HybridFinder

Description

This function takes in three mandatory inputs: (1) all denovo candidates (2) database search results and (3) the corresponding proteome fasta file. The function's role is to extract high confidence de novo peptides and to search for their existence in the proteome, whether the entire peptide sequence or its pair fragments (in one or two proteins).

Usage

HybridFinder(
  denovo_candidates,
  db_search,
  proteome_db,
  customALCcutoff = NULL,
  with_parallel = TRUE,
  customCores = 6,
  export_files = FALSE,
  export_dir = NULL
)

Arguments

Details

This function is based on the published algorithm by Faridi et al. (2018) for the identification and categorization of hybrid peptides. The function described here adopts a slightly modified version of the algorithm for computational efficiency. The function starts by extracting unassigned denovo spectra where the Average Local Confidence (assigned by PEAKS software), is equivalent to the ALC cutoff which is based on the median of the assigned spectra (between denovo and database search). The sequences of all peptides are searched against the reference proteome. If there is a hit then, then, the peptide sequence within a spectrum group considered as being linear and each spectrum group is is then filtered so as to keep the highest ALC-ranking spectra. Then, the rest of the spectra (spectra that did not contain any sequence that had an entire match in the proteome database) then undergo a "cutting" procedure where each sequence yields n-2 sequences (with n being the length of the peptide. That is if the peptide contains 9 amino acids i.e NTYASPRFK, then the sequence is cut into a combination of 7 sequences of 2 fragment pairs each i.e fragment 1: NTY and fragment 2: ASPRFK, etc).These are then searched in the proteome for hits of both peptide fragments within a same protein, spectra in which sequences have fragment pairs that match within a same protein, these are considerent to be potentially cis-spliced. Potentially cis-spliced spectrum groups are then filtered based on the highest ranking ALC. Spectrum groups not considered to be potentially cis-spliced are further checked for potential trans-splicing. The peptide sequences are cut again in the same fashion, however, this time peptide fragment pairs are searched for matches in two proteins. Peptide sequences whose fragment pairs match in 2 proteins are considerend to be potentially trans-spliced. The same filtering for the highest ranking ALC within each peptide spectrum group. The remaining spectra that were neither assigned as linear nor potentially spliced (neither cis- nor trans-) are then discarded. The result is a list of spectra along with their categorizations (Linear, potentially cis- and potentially trans-) Potentially cis- and trans-spliced peptides are then concatenated and then broken into several "fake" proteins and added to the bottom of the reference proteome. The point of this last step is to create a merged proteome (consisting of the reference proteome and the hybrid proteome) which would be used for a second database search. After the second database search the checknetmhcpan function or the step2_wo_netMHCpan function can be used in order to obtain the final list of potentially spliced peptides. Article: Faridi P, Li C, Ramarathinam SH, Vivian JP, Illing PT, Mifsud NA, Ayala R, Song J, Gearing LJ, Hertzog PJ, Ternette N, Rossjohn J, Croft NP, Purcell AW. A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands. Sci Immunol. 2018 Oct 12;3(28):eaar3947. <doi: 10.1126/sciimmunol.aar3947>. PMID: 30315122.

Value

The output is a list of 3 dataframes containing:

1. the HybridFinder output (dataframe) - the spectra that made it to the end with their respective columns (ALC, m/z, RT, Fraction, Scan) and a categorization column which denotes their potential splice type (-cis, -trans) or whether they are linear (the entire sequence was matched in proteins in the proteome database). Potential cis- & trans-spliced peptide are peptides whose fragments were matched with fragments within one protein, or two proteins, respectively.

2. character vector containing potentially hybrid peptides (cis- and trans-)

3. list containing the reference proteome and the "fake" proteins added at the end with a patterned naming convention (sp|denovo_HF_fake_protein) made up of the concatenated potential hybrid peptides.

See Also

read.fasta,s2c

Examples

## Not run: 
 hybridFinderResult_list <- HybridFinder(denovo_candidates, db_search,
 proteome, export = TRUE, output_dir)
 hybridFinderResult_list <- HybridFinder(denovo_candidates, db_search,
 proteome)
 hybridFinderResult_list <- HybridFinder(denovo_candidates, db_search,
 proteome, export = FALSE)

## End(Not run)

checknetMHCpan

Description

checknetMHCpan, utilizes the file from the second (PEAKS) run and analyzes the data with netMHCpan in order to provide the peptide binding affinity to different HLA/MHC alleles.

Usage

checknetMHCpan(
  netmhcpan_directory,
  netmhcpan_alleles,
  peptide_rerun,
  HF_step1_output,
  export_files = FALSE,
  export_dir = NULL
)

Arguments

Details

The ability to check the peptide binding affinity to the different MHC/HLA molecules is essential for assessing the antigenicity of all peptides. This function thus uses netMHCpan (Reynisson et al., 2020) for the generation of binding affinty results.

Value

1. netMHCpan results pertaining to the binding affinity of all peptides in the database search results (in long- and wide- format, with data tidying in the wide format in order to compute the amount of HLA molecules to which a peptide is strong/weak/non-binder binder)(dataframe)

2. netMHCpan results pertaining to the binding affinity of the hybrid peptides to the MHC molecules (in long- and wide- format, with data tidying in the wide format in order to compute the amount of HLA molecules to which a peptide is strong/weak/non-binder binder) (dataframe)

3. the database search rerun with the categorizations already determined in step1 (HybridFinder Function)

(datafrane)

Examples

## Not run: 
  results_checknetmhcpan_Exp1<- checknetMHCpan('/usr/local/bin', alleles,
  peptide_rerun, Exp1_HF_results[[1]])
  results_checknetmhcpan_Exp1 <- checknetMHCpan('/usr/local/bin', alleles,
  peptide_rerun, Exp1_HF_results_denovo_w_spliced)

## End(Not run)

db_Human_Liver_AUTD17

Description

database search results from the first run using PEAKS software, on the raw dataset from the HLA ligand Atlas Human Liver sample of AutDonor17

Usage

db_Human_Liver_AUTD17

Format

A data frame with 6108 rows and 18 variables:

Peptide

character Peptide

X.10lgP

double X.10lgP

Mass

double Mass

Length

integer Peptide length

ppm

double ppm

m.z

double mass-to-charge

Z

integer charge

RT

double Retention Time

Area

double Area

Fraction

integer Fraction

Id

integer Id

Scan

character Scan

from.Chimera

character from.Chimera

Source.File

character Source.File

Accession

character Accession

PTM

character Post Translational Modification

AScore

character AScore

Found.By

character Found.By

Details

database search results from the first run using PEAKS software, on the raw dataset from the HLA ligand Atlas Human Liver sample of AutDonor17

db_rerun_Human_Liver_AUTD17

Description

database search results from the second run using PEAKS software, using the raw file from HLA ligand ATLAS, Human Liver AutDonor 17

Usage

db_rerun_Human_Liver_AUTD17

Format

A data frame with 6315 rows and 18 variables:

Peptide

character Peptide

X.10lgP

double X.10lgP

Mass

double Mass

Length

integer Peptide sequence

ppm

double ppm

m.z

double mass-to-charge

Z

integer charge

RT

double Retention Time

Area

double Area

Fraction

integer Fraction

Id

integer Id

Scan

character Scan

from.Chimera

character from.Chimera

Source.File

character Source.File

Accession

character Accession

PTM

character Post-Translational Modification

AScore

character AScore

Found.By

character Found.By

Details

database search results from the second run using PEAKS software, using the raw file from HLA ligand ATLAS, Human Liver AutDonor 17

denovo_Human_Liver_AUTD17

Description

denovo sequencing results obtained using PEAKS software, and the raw file of Human liver from autDonor 17

Usage

denovo_Human_Liver_AUTD17

Format

A data frame with 50114 rows and 18 variables:

Fraction

integer Fraction

Source.File

character Source.File

Feature

character Feature

Peptide

character Peptide

Scan

character Scan

Tag.length

integer Tag.length

Denovo.score

integer Denovo.score

ALC....

integer Average Local Confidence

Length

integer peptide length

m.z

double mass-to-charge(m/z)

z

integer charge

RT

double Retention Time

Predict.RT

character Predict.RT

Mass

double Mass

ppm

double ppm

local.confidence....

character confidence score per residue

tag....0..

character tag

mode

character mode

Details

denovo sequencing results obtained using PEAKS software, and the raw file of Human liver from autDonor 17

export_HybridFinder_results

Description

this function allows to export the results list obtained in the HybridFinder() function.

Usage

export_HybridFinder_results(results_list, export_dir)

Arguments

Details

In order to be able to have the HybridFinder() results list exported, this function will come in handy. Please note that this function is also part of the HybridFinder() function, therefore if you set export_files=TRUE and you indicate the export directory in export_dir in the HybridFinder() function, you would have the exact same outcome.

Value

exports a folder containing three files

1. the HybridFinder output - the spectra that made it to the end with their respective columns (ALC, m/z, RT, Fraction, Scan) and a categorization column which denotes their potential splice type (-cis, -trans) or whether they are linear (the entire sequence was matched in proteins in the proteome database). Potential cis- & trans-spliced peptide are peptides whose fragments were matched with fragments within one protein, or two proteins, respectively.

2. list of potential hybrid peptides (excluding the linear peptides) (.csv file)

3. the merged proteome consisting of the reference proteome along with the hybrid proteome added at the end of the file with the sequence names following the pattern "sp|denovo_HF_fake_protein" along with a digit at the end (1,2,3,4,4,etc.) (.fasta file)

See Also

write.fasta

Examples

## Not run: 
 export_results(results_HybridFinder_Human_Liver_AUTD17,folder_Human_Liver_AUTD17)

## End(Not run)

export_checknetmhcpan_results

Description

this function allows to export the results generated from checknetMHCpan()

Usage

export_checknetmhcpan_results(list_checknetMHCpan_results, export_dir)

Arguments

Details

In order to be able to have the checknetMHCpan() function results exported, this function will come in handy. Please note that this function is also part of the checknetMHCpan() function (if export_files is set to TRUE and a valid export directory is indicated)

Value

exports a folder containing three files

1. netMHCpan results in long format (the original output)(.csv file)

2. netMHCpan results tidied (in wide format) so as to summarize the information per peptide (.tsv tab-separated file)

3. the updated database search results which contain the categorizatiosn of the peptides found in common between the 2nd database search and the HybridFinder function (.csv file)

Examples

## Not run: 
 export_checknetmhcpan_results(results_checknetMHCpan_Human_Liver_AUTD17, folder_Human_Liver_AUTD17)

## End(Not run)

export_step2_results

Description

this function allows to export the results generated from step2_wo_netmhcpan.

Usage

export_step2_results(step2_RHF_results_Exp1, export_dir)

Arguments

Details

Since netMHCpan is not compatible with Windows OS, the package offers an alternative by outputting the input for netMHCpan and as well the database results with their respective categorizations (cis, trans) established in step1.

Value

exports a folder containing 2 files

1. the peptide list to be entered in a netMHCpan-ready format,(.csv)

2. the updated database search results which contain the categorizatiosn of the peptides found in common between the 2nd database search and the HybridFinder function (.csv file)

Examples

## Not run: 
 export_step2_results(results_step2_Human_Liver_AUTD17, folder_Human_Liver_AUTD17)

## End(Not run)

mhc_check

Description

this function only contains the alleles list, read by netMHCpan, the list was retrieved by reading the file exported from netMHCpan, using the following command line "netMHCpan -listMHC"

Usage

mhc_check(netmhcpan_alleles)

Arguments

Details

a custom error is printed in case the allele is not written correctly

Value

• returns a custom error message if MHC/HLA allele(s) are not written correctly

• returns nothing if there are no issues. If HLA alleles are not written correctly

Examples

if (interactive()) {
 mhc_check("HLA-A02:01")
 mhc_check("HLA-A0201")
}

netmhcpan_list_alleles

Description

the list of alleles in the acceptable format for netMHCpan

Usage

netmhcpan_list_alleles

Format

A data frame with 1024 rows and 1 variables:

V1

character the alleles

Details

the list of alleles in the acceptable format for netMHCpan

step2_wo_netMHCpan

Description

This function helps retrieve the categorizations for the peptides from step 1 and apply them to those that are matched in the second database search.

Usage

step2_wo_netMHCpan(
  peptide_rerun,
  HF_step1_output,
  export_files = FALSE,
  export_dir = NULL
)

Arguments

Details

In special cases where the PC runs on windows OS, since it would only be possible to use the web version of netMHCpan, this function returns the peptide input file for the webversion of netMHCpan. Also, this function outputs the database search rerun results with their categorizations (into potentially cis and potentially trans) obtained from the first step (HybridFinder).

Value

1. the input file for the web version of netMHCpan (dataframe)

2. the database search rerun with the categorizations already determined in the previous step. (character vector)

Examples

if (interactive()) {
data(package="RHybridFinder", "db_rerun_Human_Liver_AUTD17")
results_checknetmhcpan_Human_Liver_AUTD17<- step2_wo_netMHCpan(db_rerun_Human_Liver_AUTD17,
    results_HybridFinder_Human_Liver_AUTD17[[1]])
}