Evidence of inter-sectional chloroplast capture in Corymbia among sections Torellianae and Maculatae

2018 ◽  
Vol 66 (5) ◽  
pp. 369 ◽  
Author(s):  
Adam Healey ◽  
David J. Lee ◽  
Agnelo Furtado ◽  
Robert J. Henry
Keyword(s):  
Phylogenetic Trees ◽  
Reticulate Evolution ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Wide Crosses ◽  
Chloroplast Capture ◽  
Chloroplast Genomes ◽  
External Transcribed Spacer ◽  
Corymbia Citriodora ◽  
Generation Sequencing

Chloroplast capture through hybridisation and introgression is well described within Eucalyptus. Despite the propensity of the Corymbia genus (eucalypts) to form hybrids from wide crosses, description of chloroplast capture in Corymbia has, until recently, been limited. In this study our aim was to investigate evidence of intersectional chloroplast capture between sections Torellianae and Maculatae. Using whole-genome next-generation sequencing data, the complete chloroplast genomes were assembled from four Corymbia taxa: Corymbia citriodora subspecies citriodora (Hook.) K.D.Hill & L.A.S.Johnson, Corymbia citriodora subspecies variegata (F.Muell.) A.R.Bean & M.W.McDonald, Corymbia henryi (S.T.Blake) K.D.Hill & L.A.S.Johnson, and Corymbia torelliana (F.Muell.) K.D.Hill & L.A.S.Johnson, represented by eight genotypes. Phylogenetic analysis and comparison among Corymbia chloroplast genomes and nuclear external transcribed spacer (ETS) sequences revealed chloroplast capture among Corymbia species across distinct sections Torellianae and Maculatae within subgenus Blakella. Reticulate evolution, along with Eucalyptus, likely extends into Corymbia as evidenced by incongruent plastid and nuclear phylogenetic trees, suggestive of its importance of hybridisation and introgression during the evolution of eucalypts.

scholarly journals Using a fast clustering method for viral segment lineage determination, applied to the H9 influenza hemagglutinin.

2017 ◽  
Author(s):  
Andrew Dalby ◽  
Lorna Tinworth ◽  
Joshua Sealy ◽  
Munir Iqbal
Keyword(s):  
Phylogenetic Trees ◽  
Sequence Data ◽  
Next Generation Sequencing Data ◽  
Viral Sequence ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
Influenza Hemagglutinin ◽  
Tree Construction ◽  
Alternative Approach ◽  
Generation Sequencing

Lineage determination is an important part of the analysis of viral sequence data. Previously this has depended on phylogenetic analysis in order to identify distinct clades within the phylogenetic trees. This method is time consuming and dependent on a set of empirical rules for clade identification. An alternative approach is to use clustering. Clustering is commonly used to identify operational taxonomic units in next generation sequencing data. In this paper we use clustering in order to rapidly identify viral segment lineages and clades without the need for tree construction.

scholarly journals Using a fast clustering method for viral segment lineage determination, applied to the H9 influenza hemagglutinin.

2017 ◽  
Author(s):  
Andrew Dalby ◽  
Lorna Tinworth ◽  
Joshua Sealy ◽  
Munir Iqbal
Keyword(s):  
Phylogenetic Trees ◽  
Sequence Data ◽  
Next Generation Sequencing Data ◽  
Viral Sequence ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
Influenza Hemagglutinin ◽  
Tree Construction ◽  
Alternative Approach ◽  
Generation Sequencing

Lineage determination is an important part of the analysis of viral sequence data. Previously this has depended on phylogenetic analysis in order to identify distinct clades within the phylogenetic trees. This method is time consuming and dependent on a set of empirical rules for clade identification. An alternative approach is to use clustering. Clustering is commonly used to identify operational taxonomic units in next generation sequencing data. In this paper we use clustering in order to rapidly identify viral segment lineages and clades without the need for tree construction.

scholarly journals NGSremix: A software tool for estimating pairwise relatedness between admixed individuals from next-generation sequencing data

2021 ◽  
Author(s):  
Anne Krogh Nøhr ◽  
Kristian Hanghøj ◽  
Genis Garcia Erill ◽  
Zilong Li ◽  
Ida Moltke ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Genetic Research ◽  
Likelihood Estimation ◽  
Software Tool ◽  
Estimation Methods ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Ngs Data ◽  
Generation Sequencing

Abstract Estimation of relatedness between pairs of individuals is important in many genetic research areas. When estimating relatedness, it is important to account for admixture if this is present. However, the methods that can account for admixture are all based on genotype data as input, which is a problem for low-depth next-generation sequencing (NGS) data from which genotypes are called with high uncertainty. Here we present a software tool, NGSremix, for maximum likelihood estimation of relatedness between pairs of admixed individuals from low-depth NGS data, which takes the uncertainty of the genotypes into account via genotype likelihoods. Using both simulated and real NGS data for admixed individuals with an average depth of 4x or below we show that our method works well and clearly outperforms all the commonly used state-of-the-art relatedness estimation methods PLINK, KING, relateAdmix, and ngsRelate that all perform quite poorly. Hence, NGSremix is a useful new tool for estimating relatedness in admixed populations from low-depth NGS data. NGSremix is implemented in C/C ++ in a multi-threaded software and is freely available on Github https://github.com/KHanghoj/NGSremix.

scholarly journals recoup: flexible and versatile signal visualization from next generation sequencing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Panagiotis Moulos
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation Sequencing Data ◽  
Special Focus ◽  
Next Generation ◽  
Sequencing Data ◽  
User Friendliness ◽  
Computational Environment ◽  
Level Data ◽  
Data Signal ◽  
Generation Sequencing

Abstract Background The relentless continuing emergence of new genomic sequencing protocols and the resulting generation of ever larger datasets continue to challenge the meaningful summarization and visualization of the underlying signal generated to answer important qualitative and quantitative biological questions. As a result, the need for novel software able to reliably produce quick, comprehensive, and easily repeatable genomic signal visualizations in a user-friendly manner is rapidly re-emerging. Results recoup is a Bioconductor package for quick, flexible, versatile, and accurate visualization of genomic coverage profiles generated from Next Generation Sequencing data. Coupled with a database of precalculated genomic regions for multiple organisms, recoup offers processing mechanisms for quick, efficient, and multi-level data interrogation with minimal effort, while at the same time creating publication-quality visualizations. Special focus is given on plot reusability, reproducibility, and real-time exploration and formatting options, operations rarely supported in similar visualization tools in a profound way. recoup was assessed using several qualitative user metrics and found to balance the tradeoff between important package features, including speed, visualization quality, overall friendliness, and the reusability of the results with minimal additional calculations. Conclusion While some existing solutions for the comprehensive visualization of NGS data signal offer satisfying results, they are often compromised regarding issues such as effortless tracking of processing and preparation steps under a common computational environment, visualization quality and user friendliness. recoup is a unique package presenting a balanced tradeoff for a combination of assessment criteria while remaining fast and friendly.

scholarly journals BIGpre: A Quality Assessment Package for Next-Generation Sequencing Data

2011 ◽  
Vol 9 (6) ◽  
pp. 238-244 ◽  
Author(s):  
Tongwu Zhang ◽  
Yingfeng Luo ◽  
Kan Liu ◽  
Linlin Pan ◽  
Bing Zhang ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Quality Assessment ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing
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