dtaselect manual

dtaselect manual

Download either version 1.9 or 2.0 -- you can read about the difference between these versions below. Both versions are free but you must agree to a Liceense Agreement and supply them with your name and details. Make sure all of the files in the archive are actually in the C:\dtaselect\ folder. There are only two parameters that we routinely change. The FASTA database should be a local copy of the same database you used in your SEQUEST search..Double-click to open in a web-browser. Enjoy. Furthemore, careful use of DTASelect 2.0 should lead to slightly improved sensitivity by enabling fine-tuning of quality thresholds and parameters within acceptible accuracy limits. However, DTASelect 2.0 is also a bit more sophisticated and the parameters should be used with care. For more information about what, precisely, the difference are, read on:The basis for these features is the use of reverse-concatenated protein databases. Reversed or shuffled protein sequences serve as a decoy with which quality thresholds can be empirically derived to maximize sensitivity and accuracy. To determine the accuracy of a particular parameter-set, SEQUEST is run using a reverse-concatenated database and DTASelect is used to filter the SEQUEST results according to certain quality thresholds (like XCorr, deltaCN, etc). One can then count up the number of decoy peptides that were identified. Thus, one can empirically estimate the false positive rate given a certain set of DTASelect parameters. These parameters are quite stringent and rarely lead to false-positive identifications (typical false-positive rates with these parameters are below 1%). That being the case, it is often possible to relax those thresholds to identify more true peptides without adversely affecting your false-positive rate, but manual adjustment of thresholds is tedious and difficult to do accurately.http://alatlukisindowarna.com/sitefiles/7fgu35-manual.xml

• dtaselect manual.

DTASelect2 employs a linear discriminant function to empirically determine which parameter-values should be chosen to achieve a desired false-positive rate. So, rather than explicitly specifying what XCorr values should be used (or relying on the default values) the user explicitly tells DTASelect2 to achieve a certain false-positive rate (the default is 5%) and DTASelect2 will analyze the distribution of decoy- and forward-peptides as relates to various quality scores (like XCorr) and empirically determine how to set these parameters to achieve the desired false-positive rate.DTASelect2 will try to achieve the desired false-positive rate, but sometimes does a poor job so one must keep an eye on the actual false-positive rate as well. Similarly, peptides with different tryptic status should not be evaluated together because fully-tryptic peptides fragment somewhat differently (better) than semi- or non-tryptic peptides. This usually has the added advantage of increasing your fully-tryptic peptide results because the majority of false-positive matches are usually semi-tryptic so the stringency for semi-tryptic peptides is increased which allows you to relax the stringency for fully-tryptic peptides.This can DRAMATICALLY increase your actual false-positive rate. Use with care. Download and run this file to install ActivePerl v5.8.8, or get the latest (free) build directly from ActiveState. (To install the activeperl-5-8-8.msi file, save it to your Desktop and right-click it and select 'Install'). Place the desired script into the appropriate folder, as described below. The archive consists of the following:To use, place dtall.pl in a folder that contains (1) a sequest.params file and (2) many folders, each containing SQT file(s).http://www.dottorini.com/fckupload/7kg-condenser-electrolux-dryer-edp2074pdw-manual.xml

Open a command prompt and type:This script will open up two windows that will run two copies of DTASelect simultaneously on all the sub-folders in your current directory To use, place in a folder that contains many sub-folders each containing DTASelect results.Here are two relatively non-redundant, reverse-concatenated variants of the IPI database (right-click and select 'save as.'). Abstract The components of complex peptide mixtures can be separated by liquid chromatography, fragmented by tandem mass spectrometry, and identified by the SEQUEST algorithm. Inferring a mixture's source proteins requires that the identified peptides be reassociated. This process becomes more challenging as the number of peptides increases. DTASelect, a new software package, assembles SEQUEST identifications and highlights the most significant matches. The accompanying Contrast tool compares DTASelect results from multiple experiments. The two programs improve the speed and precision of proteomic data analysis. Keywords: proteomics, protein identification, SEQUEST, data analysis, software, differential analysis, subtractive analysis, MudPIT, assembly 1. Introduction Protein identification through mass spectrometry has become a major application of genetic sequence information. Two forms of protein identification have emerged. “Mass mapping” enzymatically digests an unknown protein and then identifies it by the masses of the peptides produced.The sequences are quickly checked against the spectrum by a preliminary scoring algorithm, and obvious nonmatches are removed. A more extensive scoring algorithm cross-correlates sequence-derived theoretical spectra against the observed spectrum, and sequences are ranked on the basis of these scores. SEQUEST's automation of this process has helped the use of tandem mass spectrometry to expand from analytical chemistry to biology.

Because SEQUEST functions at the level of individual spectra, additional software is necessary to assemble information at the protein level. SEQUEST SUMMARY 15 is broadly used, but it can only coordinate the results from a single dimension of chromatography. AUTOQUEST, 15 a more extensive algorithm, can handle multidimensional results, but it has several limitations. As the number of peptides increases, the output grows in size very rapidly. It categorizes identifications by score, but the range of scores in each category is too broad for adequate discrimination. The program cannot organize results from DNA database searches. In addition, AUTOQUEST will not install alongside the commercial version of SEQUEST. Neither program allows much latitude for users to set standards on which identifications should be reported and which should not. The DTASelect package was developed to assemble and evaluate shotgun proteomics data. The program applies multiple layers of filtering to SEQUEST search results and supplies several analytical reports in text and HTML files. It is particularly useful for comparing results from different experimental conditions. The summarization phase reads individual spectrum identifications from the SEQUEST output (.out) files and assembles information for each locus (the database identity of the protein or gene from which the peptide derives). The evaluation phase applies the criteria indicated by the user for spectra and proteins, removing those that do not qualify. The reporting phase creates files to be read in a web browser or spreadsheet. At each step, the user is notified of DTASelect's progress. Summarization collects the most important information from SEQUEST identification. The peptides are sorted by locus, and peptides for each locus are sorted by sequence. Next, the same database used by SEQUEST is consulted for the full protein sequence, descriptive name, and peptide positions.

The information from the output files and database lookup is stored to the DTASelect.txt file so that this step can by bypassed in subsequent analyses of this sample. Evaluation applies user-selected criteria to the matches. An identification is included only when it passes all specified spectrum criteria (see Table 1 ). Selection is next conducted at a higher level, retaining proteins that have a sufficient number of different peptides or that have at least one peptide showing up several times (see Table 2 ). Two filters reduce the redundancy of results. DTASelect can choose the best example of multiple spectra matching the same sequence by score or by signal intensity. In addition, many proteins may be indicated by exactly the same sets of peptides; DTASelect groups together proteins that are identical in sequence coverage. These selections highlight peptides and proteins that represent the entire sample. These filters are employed in the first pass of the filtering step. Each spectrum must pass all the standard filters to be included in the DTASelect report. Changes can be made at the command line or in a DTASelect configuration file. Filtering is conducted at the locus level after it has been performed for individual spectra. A protein must pass either the -p or the -r filter to be retained in the DTASelect reports. The reporting phase generates several different files. This information is also recorded in DTASelect-filter.txt, a tab-delimited text file suitable for review in a spreadsheet. For better data portability, DTASelect includes a graphical user interface, which can be used independently of SEQUEST's support programs (see Figure 3 ). This interface provides superior support for identifying sequence ions from spectra, especially those that come from triply charged precursors. In another mode, it displays the selected protein's sequence coverage. These files and the graphical user interface present the primary information produced by DTASelect.

Open in a separate window Figure 1 Sample DTASelect.html fragment. Each protein identity is printed beside the count of peptide sequences associated with it. The number of spectra representing those sequences is also shown, along with the protein's sequence coverage, length in residues, molecular weight, calculated pI, and description from the specified database. If multiple proteins in the database correspond to the same set of peptide sequences, the proteins are grouped together. The peptides found for each collection of loci are listed beneath it. Spectra matching the same sequences but possessing different charge states (discernible by the “.2” vs “.3” suffixes on filenames) are not considered duplicates. Peptides that are uniquely found at a particular locus are indicated with asterisks. The fields enumerated for each peptide include file name, XCorr, DeltCN, precursor ion mass, Sp rank, percentage of fragment ions found, copy count, and sequence. Addition symbols (as seen with w26S.0501.0501.1) link to other proteins in the report that also contain the indicated peptide. The similarity for protein YOL055C to YPL258C is reported, showing that one peptide present for YOL055C matches to the other protein and one peptide does not. Shown are total counts for proteins, peptides, and spectra. The difference between the nonredundant and redundant protein counts reflects that some proteins have been grouped together because of identical sequence coverage. Open in a separate window Figure 3 DTASelect graphical user interface. Identified peaks are color-coded blue for y ions or red b ions. The letters along the top of the window show the correspondence between fragment ions and sequence. Clicking on a peptide will cause its spectrum to be shown. Selecting a protein will show sequence coverage. Supplementary information is presented in two auxiliary files. The first contains information about protein similarity.

Multiple proteins may feature a particular peptide, but this is not always an indicator of substantial similarity among the proteins. Similarity is calculated to be the number of peptides shared between two proteins minus the number of nonmatching peptides; a score of zero indicates that the number of peptides that match equals the number that do not match. DTASelect includes all similarity scores greater than or equal to zero in its primary report and in a separate similarity table. Another DTASelect report evaluates chromatography. After filters have been applied, the number of singly, doubly, and triply charged peptides remaining from each round of chromatography is given, along with a profile of where in each cycle the peptides eluted. This information can help users optimize their separations for both completeness and efficiency. 2.2. Contrast Algorithm Contrast extends DTASelect across multiple samples or multiple sets of criteria. Its three phases are reading, comparison, and reporting. The program is configured by changes in the Contrast.params file, and the specified DTASelect results are read from the disk. The presence of each protein is compared between the described sets, and the similarities and differences are reported in HTML and text formats. The reading phase begins with the Contrast.params file, which allows the user to specify the DTASelect results of interest and to enumerate criteria sets to be applied to the results. Contrast requires at least one sample and set of criteria to be specified, but more can be included as long as the number of samples multiplied by the number of criteria sets does not exceed 63. Each DTASelect result is processed under each set of criteria, so four samples and two criteria sets will result in eight “data sets,” corresponding to eight columns in the output. Once the Contrast.params file sets up the run, Contrast will read the DTASelect.txt file for each sample and apply the specified filters as in DTASelect.

To compare the data sets, Contrast develops a master list of proteins including each locus appearing in any of the data sets. Each data set is assigned a power of two. Each locus' pattern of presence or absence is stored by summing the powers of two for the data sets in which the protein is found. Finally, the master list of loci is sorted by these power sums and then by protein name. The effect is that loci are grouped by patterns of appearance across data sets. Contrast produces extensive output. For users who want to see how individual peptides for each protein differ among data sets, Contrast offers a “verbose” mode, which provides this level of detail (see Figure 6 ). Although a complex comparison can yield a lengthy report, Contrast's output organization ensures that details can be found in predictable locations. Open in a separate window Figure 4 Sample Contrast.html fragment. This represents a group of proteins that appear in the new MudPIT sample but not the previous experiment when the same criteria are used against each. Each row in the table represents one protein, and the numbers in the columns are the sequence coverage percentages found in each data set (or, in the Total column, the cumulative sequence coverage across multiple columns). Several such sections appear in each Contrast output file, one for each combination of presence and absence. Open in a separate window Figure 5 Sample Contrast.html summary. Each row in this table represents a particular combination of presence and absence in each of the data sets, with the “X” marks indicating this pattern. Each row's count links back to the appearance of the group above it in the Contrast.html file. Of the 118 proteins appearing, 60 were present in both samples, 18 were present only in the “new” analysis, and 40 were found only in the “prev” experiment. Open in a separate window Figure 6 Sample Verbose Contrast.html fragment.

Proteins YDR471W and YHR010W were found in both samples under this criteria set, though with different sequence coverages (17.6% and 21.3%, respectively). One peptide was found in both samples, but the other peptides were found in only one. The highest XCorr for each peptide in each sample is shown beside its sequence. Cumulatively, these peptides add up to 30.9% sequence coverage. The sequence coverage percentages for each sample lead to the relevant sections in the respective DTASelect output files. All but two of these 37 proteins had been observed before: 32 had been identified as part of the 26S proteosome, one was known to associate with the complex, 12 and two were exogenous contaminants. 8 DTASelect prepared its report in minutes (see Figure 1 ). After filtering and summarization, the 33 841 output files were pared down to 4157 spectra representing 968 distinct peptides and 77 proteins (see Figure 2B ). Upon manual evaluation of the results, only four of these 77 proteins (5.2%) passed through the filters as false positives. Such percentages are typical of the results for these experiments. Manual evaluation confirmed that none of the 60 shared proteins were passed through the filters as false positives. For this group of proteins, all were either components of the proteosome, 32 associated with the proteosome, 8 highly abundant yeast proteins that are found in numerous purifications, 18 or exogenous contaminants such as immunoglobulin and trypsin. 2 Contrast produces an analysis within a few minutes that would take a few hours to produce manually. The proteins are sorted by the experiments in which they are found, and hypertext links provide access to more detailed information for evaluative purposes (see section 2.2). When multiple positive and negative controls are part of the experiment, Contrast's automation becomes even more valuable. 3.4.

DTASelect and Contrast Applications: Miscellaneous Since a large variety of customizable filters are available in DTASelect and thus Contrast, individual researchers can adapt the programs to their own needs. Contrast can be used for a “step analysis” in which progressively more stringent criteria are applied to data from a single experiment. Those proteins that meet the highest stringency requirements are sorted to the top of the output file, and those passing only the lowest criteria are sorted to the end. This approach is well suited for stratifying the protein identifications in a complex sample. For projects focusing on a particular protein, the verbose mode of Contrast can show how individual peptides vary among samples. If modifications like phosphorylation or methylation are included in SEQUEST search parameters, DTASelect can be used to list only those peptides found to be modified, saving time for researchers interested in this subset of the data. It can also be used for chemical labeling tags such as the ICAT reagent. 17 The open-ended design of the programs accommodates many purposes. 4. Conclusions DTASelect assembles protein-level information from peptide data more quickly, flexibly, and uniformly than existing tools. DTASelect focuses attention on peptides of interest by sweeping away less likely identifications. By streamlining data analysis for proteomics, DTASelect makes more complex experiments feasible. Contrast provides extensive sample comparison capabilities. Whether stepping through multiple levels of criteria to triage the proteins identified from a single sample or differentiating the protein content of multiple, complex samples, Contrast is a potent tool for the molecular biologist. Most users of SEQUEST can benefit from DTASelect and Contrast. Just as SEQUEST automated the time-intensive process of spectrum identification, DTASelect and Contrast automate the process of SEQUEST result interpretation.

Acknowledgments We thank Rati Verma and Raymond Deshaies of the Division of Biology and Howard Hughes Medical Institute at Caltech for graciously providing proteosome samples used to demonstrate the software. D.L.T. was supported by a National Science Foundation Graduate Research Fellowship. W.H.M. was supported by the Merck Genome Research Institute. Partial support was derived from NIH Grant Nos. RR11823 and R33CA81665. Verma R, Chen S, Feldman R, Schieltz D, Yates JR, III, Dohmen J, Deshaies RJ. Hunt DF, Yates JR, III, Shabanowitz J, Winston S, Hauer CR. Tabb DL, Eng JK, Yates JR., III. Protein Identification by SEQUEST. In: James P, editor. Gygi SP, Rist B, Gerber SA, Turecek F, Gelb MH. Related topics are listed below. Ascore can be used for phosphorylation site localization. After selecting the DTASelect filter, run “PhosphoAnalysis” by clicking “Run” as shown below:Figure 10.6.1: Link for Quantitative Comparison. The Label-Free Analysis Page will show previous analysis results.To start, you can click the “Project” tab to view all existing projects with a set of experiments. Figure 10.2.1: Home Page.Bruker Scientific LLCBillerica, MA 01821Users can quantify peptides without running a search engine. Click here for more info From the experiment page, click the ProLuCID search button to open search parameter page.General analysis version 1A. Login to the IP2 server by ssh command line.Then, click the “view” link to list the DTASelect search result in the table. Figure 6.1.1 Home page.With this filter, a protein keeps only unique peptides. Further, this filter assign shared peptides to their redundant proteins.Figure 8.3.1We understand that you want to receive your results fast without trading accuracy.Figure 8.4.2Uploading Thermo experiment log file will help generate additional information.Figure 10.4.1: Link for Quantitative Comparison.Users can use either identification compare tool (see manual section 10) or the fractional analysis tool.A.

Check that tomcat server is running (as ip2 user): killtomcat (this command will restart the IP2 server)The IP2 platform creates multiple types of quantitative output files as analysis of the data and uses tabular or graphical tools for display in web browser. The graphical web user interface is a great tool to easily access data for scientists.Figure 7.7.1. Next, add the proper ppm shift value as shown in the image and click submit. This mass corrector will shift precursor masses in MS2 and peaks in MS1 spectra.From the experiment page, click the batch search link. Figure 7.8.1: Batch search link. Users can select experiments to submit batch search by clicking radio buttons.In the ProLuCID search page, differential modification search options are recommended as below:In the experiment page, click “Whole view” to display protein database search results in one page.Users need a web browser (e.g. firefox, IE, safari, Chrome, etc.) to access the IP2 system.Figure 10.3.1: Link for Quantitative Comparison.Click it to view the protein search result list.This account is not for data analysis. It is used to manage user accounts in your IP2 server, including creating new users, enabling or disabling users, and updating user information.Move the mouse to “Database” and click to navigate to the page which will show all the existing databases. Figure 4.1.1 In the pepxml file, you need to make sure the fasta database name is available in the IP2 database page.In the backend analysis, the IP2 uses Ontologizer9Batch spectra upload tool can allow users to upload multiple spectral files and create corresponding experiments automatically.Figure 9.7.1URL can be your local IP2 server, a remote one, or cloud one depending on your IP2 license type. Figure 3.1.1 Login page for IP2The advantage of the Targeted Quantitative Analysis (TQA) tool is that it looks for peptides in the entire ms1 spectra. Each one of these steps is of equal importance.

That is, if any one of these steps is inaccurate or of low confidence, the entire quantitative analysis is equally inaccurate and of low confidence. Identification of phosphopeptide sequences and measurement of phosphorylated peptides leverages the accuracy of global quantitative proteomics (i.e. SEQUEST, ProLuCID, DTASelect, and CENSUS). When the appropriate filtering methods are used, these two steps are already of high confidence. The remaining essential steps of phosphosphopeptide validation, determination of phosphorylation stoichiometry of phosphopeptides and phosphorylation site localization have been addressed by the introduction of Debunker and AScore. This manual describes the integration and usage of Debunker and Ascore in the quantitative pipeline (Phospho Quant) of the web-based Integrated Proteomics Pipeline (IP2) using Orbitrap MS data and 15N isotopic labeling. These methods can be applied to other high mass accuracy instruments (i.e. TOF, FTIRC, etc.) and labeling strategies (e.g. SILAC).The following steps will describe the use of ProLuCID, but the SEARCH MANUAL can be consulted for searching using SEQUEST. The following screen should appear: Select the location for the computational search to be performed, either “Cluster” or “Cloud”, depending on your available resources.For this example analysis, mouse was the sample origin so we select a mouse database. See the DATABASE manual for uploading databases if one is not present for your sample organism. For this example analysis, multistage-activation CID was used so we selected “CID”. An Orbitrap was used for these MS analyses, so we select “High resolution”. Although not as confident for phosphorylation analysis, if low resolution precursor data was acquired (i.e LTQ) select “Low resolution”. Please see the SEARCH MANUAL for a description of the precursor and fragment mass tolerance default values.

Trypsin was used for this sample, but see the SEARCH MANUAL for a description of settings for other proteases:Phosphorylation events near lysine or arginine residues can inhibit trypsin cleavage, so leaving the option for only one tryptic end and more than 3 missed cleavages is advantageous to identifying more phosphopeptides. If adequate computational resources are available, a “Specificity” of “none” can be used to potentially find all phosphopeptides.For this example, we selected “yes” and “N15”. If SILAC was used, select “Selected amino acids” and enter the appropriate mass shifts. A full description for this can be found in the QUANTITATION MANUAL.This is performed automatically after the search and can be repeated to adjust the filtering parameters. The primary options for this are shown in the following window and steps.Only one phosphorylation site or phosphopeptide may be present or detected for a protein.If a “Specificity” of “none” was used in the search, “0” can also be selected to maximize phosphopeptide identifications.A common FDR is 0.1% at the peptide level.A common value is 10 ppm for Orbitrap data, but can be assessed based on the precision of the instrument used.After DTASelect filter run “PhosphoAnalysis” by clicking “Run” as show in the window below.Note: For phospho peptide quantitation, PhosphoAnalysis MUST be run before Quantitative Analysis.To perform a phosphopeptide comparisonA new page will open to display the Venn diagram. The text and circles can be rearranged by left or right clicking and holding down while moving. The image can be printed to a PDF or a printer by clicking “Print”.These results can be exported to view in other programs using the different “Export options” at the bottom of the page.Select the appropriate labeling method. For this example we used 15 N. For a description of procedures for other labeling methods see the QUANTITATION MANUAL. The default quantitation values for high mass accuracy data (e.g.

Orbitrap) and appropriate masses differences for each amino acid will appear in the following table. The following screen appears with a link to run a new comparison. The list of experiments which have had PhosphoAnalysis run on them within that project will be listed with relevant experimental information. Start by entering a name for the replicate experiments to be combined for statistical quantitative comparison in the “group name” entry box. Next select the experiment to be combined by checking the boxes next to the experiments as shown below. Then click “Add to group”. The next screen that appears will have the first “Group name” listed of experiments that were combined on the last screen. Repeat the selection of experiment to be grouped for quantitative comparison to another group. Click “Add to group” again and repeat this process until all of the comparisons to be performed are grouped. Then click “Next”.Name the quantitative comparison in the “Give a name to save” entry box and click “Run it now”.To do this, enter the desired cutoff value for either or both in the entry boxes and click “Filter”. To reintegrate from earlier in the manual, if the Ascore is greater than 13 there is 95% confidence in the phosphorylation site and if greater than 19 there is 99% confidence in the phosphorylation site. While the phosphopeptide confidence value (Debunker Score) directly corresponds to the confidence on a scale of 1 for phosphoserine and phosphothreonine. Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts. The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated.