Phylogenomic analyses data of the avian phylogenomics project

Jarvis, Erich D.; Mirarab, Siavash; Aberer, Andre J.; Li, Bo; Houde, Peter; Li, Cai; Ho, Simon Y. W.; Faircloth, Brant C.; Nabholz, Benoît; Howard, Jason T.; Suh, Alexander; Weber, Claudia; Fonseca, Rute R. da; Alfaro-Núñez, Alonzo; Narula, Nitish; Liu, Liang; Burt, David W.; Ellegren, Hans; Edwards, Scott V.; Stamatakis, Alexandros; Mindell, David P.; Cracraft, Joel; Braun, Edward L.; Warnow, Tandy; Wang, Jun; Gilbert, M. Thomas P.; Zhang, Guojie

doi:10.1186/s13742-014-0038-1

“…To assess and compare the performance of the concatenation fuctions of AMAS, FASconCAT-G, and Phyutility, I used four recently published phylogenomic data sets: filtered DNA alignments of 8,295 exons of 52 vertebrates and 3,769 UCE loci of 49 bird taxa Jarvis et al (2014) (available at gigadb.org/dataset/101041), DNA and amino acid alignments of 1,478 loci from 144 arthropod taxa from Misof et al (2014) (http://datadryad.org/resource/doi:10.5061/dryad. 3c0f1), and amino acid alignments of 5,214 exons from 19 wasp taxa from Johnson et al (2013) (datadryad.org/resource/doi:10.5061/dryad.jt440).…”

Section: Methodsmentioning

confidence: 99%

AMAS: a fast tool for alignment manipulation and computing of summary statistics

Borowiec

¹

2015

Preprint

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The amount of data used in phylogenetics has grown explosively in the recent years and many phylogenies are inferred with hundreds or even thousands of loci and many taxa.These modern phylogenomic studies often entail separate analyses of each of the loci in addition to multiple analyses of subsets of genes or concatenated sequences. ABSTRACTThe amount of data used in phylogenetics has grown explosively in the recent years and many phylogenies are inferred with hundreds or even thousands of loci and many taxa. These modern phylogenomic studies often entail separate analyses of each of the loci in addition to multiple analyses of subsets of genes or concatenated sequences. Computationally efficient tools for handling and computing properties of thousands of single-locus or large concatenated alignments are needed. Here I present AMAS (Alignment Manipulation And Summary), a tool that can be used either as a stand-alone command-line utility or as a Python package. AMAS works on amino acid and nucleotide alignments and combines capabilities of sequence manipulation with a function that calculates basic statistics. The manipulation functions include conversions among popular formats, concatenation, extracting sites and splitting according to a pre-defined partitioning scheme, and creation of replicate data sets. The statistics calculated include the number of taxa, alignment length, total count of matrix cells, overall number of undetermined characters, percent of missing data, AT and GC contents (for DNA alignments), count and proportion of variable sites, count and proportion of parsimony informative sites, and counts of all characters relevant for a nucleotide or amino acid alphabet. AMAS is particularly suitable for very large alignments with hundreds of taxa and thousands of loci. It performs better at concatenation and summarizing alignments than other popular tools. AMAS is a Python 3 program that relies solely on Python's core modules. AMAS source code and manual can be downloaded from http://github.com/marekborowiec/AMAS/

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“…Phyutility allows concatenation only to the sequential NEXUS format. AMAS concatenation times ranged from about 2 seconds for the smallest data set of Johnson et al (2013) to about 22 seconds for Jarvis et al (2014) data, outperforming the other two programs by a factor of 30 or more. A comparison of times taken for concatenating the smallest data set of 5,214 amino acid loci from Johnson et al (2013) is presented in Figure 1A.…”

Section: Concatenationmentioning

confidence: 99%

“…To assess and compare the performance of the concatenation fuctions of AMAS, FASconCAT-G, and Phyutility, I used four recently published phylogenomic data sets: filtered DNA alignments of 8,295 exons of 52 vertebrates and 3,769 UCE loci of 49 bird taxa Jarvis et al (2014) (available at gigadb.org/dataset/101041), DNA and amino acid alignments of 1,478 loci from 144 arthropod taxa from Misof et al (2014) (http://datadryad.org/resource/doi:10.5061/dryad. 3c0f1), and amino acid alignments of 5,214 exons from 19 wasp taxa from Johnson et al (2013) (datadryad.org/resource/doi:10.5061/dryad.jt440).…”

Section: Methodsmentioning

confidence: 99%

“…This took about 8 and 23 minutes for the two datasets, respectively. FASconCAT-G crashed with an error when attempting to read in the nucleotide FASTA files of Jarvis et al (2014) UCE and exon data sets. Phyutility was able to concatenate all of the four FASTA data sets, in times of about 10 minutes for the smallest data set of 5,214 amino acid loci of Johnson et al (2013) to over 200 minutes for the largest data set of 8,295 exons from Jarvis et al (2014).…”

Section: Concatenationmentioning

confidence: 99%

“…AMAS wrote 5,214 files from partitions of the Johnson et al (2013) concatenated matrix in about 5 seconds, and 8,295 files from the Jarvis et al (2014) exon data set in about 30 seconds.…”

Section: Splitting By Partitionmentioning

confidence: 99%

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AMAS: a fast tool for alignment manipulation and computing of summary statistics

Borowiec

¹

2015

Preprint

View full text Add to dashboard Cite

The amount of data used in phylogenetics has grown explosively in the recent years and many phylogenies are inferred with hundreds or even thousands of loci and many taxa.These modern phylogenomic studies often entail separate analyses of each of the loci in addition to multiple analyses of subsets of genes or concatenated sequences. ABSTRACTThe amount of data used in phylogenetics has grown explosively in the recent years and many phylogenies are inferred with hundreds or even thousands of loci and many taxa. These modern phylogenomic studies often entail separate analyses of each of the loci in addition to multiple analyses of subsets of genes or concatenated sequences. Computationally efficient tools for handling and computing properties of thousands of single-locus or large concatenated alignments are needed. Here I present AMAS (Alignment Manipulation And Summary), a tool that can be used either as a stand-alone command-line utility or as a Python package. AMAS works on amino acid and nucleotide alignments and combines capabilities of sequence manipulation with a function that calculates basic statistics. The manipulation functions include conversions among popular formats, concatenation, extracting sites and splitting according to a pre-defined partitioning scheme, and creation of replicate data sets. The statistics calculated include the number of taxa, alignment length, total count of matrix cells, overall number of undetermined characters, percent of missing data, AT and GC contents (for DNA alignments), count and proportion of variable sites, count and proportion of parsimony informative sites, and counts of all characters relevant for a nucleotide or amino acid alphabet. AMAS is particularly suitable for very large alignments with hundreds of taxa and thousands of loci. It performs better at concatenation and summarizing alignments than other popular tools. AMAS is a Python 3 program that relies solely on Python's core modules. AMAS source code and manual can be downloaded from http://github.com/marekborowiec/AMAS/

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Comparative morphology of the avian bony columella

Peacock

¹

,

Spellman

²

,

Field

³

et al. 2023

The Anatomical Record

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In birds, the columella is the only bony element of the sound conducting apparatus, conveying vibrations of the cartilaginous extracolumella to the fluid of the inner ear. Although avian columellar morphology has attracted some attention over the past century, it nonetheless remains poorly described in the literature. The few existing studies mostly focus on morphological descriptions in relatively few taxa, with no taxonomically broad surveys yet published. Here we use observations of columellae from 401 extant bird species to provide a comprehensive survey of columellar morphology in a phylogenetic context. We describe the columellae of several taxa for the first time and identify derived morphologies characterizing higher‐level clades based on current phylogenies. In particular, we identify a derived columellar morphology diagnosing a major subclade of Accipitridae. Within Suliformes, we find that Fregatidae, Sulidae, and Phalacrocoracidae share a derived morphology that is absent in Anhingidae, suggesting a secondary reversal. Phylogenetically informed comparisons allow recognition of instances of homoplasy, including the distinctive bulbous columellae in suboscine passerines and taxa belonging to Eucavitaves, and bulging footplates that appear to have evolved at least twice independently in Strigiformes. We consider phylogenetic and functional factors influencing avian columellar morphology, finding that aquatic birds possess small footplates relative to columellar length, possibly related to hearing function in aquatic habitats. By contrast, the functional significance of the distinctive bulbous basal ends of the columellae of certain arboreal landbird taxa remains elusive.

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Phylogenomic analyses data of the avian phylogenomics project

Cited by 102 publications

References 19 publications

AMAS: a fast tool for alignment manipulation and computing of summary statistics

AMAS: a fast tool for alignment manipulation and computing of summary statistics

AMAS: a fast tool for alignment manipulation and computing of summary statistics

Comparative morphology of the avian bony columella

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