the ability to reproducibly and accurately 
 quantify proteins or proteomes is important for life science research , 
 and the recent development of targeted proteomics strategies , i.e. , 
 selected reaction monitoring ( srm ) , greatly increases the quantitative 
 reproducibility and accuracy compared to conventional data - dependent 
 mass spectrometry analysis .
recent srm workflows 
 have displayed a large linear dynamic range and high quantification 
 reproducibility using stable isotope standards ( sis ) . in srm ,
proteotypic 
 peptides are quantified in a triple quadrupole mass spectrometer by 
 measuring specific peptide ions and some of their most specific and 
 most frequently appearing fragments .
the 
 combination of a peptide ion and fragment ion mass - to - charge is called 
 a transition .
gathering of the a priori information 
 of what peptides and fragments to target , called srm assays , has been 
 a bottleneck in srm analysis , but the presence of large peptide identification 
 repositories such as peptideatlas , recent 
 advances in computational prediction algorithms , the use of crude synthetic peptides , and the ongoing construction of srm atlases are rapidly decreasing srm assay development time .
furthermore , 
 throughput of the method is increasing with the introduction of scheduled 
 srm and isrm , presenting the possibility to use srm for screening complete pathways 
 and even complete microbial proteomes .
however , examining this new 
 larger scope reveals two new bottlenecks : stable isotope labeling , 
 which effectively halves ms throughput , increases sample preparation 
 complexity , and is normally associated with long synthesis times and 
 high cost , and manual data analysis , which limits routine high throughput 
 and introduces bias .
previous studies have reported that label - free 
 quantitative srm may generate data of sufficient quality for analysis 
 of biological samples . however , increasing 
 the number of target proteins also requires automated data analysis , 
 and some objective measure of quality for each detection to control 
 false discoveries .
of previously published software , mprophet and the ddb workflow 
 perform detection and quantification of peptides in srm data in an 
 automated fashion while providing custom scores for quality control 
 but in return have the drawbacks of relying on decoy measurements 
 and requiring a large assay database respectively .
most current srm 
 software ( skyline , pinpoint , mrmer ) focus on assisting in construction of srm assays , 
 presenting the data , and assisting manual quantification .
skyline 
 does perform detection and quantification , but without any clear measure 
 of quality , and in addition requires a spectral library .
the audit software assists manual data analysis by highlighting 
 peptides with large variation in sis - corrected quantity or deviating 
 fragment ratios between endogenous and sis peptide . in this 
 report we present a novel algorithm that does not rely on decoy data 
 or spectral libraries but focuses on label - free srm , estimates the 
 quality of each reported detection , and supports interference correction 
 during quantification , to circumvent the drawbacks of using sis - labeling 
 and manual data analysis .
we demonstrate that the algorithm , called 
 anubis , performs srm data analysis on par with a human expert , elaborate 
 on the reproducibility and accuracy achievable with the anubis label - free 
 workflow , and apply it to study the effect of human plasma on a set 
 of targeted streptococcus pyogenes proteins .
anubis is vendor 
 independent , accepts mzml data files 
 and traml or csv transition lists , is 
 operating system independent by being implemented in scala 2.8.0 , 
 and is run in a java virtual machine .
source code , 
 compiled binaries , and usage instructions are available at http://quantitativeproteomics.org/anubis/ under an open source license . the streptococci used were grown from a single colony of s. pyogenes strain sf370 .
this culture was sampled into 
 2 sets of 10 replicates , where one set was grown in pure todd - hewitt 
 broth ( th ) and one in th supplemented with 10% citrate treated human 
 plasma ( skne university hospital , sweden ) .
cells were grown 
 to exponential phase ( od620 = 0.5 ) and were harvested , 
 suspended , and lysed using standard procedures .
protein concentrations 
 were estimated with pierce coomassie protein assay kit ( 10 l 
 sample to 240 l reagent in duplicates , 96-well plate , a595 
 victor ) , showing a reduced yield in one th sample ( supplementary table 5 ) .
the samples were prepared for srm 
 by taking 50 l of each harvested culture and adding 2.5 l 
 of 1 pmol/l adh1_yeast ( adh ) , after which 0.6 l of 0.5 
 m tcep was added and samples were left to incubate in 37 c for 
 1 h. after adding 1.2 l of 500 mm iodoacetamide , samples were 
 left in the dark for 45 min , 2 l of 0.5 g / l trypsin was added , 
 and samples were incubated in 37 c overnight .
samples were desalted 
 using c18 columns ( the nest group , southborough ) , dried in vacuo , 
 and resuspended in 50 l of 2% acetonitrile ( acn ) , 0.2% formic 
 acid ( fa ) , by sonication for 5 min , followed by centrifugation at 
 3,100 rcf for 30 s before transferring the supernatant to hplc vials .
srm assays were created using synthetic peptides ( spiketides , jpt 
 peptide technologies gmbh ) . in total 163 peptides originating from 
 41 proteins
the dried peptides ( approximately 
 50 nmol ) were dissolved by addition of 180 l of 20% acn , 1% 
 fa to each peptide well , sonication for 5 min , and shaking for 1 h. 
 five microliters was sampled from each well and pooled .
the pool was 
 dried out and redissolved in 450 l of 2% acn , 0.1% fa , and 
 100 l was transferred to an hplc vial . for each synthetic 
 peptide
1015 transitions were generated in skyline , and these were measured with unscheduled srm . 

the resulting data were used to manually reduce the number of transitions 
 per precursor to a maximum of 5 , choosing the transitions with highest 
 intensity and with not more than one transition with a product m / z smaller than the m / z of the precursor .
the retention times of the synthetic peptides 
 were used to generate a final scheduled , 5 min window , srm method .
all ms analysis was carried 
 out on a tsq quantum vantage ( thermo - fisher scientific , 
 waltham ma ) triple quadrupole instrument interfaced with an eksigent nanolc 1dplus lc system ( eksigent 
 technologies , dublin ca ) . the mobile phase consisted of solvent a , 
 0.1% aqueous formic acid and solvent b , acetonitrile with 0.1% formic 
 acid .
peptides were separated on a 10 m tip , 75 m  
 12 cm capillary column ( picotip emitter ) packed with reprosil - pur 
 c18-aq resin ( 3 m , dr .
the system was washed and 
 equilibrated in a separate water injection between each sample injection . 

sample injections were 1 l at a constant flow of 300 nl / min , 
 with a gradient of 97% solvent a at 05 min , 85% a at 8 min , 
 65% a at 42 min , 10% a at 4550 min .
tsq cycle time was 1 s 
 and q1 and q3 peak widths of 0.7 m / z . instrument raw files were converted to mzml using msconvert from 
 proteowizard .
the median protein group cv was 
 calculated as the median of the cvs of all the peptides measured for 
 proteins in the group . for detecting significant differences in center 
 between or two biological conditions ,
we have used a combination of 
 student s t test and wilcoxon s rank 
 sum test , both two - sided .
q - q plots of some arbitrary peptides show 
 that for peptides with reasonably low cv , replicate measurements are 
 roughly normally distributed , although one high cv peptide demonstrated 
 typical non - normality ( supplementary figure 24a , b ) .
because of this we consider differences significant only if both 
 the parametric and nonparametric test show significant difference 
 at p  0.05 . with the high total numbers 
 of mass spectrometry analyses in this study , we have excluded replicates 
 because of column failure , failed protein extraction , large synthetic 
 peptide carry - over , and in one case unexplainably low total signal . 

all exclusions are reported in supplementary table 
 2a , b and supplementary figure 7 .
normalization of label - free s. pyogenes data was 
 done using a house keeping protein index ri defined as the average peptide quantity of rs10_stra1 , 
 rl22_strp1 , rl1_strp1 and rs17_stra1 proteins in replicate i. peptide quantities in replicate i was 
 divided by ri and multiplied 
 by the average ri across 
 the replicates .
the original data in this work has been 
 deposited at the swestore repository : http://webdav.swegrid.se/snic/bils/lu_proteomics/pub/anubis_data.zip
peptide quantification using srm is 
 typically performed by measuring multiple transitions to ensure that 
 the signal is derived from the target peptide .
the chromatographic 
 traces of the measured transitions should all display a peak when 
 the peptide elutes , and furthermore the ratios of the peak intensities 
 should be identical to previously measured peaks of the same peptide , 
 unless the transitions are contaminated by other compounds .
the anubis algorithm was tailor - made for these 
 properties and works by comparing chromatograms from complex biological 
 samples to user - provided reference ratios to locate the target peptide . 

conventionally , chromatogram analysis is performed by searching for 
 peak - shaped sections in each fragment ( e.g. , by local maxima or by 
 a moving reference shape ) , clustering of the peak shapes , and then 
 selecting the best cluster using some heuristic , often based on intensity , 
 rank between fragments , or retention time . in anubis , we have used 
 a novel approach in which possible elution points are searched for 
 in the pairwise fragment ratios rij of the chromatogram , by comparing to target pairwise fragment 
 ratios , tij . for a given 
 instrument , collision energy and peptide ,
the frequency of each fragment 
 is constant , and we can therefore simply 
 search for retention times where a rij agrees with the respective tij ( figure 1a  d ) .
( d ) in the data , every time point during which any pairwise 
 fragment ratio is close enough to its target is marked as a possible 
 peak of peptide elution .
( e ) using wavelet analysis , p - values are estimated for each possible peak , and the most specific 
 peak is chosen .
( f ) again the target pairwise fragment ratios are 
 used to remove any substantial interference , which gives the final 
 peak . this
( g ) we validated anubis with a large srm study of 8 laboratories , 
 10 peptides , and a 9-point dilution series . using sis references , anubis achieves equal accuracy as the previously 
 published manual analysis .
( h ) label - free anubis analysis gives still 
 accurate , but slightly reduced , performance . to allow assessment of the quality of reported 
 peptide quantities ,
a local p - value is estimated 
 for each detected peak , allowing the filtering of a data set at any 
 confidence level .
we calculate p - values by deconstruction 
 and reassembly of the chromatogram using wavelet analysis , in a way 
 that preserves the general frequency content of each fragment but 
 removes any correlation between fragments , thus generating random 
 chromatograms with properties similar to the original chromatograms . 

this is performed 1,000 times to create a null distribution , and the p - value is taken as the fraction of the null distribution 
 where there is a point of equal or better agreement between rij and tij compared to that of the detected peak , 
 thus indicating a false discovery .
in addition to providing the user 
 with a sense of the peak detection quality , the p - value is also used to pick the most specific ( lowest p - value ) peak if there are multiple possible peaks in a chromatogram . 

quantification is done by summing the fragment areas , while excluding 
 interference in fragments that are not part of any agreeing rij ( figure 1e , f ) .
the size of the null distribution 
 was chosen as a trade - off between analysis time and p - value precision ( supplementary results ) .
reference ratios are preferably derived from a chromatogram 
 with a clear peak of the target peptide , using the reference creator 
 program that is supplied with anubis . for peptides from naturally 
 high abundance proteins , reference ratios
can often be measured directly 
 in the biological sample , when unambiguous peaks exist . for peptides 
 where the correct peak is not readily distinguishable in the biological 
 sample ,
, hundreds of crude synthetic peptides can be pooled and 
 analyzed for measuring reference chromatograms in a minimum of instrument 
 time . as an alternative to synthetic peptides , in vitro synthesized 
 proteins
only 
 the pairwise fragment ratios are needed from the reference chromatogram , 
 another approach that we have not evaluated would be to calculate 
 the ratios directly from spectral libraries , but for accurate results 
 the library would need to be acquired under similar instrument settings 
 as in the final srm method . note that because anubis uses fragment 
 ratios , a theoretical minimum of 2 transitions per peptide is needed , 
 but we generally find that 3 transitions are necessary for reliable 
 detection , and at least 4 transitions are needed for maximal accuracy 
 in the quantification ( supplementary results ) .
the design of the anubis algorithm has a number of advantages . 
 compared to creating peak candidates from a cluster of local intensity 
 maxima , looking directly at the pairwise ratios
allows evaluation 
 of each fragment at each individual time point , giving a much more 
 refined representation of the peak .
this makes exclusion of ill - behaving 
 fragments possible , whereas the dot product between the relative ratios 
 and a spectral library or other aggregate 
 measures are incapable of this .
as the 
 signal - to - noise decreases the fragment ratios remain constant ( supplementary figure 5 ) , meaning that peaks will 
 be detected equally well , but their assigned p - value 
 will be higher since similar peaks will occur more frequently in the 
 null distribution .
finally , we have chosen not to utilize retention 
 time in our analysis , since retention times typically fluctuate and 
 column degradation can give systematical shifts in large sample batches 
 ( supplementary figure 6 ) , which means that 
 deviations in raw retention time contain little information for distinguishing 
 target peaks within the small time windows typically used in scheduled 
 srm .
retention time has indeed been shown to be the least discriminant 
 dimension of information in srm .
efficient 
 usage of retention time for peak discrimination requires either some 
 efficient means of retention time normalization or inter - replicate 
 analysis , which we prefer to leave outside the core algorithm since 
 this is heavily experiment setup dependent .
in addition to the 
 aim of extracting the most information possible out of srm data , anubis 
 was specifically designed for being easy to use , for software pipeline 
 integration , and to support high throughput .
we support the standard 
 file formats mzml and traml , as well as transition lists exported from skyline .
the software is platform independent , and analysis 
 is easily automated using the command line interface . although the 
 need for reference ratios could imply a potential lowering of throughput 
 if synthetic peptides are used as references , these analyses only 
 have to be done once per peptide and are typically performed during 
 assay development regardless .
we validated 
 anubis performance in a label - free quantitative workflow against a 
 large previously published multisite data set .
this data set consists of 10 peptides diluted into human 
 plasma over 3 orders of magnitude , and the resulting sample set was 
 analyzed at eight different laboratories followed by expert manual 
 data analysis .
we reanalyzed this data with anubis and compared coefficients 
 of variation ( cv ) and coefficients of determination ( r ) with values reported in the original publication ( supplementary table 1 ) .
since the study was made 
 using sis labeling , we quantified both endogenous and sis peptides 
 with anubis and calculated statistics for both endogenous quantities 
 and endogenous divided by sis quantities .
anubis sis reference statistics 
 match the manually analyzed results , showing the validity of the algorithm 
 and its ability to perform unsupervised analysis of complex srm data 
 ( figure 1 g ) .
statistics for anubis quantities 
 with or without sis labels shows that labeling is beneficial for accurate 
 srm quantification as expected , but label - free quantification is still 
 reliable , demonstrating that the label - free anubis workflow allows 
 accurate quantification ( figure 1h ) .
we further 
 confirmed the validity of the anubis workflow by performing spike - in 
 experiments with a dilution series of 42 synthetic peptides in a cell 
 line lysate using skyline and automated anubis analysis in parallel 
 ( supplementary results ) .
although performing well on the spike - in data sets , 
 the utility of the label - free quantification workflow needed to be 
 confirmed on real biological experimental data , without extensive 
 assay optimization .
we thus conducted a series of label - free experiments 
 on s. pyogenes , an important microbial pathogen often 
 responsible for pharyngitis but occasionally causing severe conditions 
 such as septic shock .
s. pyogenes is responsible for more than 500,000 
 deaths worldwide , making it one of the most important human pathogens .
we cultured 9 biological replicates of s. pyogenes ( supplementary table 2a ) , with replicates grown , harvested , and prepared for srm in parallel 
 to minimize the biological and experimental variation . from a previously 
 published s. pyogenes instance of peptideatlas , we selected 10 s. pyogenes ribosomal 
 ( rib ) proteins , 14 fatty acid synthesis ( fas ) pathway proteins , and 
 29 virulence
associated or presumed virulence associated proteins 
 ( virulome ) representing a complete coverage of the intracellular dynamic 
 protein abundance range . for these proteins 
 synthetic peptides
were made for one to three previously identified 
 proteotypic peptides , and reference chromatograms 
 were established by analyzing pools of the synthetic peptides by srm , 
 giving validated assays for 161 peptides ( supplementary 
 table 3 ) . to assess the overall relationship between 
 the technical and total variability ( technical plus biological variability ) 
 in label - free srm using anubis , we processed and quantified the 9 
 biological replicates with the anubis workflow , giving total variability 
 median cvs of 18% , 19% , and 38% for the respective protein groups 

( rib , fas , and virulome ) ( figure 2a ) . to estimate 
 the influence of sample preparation steps , srm measurement , and data 
 analysis
, we made up to 10 repeated measurements on singular biological 
 replicates , giving six technical replicate sets of totally 56 successful 
 replicates ( supplementary table 2b , supplementary 
 figure 7 ) .
the median cvs for these six sets were 411% , 
 715% , and 1526% for the protein groups . compared with 
 the biological replicate cvs ( figure 2a ) ,
the 
 technical cvs are consistently smaller by about half , meaning that 
 only 1/4 of total experimental variability originates from the mass 
 spectrometer analysis and the anubis label - free workflow .
we therefore 
 estimate the technical srm variability to be considerably lower than 
 the total variability in bacterial samples with minimized biological 
 variability and that the accuracy of quantification of the label - free 
 srm workflow is sufficient for analysis of complex bacterial samples .
proteins are 
 divided into ribosomal ( rib ) , fatty - acid synthesis ( fas ) , and virulence - associated 
 proteins ( virulome ) , which are respectively high- , medium- , and low - abundant . 

( a ) median cvs across the peptides in 3 protein groups , for 6  
 10 technical replicates and 9 biological replicates .
( b ) median cv ranges for the 6  10 
 technical replicates compared to median cv ranges 2 sets separately 
 prepared replicates ( a2.1apr + a2.2apr and a2.1jun + a2.2jun ) .
( c ) 
 median cv ranges for the 6  10 technical replicates compared 
 to median cv ranges of 2 sets replicates analyzed at different times 
 ( a2.1apr + a2.1jun and a2.2apr + a2.2jun ) .
( d ) median cvs with interquartile 
 ranges for the technical replicate sets compared to all second batch 
 replicates ( a2.1apr , a2.2apr , a2.1jun and a2.2jun ) , with and without 
 normalization .
( e ) illustration of normalization by ribosomal housekeeping 
 proteins ( rs10_stra1 , rl22_strp1 , rl1_strp1 , and rs17_stra1 ) in one 
 biological replicate .
each row represents a peptide , and each column 
 a replicate  grouped into 5 sets ( supplementary 
 table 2b , supplementary data 1a - b ) .
color denotes the quantity 
 of the peptide in that replicate compared to its average across all 
 replicates . from comparing a2.1apr + a2.2apr to a2.1jun + a2.2jun 

it is clear that the time of analysis greatly affects the measured 
 quantity . after normalization these differences
complementary to single preparation back - to - back 
 technical replicate sets , combinations of sets allowed for investigation 
 of the experimental variability caused by sample processing and instrument 
 condition at the time of injection .
combining replicates from double 
 sample preparations gave a moderate increase in total variation ( figure 2b ) , while combining replicates from separate times 
 of injection resulted in more substantial variation ( figure 2c ) .
however , both of these systematic replicate - wide 
 increases in variability can be negated by proper normalization , which 
 was done by housekeeping proteins since total ion current normalization 
 is typically not possible in label - free srm .
normalization by four 
 stable ribosomal proteins gave cvs almost level with the ideal technical 
 replicate sets and allowed combination of data from different times 
 of analysis and multiple sample preparations ( figure 2d , e ) .
we also calculated the average amount of successful 
 detections in detectable peptides ( with more than one successful detection ) 
 for each of the six technical replicate sets and the nine biological 
 replicates ( in total 65 injections in seven sets ) .
the reproducibility 
 of the sets was high , with rib , fas , and virulome proteins having 
 an average success rate of 97100% , 95100% , and 7584% 
 ( figure 2e ) .
closer inspections of virulome 
 peaks revealed that the limited detection of these peptides can largely 
 be attributed to very low signal - to - noise ratios for these peptides , 
 resulting in loss of detection in some injections ( examples are shown 
 in supplementary figures 1322 ) .
we believe that the above demonstrates that automated analysis 
 of label - free srm data using anubis possesses the required properties 
 for targeted quantitative proteomics .
the technical variability ( 426% ) 
 is considerably smaller than the biological and other experimental 
 variability ( 1838% ) . with high reproducibility of detection 

( 75100% ) it presents a way of increasing throughput when absolute 
 quantification is not required . to finally demonstrate the feasibility of anubis
, 
 we compared the measurements of our 9 biological replicates grown 
 in standard medium to 9 biological replicates grown in the presence 
 of 10% human plasma ( supplementary table 2a ) , as adaption to human plasma is an important ability for s. pyogenes virulence .
growing s. pyogenes with plasma changes the proteome homeostasis 
 of the bacterium but also represents a vastly different sample as 
 there are a substantial amount of plasma proteins present .
nevertheless , 
 the increased sample complexity barely influenced the technical variability 
 ( data not shown ) .
looking at agreeing wilcoxon rank sum tests and 
 students t tests , we find that the entire fas network 
 is significantly down - regulated by about 40% in the plasma condition 
 ( figure 3a ) , which is supported and explained 
 by previous research . meanwhile , in the 
 highly abundant and therefore easily measurable ribosomes , only one 
 protein is significantly regulated . in the virulome proteins ,
no group - wide 
 trend is seen , but multiple proteins show significant regulation in 
 all measured peptides .
for example , measured up - regulation of c5a 
 peptidase ( figure 3b ) upon plasma exposure 
 agrees with previous results , as well as the suspected 
 down - regulation of streptopain ( figure 3c ) .
polyphosphoribitol 
 subunit 1 and 2 ( dltc and dlta , figure 3d ) 
 is expected as they share the same promoter region .
these biological findings have been discussed previously 
 by others , but the agreement of our results with previous studies 
 further validates the performance of the label - free setup and automated 
 analysis . in summary ,
application of label - free srm on s. pyogenes to study protein abundance differences upon 
 growth with human plasma supplement .
( a ) each row represents one peptide , 
 with peptides grouped into proteins and separated by a white row .
0% plasma and 10% plasma columns display 
 measured quantities in the 2  9 biological replicates grown 
 with and without 10% plasma ( supplementary data 
 2 ) .
wilcoxon and t test columns 
 show the p - values of wilcoxon rank sum tests and t tests between conditions , with light green indicating 
 significance 0.05 .
the fold change column 
 shows the means ratio between samples , with blue being down - regulated 
 in plasma and red up - regulated . while the high - abundant ribosomes 
 show almost no regulation , indicating that they are not affected by 
 plasma , almost the entire fas ii pathway is significantly down - regulated 
 in plasma by about 40% .
( b ) the virulome protein c5a peptidase shows 
 consistent significant up - regulation in all peptides .
( c ) streptopain 
 is reliably detected in 0% plasma samples but not at all in 10% plasma 
 samples , indicating down - regulation beyond our limit of detection . 

( d ) d - alanine  polyphosphoribitol ligase subunit 1 
 and 2 both show significant down - regulation in 10% plasma .
the maturing techniques of targeted mass 
 spectrometry have been demonstrated and utilized in numerous reports , 
 typically using isotope labeling and with limited numbers of proteins , 
 samples , and replicates .
the major advantages of the anubis workflow 
 are automated analysis , with performance equal to a human expert , 
 and the omission of costly labeling .
we still retain a median technical 
 variability of 520% for the label - free workflow in a large 
 scale experiment , and application on s. pyogenes yields 
 significant biological results in agreement with previous data .
the measured trend of inverse correlation between abundance and technical 
 variability agrees with previous work , and manual inspection of low abundance peaks indicated that the 
 vast majority of these were correctly assigned and quantified by anubis . 

we believe that this higher variability of low abundance peptides 
 is naturally close to the limit of quantification , arising from fluctuations 
 in chromatography and ionization , as well as from stochastic ion detection . in a recent smaller scale study by zhang et al . , technical cvs of 10% for sis - labeled srm and 2030% 
 for label - free srm were found , which is slightly above our values . 

they argue , however , that when performing measurements on clinical 
 samples , the inherent biological variation in clinical material is 
 large enough that a technical variability of even 20% will barely 
 affect the total variability of the experiment .
label - free 
 srm has slightly different and complementary characteristics compared 
 to other lc
the classical data - dependent 
 shotgun strategy has with recent advances in protocols , chromatography , 
 and ms instrumentation been able to reach both high proteome coverage 
 and high sensitivity .
however , it often 
 lacks the ability to reproducibly quantify a given analyte in multiple 
 samples , due to both stochastic ms / ms sampling and difficulties in 
 precursor ms data analysis .
several approaches 
 using data - independent ms / ms acquisition have also been proposed , 
 as discussed elsewhere .
for the recent 
 variant swath - ms , a data analysis workflow has been proposed where 
 fragment ion chromatograms are extracted and analyzed on the basis 
 of previously acquired ms / ms data , in 
 a manner similar to srm data analysis .
this offers an alluring compromise 
 between the srm and shotgun strategies , mimicking the reproducibility 
 and sensitivity of srm but at a lc  ms / ms - like throughput . in 
 this initial work ,
the quantitative reproducibility of swath - ms was 
 indeed comparable to that of srm .
it 
 remains to be seen whether this high performance can be repeated on 
 a standard basis , but we can note that swath - ms data potentially could 
 be analyzed using anubis . whether to use an srm strategy with 
 or without labels for any given experiment will always be a trade - off 
 between required accuracy , cost , and instrument time . if measurements 
 have to be made at different time points , or if heterogeneity between 
 biological samples is so large that normalization is troublesome , 
 a strategy incorporating labels might be the only way to control instrument 
 variation . if on the other hand large amounts of analytes need to 
 be measured and the experimenter has much control over how and when , 
 a label - free approach will give similar results faster and at a reduced 
 cost .
we believe our proposed workflow enables larger scale 
 srm experiments , with higher throughput , reduced cost , more consistent 
 data analysis , and controlled error rates .
all of this boils down 
 to the possibility to target more proteins or using more replicates 
 for additional statistical power , while relieving some highly qualified 
 srm expert of hours of daunting peptide integration .
once a small 
 set of highly interesting proteins is found , the synthesis of sis 
 is of course possible and will result in further decreased experimental 
 variability .
 selected reaction monitoring ( srm ) is a mass spectrometry 
 method with documented ability to quantify proteins accurately and 
 reproducibly using labeled reference peptides . 
 however , the use of 
 labeled reference peptides becomes impractical if large numbers of 
 peptides are targeted and when high flexibility is desired when selecting 
 peptides . 
 we have developed a label - free quantitative srm workflow 
 that relies on a new automated algorithm , anubis , for accurate peak 
 detection . 
 anubis efficiently removes interfering signals from contaminating 
 peptides to estimate the true signal of the targeted peptides . 
 we 
 evaluated the algorithm on a published multisite data set and achieved 
 results in line with manual data analysis . 
 in complex peptide mixtures 
 from whole proteome digests of streptococcus pyogenes we achieved a technical variability across the entire proteome abundance 
 range of 6.519.2% , which was considerably below the total 
 variation across biological samples . 
 our results show that the label - free 
 srm workflow with automated data analysis is feasible for large - scale 
 biological studies , opening up new possibilities for quantitative 
 proteomics and systems biology . 
Introduction
Experimental Section
Results and Discussion
Conclusions