This supplemental information accompanies the DEScore paper, and is formatted as follows:

Inside the directory DEScoreSuppData you will find 6 folders:

1_Kocot2017.zip

2_MissingData_Simulations.zip

3_NoiseSimulations.zip

4_Other.zip

5_PositionAndTaxaIncrease_DEScore.zip

6_SaturationSims.zip

7_RealDataStudies.zip

8_ComparisonWithSatuRation.zip

1. Kocot2017

This directory contains three further directories, Kocot2017_Slope, Best532_LB_DE_nRCFV and DEScore_Reselected.

Kocot2017_Slope comprises 6 directories: these are copies of the Best 106 genes through to Best 532 genes as selected by the Slope Measurements in Kocot et al 2017. These directories each contain a phylip alignment file, a partition file in txt format, and the output of a RAxML analysis: a most likely tree (RAxML_bipartitions.<Slope Sextile>.tre), bootstrap support files, and a bipartition file.

Best532_LB_DE_nRCFV contains the results of the Kocot et al 2017 reselection based on the genes in the top 5 sextiles of the LB-Score, DE-Score and nRCFV. It comprises a fasta file and a set of IQTree output files (including a newick tree).

DEScore_Reselected comprises 5 directories: these each correspond to the Best 106 genes through to the Best 532 genes as selected by the DE-Score. Each directory contains 10 files: a fasta multiple sequence alignment file, a list of selected genes in txt format, partition file in text format (formatted by cat sequences, then manually reformatted for input to IQtree), a most likely tree (DEScore.<Best Sextile>.partitions.clean.txt.treefile), a most likely tree with the taxa labels corrected to correspond to the Kocot et al naming scheme (DEScore.<Best Sextile>.partitions.txt.clean.treefile) and 5 intermediate output files supplied by IQtree, detailed the maximum likelihood distances (mildist), the logs of the phylogenetic tree run (.iqtree and .log), the checkpoint file (ckp.gz), neighbor joining tree used to initialize the tree search (bionj) 

2. MissingData_Simulation

This directory contains the Missing Data simulation data, in both Gapped and Ungapped forms, in the Gaps and NoGaps directories, respectively. The Dayhoff Group Exchange Frequency (labelled Transition Frequency), the absolute count standard deviation (Transition StdDev) and the Frequency standard deviation (TransFreqStDev) for each simulation category can be found in Missing_<SextileNumber>.TransitionTransversion.Pairwise.txt, in the Gaps and NoGaps directories. These measurements were used to originally calculate the DEScore (as seen in the Excel sheet in 4.Other).

Inside each further directory (labelled by sextile from 1of6 to 6of6) is the simulation data from the Fleming & Struck (2023) paper that is reused here, in phylip multiple sequence alignment format.

3. NoiseSimulations

This directory contains the Noise Simulations generated by NoiseMaker (which can be found at the DEScore Github, and in 4. Other).

Inside NoiseSimulations are three directories: OriginalSims, NoiseMaker and ConCatSims. OriginalSims contains 100 simulation datasets, in phylip format, that can also be found in 5_PositionAndTaxaIncrease_DEScore/250Taxa/2100Bases/. NoiseMaker contains the pure noise that was added to these datasets, formatted in 5 directories (from 10Percent Noise through the 50Percent Noise). The noise is formatted in philip format.

The ConCatSims directory contains the data analysed in the Noise Simulation analysis. The Dayhoff Group Exchange Frequency (labelled Transition Frequency), the absolute count standard deviation (Transition StdDev) and the Frequency standard deviation (TransFreqStDev) for each simulation category can be found in <NoisePercent>.TransitionTransversion.Pairwise.txt. These measurements were used to originally calculate the DEScore (as seen in the Excel sheet in 4.Other).

The ConCatSims directory also contains the alignments, in the 5 directories 10Percent, 20Percent, 30Percent, 40Percent and 50Percent. These directories each contain 100 phylip files corresponding to the concatenation of one of the original simulations (250 Taxa by 2100 positions) with the corresponding pair of additional noise (for example, OriginalSims/250by2100.aa.1.fas.phy is paired with NoiseMaker/10Percent/1.Noise.phy to produce ConCatSims/10Percent/10Percent.1.Concat.phy.

4. Other

This directory contains the software that can be found at the Github: DEScore_Calculator.pl and Noisemaker.pl, in the subdirectory DEScore-main. It also contains an additional copy of the PDF Supplementary Information file associated with the main text.

It additionally contains the DEScore Excel file, which provides a compiled form of the data used to initially calculate the DE-Score. This Excel file comprises six sheets:

1\) DayhoffJTTComparison, which assesses the ability of the DE-Score and Dayhoff Exchange Frequency to assess site saturation across increasing branch scaling factors, under two models. The source data that populates this sheet can be found in directory 6_SaturationSims

2\) ComparingSlopeAndDEScore, which compares the results of Slope and the DE-Score under the Chang et al trees, and shows the Spearman's rank correlation coefficients between each dataset, which are used in the main text. The source data that populates this sheet can be found in directory 6_SaturationSims.

3\) BiasCausedByTaxaSites: The analysis of the variation in Dayhoff Exchange Frequency and DE-Score with respect to the number of taxa and amino acid sites in the dataset. This data was used to form the DE-Score, as detailed in the supplementary text. The source data that populates this sheet can be found in directory 5_PositionAndTaxaIncrease_DEScore.

4\) MissingDataBias: The analysis of the variation in Dayhoff Exchange Frequency and DE-Score with respect to the amount of Missing Data in the dataset. Ungapped simulations were generated by alisim's alignment mimic command filling gaps with amino acids based on the proportional amino acid frequencies of the source dataset, while gapped simulations were generated by preserving the gaps present in the original source dataset. The source data that populates this sheet can be found in directory 2_MissingData_Simulations

5\) Noise: The analysis of the variation in Dayhoff Exchange Frequency and DE-Score with respect to the amount of Noise in the dataset. Noise was generated by concatenating 10, 20, 30, 40 and 50% of pure noise (generated by Noisemaker.rub, found in directory 4) to the 250by2100 dataset from directory 5_PositionAndTaxaIncrease_DEScore. The source data that populates this sheet can be found in directory 3_Noise.

6\) Kocot_RealData: The selection of the sextiles for the reselection and Real Data analysis that populates directory 1_Kocot2017. The second half of this sheet (Columns P through AI) assesses which genes appear in both datasets. #N/A denotes a gene that does not appear in that sextile in the DE-Score selected dataset, but that does appear in the Slope selected dataset.

5. PositionAndTaxaIncrease_DEScore

This directory contains the simulation data, generated in IQ-Tree using the alisim function, corresponding to amino acid datasets with increasing numbers of taxa and amino acid positions from 50 to 500 taxa and 300 to 3000 amino acid positions.
These simulation datasets were generated using the following command:

iqtree2 --alisim $SimulationPrefix --seed 2014 -m WAG+F+G -t RANDOM{bal/$taxaNumber} --length $alignmentLength --num-alignments 100 --out-format fasta -redo

This directory is first divided by taxa, and within that it is divided by position. For example, 50Taxa/50by3000 corresponds to a 3000 amino acid long alignment with 50 taxa.

In each Taxa folder, you will find 10 txt files name <Taxa>by<Position>.TransitionTransversion.Pairwise.txt. This file contains the Dayhoff Group Exchange Frequency (labelled Transition Frequency), the absolute count standard deviation (Transition StdDev) and the Frequency standard deviation (TransFreqStDev). These were used to originally calculate the DEScore (as seen in the Excel sheet in 4.Other)

Each taxa-position pair (eg: PositionAndTaxaIncrease_DEScore/50Taxa/50by3000) folder additionally contains the 100 simulations in both .fa and .phy format.

6. SaturationSims

This directory contains the simulation data, originally sourced from Hernandez & Ryan (2021), corresponding to amino acid datasets with increasing amounts of saturation (due to branch length scaling factors).

Inside this directory you will find 01-DAYHOFF and 02-JTT, as in the supplemental data of Hernandez & Ryan 2021. These directories correspond to the model the data was generated under.

Within 01-DAYHOFF you will find:

	\- Chang.tre: the original Chang et al tree file. This is the "true tree" used for the simulation comparisons.

	\- Chang.PAM.<number>.TransitionTransversion.Pairwise.txt. The Dayhoff Group Exchange Frequency (labelled Transition Frequency), the absolute count standard deviation (Transition StdDev)

	and the Frequency standard deviation (TransFreqStDev) for each simulation category, alongside the average uncorrected p-distance and the standard deviation of the p-distance of the

	simulation. These measurements were used to originally calculate the DEScore (as seen in the Excel sheet in 4.Other).

	\- 20 further directories corresponding to the 20 branch length scaling factors, each named Chang_PAM_<number>.

		Inside each of these directories, for each simulation, you will find:

		\- A phylip file

		\- A most likely tree (<simulation number>.phy.treefile)

		\- Intermediate output files supplied by IQtree, detailed the maximum likelihood distances (mldist), the logs of

		the phylogenetic tree run (.iqtree and .log), the checkpoint file (ckp), neighbor joining tree used to

		initialize the tree search (bionj)

	\- The SlopeResults directory, which contains an additional 20 directories (Chang_PAM_<number>_Slope for each simulation category.

		Inside each of these 20 directories are:

		\- Chang.PAM.<number>.alignments.txt - a list of the alignment phylip files for each category

		\- Chang.PAM.resolvedTrees.txt - a list of the tree files for each category

		\- Chang.PAM.trees.txt - the input file for a TreSpex analysis against the generating tree

		\- log.txt - the log of the TreSpex Slope analysis against the generating tree

		\- log.resolved.txt - the log of the TreSpex Slope analysis against the resolved trees

		\- Saturation_Files_used.log - output of TreSpex confirming the alignments assessed in the analysis

		\- Summary_Saturation.log - log of the command used by TreSpex to recover the resolved trees analysis

		Alongside two further directories - GeneratingCorrelation_Results and TrueCorrelation_Results:

			\- Correlation_Slope_Summary.txt - this file contains the Slope and R2 measurements for each alignment tested.

			\- Both directories comprise two directories: one containing the P Matrices for each simulation analysis in txt format, and one containing the PD matrices in txt format.

			These two values are used to calculate the Slope value.

			\- Under GeneratingCorrelation_Results, only one PDMatrix txt file exists, as all alignments were compared against the generating tree (Chang.tre). In

			RecoveredCorrelation_Results, you will find one PDMatrix for each tree.

Within 02-JTT you will find:

	\- 20 further directories corresponding to the 20 branch length scaling factors, each named Chang_JTT_<number>. These contain the simulation alignments in philip format generated under

	the JTT model in Hernandez & Ryan 2021 for each branch length scaling factor.

	\- Chang.JTT.<number>.TransitionTransversion.Pairwise.txt - This file contains the Dayhoff Group Exchange Frequency (labelled Transition Frequency), the absolute count standard deviation

	(Transition StdDev) and the Frequency standard deviation (TransFreqStDev). These were used to originally calculate the DEScore (as seen in the Excel sheet in 4.Other)

	\- Chang.JTT.<number>.phy - a phylip alignment used in Hernandez & Ryan 2021 as the seed to generate the alignments under that branch length scaling category.

7. Real Data Studies

This directory contains the results of the real data studies detailed in the main text. Inside this directory you will find two further directories: Multiple Gene Datasets and Single Gene Datasets

Multiple Gene Datasets contains the results of the twenty-one "Multiple Gene" studies re-analysed by the DE-Score inside each directory (Misof et al. 2014, Fernández et al. 2016, Fernández et al. 2017, Irisarri et al 2017, Kocot et al 2017, Peters et al. 2017, Fernandez et al 2018, Hughes et al 2018, Johnson et al. 2018, Sharma et al. 2018, Shen, Jin et al 2018, Shen, Opulente et al. 2018, Benavides et al. 2019, Evangelista et al 2019, Kawahara et al. 2019, Simon et al. 2019, Steenwyk et al 2019, Milla et al 2020, Mongiardo, Koch & Thompson 2021, Wibberg et al 2021, Herranz et al 2022). Each of these directories contains:

	\<Dataset Name>.DEScore.fastaSummary.txt - a file containing a list of the DE-Scores of individual gene alignments

	\<Dataset Name>.nRCFV.fastaSummary.txt - a file containing a list of the nRCFVs of individual gene alignments

	CompleteDataset - a directory containing the complete alignment and its corresponding DE-Score and nRCFV outputs

	IndividualGenes - a directory containing individual gene alignments and their corresponding DE-Score and nRCFV outputs

	TopSextile - a directory containing the alignment derived by selecting the top sextile of genes as determined by the DE-Score, and its corresponding DE-Score and nRCFV outputs

Single Gene Datasets contains the results of the six "Single Gene" studies re-analysed by the DE-Score inside each directory (Fleming et al 2021, Giaccomelli et al 2025, Novotna-Floriancicova et al 2023, Vieira & Rozas et al 2011). Each of these directories contains the relevant alignment and its corresponding DE-Score and nRCFV outputs.

Please note that two directories - Fleming et al 2021 and Vieira & Rozas 2011 - contain further directories A and B, as two datasets were re-analysed in each study.

8. Comparison with SatuRation

This directory contains the data used to compare the DE-Score metric with the Mean Historical Signal (Lambda) metric used by SatuRation (https://github.com/lsjermiin/SatuRation.v1.0/). As another pre-analysis entropic site saturation detection metric, it is a useful benchmark for assessing the effectiveness of the DE-Score. The implications of this assessment are detailed in our Supplemental Methods.
Inside this directory you will find two directories and one excel file.
- SatuRation_DEScoreComparison.xlsx - this Excel file presents two sheets, one comparing the Mean Lambda and DE-Score under the Dayhoff simulations that were initially used to compare the DE-Score to the Slope Measurement (Supplemental Data Part 6), and a second comparing the two metrics under the JTT simulations used to assess the efficacy of the DE-Score under non-model conditions (Supplemental Data Part 6). This is achieved by means of Pearson Correlations against both metrics and of both metrics against the branch length scaling factor of the simulation datasets.
The two directories are:
- 01-DAYHOFF
- 02-JTT
Within each of these two directories are three files, <ModelName>_1_9_MeanLambda.txt, <ModelName>_10_19_MeanLambda.txt and <ModelName>_20_MeanLambda.txt. These files contain lists of the MeanLambda, in order, over each of the scaling categories (1-9. 10-19, 20)
These two directories additionally contain 20 further directories each. These contain fasta files of the simulations contained in Part 6 (x.phy.fas), alongside the summary result output produced by SatuRate (x.phy.fas.summary.txt).

ChangeLog

This is the third upload of this supplemental dataset, and significany changes have occurred between this version and the original upload.

v3.0:

4_Other - Both scripts, Noisemaker.pl and DEScoreCalculator.pl have been significantly revised. Noisemaker has changed from a Ruby script to a Perl script, and the DEScoreCalculator has moved to Version 1.5, which includes a new, faster logic for calculating the DE-Score, a removal of perl module dependencies and a POD.

7_Real Data Studies - The multi-gene datasets have expanded from 5 to 21.

v2.0:

Most notably, this version divides the supplemental data into individual zip files, allowing users to more easily access the data they are interested in. This comes with a new data structure that groups the data into 8 categories: 1_Kocot2017.zip, 2_MissingData_Simulations.zip, 3_NoiseSimulations.zip, 4_Other.zip, 5_PositionAndTaxaIncrease_DEScore.zip, 6_SaturationSims.zip, 7_RealDataStudies.zip, 8_ComparisonWithSatuRation.zip.

In addition to this new partitioning, sections 7 and 8 are both new. Section 7 presents 6 single gene and 5 multi-gene datasets to evidence the efficacy of the DE-Score. Section 8 compares the DE-Score with SatuRation, a direct measurement of entropy in a multiple sequence alignment.