TreeBASE Search-Analyses for study 16236

@ARTICLE{TreeBASE2Ref23544, author = {Sonja L. B. Arneaud and Huong T. N. Trinh and Elizabeth A. Schlatter and Christine M. Dang and John R. Porter}, title = {Taxonomic classification of two podophyllotoxin-producing endophytic fungi.}, year = {2014}, keywords = {Dark septate endophyte, Taxonomic classification, Phialocephala, P. fortinii s.l.-A. applanata species complex, Phylogenetic analysis}, doi = {}, url = {}, pmid = {}, journal = {Fungal Biology}, volume = {}, number = {}, pages = {}, abstract = {The morphological characteristics and DNA sequences of four coding loci (the partial nuc beta-tubulin gene [bTub], the partial nuc translation elongation factor 1-alpha gene [EF1-alpha], the 5' end of the partial nuc 28S rDNA large subunit gene [28S], the partial mt cytochrome c oxidase subunit I gene [cox1]) and four non-coding loci (the ITS1-5.8S-ITS2 sequence from which was extracted the ITS1 and ITS2 sequences [ITS], pPF-018, pPF-061, pPF-076) were used to determine the taxonomic classification of two endophytic fungi isolated from the rhizomes of Podophyllum peltatum (American Mayapple) growing near Newark, Delaware. Our initial phylogenetic analysis of these fungi based solely on the analysis of the D2 variable region of the nuc large subunit 28S rDNA placed them within the Phialocephala genus. As more taxonomic data became available for cryptic members within this genus, it became evident that further phylogenetic analysis was necessary to provide a more accurate species-level placement of these organisms. In order to achieve this, five phylogenetic programs were utilized to analyze the DNA loci of each fungus in three ways: singly, concatenated, and through the generation of a species tree. Based on these analyses, we have concluded that P. peltatum endophyte 5 (PPE5) is a new strain of P. fortinii and a member of the P. fortinii s.l.-Acephala applanata complex (PAC). Additionally, we report P. peltatum endophyte 7 (PPE7) as a new species of Phialocephala that we have named P. podophylli. } }

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Analyses for Study 16236

Citation title: "Taxonomic classification of two podophyllotoxin-producing endophytic fungi.".

Study name: "Taxonomic classification of two podophyllotoxin-producing endophytic fungi.".

This study is part of submission 16236 (Status: Published).

Analyses

Analysis 1

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 pPF-061 analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the Kimura 2-parameter (K2) model (Kimura, 1980). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the MCL approach, and then selecting the topology with superior log likelihood value. The analysis involved 52 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 668 positions in the final dataset.

Output data

1 Output object(s)

Analysis 2

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 beta-tubulin analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the Kimura 2-parameter (K2) model (Kimura, 1980). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the neighbor-joining (NJ) method to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach. A discrete gamma distribution was used to model evolutionary rate differences among sites (five categories +gamma, parameter = 0.3292)). The analysis involved 122 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 584 positions in the final dataset.

Output data

1 Output object(s)

Analysis 3

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 EF1-alpha analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the Kimura 2-parameter (K2) model (Kimura, 1980). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the neighbor-joining (NJ) method to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach. A discrete gamma distribution was used to model evolutionary rate differences among sites [5 categories +gamma, parameter = 0.3329)]. The analysis involved 100 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 459 positions in the final dataset.

Output data

1 Output object(s)

Analysis 4

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 COX-1 analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the Kimura 2-parameter (K2) model (Kimura, 1980). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the neighbor-joining (NJ) method to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach. A discrete gamma distribution was used to model evolutionary rate differences among sites (five categories (+gamma, parameter = 0.3292)). The analysis involved 56 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 409 positions in the final dataset.

Output data

1 Output object(s)

Analysis 5

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 pPF-076 analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the Kimura 2-parameter (K2) model (Kimura, 1980). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the neighbor-joining (NJ) method to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach. A discrete gamma distribution was used to model evolutionary rate differences among sites (five categories (+gamma, parameter = 0.3292)). The analysis involved 121 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 478 positions in the final dataset.

Output data

1 Output object(s)

Analysis 6

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 28S analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the Kimura 2-parameter (K2) model (Kimura, 1980). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the MCL approach, and then selecting the topology with superior log likelihood value. The analysis involved 123 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 550 positions in the final dataset.

Output data

1 Output object(s)

Analysis 7

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 ITS analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the GTR model (Nei and Kumar, 2000). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained by applying the neighbor-joining (NJ) method to a matrix of pairwise distances estimated using the maximum composite likelihood (MCL) approach. A discrete gamma distribution was used to model evolutionary rate differences among sites (5 categories (+gamma, parameter = 2.8351)). The rate variation model allowed for some sites to be evolutionarily invariable ([+I], 0.0000% sites). The analysis involved 43 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 254 positions in the final dataset.

Output data

1 Output object(s)

Analysis 8

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 pPF-018 analysis --

Step details

Name

Software used

Name
Algorithm
Commands
The evolutionary history was inferred by using the ML method based on the Kimura 2-parameter (K2) model (Kimura, 1980). The tree with the highest log likelihood (-1560.2412) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. The details outlined for the construction of this phylogenetic tree are the same as described in the caption of Fig. S1. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using the MCL approach, and then selecting the topology with superior log likelihood value. The analysis involved 123 nucleotide sequences. All positions with less than 95% site coverage were eliminated. That is, fewer than 5% alignment gaps, missing data, and ambiguous bases were allowed at any position. There were a total of 550 positions in the final dataset.

Output data

1 Output object(s)

Analysis 9

Analysis details

Name

Input data

1 Input object(s)

Analysis step

PPE5&7 concatenated analysis --

Output data