Links to Data/Results

Corresponding Figures / Tables

Description

RefDB Statistics

All

Included expected value (mean), standard deviation (sd):

·      RefDB.caStat.csv: calculated mean and sd for C_a using all of the data from RefDB,

·      RefDB.cbStat.csv: calculated mean and sd for C_b using all of the data from RefDB.

RefDB Clean Data

Figure 3 - 4

Supplemental Figure 1

All the RefDB carbon data, striped off metadata and sub-filed basing on the resonance types.

Covariances Analysis

Figure 5

Covariance values calculate from different population:

·      CarbonCovTable_BC_old.csv: Covariance values calculated using all of the data from RefDB,

·      CarbonCovTable_BC.csv: Covariance values calculated using only filtered data.

Code:

·      Refer to Covariance Comparison. (Figure and table generate codes were included in the data generating codes.)

BMR6032 Results

Table 2, Supplemental Figure 2

Reference correction results calculated with different covariances for BMR6032 dataset:

·      ResultA.RData: Using covariance matrix A,

·      ResultB.RData: Using covariance matrix B,

·      ResultC.RData: Using covariance matrix C,

·      ResultD.RData: Using covariance matrix D-Revised, with cysteine separated as two different residues (ox-red),

·      ResultD_noSep.RData: Using covariance matrix D,

·      ResultE.RData: Using covariance matrix E.

Codes:

·      Refer to BMR6032 Analysis. (Figure and table generate codes were included in the data generating codes.) 

All RefDB Reference Correction Results

Supplemental Table 3, Figure 7

Reference correction results calculated with different covariances across RefDB data:

·      Results_A.RData: Using covariance matrix A,

·      Results_B.RData: Using covariance matrix B,

·      Results_C.RData: Using covariance matrix C,

·      Results_D.RData: Using covariance matrix D,

·      Results_E.RData: Using covariance matrix E,

·      Results_E_revised.RData: Using covariance matrix D-Revised,

·      Results_E_revised_OL.RData: Using covariance matrix D-Revised and overlap matrix,

·      Results_E_revised_OL_90perc.RData: Using covariance matrix D-Revised and overlap matrix for data with 90% completion.

Codes:

·      Figure_7.R: will generate the Figure 11,

·      S_Table_3.R: will generate the Table 3.

Analysis on Glycine

Supplemental Figure 4

Reference correction analysis results from methods including and no including glycine residue as classifier:

·      Results_NoGly.RData: result from method not using glycine as additional classifier,

·      Results_WithGly.RData: result from method using glycine as additional classifier.

Code:

·      GlycineAnalysis.R: will generate the Supplemental Figure 4.

Robust Analysis

Supplemental Table 4, Figure 8

Included robustness analysis data of BaMORC against varying amounts of missing alpha and beta carbon chemical shifts (95% - 5%):

·      noGlyNoPrior_XX.RData. Here XX stands for the percentage of the data completion. For example, 50% completion will be noGlyNoPrior_50.RData.

Codes:

·      Figure_8.R: will generate the Figure 8,

·      S_Table_4.R: will generate the Supplemental Table 4.

Assigned BaMORC

Figure 9

Results comparison between Assigned BaMORC and LACS for 90% completion data:

·      Assigned_BaMORC.RData: Using Assigned BaMORC Algorithm to find referencing correction.

·      LACS.RData: Using LACS Algorithm to find referencing correction.

Codes:

·      Figure_9.R: will generate the Figure 9.

JPred Results

Supplemental Table 5, Supplemental Figure 5

Reference correction results on all RefDB data by utilizing secondary structure information from RefDB and JPred4²:

·      Results_D_revised_OL_90perc.RData: Using secondary structure information from RefDB.

·      JPred_Results.RData: Using secondary structure information from JPred4.

Codes:

·      S_Figure_5.R: will generate the Supplemental Figure 5,

·      S_Table_5.R: will generate the Supplemental Table 5.

Unassigned BaMORE Results

Table 1

Reference correction results from real-life data:

·      Unassigned_BaMORC_Results.RData: testing Unassigned BaMORC on 10 real-life data with secondary structure information from JPred4.

Code:

·      Table_1.R: will generate the Table 1.

Residual of Residual vs. Reference Correction Values

Supplemental Figure 7

Amino Acid and Secondary Structure Frequency Residual of Residual vs. Reference Correction Values for RefDB datasets:

·      Resitual.vs.Reference.RData,

Code:

·      S_Figure_7.R: will generate the Supplemental Figure 7.

Global Optimization vs. Grid Search

Supplemental Figure 8

Comparison between global optimization and grid search approaches:

·      DEoptim_10.RData: Using DEoptim global optimization algorithm with max iteration equals to 10.

·      DEoptim_20.RData: Using DEoptim global optimization algorithm with max iteration equals to 20.

·      DEoptim_50.RData: Using DEoptim global optimization algorithm with max iteration equals to 50

·      Results_E_revised_OL.RData: Using grid search approach.

Code:

·      S_Figure_8.R: will generate the Supplemental Figure 8.

Source Code Folder Links

Source Code Description

Expected Outputs

(Only exist after run the code!)

Cysteine Analysis

The source codes were used to illustrate cysteine should be treated as two different residues:

·      Cysteine_Analysis.R: plot alanine and cysteine scatter plot with correlation and marginal histogram and save 3 sets of data: alanine, cysteine, and cysteine separation, which included class indicator.

Results sub-directory will be: output/Cysteine_Analysis/

Data:

·      Alanine_Beta_Strand.RData

·      Alanine_Coil.RData

·      Alanine_Helix.RData

·      Cysteine_Beta_Strand.RData

·      Cysteine_Coil.RData

·      Cysteine_Helix.RData

·      Cysteine_Seperated_Beta_Strand.RData

·      Cysteine_Seperated_Coil.RData

·      Cysteine_Seperated_Helix.RData

Figures:

·      Alanine_Beta_Strand.png

·      Alanine_Coil.png

·      Alanine_Helix.png

·      Cysteine_Beta_Strand.png

·      Cysteine_Coil.png

·      Cysteine_Helix.png

·      Cysteine_Seperated_Beta_Strand.png

·      Cysteine_Seperated_Coil.png

·      Cysteine_Seperated_Helix.png

Data Filtration and Coveriance Re-Calculation

The source codes will output the new covariance and new datasets based on RMSD values of each dataset as a criterion

·      DataFiltration_CovRecal.R: generates figures and output two result data.

Results sub-directory will be: output/DataFiltration_CovRecalculation/

Data:

·      Filtered_Cov.RData

·      Filtered_Datasets.RData

Figures:

·      Figure1.png: histogram of Ca;

·      Figure2.png: histogram of Cb;

·      Figure3.png: histogram of Ca-Cb;

·      Figure4.png: histogram of absolute value of (Ca-Cb);

·      Figure5.png: histogram of ;

·      Figure6.png: histogram of ;

·      Figure7.png: histogram of ;

Covariance Comparison

The source codes will generate figure on comparison of covariance values before and after the data filtering step:

·      Covariance_comparison.R

Results sub-directory will be: output/Covariance_Comparison/

Figure:

Cov_comparison.png

BMR6032 Analysis

The source codes were used for testing 6 different types of covariance matrices on bmr6032 dataset:

·      Covariance_A.R: testing with covariance matrix A

·      Covariance_B.R: testing with covariance matrix B

·      Covariance_C.R: testing with covariance matrix C

·      Covariance_D.R: testing with covariance matrix D

·      Covariance_E.R: testing with covariance matrix E

·      Covariance_E-Revised.R: testing with covariance matrix D-Revised, with using the RMSD values of each dataset as a criterion to further filter out datasets that are likely not derived from a single NMR experiment

Results sub-directory will be: output/BMR6032_Analysis/

Data:

·      BMR6032_Analysis/Covariance_A.RData

·      BMR6032_Analysis/Covariance_B.RData

·      BMR6032_Analysis/Covariance_C.RData

·      BMR6032_Analysis/Covariance_D.RData

·      BMR6032_Analysis/Covariance_E.RData

·      BMR6032_Analysis/Covariance_E-Revised.RData

Figures:

·      BMR6032_Analysis/Covariance_A.png

·      BMR6032_Analysis/Covariance_B.png

·      BMR6032_Analysis/Covariance_C.png

·      BMR6032_Analysis/Covariance_D.png

·      BMR6032_Analysis/Covariance_E.png

·      BMR6032_Analysis/Covariance_E-Revised.png

Overlap Matrix and Weights Calculation

The source code will generate the overlap matrix and classifier weights:

·      OverlapMatrix_Weights_Calculation.R

Results sub-directory will be: output/OverlapMatrix_Weights/

Data:

·      Overlap_Weights.RData

E-Revised + Overlap Matrix

The source code will generate reference correction results on all the RefDB data using D-Revised covariance matrix with the prediction overlap matrix

·      E-Revised_OLMatrix.R

Results sub-directory will be: output/E-Revised_OLM/

Data:

·      results_E.Revised_OLMatrix.RData

E-Revised + Overlap Matrix with 90% Completion

The source code will generate reference correction results on RefDB data with 90% completion using D-Revised covariance matrix with the prediction overlap matrix

·      E-Revised_OLMatrix_90.R

Results sub-directory will be: output/E-Revised_OLM_90/

Data:

·      results_E.Revised_OLMatrix_90.RData

Glycine Analysis

The source code will generate reference correction results on all the RefDB data with condition of including and not including glycine residue:

·      Gly.R,

·      noGly.R

Results sub-directory will be: output/Glycine_Analysis/

Data:

·      results_gly.RData

·      results_nogly.RData

JPred Analysis

The source code will generate reference correction results on 90% completion RefDB data utilizing secondary structure information from JPred4²:

·      JPred_Analysis.R

Results sub-directory will be: output/JPred_Analysis/

Data:

·      JPred_Analysis_result.RData

Robust Testing

The source code will generate robust test results with data completion from 90% to 50%:

·      Robust_Testing_XX.R. Here XX stands for the percentage of the data completion. For example, 50% completion will be Robust_Testing_50.R.

Results sub-directory will be: output/Robust_Testing/

Data:

·      Robust_Testing_XX.RData. Here XX stands for the percentage of the data completion. For example, 50% completion will be Robust_Testing_50.RData.

Assigned BaMORC

The source code will generate Assigned BaMORC results with RefDB data:

·      Assigned_BaMORC.R. Using assignment information to estimate the referencing correction value.

Results sub-directory will be: output/Assigned_BaMORC/

Data:

·      Assigned_BaMORC.RData.

BaMORC Global Optimization

The source code will generate BaMORC results with RefDB data via global optimization algorithm:

·      BaMORC_GO.R. Similar to the original BaMORC script, but using globa optimization algorithm to reduce running time.

Results sub-directory will be: output/BaMORC_GO/

Data:

·      BaMORC_GO.RData.