mass - spectrometry - based 
 proteomics , fueled by seemingly ever - increasing 
 instrument performance , has gained considerable traction as a high - throughput 
 method for biomarker discovery and systems biology applications .
new 
 and flexible workflows have extended the range of mass spectrometry 
 ( ms ) applications in life science research from biomarker discovery 
 to complex interaction mapping , whole - proteome assay 
 libraries , mrna and protein dynamics , or full structural characterization of a protein complex . in bottom - up
ms proteomics , mass - spectrometry 
 analysis of peptides , derived from intact proteins using proteolytic 
 processing , allows the measurement of thousands of isotope envelopes 
 of individual peptide ions .
these isotope envelopes appear as ion 
 intensity patterns in the retention time and m / z dimensions and are often referred to as features .
accurate 
 feature detection is an important step in many mass - spectrometry - based 
 proteomics workflows to quantify identified peptides in shotgun mass spectrometry 
 by using the integrated or apex feature intensity .
accurately determined features are also used 
 to increase the accuracy of estimated precursor masses , to determine 
 chromatography performance and optimize chromatographic gradients , and to determine the total number of detectable 
 peptides by analyzing feature charge states , masses , and retention 
 times .
features can also be used to perform untargeted analysis of 
 data - independent acquisition ( dia ) experiments .
lastly , recent work has shown that feature information 
 can increase the identification rates of chimeric ms / ms spectra , providing 
 a cheap possibility to increase the number of identified spectra and 
 the quality of the data analysis .
although listgarten 
 and emili thoroughly summarize early work on the subject , label - free quantification of shotgun proteomics 
 experiments has resulted in several additional published feature - detection 
 tools like , for example , msinspect , superhirn , openms , centwave , hardklr , and mzmine . in 2008 , maxquant introduced 
 a graph model to improve feature detection in dense proteomics data 
 derived from complex biological samples .
later work has been driven by changing data quality ; modern fourier 
 transform high - resolution instruments have reduced the need for spectral 
 noise reduction and are not suited for m / z range binning , which were both prominent parts of the 
 early algorithms .
the large number of available feature detection algorithms calls 
 for strategies to evaluate the outcome of feature detection . a straightforward 
 method for such evaluation
is to ensure that the feature list returned 
 by the feature detection algorithm contains the peaks that were confidently 
 identified by data - dependent ms / ms spectra .
first , the tool should detect and distinguish all features for the 
 detectable peptide ions in an lc 
second , it 
 should provide accurate measurements of feature retention times and 
 monoisotopic masses in addition to providing accurate quantitative 
 values .
third , it should preferably compute as fast as possible , be 
 robust against irregularities in input data , and be flexible in terms 
 of input data formats , output , and computational environment .
finally , 
 the tool should provide parameters for optimization and means for 
 the users to evaluate the effects of any modified parameter .
although 
 ms1 feature detection is a well - studied problem , we believe further progress can be made toward these 
 ideal properties . problems with existing algorithms are , for example , 
 operating system limitations , lack of support for standard file formats , 
 slow algorithm execution , limited control output , and closed or outdated 
 source code .
we also experience difficulties in understanding how 
 modification of algorithm parameters influences performance , which 
 prevents adoption of a given tool to new instruments or acquisition 
 conditions . in the work presented here
, we describe a new ms1 
 feature - detection 
 tool called dinosaur , based on concepts adapted from the feature detection 
 algorithm in the maxquant software , which 
 is widely used for analysis of shotgun mass - spectrometry data .
importantly , 
 with dinosaur , we introduce a new strategy of creating a set of quality - control 
 plots , referred to as a plot trail , to support the monitoring of computation 
 performance .
on the basis of an extensive evaluation of the plot trail , 
 we introduce several modifications to the original feature - detection 
 algorithm to improve performance and robustness . the dinosaur implementation 
 is heavily optimized and parallelized and handles data files from 
 modern mass spectrometers in minutes on any major operating system .
we evaluated the dinosaur performance by benchmarking against four 
 previously published feature - detection algorithms on a 5 orders of 
 magnitude dilution series of synthetic peptides in a complex biological 
 background .
dinosaur achieved an equally 
 high level of linearity compared to the other tools while matching 
 features to a larger percentage of identified peptides .
we also demonstrate 
 the utility of dinosaur by implementing it in a chimeric spectrum 
 identification workflow to increase the 
 number of detected unique peptides by 32% at a 1% false discovery 
 rate ( fdr ) .
the synthetic peptide 
 dilution series 
 ( pxd001091 ) and hela data sets ( pxd000999 ) were downloaded from proteomexchange . 

the eight sample - type representative samples were prepared in - lab 
 using standard protocols and have been deposited to the proteomexchange 
 consortium via the pride partner repository 
 with the data set identifier pxd003405 .
acquisition was performed 
 on a q exactive plus mass spectrometer ( thermo scientific ) in top-15 
 mode , coupled to an easy - nlc 1000 ultrahigh - pressure liquid chromatography 
 system ( thermo scientific ) .
linear gradients of between 5% and 35% 
 acetonitrile in aqueous 0.1% formic acid were run for 30 , 60 , 90 , 
 or 120 min at a flow rate of 300 nl / min .
sample is a his - tagged streptococcus pyogenes m - protein expressed in escherichia coli ;  synthetic peptides
 refers to 20 synthetic peptides 
 of 80% purity ( proteogenix ) ;  ap - ms  is an affinity 
 purification experiment using his - tagged s. pyogenes m - protein as a bait in mouse plasma ;  plasma  and 
  depl .
are nondepleted and , respectively , depleted 
 mouse plasma ;  bacteria  refers to blood agar grown s. pyogenes ;  yeast  is ms - compatible 
 yeast protein extract digest ( promega , v7461 ) ; and  tissue 
 lysate  is a complete mouse liver tissue lysate .
all raw data files were converted to gzipped and ms - numpressed mzml using the command - line version of the proteowizard 
 ( 3.0.5930 ) tool msconvert .
when we analyzed the synthetic peptide 
 dilution series , maxquant 1.3.0.5 and msinspect ( build 599 ) settings 
 were as previously published .
maxquant 
 1.5 has become available since we setup our analysis pipeline , but 
 feature results from v1.3 and v1.5 are very similar , and thus , we 
 have kept our results from v1.3 .
maxquant 1.1.0.25 was used to represent 
 the original feature detection algorithm using default settings .
identification 
 of ms / ms spectra was performed and combined within the proteios software 
 environment , using x!tandem tornado 2008.12.01.1 
 ( www.thegpm.org/tandem ) and mascot 2.3.01 ( www.matrixscience.com ) with 7 ppm precursor tolerance and 0.5 da fragment tolerance , using 
 fixed carbamidomethylation of cysteins and variable oxidation of methionines 
 as described previously .
features were matched to ms / ms identifications 
 passing a 1% peptide - spectrum - match level fdr using a 0.005 da and 
 0.2 min threshold .
when we mapped multiple identifications to a feature , 
 no control was made for identification consistency , an effect with 
 an estimated magnitude of < 0.5% .
advanced feature - identification 
 matching is available as a proteios plugin , but for our purposes , 
 we reimplemented a simplified version of this functionality to be 
 able to count the mock matches .
openms ( 1.11.1 , may 2014 ) settings for the demix workflow were 
 as originally described ; in short , a default peakpickerhires configuration 
 was used for centroiding , followed by a featurefindercentroided configuration 
 that allowed precursor charges of 27 .
the used toppas workflow is accessible along with the dinosaur 
 source code . for the demix pipeline ,
searches were performed with 
 ms - gf+ , with trypsin set as the enzyme , 
 an instrument setting of 3 , an initial precursor tolerance of 10 ppm , 
 a minimum peptide length of 7 , and fixed carbamidomethylation of cysteins 
 and variable oxidation of methionines as modifications , as in the 
 demix publication .
program run times were measured 
 by the reported total time for dinosaur ( enabled using the profiling 
 flag ) and as the total feature finder execution time for openms , excluding 
 centroiding . for the openms 
dinosaur comparison , both programs 
 were run with concurrency of eight on a 12 core computation server 
 running ubuntu server 14.04 lts .
maxquant - ref was timed by starting 
 the analysis and measuring the time taken until ms2-related files 
 were first written . for maxquant , the total time for running steps 
 14
is reported as  maxquant full  , and the time 
 taken by the  feature detection 
subprocess until the 
 appearance of ms2-related files is reported as  maxquant part  . 

maxquant comparisons were performed with concurrency 
 of one on a ssd - equipped desktop computer running windows 7 . all time 
 measurements refer to wall time .
dinosaur is implemented in 
 scala 2.10.0 and compiled for java virtual machine ( jvm ) using reactive 
 programing techniques and akka actors to enable parallelization .
scala 
 is a high - level language that gracefully blends object - oriented programming 
 with functional programming , and actors are a program construct that 
 send immutable messages to one another asynchronously and receives 
 and processes each message atomically .
these choices of language and 
 parallelization library do not affect the appearance of the final 
 program , which appears as a regular java program .
dependencies are 
 managed using maven , and in - house libraries ( https://github.com/fickludd/proteomicore ) were used for mzml parsing , supporting all mzml versions including 
 zlib , gzip and ms - numpress compression , 
 centroid or profile , indexed or not .
all dinosaur source code and 
 a self - contained standalone executable can be downloaded or compiled 
 from https://github.com/fickludd/dinosaur along with modified 
 scripts for running demix with dinosaur under the apache2 open - source 
 license .
the label - free shotgun quantification workflow using dinosaur 
 is freely available as part of proteios at www.proteios.org .
the implemented dinosaur feature detection algorithm 
 consists schematically 
 of four computational steps , as shown in figure 1 . in the first step , profile spectra are 
 centroided ( figure 1a ) . because the most common ms methods perform repeated ms measurements 
 during the elution of an analyte , the second step assembles centroid 
 peaks with similar m / z in consecutive 
 spectra into hills , meaning that one hill represents the full chromatographic 
 trace of one analyte ion isotope ( figure 1b ) . in the third step ,
the hills are clustered 
 according to plausible m / z differences 
 based on carbon and sulfur natural isotopes and considered charge 
 states ( figure 1c ) . 

because these clusters are not consistent in charge state or expected 
 isotopic prevalence , the fourth and last step is hill cluster isotopic 
 deconvolution into individual features with consistent charge ( figure 1d ) .
features are 
 detected by ( a ) centroiding ms1 spectra ; ( b ) assembling centroid peaks 
 into single isotope traces , also referred to as hills ; ( c ) clustering 
 of hills by theoretically possible m / z differences ; and ( d ) deconvolution of clusters into charge - state - consistent 
 features .
the plot trail plots randomly selected parts of the intermediary 
 data to support the tuning of parameters and increase transparency 
 of the computational steps . created plot types are ( e ) a line graph 
 of a peak centroid , ( f ) a heatmap and histogram of hill construction , 
 ( g ) an isotopic profile compared to an averagine , and ( h ) a complete 
 heatmap of a data section with annotated detected features . to improve the understanding and transparency of 
 the algorithm 
 and its parameters , dinosaur randomly selects and visualizes parts 
 of the data at each computation step .
the 
 plot trail facilitates intelligent optimization of the 50 dinosaur 
 parameters and supports the users to minimize the risk of major computational 
 mistakes or acquisition errors .
the dinosaur plot trail consists of 
 four types of plots ; a line graph of a centroided peak ( figure 1e ) , a heatmap and histogram 
 of a constructed hill ( figure 1f ) , a line graph of a feature isotopic profile ( figure 1 g ) , and a heatmap of a whole 
 section of data with annotated features ( figure 1h ) .
the centroid peak plots consist of 
 the least , most , and median intense centroided peak from one spectrum . 

the hill plots consist of the shortest , longest , and median length 
 hills with their apex at the same spectrum .
. heatmaps 
 are 15 m / z times 150 spectra by 
 default . all sampling in the m / z and spectrum ( rt ) dimensions is done uniformly .
additionally , dinosaur 
 supports a targeted plot trail , where a list of m / z and retention time coordinates creates corresponding 
 annotated heatmaps .
the targeted plot trail is practical to evaluate 
 the algorithm performance for features of particular interest .
the implementation of the plot trail supports the detailed analysis 
 of individual features in conjunction with ms / ms identifications . 

dinosaur was initially a reimplementation of the maxquant feature - finding 
 algorithm , as described in the original manuscript ( hereafter called maxquant - ref to distinguish it from later 
 maxquant versions with unpublished algorithms ) .
however , using the 
 plot trail during implementation motivated several algorithm modifications 
 to improve performance and robustness .
these improvements are documented 
 below and made the final algorithm to distinctly deviate from its 
 ancestor .
first , several changes were introduced in the 
 hill - building step 
 to reduce the occasional intermingling of traces from separate features , 
 which happens as multiple analytes frequently share almost identical 
 retention time and m / z .
the centwave algorithm , used for hill construction in maxquant - ref , 
 relies on a simple maximal offset in ppm between centroid peaks .
we 
 reduce the feature intermingling by instead comparing new centroid 
 peaks to the sliding average of the last three peaks in a hill to 
 reduce the effect of single - spectrum m / z fluctuations .
further , this matching is performed in a greedy fashion 
 in centwave , meaning that the first found match is selected , but this 
 also leads to intermingling in crowded or noisy parts of the data 
 because of the relatively large 7 ppm maximal offset .
in contrast , 
 dinosaur temporarily stores all matching hills and centroid peaks 
 in a list until the next pair is outside the maximal offset . at this 
 point ,
the hills and centroid peaks are matched from the list in the 
 order of the minimal m / z difference 
 until no more matches are within the maximal offset .
finally , hill - profile 
 smoothing in dinosaur uses only three - point sliding windows versus 
 a five - point window in maxquant - ref . after the removal of the artifacts 
 introduced by intermingling ,
the improvements of the hill construction 
 are expected to reduce the misassignment of features in the later 
 steps and , therefore , increase the algorithm s sensitivity .
this phenomenon is more frequent in highly complex 
 samples and at the end of a chromatographic run , when remnants on 
 the column are washed off by a sudden increase in organic solvent . 
 during deconvolution ,
all of the hills in a cluster are compared against 
 each other , giving a computation of at least quadratic complexity , 
 which becomes prohibitively slow for clusters with thousands of hills . 

this was solved in maxquant - ref by arbitrarily splitting clusters 
 above 100 hills into two halves and recursively running the deconvolution 
 on each half instead .
this solution may split the hills of a feature 
 into separate subclusters , resulting in failure to detect such a feature . 

dinosaur instead sorts the hills by summed intensity and only seeds 
 isotope patterns from the 100 most intense hills , after which the 
 longest pattern is selected and the process repeated on the remaining 
 hills until no further patterns are found .
this heuristic limits computation 
 complexity while avoiding arbitrary splitting of clusters and thus 
 potentially increases algorithm sensitivity .
third , the trimming 
 of isotope patterns is not well - documented 
 in the original maxquant - ref algorithm . from the original source code
, 
 it can be seen that the algorithm relies on a few heuristic rules 
 based on the seed mass .
instead , dinosaur compares the suggested isotope 
 pattern intensity profile to the shifted versions of an averagine 
 peptide of the same mass .
the shifted 
 averagine pattern is scored by the cosine correlation to the feature 
 isotope profile , multiplied by the explanation percentage of the matched 
 averagine peaks .
the highest - scoring shift is selected to determine the monoisotopic 
 mass of the isotope pattern and to discard features that are not similar 
 enough ( by default , cosine corr of 0.6 ) to the averagine isotopic 
 pattern .
this change is expected to increase the correct assignment 
 of the feature monoisotopic mass , which is crucial for matching features 
 to ms / ms identifications and for label - free alignment in any high - resolution 
 ms workflow .
apart from direct algorithm improvements , dinosaur 
 aims to be a 
 flexible tool .
it runs on all major operating systems and accepts 
 all configurations of the ms data standard format mzml , including gzipped and ms - numpressed files .
in addition , dinosaur is provided 
 in a self - contained executable jar file without any other requirement 
 than the java runtime environment 1.6 or higher .
these properties are summarized for dinosaur and other common feature 
 detection tools in table 1 .
finally , the plot trail allows users to correctly configure 
 dinosaur and transparently evaluate its performance on their samples 
 ( see the supplementary discussion section ) .
dinosaur 
 was evaluated on three data sets to test different aspects of the 
 tool .
first , we compare the raw algorithm performance to four other 
 feature - detection tools on a dilution series of synthetic peptides . 

second , we investigate the computation speed and usability of feature 
 data on eight samples that were representative of different mass - spectrometry - based 
 experiments .
third , we demonstrate an application of dinosaur in a 
 workflow using three hela cell lysate injections in a chimeric spectrum 
 identification workflow .
we evaluated the performance of dinosaur 
 by comparing dinosaur results to those obtained with msinspect , maxquant 1.1 ( representing maxquant - ref ) , maxquant 
 1.3 , and the openms featurefindercentroided using a dilution series of synthetic peptides in a bacterial background .
the dilution series consisted of 273 crude synthetic 
 peptides , log  linearly cross - diluted at 12 dilution levels 
 ranging from 20 pmol/l to 200 amol/l .
each dilution 
 point was analyzed in four or seven replicates for a total of 57 separate 
 injections .
data was collected using a standard data - dependent acquisition 
 method on an orbitrap xl mass spectrometer . in our hands ,
we used 
 the same set of 350  551 synthetic and bacterial peptide spectrum 
 matches at 1% fdr to evaluate the performance of the different feature 
 detection tools .
the percentage of feature - matched ms / ms identifications 
 was higher for dinosaur ( 97.8  0.5% ) than maxquant ( 94.5  
 0.9% ) , open ms ( 95.7%  0.7% ) , maxquant - ref ( 89.0%  1.4% ) , 
 and msinspect ( 81.3  0.9% ) ( figure 2a ) .
these numbers of matched ms / ms identifications 
 were compensated for spurious pairings by control - matching with shuffled 
 ms2 events ( see the supplementary material section ) and therefore represents a quality measure of the detected 
 features because identified peptides were filtered at 1% fdr and should 
 in almost all cases have corresponding ms1 features .
the tests with 
 matching of shuffled ms2 events also indicated that the high number 
 of ms / ms matches seen for dinosaur was not an effect of matches due 
 to high numbers of randomly detected features , as the algorithms matched 
 27% of mock ms / ms events ( figure s-2 ) .
dinosaur , msinspect , maxquant , maxquant - ref , and open ms feature 
 detection performance based on 57 lc  ms injections of a dilution 
 series of synthetic peptides in bacterial background .
( a ) the proportion 
 of ms / ms identifications at 1% fdr that were matched to a feature . 

( b ) log  log coefficients of correlation of feature summed intensities 
 vs theoretical synthetic peptide concentration . in terms of quantitative linearity versus theoretical concentrations 
 of the synthetic peptides , results seem limited by instrumentation 
 rather than algorithm , as all tested algorithms have similar median 
 coefficients of correlation ( r ) around 
 0.95 ( figure 2b ) . in 
 total , all algorithms detected features for a median 17% of the synthetic 
 peptides across all of the analyzed lc  ms injections . the low 
 coverage is expected because of the sensitivity limitations of shotgun 
 ms / ms identifications and the very diluted synthetic peptides in the 
 sample and because no identity propagation between samples was performed .
we conclude that dinosaur achieves higher sensitivity 
 of matched ms / ms identifications without sacrificing quantitative 
 accuracy compared to the other algorithms .
to deeper characterize 
 the observed increased coverage of ms / ms 
 identifications , we normalized the reported features from all tools 
 by the median intensity of each tool on identifications that were 
 matched by all , and we compared feature intensity distributions ( figure 3 ) . in absolute numbers , 
 the feature - intensity distributions of the five tools are highly similar , 
 presumably reflecting the overall composition of detectable peptide 
 ions in the sample ( figure 3a ) .
if we , however , account for this by analyzing the relative 
 number of features in each intensity bin , differences in the tools 
 become apparent ( figure 3b ) .
the maxquant tools have a relatively strong performance on the 
 lower end of feature intensities , and the openms feature detector 
 performs very well in the high - intensity end .
dinosaur , however , achieves 
 top numbers of matched features across the normalized intensity scale , 
 explaining the observed high degree of matched ms / ms identifications . 

we thus conclude that dinosaur is capable of full - dynamic - range feature 
 detection and appears to match the differential strength of two previous 
 top - performing tools .
intensity distribution of id - matched features for compared 
 feature - detection 
 tools .
feature intensities from each tool were normalized by the division 
 by the median intensity of features linked to ids that all tools matched . 

( a ) absolute distribution of features over the normalized intensity 
 range . ( b )
maxquant tools are relatively strong at low - intensity features , and 
 the openms tool is relatively strong at high - intensity features .
apart from hard performance metrics , a mature software tool 
 should 
 be applicable in a variety of contexts and be reasonably fast .
we 
 challenged dinosaur by analyzing ms data from eight different samples 
 ranging from a purified protein to full tissue lysates of thousands 
 of proteins .
the eight different samples represent typical samples 
 analyzed in proteomics studies with a representative range of complexities 
 ( figure 4a  d ) . 
 in total
, dinosaur detected 412 331 features of charge 2 or higher 
 in the eight files .
these features were used to plot the empirical 
 distributions of feature intensities and retention times and to characterize 
 each sample s complexity and chromatographic performance .
the 
 number of features and their intensity was clearly dependent on the 
 expected sample complexity ( figure 4a ) , with the highest number of features being detected 
 in a murine liver tissue lysate and the least in the injection of 
 a purified protein .
the highest rate of detected 
 features per minute was found in a 60 min gradient of a yeast sample 
 ( figure 4b ) , where 
 dinosaur detected roughly 1000 features per min .
these results are 
 in accordance with a previously reported rate of 1500 features per 
 min . finally , we profiled the speed of 
 dinosaur by measuring the computation time of feature detection in 
 the eight representative files .
dinosaur s speed was compared 
 to maxquant- , maxquant - ref- , and the openms - based feature - finding 
 workflow . in our results ,
no tool displayed a strong correlation between 
 computation time and sample complexity , but dinosaur often achieved 
 the shortest runtime , reducing runtimes by 550% over the maxquant 
 versions and by 70% over openms ( figure 4c , d ) . in summary ,
the analysis of the eight 
 representative samples show the robustness , speed , and usability of 
 dinosaur and how descriptive information regarding sample complexity 
 and chromatography can be obtained from dinosaur - derived features .
( a  d ) typical proteomics samples 
 were represented by eight samples of different complexity . in total , 
 583  793 features were detected , of which the 412  331 
 of charge two or higher are included here .
( c ) computation times of the 
 eight samples for dinosaur compared to maxquant and maxquant - ref . 

because of difficulties with timing the feature - detection part of 
 maxquant , two alternative measures are reported .
( d ) computation times 
 as in ( c ) but for dinosaur compared to an openms feature finder .
the 
 missing measurement of the synthetic peptide openms sample is likely 
 due to some corner - case implementation issue .
( e ) the number of unique 
 peptides identified in three hela cell replicates using a new dinosaur - based 
 implementation of the demix workflow compared to the original workflow 
 and analysis without demixing .
an important parameter for a feature - detection tool is the 
 applicability 
 of the tool to different workflows . to illustrate the ability to integrate 
 dinosaur in downstream workflows ,
we first incorporated it into the 
 label - free quantification workflow in 
 the proteios software environment as 
 one of the selectable feature detectors .
we further used dinosaur 
 to implement the recently presented demix workflow for searching chimeric 
 spectra .
we exchanged the original feature 
 finder to dinosaur with some minor modifications in the original workflow 
 script .
for an unbiased comparison with the original workflow , we 
 used the three hela lysate injections from the demix publication . 

the demix workflow with dinosaur increased the number of unique identified 
 peptides ( 1% peptide - spectrum match fdr ) by 32% , compared to a standard 
 search on the total 151  260 ms / ms spectra in the three replicate 
 injections , and the openms feature finder of the original publication 
 produced a 26% increase on the same files ( figure 4e ) .
the increase in demix effectiveness should 
 be directly related to the increased ms / ms matching of dinosaur over 
 open ms , as seen in figure 2a , and is likely a general property that is transferrable 
 to other samples . with the successful incorporation into a label - free 
 quantification workflow and a chimeric spectrum workflow ,
in this work , we have devised an improved ms1 feature detection 
 tool called dinosaur .
this tool includes a plot trail , a new strategy 
 for performance monitoring , which was used to improve the algorithm 
 in most major computational steps . from these improvements ,
we demonstrate 
 an increased percentage of feature - matched 1% fdr ms / ms identifications 
 and equal levels of quantitative accuracy compared to other feature - detection 
 tools .
we further demonstrate runtime decreases of 570% over 
 alternative tools and the applicability of dinosaur , both by itself 
 on eight representative sample injections and as a part of a label - free 
 quantification workflow and a chimeric - spectrum - identification workflow . 

given the high data - generation rate in ms - based proteomics , a natural 
 focus is the post - acquisition data analysis .
the past years have resulted 
 in numerous developed and published feature detection algorithms in 
 which some are heavily used and others are not .
although most algorithms 
 are useful for specific purposes , underdeveloped and under - tested 
 software , possibly related to difficulties in publishing refined software , tends to limit common usage .
it can not be ruled 
 out that many of the best available algorithms remain unused due to 
 implementation problems like noncompatible input or output formats , 
 no support for the used operating system , or simply undocumented and 
 unexplainable crashes on apparently valid input . in this paper , we 
 have attempted to address the need for an accurate and powerful ms1 
 feature detection program by consolidating and further developing 
 a successful existing algorithm . although the ideal feature 
 detection tool might not be achievable , 
 we believe dinosaur closes the gap a little , given the observed increase 
 in computation speed and percentage of feature - matched ms / ms identifications . 

it should be noted that acquiring 100% ms / ms identification coverage 
 is a game of diminishing returns , as small improvements in absolute 
 numbers result in increasingly complex algorithms with associated 
 increasing computation time .
for the achievement of high coverage 
 in complex proteomes , a dia - based workflow is possibly a better alternative because these workflows avoid the stochastic sampling of shotgun 
 ms and rely on quantification at the ms / ms level .
the algorithm 
 improvements that differentiate dinosaur from maxquant - ref 
 increased the percentage of feature - matched ms / ms identifications 
 in the synthetic peptide dilution data set from 89% to 98% , due to 
 decreased feature intermingling , avoided feature - splitting during 
 cluster deconvolution , and improved averagine matching and monoisotopic 
 assignment .
we used the dinosaur targeted plot trail to manually assess 
 158 coordinates mapping to the 2% of nonmatched identifications 
 in a representative file . for most nonmatched identifications , the 
 corresponding features are manually detectable in the ms data
. missed 
 features are often caused by mistakes in cluster deconvolution , but 
 often , features are entangled with other features in such a way that 
 it would not be possible to separate them without considering combinations 
 of isotope patterns , which is not readily possible in the current 
 algorithm .
this could potentially be solved by using the harklr decharging algorithm or a two - dimensional template - based 
 feature detector , although using such a technique on high - resolution 
 data might be computationally prohibitive and would warrant further 
 investigation .
we were also unable to devise a way to correctly evaluate 
 the precision of the reported features as the absence of an associated 
 identification does not necessarily indicate that the feature is incorrect 
 because of the limited sequencing speed of the instrument .
after the 
 completion of the dinosaur project , we have become aware of a small 
 manually annotated feature data set that could be used for this purpose 
 in future studies . apart from the 
 missing 2% of the identifications , it should be 
 noted that dinosaur has been developed and optimized for orbitrap 
 data
. an initial test of application on high - resolution q - tof data 
 was , for example , not satisfactory according to the plot trail , and 
 no data from other mass analyzers such as low - resolution ion traps 
 has been analyzed . here is a clear avenue for further study and algorithm 
 tuning .
finally , one might consider reproducing the final parts of 
 the maxquant algorithm for silac data analysis to enable operating - system - neutral 
 and open - source analysis of silac data using dinosaur .
dinosaur 
 exposes more than 50 different configuration parameters 
 ( table s-1 ) , which are configurable directly 
 on the command line or in a parameter file . to guide the user in tuning 
 and evaluating these parameters , we have implemented a plot trail 
 to stochastically sample the results of the major computation steps 
 and to create plots describing computation effect on real data .
the 
 plot trail was used in this paper to improve the dinosaur algorithm , 
 but the main purposes are computational quality control and tuning 
 dinosaur parameters for application on data from other ms instruments . 

plot trail plots are stored in a quality control folder or zip folder 
 and can quickly be opened to check analysis performance and evaluate 
 the effects of particular parameter settings ( see the supplemental discussion section ) .
the plot trail 
 is distinctly different from the visualizations offered by programs 
 like toppview ( openms ) , maxquant , and skyline . in these programs , 

plots need to be manually generated by loading the corresponding ms 
 data and result files and the plot space manually selected , leaving 
 room for human bias . also
, all metadata present in the dinosaur plot 
 trail plots is not available in the final feature results . in summary
, 
 we believe the plot trail is a novel concept that is useful for explaining 
 the software function to users and to enable the earlier detection 
 of errors in data or feature - detection computation with minimal user 
 effort .
multiple groups have recently reported alternative techniques 
 for 
 identifying and analyzing chimeric ms / ms 
 spectra using either ms1 features , ms / ms - based 
 precursor mass guessing , or iterative 
 searching with fragment attenuation .
these 
 recent studies consistently report up to 30% gains in the number of 
 unique peptides identified and examples of coisolation of four or 
 more peptides . in this way
, peptide information in the available measured 
 data that was previously ignored becomes accessible .
our results using 
 dinosaur concur with previous reports , contributing to a growing body 
 of evidence that suggests that chimeric spectral searching should 
 be a part of standard workflows .
the intention of dinosaur is 
 to provide a flexible , fast , and robust 
 feature finding algorithm .
even though the devised improvements give 
 some small increase in feature sensitivity , we emphasize that the 
 main benefit of dinosaur lies in its usability and fast incorporation 
 in various workflows ( for example , as demonstrated in the implemented 
 label - free quantification and chimeric - spectral - search workflows ) .
 in bottom - up mass spectrometry ( ms)-based 
 proteomics , peptide isotopic 
 and chromatographic traces ( features ) are frequently used for label - free 
 quantification in data - dependent acquisition ms but can also be used 
 for the improved identification of chimeric spectra or sample complexity 
 characterization . 
 feature detection is difficult because of the high 
 complexity of ms proteomics data from biological samples , which frequently 
 causes features to intermingle . 
 in addition , existing feature detection 
 algorithms commonly suffer from compatibility issues , long computation 
 times , or poor performance on high - resolution data . 
 because of these 
 limitations , we developed a new tool , dinosaur , with increased speed 
 and versatility . 
 dinosaur has the functionality to sample algorithm 
 computations through quality - control plots , which we call a plot trail . 
 from the evaluation of this plot trail 
 , we introduce several algorithmic 
 improvements to further improve the robustness and performance of 
 dinosaur , with the detection of features for 98% of ms / ms identifications 
 in a benchmark data set , and no other algorithm tested in this study 
 passed 96% feature detection . 
 we finally used dinosaur to reimplement 
 a published workflow for peptide identification in chimeric spectra , 
 increasing chimeric identification from 26% to 32% over the standard 
 workflow . 
 dinosaur is operating - system - independent and is freely available 
 as open source on https://github.com/fickludd/dinosaur . 
Introduction
Experimental Methods
Results
Discussion