with phenotypic resistance to bedaquiline (BDQ) and delamanid (DLM) in Mycobacterium tuberculosis complex (MTBc) strains is poorly characterized. A clear understanding of the genetic variants’ role is crucial to guide the development of molecular-based drug susceptibility testing (DST). In this work, we analyzed all mutations in candidate genomic regions associated with BDQ- and DLM-resistant phenotypes using a whole-genome sequencing (WGS) data set from a collection of 4,795 MTBc clinical isolates from six countries with a high burden of tuberculosis (TB). From WGS analysis, we identified 61 and 163 unique mutations in genomic regions potentially involved in BDQ- and DLM-resistant phenotypes, respectively. Importantly, all strains were isolated from patients who likely have never been exposed to these medicines. To characterize the role of mutations, we calculated the free energy variation upon mutations in the available protein structures of Ddn (DLM), Fgd1 (DLM), and Rv0678 (BDQ) and performed MIC assays on a subset of MTBc strains carrying mutations to assess their phenotypic effect. The combination of structural and phenotypic data allowed for cataloguing the mutations clearly associated with resistance to BDQ (n = 4) and DLM (n = 35), only two of which were previously described, as well as about a hundred genetic variants without any correlation with resistance. Significantly, these results show that both BDQ and DLM resistance-related mutations are diverse and distributed across the entire region of each gene target, which is of critical importance for the development of comprehensive molecular diagnostic tools.

Discovering genetic variants in Crohn's disease by exploring genomic regions enriched of weak association signals

2011 ◽  
Vol 43 (8) ◽  
pp. 623-631 ◽  
Author(s):  
Annarita D’Addabbo ◽  
Orazio Palmieri ◽  
Rosalia Maglietta ◽  
Anna Latiano ◽  
Sayan Mukherjee ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Genetic Variants ◽  
Weak Association ◽  
Genomic Regions

scholarly journals aCGH Analysis to Estimate Genetic Variations among Domesticated Chickens

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Tomoyoshi Komiyama ◽  
Mengjie Lin ◽  
Atsushi Ogura
Keyword(s):  
Comparative Genomic Hybridization ◽  
Selection Pressure ◽  
Array Comparative Genomic Hybridization ◽  
Comparative Genomic ◽  
Genomic Hybridization ◽  
Selective Pressures ◽  
Number Of Genes ◽  
Chicken Breeds ◽  
Ancient Times ◽  
Genomic Regions

Chickens have been familiar to humans since ancient times and have been used not only for culinary purposes but also for cultural purposes including ritual ceremonies and traditional entertainment. The various chicken breeds developed for these purposes often display distinct morphological and/or behavioural traits. For example, the JapaneseShamois larger and more aggressive than other domesticated chickens, reflecting its role as a fighting cock breed, whereas JapaneseNaganakidoribreeds, which have long-crowing behaviour, were bred instead for their entertaining and aesthetic qualities. However, the genetic backgrounds of these distinct morphological and behavioural traits remain unclear. Therefore, the question arises as to which genomic regions in these chickens were acted upon by selective pressures through breeding. We compared the entire genomes of six chicken breeds domesticated for various cultural purposes by utilizing array comparative genomic hybridization. From these analyses, we identified 782 regions that underwent insertions, deletions, or mutations, representing man-made selection pressure in these chickens. Furthermore, we found that a number of genes diversified in domesticated chickens bred for cultural or entertainment purposes were different from those diversified in chickens bred for food, such as broilers and layers.

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