Commercially acquired proteins (α-amylase, amyloglucosidase, apo-transferrin, β-galactidase, carbonic anhydrase, catalase, phosphorylase B, glutamic dehydrogenase, glutathione transferase, immunoglobulin γ, lactic dehydrogenase, lactoperoxidase, myoglobin) were used, each in two independent preparations (each with a concentration of 100 fmol). For chromatography, a UltiMate Plus Nano-LC system. LC-Packings - A Dionex Co was used. Chromatographic mobile phases were: loading mobile phase 0.1% TFA in water, separation mobile phase A 5% acetonitrile in 0.1% aqueous formic acid and mobile phase B 80% acetonitrile, 20% water with 0.08% formic acid. The sample was loaded for 10 min onto a reversed phase trap column (PepMap C18, 300 μm ID × 5 mm length, 5 μm particle size, 100 Å pore size, LC Packings - A Dionex Co., not online with the separation column) at a flow rate of 20 μl/min and washed free of ion pairing agents and other impurities.
The gradient for separation of analytes starts at 10 min when the trap column is switched online with the separation column (PepMapC18, 75 μm ID × 15 cm length, 3μm particle size, 100 Å pore size) at 0.275 μl/min. The gradient used starts at 100% mobile phase A and changes to 50% mobile phase B from 10 minutes (trap column and separation column online) to 40 minutes. Additional wash step of 90% mobile phase B is incorporated in order to clean the separation column and elute hydrophobic analytes. After the separation, the trap column is switched offline and equilibrated with loading mobile phase. The analytical nano column is equilibrated with separation mobile phase A. The mass spectrometric data are only recorded for the time both columns are online.
The mass spectra were recorded with a Thermo Finnigan LTQ (positive nano-ESI mode, ionizing spray voltage: 1.5 kV, enhanced mass-spec full-scan range: 220 - 2000 amu). The much smaller datasets for bovine serum albumin (BSA), yeast alcohol dehydrogenase (ADH) and human transferrin (TRF) recorded with a 3D IT mass spectrometer (model DecaXP Thermo Finnigan) were reused from our previous work .
File processing and MS/MS data analysis
The MS/MS output was converted into mgf-files (MASCOT generic format). Each dataset was then separately processed using the MS Cleaner program (with default internal parameters), generating two new mgf-files with cleaned and bad (non-interpretable) spectra respectively. The MASCOT search parameters were the same in all runs (enzyme: trypsin; fixed modifications: carbamidomethyl (at cysteines) for BSA, ADH and TRF, carboxymethyl (at cysteiness) for other proteins; variable modifications: oxidation (at methionines); peptide charges: 1+, 2+ and 3+; mass values: monoisotopic; protein mass: unrestricted; peptide mass tolerance: ± 2 Da; fragment mass tolerance: ± 0.8 Da; max. missed cleavages: 1). The MASCOT search results output html-file was formatted with standard scoring, a significance threshold of p < 0.05, and an ion score cut-off for each peptide of 30. The non-redundant protein database (NCBI) was used (both for the local PC MASCOT installation and for the MASCOT Linux cluster).
In this work, we compare the MASCOT interpretation results of non-pre-processed tandem MS datasets with those obtained in a two-step preprocessing. First, each spectrum (.dta-file) is analyzed with the sequence ladder algorithm. Only those spectra that pass this test, are then processed with the background removal routines described in our previous publication .
The sequence ladder algorithm
For this algorithm, two parameters are critical - the values n(in amino acid residues), the minimal length of the sequence ladder, and s(in per cent), the fraction of peaks from the spectrum that is considered of high intensity. The number n can theoretically be just one (i.e., we would require just two high intensity peaks that are spaced by the mass difference corresponding to the mass of one of the amino acids); yet, larger values of n(for example, between two and six residues) represent stricter requirements to the sequence ladder. The other parameter s restricts the search space. For this purpose, the peaks in the spectrum considered (i.e., in one .dta-file) are sorted by intensity into a list with descending order. Only the first part of this list (the fraction s of the total set) is used for searching sequence ladders. The condition of s= 100% implies that all peaks are included; yet, considerably smaller values of s are desirable since they would help unselecting more non-interpretable spectra. Once the set of high-intensity peaks is defined, their pair-wise mass differences are compared in a systematic enumeration with the masses of amino acids residues (to select pairs of peaks separated by the mass of any of the amino acids within a user-defined accuracy) and it is tested whether a subset of peaks forms a sequence ladder of the required minimal length. If at least one such ladder is found, the search is stopped and the procedure is restarted with the next tandem MS spectrum in the dataset.
Modifications of the noise detection algorithm
If a spectrum has passed the sequence ladder test, it is handed over to a series of routines for noise and background detection. The procedures for removing multiply charged peak clusters with the etalon method and for the suppression of high-frequency noise with a low-pass filter after Fourier transformation have been described in a previous publication  in detail and have been applied without changes here.
The algorithm for the removal of latent periodic background (including deisotoping) received another option with respect to the determination of the base frequency of the noise. We observed that the determination of the base frequency f
in the first power spectrum (see sections 3.3 and 3.5 in ref. ) is, in rare cases, not always as unambiguous as in Figure 2A of ref.  since several almost equally intense peaks may appear in the second-level Fourier transform. Wrong base frequency f
detection leads to wrong multi-band rejection filter creation and a few interpretable spectra can be lost after applying this technique. This ambiguity can be avoided by not choosing the frequency of the most intense peak in the second-level Fourier transform. Rather, we propose to iterate through all possible base frequencies detected in this spectrum. For each of these frequencies, theoretical maxima and minima expected in first level Fourier transform are calculated. Best matching between the theoretical and experimental maxima and minima (see Figure 3 in ref. ) confirms the right base frequency. We call this method "soft recognition" of latent periodic noise which should be applied if minor improvements in sequence coverage (in rare cases, a single additional peptide) are more important than data size reduction; yet, it leads to an increment of about 10% of the computation time compared with the previous method .
Standalone implementation and cluster version
We created two implementations for MS Cleaner 2.0. A single-machine Windows version was used for most of the computations in this article and it is available for free download at the associated WWW site. A Unix-Port of the MS Cleaner 2.0 software is deployed in a clustered environment in order to guarantee scalability. The spectrum file is partitioned into workpackages, which are then handed over to a batch queuing system for scheduling on available nodes. Each node processes the spectra in its workpackage and transfers the results back to the controlling application where they are post-processed into the final good/bad spectra output. This version is the engine behind the MS Cleaner 2.0 WWW server.
At the WWW-site http://mendel.bii.a-star.edu.sg/mass-spectrometry/MSCleaner-2.0/, supplementary resources are available: all experimental mass-spectrometry data used in this work, the processed spectra, the user manual, default parameter datasets and a free downloadable Windows version of the program MSCleaner 2.0 as well as free access to a MSCleaner 2.0 WWW server accessing a local Linux cluster. Other implementations can be obtained on request.