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The nature of the Last Universal Common Ancestor and its impact on the early Earth system -- timetree inference analyses

In this repository, you will find a very detailed tutorial with all the steps you need to follow to reproduce the results for the timetree inference analyses we carried out as part of our study: The nature of the Last Universal Common Ancestor and its impact on the early Earth system.

To get started, you can clone this repository on your PC and follow all the guidelines given in the various README.md files that you shall find inside each directory so that you can go through all the steps we carried out for timetree inference. A summary of this workflow is given below:

  • Parsing and formatting the input data required to run PAML programs BASEML and MCMCtree (Yang, 2007): sequence files (concatenated and partitioned), calibrated tree files (three calibration strategies, more details below), and control files.
  • Inferring the mean evolutionary rate to specify a sensible rate prior.
  • Running PAML programs for timetree inference:
    • Using various in-house pipelines to set up the working environment, the file structure, and the control files required to run PAML programs.
    • Running CODEML to calculate the branch lengths, the gradient, and the Hessian; required by MCMCtree to enable the approximate likelihood calculation dos Reis and Yang, 2011.
    • Running MCMCtree with the approximate likelihood calculation enabled for timetree inference. Our main analysis consisted of inferring the timetree for the partitioned dataset under both the Geometric Brownian motion (GBM, Thorne et al., 1998, Yang and Rannala, 2006) and the independent-rates log-normal (ILN, Rannala and Yang, 2007, Lemey et al., 2010) relaxed-clock models, where nodes that correspond to the same divergences are cross-braced (i.e., hereby referred to as calibration strategy “cross-bracing A”). We then ran 10 additional inference analyses to benchmark the effect that partitioning, cross-bracing, and relaxed-clock models can have on species divergence times estimation:
      • GBM + concatenated alignment + cross-bracing A.
      • GBM + concatenated alignment + cross-bracing B (i.e., only nodes that correspond to the same divergences for which there are fossil constraints are cross-braced).
      • GBM + concatenated alignment + without cross-bracing.
      • GBM + partitioned alignment + cross-bracing B.
      • GBM + partitioned alignment + without cross-bracing.
      • ILN + concatenated alignment + cross-bracing A.
      • ILN + concatenated alignment + cross-bracing B.
      • ILN + concatenated alignment + without cross-bracing.
      • ILN + partitioned alignment + cross-bracing B.
      • ILN + partitioned alignment + without cross-bracing.
  • Running MCMC diagnostics for all the chains under each analysis.

To make it easier for you to navigate this GitHub repository, below you can find a summary of the content you shall find inside each directory.

Data formatting

  • Molecular alignment: we analysed a molecular alignment with 5 pre-LUCA paralogues (i.e., F- and V- type subunits from ATPases; Elongation Factor Tu and G; Signal Recognition Protein and Signal Recognition Particle Receptor; Tyrosyl-tRNA and Tryptophanyl-tRNA synthetases; and Leucyl- and Valyl- tRNA synthetases) for 246 taxa. You can read the README.md file under 00_data_formatting to reproduce all the steps that we carried out to parse the raw data.
    • Concatenated molecular alignment (raw): Eight paralogues were initially selected based on previous work showing a likely duplication event before LUCA: the amino- and carboxy-terminal regions from carbamoyl phosphate synthetase, aspartate and ornithine transcarbamoylases, histidine biosynthesis genes A and F, catalytic and non-catalytic subunits from ATP synthase (ATP), elongation factor Tu and G (EF), signal recognition protein and signal recognition particle receptor (SRP), tyrosyl-tRNA and tryptophanyl-tRNA synthetases (Tyr), and leucyl- and valyl-tRNA synthetases (Leu) (Zhaxybayeva et al., 2005). Gene families were identified using BLAST (Altschul 1990). Sequences were downloaded from the NCBI (Sayers et al., 2010), aligned with MUSCLE (Edgar 2004) and trimmed with TrimAl (Capella-Gutiérrez 2009) (option -strict). Individual gene trees were inferred under the LG+C20+F+G4 substitution model implemented in IQ-TREE2 (Minh et al. 2020). These trees were manually inspected and curated to remove non-homologous sequences, horizontal gene transfers, exceptionally short or long sequences and extremely long branches. Recent paralogues or taxa of inconsistent and/or uncertain placement inferred with RogueNaRok (Aberer et al., 2013) were also removed. Independent verification of an archaeal or bacterial deep split was achieved using minimal ancestor deviation (Tria et al., 2017). This filtering process resulted in the five pairs of paralogous gene families (Zhaxybayeva et al., 2005) (ATP, EF, SRP, Tyr and Leu) that we used to estimate the origination time of LUCA. According to the filters applied, the resulting concatenated gene alignment included 246 species.
    • Individual gene alignments for each paralog (raw): you will find the five gene alignments (i.e., one for each paralog) under the partitioned directory.
    • Input concatenated alignment: after parsing the raw concatenated alignment aforementioned, this sequence file contains the input concatenated alignment that we used for all timetree inference analyses.
    • Input partitioned alignment: after parsing the raw individual gene alignments aforementioned, this sequence file contains the input partitioned alignment (i.e., five alignment blocks, one for each gene alignment) that we used for all timetree inference analyses.
  • Phylogeny: given that a fixed tree topology is required for timetree inference with MCMCtree, we decided to infer the best-scoring ML tree with IQ-TREE 2 under the LG+C20+F+G4 model (Hoang 2018) following our previous phylogenetic analyses. We then modified the resulting inferred tree topology to implement the available fossil calibrations (see below) and following consensus views of species-level relationships (Coleman et al., 2021, Aouad et al., 202, Burki et al., 2019).
  • Calibrations: we used previously established dates for four fossil calibrations and included nine additional calibrations with justifications provided in the supplementary material of our paper. In addition, five of the newly defined calibrations (i.e., total group Eukarya for the archaeal lineage, Embryophyta, Archaeplastida, and Eumetazoa) have been revised to follow the latest geochronological updates and literature as of December 20233, and so differ from those used in previous studies (Betts et al., 2018, Morris et al., 2018, Mahendrarajah et al., 2023). Note that the root of a phylogeny requires constraining in molecular clock-dating analyses. Therefore, we constrained the root (representing the duplication of pre-LUCA paralogues) with a hard upper bound equal to the age of the moon forming impact. We generated three control files that we used as input files for our R in-house scripts to semi-automatically calibrate the tree topologies with the MCMCtree notation:

PAML analyses

Note that all the directories that you shall find inside the 01_PAML directory have their own README.md file. To this end, you can navigate to each of these directories to read more about how we ran CODEML and MCMCtree when using either the concatenated or the partitioned alignments and when running the inference analyses under different settings (e.g., data type, calibration strategy, relaxed-clock model).

Below, you can find a short description of what you can find in each directory:

  • 00_CODEML: guidelines to infer the branch lengths, the gradient, and the Hessian for the concatenated alignment with CODEML.
  • 01_CODEML: guidelines to infer the branch lengths, the gradient, and the Hessian for each gene alignment with CODEML. Remember that each gene alignment is part of the partitioned alignment as individual alignment blocks.
  • 02_MCMCtree: guidelines to run MCMCtree by enabling the approximate likelihood calculation (dos Reis and Yang, 2011) and the newly implemented cross-bracing approach with the concatenated dataset under the GBM and ILN relaxed-clock models. Note that all nodes that correspond to the same speciation event were cross-braced during this timetree inference analysis (i.e., "cross-bracing A").
  • 03_MCMCtree_nonbraced: guidelines to run MCMCtree by enabling the approximate likelihood calculation (dos Reis and Yang, 2011) under the GBM and ILN relaxed-clock models with the concatenated dataset. Note that cross-bracing was not enabled during this timetree inference analysis.
  • 04_MCMCtree_fossbraced: guidelines to run MCMCtree by enabling the approximate likelihood calculation (dos Reis and Yang, 2011) and cross-bracing with the concatenated dataset under the GBM and ILN relaxed-clock models. Note that only those nodes for which a fossil calibration was available were cross-braced during this timetree inference analysis (i.e., "cross-bracing B").
  • 05_MCMCtree_part: guidelines to run MCMCtree by enabling the approximate likelihood calculation (dos Reis and Yang, 2011) and cross-bracing with the partitioned dataset under the GBM and ILN relaxed-clock models. Note that all nodes that correspond to the same speciation event were cross-braced during this timetree inference analysis (i.e., "cross-bracing A").
  • 06_MCMCtree_part_nonbraced: guidelines to run MCMCtree by enabling the approximate likelihood calculation (dos Reis and Yang, 2011) under the GBM and ILN relaxed-clock models with the partitioned dataset. Note that cross-bracing was not enabled during this timetree inference analysis.
  • 07_MCMCtree_part_fossbraced: guidelines to run MCMCtree by enabling the approximate likelihood calculation (dos Reis and Yang, 2011) and cross-bracing with the partitioned dataset under the GBM and ILN relaxed-clock models. Note that only those nodes for which a fossil calibration was available were cross-braced during this timetree inference analysis (i.e., "cross-bracing B").

Please note that each directory has all the data and scripts that you will need to reproduce our analyses if you clone this repository on your PC. Nevertheless, note that the MCMCtree output files with all the collected samples during the MCMC that we used to summarise our results are too large to be stored here. If you want to use our results (e.g., compare to the results you get when you re-run our analyses, reproduce our figures or plots, etc.), please download the GitHub repository we compressed in June 2024 with all the files we have used and genreated as part of this project, which is available at the University of Bristol data repository. Depending on how you want to use our step-by-step guidelines, you may do one of the following:

  • If you want to run the tutorials under the 01_PAML/*MCMCtree* and 03_additional_analyses directories to reproduce the results we report in our manuscript, then you will not initially need initially our output files: you will run PAML programs following our guidelines to generate your own results, which you can analyse with our scripts. Please bear in mind that the your time estimates may vary (i.e., decimals) because you will be using different seed numbers: you will be carrying out independent runs with MCMCtree, and such variation is expected (unless the same seed number is used!).
  • If you want to either (i) reproduce our figures or plots using our own results or (ii) you have re-run our analysis and want to compare your results to ours, please make sure that you do the following:
    • Download the compressed file with all our timetree inference results from the archive we stored at the University of Bristol data repository (~71Gb). Please note that we may have changed the content of some of the README.md files and/or other files since we generated our archive, and so we ask that you use such archive to access the files that you may not find in this repository (due to size limitation) and you may need as input files to run some diagnostics or generate plots/tables/figures/etc. Please keep an eye on this GitHub repository and our releases to track all the changes/updates since the publication of our paper.
    • Once downloaded, please decompress the LUCAdivtimes.zip file and find the 00_prior and 01_posterior directories inside each 01_PAML/*MCMCtree*/sum_analyses directory (depending on which analysis you want to reproduce). You will see that the 00_prior and 01_posterior directories are already inside the 01_PAML/*MCMCtree* directories that we provide you with in this GitHub repository. Nevertheless, they are all missing the mcmc.txt files that you shall find inside the downloaded 00_prior/CLK/*/ and 01_posterior/[GBM|ILN]/*/ directories, which are the output files generated by MCMCtree with all the samples collected for each parameter of interest. Our in-house scripts use such files to summarise the results. We recommend that you do the following:
      • If you just want to recreate figures/tables/plots we have included in our paper with our results, please navigate to each 01_PAML/*MCMCtree in the decompressed file you should have already downloaded, locate the 00_prior and 01_posterior directories, and save them in the corresponding sum_analyses directories in the file structure you are following as per this repository. Make sure that you save them in the same 01_PAML/*MCMCtre* directory following the file structure! Then, you can run our scripts to generate the files you wanted.
      • If you want to compare your results to ours, please change the name of your sum_analyses directory with your own results so that you do not overwrite your files. Then, you can locate our sum_analyses directories in the downloaded archive and place them in the corresponding location in your file structure. You can then re-run our scripts with both your results and ours if you want to also test that you can recreate the same results that we report in our paper.

In a nutshell, if you follow the instructions and run the code snippets and in-house scripts as detailed in each README.md file under directories 01_PAML/*MCMCtree and 03_additional_analyses, you will be able to reproduce all our results! If you had re-run these analyses, then you will be able to compare your results to ours!

Figures and tables

The figures and tables that we included in our supplementary file can also be found in this directory. Please refer to the supplementary material document for figure and table captions (i.e., some numbers may have been updated during the final publication process when rearranging figures/tables, so the numbers in this repository may not correspond to the final ones). All the plots an tables included in these documents are generated by the scripts you will find inside the figs_tables directory.

Additional analyses

We carried out three additional sensitivity analyses to address some questions asked during the revisions of our paper:

  • Single-gene alignments strategy: we ran a timetree inference analysis for each paralog separately, that is, a total of 5 timetree inference analysis for each gene alignment: "ATP", "EF", "Leu", "SRP", and "Tyr". The link above will redirect you to the workflow we followed to carry out these analyses.
  • "Leave-one-out" strategy: we inferred 5 gene alignments in which each paralog had been iteratively removed (e.g., remove "ATP" but keep genes "EF", "Leu", "SRP", and "Tyr" concatenated in a unique alignment block; follow the same procedure for each gene family). The link above will redirect you to the workflow we followed to carry out these analyses.
  • "bs_inBV" strategy: we used the new bs_inBV approach to assess the effect that using branch lengths, gradient, and Hessian estimated under a complex substitution model for timetree inference could have on our time estimates. The link above will redirect you to the workflow we followed to carry out these analyses.

After running all these analyses, we then generated summary plots and tables to compare the estimates we obtained for these sensitivity tests with our core analyses. You can find these files in the figs_tables together with the scripts we used to generate them.

In-house R functions

The main functions that you call when you execute the various in-house scripts that are mentioned in this tutorial can be found in these three R scripts:

If you open the scripts aforementioned, you will find all the details regarding the arguments they need and how to run them (which is also referred to when they are called when summarising our results and/or generating output files such as plots or tables).

What do you need to run our analyses?

Software

Before you go through this step-by-step tutorial to reproduce our results, please make sure you have the following software installed on your PCs/HPCs:

Note

Linux users: you may need to install the intel compilers before you run make -f Makefile. Please visit this link to download the Intel oneAPI Base Toolkit. Thank you, @ERRMoody, for pointing this out!

  • R and RStudio: please download R (i.e., select either Download R for Linux, Download R for macOS, or Download R for Windows depending on your OS; then follow the installation instructions) and RStudio as you will need them to run various of our in-house R scripts. The packages you will need to install work with R versions that are either newer than or equal to v4.1.2 (we used v4.1.2). If you are a Windows user, please make sure that you have the correct version of RTools installed, which will allow you to install packages from the source code if required. For instance, if you have R v4.1.2, then installing RTools4.0 shall be fine. If you have another R version installed on your PC, please check whether you need to install RTools 4.2 or RTools 4.3. For more information on which version you should download, please go to the CRAN website by following this link and download the version you need.

    Before you proceed, however, please make sure that you install the following packages too:

    # Run from the R console in RStudio
# Check that you have at least R v4.1.2
version$version.string
# Now, install the packages you will need to run
# our in-house R scripts
# Note that it may take a while to install them all
install.packages( c('rstudioapi', 'ape', 'phytools', 'sn', 'stringr', 'rstan'), dep = TRUE )
## NOTE: If you are a Windows user and see the message "Do you want to install from sources the 
## packages which need compilarion?", please make sure that you have installed the `RTools`
## aforementioned. Otherwise, you will get errors during the installation.
## The versions we used for each of the packages aforementioned are the following:
##   rstudioapi: v0.14
##   ape: v5.7.1
##   phytools: v1.5.1
##   sn: v2.1.1
##   stringr: v1.5.0
##   rstan: v2.21.7

Note

Linux users: you may experience some problems when you try to execute the install.packages() command that you see in the code snippet above. If that is the case, please install each package separately (e.g., install.packages( 'rstudioapi' ) to install rstudioapi, etc.). If you experience problems with stringr, please follow the suggestions given in this StackOverflow page -- despite the question being addressed for Fedora, the solution suggested also works for Ubuntu. Thank you, @ERRMoody, for pointing this out!

Additional note for Mac users when using the sed command

If you are running a UNIX-based system (e.g., Mac users), you will experience some problems with the Linux-based sed command that you shall see (i) in the various code snippets that you need to run as part of the tutorial described in this repository and (ii) used in most of our in-house bash scripts. By default, this command is different from Linux-based systems, and hence you will have problems to execute it as intended unless you follow one of these two approaches:

  • (EASY): Instead of running the sed commands using the format sed -i 's/PATTERN/REPLACEMENT/' (i.e., what you shall see in the code snippets), you should include '' between -i and 's/PATTERN/REPLACEMENT/': sed -i '' 's/PATTERN/REPLACEMENT/'. Please remember to modify the commands in this tutorial accordingly before you copy and paste them on your terminal!
  • (MORE DIFFICULT): You should install GNU sed and establish it as the "standard" sed command instead of the one you will have by default. At the time of writing, this post is available and has a detailed explanation that you could follow to install GNU sed. Nevertheless, there are many other tutorials out there that you can follow to achieve the same goal. If you decide to follow this approach, you will not need to change the commands in this tutorial!

Once the sed incompatibility is sorted out, there should not be any problems with regards to following this tutorial and/or running our in-house bash scripts!



If you have gone through the previous sections and have a clear understanding of the data we used, the workflow we followed (which you shall follow to reproduce our analyses), and have installed the required software aforementioned... Then you are ready to go!

You can start by taking a look at how we formatted the raw dataset with which we started the timetree inference analyses by following this link.

Happy timetree inference! :)

Contact

This repository and its content was created by Dr Sandra Álvarez-Carretero (@sabifo4), who actively maintains this repository too. If you have any queries with regards to the analyses detailed in this repository, please do not hesitate to reach me via email!

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Methods for the analyses described in Moody et al. 2024 (https://www.nature.com/articles/s41559-024-02461-1)

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