# RHybridFinder

#### Frederic Saab, Peter Kubiniok

#### 2021-08-16

RHybridFinder is a package for the analysis of Mass spectrometry (MS) for the discovery of putative hybrid peptides. For the analysis of your sample, please note that the proposed workflow in the context of this package consists of two major steps:

- step 1: HybridFinder
- Interim: second database search using the merged proteome.
- step 2: Either checknetMHCpan or step2_wo_netMHCpan

## Loading the package

After installing the package and in order to be able to use the package, it has to be loaded

    library(RHybridFinder)

## Example data

For demonstration purposes, the data showcased in this vignette, which is also available in the package (denovo sequencing and database search results, in .csv format) is from HLA Ligand Atlas (Human liver, Autonomous Donor 17) (Marcu et al., 2020). In order to download the human proteome database .fasta file, please visit the uniProt website.

In order to access the example denovo sequencing results and database search results through the package:

    #retrieve the denovo sequencing results for the example data
    data(package="RHybridFinder", denovo_Human_Liver_AUTD17)

    #retrieve the database search results for the example data
    data(package="RHybridFinder", db_Human_Liver_AUTD17)

The RAW Mass Spectrometry (MS) Files for the dataset are provided by the authors on Proteomics Identifications Database (PRIDE): PXD019643

## Step 1

The step 1 consists of running the HybridFinder function.

### HybridFinder

#### Description

The HybridFinder function is based on the workflow proposed by Faridi et al. (2018), with some modifications. Whereby, while using denovo sequencing results with database search results and the proteome database, HybridFinder extracts High confidence denovo peptides and then goes through a 3-step search of these into the proteome. If peptide sequences are matched fully within proteins then they are considered as being “Linear” and Linear peptides within a given spectrum are filtered based on the highest ALC (Average Local Confidence: a significance score for the sequence) score. The rest of the spectra go through the second step during which lists of pair fragments from each peptide sequence are created and then searched in the proteome database, if pair fragments are matched within one protein, these are considered to be potentially cis-spliced. Then, only the highest ALC peptides from each spectrum group are kept. The rest of the spectra goes through the last step, which consists of searching for pair combination matches within two proteins, those that match are considered as being potentially trans-spliced, and only the highest ALC peptides within each spectrum group are kept. Finally, the list of hybrid candidates are concatenated into different ‘fake’ proteins, with the goal being the creation of a hybrid proteome which would mimic the actual proteome. And this hybrid proteome is merged with the reference proteome.

#### Loading data

In order to run HybridFinder, three inputs must be provided to `HybridFinder`

1. all de novo candidates export file - all denovo sequencing candidates, loaded into R as dataframe
2. DB search psm export file - database search results, loaded into R as dataframe.
3. folder path to the proteome database used.

Please note that it is recommended to have a folder structure that looks as follows, as it helps keep all results organized:

- (parent folder)- Exp 1 (could be any name )
  - (child folder): first_run
    - denovo
    - db
  - (2nd child folder): second_run

    folder_Human_Liver_AUTD17 <- file.path("./data/Human_Liver_AUTD17")
    denovo_Human_Liver_AUTD17 <- read.csv(file.path(folder_Human_Liver_AUTD17, "first_run","all de novo candidates.csv"), sep=",", head=TRUE,stringsAsFactors = FALSE)
    db_Human_Liver_AUTD17 <- read.csv(file.path(folder_Human_Liver_AUTD17, "first_run","DB search psm.csv"), sep=",", head=TRUE,stringsAsFactors = FALSE)
    proteome_Human_Liver_AUTD17<- file.path(folder_Human_Liver_AUTD17, "uniprot-proteome-human_UP000005640-reviewed_validated.fasta")

#### Run HybridFinder

Once the inputs are loaded, running HybridFinder is a piece of cake. Please note that the HybridFinder function can use parallel computing in order to obtain results fast. It will be good to make sure whether the PC used can support that.

It is possible to set the amount of cores (customCores) for the HybridFinder function to run (given that these are >5). Additionally, it is possible to set a custom ALC cutoff (through the customALCcutoff parameter), setting this allows to filter unassigned spectra based on the newly set custom ALC cutoff (instead of it being calculated). The minimum customALCcutoff score that can be set is 85. Anything set lower than 85 will be set at 85.

    results_HybridFinder_Human_Liver_AUTD17<- HybridFinder(denovo_candidates =  denovo_Human_Liver_AUTD17, db_search =  db_Human_Liver_AUTD17, proteome_db = proteome_Human_Liver_AUTD17, customALCcutoff = NULL, with_parallel = FALSE, customCores = 8, export_files = TRUE, export_dir=folder_Human_Liver_AUTD17)

#### Output

The function returns a list composed of 3 elements

- a dataframe representing the HybridFinder output, containing the high confidence denovo peptides that made it through the different searches (listed above).
- a vector containing all the hybrid candidate sequences
- a list consisting of the merged reference+hybrid proteome.

##### Ouput 1: HybridFinder Step1 output (HF_step1_output)

    #display HybridFinder(HF) step1 output
    print(head(results_HybridFinder_Human_Liver_AUTD17[[1]]))
    #>     Fraction     Scan      m/z    RT    Peptide Length Potential_spliceType ALC
    #> 394        3  F3:8061 584.3079 45.51 SLVMTQTPKF     10                trans  86
    #> 23         1 F1:16116 575.7930 72.95  SYLEHLFEL      9               Linear  95
    #> 375        3 F3:15872 558.8188 70.76 LPVDLATFQL     10                trans  83
    #> 130        3 F3:13743 533.3030 64.36  SYLLRPVAF      9               Linear  79
    #> 175        1 F1:11624 531.2999 59.30  YPEDKLLLA      9                  cis  78
    #> 15         1 F1:15777 575.7930 72.95  SYLEHLFEL      9               Linear  92
    #>                             proteome_database_used
    #> 394 human_proteome_dropbox_for_HF_validation.fasta
    #> 23  human_proteome_dropbox_for_HF_validation.fasta
    #> 375 human_proteome_dropbox_for_HF_validation.fasta
    #> 130 human_proteome_dropbox_for_HF_validation.fasta
    #> 175 human_proteome_dropbox_for_HF_validation.fasta
    #> 15  human_proteome_dropbox_for_HF_validation.fasta

##### Ouput 2: List of step1 candidate hybrid peptides

    #display list of candidate hybrid peptides
    print(head(results_HybridFinder_Human_Liver_AUTD17[[2]]))
    #> [1] "YPEDKLLLA" "ANAVLARFY" "YNLPWLENL" "LLYYALMPY" "LPVDLQRYL" "MEDLLKLLA"

##### Ouput 3: merged proteome

    #display the merged proteome
    print(tail(results_HybridFinder_Human_Liver_AUTD17[[3]]))
    #> $`sp|Q8TDM6-2|DLG5_HUMAN`
    #> [1] " "
    #> attr(,"name")
    #> [1] "sp|Q8TDM6-2|DLG5_HUMAN"
    #> attr(,"Annot")
    #> [1] ">sp|Q8TDM6-2|DLG5_HUMAN Isoform 2 of Disks large homolog 5 OS=Homo sapiens OX=9606 GN=DLG5"
    #> attr(,"class")
    #> [1] "SeqFastaAA"
    #>
    #> $`sp|Q8TDM6-3|DLG5_HUMAN`
    #> [1] " "
    #> attr(,"name")
    #> [1] "sp|Q8TDM6-3|DLG5_HUMAN"
    #> attr(,"Annot")
    #> [1] ">sp|Q8TDM6-3|DLG5_HUMAN Isoform 3 of Disks large homolog 5 OS=Homo sapiens OX=9606 GN=DLG5"
    #> attr(,"class")
    #> [1] "SeqFastaAA"
    #>
    #> $`sp|Q8TDM6-4|DLG5_HUMAN`
    #> [1] " "
    #> attr(,"name")
    #> [1] "sp|Q8TDM6-4|DLG5_HUMAN"
    #> attr(,"Annot")
    #> [1] ">sp|Q8TDM6-4|DLG5_HUMAN Isoform 4 of Disks large homolog 5 OS=Homo sapiens OX=9606 GN=DLG5"
    #> attr(,"class")
    #> [1] "SeqFastaAA"
    #>
    #> $`sp|Q8TDM6-5|DLG5_HUMAN`
    #> [1] " "
    #> attr(,"name")
    #> [1] "sp|Q8TDM6-5|DLG5_HUMAN"
    #> attr(,"Annot")
    #> [1] ">sp|Q8TDM6-5|DLG5_HUMAN Isoform 5 of Disks large homolog 5 OS=Homo sapiens OX=9606 GN=DLG5"
    #> attr(,"class")
    #> [1] "SeqFastaAA"
    #>
    #> $`sp|denovo_HF_fake_protein1`
    #> [1] "YPEDKLLLANYGELFEKFDYGELFEKFDYGELFQKFANAVLARFYKLADFRLLYKLADFLRLYSSYFLLEAFYNLPWLENLLSLNYCLLLLEPFLLPTLPELFLLPTLPLEFLLPTLLTTSWMSLKMEDLLKLLAMENLLKLLALHLSRLQYFLPVDLQRYLLPVNLQRYLLWDLSLTRLFPYYAPELLLLYYASNYRRFLVGSLPKFENGEWRELQLADLFRLYEHVVKVFSLLYEVLLKNFFNLPWTQRF"
    #>
    #> $`sp|denovo_HF_fake_protein2`
    #> [1] "FEVPWTQRFDQDLRSMATALLYYASNRYLLYYASRNYLLYYAPLMYLLYYALMPYSLVMTQTPKFYKVYTSVSWMMALLTHGLVLPRRFPQFNYLPWLELNKYPSDEFVFYKPSDEFVFLLYYANSRYLLYYARSNYTVVMTQTPKFTVVMTQTPFKVVYPSMTQRFVVYPAMTQRFRYFSTSVSWYRFSTSVSWLPVDLTAFQLLPVNLATFQLLPVDLATFQLLPVNLKSLTMSYLEHLFYTSYLHELFELANVENLGYLTF"

#### Export

If export is set to `TRUE` and a valid directory is provided in `export_dir`, then the results are exported .csv, .csv and .fasta format, respectively.

Even if the export parameters were not set at the beginning, the results returned can always be exported with the `export_HybridFinder_results` function as long as as the results obtained from the HybridFinder function are stored which is also indicated in the results_list parameter of the `export_HybridFinder_results` function.

## Interim external step: Second database search using the merged proteome

After finishing this, a second database search has to be done on the raw MS however with the merged proteome (.fasta) exported from the HybridFinder function results.

## Step2

The second step in RHybridFinder consists of either using `checknetMHCpan` or `step2_wo_netMHCpan`, while using the results from step 1 in order to retrieve for the final list of peptides which includes the hybrid candidates, their potential splice types.

### checknetMHCpan

#### Description

the `checknetMHCpan` function represents step 2 of Faridi et al. (2018)’s workflow and also features the use of netMHCpan (Jurtz et al., 2017, Reynisson et al., 2020) for obtaining the peptide-MHC-I predicted binding affinities. Please note that netMHCpan needs to be installed in order to be able to run this function. The package also contains a function that runs step2 without netMHCpan (Please refer to the `step2_wo_netMHCpan` part).

#### Loading data

In order to run checknetMHCpan, four inputs must be provided to `checknetMHCpan`

1. netmhcpan_directory: the directory in which netMHCpan is located in (i.e ‘/usr/bin/’ or ‘/usr/bin/local/’)
2. netmhcpan_alleles: the alleles to be tested again, in a vector format if multiple (i.e alleles<- c(‘HLA-A03:01’, ’HLA-A24:02’))
3. peptide_rerun: the database search results from the 2nd run loaded into R as a dataframe
4. HF_step1_output: the dataframe of the first element of the `HybridFinder` output.

    netmhcpan_dir<- '/usr/bin/'

    alleles_Human_liver_AUTD17<- c("HLA-A*03:01", "HLA-A*24:02", "HLA-B*35:03", "HLA-B*45:01", "HLA-C*04:01", "HLA-C*16:01")

    db_rerun_Human_liver_AUTD17 <- read.csv(file.path(folder_Human_Liver_AUTD17, "second_run","DB search psm.csv"), sep=",", head=TRUE,stringsAsFactors = FALSE)

    HF_output_Human_liver_AUTD17<- results_HybridFinder_Human_Liver_AUTD17[[1]]

#### Run checknetMHCpan

Once the inputs are loaded, running checknetMHCpan is easier than ABC.

    results_checknetMHCpan_Human_Liver_AUTD17<- checknetMHCpan(netmhcpan_directory = netmhcpan_dir, netmhcpan_alleles = alleles_Human_liver_AUTD17, peptide_rerun = db_rerun_Human_liver_AUTD17, HF_step1_output = HF_output_Human_liver_AUTD17, export_files = TRUE, export_dir=folder_Human_Liver_AUTD17)

#### Output

The function returns a list composed of 3 elements: - the netMHCpan results in long format, that is the binding affinity results are displayed for each peptide with a given allele from those chosen. - the netMHCpan results in wide format, that is the binding affinity levels per peptide summarized for all HLA alleles chosen. - the database results with the respective potential splice types retrieved from step 1

##### Ouput 1: netMHCpan results in long format

    #display netmhcpan output(long version)
    print(head(results_checknetMHCpan_Human_Liver_AUTD17[[1]]))
    #>      Pos         HLA      Peptide      Core Of Gp Gl Ip Il        Icore
    #> 431    1 HLA-A*03:01   VTTYPQGFKL VTTYPQGFK  0  0  0  0  0    VTTYPQGFK
    #> 2846   1 HLA-C*16:01   VTTYPQGFKL VTYPQGFKL  0  1  1  0  0   VTTYPQGFKL
    #> 2789   1 HLA-C*16:01    EYLLKVNEL EYLLKVNEL  0  0  0  0  0    EYLLKVNEL
    #> 512    1 HLA-A*24:02 GLFEVGAGWLGK GLFAGWLGK  0  3  3  0  0 GLFEVGAGWLGK
    #> 804    1 HLA-A*24:02   LPVDLATFQL LVDLATFQL  0  1  1  0  0   LPVDLATFQL
    #> 1687   1 HLA-B*45:01    FEVPWTQRF FEVPWTQRF  0  0  0  0  0    FEVPWTQRF
    #>      Identity     Score Aff(nM)   %Rank  BindLevel strongBinder weakBinder
    #> 431   PEPLIST 0.2355170  3911.0  4.0177 Non binder
    #> 2846  PEPLIST 0.3118960  1711.5  3.3500 Non binder
    #> 2789  PEPLIST 0.1848720  6764.9  9.4126 Non binder
    #> 512   PEPLIST 0.0277830 37018.5 36.2242 Non binder
    #> 804   PEPLIST 0.0686400 23792.1 14.4521 Non binder
    #> 1687  PEPLIST 0.1722820  7752.1  3.4859 Non binder
    #>       noneBinder Potential_spliceType
    #> 431  HLA-A*03:01               Linear
    #> 2846 HLA-C*16:01               Linear
    #> 2789 HLA-C*16:01               Linear
    #> 512  HLA-A*24:02               Linear
    #> 804  HLA-A*24:02                trans
    #> 1687 HLA-B*45:01                trans

##### Ouput 2: netMHCpan results in wide format

    #display netmhcpan output tidied version (wide)
    print(head(results_checknetMHCpan_Human_Liver_AUTD17[[2]]))
    #>           Peptide            strongBinder              weakBinder
    #> 799  GMEGANSLFSGF
    #> 2311  VPLDEKLLPQL
    #> 2017    SYNNFFRMF HLA-A*24:02,HLA-C*04:01
    #> 2341   VVYPSMTQRF             HLA-A*24:02 HLA-C*04:01,HLA-C*16:01
    #> 1117   LFVPSYLNLF             HLA-A*24:02             HLA-C*04:01
    #> 529     FAAFEEPEL HLA-B*35:03,HLA-C*16:01
    #>                                                                   noneBinder
    #> 799  HLA-A*03:01,HLA-A*24:02,HLA-B*35:03,HLA-B*45:01,HLA-C*04:01,HLA-C*16:01
    #> 2311 HLA-A*03:01,HLA-A*24:02,HLA-B*35:03,HLA-B*45:01,HLA-C*04:01,HLA-C*16:01
    #> 2017                         HLA-A*03:01,HLA-B*35:03,HLA-B*45:01,HLA-C*16:01
    #> 2341                                     HLA-A*03:01,HLA-B*35:03,HLA-B*45:01
    #> 1117                         HLA-A*03:01,HLA-B*35:03,HLA-B*45:01,HLA-C*16:01
    #> 529                          HLA-A*03:01,HLA-A*24:02,HLA-B*45:01,HLA-C*04:01
    #>      %Rank.HLA-C*16:01 %Rank.HLA-B*45:01 %Rank.HLA-B*35:03 %Rank.HLA-A*03:01
    #> 799            23.1799            6.9626           27.5516           17.2915
    #> 2311           25.8328           84.5494            2.6277           66.2649
    #> 2017            2.6310           16.6140           20.2119           19.6749
    #> 2341            1.8172           55.9481           15.6904            3.0010
    #> 1117            7.2039           39.4305           13.8101           15.3994
    #> 529             0.4844           67.7349            0.1084           72.6386
    #>      %Rank.HLA-C*04:01 %Rank.HLA-A*24:02 strongBinder_count weakBinder_count
    #> 799            12.6396            8.4755                  0                0
    #> 2311           23.1188           37.9233                  0                0
    #> 2017            0.3332            0.0133                  2                0
    #> 2341            1.6691            0.0367                  1                2
    #> 1117            0.7323            0.0734                  1                1
    #> 529             2.8687           23.6958                  2                0
    #>      noneBinder_count Potential_spliceType
    #> 799                 6               Linear
    #> 2311                6               Linear
    #> 2017                4               Linear
    #> 2341                3                trans
    #> 1117                4               Linear
    #> 529                 4               Linear

##### Ouput 3: Database search results updated

    #display the updated database search results with the categorizations from step1
    print(head(results_checknetMHCpan_Human_Liver_AUTD17[[3]]))
    #>                Peptide X.10lgP      Mass Length ppm      m.z Z    RT   Area
    #> 1758       AELLNGKELSA   33.77 1143.6135     11 0.5 572.8143 2 40.00 114240
    #> 4124 AYLERM(+15.99)NYL   32.28 1187.5645      9 0.3 594.7897 2 46.69 600890
    #> 3001 VYTVVDEM(+15.99)F   35.37 1117.5001      9 0.3 559.7575 2 69.18 354900
    #> 3305           YPWTQRF   23.22  996.4818      7 0.5 499.2484 2 57.02      0
    #> 4095         LYQELLHYF   32.28 1224.6179      9 0.5 613.3165 2 70.50  87683
    #> 5386 AYPHNLM(+15.99)TF   24.19 1108.5011      9 0.4 555.2581 2 48.85 426960
    #>      Fraction    Id     Scan from.Chimera
    #> 1758        3 37741  F3:6305           No
    #> 4124        1  2628  F1:7714           No
    #> 3001        2 25937 F2:15065           No
    #> 3305        1 14824 F1:10950           No
    #> 4095        3 43871 F3:15652           No
    #> 5386        1  3035  F1:8562           No
    #>                                                    Source.File
    #> 1758 171002_AM_BD-ZH17_Liver_W_10%_DDA_#3_400-650mz_msms6.mzML
    #> 4124 171002_AM_BD-ZH17_Liver_W_10%_DDA_#1_400-650mz_msms4.mzML
    #> 3001 171002_AM_BD-ZH17_Liver_W_10%_DDA_#2_400-650mz_msms5.mzML
    #> 3305 171002_AM_BD-ZH17_Liver_W_10%_DDA_#1_400-650mz_msms4.mzML
    #> 4095 171002_AM_BD-ZH17_Liver_W_10%_DDA_#3_400-650mz_msms6.mzML
    #> 5386 171002_AM_BD-ZH17_Liver_W_10%_DDA_#1_400-650mz_msms4.mzML
    #>                        Accession           PTM                   AScore
    #> 1758                      P11586
    #> 4124 P06241:P06241:P06241:P07947 Oxidation (M) M6:Oxidation (M):1000.00
    #> 3001                      P53680 Oxidation (M) M8:Oxidation (M):1000.00
    #> 3305                      P68871
    #> 4095                      Q14185
    #> 5386               Q15629:Q15629 Oxidation (M) M7:Oxidation (M):1000.00
    #>      Found.By Peptide_no_mods Potential_spliceType
    #> 1758 PEAKS DB     AELLNGKELSA               Linear
    #> 4124 PEAKS DB       AYLERMNYL               Linear
    #> 3001 PEAKS DB       VYTVVDEMF               Linear
    #> 3305 PEAKS DB         YPWTQRF               Linear
    #> 4095 PEAKS DB       LYQELLHYF               Linear
    #> 5386 PEAKS DB       AYPHNLMTF               Linear

#### Export

If export is set to `TRUE` and a valid directory is provided in `export_dir`, then the results are exported .csv, .tsv (tab-separated) and .csv format, respectively.

Even if the export parameters were not set at the beginning, the results returned can always be exported with the `export_checknetMHCpan_results` function as long as as the results obtained from the checknetMHCpan function are stored which is also indicated in the results_list parameter of the `export_checknetMHCpan_results` function.

### step2_wo_netMHCpan

The `step2_wo_netMHCpan`, removes peptide modifications and prepare a peptide (.pep) file for use in webversion of netMHCpan, in case netMHCpan is not installed, OS is windows or the user would like to run in another software. Additionally, the function matches peptide sequences in the database search rerun (the second database search where the merged proteome was used), with the predicted splice type obtained from step 1.

#### Description

The step2_wo_netMHCpan, removes peptide modifications and runs netMHCpan on peptides between 9 and 12-mers. Additionally, the function matches peptide sequences in the database search rerun (the second database search where the merged proteome was used), with predicted splice type obtained from step 1.

#### Loading data

In order to run checknetMHCpan, four inputs must be provided to `checknetMHCpan`

1. peptide_rerun: the database search results from the 2nd run loaded into R as a dataframe
2. HF_step1_output: the dataframe of the first element of the `HybridFinder` output.

    db_rerun_Human_liver_AUTD17 <- read.csv(file.path(folder_Human_Liver_AUTD17, "second_run","DB search psm.csv"), sep=",", head=TRUE,stringsAsFactors = FALSE)

    HF_output_Human_liver_AUTD17<- results_HybridFinder_Human_Liver_AUTD17[[1]]

#### Run step2_wo_netMHCpan

Once the inputs are loaded, running step2_wo_netMHCpan is easier than ABC.

    results_step2_Human_Liver_AUTD17<- step2_wo_netMHCpan(peptide_rerun = db_rerun_Human_liver_AUTD17, HF_step1_output = HF_output_Human_liver_AUTD17, export_files = TRUE, export_dir=folder_Human_Liver_AUTD17)

#### Output

The function returns a list composed of 2 elements: - a character vector containing the list of unique peptides from the database search rerun without modifications and of length 9 to 12 amino acids - the database results with the respective potential splice types retrieved from step 1

##### Ouput 1: netMHCpan-ready input

    #display the netmhcpan-ready input / list of all peptides 9-12 aa, without
    #modifications
    print(head(results_step2_Human_Liver_AUTD17[[1]]))
    #> [1] "LYPDSFTVL"   "LDFPKPLLA"   "YYTPLTPHL"   "LYEPNFLFF"   "VAHVDDMPNAL"
    #> [6] "FLPLTPQFVTE"

##### Ouput 2: Database search results updated

    #display the updated database search results table with the categorizations from
    #step1
    print(head(results_step2_Human_Liver_AUTD17[[2]]))
    #>          Peptide X.10lgP      Mass Length  ppm      m.z Z    RT    Area
    #> 3078 LRVAPEEHPVL   29.89 1258.7034     11  0.0 420.5751 3 43.15 1350800
    #> 5633   NEQVKNFVA   23.25 1047.5349      9 -0.7 524.7744 2 29.43   65130
    #> 4017   SALPVTYVF   29.62  995.5328      9  0.2 498.7738 2 75.76  101290
    #> 707    VVYPWTQRF   31.33 1194.6185      9  0.6 598.3169 2 61.47 3779700
    #> 5028   AYLERMNYL   33.65 1171.5696      9  0.6 586.7924 2 51.78   66751
    #> 5789  VVYPAMTQRF   24.10 1210.6168     10 -2.4 606.3142 2 56.05  349750
    #>      Fraction    Id     Scan from.Chimera
    #> 3078        3 38292  F3:7364           No
    #> 5633        1   322  F1:3088           No
    #> 4017        3 44945 F3:17185           No
    #> 707         1  5670 F1:13116           No
    #> 5028        3 39990  F3:9972           No
    #> 5789        3 40918 F3:10996           No
    #>                                                    Source.File
    #> 3078 171002_AM_BD-ZH17_Liver_W_10%_DDA_#3_400-650mz_msms6.mzML
    #> 5633 171002_AM_BD-ZH17_Liver_W_10%_DDA_#1_400-650mz_msms4.mzML
    #> 4017 171002_AM_BD-ZH17_Liver_W_10%_DDA_#3_400-650mz_msms6.mzML
    #> 707  171002_AM_BD-ZH17_Liver_W_10%_DDA_#1_400-650mz_msms4.mzML
    #> 5028 171002_AM_BD-ZH17_Liver_W_10%_DDA_#3_400-650mz_msms6.mzML
    #> 5789 171002_AM_BD-ZH17_Liver_W_10%_DDA_#3_400-650mz_msms6.mzML
    #>                        Accession PTM AScore Found.By Peptide_no_mods
    #> 3078               P63261:P60709            PEAKS DB     LRVAPEEHPVL
    #> 5633                      P04114            PEAKS DB       NEQVKNFVA
    #> 4017               Q9BZW5:Q9BZW5            PEAKS DB       SALPVTYVF
    #> 707                       P68871            PEAKS DB       VVYPWTQRF
    #> 5028 P06241:P06241:P06241:P07947            PEAKS DB       AYLERMNYL
    #> 5789    |denovo_HF_fake_protein2            PEAKS DB      VVYPAMTQRF
    #>      Potential_spliceType
    #> 3078               Linear
    #> 5633               Linear
    #> 4017               Linear
    #> 707                Linear
    #> 5028               Linear
    #> 5789                trans

#### Export

If export is set to `TRUE` and a valid directory is provided in `export_dir`, then the results are exported .csv, .csv and csv format, respectively.

Even if the export parameters were not set at the beginning, the results returned can always be exported with the `export_step2_results` function as long as as the results obtained from the step2_wo_netMHCpan function are stored which is also indicated in the results_list parameter of the `export_step2_results` function.

## References

Faridi, P., Li, C., Ramarathinam, S. H., Vivian, J. P., Illing, P. T., Mifsud, N. A., Ayala, R., Song, J., Gearing, L. J., Hertzog, P. J., Ternette, N., Rossjohn, J., Croft, N. P., & Purcell, A. W. (2018). A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands. Science Immunology, 3(28), eaar3947. /sciimmunol.aar3947, Link

Hanada K, Yewdell JW, Yang JC. Immune recognition of a human renal cancer antigen through post-translational protein splicing. Nature. 2004 Jan 15;427(6971):252-6. DOI /nature02240, Link

Marcu A, Bichmann L, Kuchenbecker L, et al HLA Ligand Atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapyJournal for ImmunoTherapy of Cancer 2021;9:e002071. /jitc-2020-002071, Link

Birkir Reynisson, Bruno Alvarez, Sinu Paul, Bjoern Peters, Morten Nielsen, NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data, Nucleic Acids Research, Volume 48, Issue W1, 02 July 2020, Pages W449–W454, /nar/gkaa379, Link

Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data. J Immunol. 2017 Nov 1;199(9):3360-3368. Epub 2017 Oct 4. PMID: 28978689; PMCID: PMC5679736 /jimmunol.1700893, Link

The UniProt Consortium, UniProt: the universal protein knowledgebase in 2021, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D480–D489, /nar/gkaa1100, Link