Phylogenetic methods - computer practicals
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Department of Botany, Charles University, Prague (2021-2024)

Maximum likelihood

A ZIP file with necessary software is here. Download it and unzip.
RAxML - Randomized Axelerated Maximum Likelihood
FigTree - phylogenetic tree viewer

Other software recommended to install

• Notepad++
• Total Commander

Tasks 1 (to work with Cardamine dataset)

after you are done submit the answers using this Google form
If an advice is required, contact Tomáš Fér: tomas.fer_AT_natur.cuni.cz

1. Create the maximum likelihood tree of the Cardamine AFLP dataset

• put the matrix in FASTA format to the same folder where is the RAxML binary

• navigate to that folder in Total Commander and click on Commands - Open command prompt window

• type this command and press Enter
  raxmlHPC -m BINGAMMA -p 12345 -s impatiens_01.fas -# 5 -n resultBIN
  Question 1: What is the likelihood of the best tree?

• open the resulting tree (RAxML_bestTree.resultBIN) in FigTree and display as unrooted
  Question 2: How many groups of individuals do you see?

Tasks 2 (to work with Amomum dataset)

2. Create best ML tree for the ITS dataset using GTRGAMMA model

• run this command
  raxmlHPC -m GTRGAMMA -p 12345 -s Amomum_ITS.trimmed.fas -# 10 -n resultDNA
  Question 3: What is the difference in likelihood between the tree with highest and the smallest likelihood?
• open all 10 resulting trees in FigTree (*.RUN0 - *.RUN9), root trees with outgroup (Siphonochilus kirkii)
  Question 4: What are the differences among trees?

3. Create the best ML tree for the matK dataset

• repeat the previous steps for the Amomum_matK.trimmed.fas (change the parameter -n to bestML)
• open best tree (RAxML_bestTree.*) in FigTree
  Question 5: What is the difference among the best ITS and the best matK tree?

4. Run the rapid bootstrap for the matK dataset

• run the full analysis using this command
  raxmlHPC -f a -m GTRGAMMA -p 12345 -x 12345 -# 100 -s Amomum_matK.trimmed.fas -n rbs
• open the resulting tree (RAxML_bipartitions.rbs) in FigTree
  Question 6: What is the lowest bootstrap support value?
• run the rapid bootstrap analysis again (but change the -n parameter to, e.g., rbs2, and -p and -x to, e.g., 12346)
  Question 7: Are all the bootstrap values the same? If not, why?
• repeat the analysis twice again but now with 1,000 bootstrap replicates (-# 1000), always changing -p, -x and -n

5. Run the standard bootstrap analysis for the matK dataset

• the best ML tree was already created in the step 3
• create 100 standard bootstrap replicate trees
  raxmlHPC -m GTRGAMMA -p 12345 -b 12345 -# 100 -s Amomum_matK.trimmed.fas -n boot
• map bootstrap values onto the best ML tree
  raxmlHPC -m GTRGAMMA -p 12345 -f b -t RAxML_bestTree.bestML -z RAxML_bootstrap.boot -n finalboot
  Question 8: Are the standard bootstrap values same as rapid bootstrap values? Is there any trend?

6. Create the rapid and standard bootstrap analysis for the concatenated and partitioned dataset

• look at the file Amomum_partitions.trimmed.txt how the partitions are specified
• run similar analysis as in steps 3-5 but specify the partition file using parameter -q (and do not forget to specify different name for parameter -n)
  raxmlHPC -m GTRGAMMA -p 12345 -s Amomum_concatenated.trimmed.fas -q Amomum_partitions.trimmed.txt -# 10 -n concatbestML
• continue running bootstrap replicates and mapping to the bestML tree (similar to step 5), again, do not forget -q
• do the rapid bootstrap analysis (similar to step 4), specify -q
  Question 9: Is the topology of ITS, matK and concatenated tree the same or what are the differences? (Always compare rooted trees)
  Question 10: Compare standard and rapid bootstrap values for the concatenated dataset.

Thank you for participating in RAxML practicals...