README.md

In Linda24bc/VariantsID: Automatic Identification of Hb variants and database expansion

VariantsID

The goal of VariantsID is to identify Hb variants by deconvoluved MS2 data and predict diagnostic product ions of Hb beta variants by referring to the experimentally determined fragments of HBA beta.

More details are avaiable in the publication in JASMS (https://doi.org/10.1021/jasms.1c00291)

Please cite: Yuan Lin, Archana M. Agarwal, Alan G. Marshall, and Lissa C. Anderson Journal of the American Society for Mass Spectrometry 2022 33 (1), 123-130 DOI: 10.1021/jasms.1c00291

Installation

You can install the development version of VariantsID like so:

devtools::install_github("Linda24bc/VariantsID")

Introduction

• Identify the Hb variants by comparing the experimental data in the database
• Original database contains the theoretical diagnostic ions of Hb variants been tested by mass spectrometry so far

Example

library(VariantsID)

Step 1. Input the database including the diagnostic ions of Hb varints and use MS1 data to narrow down the database - subset the database

1.1 Input the original database

HbDatabase <- read_csv(“Hb Variants_OriginalDatabase.csv”)

1.2 Use the MS1 data to narrow down the database, if the mass shift is about -0.93 Da, then the Mshift is -0.93 Da and the error tolerence is 0.06 Da. Thoese two values are changable and depend on the accuracy of deconvolution.

ref <- SubDatabase(HbDatabase, Mshift= -0.93, error_Da_L=-0.05, error_Da_R=0.06)

Step 2. Input the deconvolve MS2 results

The list should contain two columns, Exp_m/z vs Exp_Intensity)

exp <- read_csv(“expt mass_cHbSS.csv”)

Step 3. Search the experimental results in the subset database with Variant Identifier

Run the function Variants.Identifier, the ppm_error range is changable and depends on the accuracy of the MS2 data. View the result list and get the identification.

ID.results <- Variants.Identifier(ref, exp, ppm_error_start=-2, ppm_error_end=5)

Step 4. Output the results in .csv

write.csv(ID.results, “ID_cHbSS.csv”, row.names = FALSE)

PredictDiag

Introduction

• This procedure can predict the diagnostic ions of Hb beta variants that have been experimentally teseted by refering to the product ions of Hb A beta.
• The output list from step 5 can be combined with the original databse in Variants Identifier as the updated database for sereaching.

Step 1: Input the list of residue numbers of possible diagnostic ions for each AA in the Hb beta,and the list of reference product ions for HbA beta

diag_ref <- read.csv(“finddiag.csv”)

WT_ref <- read.csv(“ref mass list_pro_1.csv”)

Step 2: Input the sequennces of HbA beta and Hb beta variants

Multiple sequences of variants sequences can be included in one .fasta file, the sequences should have the N-terminal Met while the comparison results exclude the N-ternimal Met.

Hbvariants <- seqinr::read.fasta(file = “Hbvariants.fasta”, seqtype = “AA”,as.string = FALSE)

WT <- seqinr::read.fasta(file = “HbA.fasta”, seqtype = “AA”,as.string = FALSE)

Step 3: Predict the diagnostic ions by running the function

PD.result <- PredictDiag(WT,WT_ref,diag_ref,Hbvarinats)

Step 4:Output results in .csv file

write.csv(PD.result, “PredictDiag_variants20.csv”, row.names = FALSE)

Linda24bc/VariantsID documentation built on April 12, 2022, 12:20 a.m.