the first step in a 
 typical computational pipeline for protein 
 identification through database searching is comparing ms / ms spectra 
 to peptide sequences to identify the best matching peptide for each 
 spectrum , referred to as peptide - to - spectrum matches ( psms ) .
these 
 psms are then processed by protein inference algorithms to produce a minimal list of proteins that would 
 need to be present in the sample to explain the identified psms .
the 
 proteins in this list are also assigned a confidence score or protein 
 probability calculated on the basis of several factors , including 
 the number of psms supporting the protein and the match score of the 
 psms . to determine the confident identifications from the results 
 of protein inference , false discovery rates ( fdr )
only proteins identified at or above a certain 
 fdr threshold ( typically 1 or 5% ) are chosen as high - confidence identifications 
 for further analyses .
although sophisticated algorithms for 
 spectral matching and analysis 
 have been developed , protein identification can still be hampered 
 by issues such as low efficiency of peptide ionization , low - quality 
 or noisy spectra , dynamic range of protein abundances , and the complexity 
 of protein samples . to deal with such 
 issues
, there have been continued attempts to incorporate additional 
 information about the ms / ms experiment into analysis pipelines , such 
 as peptide chromatographic retention time , pi , or mass accuracy , some of which are now a routine part of many proteomic 
 analysis pipelines .
studies have also 
 investigated using matching ms and ms information , match scores from multiple search engines , and various other information sources to rescore or adjust protein 
 identification probability .
another category of methods have 
 investigated utilizing external 
 information ( information from outside the ms / ms experiment ) such as 
 microarray data , protein 
protein 
 interaction networks , or gene functional 
 networks to improve protein identifications . 
 a recent study by wang et al . described 
 an approach to utilize rna - seq abundance information to limit the 
 size of protein sequence databases and
, we describe an alternate method 
 for incorporating 
 external information such as rna - seq abundance and gpmdb identification 
 frequency into proteomic analysis pipelines , through rescoring or 
 adjustment of protein identification probabilities .
rna - seq uses short 
 read sequencing technologies to sequence the rna content ( transcriptome 
 profile ) of a sample . on the basis of 
 the central dogma of molecular biology , it is a reasonable assumption 
 that proteins corresponding to high - abundance transcripts are more 
 likely to be found in a sample .
the global proteome machine database 
 ( gpmdb ) is a repository storing the results 
 of proteomics experiments . with the large volume of data aggregated 
 in gpmdb , the frequency of identification of a protein in gpmdb
can 
 be used as a surrogate measure of a protein s propensity to 
 be observed in a ms / ms experiment . in other words
, we can reasonably 
 assume that proteins with a high gpmdb identification frequency ( gpmfreq ) 
 are more likely to be identified in an ms / ms experiment .
in this study , 
 we evaluate the utility of incorporating both of the above types of 
 information into proteomics analysis pipelines .
data from vcap , a human prostate cancer 
 cell line , and hek293 , a cell line derived 
 from human embryonic kidney cells , 
 were used in this study .
the ms / ms and rna - seq data for the vcap cell 
 line were generated in parallel at the same lab .
this rna - seq data 
 was also used as the control sample in a paper by sam et al . and is available for download from the ncbi 
 short read archive , sra ( sra accession nos .
, ms / ms data was obtained from control samples 
 in a publication by fonslow et al . , whereas 
 the rna - seq data was downloaded from data generated by sultan et al .
the gpmdb identification frequencies , which are not cell line specific , 
 were obtained by querying gpmdb for every ensembl protein i d using 
 a perl script .
collection of vcap whole cellular protein extract was done in 
 ripa complete buffer supplemented with halt protease and phosphatase 
 inhibitor cocktail ( peirce biotechnology ) .
fifty milligram aliquots of 
 total cellullar proteins were first separated by 1d sds - page ( 412% 
 bis - tris novex - invitrogen , carlsbad , ca ) .
forty equal - sized gel bands 
 were excised and subjected to in - gel digestion as previously described .
extracted peptides were reconstituted with mobile 
 phase a prior to online reverse - phase nanolc 
peptides were eluted online 
 to the mass spectrometer with a reverse - phase linear gradient from 
 97% a ( 0.1% formic acid in water ) to 45% b ( 0.1% formic acid in acetonitrille ) . 

peptides were detected and fragmented in the mass spectrometer in 
 a data - dependent manner , sending the top 12 precursor ions , excluding 
 singly charged ions , for collisional induced dissociation .
raw spectra 
 files were converted into mzxml by an in - house version of readw . both of 
 the ms / ms experiments 
 ( vcap and hek293 ) used in this study were seen to have very deep proteome 
 coverage , with about 40006000 protein identifications at 1% 
 fdr . however , most ms / ms experiments do not achieve this level of 
 proteome coverage . to investigate performance under experimental conditions 
 with varying depths of proteome coverage ,
the ms / ms data was sampled 
 at the level of individual mzxml files to create various subsets of 
 data of varying sizes ( fewer files would be included for a smaller 
 subset , and more files , to get a larger subset ) .
the vcap data had 
 a total of 40 mzxml files , whereas the hek293 data consisted of 60 
 mzxml files .
the number of protein identifications in these subsets 
 ranged between about 500 and 5000 protein identifications at 1% fdr .
the ms / ms data was searched 
 using the x!tandem ( cyclone ; 2010.12.01.1 ) search engine with a k - score plugin provided by 
 the trans - proteomic pipeline .
the search was performed against the 
 ensembl v.66 human proteome with reversed protein sequences appended 
 as decoys .
trypsin was specified as the enzyme with no missed cleavages 
 allowed , and cysteine carbamidomethylation and methionine oxidation 
 were set as fixed and variable modifications , respectively .
vcap data 
 was searched using a precursor mass error of 1 to + 4 da , whereas 
 the hek293 data ( high mass accuracy data ) was searched with a precursor 
 mass error of 50 ppm .
statistical validation of psms was performed 
 using the trans - proteomic pipeline ( tpp v4.6 occupy rev 2 ) software 
 suite .
vcap data was processed with + 1 
 charge state ions set to be ignored and using a semisupervised model for estimating negative distributions . hek293 

data was processed using the same settings as above along with additional 
 parameters to use accurate mass binning and the ppm scale for the 
 mass models .
the output protxml files from tpp were processed using 
 the abacus software tool to select a 
 representative protein for each protein group , according to heuristic 
 filters built into the tool .
rna - seq 
 data was aligned 
 to the ensembl v.66 human genome ( hg19 build 37 ) using the tophat 
 aligner ( tophat v.1.3.2 ) .
parameters 
 were set to allow up to one mismatch per alignment , and a gtf file 
 containing ensembl v.66 gene annotations was provided to tophat , using 
 the  g 
transcript abundance , in the 
 form of reads per kilobase per million mapped reads ( rpkm ) ( read count normalized to transcript length 
 and total number of reads in the experiment ) , was calculated for each 
 transcript from the bam file output from tophat .
rpkm calculation 
 was performed with a custom r script utilizing functions from the 
 bioconductor packages rsamtools ( v.1.6.3 ) and genomicfeatures ( v.1.6.9 ) .
our approach to incorporating 
 rna - seq or gpmdb frequency information ( figure 1 ) is built upon a statistical adjustment of 
 the protein probability .
the probability adjustment increases the 
 identification confidence scores of proteins that have significant 
 supporting evidence from external data ( high transcript abundance 
 in rna - seq or high frequency of identification in gpmdb ) , relative 
 to other proteins without such supporting evidence .
protein identifications 
 that previously fell just below the fdr threshold based on ms / ms evidence 
 alone , in a 
gray zone  of identification confidence , 
 can be promoted above the threshold when ranked by the adjusted probability . 

therefore , we are able to obtain more protein identifications at the 
 same fdr .
( a ) external information is added to protein 
 identifications from the analysis pipeline .
protein probabilities 
 are adjusted on the basis of transcript abundance ( rpkm ) or gpmdb 
 identification frequency ( gpmfreq ) .
( b ) decoys sequences used to estimate 
 fdr thresholds do not have native rpkm ( or gpmfreq ) values ; they are 
 assigned values by sampling from a set of all forward sequences with 
 similar length ( see methods ) .
decoy 
 approach ( reversed decoy sequences are appended to the forward protein 
 sequence database before performing database searching . the number 
 of identifications matched to decoy sequences is used to estimate 
 the rate of random matches in the identifications mapped to forward 
 sequences ) . because decoy sequences do not have inherent rpkm / gpmfreq 
 values
, their identification probabilities would be selectively decreased 
 during probability adjustment based on the external information . to 
 be able to estimate fdr after probability adjustment in an unbiased 
 manner , a rational method for assigning rpkm / gpmfreq values to decoy 
 sequences
the density distribution of rpkm / gpmfreq 
 values for proteins identified in the ms / ms experiment at 1% fdr ( confident 
 true positive identifications ) is seen to be appreciably different 
 from that of rpkm / gpmfreq for all proteins ( figure 2 ) . on the basis of this observation , sampling from the  all 
 proteins 
distribution allows for the unbiased assignment of 
 rpkm / gpmfreq values to decoy sequences while maintaining discrimination 
 between decoy and forward identifications .
weak correlations among 
 the rpkm , gpmfreq values , and protein length were also observed in 
 the data ( data not shown ) . to preserve this structure in the data , 

our sampling approach was designed to sample rpkm / gpmfreq values together 
 from forward sequences and assign them only to decoys of similar length 
 ( see supporting information methods for 
 a more detailed description of the sampling process ) . in further analysis
, 
 it was observed that the improvement from probability could be slightly 
 increased if the sampling for decoy values was weighted to prefer 
 values from proteins not identified in the ms / ms experiment , instead 
 of using a completely random sampling ( supporting 
 information figure 5 ) .
however , only results from the more 
 statistically rigorous approach of completely random sampling are 
 reported here .
density distributions of rpkm ( a ) and gpmfreq ( b ) values 
 ( log - scaled ) 
 for proteins identified at 1% fdr in the vcap cell line and all proteins 
 in ensembl are plotted .
the difference between the two distributions 
 allows us to sample the  all proteins  distribution 
 to assign values to decoys in an unbiased manner while still maintaining 
 discrimination between true positive and decoy identifications .
when performing probability 
 adjustment , pprot , a protein probability score calculated on the basis 
 of maximum peptide probability , was used as the prior probability 
 ( see supporting information methods for 
 details of pprot calculation ) .
pprot was used instead of the native 
 protein probability reported by tpp because it has been observed that 
 the maximum peptide probability is a more reliable indicator of true 
 protein identifications than the protein probability value ( supporting information figure 1 ) , especially 
 for large samples . using bayes theorem , the probability 
 adjustment estimates the probability of a protein identification being 
 a true positive given its rpkm / gpmfreq value ( eq 1).1where v can be 
 either an 
 rpkm or gpmfreq value .
the prior probability terms p(+ ) and p( ) were substituted with pprot 
 and 1  pprot , respectively . 
 to estimate the conditional probabilities of decoy or forward identifications 
 having value v , p(v| ) and p(v|+ )
, the density 
 distribution of log - scaled rpkm / gpmfreq values was placed into bins 
 of equal width ( supporting information figure 
 2 ) .
conditional probabilities for each bin were calculated 
 as p(vi| ) = ndi / ndt , where vi is any rpkm / gpmfreq value that falls within bin i , ndi is the number 
 of decoys having rpkm / gpmfreq values within bin i , and ndt is the total number of decoys 
 in the sample .
values for p(vi|+ ) were also estimated 
 similarly , but instead of number of decoys , the number of forward 
 hits with pprot > 0.5 ( i.e. , forward identifications that are more 
 likely to be true positive than false positive ) were used .
because the rpkm / gpmfreq 
 values are assigned through random sampling , the assignment and probability 
 adjustment ( figure 1a ) are repeated multiple 
 times to nullify any sampling artifacts and to obtain stable mean 
 adjusted probability values . in our study , the mean values were typically 
 seen to stabilize after about 200 iterations ( supporting information figure 3 ) , but the process was repeated 
 to 500 iterations for the results reported here .
the effect of the 
 probability adjustment was measured by comparing the number of protein 
 identifications at 1% fdr without adjustment to the number of protein 
 identifications at 1% fdr after probability adjustment ( rpkm or gpmfreq 
 based ) .
the percent improvement from all of the various subsets was 
 calculated and plotted , as shown in figure 3a , b .
percentage 
 improvement due to the probability adjustments ( rpkm 
 and gpmfreq ) for vcap ( a ) and hek293 ( b ) cell lines plotted at various 
 depths of proteome coverage ( no . of proteins ) .
the probability adjustment results in improvements of almost 
 8% 
 in the hek293 cell line and up to 4% in vcap ( figure 3 ) .
notably , the amount of improvement observed is similar 
 for both the rpkm and gpmfreq adjustments .
furthermore , it appears 
 that using rna - seq data generated in parallel to the ms / ms data ( vcap ) 
 or rna - seq generated at a different time and location from the ms / ms 
 data ( hek293 ) does not significantly affect the results .
we 
 believe the probability adjustment works by boosting protein 
 identifications that fall in a gray zone of confidence of identification . 
 to test this hypothesis , the entire analysis described above was repeated 
 using maximum hyperscore instead of maximum peptide probability as 
 the identification confidence score .
hyperscore is a spectral matching 
 score calculated and reported by the x!tandem search engine .
the maximum 
 hyperscore for a protein can be used as an alternate , albeit less 
 effective than the maximum peptide probability , confidence score for 
 sorting protein identifications and estimating fdr thresholds . because 
 maximum hyperscore is a suboptimal score compared to maximum peptide 
 probability
, the resulting protein identifications should have more 
 proteins in the gray zone and therefore the probability adjustment 
 on these identifications should provide increased improvement . as 
 expected ,
figure 4a , b shows that the percentage 
 improvement is much greater ( 720% ) in the maximum hyperscore - based 
 analysis .
these results support the idea that the amount of improvement 
 obtained from probability adjustment is dependent on the number of 
 proteins falling in the gray zone of the confidence of identification .
percentage 
 improvement at various depths of proteome coverage when 
 probability adjustment is performed on a maximum hyperscore - based 
 protein identification probability .
as expected , the improvement is 
 significantly greater when the suboptimal maximum hyperscore is used 
 instead of maximum peptide probability ( figure 3 ) . in our analysis , a clear trend 
 of the percentage improvement from 
 probability adjustment decreasing as the depth of proteome coverage 
 ( i.e.
, number of proteins identified in the data set ) increases can 
 be seen ( figure 3 ) . with deeper coverage of 
 the proteome ,
such proteins would have low rna - seq abundance 
 and/or low frequency of identification in gpmdb .
therefore , these 
 proteins will not benefit from a probability adjustment based on rpkm / gpmfreq 
 evidence and , in fact , may have their confidence scores decreased 
 by it . furthermore , increasing depth of proteome coverage not only 
 increases the number of proteins identified but also increases the 
 amount of ms / ms or spectral evidence collected for each identified 
 protein .
this would lead to a decrease in the number of proteins falling 
 in the gray zone .
on the basis of this , we believe that the observation 
 of decreased improvement in deeper coverage data sets reflects the 
 fact that in these data sets there are fewer proteins that would benefit 
 from the probability adjustment .
a more detailed analysis of the 
 effects of probability adjustment 
 was carried out on one of the sampled data subsets from each cell 
 line , the results of which are shown in table 1 .
proteins that were promoted above the 1% fdr threshold as a result 
 of the probability adjustment were selected for manual validation . 

these selected proteins were compared with the list of proteins identified 
 at 1% fdr in the complete data set ( largest data set without any sub 
 sampling ) of that cell line .
a promoted protein being found in the 
 complete sample would suggest that the protein is indeed a true identification . 

it is possible that there was not sufficient ms / ms evidence in the 
 smaller sampled data set for the protein to be confidently identified , 
 but the probability adjustment using rpkm / gpmfreq information provided 
 the necessary boost to promote it above the fdr threshold . in our 
 analysis ,
the remaining 2030% of promoted proteins , 
 which were not 
 observed in the complete data set , were seen to have high confidence 
 scores in the same range as that of the validated proteins . in other 
 words ,
it is possible 
 that these proteins were not observed in the complete data set because , 
 even with the increased amount of ms / ms evidence collected in the 
 complete data set , there still is not sufficient evidence to confidently 
 identify them solely by ms / ms evidence without the aid of external 
 information . in our analysis ,
a larger proportion of proteins are 
 validated in the vcap sample , which has more ms / ms data collected 
 ( 6000 proteins ) , than in the hek293 sample ( 3000 proteins ) , 
 which appears to support this interpretation .
the probability adjustment method described here allows us to utilize 
 external data , such as rna - seq abundance , or gpmdb identification 
 frequency , to improve the sensitivity of protein identification through 
 database searching .
although some studies generate rna - seq data in 
 parallel to proteomics data , large amounts of rna - seq data for many 
 common organisms and/or cell lines used in biological research are 
 already freely available from public resources such as the sequence 
 read archive ( sra ) . as we can see from 
 figure 3 , whether rna - seq data is generated 
 in parallel with proteomics data ( vcap ) or independently ( hek293 ) 

this will allow us effectively leverage the large amounts 
 of publicly available rna - seq data .
furthermore , the improvement 
 obtained by adjusting probability 
 based on gpmfreq is similar to , and sometimes better than , improvement 
 from rpkm adjusted probability .
this is very convenient , allowing 
 us to make use of readily available gpmdb information in our proteome 
 analysis pipelines .
of course , this requires that the gpmdb repository 
 contains enough experiments for the organism of interest for the gpmfreq 
 values to be meaningful .
however , for commonly studied organisms of 
 interest such as human or mouse , with numerous experiments in gpmdb , 
 it can be a useful source of external information .
computing a combined 
 adjusted probability from both rna - seq and gpmdb information does 
 not result in a marked improvement in protein identification over 
 the individual probability adjustments ( supporting 
 information figure 4 ) , suggesting that rna - seq and gpmdb capture 
 similar types of information about a sample for the purposes of probability 
 adjustment .
as mentioned earlier , the improvement obtained from 
 probability 
 adjustment decreases as the depth of proteome coverage of the experiment 
 increases because there are fewer proteins in the gray zone that would 
 benefit from the probability adjustment and there are more rare and 
 low - abundance proteins that could be penalized by it .
this is an inherent 
 upper limit to the amount of improvement that is possible by this 
 method and must be taken into consideration when applying this probability 
 adjustment to large samples .
however , this method remains useful for 
 ms / ms data of low - to - medium levels of proteome coverage .
hence , one 
 potential application of this method may be for data obtained from 
 older instruments or experiments where the amount of instrument time 
 available was low .
compared to the customized database approach described 
 by wang et al . , the probability adjustment 
 method was seen to provide better improvement for low - to - medium - coverage 
 samples , whereas the customized database approach performed better 
 for deep - coverage samples ( supporting information 
 figure 9 ) , suggesting nonoverlapping scenarios of usage for 
 the two methods .
although the probability adjustment approach 
 has been demonstrated 
 using rna - seq and gpmdb data , it does not include any assumptions 
 that would limit it to only these two kinds of data .
therefore , this 
 approach can be utilized to incorporate any external source of data 
 ( with a significant association with protein presence or abundance ) 
 into proteomic analysis pipelines to improve the sensitivity of protein 
 identification .
 tandem mass spectrometry ( ms / ms ) 
 followed by database search is 
 the method of choice for protein identification in proteomic studies . 
 
 database searching methods employ spectral matching algorithms and 
 statistical models to identify and quantify proteins in a sample . 
 in general , these methods do not utilize any information other than 
 spectral data for protein identification . 
 however , considering the 
 wealth of external data available for many biological systems , analysis 
 methods can incorporate such information to improve the sensitivity 
 of protein identification . in this study , we present a method to utilize 
 global proteome machine database identification frequencies and rna - seq 
 transcript abundances to adjust the confidence scores of protein identifications . 
 
 the method described is particularly useful for samples with low - to - moderate 
 proteome coverage ( i.e. , < 20003000 proteins ) , where we 
 observe up to an 8% improvement in the number of proteins identified 
 at a 1% false discovery rate . 