scholarly journals Reconstructing evolutionary timescales using phylogenomics

2016 ◽  
Author(s):  
K. Jun Tong ◽  
Nathan Lo ◽  
Simon Y W Ho
Keyword(s):  
High Throughput ◽  
Molecular Clock ◽  
Evolutionary Biology ◽  
High Throughput Sequencing ◽  
Genetic Data ◽  
Biological Information ◽  
Data Sets ◽  
Sequencing Technology ◽  
Genome Scale ◽  
Scale Data

Reconstructing the timescale of the Tree of Life is one of the principal aims of evolutionary biology. This has been greatly aided by the development of the molecular clock, which enables evolutionary timescales to be estimated from genetic data. In recent years, high-throughput sequencing technology has led to an increase in the feasibility and availability of genome-scale data sets. These represent a rich source of biological information, but they also bring a set of analytical challenges. In this review, we provide an overview of phylogenomic dating and describe the challenges associated with analysing genome-scale data. We also report on recent phylogenomic estimates of the evolutionary timescales of mammals, birds, and insects.

scholarly journals Reconstructing evolutionary timescales using phylogenomics

2016 ◽  
Author(s):  
K. Jun Tong ◽  
Nathan Lo ◽  
Simon Y W Ho
Keyword(s):  
High Throughput ◽  
Molecular Clock ◽  
Evolutionary Biology ◽  
High Throughput Sequencing ◽  
Genetic Data ◽  
Biological Information ◽  
Data Sets ◽  
Sequencing Technology ◽  
Genome Scale ◽  
Scale Data

Reconstructing the timescale of the Tree of Life is one of the principal aims of evolutionary biology. This has been greatly aided by the development of the molecular clock, which enables evolutionary timescales to be estimated from genetic data. In recent years, high-throughput sequencing technology has led to an increase in the feasibility and availability of genome-scale data sets. These represent a rich source of biological information, but they also bring a set of analytical challenges. In this review, we provide an overview of phylogenomic dating and describe the challenges associated with analysing genome-scale data. We also report on recent phylogenomic estimates of the evolutionary timescales of mammals, birds, and insects.

scholarly journals Research Progress of Soil Microorganism Application Based on High-Throughput Sequencing Technology

2021 ◽  
Vol 692 (4) ◽  
pp. 042059
Author(s):  
Yujun Zhang ◽  
Puchang Wang ◽  
Zhongfu Long ◽  
Leilei Ding ◽  
Wen Zhang ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Soil Microorganism ◽  
Research Progress ◽  
Sequencing Technology

scholarly journals Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computation Time ◽  
Potential Effect ◽  
Data Sets ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
High Throughput Sequencing Data ◽  
Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

scholarly journals Using target enrichment sequencing to study the higher-level phylogeny of the largest lichen-forming fungi family: Parmeliaceae (Ascomycota)

IMA Fungus ◽  
2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Felix Grewe ◽  
Claudio Ametrano ◽  
Todd J. Widhelm ◽  
Steven Leavitt ◽  
Isabel Distefano ◽  
...  
Keyword(s):  
Data Sets ◽  
Target Enrichment ◽  
Data Set ◽  
Reduced Genome ◽  
Genome Data ◽  
Worldwide Distribution ◽  
Phylogenetic Studies ◽  
Genome Scale ◽  
Scale Data ◽  
Promising Avenue

AbstractParmeliaceae is the largest family of lichen-forming fungi with a worldwide distribution. We used a target enrichment data set and a qualitative selection method for 250 out of 350 genes to infer the phylogeny of the major clades in this family including 81 taxa, with both subfamilies and all seven major clades previously recognized in the subfamily Parmelioideae. The reduced genome-scale data set was analyzed using concatenated-based Bayesian inference and two different Maximum Likelihood analyses, and a coalescent-based species tree method. The resulting topology was strongly supported with the majority of nodes being fully supported in all three concatenated-based analyses. The two subfamilies and each of the seven major clades in Parmelioideae were strongly supported as monophyletic. In addition, most backbone relationships in the topology were recovered with high nodal support. The genus Parmotrema was found to be polyphyletic and consequently, it is suggested to accept the genus Crespoa to accommodate the species previously placed in Parmotrema subgen. Crespoa. This study demonstrates the power of reduced genome-scale data sets to resolve phylogenetic relationships with high support. Due to lower costs, target enrichment methods provide a promising avenue for phylogenetic studies including larger taxonomic/specimen sampling than whole genome data would allow.

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