DOI: 10.5061/dryad.34tmpg4tm
title: The Dayhoff Exchange Score: A new metric to quantify site saturation in amino acid datasets prior to phylogenetic analysis

description: Entropic site saturation is a persistent problem in phylogenetic analyses,
 where it can hinder the accuracy of topology reconstruction. It is
 fundamentally caused by large amounts of independent change along
 branches, causing the model to be unable to distinguish phylogenetic
 signal from noise. The Dayhoff Exchange Score (DE-score) is a new metric
 to assess this form of site saturation within and between amino acid
 datasets, which provides both a whole dataset overview and taxon-specific
 values that represent the contribution of a given taxon to the whole
 dataset entropic site saturation. We first assess the efficacy of this
 score at detecting increased entropic site saturation over 20,000
 simulation datasets, compare it to the existing Slope R2 score, and then
 assess its efficacy in the face of the potentially confounding factors of
 increasing taxon number, number of positions in the alignment, missing
 data, and noise. Finally, we use the DE-Score to re-evaluate several
 previously published datasets to illustrate its efficacy.
description: <strong>The methods and their implications are explored in
 greater detail in the pdf file that can be found inside folder
 4_Other.</strong> <strong>1_Kocot2017:
 Applying the DE-Score to real data: Reselection
 Datasets</strong>             The work of Kocot
 et al (2017) (Kocot, K.M., Struck, T.H., et al. 2017) was chosen for more
 thorough examination. This paper was chosen for reanalysis as the Slope
 score (Nosenko, T., Schreiber, F., et al. 2013) was previously used to
 assess site saturation within genes in this dataset, which allowed us to
 assess the coherence between the two metrics and the effect of their
 differences.             For this reselection study, a
 phylogenetic tree was recovered using IQTree-mpi (v1.6.12) (Nguyen, L.-T.,
 Schmidt, H.A., et al. 2015) and the LG+F model, as in the original
 analysis, but instead using the sextile of least saturated genes, as
 selected by the DE-Score, and then a concatenated dataset comprising the
 145 genes that were placed in the highest five out of six sextiles (523
 genes) of the DE-Score, nRCFV, occupancy and LB-Score at once. These
 topologies were then compared to the sextiles selected by the Slope score
 and the highest five out of six tree in the initial study (Kocot, K.M.,
 Struck, T.H., et al. 2017) .             The command
 used to generate the topologies in IQTree was:
             iqtree-mpi -s -sp -m LG+F
 <strong>2_MissingData_Simulations: Assessing the Effects of
 Missing Data</strong>             To assess the
 effect of missing data on the Dayhoff Category Exchange Ratio and the
 DE-Score, we used the 1,200 simulation datasets that were used to assess
 the effect of Missing Data on nRCFV in (Fleming, J.F. and Struck, T.H.
 2023). These simulation datasets used the six "Missing Data"
 categories of Kocot et al (2017)(Kocot, K.M., Struck, T.H., et al. 2017).
 These datasets divided the total dataset assessed in that study into
 sextiles based on increasing percentage Missing Data, ranging from 18.17%
 to 38.43%. As per Kocot et al's methodology, missing data was
 classified as the presence of ambiguity characters, gaps or a lack of
 sampling (or absence) of the target gene in the taxon. 
             Simulation datasets were created using the alignment
 mimic command in IQTree2's alisim, which replicates the conditions of
 the source dataset - including missing data. This resulted in 600
 simulation datasets including missing data, which we named
 "gapped" datasets. Simulated replicates of
 the same Kocot et al datasets were then generated using Alisim's
 -no-copy-gaps command, which generates gapless simulation datasets that
 otherwise replicate the source alignment. The following commands were used
 in IQTree2.2.0:             For “Gapped” datasets:
 iqtree2 –alisim &lt; Output &gt; -s &lt; Missing Data
 Dataset &gt;              For “Ungapped” datasets:
 iqtree2 –alisim &lt; Output &gt; -s &lt; Missing Data
 Dataset &gt; –no-copy-gaps.
 <strong>3_NoiseSimulations: Assessing the Effects of
 Noise</strong>             To assess the effect
 of noise on the ratio on the Dayhoff Category Exchange Ratio and the
 DE-Score, we first selected one category of the simulation datasets used
 to initially assess changes in the number of taxa and positions - 100
 simulation datasets with 250 taxa and 2100 positions. This dataset was
 chosen as it represented a medium-sized dataset among our simulation
 datasets. New simulation datasets were generated by combining the 100
 simulated datasets with an additional 10%, 20%, 30%, 40% and 50% noise,
 resulting in datasets that were 2333, 2625, 3000, 3500 and 4200 amino
 acids long, respectively. Noise was generated by randomly generating a
 number between 1 and 20 for each site within each taxon in the alignment.
 Each number was assigned an amino acid prior to the generation of the
 random number string, and the randomly generated numbers were then
 transformed into amino acid noise. The script used to do this, Noisemaker,
 can be found inside section <strong>4_Other</strong>, and at
 the DE-Score Calculator Github, here:             https://github.com/JFFleming/DEScore <strong>5_PositionAndTaxaIncrease_DEScore: Assessing the Effects of an Increasing Number of Taxa and Positions on the Dayhoff Category Exchange Ratio</strong>             To assess the effect of changes in the number of taxa and positions in an alignment on the Dayhoff Category Exchange Ratio and the DE-Score, we generated 10,000 simulation datasets under the WAG+F+G model on a balanced tree, using the alisim function of IQTree version 2.3.6 (Nguyen, L.-T., Schmidt, H.A., et al. 2015, Ly-Trong, N., Naser-Khdour, S., et al. 2022), under 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             WAG+F+G was used to create simulations that contained more compositional variability than the GTR model, and thereby more realistic variation in site saturation between simulation datasets. Simulation datasets were created in bins of 100 datasets each—taxa at intervals of 50 from 50 taxa to 500 and sequences at intervals of 300 from 300 to 3000 positions, producing an end product of 10,000 simulation datasets. <strong>6_SaturationSims: Establishing the ability of the DE-Score to detect Site Saturation and comparing the DE-Score to the slope of the regression line of patristic vs. p-distances</strong>             To assess the utility of directly measuring the ratio of the DE-Score to assess site saturation within a dataset, we used simulation datasets previously used to assess site saturation by Hernandez & Ryan (2021) (Hernandez, A.M. and Ryan, J.F. 2021). These datasets, originally based on the Chang et al (2015) tree (Chang, E.S., Neuhof, M., et al. 2015), were generated by applying a scaling factor to all branches of the original tree, from 1 to 20. The datasets were generated under two models, the Dayhoff model and the JTT model, using Seq-Gen, as described in Hernandez & Ryan (2021) (Hernandez, A.M. and Ryan, J.F. 2021). Each scaling factor category comprises 1,000 datasets, resulting in a total of 20,000 datasets for each model category: for a final 40,000 datasets.             To assess the efficacy of our new metric in comparison to existing metrics, we calculated the slope of the regression line of patristic vs. p-distances as described in Nosenko et al (2013) and implemented in TreSpEx (Nosenko, T., Schreiber, F., et al. 2013, Struck, T.H. 2014). As the Slope R<sup>2</sup> score has not yet been assessed on simulation datasets, we assessed its efficacy against the 20,000 simulation datasets generated under the Dayhoff model by Hernandez & Ryan (2021) (Hernandez, A.M. and Ryan, J.F. 2021) that were initially used to assess the efficacy of the DaCER.             We generated two variations of the Slope R<sup>2</sup> score - one by comparing the patristic distances of the true tree to the p-distances and one by comparing the patristic distances of the trees generated by the Dayhoff model simulation datasets, analysed under the simpler JTT model. This was intended to model the effect of phylogenetic topological artifacts caused by site saturation on the Slope R<sup>2</sup> score.             We then used the Spearman's rank correlation coefficient to assess each pair of conditions, by ranking the Slope R<sup>2</sup> or DE-score of each dataset and comparing these ranks to assess correlation between the two metrics. <strong>7_RealDataStudies: Applying the DE-Score to real data: Overview Datasets</strong>             To better understand the relationship between the DaCER and the DE-Score on real data, and to better explore its efficacy and use cases, we selected 6 previously published single protein phylogenetic datasets across 4 previously published papers (Vieira, F.G. and Rozas, J. 2011, Fleming, J.F., Pisani, D., et al. 2021, Novotná Floriančičová, K., Baltzis, A., et al. 2023, Giacomelli, M., Vecchi, M., et al. 2025), and 21 previously published multi-protein phylogenetic datasets from 21 previously published papers (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). <strong>8_ComparisonWithSatuRation: Comparing the DE-Score to the Mean Lambda Entropy</strong>             Using the branch scaling factor datasets previously used to compare the DE-Score to the Slope measurement <strong>(6_SaturationSims</strong>, we further examined the effectiveness of the DE-Score in comparison to another metric of site saturation in amino acid datasets: mean historical signal strength (λ).
description: # **README** 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..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..partitions.clean.txt.treefile), a most likely tree with the
 taxa labels corrected to correspond to the Kocot et al naming scheme
 (DEScore..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_.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
 .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
 by.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..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_. Inside each of these directories, for
 each simulation, you will find: \- A phylip file \- A most likely tree
 (.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__Slope for each simulation category.
 Inside each of these 20 directories are: \-
 Chang.PAM..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_.
 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..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..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: \.DEScore.fastaSummary.txt - a file containing a list of the
 DE-Scores of individual gene alignments \.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, _1_9_MeanLambda.txt,
 _10_19_MeanLambda.txt and
 _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.