Tutorial 3: Library Generation with DIA-UMPIRE

Transcription

1 Tutrial 3: Library Generatin with DIA-UMPIRE In this tutrial, we will use the java-based tl DIA-Umpire t perfrm untargeted analysis f DIA data. We will take tw DIA files f the LFQbench dataset (see tutrial verview) t perfrm DIA-Umpire signal-extractin (Step1) and database searching fr spectral library and query parameter generatin (Step2). This library will then be used in the next tutrial fr targeted reextractin and quantificatin with OpenSWATH. 1. Overview The input fr DIA-Umpire are cnverted and centrided mzxml files cntaining measured DIA spectra. The signal extractin algrithm detects all pssible precursr and fragment in features in the prvided MS1 and MS2 data and grups them based n the crrelatin f their elutin prfiles. Thus, the mdule can generate pseud-ms2 spectra that lk like spectra generated frm measurements in DDA mde. The pseud-ms2 spectra generated frm the DIA-Umpire signal extractin mdule can be used fr a regular database search t identify the peptides and prteins present in the measured DIA data. Due t limited sensitivity and quantitative accuracy in the analysis f a single DIA file with DIA- Umpire signal extractin and database searching nly, an additinal quantificatin step is strngly recmmended. Fr this, yu can either use the DIA-Umpire quantificatin mdule directly r use multiple DIA runs t generate a spectral library, like yu have dne in the last tutrial, fr targeted re-extractin and quantificatin with ther tls. In this tutrial we will use the results frm the database search f the pseud-ms2 spectra t generate a spectral library, which can be used fr targeted re-extractin by OpenSWATH tmrrw. 2. DIA-Umpire wrkflw 2.1. DIA-Umpire signal extractin In this first part f the tutrial we will use the signal extractin mdule f DIA-Umpire t extract all pssible precursr and fragment in features based n the crrelatin f their elutin prfiles. The end result will be the generatin f a cllectin f pseud-ms2 spectra in the frm f mgf files, which we will then cnvert t mzxml files ready fr database search. We will run DIA-Umpire n nly tw exemplary DIA file cntaining mixture spectra frm human, E.cli and yeast: C:\DIA_Curse\Data\DIA_data\TTOF\cllinsb_I180316_001_SW-A.mzXML C:\DIA_Curse\Data\DIA_data\TTOF\cllinsb_I180316_002_SW-B.mzXML Or 1

2 C:\DIA_Curse\Data\DIA_data\QE\cllinsb_X1803_171.mzXML C:\DIA_Curse\Data\DIA_data\QE\cllinsb_X1803_172.mzXML Befre starting the signal extractin mdule f DIA-Umpire ne needs t revise the parameters file and tune it accrding t the experimental and machine setup. DIA-Umpire prvides template parameter files fr different instrument types. We will adjust the parameter file fr ABSciex data: diaumpire_se_absciex_params.txt r fr QE data: diaumpire_se_therm_params.txt In RStudi pen the respective file frm the C: drive flder n yur VM and mve t the Tutrial3_DIAUmpire Here yu find the default parameter files f DIA-Umpire in the parameter_files flder We will d ur changes again in the RStudi envirnment, s please pen the respective parameter template file. Review the fllwing parameters and make sure yu understand them. We will nly mentin the mst imprtant parameters here, the additinal parameters nt mentined here usually d nt need adjustments. #Fragment intensity adjustment BstCmplementaryIn Fr each detected cmplementary y/b in pair (tw peaks with masses that sum up t the precursr peptide mass), it sets the intensities f bth cmplementary ins t the same (largest f the tw intensities) value. When building spectral libraries using DIA-Umpire extracted pseud-ms/ms spectra, such bsting is nt recmmended since lw intensity fragments can be selected fr inclusin in the library (at the expense f excluding better fragments) nly because their intensities gt artificially bsted. Turning this bsting ptin ff decreases the number f IDs nly slightly. In several representative AB Sciex 6600 and Therm Fusin datasets tested, with bsting, the number f IDs (at 1% FDR) drpped by ~ 3% at the prtein level and ~ 6% at the peptide in level. Set this t 'false' if yu plan t build spectral libraries using DIA-Umpire results fr subsequent targeted re-extractin using OpenSWATH r Skyline. Cautin! Change BstCmplementaryIn t false because we build a spectral library frm the DIA-Umpire results! #Signal extractin SE.MS1PPM and SE.MS2PPM: These parameters establish the mass errr (in ppm) t be used fr MS1 and MS2 signal extractin. Fr Orbitrap data the suggested values are ppm (MS1) and ppm (MS2), whereas fr TOF instruments the suggested values are ppm. Yu dn t have t adjust this value fr the tutrial. SE.Reslutin: This defines the reslutin used during data acquisitin and it is crucial fr data generated in prfile mde. In that case, recmmended values are (Therm Fusin r QE HF) and (Sciex), but these values may need t be adjusted depending n the actual reslutin settings f the instrument during data acquisitin. SE.SN and SE.MS2SN These parameters establish the minimal signal-t-nise rati t perfrm signal extractin and they d nt matter much as lng as the values are lw enugh. Fr Orbitrap data the recmmended value is 1.1 whereas fr Sciex TOF data the recmmended value is 1.5. SE.EstimateBG: This parameter is used t autmatically set a backgrund nise. Fr Sciex data this must be set t true as Sciex data typically cntain a lt f lw intensity nise that needs t be remved befre feature detectin. Setting this parameter t true will result in autmatic detectin and remval f the backgrund 2

3 nise signal. In cntrast, fr Therm data, n filtering is typically needed and this parameter shuld be set t false. SE.MinMSIntensity and SE.MinMSMSIntensity: If SE.EstimateBG = true (see abve), these parameters are nt used. If SE.EstimateBG = false, then ne can apply these user-defined minimum intensity filters t remve the backgrund befre signal extractin. As mentined abve, fr Therm data n filtering is typically needed, and these parameters can be set t a small value, e.g. 1. SE.NMissedScan: Determines hw many cnsecutive missing scans are allwed. Fr Therm data, set this t 2, whereas fr Sciex, set this parameter t 1. SE.StartCharge and SE.EndCharge: These parameters define the charge state range fr precursr in detectin in MS1 scans. Nrmally is set frm +1 t +5. SE.MS2StartCharge and SE.MS2EndCharge: These parameters define the charge state range fr precursr in detectin in MS2 scans. Nrmally is set frm +2 t +5. SE.MinFrag: Minimum number f fragments needed fr peak extractin. This parameter is nrmally set at 5. SE.StartRT and SE.EndRT: These parameters define the range f the chrmatgram at which the signal extractin will take place (units in minutes). Yu can leave the default values that cnsider the entire chrmatgraphic RT dimensin. SE.MinMZ: Defines the minimal m/z value f the chrmatgram at which the signal extractin ends. Yu can keep the 200 m/z that is the default. SE.MinPrecursrMass and SE.MaxPrecursrMass: These parameters define the range f the precursr mass fr signal extractin. Nrmally it ranges frm 400 t #Islatin windw setting WindwType: This parameter defines the islatin windw schema and currently it supprts the fllwing windw type: SWATH (fixed windw size), V_SWATH (variable SWATH windw), MSX, MSE, psmart. Set this t V_SWATH WindwSize: It defined the size fr fixed windws. Only valid fr Sciex data and nt required fr Therm data. Variable SWATH windw setting: Yu can define the windw islatin schema using the start m/z, end m/z, separated by Tab. This is what we will d! Cautin! We need t specify the variable SWATH windws. Fr this yu need t g t the Data flder and pen swath64_nheader.txt r dia_qe19_w_header.txt in Ntepad++. Cpy the entire file cntent. Paste in between: ==windw setting begin ==windw setting end Save the parameter file as: diaumpire_se_absciex_params_diacurse.txt r diaumpire_se_therm_params_diacurse.txt respectively. Nw we are ready t run DIA-Umpire frm the cmmand line. Open the bash script run_diaumpire_librarygeneratin.sh frm the flder Tutrial3_DIAUmpire. Like in Tutrial 1 we will first change the wrking directry. Therefre run the mmand n line 2: cd /c/dia_curse/tutrial3_diaumpire/ 3

4 DIA-Umpire writes all utput files t the directry f the input sample. Since we want t keep yur data directry clean, we will cpy ur DIA file f interest t the flder fr tday s tutrial. The fist cmmand fr cpying is already written in yur script. Cpy the secnd cmmand as well (line 9) in rder t cpy the secnd file, t. Change the file names and directry if yu are using the QE data. Run thse cmmands: cp /c/dia_curse/data/dia_data/ttof/cllinsb_i180316_001_sw-a.mzxml \ /c/dia_curse/tutrial3_diaumpire/ cp /c/dia_curse/data/dia_data/ttof/cllinsb_i180316_002_sw-b.mzxml \ /c/dia_curse/tutrial3_diaumpire/ Nte! cp stands fr cpy and it mves the file specified in the first argument t the directry specified in the secnd argument. Nw run the signal extractin mdule f DIA-Umpire using the fllwing cmmand (Again: Remember t change the file names fr the data and the parameter file if yu use QE data): java -jar -Xmx8G DIA_Umpire_SE.jar \ cllinsb_i180316_001_sw-a.mzxml \./parameter_files/diaumpire_se_absciex_params_diacurse.txt \ java -jar -Xmx8G DIA_Umpire_SE.jar \ cllinsb_i180316_002_sw-b.mzxml \./parameter_files/diaumpire_se_absciex_params_diacurse.txt \ Nte! java jar is the standard cmmand t run a java tl frm the cmmand line. The ptin Xmx8G specifies that 8 GB f ram shuld be used fr the jb. This might have t be adjusted depending n the files t be analyzed with DIA-Umpire. Generally, all paths need t be adjusted t the file system yu are using. Yu will see that yur directry is being filled with different utput files frm DIA-Umpire. Fr tday s tutrial, we will nly fcus n the final utput mgf files that cntain all the pseud-ms2 spectra classified in different quality levels accrding t hw the precursr in was detected: Q1, Q2 and Q3. Q1 file cntains MS2 spectra crrespnding t peak grups with mre than tw istpic peaks detected in MS1 spectra Q2 file cntains MS2 spectra crrespnding t peak grups with nly tw istpic peaks detected in MS1 spectra Q3 file cntains MS2 spectra crrespnding t peak grups fr which the precursr infrmatin was derived frm the detected unfragmented precursr in MS2 spectra Nte! Each file crrespnds t a different quality level f precursr ins. These spectra are written t separate files because they must be searched separately against a prtein sequence database as a cnsequence f differences in FDR estimates fr these varying quality data In rder t prepare the DIA-Umpire utput fr the database searching and library generatin step, we nw cnvert all mgf files t mzxml files using MSCnvert. Run MSCnvert using the fllwing cmmand: 4

5 mscnvert --mzxml *.mgf \ Nte! The ptin --mzxml means that yu cnvert t mzxml. The asterisk (*.mgf) means that any file that ends with the extensin mgf will be cnverted Database search & spectral library generatin In this secnd part f the tutrial we will generate a spectral library frm the pseud-ms2 spectra we generated in step 1 with DIA-Umpire. We will generally fllw exactly the same prtcl as in Tutrial 1 fr library generatin frm DDA files. Like this yu can repeat and further extend n what yu have already learned. We dn t explain all parameters in detail again in this tutrial, fr references please lk int Tutrial 1 in case yu need a recap. First, we perfrm a database search fr the three mzxml files by using the cmet parameter file we generated in Tutrial 1: cmet -P/c/DIA_Curse/Tutrial1_Library/parameter_files/cmet.params.highhigh_TTOF \ cllinsb_*q*.mzxml \ Nte! We run all cmet searches in ne cmmand by using the asterisk (cllinsb_*q*.mzxml). This means that all files (Q1, Q2 and Q3 frm bth inputs) are prcessed after each ther by cmet. If yu remember frm the first tutrial, the next step is the scring f PSMs with PeptidePrphet: xinteract -dreverse_ \ -OARPd \ -Ninteract_Q1.cmet.pep.xml \ cllinsb_*q1*.pep.xml \ xinteract -dreverse_ \ -OARPd \ -Ninteract_Q2.cmet.pep.xml \ cllinsb_*q2*.pep.xml \ xinteract -dreverse_ \ -OARPd \ -Ninteract_Q3.cmet.pep.xml \ cllinsb_*q3*.pep.xml \ Fllwed by cmbinatin and rescring f all PSMs within iprphet: InterPrphetParser DECOY=reverse_ \ interact*.pep.xml \ iprphet.pep.xml \ 5

6 Next, we perfrm a MAYU analysis t subsequently select an iprphet prbability threshld that crrespnds t a 1% glbal prtein FDR. perl /c/tpp/bin/mayu.pl \ -A iprphet.pep.xml \ -C /c/dia_curse/data/napedr_3mixed_human_yeast_ecli_ _irt_reverse.fasta \ -E reverse_ \ -G 0.01 \ -H 101 \ -I 0 \ Nte! At this step, yu have t perfrm the manual inspectin f the MAYU utput file in rder t select an iprphet prbability scre that crrespnds t a glbal prtein FDR f 1%. Open the file ending with _main_1.07.csv in Excel Identify the clumn with the name prtfdr G dwn until yu reach the rw with the last value that is smaller 0.01 Mark the rw and find the clumn IP/PP, which is the iprphet prbability scre Write dwn the number in yur marked rw this is the scre cutff yu use fr the further analysis (result shuld be in the range f: in ur example) Nw it s time t generate a spectral library using SpectraST. First, spectra are filtered and retentin time alignment is perfrmed Nte! Remember t change the acquisitin setting t cihcd fr the QE data spectrast -cnspeclib \ -cicid-qtof \ -cf "Prtein! ~ reverse_" \ -cp \ -c_irt/data/data/irtkit.txt \ -c_irr iprphet.pep.xml \ 6

7 Afterwards, cnsensus spectra are generated fr each peptide in: Nte! Remember t change the acquisitin setting t cihcd fr the QE data spectrast -cnspeclib_cns \ -cicid-qtof \ -cac SpecLib.splib \ Generate a SpectraST MRM transitin list: Nte! Remember t change the acquisitin setting t cihcd fr the QE data spectrast -cnspeclib_pqp \ -cicid-qtof \ -cm \ SpecLib_cns.splib \ Cnvert the SpectraST MRM t TraML TargetedFileCnverter \ -in SpecLib_pqp.mrm \ -ut transitinlist.traml \ Generate target assays by cpying this cmmand in yur script and run it: Nte: When yu use the QE data, adjust the windws file accrdingly. OpenSwathAssayGeneratr \ -in transitinlist.traml \ -ut transitinlist_ptimized.traml \ -swath_windws_file /c/dia_curse/data/swath64_w_header.txt \ In rder t enable decy-based errr-rate cntrl with pyprphet dwnstream f OpenSWATH, it is imprtant t append the assay library with decy-query parameters. Append decy transitins t the spectral library: OpenSwathDecyGeneratr \ -in transitinlist_ptimized.traml \ -ut transitinlist_ptimized_decys.traml \ -methd shuffle \ Cnvert the library t the pqp frmat fr the further OpenSWATH analysis: TargetedFileCnverter \ -in transitinlist_ptimized_decys.traml \ -ut transitinlist_ptimized_decys.pqp \ Nw yu have a final library that can be used fr targeted, peptide-centric DIA analysis with OpenSWATH. 7

8 Finally, t inspect the library, we will cnvert it back t the tsv frmat and take a clser lk. Run TargetedFileCnverter TargetedFileCnverter \ -in transitinlist_ptimized.traml \ -ut transitinlist_ptimized.tsv \ Yu n gt thrugh the entire library generatin wrkflw and have a script that yu can use as a reference and scaffld fr the library generatin f ther datasets. Save the shell script and exit RStudi. In Excel, cunt the number f prteins and peptides fr each rganism as explained in Tutrial 1. Hw d the libraries cmpare? Can yu identify a specific difference between the tw libraries? We wuld like t thank SystemsX fr supprting the Zurich DIA / SWATH Curse