A strategy is presented to prioritize the functional modules that mediate genetic perturbations and their phenotypic effects among candidate modules. Saccharomyces cerevisiae and human cancer as two examples to show the effectiveness of this approach. We discovered that lethality is more conserved at the module level than at the gene level and we identified several potentially 'new' cancer-related biological processes.We have developed a global strategy based on the Bayesian network framework to prioritize the functional modules mediating genetic perturbations and their phenotypic effects among a set of overlapping candidate modules. We take lethality in  Saccharomyces cerevisiae [C. elegans [D. melanogaster [How to interpret the nature of biological processes, which, when perturbed, cause certain phenotypes, such as human disease, is a major challenge. The completion of sequencing of many model organisms has made 'reverse genetic approaches'  efficienrevisiae ,3, and d elegans , D. melanogaster  and mammnogaster ,7.S. cerevisiae and suggested that essentiality is the product of protein complexes rather than individual genes. Other studies have made use of the modular nature of phenotypes to predict unknown causal genes [Compared to the direct genotype-phenotype correlation observed in the above experiments, what is less obvious is how genetic perturbation leads to the change of phenotypes in the complex of biological systems. That is, we might perceive the cell or organism as a dynamic system composed of interacting functional modules that are defined as discrete entities whose functions are separable from those of other modules . For exaal genes . In a real genes  mapped dS. cerevisiae and human cancer. In both cases, as summarized below, we provide evidence indicating that the causal modules prioritized by the BN model are more accurate than those prioritized by such local strategies as the HG enrichment test. With lethality and human cancers as two illustrating examples, we aim to provide a general framework for module-based decoding of phenotypic variation caused by genetic perturbation, which could be applied to the understanding of diverse phenotypes in various organisms.Despite these successful studies, the task of computationally inferring the functional modules that mediate genetic perturbations and their phenotypic effects might not be as easy as it appears. On the one hand, different modules could share common components. On the other hand, modules are believed to be hierarchically organized in biological systems  such thaS. cerevisiae [In the first case, we used gene lethality data observed from a genome-wide gene deletion study in revisiae . Using tIn the second case, we applied our strategy to the study of human cancer. Human cancer is believed to be caused by the accumulation of mutations in cancer genes, for example, oncogenes and tumor suppressor genes. It has been suggested that a limited number of biological pathways might include most cancer genes . Based oS. cerevisiae obtained from a genome-wide gene deletion study [We prioritized lethal modules from the gene lethality data in on study   and lethal genes whose protein products have not been involved in any lethal protein complexes (referred to as LGNLCs for simplicity). It should be noted that such classification is simply for the purpose of elucidation. Not all lethal modules are included in our dataset. Thus, the existence of LGNLCs that have not been associated with any lethal modules in our dataset largely results from data incompleteness.We can categorize non-lethal genes into two classes according to the lethality of protein complexes in which they participate. For simplicity, we refer to non-lethal genes whose protein products have been involved in certain lethal protein complexes as NLGLCs, and we refer to non-lethal genes whose protein products have not been involved in any lethal protein complexes as NLGNLCs. A key computational measurement we use is termed 'ortholog lethal ratio,' which refers to the proportion of genes in species A, specifically S. cerevisiae. More specifically, we assume each protein complex as a random sample from a set of 5,916 genes, 1,105 of which are lethal. We called a complex with Nc genes and Lc lethal genes lethal if the probability of having at least Lc lethal genes out of Nc genes is less than 0.05 based on the hypergeometric distribution. We obtained a total of 149 lethal protein complexes in this way. We then classified genes into four groups according to their gene lethality and the lethality of protein complexes in which they participate: LGLC, LGNLC, NLGLC and NLGNLC. To estimate the 'ortholog lethal ratio' for each group of genes, we calculated the proportion of genes whose orthologs in C. elegans are lethal among all the genes whose orthologs in C. elegans exist with known lethality . As shown in Figure p-value of chi-square test between the two groups > 0.1). However, as discussed in the Background, the above HG method might overestimate the number of lethal complexes by including 'overlapping protein complexes' whose enrichment of lethal genes would most likely result from the sharing of gene members with actual lethal protein complexes. Thus, we then used the BN model to filter out those 'overlapping protein complexes'. Out of the above 149 protein complexes with an HG p-value < 0.05, we filtered out 55 protein complexes whose probability of being lethal, as derived from the BN model, was < 0.7 and treated them as non-lethal protein complexes. In this case, the 'ortholog lethal ratio' is significantly higher for NLGLCs than for NLGNLCs after filtering out the 'overlapping protein complexes' . It has to be mentioned that those protein complexes that are not significantly enriched with lethal genes  are not considered as candidate lethal protein complexes in the BN model to speed up the algorithm, since those HG insignificant complexes are of less practical use and could add a substantial amount of computational burden to the BN model, particularly when GO BPs are considered in later analysis. Other preprocessing strategies to speed up the algorithm might work as well, for instance, removing protein complexes with the number of lethal genes less than a threshold.Since we are able to distinguish lethal from non-lethal protein complexes based on the 'ortholog lethal ratio' of their associated non-lethal genes, we could expect that a list of protein complexes with a higher enrichment of lethal protein complexes will show a higher 'ortholog lethal ratio' of non-lethal genes than otherwise. We therefore carried out the following analysis to compare the capacity of the HG enrichment test with the BN model in distinguishing lethal from non-lethal protein complexes. To determine the lethality of protein complexes, we first employed the HG enrichment test to evaluate the enrichment of lethal genes in 390 curated protein complexes in p-value of chi-square test < 0.05). In other words, by successfully filtering out these 'overlapping protein complexes,' the resulting list of lethal protein complexes becomes more enriched when quantified by the 'ortholog lethal ratio'. Second, in the absence of the BN model, it is unlikely that those 'overlapping protein complexes' could have been effectively filtered out by the HG enrichment test, even by setting a more stringent p-value cutoff, since, based on the Wilcoxon rank-sum test, there is no significant difference between the HG p-value of those 'overlapping protein complexes' filtered out by the BN model and the HG p-value of the remaining lethal protein complexes. Third, the coverage of lethal genes by lethal protein complexes remains similar, both before and after filtering out 'overlapping protein complexes'. Because the 'overlapping protein complexes' filtered out by the BN model are those sharing lethal gene members with the remaining lethal protein complexes, it can be seen from the data in Figure p-value is set for the HG enrichment test, the coverage of lethal genes by lethal protein complexes will be dramatically decreased (data not shown). Fourth, even when the coverage of lethal genes is not considered, the BN model still performs better than the HG enrichment test in distinguishing lethal protein complexes from non-lethal protein complexes as measured by the 'ortholog lethal ratio' of non-lethal genes. Figure p-value of the HG enrichment test or the probability of being lethal protein complexes derived from the BN model are used. Compared to the HG enrichment test, it can clearly be seen that the 'ortholog lethal ratio' shows more striking differences between NLGLCs and NLGNLCs when the BN model is used.Based on the results of the above analysis, we conclude that the BN model is superior to the HG enrichment test in distinguishing lethal protein complexes from non-lethal protein complexes as indicated by the following four findings. First, as indicated by the 'ortholog lethal ratio,' those 'overlapping protein complexes' filtered out by the BN model are very likely to be non-lethal protein complexes. To be more specific, the 'ortholog lethal ratio' for non-lethal genes only involved in the 'overlapping protein complexes' was not found to be significantly different 20%) from that of NLGNLCs before filtering . This indicates that our observations here are not restricted to one dataset or one species. Since the genome-wide whole organism screening is not available for D. melanogaster, the gene lethality in D. melanogaster is defined based on cell-based RNA interference screening [The analysis of the lethality of ortholog genes in the context of protein complexes also reveals that lethality is more conserved at the module level than at the gene level. In other words, compared with the lethality of a gene itself, the lethality of the protein complexes in which that gene participates appears to be a more relevant predictor for the lethality of its orthologs in other organisms. It can be seen that both LGLCs and NLGLCs show a similar 'ortholog lethal ratio'  of them are lethal; more than half of the members are lethal in all except for one of them. In addition, the proportion of lethal genes in a lethal complex appears to differ based on their functions. For example, as listed in Table S1, lethal complexes related to chromatin remodeling, such as the RSC complex and the INO80 complex, or protein transport and translocation, such as the mitochondrial outer membrane translocase complex, nuclear pore complex, and ER protein-translocation complex, have a relatively low proportion of lethal genes. The relatively low proportion of lethal genes indicates functional redundancy within those complexes. For example, the nuclear pore complex has the principal function of regulating the high throughput of nucleocytoplasmic transport in a highly selective manner . The facS. cerevisiae, this is not necessary since the lethality of almost all the genes is known. Nonetheless, S. cerevisiae does provide a good system for evaluating prediction accuracy of gene lethality through cross-validation. In the context of our study, if, by such evaluation, we assume that more accurate prediction of gene lethality is a consequence of more accurate inference of module lethality, then prediction accuracy of the former could reflect prediction accuracy of the latter.Besides the above ortholog lethality analysis, we also compared the power of the BN model with the local strategy in predicting unknown lethal genes. The module lethality inferred from the BN model is superior to the results obtained by the local strategy in predicting unknown lethal genes as evaluated by cross-validation. As mentioned before, one of the applications of identifying causal modules is the prediction of unknown causal genes. However, for gene lethality in p-value of the HG enrichment test in prioritizing lethal protein complexes. In this case, we chose to compare the BN model with the LM model instead of the HG enrichment test. Compared with the p-value derived from the HG enrichment test, the output of the LM model is more like the BN model and, therefore, it is easier to infer gene lethality with it. We used the receiver operating characteristic (ROC) curve [To evaluate prediction accuracy of gene lethality through cross-validation, we randomly chose part of the gene lethality data (training data) as a known to estimate module lethality. The estimation results were then used to infer the probability of being lethal for the remaining genes . In the step where the lethality of each candidate module is inferred, we employed the BN model as our global strategy and the LM model as our local strategy with the purpose of comparing how the results of these two methods could affect prediction accuracy of gene lethality. The LM model differs from the BN model only in that only the subnetwork for a candidate module is considered as if none of its components participates in other modules . In this sense, the probability of being lethal for each protein complex inferred by the LM model is similar to the C) curve  and the C) curve  at a falC) curve , which aWhen the candidate modules consist of only curated protein complexes, the pAUC.2 of our BN model increases by 8.5% compared with that of the LM Figure . The relp-value < 0.05 are treated as candidate modules in the BN model, and the same empirical cutoff was used to filter out 'overlapping GO BP nodes'. Table In order to show how the BN model could be applied to more complicated phenotypes, such as human diseases, we prioritized GO BPs that are causally implicated in human cancers (CAN-processes) based on cancer genes documented in 'cancer-gene census', a curated cancer gene database assembled from previous studies . Comparep-value of the HG enrichment test.) As noted above, if one GO node is enriched with cancer genes, many of its ancestor and offspring nodes will also become enriched. The results shown in Additional data file 2 are, therefore, consistent with this observation. It can also be seen that most GO BP nodes prioritized by the HG enrichment test  conditional on at least one of its child nodes [p-value of the HG enrichment test of node A conditional on node B, we removed genes included in node B from node A and calculated the p-value of the enrichment of cancer genes for the remaining genes in node A. As a comparison, since the 27 GO BP nodes prioritized by the BN model are close to the leaf nodes, their enrichment of cancer genes is less likely to result from being ancestor nodes of actual CAN-processes. As a matter of fact, out of 16 nodes that are not leaf nodes, only 12.5% (2 out of 16) become insignificant conditional on at least one of their child nodes. Moreover, for the two nodes that become insignificant conditional on their child nodes, none of their child nodes is significantly enriched with cancer genes . In this sense, their child nodes are not better representatives of actual CAN-processes than the two nodes themselves.Since most GO BP nodes prioritized by the HG enrichment test are close to the root node, it is suspected that the enrichment of cancer genes for most of them might actually result from being ancestor nodes of actual CAN-processes. As a matter of fact, the enrichment of cancer genes for 63.0% of these nodes (17 out of 27) becomes insignificant  of their parent nodes were conditionally significant . We then extended such a conditional HG enrichment test to all 649 GO BP nodes that are enriched with cancer genes . The distribution of the original p-values of the HG enrichment test and the p-values of the HG enrichment test conditional on the 27 GO BP nodes are shown in Figure p-value > 0.05); only 13 have a p-value < 0.001 and none have a p-value < 1e-5. It can also be seen in Figure On the other hand, although most GO BP nodes prioritized by the BN model are of smaller size and close to the leaf nodes, their enrichment of cancer genes is less likely to result from being the offspring nodes of actual CAN-processes. This means that only a few of their ancestor nodes will remain significantly enriched conditional on the 27 GO BP nodes prioritized by the BN model. In order to demonstrate this, for each parent node of the 27 GO BP nodes prioritized by the BN model, we calculated the 3 out of 4 of theiSecond, the results obtained from implementing the BN model are more consistent with previous knowledge of cancer-related processes than the results obtained through HG enrichment test results. As shown in Table p-values of 2e-07, 2e-04 and 2e-06, respectively. However, under the BN model, only GG-NER was prioritized among the top list, while TC-NER and NER are filtered out as 'overlapping GO BPs'. When we take a close look at the exact position of those cancer genes in the two subpathways, it can be seen that all three cancer genes involved in TC-NER, that is, XPB (ERCC3), XPD (ERCC2) and XPG (ERCC5), function after the two subpathways converge. None of the genes involved in the initial damage recognition, which is specific to TC-NER, for example, CSA (ERCC8) and CSB (ERCC6), has yet been documented as a cancer gene in 'cancer-gene census'. On the other hand, a number of genes specific to GG-NER, for example, XPE (DDB2) and XPC, have been documented as cancer genes. Therefore, it is speculated that TC-NER itself might not be a CAN-process. Such a hypothesis has been supported by previous studies. For example, it has been shown that skin cancer is not a feature of pure Cockayne syndrome, a disease that could be caused by defects in gene CSA or CSB [Previous knowledge also indicates that some of the 'overlapping GO BPs' filtered out by the BN model might be independent of cancer. Importantly, in the absence of such a global approach, these 'overlapping GO BPs' are less distinguishable from actual CAN-processes based on the HG enrichment test. One example is nuclear excision repair (NER), which can be categorized into two classes: global genome NER (GG-NER) and transcription coupled NER (TC-NER) . The twoA or CSB . Since, A or CSB . NeverthThird, the CAN-processes inferred from the BN model are superior to the results obtained by the local strategy in predicting unknown cancer genes as evaluated by cross-validation. Similar to the case of lethality, we employed cross-validation to compare the BN model and the LM model in predicting cancer genes in 'cancer-gene census'. We measured both the standard AUC and pAUC.2 as before. The results shown in Figure p-value for overrepresentation at 0.002. On the contrary, when the HG enrichment test was used, genes involved in the CAN-processes prioritized in Wood's data are significantly underrepresented when compared to those involved in the CAN-processes prioritized in 'cancer gene census' . Therefore, the BN model results are more consistent with each other than the HG enrichment test results when different datasets are used.Last, but equally important, comparison of CAN-processes prioritized in different cancer gene datasets shows that the BN model results are more consistent with each other than the HG enrichment test results. In order to show the consistency of CAN-processes prioritized in different cancer gene datasets, a second group of cancer genes was considered. These cancer genes were identified recently through systematic sequencing of colorectal and breast cancer genomes for somatic mutations  and are Although statistically significant, the overlap between the two sets of CAN-processes prioritized based on the two cancer gene datasets by the BN model is only 5% (intersection/union of genes). Since the two datasets of cancer genes differ in many respects, such a small overlap could reflect the different focus of the two datasets. Particularly, since the 'cancer-gene census' is assembled from previous studies and the Wood's dataset is derived from a recent study with new techniques, the small overlap could indicate the discovery of potentially 'new' CAN-processes by the Wood's study. To be more specific, while the majority of cancer genes in 'cancer-gene census' were detected from liquid tumors such as leukemias and lymphomas, those in Wood's dataset were identified from colorectal and breast tumors, which are normally solid tumors. In solid tumors, precursor cells must become mobile and invasive in order to become malignant. Consequently, some 'new' CAN-processes might be 'overlooked' because their disruption is not required in liquid tumors since their precursor cells are already mobile and invasive . AlternaCytoskeletal anchoring and lipid transport are two examples of such potentially 'new' CAN-processes prioritized in the Wood's dataset by the BN model. None of the genes associated with the two GO BPs has been documented as cancer genes in 'cancer gene census'. Their potential roles in tumorigenesis or metastasis are discussed below. These findings might give insight into further study and treatment of human cancers.FLNB and TLN1 are two cytoskeletal anchoring genes detected in Wood's dataset. Both of them have been detected to interact with integrins [The involvement of cytoskeletal anchoring in cancer development is not unexpected, especially considering that it functions as a direct or indirect connection between two groups of cancer-related molecules, for example, transmembrane or membrane-associated proteins and cytoskeletal filaments, both of which are actively involved in signaling transduction, cell-cell adhesion, and other cancer-related biological processes. For example, ntegrins , a familntegrins . Thus, integrins , which wntegrins .Compared to cytoskeletal anchoring, the roles of lipid transport in cancers are more complicated. On the one hand, in rapidly dividing cancer cells, the availability of cholesterol is essential for proliferation and progression of the cancer . On the In this paper, we attempt to decode phenotypic effects caused by genetic perturbation through known functional modules. By decoding gene lethality through protein complexes and investigating the conservation of gene lethality across different species in the context of lethal and non-lethal protein complexes, we provide evolutionary evidence supporting the modular nature of lethality. Based on human cancer genes, we prioritized many biological processes causally implicated in cancers, which are consistent with previous knowledge. We also identified some 'new' biological processes whose roles in cancer development are less well understood: cytoskeletal anchoring and lipid transport.Motivated by the overlapping structure of functional modules in biological systems, we provide a global strategy to distinguish functional modules that are most likely to be actual causal modules from a large number of other 'overlapping modules' whose only relatedness with the phenotypes most likely results from the sharing of gene members with the causal modules. Local strategies, such as the HG enrichment test, ignores overlapping structures among modules and is thus less effective in distinguishing actual causal modules from the 'overlapping modules'. In contrast, the BN model filters out 'overlapping modules,' which generates a more accurate list of causal modules. Compared to either the HG enrichment test, or the LM model, in the case of prediction of gene lethality, the results consistently show that the modules prioritized by the BN model are better representatives of the actual causal modules, even though it can never be ascertained whether or not the modules prioritized by the global strategy are, indeed, true causal modules in the absence of any direct biological benchmark.In summary, our results indicate that modularity, which is believed by investigators to be a true property of biological systems, can be applied to the interpretation of phenotypic variations from perturbations in genetic variation. This might shed light on the study of more complicated phenotypes, such as human disease. With proper modeling, the global strategy could potentially be applied to a variety of fields. For example, it might be interesting to see how it helps identify differentially expressed gene sets in microarray data analysis. More importantly, a module-based prediction strategy will benefit the study of human diseases by transferring phenotypic data learned from other organisms to human beings. This has significant implications for the treatment of human cancers.In this section, we explain the BN model we used to prioritize functional modules mediating genetic variation and its phenotypic effects. For illustration, we take single gene deletion, lethality and protein complexes as examples of the genetic perturbation, the phenotypic effect and the candidate functional modules, respectively. Figure G: a set of genes whose lethality is known; in Figure G = {g1, g2 ... g5}: a set of known protein complexes; in Figure AGM = {gi ∈ G, mj ∈ M}: is the association matrix between genes and complexes.mj contains protein product of gene gi, and 0 otherwise. In Figure LG = {gi ∈ G}: is the lethality of each gene that is observed from genome-wide knockout experiments. gi leads to inviable phenotype and 0 otherwise. In Figure LM = {mj ∈ M}: is the unknown lethality of each protein complex. mj leads to inviable phenotype, and 0 otherwise. We refer to those protein complexes with LM from the observed LG becomes difficult when the protein products of gene gi participate in more than one protein complex. For example, in Figure g2 participate in both protein complexes m1 and m2. If g2 in protein complex m1 while keeping protein products of gene g2 in protein complex m2 unchanged, and vice versa. The inviable or viable phenotypes observed in such complex-specific protein inactivation experiments could reflect the lethality of protein complexes more directly. Motivated by this, we introduce a set of hidden variables that denote the outcome of complex-specific protein inactivation experiments and function as bridges between LM and LG.As discussed in the introduction, inference of gi that participates in protein complex mj (while keeping other protein complexes active) leads to inviable phenotype, and 0 otherwise. In Figure The following explains the causality relationship among the three sets of variables and derives the conditional probability tables for the Bayesian network.gi is knocked out from the genome, all of its protein products are knocked out and hence inactivated. Thus, obviously, deletion of gene gi will lead to an inviable phenotype if one complex-specific inactivation of its protein products leads to an inviable phenotype. However, even when no such complex-specific inactivation leads to an inviable phenotype, there is still a possibility that the deletion of gene gi will lead to an inviable phenotype. One possibility is that there are agonistic interactions among protein products of gene gi that participate in different protein complexes such that simultaneous inactivation of them, which is the case in deletion of gene gi, leads to an inviable phenotype. Other possible explanations may include protein products of gene gi possibly acting in certain lethal modules that are not included in our dataset of candidate modules. For simplicity, we refer to all those other events or effects that cause an inviable phenotype, besides those reflected by complex-specific protein inactivation, as 'other effects' in our paper and assign them probability Pother. According to the above arguments, the conditional probability table for each LG can be derived as follows:In reality, when gene mj, denoted as lGM can be expressed as:Not all members in a complex are equally important. Although individual inactivation of some members of a protein complex will cause that protein complex to 'break down' or lose its function, individual inactivation of others might not. Functional redundancy within protein complexes might be one of many possible reasons. Thus, even for a lethal protein complex, inactivation of certain members might not lead to an inviable phenotype. To accommodate such phenomena, we assign a lethal probability to each protein complex LM in the above Bayesian network, given the observed values in LG and the known network structure. Since there are other unknown variables LGM and unknown parameters Pother is set to be a fixed value for simplicity), we employed an expectation-maximization strategy with details given below.Our goal is to infer each variable in mj ∈ MInitiate each LGM and LM by Gibbs sampling [E-step: estimate each variable in the set of sampling , denotedM-step: calculate the maximum likelihood estimation of each parameter In the prediction of gene lethality through cross-validation, we randomly chose part of the gene lethality data (training data) as known to estimate the parameters As a comparison to the BN model, we also employed the LM model to infer the module lethality given gene lethality. The LM model differs from the BN model only in that only the subnetwork for a candidate module is considered as if none of its components participate in other modules . A detailed description can be found in [Protein complexes were treated as one type of functional module in the case of gene lethality. Both curated and HTP protein complex data were used in our analysis. The manually curated protein complex dataset was downloaded from the ScISIC dataset in the Bioconductor  R packagfound in . It consfound in . In totafound in . It consYEAST (version 2.0.1) and processed with the package GOstats [For more broadly defined functional modules, we used GO BPs . The dat GOstats . It was S. cerevisiae. In total, there are 14,371 human genes involved in 4,644 biological processes. Two datasets of cancer genes were considered. The first dataset was downloaded from the 'cancer-gene census' database, a curated cancer gene database assembled from previous studies [We then applied our strategy to prioritize biological processes causally implicated in human cancers. We downloaded and retrieved GO annotations for human genes from the NCBI website . The dat studies . The sec studies . In this studies  accordin studies  are consAUC: area under the ROC curve; BN: Bayesian network; BP: biological process; CAN-processes: biological processes causally implicated in cancers; GG-NER: global genome NER; GO: Gene Ontology; HG: hypergeometric; LGLC: lethal gene whose protein product has been involved in certain lethal protein complexes; LGNLC: lethal gene whose protein product has not been involved in any lethal protein complexes; LM: local Bayesian model; NER: nuclear excision repair; NLGLC: non-lethal gene whose protein product has been involved in certain lethal protein complexes; NLGNLC: non-lethal gene whose protein product has not been involved in any lethal protein complexes; pAUC: partial AUC; ROC: receiver operating characteristic; TC-NER: transcription-coupled NER.TC, FS and LW conceived of and designed the study. LW carried out all the analysis and wrote the manuscript. All authors viewed and approved the manuscript.p-value of the HG enrichment test for the cancer genes. Those nodes with size zero are insignificant nodes by HG enrichment test .The following additional data are available with the online version of this paper. Additional data file S. cerevisiae are classified into four groups according to their lethality and the lethality of protein complexes to which they belong. Within each group, the pie chart represents the distribution of genes with respect to the lethality of their orthologs in D. melanogaster. The lethal protein complexes were identified as in Figure Table S1: 94 curated lethal protein complexes identified by the BN model. The complex ID starting with 'GO', 'MIPS' and 'EBI' represents GO, MIPS and Intact ID, respectively. Total # denotes the total number of genes whose lethality is known. Lethal # denotes the number of genes that are lethal. Figure S1: genes in Click here for filep-value of the HG enrichment test for the cancer genes. Those nodes with size zero are insignificant nodes by the HG enrichment test .The 27 GO CAN-processes prioritized by the BN model (yellow) and their offspring and ancestor nodes (blue). The nodes with red circles represent 23 out of 27 GO CAN-processes prioritized by the HG enrichment test. The size of the nodes is proportional to the minus log Click here for file


Domains are the building blocks of all globular proteins and present one of the most useful levels at which protein function can be understood. Through recombination and duplication of a limited set of domains, proteomes evolved and the collection of protein superfamilies in an organism formed. As such, the presence of a shared domain can be regarded as an indicator of similar function and evolutionary history, but it does not necessarily imply it since convergent evolution may give rise to similar gene functions as well as architectures.via homology relationships and comparative genomics, structural modeling and phylogenetics. The goal hereby is not only to predict the function of newly discovered proteins, but also to spell out their pathway of evolution and, possibly, identify their most likely origin. This can ultimately help to understand protein function and functional relationships of protein families. Additionally, through comparison with transcriptional data, evolutionary data can be linked to gene (and genome) activity and thus allow for the identification of common principles behind fast evolving proteins and relatively stable ones.Through the wealth of sequences and annotation data brought about by genomics, evolutionary links can be sought for In this review, we describe the basic principles of studying protein (domain) evolution and illustrate recent developments in molecular evolution and give valuable new insights in the field of comparative genomics. As an example, we include here molecular models of the multiple PDZ domain protein MUPP-1 and present a simple comparative genomic view on its structural course of evolution. The genome projects of the last decade have produced a staggering amount of sequence data, but most of the identified genes lack experimental determination of biological function or even in some instances identification. The advances in bioinformatics have allowed large-scale genome comparisons, and efforts are well under way to make similar use of comparative functional and structural genomic approaches. However, the wealth of comparative genomic data generated has yet to be followed by a comparable gain of structural and functional information.The annotation of genes, the prediction of new genes and the allocation of regulatory elements to date largely relies on evolutionary relationships for which genome comparison is fundamental , 2. In ei.e., point mutations or insertion-deletions [indels]) and selection pressures which can be positive, negative or neutral with regard to the presence of the mutation in the next generation  and Homo sapiens structures [PDZs 8-10]). Indeed, PDZ 8 of Tetraodon nigroviridis seems structurally highly related to domain 9 of Xenopus and domain 9 and 10 of Homo sapiens. In other words, either of these structures appears more structurally related to the others in this small group than to any of the other (flanking) PDZ domains, which suggests duplication. The PDZs 8 of Xenopus and Homo sapiens form, however, a separate structural group as the phylogenetic analysis predicted. We therefore propose that the Homo sapiens PDZ 9 originates from a duplication event of the Xenopus PDZ 9 and that Homo sapiens PDZ 8 is a result of an insertion in the common ancestor of Xenopus and Homo sapiens. Our evolutionary model shown in Fig. (2B) can thus be used to confirm the phylogenetic tree shown in Fig. (1B).It should be clear from the above that the process of structural evolution is of a completely different order than that of sequence evolution, which is much faster. The tertiary sequence of a protein contains therefore much more phylogenetic signal and makes it far more likely to find linkages beyond the timeframe of standard sequence alignments . Indeed,i.e., multidomain proteins) and have evolved through a process of duplication and recombination of the limited set of protein domains available [e.g., a catalytic domain and an ATP binding domain resulting in a helicase or kinase), but also combined domains that could co-evolve into one larger superdomain. An example of the latter can be found in the MAGUK family of proteins in which the Src homology 3 (SH3) domain and the Guanylate Kinase (GUK) domain interact intramolecularly to form a superdomain involved in protein-protein interactions [Even though domains are recognized by prediction programs, like Pfam and SMART, the actual fold may be different due to intermolecular interactions. Proteins usually contain more than one domain species were made for conditions like embryogenesis, metamorphosis, sex-dependency and mutation rates , 69-72. However, more evolutionary distant organisms may react differently to the same stimulus, which undermines comparison of gene expression data. To overcome this limitation, the association of co-expression data of genes and of expression signatures has been developed in addition to a direct comparison of individual gene expression changes . FirstlyAnother benefit of comparing co-expression of genes is that often functional entities can be discovered and, subsequently, new leads can be gained for functional interpretation. The approach can be combined with the search for common cis-regulatory elements at the promoter regions or applied to other similarity measures between genes, such as protein-protein interactions, phosphorylation networks or ligand-binding specificities -79.Finding evolutionary relationships for genes, proteins or protein domains is mostly based on orthology and thus on best sequence matches. Identifying these and categorizing them depends largely on multiple sequence alignments and this will in most cases give good indications for function and fold. However, this approach usually discards apparent ambiguities that arise from species-specific duplications or losses and may therefore introduce extensive biases . Biases Genome and proteome comparisons can be performed by looking at expression data and, preferably, co-expression patterns or protein-protein and phosphorylation interactions. In the end, it will be the ultimate challenge to combine all comparative data  into one biological network. Indeed, only through putting together data obtained from protein-protein interactions and co-expression networks, conserved functional cell cycle complexes shared among yeast, plants, worms and humans have been revealed . Expecta


The Structural Genomics Consortium (SGC) is a not-for-profit, public-private partnership established to deliver novel structural biology knowledge on proteins of medical relevance and place this information into the public domain without restriction, spearheading the concept of “Open-Source Science” to enable drug discovery. The SGC is a major provider of structural information focussed on proteins related to human health, contributing 20.5% of novel structures released by the PDB in 2008. In this article we describe the PLoS ONE Collection entitled ‘Structural Biology and Human Health: Medically Relevant Proteins from the SGC’. This Collection contains a series of articles documenting many of the novel protein structures determined by the SGC and work to further characterise their function. Each article in this Collection can be read in an enhanced version where we have integrated our interactive and intuitive 3D visualisation platform, known as iSee. This publishing platform enables the communication of complex structural biology and related data to a wide audience of non-structural biologists. With the use of iSee as the first example of an interactive and intuitive 3D document publication method as part of PLoS ONE, we are pushing the boundaries of structural biology data delivery and peer-review. Our strong desire is that this step forward will encourage others to consider the need for publication of three dimensional and associated data in a similar manner.Since its advent, structural biology has played a major role in the understanding of biological processes at the molecular level. This, in turn, has enabled the progression and development of several fields in biological sciences. As a consequence there has been a continuing demand for faster and more cost-effective determination of protein structures and their in-depth functional, mechanistic and biological analysis. The lack of a well-established process historically meant that structures were been solved in a case-by-case manner, with academic researchers establishing specific protocols over a relatively long period of time that are difficult to implement or inappropriate for high-throughput approaches. As the sequencing of the human genome was completed the gap between known genes and solved protein structures became wider. As a result concerted and systematic technology-driven ‘structural genomics’ (SG) efforts to solve protein structures in a high throughput manner were begun (for thorough reviews see http://www.nigms.nih.gov/Initiatives/PSI), European initiatives ‘The Protein Structure Factory  and ‘Structural Proteomics in Europe  and Japanese Protein 3000 and RIKEN Structural Genomics/Proteomics programs (http://protein.gsc.riken.jp). The majority of these early programmes were tasked with exploring the breadth of the protein structural universe by focussing on prokaryotic targets whilst driving novel structural biology technology for a high-throughput environment First formalised in 1998 New avenues have been opened by the availability of detailed structural descriptions of proteins and enzymes involved in the normal  functioning of the human body and associated pathogens. In particular, this information provides a better understanding of the basis by which different proteins achieve selectivity and specific binding to their cognate ligands and drugs http://www.thesgc.org) was created in 2003 as a response to this challenge.Despite these successes, determining the structures of human proteins in a high-throughput manner was seen to be technically challenging relative to those of less complex organisms (e.g. prokaryotes), where issues such as the lack of solubility of expressed proteins in simple bacterial systems pose major bottlenecks to scaling the structure determination process The SGC is a not-for-profit organisation that aims to solve the three dimensional structures of proteins of medical relevance and place them into the public domain without encumbrance or restriction. The SGC has adopted a protein-family based approach whereby protein targets are chosen from discrete and medically-relevant human protein families. This approach ensures that comparative analysis and “blanket” methods can be applied to members of same family Since the production phase of the SGC began in 2004, we have released the coordinates of over 800 protein structures related to several diseases and metabolic disorders into the public domain of which over 700 are novel and unique. Whilst the SGC focuses primarily on human targets, we also pursue the structure determination of proteins from parasitic organisms associated with neglected diseases, such as malaria and toxoplasmosis. We have deposited more than 70 of these structures into the public domain. These structures are providing a significant impact with the SGC providing 61% of publicly available apicomplexan structures. The SGC has also developed new protocols and methods that are now widely used In this PLoS ONE Collection we present articles covering the structure determination of a number of targets by the SGC along with additional work carried out to further characterise these proteins. Since SG data is intrinsically heterogeneous and not always accessible to non-specialists, all the articles from this collection are available also as an enhanced version using our interactive visualisation platform – known as iSee – which allows the reader to interact and manipulate the 3D scenes prepared by the authors to highlight and support information given in the main text of the article A clear mandate of the SGC is to make results readily available to the scientific community. This data is necessarily heterogeneous in nature, ranging from materials and methods, construct and clone design, protein expression and purification to small molecule screening and protein structure determination information. The primary ‘customers’ for this information are not necessarily structural biologists but scientists with little if any structural biology understanding such as medicinal chemists, clinicians, pathologists etc.Historically, SG efforts have solved structures of proteins with unknown function, relying on the scientific community to take these proteins, characterise them annotate the structures. Since the structures released by the SGC are of targets with known biochemical and, often, biological function, it is possible to interpret the structures in these contexts thereby providing new insights into the role of the targets.However, the deposition into specialist databases such as the Protein Data Bank of the protein structures is not sufficient to reach the ‘customers’ of this data. There are a number of significant challenges that face an interested scientist who is not a structural biologist. Firstly, the scientist must be aware of where and how a protein structure can be retrieved. Secondly, it is necessary to visualise the structure using software that may not be intuitive to a non structural-biologist. Thirdly, the interpretation of the structure is dependent not only on an understanding of the biology and function of the protein, but also the structure itself. Alternatively, protein structures may be published in peer-reviewed journals. However, this medium often suffers from stringent restrictions in size by the publishers, forcing the authors to use jargon and cutting down the elementary information needed for the comprehension by non-structural biologist.http://www.yasara.org)) are an alternative to convey a sense of depth and perspective to structures. However, this approach provides ‘fixed’ set of visualisations as defined by the movie authors resulting the viewer being unable to interact with the structure displayed. This hurdle is overcome using specialist visualisation software (e.g. PyMol (http://www.pymol.org), Jmol (http://jmol.sourceforge.net), Kinemage/KiNG http://molvis.sdsc.edu/fgij), RCSB Protein Workshop Representing a 3D protein structure using media that is in reality 2D also poses an obstacle. The visualisation of protein structure on a computer screen is a clear improvement over a paper figure, even if stereo images are provided. Several movie-making tools  and provide high quality graphical visualisations of that data. Each file – which we dub an ‘iSee datapack’ – is usually smaller than 10 Mb and can be downloaded freely from the SGC website (www.thesgc.org/iSee). To open these datapacks online, a free plugin is available for the most common internet browsers and platforms such as Windows, Linux and Mac OS/X with Internet Explorer, Mozilla Firefox, Google Chrome and Safari  explores the advantages of various modes in which a web-based publication of the future may be delivered. The Journal of Biological Chemistry also encourages the use of 3D PDFs to communicate structural information Recently publishers have indicated their awareness and desire to explore this field further. The initiative from Cell Press and Elsevier entitled ‘Article of the Future’  maintained by specialists to address the most common questions and to provide usage and authoring tips.Further discussion and feedback of our articles is encouraged and enabled on the PLoS ONE website. Both the HTML/PDF and the enhanced iSee versions will remain constant in terms of content and location. This is critically important for future reference and also archival purposes. For technical questions and feedback we have implemented an iSee-Wiki (Currently, iSee datapacks are provided in a binary file format to enable maximal compactness and fast download to the browser. A full text file format would increase the files' size resulting in relatively slow downloads into the browser/plugin. However we strongly believe that it is vital that we and others are able to provide similar data and its interpretation in a completely open manner. Not only is this important for the ability to curate and archive such work, it is also critical to allow the data to be opened and displayed in other visualisation platforms. To enable the portable delivery of such content we are working with the structural biology visualisation community to begin work on an open file format which can provide a human readable and editable version of any set of data, visualisations and annotations.We are excited to be making the SGC's work available to a greater audience via this PLoS ONE collection. With the use of iSee as the first example of an interactive and intuitive 3D document publication method as part of PLoS ONE, we are pushing the boundaries of structural biology data delivery and peer-review. Our strong hope is that this step forward will encourage other publishing houses to consider the need for publication of three dimensional data in a similar manner.


PLoS Computational Biology tutorial was presented at ISMB 2009This Decades of research into cell biology, molecular biology, biochemistry, structural biology, and biophysics have produced a remarkable compendium of knowledge on the function and molecular properties of individual proteins. This knowledge is well recorded and manually curated into major protein databases like UniProt http://www.pathguide.org/.Efficient large-scale technologies that measure proteome-wide physical connections between protein pairs are essential for accomplishing a comprehensive knowledge of the protein interactomes. In recent years, given an explosive development of high-throughput experimental technologies, the number of reported protein–protein interactions (PPIs) has increased substantially. Large collections of PPIs produce “omic” scale views of protein partners and protein memberships in complexes and assemblies To achieve appropriate understanding of PPIs and to design better ways for analyzing and interpreting them, this educational review presents several essential concepts and definitions intended to facilitate the use of PPI information both by computational and experimental biologists.PPI definition; a definition of a protein-to-protein interaction compared to other biomolecular relationships or associations. (b) PPI determination by two alternative approaches: binary and co-complex; a description of the PPIs determined by the two main types of experimental technologies. (c) The main databases and repositories that include PPIs; a description and comparison of the main databases and repositories that include PPIs, indicating the type of data that they collect with a special distinction between experimental and predicted data. (d) Analysis of coverage and ways to improve PPI reliability; a comparative study of the current coverage on PPIs and presentation of some strategies to improve the reliability of PPI data. (e) Networks derived from PPIs compared to canonical pathways; a practical example that compares the characteristics and information provided by a canonical pathway and the PPI network built for the same proteins. Last, a short summary and guidance for learning more is provided.The report is divided into five sections and a summary: (a) The first step needed is to define precisely what protein–protein interactions are. Commonly they are understood as physical contacts with molecular docking between proteins that occur in a cell or in a living organism in vivo. As discussed previously The physical contact considered in PPIs should be specific, not just all proteins that bump into each other by chance. It also should exclude interactions that a protein experiences when it is being made, folded, quality checked, or degraded. For example, all proteins at one point “touch” the ribosome, many touch chaperones, and most make contact with the degradation machinery. In many experimental assays, such generic interactions are rightfully filtered out. Therefore, the definition of PPI has to consider (1st) the interaction interface should be intentional and not accidental, i.e., the result of specific selected biomolecular events/forces; and (2nd) the interaction interface should be non-generic, i.e., evolved for a specific purpose distinct from totally generic functions such as protein production, degradation, and others.That PPIs imply physical contact between proteins does not mean that such contacts are static or permanent. The cell machinery undergoes continuous turnover and reassembly. Some protein assemblies are stable because they constitute macromolecular protein complexes and cellular machines, for example ATP synthase  or cytochrome oxidase . These proteins included in complexes are called “subunits”. Other protein assemblies are only built to carry out transient actions, for example, the activation of gene expression by the binding of transcription factors and activators on the DNA promoter region of a gene.Another essential element for defining PPIs is the biological context. Not all possible interactions will occur in any cell at any time. Instead, interactions depend on cell type, cell cycle phase and state, developmental stage, environmental conditions, protein modifications , presence of cofactors, and presence of other binding partners.Experimental determinations of interactions between proteins are done at either a large or small scale with two main technologies that produce different types of PPI data. The techniques that measure direct physical interactions between protein pairs are “binary” methods, while the techniques that measure physical interactions among groups of proteins, without pairwise determination of protein partners, are “co-complex” methods Several previous publications describe databases related to protein interactions A comparison of the main databases and repositories that include protein interactions is shown in There is a strong need to distinguish between “experimental” PPIs and “predicted” PPIs in order to avoid misinterpretation of the results provided by one or the other approach. Both types of data can be useful, but it is not the same to test an interaction between protein A and B by Y2H as it is to infer a possible interaction between protein A and B based on their gene co-expression profile. In the first situation, the PPI is experimentally proven, while in the second the PPI is predicted from experimental data obtained for the corresponding genes, which does not prove a direct protein interaction.http://www.psidev.info/), has defined the standard MIMIx  Some of the primary databases are DIP Saccharomyces cerevisiae. An empirical estimate of the complete binary protein interactome in S. cerevisiaeThere are clear discrepancies in current estimations of the real size of the protein interactomes, even for the well-studied unicellular model organism A first obstacle to evaluate the reliability of PPIs is the low coverage of the databases for each specific interactome. One way to increase coverage is to integrate data reported by different primary databases. Each database lacks a substantial proportion of the total reported PPIs Once the coverage is the best possible for a given interactome, strategies for selecting reliable PPIs are needed. A possible solution is to incorporate 3-D structural information about the interacting proteins. This is based on the principle that direct physical PPIs occur via specific structural interfaces, which can often be associated to domain pairs of known 3-D structure, i.e., to structural domain–domain interactions (sddis). Integration of sddi data with PPI data may help to reduce false positives and can be used to validate large-scale protein interaction data Pfam domain assigned: 69,079 interactions, called ppihs_all . Besides these three groups, we built another subset including all protein pairs supported by structural domain–domain interactions , selecting human PPIs that had at least one structural domain pair reported by one sddi resource. The sddi repositories are based on the analysis of 3-D structural interactions between protein domains taken from the PDB database ppihsxsdd subset includes 3,688 human proteins and 9,879 interactions. The Venn diagrams (ppihsx2meth).To show the coverage of 3-D structural data on the known human protein–protein interactome, we produced three different subsets of this interactome at three levels of confidence: (i) a subset of the complete human PPI data including only the proteins that have at least one pihs_all ; (ii) a hsx2meth ; (iii) aihsxKEGG  (http://diagrams  indicatehttp://www.cytoscape.org/), an open-source bioinformatics software platform for visualizing molecular interaction networks and biological pathways and for integrating these networks with annotations and other types of data http://www.pathwaycommons.org/).In several PPI repositories, it is a straightforward process to obtain all the proteins that interact with a given query protein and from those to build a corresponding network of molecular interactions. Several bioinformatic tools have been developed to represent and explore such PPI networks. Probably the most useful ones are associated with Cytoscape  in both networks are generally similar, and the information that can be deduced from them is complementary, each single view being enriched by the other. The γ-secretase complex is not included in the PPI network, while the interaction of NOTCH with the SMAD pathway is not present in the KEGG network. The central role of NOTCH and RBPJ is represented in both views , showingspecific physical contacts between protein pairs that occur by selective molecular docking in a particular biological context. Following this definition, we present some concepts related to the experimental methods used to determine PPIs, the types of biological repositories that include PPIs, and some strategies for analyzing the quality of protein interactions. Adequate description of the main characteristics of each PPI, including complete biological information about the proteins, is essential for building reliable protein interaction networks. As a guide for building and analyzing interactome networks, the tutorial provides a broad collection of references about PPI data resources This tutorial presents an up to date overview of PPIs, which are defined as


Protein identification using mass spectrometry is an indispensable computational tool in the life sciences. A dramatic increase in the use of proteomic strategies to understand the biology of living systems generates an ongoing need for more effective, efficient, and accurate computational methods for protein identification. A wide range of computational methods, each with various implementations, are available to complement different proteomic approaches. A solid knowledge of the range of algorithms available and, more critically, the accuracy and effectiveness of these techniques is essential to ensure as many of the proteins as possible, within any particular experiment, are correctly identified. Here, we undertake a systematic review of the currently available methods and algorithms for interpreting, managing, and analyzing biological data associated with protein identification. We summarize the advances in computational solutions as they have responded to corresponding advances in mass spectrometry hardware. The evolution of scoring algorithms and metrics for automated protein identification are also discussed with a focus on the relative performance of different techniques. We also consider the relative advantages and limitations of different techniques in particular biological contexts. Finally, we present our perspective on future developments in the area of computational protein identification by considering the most recent literature on new and promising approaches to the problem as well as identifying areas yet to be explored and the potential application of methods from other areas of computational biology. Proteomics is a relatively new but rapidly maturing discipline within life science research for understanding the biology of an organism via the large-scale study of the proteins expressed by the organism. There is already a vast body of literature applying proteomics in many different areas of clinical and biochemical interest and in the study of the pathogenesis, development, prevention, and treatment of a wide range of diseases .Protein identification is a key and essential step in the field of proteomics. The examination of patterns of protein expression alone can, of course, lead to important discoveries, including, for example, classification of samples on the basis of a particular pattern. However, without identifying the proteins known to be critically involved in the system under investigation, it is not possible to delve into the biological explanation for these patterns or to develop hypotheses as to the underlying biology of the system of interest. Thus, while protein identification may often be overlooked or taken for granted, it remains the key initial step in elucidating the biology of an organism by studying its protein expression. Our ability to maximize the benefit of proteomics to life science research is often dependent on our ability to accurately, quickly, and completely identify the full complement of proteins found in our samples of interest.The exponential growth in DNA sequence and protein databases, coupled with a similar growth in machine throughput, and the critical nature of protein identification to the proteomics process, has seen an explosion in interest in protein identification. For example, both the number and proportion of National Center for Biotechnology (NCBI) articles containing the phrase “protein identification” has seen exponential growth in the past decade.Mass spectrometry has emerged as the primary tool for protein identification and is the cornerstone of proteomics. While it was first used almost a century ago , the useThe speed and accuracy of these machines make them amenable to the high-throughput applications required not just in proteomics, but also in many other areas of the life sciences, resulting in rapid developments in hardware, software, and data management in the last decade. When we consider the use of mass spectrometers for protein identification, these rapid developments have lead to a bewildering number of instrument configurations, analysis algorithms, and data formats. Mass spectrometry is often critically important in a number of research pipelines. As such, biologists, and computational biologists especially, are often expected to meaningfully manage and interpret mass spectrometry data, requiring an understanding of the most up-to-date methods available to maximize true protein identifications and minimize false identifications for their particular application. This insight into protein identification algorithms is important because often the results may be ambiguous, and the biases chosen to make the problem computationally tractable can radically affect the result.Despite the improvements in mass spectrometry hardware and the reliability of modern protein identification software, several studies involving a range of mass spectrometers, datasets, and identification algorithms have shown in each case that fewer than half of the proteins in a complex proteomic sample can be identified –13. GiveThe computational biologist is often confronted with data at a point in the research pipeline where many previous steps have been completed. For example, sample preparation, instrument choice, data acquisition, and peak picking will usually have been completed to produce the mass spectrum to be used for protein identification. These preliminary steps can be as important as the computational protein identification process itself. As an example of the importance of “upstream” steps in the proteomic process, up to 90% of tandem mass spectra in a typical liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis cannot be identified using database search algorithms due to the poor quality of the spectra ,14. SpecOnce the data have been acquired by the mass spectrometer, there are features of these data that add further complexity and must be considered before a mass spectrum can produced. For example, the challenges involved in defining the center or presence of a peak –18 in tuThe challenges of allowing for potentially poor, fragmented, or incomplete database annotation are, however, the responsibility of the user interpreting results returned from protein identification software. Studies show that for less well-characterized species, these choices can have significant effects on the interpretation of protein identification results ,25.The greatest challenges for both users and vendors of protein identification software revolve around the issue of simplicity versus versatility. The diversity in hardware and experimental parameters has forced software developers to produce products servicing a vast number of experimental and machine configurations, in many different laboratory environments. These software applications must also work well for the full range of subtleties existing between different biological experiments. Furthermore, the software must integrate into existing systems, and allow for changes in instrumentation while seamlessly integrating the most up-to-date information sourced from third parties, such as protein sequence databases .This creates the design issue of how to allow enough flexibility to cover this diversity in hardware while incorporating all relevant knowledge about the biological question being asked. Inevitably, the user is heavily involved in the protein identification process by being forced to manually select many of the parameters for the search, a large proportion of which may not even be relevant to the particular experiment. This transfers much of the complexity of the task to the user, as evidenced by the length (around 90 pages!) of the typical basic user manual for a protein identification application. This highlights the need for both life scientists and computational biologists to understand the consequences their decisions at the searching stage will have on their ability to identify their proteins.Finally, mass spectrometry data also raises issues of reporting and data management. Under what criteria are proteins “identified”? How should mass spectrometry related data be stored for later use and how are previous results affected by ever increasing database size? At present, there are few accepted standards for addressing such questions –30, althThe heart of all protein identification methods is the scoring system. Mass spectrometry data derived from the unidentified protein are compared with theoretical data from known proteins, and a score is assigned according to how well the two sets of data compare. Any score above an arbitrary confidence threshold is termed a “hit.” The top such hit is expected to identify the unknown protein. If there are no scores above this threshold (“no hits”), then the protein remains unidentified.The development of scoring systems for protein identification began with the adaptation of well-developed general statistical methods common to many areas of science and engineering using methods such as cross correlation , BayesiaEscherichia coli databases using data collected on a high-performance LC-MS/MS LCQ ion trap mass spectrometer. They find correct proteins to have scores between 20 and 117, and incorrect proteins to have scores of up to 60. This demonstrates a fundamental property of protein identification software. As shown in The limiting factor on all protein identification tools is the tradeoff between false positives and false negatives. It is absolutely essential to keep false positives to a minimum during protein identification because identifying the wrong protein can lead to a costly waste of time and resources. At the same time, it is clearly desirable to identify as many proteins as possible to draw maximum benefit from the experimental data. The ability of an algorithm to identify a protein is said to be its sensitivity, and its ability to distinguish true positives from false positives is said to be its specificity. As may be expected, there is a tradeoff between the two, embodied in a numerical threshold often called the confidence level, above which proteins are classed as identified. This is important for the researcher to bear in mind, since the balance between sensitivity and specificity will have a bearing on the threshold above which they are prepared to accept a protein as “identified.” For example, Chen et al.  report rUsing a mass-based approach, each protein in a database is theoretically subjected to the same experimental conditions as the protein to be identified. Typically, this will involve an enzymatic digestion and possible secondary fragmentation. This produces a theoretical mass spectrum (or spectra) for each protein in the database. These theoretical mass spectra are compared with the experimental spectrum. In theory, any method of comparison between two spectra can be a candidate for a scoring system, and in practice a variety of methods are used. Details of these methods are included later in this review. As an example, and possibly the most basic method for comparison, one can consider the shared peak count. The shared peak count, as the name implies, counts the number of peaks in the same position (shared) in both the experimental and theoretical spectra. The theoretical spectrum with the highest shared peak count is then said to be the closest match. Another widely used related scoring function is the “coverage.” Given each peak represents a fragment of the peptide, the coverage is the proportion of the protein covered by these matching fragments. Practical scoring systems use the combination of many such metrics to produce a final score.Peptide mass fingerprinting (PMF) was the first available method of protein identification using mass spectrometry, and is still widely used . This mePMF is popular and works well in practice because it is relatively fast to compute PMF scores against a database. For good quality samples belonging to well-characterized model organisms, PMF can in many cases produce protein identifications with high confidence, especially in organisms with smaller genomes. Unfortunately, sometimes a sample spectrum does not resemble any theoretical spectra in the protein database closely enough to make a confident identification. This can happen for many reasons, such as unexpected post-translational or chemical modifications, splice variants, individual sequence variants , or omissions and errors in the database. As more sophisticated methods for scoring PMF have been developed, more proteins can now be identified with confidence. This corresponds to a better separation of true from false positives using the scoring system. A wide variety of methods for attributing a score to the similarity between two spectra has been explored, with the most successful algorithms taking into consideration a number of factors to derive the final score. The next step involves deciding on a threshold: proteins whose scores are above this threshold are said to be identified. The definition of a threshold is difficult because setting a threshold too low will incorrectly identify a number of proteins , while setting the threshold too high may result in the correct protein not being identified . Statistical methods have been developed to determine the optimal threshold to keep the number of false positives below a given percentage ,43. The One of the limitations of PMF is its sensitivity to database size. There is a direct effect on the statistical confidence a PMF algorithm can ascribe to protein identification as the search database grows. A larger database has an elevated chance of the experimental masses randomly matching theoretical peptide masses in these databases, thereby decreasing the confidence of protein identifications using PMF. Many experimentalists use PMF as a “first pass” to identify a protein, and if the identification fails or remains tentative, move on to methods such as PFF . The mosAldente  is hosteMascot  uses a p−5. This may sound like a promising result but, if the real database contains 106 sequences, several scores of this magnitude may be expected by chance. A widely used significance threshold ..... is p < 0.05.“The fundamental approach is to calculate the probability that the observed match between the experimental data set and each sequence database entry is a chance event. The match with the lowest probability is reported as the best match. Whether this match is also a significant match depends on the size of the database. To take a simple example, the calculated probability of matching six out of ten peptide masses to a particular sequence might be 106 entries, this would mean that those with significant matches were those with probabilities of less than 5 × 10−8. .... we have adopted a convention often used in sequence similarity searches, and report a score which is −10Log10(P), where P is the probability. A significant match is typically a score of the order of 70.”For a database of 10p < 0.05. Examples of the input data format and results format are available from the Matrix Science Web site [This means searches in smaller protein databases, such as bacterial databases, will generally have lower threshold scores for confidence than those conducted in larger databases for higher organisms. We can also infer that for noisy experimental spectra, for example those with contamination, these extra peaks contribute to the possibility of a random match, and thus raise the confidence score threshold for a given probability. Mascot automatically returns a score threshold with its results calculated to represent a confidence level of Web site .MS-Fit  is also −10, with unrelated proteins returning a probability of less than 10−33. The flexibility of a Bayesian approach has allowed Profound to remain competitive since its introduction in 2000.Profound  uses a BA recent study by Chamrad et al.  using maA study in yeast using 266 spectra gathered on MALDI-TOF instruments from three different manufacturers  found MaThe claims for the highest rate of protein identifications belong to groups using consensus methods. These methods submit the query data independently to multiple search engines and combine the results. The rationale for this process is that marginal identifications may be corroborated or rejected by complementary packages. Experimentalists have been doing this independently for some time , usuallyDevelopments in mass spectrometry hardware, the explosive growth of database sizes, and computational advances necessitate the need for ever-higher sensitivity and accuracy in order to deliver gains in protein identification rates. These requirements have resulted in the emergence of a technique known as PFF, particularly in the high-throughput proteomics domain.Approaches using PFF data are the current mainstream of high-throughput protein identification. Proteins are first digested with an enzyme, and then individual peptides are selected to undergo further fragmentation to yield PFF spectra such as the one shown in PFF is the method of choice for high-throughput applications due to the additional information gained from secondary fragmentation. This information makes the protein identification process less sensitive to effects such as protein modifications and can generate higher statistical confidence in the correct identification than traditional PMF. Some of the more popular PFF packages are listed below in Sequest and Mascot are arguably the two most popular packages for protein identification using PFF. Mascot is probabilistically based while also using some heuristics to improve scoring, but, like Spectrum Mill, ProteinProspector, and the most recent commercial PFF package, Phenyx, the details of the scoring process have not been published. Mascot, however, is known to be based on the probabilistic MOWSE algorithm , which uSp calculation can be determined by “a number of different scoring algorithms” [Sequest uses a patented scoring algorithm utilising a cross-correlation approach. orithms” , but theSp: this is a preliminary score based on a computationally cheap increment/decrement schedule for identifying alignments between ions in the experimental and theoretical spectra. Sp is used as a first pass to gather a set of 500 peptides for further analysis. Larger peptides have a higher Sp. For example, a good hit for a 20-residue peptide will often have a Sp over 1,000, while a good hit for a 6-residue peptide will often be below 500.Cn: the normalized correlation score is a measure of how well the theoretical y/b ion spectrum correlates to the experimental spectrum, corrected for peptide length. The uncorrected value is known as XCorr, the cross correlation. Cn has a range between 1 and 0 , with 1 being perfect correlation between experimental and theoretical spectra and a score of 0 indicative that there are no aligned peaks between the spectra.ΔCn: this score gives a measure of how far the top score is above the other candidates. It is simply the difference between the top-ranking Cn and the Cn for each other peptide.Ions: this value tells the user how many of the experimental ions align with theoretical ions. For example, 10/12 means that from the 12 peaks in the experimental input, 10 were matched in the theoretical peak list for that candidate peptide.These measures provide important information for interpreting Sequest results. Obvious criteria for a confident identification are a high normalized correlation score (close to 1), a significant proportion of aligned ion peaks, and good separation from other candidate peptides. The distillation of Sequest results into identifications with corresponding confidence scores has been an area of active research –59, and A competing package, Popitam, was released in 2003 and uses a novel parsing of the MS/MS spectrum for matching against a database, followed by correlation scoring. This package has been specifically designed to address the limitations of other algorithms in the case of modifications or mutations in the source peptide . The comA recent benchmarking paper produced by Kapp et al.  comparedResing et al.  report sp < 0.05 threshold. Sequest was found to be less sensitive to parameter changes, including fragment and parent ion tolerances and especially variable modifications. Restricting the taxonomy of the search from “all” to “mouse” improved correct identifications for both engines by roughly 30%. The overall recommendations of the study include “a careful and sparing” use of variable modifications, restricting the taxonomy search as much as is appropriate, and searching with two missed cleavages as a good starting point if speed is not critical.In 2004, Chamrad et al.  reportedThe results of these studies suggest: (1) there is a high level of overlap in the identified sets of proteins between PFF packages; (2) the thresholds used to exclude false positives also exclude up to half of the true positives; and (3) some algorithms were better at identifying certain types of proteins than others.Despite the extensive literature on algorithm improvements, and the number of free implementations of these algorithms, many of these improvements are not being adopted by life scientists. In a review of protein identification packages, Shadforth  speculatAlthough it is difficult to compare the various packages for protein identification, Kapp et al.  have pubMeta scoring incorporating scores from multiple PFF packages has been well explored and successfully applied to significantly reduce the number of false positives, thereby allowing significantly more identifications for a given false positive rate ,66–68. WTag-based approaches begin with an attempt to extract peptide sequence information directly from the peptide fragmentation spectra. These methods are based on casting the problem into one of finding a maximum path length through a graph, a problem already known to have efficient solutions, and are based on a seminal paper by Dancik et al. . The proNot surprisingly, tag-based approaches appeared as the first de novo sequencing methods were becoming available . A numbeGutenTag  is a popPEAKS is the current standard for de novo sequencing tasks prior to submission to an alignment program for protein identification. It reports results with a confidence measure and has been shown to outperform the popular Lutefisk as well as various software packages from instrument manufacturers . For quaDe novo sequencing quality is highly dependent on the precision of the mass spectrometer and the quality of the spectra. Advances in hardware accuracy and precision have a great effect on the ability of de novo algorithms to correctly and accurately infer longer stretches of protein sequence. Quality spectra as well as high precision greatly constrain the possible sequences capable of generating the observed spectrum. Thus, the short list of possible peptides, to be later submitted to a tag-based search, may contain longer and therefore more specific sequences, resulting in more confident identifications.Preparatory methods to improve the quality of spectra intended for de novo sequencing is an active area of research –80. For Most current tag-based methods use a basic adaptation of the BLAST  or FASTATag-based approaches are much faster than PFF searches; Tanner et al.  report aTag-based methods have been designed to function in environments where exact matches are not expected—for example, searching against databases of other species—and as such have different methods for determining the statistical significance of a result under these conditions. This translates to a much higher sensitivity in these types of applications when compared with other methods such as the PFF methods already discussed ,89. Tag-A study by Habermann et al.  of crosshttp://www.molecularstation.com/bioinformatics/link/Proteomics/Protein_Identification_Tools and http://www.proteomesoftware.com/Proteome_software_link_software.html.There is a great number and variety of protein identification packages other than those listed in this review. Many of these packages have been tailored to provide identifications for particular classes of proteins, or even glycans , or use Much effort has been made by various groups to devise metrics allowing the various protein identification packages to be compared. Such metrics include: (1) calculating expectation values for the number of hits expected for a given score –30; (2) p-values; (4) justifying evidence for identifications made on single peptides, for a particular protein within a protein family, or proteins identified in another species; and (5) multiple replicates for complex analyses.However, these metrics are not regularly used in the literature, and there is still no firm consensus on how the results of protein identifications can be reported and compared. Recognizing the critical need to clearly compare experiments, some proteomics journals have already introduced standards for protein identification reporting ,33, and The problem of standardizing mass spectrometry–related data formats and vocabulary is being addressed by the HUPO Proteomics Standards Initiative . This grA consistent message found in this review of protein identification algorithms is that the best results for protein identification are extracted through the use of consensus programs used to collect the results from various packages and distill their results. This is particularly the case in the mass-based approaches of PMF and PFF. Through such methods, the strengths of some packages can be exploited, while weaknesses in others are mitigated. The only difficulty with this approach is the added expense and difficulty of operating multiple-search algorithms as well as the consensus-scoring software for compiling and analyzing the different results. However, the added performance gained, in terms of correct protein identification, would appear to encourage the investment in using consensus-based methods.All of the methods require the use, at some point, of a reference sequence database for identifying the proteins expressed in the sample. This presents extra challenges for researchers working with less well-characterized species. In this scenario, tag-based methods are preferred because of the reduced computational complexity of searching for diverged proteins. Mass-based methods require matching of peptide or peptide fragment masses to their theoretical equivalents derived from a sequence database. A single amino acid change, with the exception of a change between leucine and isoleucine, will change the mass of the peptide or peptide fragment with a resultant effect on the ability of the algorithm to correctly identify the protein. In contrast, with tag-based methods, particularly if the tag-matching process is tolerant of sequence variation, sequence changes have less of an impact on the ability to correctly match database entries. Thus, cross-species databases can be more effectively used to aid in protein identification.Identifying the single best package for each application from the available literature is at present extremely difficult due to a number of factors. Each package claims advantages over a number of others. These claims are often backed up with compelling results. While some of the comparative studies cited above have produced work of excellent scope and quality, many of these results show marginal differences between packages or show contradictory results to other studies. Furthermore, across all the studies, only a small fraction of the available packages have been considered and evaluated. Similarly contradictory results are reported not only in comparisons between various packages, but also between approaches, such as between mass-based and tag-based approaches. This indicates the datasets and thresholds used in such comparisons have a critical importance on the outcome of such experiments, and that the high variability in machine and experimental setups complicates analysis. The state of data standards and lack of benchmarks therefore makes it difficult to make an effective comparison, implying the need for sustained directed research on the creation of suitable benchmarks. While the increasing availability of data in public repositories and tightening standards will no doubt ameliorate the problem, until this basic benchmarking problem is overcome, no single package or approach can conclusively be declared to outperform all others, expect, perhaps, in the specific circumstances used in particular studies. For such benchmarking work to be successful, it is important that it be broad, replicable, and routine, because each software package is constantly evolving, so a benchmark can at best produce a comparison likely to quickly become redundant as newer versions of packages are released. This, in turn, points to the need for an ongoing process of benchmark-based testing, in which new algorithms and techniques, or developments in existing packages, are regularly re-evaluated to measure performance and provide guidance to life-science researchers seeking to extract the most from their proteomic experiments.It is clear there is much room for improvement in protein identification techniques as, despite the many advances in the field, it is still evident that fewer than half of the proteins in a typical proteomics study can be identified –11,87. PThe uptake of new standards for reporting and particularly for data formats holds the promise of direct comparison between experiments, and more importantly, a solid suite of benchmarks against which new methods and techniques can be measured. These standards are being eagerly adopted by the computational biology community and will serve to streamline development efforts.At present, the greatest focus in improving protein identification software is on the following: (1) developing better scoring metrics or including additional information –94; (2) These methods can massively speed searches by reducing the size of the database being searched through the use of statistical methods to cheaply reject the majority of non-matching database entries, or by improving the speed at which comparisons can be made.The above, purely algorithmic, improvements complement new techniques for sample preparation and data acquisition such as chemical derivatization ,81 as weProtein identification software faces the ongoing challenge of incorporating these diverse advances in hardware, software, and methodology as we move toward the ability to rapidly and confidently identify a greater proportion of proteins from every experiment.