BACKGROUND
proteomics based on lc-ms is well established technology for discovery of disease biomarkers, drug target identification, mode of action  studies and safety marker identification in drug research.
in particular, most of these analyses are differential, i.e., comparing samples from drug-treated and untreated subjects, diseased subjects and healthy controls, mutant and wild-type samples and so on.
in cases of the differential analysis, mere identification of proteins and metabolites from the complex biological samples is not sufficient, and quantitative analysis is required.
there are two main types of quantitation methods for differential analysis using lc-ms, one being the standard-free approach  and the other being the external/internal standards approach to normalize variations by the use of stable isotope labeling methods  <cit> .
the latter approach offers high accuracy and reliability, but has the disadvantages of requiring expensive reagents and time-consuming preparation of standards in practical experiments.
the former approach offers simplicity and easy experimental design with relatively low cost, but there remain many challenges related to bioinformatics, e.g., to increase true positives, to decrease false positives, to improve analysis performance for large scale data sets and to increase reliability by normalization of data.
meanwhile, proteomics data sets have become bigger and more complex in the last decade due to the increased sensitivity, resolution and throughput of lc-ms, thus to improve automated large-scale data handling is another challenge.
in particular, it is also important to note that only 10-50% of spectra generated from lc-ms/ms have been correctly assigned for the identification in proteomics field  <cit> ; this means that about half of the spectra, which were filtered during the identification process, may sometimes play important biological roles such as known and/or unknown post translational modifications.
therefore, label-free quantitation is a reliable, versatile and cost-effective method in the biomarker discovery field.
to date, there are two major sub-approaches reported in label-free quantitation proteomics, i.e., 1) spectral counting based quantitation; 2) spectra intensity based quantitation.
in this paper we will mainly focus on the latter one.
several label-free quantitative algorithms/tools are available for proteomics based on lc-ms data .
however, one of the current challenges is to develop a highly reliable and flexible lc-ms based quantitation method for large scale biomarker discovery.
there are also only a few software tools which allow us to customize and/or add newly developed algorithms as plug-ins.
moreover, in most cases, existing algorithms were developed for only specific purposes, thus there are quite a few papers to date which compared different aspects of existing algorithms using the same data set.
here, we present a simple algorithm, which we call ab3d, which allows peak picking, isotope cluster recognition and quantitation using ms <dig> data with high reliability and with a sophisticated graphical user interface for verification using the universal mass spectrometry data visualization and analysis tool mass++  <cit> , in addition to comparing different types of existing label-free quantitation algorithm.
algorithm
data processing
in our laboratory, usually over a hundred megabyte raw data is generated per run by the most advanced lc-ms, thus it is important to considering high speed computational methods for data processing and spectra manipulation.
in addition, to adapt the same algorithm for all the different types of mass spectrometry data format, it is useful to have a universal text format for internal pre-processing.
ab3d first roughly picks all local maxima whose intensity is larger than a given threshold  as candidate peaks from the entire spectrum and then stores all the candidate peaks data into the memory, which consists of 3-dimensional mass spectrometry data, i.e., m/z, intensity and their corresponding retention time.
alternatively, all candidate peaks can be generated through an api  which is provided from different mass spectrometry vendors such as msfilereader  and lcmssolution.
ab3d generates extracted ion chromatograms  for all candidate peaks in the following steps ;figure  <dig>
a schematic overview of the ab3d peak detection algorithm.
ab3d sequentially processes all candidate peaks from the maximum intensities to minimum intensities.
to extract the xic  for a target peak a , all related peak information such as m/z, retention time and intensity were obtained by searching adjacent peaks in the range of m/z and retention time tolerances.
b and c are the minimum and maximum m/z values respectively within the tolerance.
select the highest intensity peak a  among all the candidate peaks, then seek all the possible neighbour peaks within the range of m/z and retention time.
if no neighbour peaks were found, then skip the second and third steps and move to the next highest intensity peak.
determine the start data point b and end data point c m/z values for a xic within a given m/z tolerance window.
generate the xic based on the ranges of m/z and retention time determined in step  <dig>  and then the target peak and all its neighbour peaks will be removed from the candidate peaks.
xic peak detection and quantitation
in shotgun proteomics, many peaks may be buried in background noise and the complexity of the data is very high, therefore, it is difficult to assign all peaks.
in addition, when samples are of very high complexity and the intensity of each peak is quite low, it was difficult to define the baseline in many cases.
thus, xic peak detection is another challenge for performing highly reliable quantitative analysis.
to address this, we have developed a novel mass chromatogram peak detection algorithm, in which we combine the local minimum and the weighted average peak detection algorithm to extract and compare peaks from highly complex data.
figure  <dig> shows the principle of our xic peak detection algorithm.
first, a point of highest intensity  is found using a local maximum algorithm.
secondly, a check point at a given percentage  of the peak height of the apex is used to discriminate the peak from noise, i.e., a horizontal line is drawn at the 50% height position along the rt axis, and if the line has two crossing points with the peak candidate, then the apex can be considered as a peak top.
for calculating the peak area value, the start and the end points of the peak have to be determined, so next the lowest left and right points were found below the given percentage height position by using a local minimum algorithm, and these are considered to be the start  and the end  points, respectively.
finally, the peak area value can be calculated using these points, i.e., the start, apex, and end points.
this algorithm is useful for analysis of complex samples, because the percentage from the peak top can be changed as required for particular experiments; usually a higher percentage is used to detect the individual components of a complex xic  and a lower percentage is used to detect the intact complex xic.figure  <dig>
the principle of the ab3d xic peak detection algorithm.
for local maximum peaks a and d, b-a-c and c-d-c’ were picked as two candidate peaks when a horizontal line was drawn at 50% of the highest intensity , while b-a-c’ were picked as one candidate xic peak when a horizontal lines was drawn at 30% of the top intensity .
xic peak filtering
in general, there are considerable pseudo-peaks originating from electronic and/or chemical noise for mass spectrometry-based quantitation  <cit> , therefore, reduction of the noise peaks is another challenge for label-free quantitation.
we have evaluated a large number of biological samples for optimizing a proper method of signal to noise ratio  calculation and some useful filters for reducing false positive rate as follows.
s/n ratio:  <dig> pbaseline=in+ <dig> ifnisodd.12in2+in2+ <dig> ifniseven.
<dig> pnoise=i1−pbaseline2+i2−pbaseline2+⋯+in−pbaseline2n   <dig> psnratio=i1−pbaseline/pnoise
where n is the total number of candidate peaks, i is the intensity value sorted in descending order as i <dig>  i <dig>  … in.
pbaseline is the baseline and pnoise is the noise level, respectively.
as shown in formulas  <dig> and  <dig>  the median value of intensities  is used as the baseline and standard deviation is used as the noise level in ab3d.
other major noise filters and isotopic clustering:fwhm: full width at half maximum  is well known as a noise reduction filter for spectra manipulation, it is also useful for reducing chemical and/or electrical noise for xic peaks.
the fwhm  can be adjusted according to the experimental condition and the instrument type.
peak undulation: some ugly and bumpy xic peak shapes might be generated due to insufficient data acquiring points, over loading of samples, low-spray status, and influence of overlapped other large peaks, we have focused on the number of up-downs within a xic peak and after processing and analysing over hundreds of biological samples, we found that peak undulation is one of the useful factors to reduce false positive peaks in most cases, the more numbers of peak undulation the lower data quality of xic.
therefore we employed the peak undulation filter in ab3d as an adjustable parameter .
number of spectra  for each xic peak: generally, xic is constructed from multiple mass spectra and the number of spectra for each xic peak can be considered as a factor to evaluate the quality of xic peaks; a too small number of spectra for a xic can be considered as a lack of confidence for advanced quantitation and identification, thus the number of spectra of the xic filter is pre-set in ab3d.
isotopic clustering: ab3d processes all sorted candidate peaks in descending order, first selects most intense peak as a target peak, then a m/z  moving window is stepped backward and forward the target peak along m/z axis.
if there is a peak signal within the moving window and the m/z tolerance, then the peak will be assigned as a member of an isotopic cluster and move to next, when there is no peak within the moving window and the m/z tolerance, the isotopic cluster is finally formed and a monoisotopic peak will be determined by using reported pattern matching method  <cit> .
in order to address the overlapped peaks and the isotopic clusters, only the monoisotopic peak was removed from the candidate peaks before moving to the next target peak.
the program ends when all the candidate peaks were assigned.
in addition, one feature of our quantitative algorithm is that it has the ability to deal with highly complex proteomics data by merging ions that originate from the same molecule, even though they have different charge states and isotopes.
RESULTS
to evaluate the performance of the ab3d algorithm, comparison analyses were carried out by focusing on three aspects, i.e., 1) to evaluate the false positives and false negatives; 2) to evaluate the reliability of quantitation results; 3) to evaluate the execution time for each algorithm.
while target software for comparison, were carefully selected using the following criteria, which have a similar concept as ab3d; 1) label-free quantitation using ms1; 2) widely used and freely available; 3) run on windows os, therefore, mzmine  <dig>  <cit> , msight  <cit> , superhirn  <cit>  and openms  <cit>  were chosen finally for comparison in this study.
mzmine  <dig> is java-based label-free software package using ms <dig> data for quantitation and has capabilities for gui and batch based multi-runs.
msight is a peak detection algorithm based on methodology for handling and analysing two dimensional gel image data.
superhirn is a c++ program with capabilities for alignment of all lc-ms runs as well as peak picking and quantitation.
openms is a c++ library based open source software which provides functionalities for handling and analysing proteomics data.
all the software packages  were downloaded from their recommended site and run locally.
peak detection results comparison
to assess the performance of ab3d and the previous algorithms, two data sets which acquired from different types of instruments, were prepared .
for data set  <dig>  we conducted a lc-ms experiment using a standard peptide mixture consisting of bsa peptides with different concentrations and peptides derived from four standard proteins such as beta galactosidase, phosphorylase b, myoglobin and cytochrome c with the same concentration .
the mass spectra were collected using ltq-orbitrap  mass spectrometer and four technical replicates of prepared samples were conducted for each bsa peptide concentration and all acquired raw ms <dig> and ms/ms data were input into all the peak detection algorithms  to produce peak lists for each peptide spectrum.
peaks identified as peptides of bsa or four standard proteins by either mascot  <cit>  or x!
tandem  <cit>  from multiple samples and with higher peptide scores  for mascot and the expectation value < <dig>  for x!tandem, were set as true positive .
peaks which were not identified as peptides from known bsa or the other four standard proteins were considered as false positive  in this study.
parameter settings for each software package are provided in additional file  <dig>  all identification results using mascot and x!tandem and features detected by each of all five algorithms are provided in additional file  <dig> and additional file  <dig>  respectively.
furthermore, to examine our algorithm can adapt for other instruments as well, a real biological dataset was obtained from the pride database  <cit>  as data set  <dig>  in which fabietti et al reported an extensive shotgun proteomic analysis of bartonella quintana, and the mass spectra were acquired using qstar xl  for oklahoma and jk <dig> strains  <cit> .
we used jk <dig> stain raw data which contains  <dig> fractionations and  <dig> replicates for each fractionation in total  <dig> raw files .
all identification results were obtained from supporting information provided at the publisher’s web-site , and the peptides/proteins identified in the literature were set as tp in our study.
raw  data was converted to mzxml format files by using proteowizard  <cit> , then the peak lists were produced using ab3d, mzmine  <dig>  msight, superhirn and openms algorithms, respectively.
parameter tuning was conducted 9- <dig> times for each algorithm and the best result was used for comparison finally.
the different times of parameter tuning are due to the different number of parameters for each algorithm.
we focused on the critical parameters, and tuned as many patterns as possible for each algorithm.
the tuning parameters for each algorithm are provided in additional file  <dig>
table  <dig> summarises peak picking results generated by each software package for bsa standard peptide concentrations from  <dig> amol to  <dig> fmol with mixed standard peptides from four proteins.
the details about peptide identification and parameters for all peak lists generated by the five algorithms using data set  <dig> are described in methods.
similarly, table  <dig> shows peak picking results produced by each algorithm for data set  <dig>  results shown in tables  <dig> and  <dig>  demonstrate that ab3d has the least number of total generated peaks but has relatively higher tp while keeping lower fp comparing with the other four existing algorithms for data sets  <dig> and  <dig>  the total numbers of unique peptides identified from all replicates were also listed for each data set; clearly ab3d has the distinction of relatively higher numbers of tp while keeping reasonable false positives compared with other software algorithms.
furthermore, tp and fp rates of the unique peptides were calculated  from each peak list and scatter plots were obtained for comparison.table  <dig>
summary of the numbers of peaks generated by ab3d, mzmine  <dig>  msight, superhirn and openms for standard peptide mixture consisting of bsa peptides with different concentrations  and peptides derived from four standard proteins such as beta galactosidase, phosphorylase b, myoglobin and cytochrome c with the same concentrations, and their replicates  using data set 1
bsa conc.
n
ab3d
mzmine2
msight
superhirn
openms
total
tp
fp
total
tp
fp
total
tp
fp
total
tp
fp
total
tp
fp
values in individual rows for each concentration represent the individual number of known peptides of bsa and the four other standard proteins from  <dig> replicates.
values in ukp rows represent the total numbers of unique known peptides identified from  <dig> replicates.
tp and fp represent true positive and false positive, respectively.
summary of the numbers of peaks generated by ab3d, mzmine  <dig>  msight, superhirn and openms for the real biological data
fraction.
n
ab3d
mzmine2
msight
superhirn
openms
total
tp
fp
total
tp
fp
total
tp
fp
total
tp
fp
total
tp
fp
values in individual rows for each fraction represent the individual numbers of identified peptides from  <dig> replicates.
in fraction  <dig>  results for two replicates were presented because there was one missing replicate.
values in ukp rows represent the total numbers of unique known peptides identified from 2- <dig> replicates.
tp and fp represent true positive and false positive, respectively.
<dig> tpr=xt   <dig> fpr=y−xz
where tpr is the tp rate, fpr is the fp rate, x is the number of unique peaks identified as known peptides from all replicates for data set  <dig> and  <dig>  t is the total number of unique peptides identified as from bsa and the four standard proteins for data set  <dig>  or the total number of identified unique peptides in each fraction for data set  <dig>  y is the number of peaks detected for each individual ms data file and z is the average peaks detected from each concentration of data set  <dig> and each fraction of data set  <dig>  respectively.table  <dig>
summary of bsa and four other standard proteins used in data set 1
length
peptides
peaks
coverage
length is the total sequence length of each standard protein.
peptides is the number of peptides identified in this experiment.
peaks is the total number of peaks observed and coverage  is the coverage for identified peptides.
figure  <dig> to  illustrate scatter plots of tpr against fpr of the unique known peptides shown in table  <dig> for bsa standard peptide concentrations from  <dig> amol to  <dig> fmol with mixed standard peptides from four proteins.
similarly, figure  <dig> to  shows the scatter plots of tpr against fpr of the unique peptides shown in table  <dig> for data set  <dig> figure  <dig>
scatter plots of fpr and tpr for unique known peptides identified from four replicates for each algorithm.
standard peptide mixture with bsa concentration   <dig> amol,   <dig> amol,   <dig> amol,   <dig> fmol,   <dig> fmol and   <dig> fmol.
ab3d , mzmine  <dig> , msight , superhirn , and openms .
the same datasets  were used to generate peak lists from those five algorithms.
fpr for unique known peptides are calculated using the average of each replicate fpr.
scatter plots of fpr and tpr for unique peptides identified from three replicates for each algorithm using data set  <dig>   fraction  <dig>   fraction  <dig>   fraction  <dig>   fraction  <dig> and  fraction <dig>  ab3d , mzmine  <dig> , msight , superhirn  and openms .
the same datasets  were used to generate peak lists from those five algorithms.
fpr for unique peptides are calculated using the average of each replicate fpr.
in general for a roc plot, the closer to the top left the better for performance, by comparing the known unique peptides shown in figure  <dig> to , ab3d has a better balance of tpr and fpr, i.e., ab3d is mostly located on the upper left, shows the higher true positive rate while keeping lower false positive rate in all bsa peptide concentrations.
figure  <dig> shows similar results as those shown in figure  <dig> even for the real complex biological samples , which demonstrated that ab3d has the better performance for label-free quantitation using mass spectrometry data.
quantitation and performance comparison
to test the quantitative accuracy of ab3d, correlation analysis was carried out by plotting peak area vs bsa peptide concentration for  <dig> identified bsa peptides which have no or fewer missing peak area values for all bsa peptide concentrations  from  <dig> amol to  <dig> fmol and the results shown in figure  <dig> to .
overall the results indicated that there are good positive correlations between quantitative value  and bsa peptide concentrations for all  <dig> algorithms, while ab3d and superhirn show better linearity than the others from low to high bsa concentrations.
some peptides showed less linearity at low concentration ranges, which was considered to be due to peptide adsorption on the sample plate.
furthermore, to assess reliability of ab3d and the other algorithms in their practical case for differential analysis, mixture samples consisting of peptides prepared from hela cells and bsa standard peptides with concentration of  <dig> amol, were prepared as a third data set  for this study.
the differences of peak picking and quantitative results for two technical replicates were then evaluated by the scatter and bland-altman plot  <cit>  .
in figures  <dig> and  <dig>  peak area values were used for ab3d, mzmine  <dig> and msight, volume values were used for superhirn and intensity values were used for openms, which does not provide area values.
parameters and detected features  using data set  <dig> for each software package are provided in additional file  <dig> and additional file  <dig>  respectively.
moreover, after analysing rt drift of bsa peptides between two replicates, peak matching tolerances for m/z and rt between two replicates are set as  <dig>  da and  <dig>  min, respectively.
in general, for figure  <dig>  the closer to the y = x line the better reproducibility for each algorithm.
as shown in figure  <dig>  ab3d showed the smallest range of 2sd and the best correlation between two replicates with a correlation coefficient of r2 =  <dig>  comparing with the other four algorithms.
the corresponding bland-altman plots for figure  <dig> were produced as shown in figure  <dig>  where the mean value is represented as a percentage of average difference, the range of 2sd is represented as the ability to reduce the ambiguous results and peaks located outside of 2sd  considered as there are significant differences between two replicates, respectively.
as shown in figure  <dig>  clearly the ab3d algorithm has the smallest range of 2sd with a value of  <dig> %, and the smallest outliers with a value of  <dig> % comparing with the other four algorithms.
these results demonstrated that ab3d has a higher potential to reduce the false positive peaks and find the true changed analytes in differential analysis even using complex biological samples.figure  <dig>
correlations between peak quantitative values of  ab3d,  mzmine  <dig>   msight,  superhirn and  openms
vs
the bsa digestion standard concentration  for  <dig> peptides using data set  <dig>  pep <dig>  pep <dig>  pep <dig>  pep <dig>  pep <dig>  pep <dig>  pep <dig> and pep <dig> represented fkdlgeehfk, ylyeiar, slhtlfgdelck, rhpeyavsvllr, tcvadeshagcek, lkeccdkpllek, kvpqvstptlvevsr and lvneltefak peptides of bsa, respectively.
the scatter plot of peak picking and quantitative results for each algorithm using data set  <dig>  the total number of detected peaks are  <dig>   <dig>   <dig>   <dig>   <dig> and the correlation coefficients between two replicates are r <dig> =  <dig> ,  <dig> , - <dig> ,  <dig> ,  <dig>  for a , b , c , d  and e , respectively.
2sd  was also plotted for each algorithm.
the bland-altman plot for peak picking and quantitation results utilizing a complex peptide mixture of hela cell proteins digested peptides and  <dig> amol concentration bsa digestion standard  with  <dig>  and  <dig>  sd of mean  for a , b , c , d  and e , respectively.
2sd in represented the number of peaks which fall within 2sd, and 2sd out represented the number of peaks which fall outside of 2sd, respectively.
to evaluate the computational performance of the ab3d algorithm, execution time analysis was conducted for comparison.
all five algorithms used the same raw files  and computer in the same conditions, execution times for each algorithm were measured from data input to finishing peak detection and their corresponding quantitative values; note the ms data format conversion time was not included in the measured time.
for a lc-ms file, triplicate measurements were carried out and the average execution values for each algorithm were used for comparison.
since superhirn only accept mzxml format, therefore we performed execution time comparison by using mzxml files firstly although currently the recommended standard format is mzml; however, if the software can also handle raw format, execution times for raw input files were also measured and listed up for comparison, and we selected the best one for software which has more than two executing time values for final evaluation.
figure  <dig> shows the results for these five algorithms, and it demonstrates that the execution time for ab3d is about  <dig>  to  <dig> times faster compared with mzmine  <dig>  msight, superhirn and openms.
this is one of the motivations for using ab3d to save the computational time and to allow research efficiently handling bigger and complex data sets due to either the increased sensitivity, resolution, throughput of lc-ms or the increase of biological sample numbers.
ab3d is a 2d based peak picking algorithm and faithfully detects 2d peaks from largest to smallest in descending order.
moreover, the plug-in style development and the fewer model fitting approaches are the other major features that need to be highlighted.
in contrast, mzmine  <dig>  openms are using different types of models for fitting the peaks and msight generates images from the raw data file for adapting the image-based peak detection, which would be possible reasons for relatively expensive computational tasks.
superhirn uses fewer model fitting algorithms but only accept mzxml format.
recently, maxquant  <cit>  algorithm was developed by using 3d features, and it was reported as a more effective way of peak picking but it was optimized for orbitrap and mainly focusing on silac based quantitation although it has label-free functionality also, therefore maxquant was not used as a benchmark candidate in this study.figure  <dig>
comparison of the execution time  for peak detection and quantitation using ab3d, mzmine  <dig>  msight, superhirn and openms, respectively.
the same data  and workstation were used for all five algorithms and the workstation specification is shown as following: os: windows  <dig> professional  service pack 1; cpu: intel xeon e <dig>  <dig>  ghz  <dig>  ghz ; ram:  <dig>  gb.
in general, the more functionality the more complicated the software operation, in ab3d, only  <dig> steps are needed to perform peak detection and quantitation, i.e., 1) read ms file; 2) optimize ab3d parameters using a heatmap; and 3) process peak detection and quantitation.
these results demonstrated that ab3d has the capability for large scale biomarker discovery with high performance and accuracy and as examples, mass++/ab3d algorithms were successfully applied in real biomarker researches  <cit> .
CONCLUSIONS
a simple and faster quantitative algorithm called ab3d for large-scale data analysis has been developed as a plug-in for mass++.
the comparison analysis demonstrated that ab3d could properly identify and quantify known peptides with higher true positives and lower false positives comparing with the  <dig> other previously existing software tools using either the standard peptide mixture or the real complex biological samples of bartonella quintana .
furthermore, ab3d demonstrated the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of hela and bsa biological samples.
for performance, the ab3d algorithm is about  <dig> -  <dig> times faster than those of existing  <dig> software tools such as mzmine  <dig>  msight, superhirn and openms.
ab3d is very easy to operate with only  <dig> clicks.
in summary, ab3d makes it easier to analyse a large amount of ms data sets with high performance, and provides more reliable information for researchers.
currently, ab3d is implemented as one of the quantitation plug-ins in mass++, which is universal free software for mass spectrometric data .
as one of the plug-ins in mass++, the source code for ab3d is not publicly opened in accordance to the policy of the mass++ software.
finally, the authors want to emphasize here that to carry out large scale biological data analysis, sophisticated chromatographic alignment algorithms which were reported and/or to be newly developed are also another key point, currently ab3d employs the chromatographic alignment algorithms developed in the mass++ software for large scale analysis, other types of chromatographic alignment algorithms will be integrated in future work.

 proteomics based on lc-ms is well established technology for discovery of disease biomarkers, drug target identification, mode of action studies and safety marker identification in drug research. in particular, most of these analyses are differential, i.e., comparing samples from drug-treated and untreated subjects, diseased subjects and healthy controls, mutant and wild-type samples and so on. in cases of the differential analysis, mere identification of proteins and metabolites from the complex biological samples is not sufficient, and quantitative analysis is required. there are two main types of quantitation methods for differential analysis using lc-ms, one being the standard-free approach and the other being the external/internal standards approach to normalize variations by the use of stable isotope labeling methods. the latter approach offers high accuracy and reliability, but has the disadvantages of requiring expensive reagents and time-consuming preparation of standards in practical experiments. the former approach offers simplicity and easy experimental design with relatively low cost, but there remain many challenges related to bioinformatics, e.g., to increase true positives, to decrease false positives, to improve analysis performance for large scale data sets and to increase reliability by normalization of data. meanwhile, proteomics data sets have become bigger and more complex in the last decade due to the increased sensitivity, resolution and throughput of lc-ms, thus to improve automated large-scale data handling is another challenge. in particular, it is also important to note that only 10-50% of spectra generated from lc-ms/ms have been correctly assigned for the identification in proteomics field; this means that about half of the spectra, which were filtered during the identification process, may sometimes play important biological roles such as known and/or unknown post translational modifications. therefore, label-free quantitation is a reliable, versatile and cost-effective method in the biomarker discovery field. to date, there are two major sub-approaches reported in label-free quantitation proteomics, i.e., 1) spectral counting based quantitation; 2) spectra intensity based quantitation. in this paper we will mainly focus on the latter one. several label-free quantitative algorithms/tools are available for proteomics based on lc-ms data. however, one of the current challenges is to develop a highly reliable and flexible lc-ms based quantitation method for large scale biomarker discovery. there are also only a few software tools which allow us to customize and/or add newly developed algorithms as plug-ins. moreover, in most cases, existing algorithms were developed for only specific purposes, thus there are quite a few papers to date which compared different aspects of existing algorithms using the same data set. here, we present a simple algorithm, which we call ab3d, which allows peak picking, isotope cluster recognition and quantitation using ms data with high reliability and with a sophisticated graphical user interface for verification using the universal mass spectrometry data visualization and analysis tool mass++, in addition to comparing different types of existing label-free quantitation algorithm. algorithm data processing in our laboratory, usually over a hundred megabyte raw data is generated per run by the most advanced lc-ms, thus it is important to considering high speed computational methods for data processing and spectra manipulation. in addition, to adapt the same algorithm for all the different types of mass spectrometry data format, it is useful to have a universal text format for internal pre-processing. ab3d first roughly picks all local maxima whose intensity is larger than a given threshold as candidate peaks from the entire spectrum and then stores all the candidate peaks data into the memory, which consists of 3-dimensional mass spectrometry data, i.e., m/z, intensity and their corresponding retention time. alternatively, all candidate peaks can be generated through an api which is provided from different mass spectrometry vendors such as msfilereader and lcmssolution. ab3d generates extracted ion chromatograms for all candidate peaks in the following steps;figure a schematic overview of the ab3d peak detection algorithm. ab3d sequentially processes all candidate peaks from the maximum intensities to minimum intensities. to extract the xic for a target peak a, all related peak information such as m/z, retention time and intensity were obtained by searching adjacent peaks in the range of m/z and retention time tolerances. b and c are the minimum and maximum m/z values respectively within the tolerance. select the highest intensity peak a among all the candidate peaks, then seek all the possible neighbour peaks within the range of m/z and retention time. if no neighbour peaks were found, then skip the second and third steps and move to the next highest intensity peak. determine the start data point b and end data point c m/z values for a xic within a given m/z tolerance window. generate the xic based on the ranges of m/z and retention time determined in step and then the target peak and all its neighbour peaks will be removed from the candidate peaks. xic peak detection and quantitation in shotgun proteomics, many peaks may be buried in background noise and the complexity of the data is very high, therefore, it is difficult to assign all peaks. in addition, when samples are of very high complexity and the intensity of each peak is quite low, it was difficult to define the baseline in many cases. thus, xic peak detection is another challenge for performing highly reliable quantitative analysis. to address this, we have developed a novel mass chromatogram peak detection algorithm, in which we combine the local minimum and the weighted average peak detection algorithm to extract and compare peaks from highly complex data. figure  shows the principle of our xic peak detection algorithm. first, a point of highest intensity is found using a local maximum algorithm. secondly, a check point at a given percentage of the peak height of the apex is used to discriminate the peak from noise, i.e., a horizontal line is drawn at the 50% height position along the rt axis, and if the line has two crossing points with the peak candidate, then the apex can be considered as a peak top. for calculating the peak area value, the start and the end points of the peak have to be determined, so next the lowest left and right points were found below the given percentage height position by using a local minimum algorithm, and these are considered to be the start and the end points, respectively. finally, the peak area value can be calculated using these points, i.e., the start, apex, and end points. this algorithm is useful for analysis of complex samples, because the percentage from the peak top can be changed as required for particular experiments; usually a higher percentage is used to detect the individual components of a complex xic and a lower percentage is used to detect the intact complex xic.figure the principle of the ab3d xic peak detection algorithm. for local maximum peaks a and d, b-a-c and c-d-c’ were picked as two candidate peaks when a horizontal line was drawn at 50% of the highest intensity, while b-a-c’ were picked as one candidate xic peak when a horizontal lines was drawn at 30% of the top intensity. xic peak filtering in general, there are considerable pseudo-peaks originating from electronic and/or chemical noise for mass spectrometry-based quantitation, therefore, reduction of the noise peaks is another challenge for label-free quantitation. we have evaluated a large number of biological samples for optimizing a proper method of signal to noise ratio calculation and some useful filters for reducing false positive rate as follows. s/n ratio: pbaseline=in+ ifnisodd.12in2+in2+ ifniseven. pnoise=i1−pbaseline2+i2−pbaseline2+⋯+in−pbaseline2n psnratio=i1−pbaseline/pnoise where n is the total number of candidate peaks, i is the intensity value sorted in descending order as i i … in. pbaseline is the baseline and pnoise is the noise level, respectively. as shown in formulas and the median value of intensities is used as the baseline and standard deviation is used as the noise level in ab3d. other major noise filters and isotopic clustering:fwhm: full width at half maximum is well known as a noise reduction filter for spectra manipulation, it is also useful for reducing chemical and/or electrical noise for xic peaks. the fwhm can be adjusted according to the experimental condition and the instrument type. peak undulation: some ugly and bumpy xic peak shapes might be generated due to insufficient data acquiring points, over loading of samples, low-spray status, and influence of overlapped other large peaks, we have focused on the number of up-downs within a xic peak and after processing and analysing over hundreds of biological samples, we found that peak undulation is one of the useful factors to reduce false positive peaks in most cases, the more numbers of peak undulation the lower data quality of xic. therefore we employed the peak undulation filter in ab3d as an adjustable parameter. number of spectra for each xic peak: generally, xic is constructed from multiple mass spectra and the number of spectra for each xic peak can be considered as a factor to evaluate the quality of xic peaks; a too small number of spectra for a xic can be considered as a lack of confidence for advanced quantitation and identification, thus the number of spectra of the xic filter is pre-set in ab3d. isotopic clustering: ab3d processes all sorted candidate peaks in descending order, first selects most intense peak as a target peak, then a m/z moving window is stepped backward and forward the target peak along m/z axis. if there is a peak signal within the moving window and the m/z tolerance, then the peak will be assigned as a member of an isotopic cluster and move to next, when there is no peak within the moving window and the m/z tolerance, the isotopic cluster is finally formed and a monoisotopic peak will be determined by using reported pattern matching method. in order to address the overlapped peaks and the isotopic clusters, only the monoisotopic peak was removed from the candidate peaks before moving to the next target peak. the program ends when all the candidate peaks were assigned. in addition, one feature of our quantitative algorithm is that it has the ability to deal with highly complex proteomics data by merging ions that originate from the same molecule, even though they have different charge states and isotopes.  to evaluate the performance of the ab3d algorithm, comparison analyses were carried out by focusing on three aspects, i.e., 1) to evaluate the false positives and false negatives; 2) to evaluate the reliability of quantitation results; 3) to evaluate the execution time for each algorithm. while target software for comparison, were carefully selected using the following criteria, which have a similar concept as ab3d; 1) label-free quantitation using ms1; 2) widely used and freely available; 3) run on windows os, therefore, mzmine, msight, superhirn and openms were chosen finally for comparison in this study. mzmine is java-based label-free software package using ms data for quantitation and has capabilities for gui and batch based multi-runs. msight is a peak detection algorithm based on methodology for handling and analysing two dimensional gel image data. superhirn is a c++ program with capabilities for alignment of all lc-ms runs as well as peak picking and quantitation. openms is a c++ library based open source software which provides functionalities for handling and analysing proteomics data. all the software packages were downloaded from their recommended site and run locally. peak detection results comparison to assess the performance of ab3d and the previous algorithms, two data sets which acquired from different types of instruments, were prepared. for data set we conducted a lc-ms experiment using a standard peptide mixture consisting of bsa peptides with different concentrations and peptides derived from four standard proteins such as beta galactosidase, phosphorylase b, myoglobin and cytochrome c with the same concentration. the mass spectra were collected using ltq-orbitrap mass spectrometer and four technical replicates of prepared samples were conducted for each bsa peptide concentration and all acquired raw ms and ms/ms data were input into all the peak detection algorithms to produce peak lists for each peptide spectrum. peaks identified as peptides of bsa or four standard proteins by either mascot or x! tandem from multiple samples and with higher peptide scores for mascot and the expectation value < for x!tandem, were set as true positive. peaks which were not identified as peptides from known bsa or the other four standard proteins were considered as false positive in this study. parameter settings for each software package are provided in additional file all identification results using mascot and x!tandem and features detected by each of all five algorithms are provided in additional file and additional file respectively. furthermore, to examine our algorithm can adapt for other instruments as well, a real biological dataset was obtained from the pride database as data set in which fabietti et al reported an extensive shotgun proteomic analysis of bartonella quintana, and the mass spectra were acquired using qstar xl for oklahoma and jk strains. we used jk stain raw data which contains fractionations and replicates for each fractionation in total raw files. all identification results were obtained from supporting information provided at the publisher’s web-site, and the peptides/proteins identified in the literature were set as tp in our study. raw data was converted to mzxml format files by using proteowizard, then the peak lists were produced using ab3d, mzmine msight, superhirn and openms algorithms, respectively. parameter tuning was conducted 9- times for each algorithm and the best result was used for comparison finally. the different times of parameter tuning are due to the different number of parameters for each algorithm. we focused on the critical parameters, and tuned as many patterns as possible for each algorithm. the tuning parameters for each algorithm are provided in additional file table  summarises peak picking results generated by each software package for bsa standard peptide concentrations from amol to fmol with mixed standard peptides from four proteins. the details about peptide identification and parameters for all peak lists generated by the five algorithms using data set are described in methods. similarly, table  shows peak picking results produced by each algorithm for data set results shown in tables  and demonstrate that ab3d has the least number of total generated peaks but has relatively higher tp while keeping lower fp comparing with the other four existing algorithms for data sets and the total numbers of unique peptides identified from all replicates were also listed for each data set; clearly ab3d has the distinction of relatively higher numbers of tp while keeping reasonable false positives compared with other software algorithms. furthermore, tp and fp rates of the unique peptides were calculated from each peak list and scatter plots were obtained for comparison.table summary of the numbers of peaks generated by ab3d, mzmine msight, superhirn and openms for standard peptide mixture consisting of bsa peptides with different concentrations and peptides derived from four standard proteins such as beta galactosidase, phosphorylase b, myoglobin and cytochrome c with the same concentrations, and their replicates using data set 1 bsa conc. n ab3d mzmine2 msight superhirn openms total tp fp total tp fp total tp fp total tp fp total tp fp values in individual rows for each concentration represent the individual number of known peptides of bsa and the four other standard proteins from replicates. values in ukp rows represent the total numbers of unique known peptides identified from replicates. tp and fp represent true positive and false positive, respectively. summary of the numbers of peaks generated by ab3d, mzmine msight, superhirn and openms for the real biological data fraction. n ab3d mzmine2 msight superhirn openms total tp fp total tp fp total tp fp total tp fp total tp fp values in individual rows for each fraction represent the individual numbers of identified peptides from replicates. in fraction results for two replicates were presented because there was one missing replicate. values in ukp rows represent the total numbers of unique known peptides identified from 2- replicates. tp and fp represent true positive and false positive, respectively. tpr=xt fpr=y−xz where tpr is the tp rate, fpr is the fp rate, x is the number of unique peaks identified as known peptides from all replicates for data set and t is the total number of unique peptides identified as from bsa and the four standard proteins for data set or the total number of identified unique peptides in each fraction for data set y is the number of peaks detected for each individual ms data file and z is the average peaks detected from each concentration of data set and each fraction of data set respectively.table summary of bsa and four other standard proteins used in data set 1 length peptides peaks coverage length is the total sequence length of each standard protein. peptides is the number of peptides identified in this experiment. peaks is the total number of peaks observed and coverage is the coverage for identified peptides. figure  to illustrate scatter plots of tpr against fpr of the unique known peptides shown in table  for bsa standard peptide concentrations from amol to fmol with mixed standard peptides from four proteins. similarly, figure  to shows the scatter plots of tpr against fpr of the unique peptides shown in table  for data set figure scatter plots of fpr and tpr for unique known peptides identified from four replicates for each algorithm. standard peptide mixture with bsa concentration amol, amol, amol, fmol, fmol and fmol. ab3d, mzmine, msight, superhirn, and openms. the same datasets were used to generate peak lists from those five algorithms. fpr for unique known peptides are calculated using the average of each replicate fpr. scatter plots of fpr and tpr for unique peptides identified from three replicates for each algorithm using data set fraction fraction fraction fraction and fraction ab3d, mzmine, msight, superhirn and openms. the same datasets were used to generate peak lists from those five algorithms. fpr for unique peptides are calculated using the average of each replicate fpr. in general for a roc plot, the closer to the top left the better for performance, by comparing the known unique peptides shown in figure  to, ab3d has a better balance of tpr and fpr, i.e., ab3d is mostly located on the upper left, shows the higher true positive rate while keeping lower false positive rate in all bsa peptide concentrations. figure  shows similar results as those shown in figure  even for the real complex biological samples, which demonstrated that ab3d has the better performance for label-free quantitation using mass spectrometry data. quantitation and performance comparison to test the quantitative accuracy of ab3d, correlation analysis was carried out by plotting peak area vs bsa peptide concentration for identified bsa peptides which have no or fewer missing peak area values for all bsa peptide concentrations from amol to fmol and the results shown in figure  to. overall the results indicated that there are good positive correlations between quantitative value and bsa peptide concentrations for all algorithms, while ab3d and superhirn show better linearity than the others from low to high bsa concentrations. some peptides showed less linearity at low concentration ranges, which was considered to be due to peptide adsorption on the sample plate. furthermore, to assess reliability of ab3d and the other algorithms in their practical case for differential analysis, mixture samples consisting of peptides prepared from hela cells and bsa standard peptides with concentration of amol, were prepared as a third data set for this study. the differences of peak picking and quantitative results for two technical replicates were then evaluated by the scatter and bland-altman plot. in figures  and peak area values were used for ab3d, mzmine and msight, volume values were used for superhirn and intensity values were used for openms, which does not provide area values. parameters and detected features using data set for each software package are provided in additional file and additional file respectively. moreover, after analysing rt drift of bsa peptides between two replicates, peak matching tolerances for m/z and rt between two replicates are set as  da and  min, respectively. in general, for figure  the closer to the y = x line the better reproducibility for each algorithm. as shown in figure  ab3d showed the smallest range of 2sd and the best correlation between two replicates with a correlation coefficient of r2 =  comparing with the other four algorithms. the corresponding bland-altman plots for figure  were produced as shown in figure  where the mean value is represented as a percentage of average difference, the range of 2sd is represented as the ability to reduce the ambiguous results and peaks located outside of 2sd considered as there are significant differences between two replicates, respectively. as shown in figure  clearly the ab3d algorithm has the smallest range of 2sd with a value of %, and the smallest outliers with a value of % comparing with the other four algorithms. these results demonstrated that ab3d has a higher potential to reduce the false positive peaks and find the true changed analytes in differential analysis even using complex biological samples.figure correlations between peak quantitative values of ab3d, mzmine msight, superhirn and openms vs the bsa digestion standard concentration for peptides using data set pep pep pep pep pep pep pep and pep represented fkdlgeehfk, ylyeiar, slhtlfgdelck, rhpeyavsvllr, tcvadeshagcek, lkeccdkpllek, kvpqvstptlvevsr and lvneltefak peptides of bsa, respectively. the scatter plot of peak picking and quantitative results for each algorithm using data set the total number of detected peaks are and the correlation coefficients between two replicates are r =,, -,, for a, b, c, d and e, respectively. 2sd was also plotted for each algorithm. the bland-altman plot for peak picking and quantitation results utilizing a complex peptide mixture of hela cell proteins digested peptides and amol concentration bsa digestion standard with and sd of mean for a, b, c, d and e, respectively. 2sd in represented the number of peaks which fall within 2sd, and 2sd out represented the number of peaks which fall outside of 2sd, respectively. to evaluate the computational performance of the ab3d algorithm, execution time analysis was conducted for comparison. all five algorithms used the same raw files and computer in the same conditions, execution times for each algorithm were measured from data input to finishing peak detection and their corresponding quantitative values; note the ms data format conversion time was not included in the measured time. for a lc-ms file, triplicate measurements were carried out and the average execution values for each algorithm were used for comparison. since superhirn only accept mzxml format, therefore we performed execution time comparison by using mzxml files firstly although currently the recommended standard format is mzml; however, if the software can also handle raw format, execution times for raw input files were also measured and listed up for comparison, and we selected the best one for software which has more than two executing time values for final evaluation. figure  shows the results for these five algorithms, and it demonstrates that the execution time for ab3d is about to times faster compared with mzmine msight, superhirn and openms. this is one of the motivations for using ab3d to save the computational time and to allow research efficiently handling bigger and complex data sets due to either the increased sensitivity, resolution, throughput of lc-ms or the increase of biological sample numbers. ab3d is a 2d based peak picking algorithm and faithfully detects 2d peaks from largest to smallest in descending order. moreover, the plug-in style development and the fewer model fitting approaches are the other major features that need to be highlighted. in contrast, mzmine openms are using different types of models for fitting the peaks and msight generates images from the raw data file for adapting the image-based peak detection, which would be possible reasons for relatively expensive computational tasks. superhirn uses fewer model fitting algorithms but only accept mzxml format. recently, maxquant algorithm was developed by using 3d features, and it was reported as a more effective way of peak picking but it was optimized for orbitrap and mainly focusing on silac based quantitation although it has label-free functionality also, therefore maxquant was not used as a benchmark candidate in this study.figure comparison of the execution time for peak detection and quantitation using ab3d, mzmine msight, superhirn and openms, respectively. the same data and workstation were used for all five algorithms and the workstation specification is shown as following: os: windows professional service pack 1; cpu: intel xeon e ghz ghz; ram: gb. in general, the more functionality the more complicated the software operation, in ab3d, only steps are needed to perform peak detection and quantitation, i.e., 1) read ms file; 2) optimize ab3d parameters using a heatmap; and 3) process peak detection and quantitation. these results demonstrated that ab3d has the capability for large scale biomarker discovery with high performance and accuracy and as examples, mass++/ab3d algorithms were successfully applied in real biomarker researches.  a simple and faster quantitative algorithm called ab3d for large-scale data analysis has been developed as a plug-in for mass++. the comparison analysis demonstrated that ab3d could properly identify and quantify known peptides with higher true positives and lower false positives comparing with the other previously existing software tools using either the standard peptide mixture or the real complex biological samples of bartonella quintana. furthermore, ab3d demonstrated the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of hela and bsa biological samples. for performance, the ab3d algorithm is about - times faster than those of existing software tools such as mzmine msight, superhirn and openms. ab3d is very easy to operate with only clicks. in summary, ab3d makes it easier to analyse a large amount of ms data sets with high performance, and provides more reliable information for researchers. currently, ab3d is implemented as one of the quantitation plug-ins in mass++, which is universal free software for mass spectrometric data. as one of the plug-ins in mass++, the source code for ab3d is not publicly opened in accordance to the policy of the mass++ software. finally, the authors want to emphasize here that to carry out large scale biological data analysis, sophisticated chromatographic alignment algorithms which were reported and/or to be newly developed are also another key point, currently ab3d employs the chromatographic alignment algorithms developed in the mass++ software for large scale analysis, other types of chromatographic alignment algorithms will be integrated in future work. 
we developed an algorithm called ab3d, a label free peak detection and quantitative algorithm using ms spectral data. label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. the need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. ab3d has addressed these issues and has the capability to perform label-free quantitation using ms for proteomics study. ab3d is a simple and fast algorithm for label-free quantitation using ms mass spectrometry data for large scale lc-ms data analysis with higher true positive and reasonable false positive rates. algorithmmass spectrometrylabel-free quantitationpeak pickingcomparisonissue-copyright-statement© the author 2014 all quantitative results were confirmed manually, and we found that ab3d could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of bartonella quintana. for performance, the ab3d algorithm is about - times faster than the four other existing software tools. however, recent most advanced types of lc-ms generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. to test our algorithm, practical applications of ab3d for lc-ms data sets were evaluated using datasets. comparisons were then carried out between widely used software tools such as mzmine msight, superhirn, openms and our algorithm ab3d, using the same lc-ms datasets. moreover, ab3d showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of hela and bsa samples. furthermore, ab3d demonstrated the best reproducibility and is about - times faster than those of existing software tools.
 label-free quantitation of mass spectrometric data is one of the simplest and least expensive methods for differential expression profiling of proteins and metabolites. the need for high accuracy and performance computational label-free quantitation methods is still high in the biomarker and drug discovery research field. however, recent most advanced types of lc-ms generate huge amounts of analytical data with high scan speed, high accuracy and resolution, which is often impossible to interpret manually. moreover, there are still issues to be improved for recent label-free methods, such as how to reduce false positive/negatives of the candidate peaks, how to expand scalability and how to enhance and automate data processing. ab3d has addressed these issues and has the capability to perform label-free quantitation using ms for proteomics study.  we developed an algorithm called ab3d, a label free peak detection and quantitative algorithm using ms spectral data. to test our algorithm, practical applications of ab3d for lc-ms data sets were evaluated using datasets. comparisons were then carried out between widely used software tools such as mzmine msight, superhirn, openms and our algorithm ab3d, using the same lc-ms datasets. all quantitative results were confirmed manually, and we found that ab3d could properly identify and quantify known peptides with fewer false positives and false negatives compared to four other existing software tools using either the standard peptide mixture or the real complex biological samples of bartonella quintana. moreover, ab3d showed the best reliability by comparing the variability between two technical replicates using a complex peptide mixture of hela and bsa samples. for performance, the ab3d algorithm is about - times faster than the four other existing software tools.  ab3d is a simple and fast algorithm for label-free quantitation using ms mass spectrometry data for large scale lc-ms data analysis with higher true positive and reasonable false positive rates. furthermore, ab3d demonstrated the best reproducibility and is about - times faster than those of existing software tools. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords algorithmmass spectrometrylabel-free quantitationpeak pickingcomparisonissue-copyright-statement© the author 2014 