Prediction of functional potential of bacteria can only be ascertained by the accurate annotation of its metabolic pathways. Homology based methods decipher metabolic gene content but ignore the fact that homologs of same protein can function in different pathways. Therefore, mere presence of all constituent genes in an organism is not sufficient to indicate a pathway. Contextual occurrence of genes belonging to a pathway on the bacterial genome can hence be exploited for an accurate estimation of functional potential of a bacterium. In this communication, we present a novel annotation resource to accurately identify pathway presence by using gene context. Our tool FLIM-MAP  predicts biologically relevant functional units called ‘GCMs’ (Gene Context based Modules) from a given metabolic reaction network. We benchmark the accuracy of our tool on amino acids and carbohydrate metabolism pathways. Figure 1 which depicts a demonstrative example of a hypothetical pathway on a bacterial genome comprising six genes (A–F). While Genome 4 in Figure 1 depicts the correct annotation of the pathway, the other three Genomes (1–3) represent mis-annotations due to homology based methods. An overestimation of functions might occur in cases where homologs of some enzymes constituting a candidate pathway participate in other pathways within the same bacterium  . Thus, it is important to distinguish cognate homologs of each enzyme functioning in a pathway from their other homologs on a bacterial genome in order to accurately decipher functional potential of an organism. Further, accounting for mere presence of homologous enzymes often leads to an inaccurate annotation of a pathway in cases where all constituent enzymes have homologs that function in multiple pathways  . In these genomes, presence of all homologs which might be separately functioning in multiple pathways might be misinterpreted as indicative of pathway presence. In other words, homology based methods may successfully provide a phenotypic function of a protein but might prove inaccurate in identifying its molecular function  utilized for predicting enzymes functional in a pathway from a bacterial genome sequence, rely only on sequence homology and fail to identify proteins with divergent sequences  . In addifunction . Thus, pfunction  which moFigure 1 Genome 4) . Therefore, utilization of gene context information can help in correct pathway annotations leaving out spurious results in genomes which merely contain homologs of genes constituting a pathway.Reports have indicated that physical as well as functional interactions between proteins are directly correlated with the spatial proximity of their genes on the genome. It is now well established that bacterial metabolic pathways are encoded by co-regulated genes lying in juxtaposition (evolutionarily conserved gene clusters and/or operons) on the genome for improved efficiency . These fenome 4) . Earlierenome 4) . FurtherSupplementary Figure S1). For example, prediction of methane producing capability of a bacterium would require annotation of the gene cluster which is involved in methanogenesis on the genome  depict the pathways using a network view, with nodes as compounds (either reactants or products) and edges as reactions. Although the graphical network displays the unified entirety of the pathway, it may not be a biologically relevant representation. For example, in addition to connections involving sets of reactants and products which represent the pathway under consideration, the depiction of pathway in such repositories include other reactions that are not part of this main pathway . Considering the ubiquity of gene clusters in bacterial metabolic pathways, FLIM-MAP assigns appropriate relevance to each cluster  by first ensuring connections between compounds (reactants and products) within a metabolic network and further checking the gene neighborhood of the catalyzing enzymes. A valid functional unit is that which not only forms appropriate edge connections (reactions) in a network but also has genes which occur in context within the genome.The present tool, called FLIM-MAP uses a novel method for identifying functional units within a metabolic pathway by utilizing prioritization of gene context information in addition to sequence similarity as well as edge connectivity information (A metabolic pathway (as depicted in repositories like KEGG) can be segregated into several functional units, each responsible for converting a specific reactant into a product catalyzed by a set of enzyme(s). These functional units are usually encoded by a set of co-regulated genes which occur in juxtaposed arrangement in the genome. In the current work, a methodology has been devised which allows to augment the modules derived by network parameters with gene context information. The FLIM-MAP algorithm identifies functional units, termed as ‘Gene Context based Modules’ (GCMs), which represent biologically relevant entities with defined metabolic activities within a metabolic pathway. The identification of these functional units requires accounting for edge connections within a metabolic network as well as genomic organization of the constituent genes, making it imperative to create a knowledgebase comprising this information.Supplementary Table S1). The constitutively expressed pathways like Glycolysis, Pentose Phosphate pathway and Citric acid cycle were not included as most of their enzymes do not occur as clusters on the genome. Similarly, Glycine, Serine, and Threonine metabolism pathways were also excluded from the analysis. Community detection algorithm  resulted in identification of 99 and 186  IRMs in carbohydrate metabolism and amino acid metabolism pathways, respectively.Initial Reference Modules (IRMs) were created  using information on edge connectivity between compounds (set of reactants and products) in KEGG repository corresponding to 11 carbohydrates and 13 amino acid metabolism KEGG pathways  which exist in bacteria can be obtained by augmenting the edge connectivity in pathways with information of genomic locations of constituent genes. Therefore, the juxtaposed occurrence of co-regulated genes on the genome can be utilized to obtain functional units/modules which correctly represent various functions within a bacterium. The outcomes can be further validated using literature mining for each of these source and target. Details of the created IRM along with validation are described below.A genome-Pfam map, termed as ‘DB1,’ was created based on the HMM profiling of 2745 completely sequenced bacterial genomes. This database comprised of the genes in each organism arranged according to their genomic locations and their corresponding domain (Pfams) annotations. This map therefore acts as a master catalog for querying the specific genes while performing module refinement. The created map has been incorporated into the backend database of the web interface called ‘FLIM-MAP’ for faster and efficient querying.The algorithm for refinement of ‘Initial Reference Modules (IRM)’ on the basis of genomic distances between constituent genes  has been applied on the 2745 bacterial genomes and benchmarked on all carbohydrate (11) and amino acid (13) KEGG pathways. Initially the algorithm redefined 82 (out of 186 IRMs) amino acid and 93 (out of 99 IRMs) carbohydrate ‘Intermediate modules’ (IM) after reassigning the genes in the IRMs on the basis of genomic locations . In other words, these IMs corresponded to sub-pathways obtained from clustering of homologs of the constituent genes in each of the KEGG pathway on the basis of genomic locations on the bacterial genomes. Since the IMs were defined using locations of genes present in IRMs, an IM might be formed by one IRM, merging of some IRMs or splitting an IRM into multiple IMs. The Pfam domain information corresponding to genes within each of IMs was also cataloged. Although, these IMs comprised of juxtaposed genes which were expected to represent a biologically functional unit, they were further validated using information in literature regarding experimental characterization of these sub-pathways. This database included only those sub-pathways (IMs) for which experimental details could be verified from literature. The literature based validation involved a combination of automated search in public literature engines using ‘6Q method’ as well as manual curation  and the obtained results are discussed below.Supplementary Table S2).The modules obtained after filtering IRMs based on gene context to yield IMs as well as verification in published literature were considered as biologically relevant functional sub-pathways within a KEGG pathway and termed as ‘Gene Context based Modules’ (GCMs). The information for each of the GCMs was cataloged as a database (DB2) along with features such as compounds involved, their order of occurrence and the key domains involved in carrying out enzymatic reactions. A total of 54 carbohydrate and 51 amino acid biologically validated GCMs  were picked from the initial 285 IRMs . Thus, these edge connectivity based communities do not represent the actual biologically relevant functional modules. Module definition using gene context in addition to edge connectivity, as implemented in the created GCMs in this study, was able to correctly put all these genes in one module . Further, since these modules were defined after literature verification pertaining to bacterial pathways, eukaryotic sub-pathways within KEGG pathways could also be removed. For example, mannan production is specific to eukaryotes and is absent in bacteria. This demonstrates the advantage of utilizing gene context information in identifying biologically relevant modules in bacterial systems. Additionally, man gene cluster  comprises of all genes with promiscuous domains and therefore multiple homologs might exist within a bacterial genome. For example, the homologs of this pathway occur in E. coli  as well as B. thetaiotaomicron . The man gene cluster was experimentally characterized in E. coli and the conversion pathway is absent in B. thetaiotaomicron. B. thetaiotaomicron only contains dispersed homologs of constituent enzymes on the genome. In such scenarios, tools like KEGG, BRENDA, and MetaCyc which determine gene function on basis of sequence homology assign and map all the enzymes to this pathway in both these organisms. This would lead to false prediction of the pathway presence in B. thetaiotaomicron. Utilization of gene context information can help in obtaining correct pathway annotations (E. coli), leaving out spurious results in genomes which merely contain homologs of genes (B. thetaiotaomicron).The community detection algorithm divided the Fructose and Mannose metabolism pathway in KEGG (map00051) into 7 modules on the basis of edge connectivity. Studies have established that bacteria utilize evolutionarily conserved e module . On the paa gene cluster)  into three clusters which did not represent the actual occurrence of pathway in bacteria correctly. The definition of gene context based modules (GCM) which accounts for gene location and edge connectivities can overcome these discrepancies by clubbing all the 14 genes with their respective reactions in the pathway into a single module (GCM Phenylacetate _Succinyl-CoA Metabolism). In this case also, the genes constituting paa gene cluster contain multiple homologs in bacteria which might lead to misannotations. Identification of GCMs allows to handle these false positives generated due to homology based annotation as well. Furthermore, it is to be noted that homologs of proteins like paaF (enoyl-CoA hydratase [EC:4.2.1.17]) and paaH (3-hydroxyadipyl-CoA dehydrogenase [1.1.1.157]) form part of other metabolic pathways like Butanoate metabolism and Fatty acid metabolism. Therefore, the two genes  are also included as part of the above mentioned pathways in homology based methods . KEGG maps b1393 and b1395 to Butanoate metabolism pathway in E. coli and shows presence of gene repertoire for conversion of Pyruvate to Crotonyl-CoA. It is known that E. coli lacks the ability to produce butanoate using this pathway thereby illustrating that homology based methods  annotate the pathway incorrectly in such cases. On the contrary, since the genes (b1393 and b1395) would only lie in within ‘paa’ gene cluster, FLIM-MAP would lead to an appropriate assignment of these genes exclusively to the Phenylacetate to Succinyl-CoA Metabolism where they actually function.The example of phenylacetate metabolism in bacteria also provides an illustration of importance of GCMs in defining functional units within a pathway. Post the community detection algorithm, the Phenylalanine metabolism pathway was distributed into six modules based on edge connectivities. Bacteria metabolize Phenylalanine, an essential amino acid, using phenylacetate as starting intermediate. It has been shown that Phenylacetate metabolism is brought about by an evolutionarily conserved gene cluster (cluster)  which waL-Xylonate/L-Lyxonate Metabolism from list of IMs for Carbohydrate Metabolism and addition of the IM, 2-Oxoisovalerate to 2-Isopropyl-3-oxosuccinate Synthesis to the intermediate module list of amino acids). Some modules were also redefined to get relevant and pathway specific clusters, for instance, metabolism of Homocitrate to Oxoadipate. This yielded 148 final IMs which were further used as input for validation using ‘6Q-Strategy’ . It should be noted that IMs removed manually in previous step as false positives were also subjected to literature mining in order to further confirm that they did not represent any functional module.The 175 IMs defined using the described algorithm for clustering and reassignment of genes was further refined using manual intervention to remove false positives. These false positives occurred in cases where an IRM contained Pfams which are very commonly found in bacteria and therefore their juxtaposed occurrence on the genome might be a chance event and not actually correspond to a pathway. The elimination of these false positives involved identifying the Pfams which were more specific to a module and could help to distinguish these false positive occurrences from actual presence of a module on a genome  (Supplementary Table S3). Further, there were modules where reference organisms were eukaryotes and not bacteria. These eight modules were removed after it was confirmed by manual search also that they belonged to eukaryotic pathways and contained no relevance in bacterial systems . Additionally, it should be noted that query structure of 6Q approach includes matches with only reactant string, only product string, reactant and product string combined as well as Reactant string, Product String and the keyword ‘bacteria’ combined . Therefore, considering pubmed IDs present in four out of six query searches as consensus pubmed IDs would even include the Pubmed IDs which only match these four criteria and obtain no publication corresponding to keywords like ‘gene cluster’ and ‘operon.’ In such cases, although the conversion between initial reactant and final product may be captured in a publication but the genes responsible might not occur in gene context (operon or gene cluster) on the genome. As an example, conversion of D-Ribulose-5-phosphate to CDP-ribitol has been discussed in the publication with Pubmed ID 11305920  as no publications corresponding to last two query structures are available. On the contrary, in other cases the last two queries become necessary to narrow down to more relevant reference organisms. Despite the extraction of ∼80% of modules correctly using automation methods, 20 modules had to be searched manually and added to the database due to no consensus publications referring to those modules using these six query structures. In summary, the application of 6Q method along with extensive manual curation allowed to define biologically functional modules in each of the 2745 genomes.D-Mannose 6-phosphate to GDP-L-Fucose of the KEGG pathway-00051 (fructose mannose pathway), has been experimentally proven to exist as an evolutionarily conserved man gene cluster. KEGG module entry shows that D-Mannose 6-phosphate, D-Mannose-1-Phosphate and GDP-D-mannose  are mapped to the KEGG Module M00114 (ascorbate biosynthesis). This affiliation in KEGG refers to a plant metabolism pathway where GDP-D-Mannose is further channeled into ascorbate biosynthesis via formation of GDP-L-Galactose. Thus, module definition in KEGG completely misses out on utilization of GDP-D-Mannose in bacteria. Therefore, incomplete curation of information might lead to inaccurate definition of metabolic potential in case of bacteria.Our approach utilizes three strategies (i) identifying domain constitution on the genome and assignment of gene clusters (ii) identification of functional units/modules based on this gene context information (iii) validation using the novel ‘6Q’ literature mining strategy. Although, methods like KEGG MODULE identify modules within pathways using manual curation, information about various modules is still not curated completely. For example, despite the importance of butyrate production by gut commensal bacteria in human health and the presence of well characterized metabolic pathways, the modules corresponding to butanoate metabolism pathway (map00650) are absent in the KEGG MODULE repository. Our analysis revealed presence of five modules in this KEGG pathway which were validated for their biological relevance by literature mining. Gene context based methods help to identify modules  which have been extensively studied in literature . In addiFigure 3). User is provided with the option of following two pipelines while using FLIM-MAP namely ‘Upload KGML’ and ‘Selection’ of a pathway in an organism. A KEGG ‘pathway’ is a graphical object comprising of ‘entry’ element  as its nodes and edges are formed by the ‘relation’ and ‘reaction’ elements. Such a graphical outline indicates the connection pattern between substrate and product catalyzed by a gene product . The former pipeline allows the user to upload an organism specific KGML file (as obtained from KEGG pathway database or user created file in similar format) for a carbohydrate or amino acid pathway listed in KEGG. User can further choose the cut-off for the percentage of total Pfams tagged to a GCM in DB2 which need to be present in order for a gene set in a query genome to be considered a valid GCM. It should be noted that for GCMs where partial matches can also be considered valid use of lower cut-offs is recommended. The interface also provides the user with an option to choose the window size which refers to the number of flanking genes to be considered while defining gene context. These two features control the stringency of the query search. A demonstrative example has been made available on the page for the user’s convenience. The latter ‘Selection’ pipeline lets the user select any one of the 2745 complete NCBI genomes for a selected carbohydrate/amino acid KEGG defined pathway (using the KEGG mapid). For a selected cut-off value and window size, all the potential modules and the extent of domain presence within the chosen window size for gene context is estimated. Results of the algorithm for both pipelines can be viewed in a network representation as well as in a tabular format and can be further downloaded as a text file. A detailed tutorial is available on the server for ease of understanding.In order to view and identify the extent of presence of ‘Gene Context based Modules’ (GCM’s) within a KEGG defined pathway in any prokaryotic organism; a tool called ‘FLIM-MAP’ has been developed   provide Micrococcus luteus) and it will not be possible to identify actual BCKD gene cluster using just the gene context based methods , an important part of Branched Chain Amino Acid (BCAA) catabolism, forms a gene cluster containing three genes. This cluster comprises of two subunits of the branched-chain α-keto acid decarboxylase (E1), a lipoamide acyltransferase unit (E2), and a lipoamide dehydrogenase unit (E3). Although, the enzyme forms a gene cluster, similar domain constitution is observed in other enzymes like Pyruvate dehydrogenase as well as 2-oxoglutarate dehydrogenase. In such cases, multiple copies of this gene cluster can be present on a bacterial genome  based profiling against the Pfam database using HMMER tool . The reference kgml files representing the structure of metabolic pathways (carbohydrates and amino acids) were first downloaded to create an edge list depicting the connections between compounds  involved in the pathway. Constitutive pathways like Glycolysis, which mostly comprise of housekeeping genes, were excluded from the analysis. Community detection algorithms were used to create edge connectivity based modules (using iGraph in R package). The formed clusters were derived based on the edge connectivity and were used as the ‘Initial Reference Modules (IRM).’2. A map was created using connection information between compounds within an IRM and the genes catalyzing the reaction. Gene information for a prokaryotic pathway was retrieved from the individual organism specific kgml file. Functional domains (Pfam) for its corresponding gene within a module were mapped using the HMM based profiling as mentioned above. The next task was to assign module identifier to those genes which connected two modules and were not part of any of the formed clusters. Such genes, termed as ‘linker genes,’ were reassigned to both the connecting modules which were later segregated using our algorithm (described below).Organism specific pathway files (kgmls) for 2745 completely sequenced bacterial genomes were downloaded from KEGGn represented the number of IRMs in a pathway. Information on gene locations which were recorded during HMM profiling was used to calculate the average distances between the genes. Both inter-modular gene distances as well as intra modular gene distances were determined. Proximal genes were clustered together to form new intermediate modules (IM). This step was recursively performed till all the genes were segregated into the clusters which were classified on the basis of gene context. Inter cluster distances were calculated and based on minimum distance score (chosen as 10) the clusters were either merged or kept as they were, thus forming redefined modules. This methodology was executed for all the 2745 complete bacterial genomes.The created Modules were compared in pairs, extending over nC2 combinations, where Figure 3).Validation as well as refinement of the redefined modules was done using extensive literature mining and manual curation. Literature mining was performed using the ‘6Q Approach’ (detailed below), results of which contained the list of consensus organisms for each query sub-module within a KEGG defined pathway. Functionally relevant domains associated within a module were manually curated and analyzed across the consensus organisms obtained from the literature search. Post shuffling and refinement, a mapping between each module and the compounds as well as functionally important Pfams was created. These reformulated biologically relevant modules with specific metabolic activities are termed as ‘Gene Context based Modules’ (GCMs)  explicated as:•‘ReactantString’ (Query 1)•‘ProductString’ (Query 2)•‘ReactantString’ + ‘ProductString’ (Query 3)•‘ReactantString’ + ‘ProductString’ + ‘bacteria’ (Query 4)•‘ReactantString’ + ‘ProductString’ + ‘operon’ (Query 5)•‘ReactantString’ + ‘ProductString’ + ‘gene cluster’ (Query 6)The ‘ReactantString’ is a manually curated string identifier for source metabolite/reactant and ‘ProductString’ is a string identifier for target metabolite/product. The ReactantString and ProductString identify the first reactant and the final product of the corresponding module, respectively. KEGG repository was further mined to obtain all synonyms for each of the compounds used as ReactantString and ProductString. All these synonyms for ‘ReactantString’ and ‘ProductString’ were used as query while performing literature search. The above mentioned queries were then used as input to search against the curated biomedical literature resources, namely Pubmed and Pubtator . The resIn order for the user to view and identify the extent of presence of GCM’s within a KEGG defined pathway, we developed the tool called ‘FLIM-MAP.’ The identified GCMs are graphically presented with distinctive colors in an interaction network. This web application was designed on the ‘Model View Controller (MVC)’ architecture. Model and Controller elements were built using MySql and PHP, Python, respectively. User interface and graphical visualization was created using HTML5, Javascript, Jquery and d3.js.https://web.rniapps.net/FlimMap [Freely available for academic use].SA, BK, and SM conceived the idea. VB and AM designed the overall methodology, implemented the algorithms, and developed the web server. SA, VB, and AM performed the case studies. VB, AM, SA, BK, and SM analyzed the results and drafted the manuscript. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The aims were to review practices concerning Differential Item Functioning (DIF) detection in composite measurement scales, particularly those used in health research, and to provide guidance on how to proceed if statistically significant DIF is detected.This work specifically addressed the Rasch model which is the subject of growing interest in the field of health owing to its particularly advantageous properties. There were three steps: 1) Literature review to describe current practices; 2) Simulation study to determine under which conditions encountered in health research studies can erroneous conclusions be drawn from group comparisons when a scale is affected by DIF but which is not considered; 3) Based on steps 1 and 2, formulation of recommendations that were subsequently reviewed by leading internationally recognized experts.Four key recommendations were formulated to help researchers to determine whether statistically significant DIF is meaningful in practice, according to the kind of DIF (uniform or non-uniform) and the DIF effect size.This work provides the first recommendations on how to deal in practice with the presence of DIF in composite measurement scales used in health research studies. Other than some purely descriptive studies, almost all health research studies include group comparisons: typical study designs involve a primary outcome measured in every subject, whose occurrence  or mean (if continuous) is compared between groups defined by a characteristic or exposure of interest. More complex designs require multivariate analyses or subgroup analyses. To be accurate, the measurement of the outcome must be valid in all groups studied. Otherwise, the difference (or the absence of difference) observed between groups may be, partly or totally, an artifact due to the measurement instrument not being valid in one or several groups. Accurate group comparisons require measurement invariance: “the measuring device should function in the same way across varied conditions, so long as those varied conditions are irrelevant to the attribute being measured” .Composite measurement scales are used in epidemiological studies to measure complex health related constructs, including quality of life, depression and satisfaction with care. They consist of items that assess the various dimensions of the construct to be measured. Several standards and guidelines have been published concerning their validation and measurement invariance is considered to be essential –6. StatiOther observed variable methods to detect DIF have been developed  but have drawbacks, particularly the use of a proxy as the matching variable, motivating the development of more modern DIF detection methods ,11–13. MThe Rasch model is often used to analyze questionnaire data in health research studies . It exprThe purpose of this paper was to review practices concerning DIF detection and handling in composite measurement scales used in health research studies and to provide guidance on how to proceed when DIF is statistically significant. We focused on the Rasch model which is the subject of growing interest in the field of health owing to its particularly advantageous properties. As theoretical background on meaningfulness of consequences of DIF is scarce in literature, this work was designed to gather information from various sources. It included three steps:Literature review: description of current practices in health research studies concerning detection and handling of DIF in a scale, with a particular attention to the use of Rasch models in this context to bring out typical scenarios studied within the Rasch framework;Simulation study: results from the literature review were used to enrich a recently published simulation study aiming to determine the conditions in which erroneous conclusions may be drawn from group comparisons in health research studies, when DIF is present within a scale ;Recommendations formulation: the literature review and simulation study were exploited to establish recommendations that were then reviewed by a panel of experts.To describe current practice in health research studies to detect and handle DIF in a scale, with particular focus on the Rasch framework.“differential item functioning” or “item bias” or “measurement invariance” or “factorial invariance” or “measurement equivalence”). The Medline database was chosen as the most appropriate to describe current practices in health research studies and the time frame restricted to one year as the number of published articles allowed the whole of known DIF detection methods to be covered. As we focused on current practices, only articles describing empirical studies in which measurement invariance of composite measurement scale(s) was evaluated were included . Articles were excluded if DIF was only evaluated across collection times .Articles written in English and published between November 11, 2013 and November 10, 2014 were selected from Medline using the following query to search title, abstract and keyword fields:  to which the scale was related were recorded for each article. Characteristics of the scales studied  and practices for DIF assessment in articles using IRT for DIF detection were identified: the stated study aim, characteristic(s) determining the groups considered for DIF and sample size. Practices for DIF assessment included: assessment of non-uniform DIF, use of criteria based on DIF effect-size to classify statistically significant DIF as meaningful or not, empirical evaluation of the impact of the presence of DIF within the scale, practices and/or recommendations produced by authors on how to handle DIF detected.Two of the investigators (AV and AR) did the data extraction and literature analysis: AV extracted all the data and AR checked studies in which IRT-based methods were used or in which data extraction was considered challenging by AV. All disagreements were resolved by discussion between AV and AR. Descriptive analysis was performed for quantitative data and qualitative content analysis was performed for studies with statistically significant DIF to reveal the methods employed to evaluate the impact of DIF and the practices and/or recommendations for its management.A total of 285 articles met the inclusion criteria . In 156 st-3rd quartiles: 1–4), and the maximum was nine. Sex  and age  were the most studied, followed by culture, country or ethnicity  and education .Within articles using an IRT framework, 16 were excluded from further analysis as DIF was assessed in items from item banks and not in composite measurement scales . The aimst-3rd quartiles: 6–15). The median number of dimensions in multidimensional scales was four (1st-3rd quartiles: 2–5). Items were polytomous in 60 (86%) scales with a median number of response categories of five (1st-3rd quartiles: 4–5). st-3rd quartiles: 166–610) than the other two (median>950) methods. Non-uniform DIF was assessed in fewer than a quarter of the studies using the Rasch framework or the 2PL model whereas it was assessed in most articles using the LORDIF method . Criteria based on DIF effect-size were used in fewer than one third of articles using the Rasch framework  and various thresholds were used to consider DIF as meaningful: group difference in item parameter higher than 0.43 logit (n = 2) or 0.5 logit (n = 12) or 0.64 logit (n = 1) or 1 logit (n = 2). DIF effect-size criteria were used in most articles using LORDIF methods  but in fewer of the articles using 2PL models .Thirty-four (49%) of the 70 scales studied in the 62 articles using Rasch framework models were multidimensional, and within a dimension, the median number of items was nine  articles in the Rasch subgroup, 14 (87%) articles in the 2PL subgroup and eight (73%) articles in the LORDIF subgroup. In 18 (25%) of these 71 articles, the impact of the DIF was evaluated empirically and was considered to be meaningful in only four (22%) articles . ModificTo determine the conditions in which erroneous conclusions may be drawn from group comparisons due to DIF within a scale in health research studies.The methods and results described below are from a published study in which 632 realistic scenarios encountered in health research studies were simulated . These sDatasets were generated using a partial credit model, a model for polytomous data that shares the statistical properties of the Rasch model and defines k item location parameters for items with k+1 response categories . Like biFactor values conforming to the conditions encountered in health research studies were selected : small nFour kinds of DIF were simulated using the DIF effect-size factor :Uniform DIF: the relation between the group and the response to the item is identical at every level of the latent trait, i.e. item characteristics curves for the two groups are parallel . Four multivariate models of analysis of variance (ANOVA), one for each kind of DIF studied, were used to investigate the effects on the measurement bias of each factor manipulated in the simulation model. However, the number of simulations studied by ANOVA was huge, so a benchmark was set to distinguish between negligible and non-negligible statistically significant factor effects. To do this, the same analyses were performed on the 500 datasets generated for each of the 12 scenarios resulting from the combination of the three factors not related to DIF , with all factors related to DIF set to zero; the resulting delta between the estimated and the “true” difference between the two groups was termed random error instead of measurement bias as there was no source of DIF-related bias in the simulation model. The maximum of the mean random error over these 12 scenarios was used as the benchmark.A latent regression partial credit model was applied to each of the 500 datasets generated for each scenario to estimate differences between groups without taking into account the presence of DIF. The The effect of the three factors not related to DIF was studied in the case of uniform DIF and was found to be negligible. To facilitate the simulation study, it was decided to investigate each kind of non-uniform DIF with the number of items in the scale set at 8, the group size set at 200 and the magnitude of the mean latent trait difference between the two groups set at 0.1 logit . Larger random error over the 500 datasets generated for each of the 12 combinations was never higher than 0.013 logit and was not affected by any of the three factors manipulated. In the following analyses, a factor was considered to have a non-negligible effect on measurement bias if its estimated coefficient was statistically significant and higher than 0.015 logit; it was considered to have a major effect if its estimated coefficient was higher than 0.1 logit, i.e. half of a small effect-size as defined by Cohen . VS. VS27]. measurement bias. According to the information from the literature review, results from our simulation study, which allowed analysis of most situations encountered in practice, thus appear to be a suitable basis for recommendations.Nearly 70% of the 285 articles reviewed in which the IRT framework was used involved the Rasch framework. It was the most used in “quality of life” and “medical disciplines” research fields. These recommendations related to DIF assessment in composite measurement scales using Rasch framework models are therefore relevant to most medical epidemiologists. Sample size and characteristics of the scales studied in these articles were close to the values used in the simulation model. The number of items per dimension (median = 9–15) was Recommendation 1: Distinguish between uniform, balanced non-uniform and unbalanced non-uniform DIF.Justification: In the simulation study, there were large effects of DIF effect-size, proportion of DIF items and their interaction on measurement bias in case of uniform or unbalanced non-uniform DIF. However, non-uniform DIF was assessed in less than a quarter of the published articles using the Rasch framework. While in most of software that allow estimation of Rasch model item parameters, DIF tests are accessible or may be programmed, only few of them, the most popular ones, include programs aimed at distinguishing uniform from non-uniform DIF: RUMM (used in 25 articles), WINSTEPS (used in 17 articles),29.Recommendation 2: When statistically significant DIF is found, its effect-size should be estimated as the group difference in the item location parameter(s).Justification: DIF effect size estimates were provided in only 18 (37%) of the 49 articles in which statistically significant DIF was detected within the Rasch framework. However, the simulation study revealed that this information is essential to evaluate the consequences of DIF. Indeed, ANOVA coefficients of this factor and of its interaction with the proportion of items in the scale affected by DIF were higher than 0.1 logit for both uniform and unbalanced non-uniform DIF.Recommendation 3: Due to the presence of statistically significant DIF:If the scale has been modified, provide information on how it has been modifiedIf the scale has not been modified, provide recommendations on how to take the presence of DIF with meaningful consequences into account when using the scale in practice, i.e. when:Statistically significant uniform DIF is found in more than 25% of the items within a dimensionStatistically significant uniform DIF is found with an effect-size higher than 0.25 logitStatistically significant unbalanced non-uniform DIF is foundJustification: In 31 (44%) of the 71 articles in which statistically significant DIF was found, there was no modification of the scale, and no recommendation on how to handle the presence of DIF in practice. DIF effect-size thresholds found in the simulation study were different to those encountered in most published studies; this should be underlined as the threshold can have substantial effects on measurement bias in practice ,18.Recommendation 4: If several items exhibit statistically significant DIF within the dimension but in opposite direction, assess its effects at the dimension level empirically.Justification: If the effects from items exhibiting DIF for one group are cancelled by those of other items that exhibit DIF for another group in the dimension, this is called the “DIF cancellation” phenomenon –33. ThisFollowing the review, recommendations were not modified but their justification was improved and their application field more precisely defined. Median of reviewers’ ratings to each AGREE item and scores to the six AGREE domains are reported in the One of the major strengths of these recommendations is that they are based on results from a simulation study closely linked to their field of application, i.e. health research studies in which the Rasch framework is used, as revealed by the literature review. However, in more than half of the articles reviewed, measurement invariance was assessed using methods from the CFA framework. As perspective, similar work to establish recommendations for the CFA framework would therefore be valuable, as well as for 2PL IRT models.We propose only four recommendations to facilitate their application, and recommendation 3 provides practical criteria to identify statistically significant DIF as not meaningful in many situations. Applying these recommendations will thus reduce resources as, in these situations, it will reduce the need for statisticians with expertise with models for taking DIF into account. Finally, using a threshold of 0.1 logit , there was no effect sufficiently large to be relevant in practice found in the simulation study concerning the position of the item affected by DIF along the latent trait continuum. No recommendation was thus formulated on this factor but future updates of these recommendations (within 10 years) will be performed based on scenarios which were not considered in our simulation study. It will also involve literature reviews to assess their application in practice and consequences on how DIF is handled by scale users, as well as any new theoretical and methodological approaches to study DIF. Finally, although some statistical software facilitate study of DIF using the Rasch model , their use to characterize DIF as meaningful or not can be complicated and thus impede the application of these recommendations ,34,35. TThis study has some limitations that highlight the need for further works on detection and characterization of statistically significant DIF. Indeed, these recommendations do not concern best methods for DIF detection and, as revealed in the literature study, these methods are numerous, so researchers should be interested in some guidance in this area. For example, guidance for assessing DIF iteratively to distinguish between real and artificial DIF has been published in the last decade and this is an imperative step before applying the present recommendations ,37. AlsoOur combination of a literature review and a simulation study led to the first consensual recommendations reviewed by international experts for the assessment of DIF within the Rasch framework in short composite measurement scales. There is a growing interest in DIF in health research, and these recommendations will help researchers conducting DIF assessment studies. They will also help scale users because the information provided in DIF assessment studies will be clearer on how to report and handle DIF into account in practice. This will contribute to better accuracy of results in epidemiological studies by improving the measurement properties of composite measurement scales, increasingly used in health research.S1 TableN = 71 articles in which statistically significant DIF was found.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S1 ListReferences with 2015 as year of publication were published online during the search period: November 11, 2013 and November 10, 2014.(DOCX)Click here for additional data file.S1 Box(DOCX)Click here for additional data file.


The past decades have witnessed a paradigm shift from the traditional drug discovery shaped around the idea of “one target, one disease” to polypharmacology . Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is to target common biological pathways involved in diseases via endopharmacology . In this study, we present proximal pathway enrichment analysis (PxEA) for pinpointing drugs that target common disease pathways towards network endopharmacology. PxEA uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs by their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. Using PxEA, we show that many drugs indicated for autoimmune disorders are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimer’s disease, two conditions that have recently gained attention due to the increased comorbidity among patients. Following Paul Ehrlich’s more-than-a-century-old proposition on magic bullets , the drug discovery pipeline traditionally pursues a handful of leads identified in vitro based on their potential to bind to target(s) known to modulate the disease . The sucThe high attrition rates can be attributed to the immense response heterogeneity across patients, likely stemming from a polygenic nature of most complex diseases. Consequently, researchers have turned their attention to polypharmacology, where novel therapies aim to alter multiple targets involved in the pathway cross-talk pertinent to the disease pathology, rather than single proteins ,6. This Over the past years, reusing existing drugs for conditions different from their intended indications has emerged as a cost effective alternative to traditional drug discovery. Various drug repurposing methods aim to mimic the most likely therapeutic and safety outcomes of candidate compounds based on similarities between compounds and diseases characterized by high-throughput omics data ,10,11. MProximal pathway Enrichment Analysis (PxEA), to specifically target intertangled biological pathways involved in the common pathology of complex diseases. We first identify pathways proximal to disease genes across various autoimmune disorders. Then we use PxEA to investigate whether the drugs promiscuously used in these disorders target specifically the pathways associated with one disease or the pathways shared across the diseases. We find several examples of anti-inflammatory drugs where the pathways proximal to the drug targets in the interactome correspond to the pathways shared between two autoimmune disorders. The observed lack of specificity among these drugs points to the existence of immune system related endophenotypes, motivating us to explore shared disease mechanisms for repurposing drugs. We demonstrate that PxEA is a powerful computational strategy for targeting multiple pathologies involving common biological pathways, such as type 2 diabetes (T2D) and Alzheimer’s disease (AD). Based on these findings, we argue that PxEA paves the way for simultaneously targeting endophenotypes that manifest across various diseases, a concept which we refer to as endopharmacology.Here, we propose a novel drug repurposing approach, conventional approach) often misses key biological processes involved in the disease due to the limited overlap between the disease and pathway genes. To show that this is the case, we focus on nine autoimmune disorders for which we obtain genes associated with the disease in the literature and we calculate p-values based on the overlap between these genes and the pathway genes for each of the 674 pathways in the Reactome database . Using known pathway genes, one can identify pathways associated with the disease via a statistical test . We start with the observation that such an approach  using the genes and symptoms shared between these diseases as well as the comorbidity information extracted from medical insurance claim records (see Methods). The autoimmune diseasome a is extrWe compare the autoimmune diseasome generated using shared genes, common symptoms and comorbidity, to the disease network in which the disease-disease connections are identified using the pathways they share. We identify the pathways enriched in the diseases using both the conventional and proximity approaches mentioned above and check whether the number of common pathways between two diseases is significant  and lymphocytes  are overrepresented. While conventional enrichment finds that most of these pathways are shared among only 4–5 diseases, proximity based enrichment points to the commonality of these pathways among almost all the diseases. Furthermore, the proximity based enrichment uncovers the involvement of additional interleukin  and lymphocyte (BCR) molecules ubiquitously in autoimmune disorders. These findings suggest that proximity-based pathway enrichment identifies biological processes relevant to the diseases, highlighting the common etiology across autoimmune disorders.Having observed that pathway proximity to diseases in the interactome captures the underlying biological mechanisms across diseases, we seek to investigate the potential implications of the connections between diseases for drug discovery. We hypothesize that a drug indicated for several autoimmune disorders would exert its effect by targeting the shared biological pathways across these diseases. To test this, we use 25 drugs that are indicated for two or more of the autoimmune disorders in Hetionet  and spliProximal pathway Enrichment Analysis that scores the likelihood of a set of pathways  to be represented among another set of pathways  based on the proximity of the pathway genes in the interactome. As opposed to the Gene Set Enrichment Analysis (GSEA) [The results indicating that the drugs used for multiple autoimmune disorders potentially target common pathways raise the following question: “Can pathway level commonalities between diseases be leveraged to quantify the impact of a given drug on these diseases?” To this end, we propose PxEA, a novel method for s (GSEA)  which usWe employ PxEA to score 25 drugs indicated for at least two of the seven autoimmune disorders (there were no common drugs for celiac and Graves’ diseases). For each disease, we first run PxEA using the pathways proximal to the disease and the proximity of the drugs used for that disease to these pathways. We then run PxEA for each disease pair, using the pathways proximal to both of the diseases in the pair and the drugs commonly used for the two diseases. We notice that several drugs indicated for multiple conditions score higher using common pathways between two diseases than using the pathways of the disease they are indicated for . This isSimilarly, prednisone, a synthetic anti-inflammatory glucocorticoid agent that is indicated for six of the autoimmune disorders, is assigned a higher PxEA score using the pathways shared by Crohn’s disease and systemic lupus erythematosus compared to using the pathways involved only in Crohn’s disease, systemic lupus erythematosus, multiple sclerosis, psoriasis, rheumatoid arthritis, or ulcerative colitis. Thus, prednisone does not specifically target any of the six autoimmune disorders but rather acts on the endophenotypes that manifest across these diseases. We observe a similar trend in meloxicam, an anti-inflammatory drug that shows analgesic and antipyretic effects by inhibiting prostaglandin synthesis. Consistent with its known mechanism of action, meloxicam is proximal to “cholesterol biosynthesis”  for the top candidates are all below It is noteworthy that the top scoring drugs belong to a diverse set of Anatomical Therapeutic Chemical (ATC) classes, covering alimentary tract and metabolism drugs , blood substitutes (B05), dermatologicals (D11) as well as cardiovascular , genito-urinary (G02), nervous (N07), and respiratory (R03) system drugs. The diversity of the ATC classes of top scoring drugs indicates that PxEA is not biased towards any particular ATC class. We also calculate the significance of the PxEA scores by permuting the ranking of the pathways. We find that the adjusted The past decades have witnessed a substantial increase in human life expectancy owing to major breakthroughs in translational medicine. Yet, the increase on average age and changes in life style, have given rise to a spectra of problems challenging human health like cancer, neurodegenerative disorders and diabetes. These diseases do not only limit the life expectancy but also induce a high burden on public healthcare costs. In the US alone, more than 20 and 5 million people have been affected by T2D and AD, respectively, ranking these diseases among the most prevalent health problems .Mainly characterized by hyperglycemia due to resistance to insulin, the disease mechanism of T2D involves a combination of multiple genetic and dietary factors. On the other hand, AD is relatively less understood and several hypotheses have been proposed for its cause: reduced synthesis of neurotransmitter acetylcholine, accumulation of amyloid beta plaques and/or tau protein abnormalities, giving rise to neurofibrillary tangles. Accordingly, most available treatments in AD are palliative (treating symptoms rather than the cause). Given the comorbidity between T2D and AD ,30 severIn this study, we first show that diseases that share drugs also tend to share biological pathways and hypothesize that these pathways can be targeted to exploit novel drug repurposing opportunities. We introduce PxEA, a method based on (i) pathways that are proximal to diseases and (ii) the ranking of the pathways targeted by a drug using the topology information encoded in the human interactome. We show that PxEA picks up whether drugs target specifically the pathways associated with a disease or common pathways shared across various conditions. We observe that many anti-inflammatory drugs are not specific to the condition they are used for and likely to target pathways involved in the autoimmune endophenotypes.To further explore shared disease mechanisms for repurposing drugs, we use PxEA and rank drugs for their therapeutic potential in targeting the common disease pathology between T2D and AD. We identify orlistat, a semisynthetic derivative of lipstatin that inhibits lipase—a pancreatic enzyme that breaks down fat—as the top repurposing candidate. Orlistat inhibits hydrolysis of triglycerides, which in turn, reduces the absorption of monoaclglycerides and free fatty acids . Recent PxEA can suggest rather counter-intuitive repositioning opportunities such as the use of clenbuterol, an asthmatic drug, in the treatment of metabolic and neurodegenerative diseases such as T2D and AD. In fact, the potential use of clenbuterol in these diseases is not too far fetched: it enhances cognitive performance in aging rats and monkeys , improveTo define a global map of interactions between human proteins, we obtained the physical protein interaction data from a previous study that integrated various publicly available resources . We downS to nodes T in a network u and v in G. We then calculated a z-score based on the distribution of the average shortest path lengths across random gene sets pathway span of the disease throughout text.Network-based proximity is a graph theoretic approach that incorporates the interactions of a set of genes  with other proteins in the human interactome and contextual information as to where the genes involved in pathways reside with respect to the original set of genes . To quanNote that, instead of average shortest path distances, one can also use random-walk based distances to calculate proximity between gene sets . Howeverk of known disease genes in T2D and AD . Hence, for each value of k, we created 10 groups of genes, containing a random sampling of k% of the T2D-associated genes. We repeated the procedure using the AD-associated genes, yielding 100 gold standard pathways  that were subsets of the known disease genes. For each gold standard pathway, we then generated so called control pathway, that is, randomly selected group of genes in the interactome that match the size of the gold standard pathway under consideration. Next, we assessed the shortest path distance based proximity between the gold standard pathways and the disease genes (proximity of the gold standard T2D pathways to the T2D disease genes and of the gold standard AD pathways to the AD disease genes) and compared it to the proximity of the control pathways to the same disease genes. We also calculated the proximity using random walk scores as proposed in a previous study [k in the gold pathways).To investigate the effect of noise in the pathway data, following the procedure proposed in , we creaus study  with theproaches . On the We compiled genes associated with nine autoimmune disorders listed in We retrieved drug target information from DrugBank for 1489 drugs in the version 5.0.6 of the database , 1466 ofThe ENTREZ gene ids of the proteins involved in biological pathways were taken from the version 5.0 of MSigDB curated gene sets . In our To identify relationships across disease pairs (autoimmune diseasome), we used the similarities between diseases in terms of the genes and symptoms they share. We assessed the significance of the overlap between genes (or symptoms) associated with two diseases using Fisher’s exact test. An alpha value of 0.05 was set to deem the connections significant  of the overlap between the pathway and disease genes assessed by a one-tailed Fisher’s exact test and (ii) of the proximity between the pathway and disease genes in the interactome. We considered the pathways that had Proximal pathway Enrichment Analysis (PxEA), a novel method that scores drugs based on the proximity of drug targets to pathway genes in the interactome. PxEA uses a GSEA-like running sum score [D, the pathways ranked with respect to their proximity to drug targets, D, and C, the set of pathways of interest, the running score is defined as follows [Toward the goal of pathway level characterization of the common pathology of diseases and to evaluate the therapeutic potential of drugs based on their impact on the common pathways, we developed um score , where t follows :ES=p-values for the case study, we repeat the procedure above 10,000 times, shuffling randomly D to calculate the expected enrichment score p-value for the enrichment usingTo calculate p-values were corrected for multiple hypothesis testing using Benjamini-Hochberg procedure [The rocedure .github.com/emreg00/toolbox. The proximity was calculated using networkx package that implements Dijkstra’s shortest path algorithm. The statistical tests were conducted in R (www.R-project.org) and Python (www.python.org). The network visualizations were generated using Cytoscape [We used the toolbox Python package for running PxEA, available at ytoscape  and the ytoscape  or ggploytoscape .


Although there have been several analyses for identifying cancer-associated pathways, based on gene expression data, most of these are based on single pathway analyses, and thus do not consider correlations between pathways. In this paper, we propose a hierarchical structural component model for pathway analysis of gene expression data (HisCoM-PAGE), which accounts for the hierarchical structure of genes and pathways, as well as the correlations among pathways. Specifically, HisCoM-PAGE focuses on the survival phenotype and identifies its associated pathways. Moreover, its application to real biological data analysis of pancreatic cancer data demonstrated that HisCoM-PAGE could successfully identify pathways associated with pancreatic cancer prognosis. Simulation studies comparing the performance of HisCoM-PAGE with other competing methods such as Gene Set Enrichment Analysis (GSEA), Global Test, and Wald-type Test showed HisCoM-PAGE to have the highest power to detect causal pathways in most simulation scenarios. Over the past several decades, gene expression data analysis has been the most common approach to investigate human diseases at the RNA level ,2. By anAnalyzing high-throughput gene expression data, at the pathway level, is very effective in two ways. Firstly, grouping thousands of genes by their respective pathways reduces complexity to just several hundred pathways. Secondly, identifying active pathways that differ between two conditions, such as normal and tumor tissues, can have more explanatory power than a simple list of differentially expressed genes (DEGs) ,6. WhileVarious cancer prognosis and survival analysis have been reported ,10. For To build a good prediction model, using gene expression data, for actual clinical practice and medical intervention, it is first necessary to identify genes (features) related to prognosis. Exploring the pathways to which genes belong can provide valuable biological interpretation and help screen out false-positive genes. In this study, we mainly focus on finding significant pathways that are relevant to the prognosis of pancreatic cancer. Through pathway analysis, our ultimate goal is to identify biological mechanisms that influence the prognosis of disease more clearly.Since gene set enrichment analysis (GSEA) was proposed, which uses the Kolmogorov-Smirnov statistic for measuring differentially expressed gene sets, many other pathway-based methods have been developed . RecentlTo account for this issue, we previously developed our Pathway-based approach using HierArchical structure of collapsed Rare variant Of High-throughput sequencing data (PHARAOH) method for discovering rare variants by constructing a hierarchical model that consists of collapsed gene-level summaries and entire pathways . PHARAOHIn this study, we developed a new pathway-based model for survival phenotypes, based on gene expression data, by taking advantage of our earlier hierarchical model, referred to as HisCoM-PAGE which represents Hierarchical structural Component Models for Pathway Analysis for Gene Expression data. As an extension of HisCoM-mimi, HisCoM-PAGE considers the biological context of gene and pathway hierarchies, in the form of structured components. HisCoM-PAGE collapses genes into the pathway in a structured form using latent variables. Unlike other methods analyzing one pathway at a time, HisCoM-PAGE analyze all pathways simultaneously by one model, which enables considering the correlation of all pathways by using a ridge penalty in parameter estimation. HisCoM-PAGE can also successfully examine the effects of individual genes within the pathways. http://statgen.snu.ac.kr/software/HisCom-PAGE/).Through simulation studies, we showed that HisCoM-PAGE performed well, compared to other existing pathway methods for survival phenotype. Application to real microarray data of pancreatic ductal adenocarcinoma (PDAC) patients from Seoul National University Hospital (SNUH) showed that HisCoM-PAGE could well identify prognosis-related pathways. HisCoM-PAGE is available at  HuGene 1.0 ST arrays. A total of 32,321 genes were normalized by the Robust Multi-array Average (RMA) method . Of the https://portal.gdc.cancer.gov)  and ijx = 0.02; for Scenario 3, A = 0.2[ijx], and ijx = 0.1i−j||. Scenario 4 has random variances and covariance, such that A is given as follows: A = 0.2[ijx], ijx = ijρ, when i is not equal to j, and 1, when i is equal to j, and ρij is generated from N. For all scenarios, three different significant gene proportion was considered. The survival time was generated from a Cox model with a constant baseline hazard rate of 0.005 whereas the censoring time was generated from an exponential distribution with a parameter of U. For type 1 error estimation, the regression coefficient Four different scenarios of covariance matrix Σ were considered. For each scenario, fraction . The suriy denote a survival time . Let ijkx denote the kth gene  expression corresponding to the jth pathway  for the ith patient. As shown in jkw denote the weight assigned to ijkx. The coefficient jβ represents the effect of the latent variable ijf on the phenotype. Wj = ’, B = ’. Considering this structure, we designed the following Cox proportional hazard model.Let To estimate the model parameters for HisCoM-PAGE, we maximized the penalized partial log likelihood using a ridge penalty. The following Equation (1) then represents the objective function, which is a partial log likelihood from a Cox model.1 < y2 <…<ydy denote the distinct and ordered d(≤I) survival times and R(iy) is the risk set at time iy The objective function can be maximized by an alternating least squares (ALS) algorithm, which iterates the following two steps until convergence. In the first step, the pathway coefficients are estimated and updated, using a least-squares approach. For the second step, the weight coefficients are updated for fixed-path coefficient estimates [geneλ and pathwayλ values, we conducted 5-fold cross-validation to obtain optimal values for geneλ and pathwayλ The process of estimating the coefficients, using the ALS algorithm, with penalty, proceeds as follows:Here stimates . In HisCInitialize Compute η, u, A, and Compute η, u, A, and z, based on the latest value of Iterate steps 2 and 3 until −4. Since it is not straightforward to obtain asymptotic variance estimates of parameters from ridge estimation, we used a permutation test to obtain a statistical significance level for HisCoM-PAGE.As a convergence criterion, we used the difference between K and the gene set S is predefined. For the first step, compute the regression coefficients of K genes, by fitting a univariate Cox models. The regression coefficient is used as an association measure between phenotype and genes. Secondly, order K genes by the absolute value of t statistics in descending order. Thirdly, calculate the enrichment score. While computing the enrichment score, GSEA method consider two methods of weighting, including GSEA1, the case when the weight term is 0, and GSEA2, the modified version of the original GSEA method when the weight term is 1 [m test statistics calculated from each m individual gene, constituting a pathway by itself. Although the p-values can be calculated using the permutation and asymptotic method, we used the permutation approach. Thirdly, the Wald type test is based on the unified pathway method proposed which combined component-wise test statistics for significance of a subset of genes [The following pathway methods were considered to compare the results of HisCoM-PAGE. We compared other pathway methods such as Gene Set Enrichment Analysis (GSEA) , Global erm is 1 ,29. Lastof genes . The WalThe Affymetrix gene identifiers were mapped to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Biocarta databases ,33. For q-value as a criterion [q-value smaller than 0.05. The upper part of For multiple test adjustment, we used the False Discovery Rate (FDR) analysis to calculate the FDR adjusted riterion . From thq-values smaller than 0.05. The lower part of From the KEGG database, HisCoM-PAGE identified 23 pathways with FDR-adjusted The pathways reported to be related to PDAC or pancreatic cancer are as follows. Hedgehog-signaling dysregulation, due to mutation or overexpression of pathway components and pathway ligands, induces pancreatic cancer . The WntWe performed RNA-seq data analysis for a replication purpose of SNUH microarray analysis results. Thus, only the genes used for SNUH microarray data analysis were selected for RNA-seq analysis. As a result, for the KEGG database, 3,258 genes were mapped to 185 pathways. For the Biocarta database, 1,162 genes were mapped to 216 pathways. q-value smaller than 0.05. From the Biocarta database, TGF-β pathway’s nominal p-value was 0.053. For the KEGG database, the Amyotrophic lateral sclerosis pathway and MTOR signaling pathway were significantly identified with a nominal p-value Less than 0.05. q-value close to 0.05 by microarray data analysis . The neurotrophic signaling pathway is known to promote pancreatic cancer invasion [In addition, among the pathways significant at the nominal level by RNA-seq analysis, the Neurotrophic signaling pathway showed the FDR-adjusted invasion .gene × βpathw value for each gene. As a result, it was possible to simultaneously consider the effect of the matched gene to the pathway, and the effect size of the pathway to the phenotype. After calculating each coefficient, significance testing was performed, using a permutation method. If the marker was selected based only on a nominal p-value, obtained by adapting the entire gene, a type 2 error and the false negative error can be large. Therefore, we used FDR analysis to calculate the FDR-adjusted q-value as a criterion.With pathways associated with prognosis, we could also find genes meaningfully related to PDAC prognosis, as well as considering hierarchies of genes and pathways. q-values < 0.05. SMAD3, BCL2, and TGF-β1 are well known to be related to PDAC. Upregulated SMAD3 promotes epithelial mesenchymal transition and predicts poor PDAC [BCL2 downregulated expression is a poor prognostic factor for PDAC [TGF-β1 levels are significantly related to PDAC patients’ prognosis [TGF-β1 showed higher overall survival times. Some other PDAC-related genes are as follows. ETS1 is known to have resistance to pancreatic cancer chemotherapy. Furthermore, ETS1 can exacerbate poor PDAC prognosis after radiation therapy [HIF1A was reported as a significant indicator of PDAC prognosis [GNAI1 is known to be a suppressor of tumor cell migration and invasion that is post-transcriptionally targeted by mir-320a/c/d [Mir-320a is found to confer 5-FU chemo-resistance upon human pancreatic cancer cells [oor PDAC ,61. Alsofor PDAC . TGF-β1 rognosis . That is therapy . HIF1A wrognosis ,66,67. G320a/c/d . Mir-320er cells .GNAI1 was also significant with a p-value less than BCL2L1 was significant with a p-value less than p-value of SMAD3 was close to 0.1. However, TGF-β1 and ETS1 were not replicated. For the KEGG database, the p-value of SMAD3 was less than 0.1.I) = 80, gene set size (𝑚𝑠)= 50, and censoring proportion (pc) = 0, 0.1, 0.2, 0.3, 0.4, 0.5. The number of permutations for significance testing was 1000. We checked the 95% confidence interval level of the estimated type 1 error. Overall, type 1 errors were shown to be well controlled in various scenario settings; especially in the HisCoM-PAGE method, the type 1 error is well controlled, even when the censoring fraction is high.K) = 200 sample size (I) = 80, pathway size (sm) = 50, significant gene proportion (pm) = 0.1, 0.3, 0.5 and censoring proportion (pc) = 0, 0.3. HisCoM-PAGE showed better performance than the other methods, when the significant gene proportions were not high, and the power was close to 1, when the significant gene proportion increases. For power analysis, we varied the censoring proportion and the proportion of significant genes in the causal pathway. We set the parameters as follows: total genes s was low, with Pearson correlation coefficient of 0.25 and Spearman correlation coefficient of 0.23. This low correlation was caused by the different survival curves between microarray data and RNA-seq data. The two Kaplan-Meier curves from both microarray data and RNA-seq data were quite different and the log-rank test for the equivalence for these two survival curves has a p-value of 0.046 [There have been several studies for comparing microarray and RNA-seq platforms ,74. For of 0.046 . Since HNext, we are planning to improve the performance of HisCoM-PAGE by taking the characteristics of RNA-seq data into account. Then, we need to conduct a simulation study to investigate its properties and compare it with other methods for RNA-seq data. We believe that HisCoM-PAGE can be easily applicable to other types of omics data such as integration analysis of multi-omics data. Furthermore, we can also use the HisCoM-PAGE approach to construct predictive models for prognosis. We could study a design of a prognostic prediction model using the latent variable pathway as a marker, as well as the genetic marker . In thisIn summary, we proposed a new pathway analysis method, Hierarchical Structural Component Models of Pathway Analysis for Gene Expression (HisCoM-PAGE), to identify disease prognosis-related pathways. Using simulated data, PDAC microarray and RNA-seq data, we can confirm that HisCoM-PAGE can identify pathways and genes that have an association with a survival phenotype. Moreover, HisCoM-PAGE can also find interpretable and meaningful pathways and prognostic genetic markers.


Lithium has many widely varying biochemical and phenomenological effects, suggesting that a systems biology approach is required to understand its action. Multiple lines of evidence point to lithium intake and consequent blood levels as important determinants of incidence of neurodegenerative disease, showing that understanding lithium action is of high importance. In this paper we undertake first steps toward a systems approach by analyzing mutual enrichment between the interactomes of lithium-sensitive enzymes and the pathways associated with affective and neurodegenerative disorders. This work integrates information from two important databases, STRING and KEGG pathways. We find that for the majority of neurodegenerative disorders the mutual enrichment is many times greater than chance, reinforcing previous lines of evidence that lithium is an important influence on incidence of neurodegeneration. Our work suggests rational prioritization for which disorders are likely to be most sensitive to lithium and identifies genes that are likely to be useful targets for therapy adjunct to lithium. Lithium is typically a first line therapy for bipolar disorder, including associated depression as well as mania Post, . A comprin vivo , initially presents with behavioral symptoms resembling mania  lithium, (2) affective disorders, and (3) neurodegenerative disorders by analyzing the mutual enrichment between STRING-derived interactomes of lithium-sensitive enzymes, and the KEGG pathways associated with affective and neurodegenerative disorders.https://string-db.org). The search within STRING is adjustable with respect to two parameters; (1) The confidence level associated with each interaction and (2) What level of interaction each of the returns has with the lithium sensitive gene—first order direct interaction or second order interaction through one intermediary. For each key gene, we adjust confidence level and order of neighbors (nearest only or next nearest included), so that each set contains a few 100 genes. This size is large enough for statistically reliable enrichment analysis. Table Analysis was performed on the interactomes of lithium-sensitive genes, as identified by prior literature search . A very low p-value implies that the degree of overlap is highly significant statistically and very likely is significant biologically. In our study the gene lists we are comparing are the interactomes of lithium sensitive enzymes on the one hand, and KEGG pathways or otherwise derived lists associated with neural disease on the other hand. For each KEGG term retrieved, a null distribution of uncorrected p-value is generated by the 1,000 null sets. This gives us a measure of the false discovery rate, since any overlap between the null sets and the KEGG pathways is purely accidentally. Then the fraction of null set uncorrected p-values smaller than or equal to the lithium-sensitive set uncorrected p-value would be the empirical p-value. For a detailed discussion of empirical p-value determination see Ge et al.  are corrected for false discovery rate by random resampling. Thus, no further false discovery rate correction is necessary for computing pmean. Note also that our method is bounded at the low end of p-values by the number of null samples it is reasonable to compute, given compute time constraints. For 1,000 null sets as used in this paper, the computed p-value will be zero when none of the thousand null sets shows the degree of enrichment of the test sets. For purposes of computing the pmean in equation (1) we substitute 10−4 for zero for each of these cases.The method of averaging in Equation (1) ensures that both strong and weak enrichments contribute significant weight to the mean. Note that all of the mean.Table As a control on our methods, we compared the statistical distribution of scores for neural disease with corresponding scores for metabolic pathways , and with random gene sets (null sets). This comparison is shown in box plots in Figure In addition to pathways we examined our results to identify specific genes within the lithium sensitive interactomes that may be important in modulating lithium effect on disease. Table The genes in Table For example, MAPK3 is a shared gene by Alzheimer's Disease (AD), Prion, Major Depressive Disorders (MDD), and Frontotemporal lobar degeneration (FTLD), indicating that these diseases might have some shared mechanism. MAPK3 appeared in 4 interactome-pathway cross-sections in AD, Prion and MDD, and on average associated with 1.33 interactome-pathway cross-section in FTLD. MAPK3 is an essential component of the MAP signal transduction pathway that carries signals from cell surface to the nucleus. In analysis of normal as compared to AD brain tissue, MAPK3 is one of a small number of genes found to have alternative promoter usage and splicing  did not incur any renal damage  is an elderly person (79) and has found the evidence cited above sufficiently compelling that he self-administers lithium calibrated to a blood level of 0.3–0.4 mM, in order to reduce the pace of age-related neurodegeneration and also as a possible protection against cancer. His outcome, however important it may be to him personally, has no statistical significance. We need a clinical study involving many subjects addressing the same question.In general, it seems clear that whatever other studies are undertaken with respect to affective and neurodegenerative disorders, lithium blood levels should be monitored for all patients, since even geographical variations may have significant effects. The cost of adding lithium level to the routine blood tests is minimal, especially compared to the potential benefits. Beyond that, multiple studies should be undertaken in which low- to moderate-level lithium supplements are administered, since these are likely to be safe .Perhaps our results, especially as scored in Table p-value for FTLD pathway mutual enrichment with lithium-sensitive interactomes is.0132, which is highly significant. While not as common as Alzheimer's, FTLD is not rare. Estimated lifetime risk is 1/742; many millions of people each year die of FTLD (Coyle-Gilchrist et al., Many conditions that score highly in Table We will be happy to collaborate on further specific pathway or network analysis relevant to any of the neural diseases for which lithium may be a promising component of therapy.The work was planned jointly in conversations between EJ and WG. WG did the computations and prepared the figures and tables. WG wrote the first draft of the Methods and Results sections. EJ wrote the first draft of the Introduction and Conclusions sections. Both authors shared in the final refinement of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer VDJRDP and handling editor declared their shared affiliation at the time of the review.


The potential biological relationship between type‐2 diabetes mellitus (T2DM) has been focused in numerous studies. To investigate the molecular associations among T2DM, prostate cancer (PCa), and chronic myeloid leukemia (CML), using a biomolecular network enrichment analysis. We obtained a list of disease‐related genes and constructed disease networks. Then, GO enrichment analysis was performed to identify the significant functions and pathways of overlapping modules in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. More than 75% of these overlapping genes were found to be consistent with the findings of previous studies. In the three diseases, we found that Sarcoglycan delta (SGCD) and Rho family GTPase 3 (RND3) were the overlapping genes and identified negative regulation of apoptotic process and negative regulation of transcription from RNA polymerase II promoter RNA as the two overlapping biological functions. CML and PCa were the most closely related, with 34 overlapping genes, five overlapping modules, 27 overlapping biological functions, and nine overlapping pathways. There were 13 overlapping genes, one overlapping modules, four overlapping biological functions and one overlapping pathway  were found in T2DM and CML.And T2DM and PCa were the least related pair in our study, with only six overlapping genes, five overlapping modules, and one overlapping biological function. SGCD and RND3 were the main gene‐to‐gene relationship among T2DM, CML, and PCa; apoptosis, development, and transcription from RNA polymerase II promote processes were the main functional connections among T2DM, CML, and PCa by network enrichment analysis. There is a “scalene” relationship among T2DM, CML, and PCa at gene, pathway, biological process, and module levels: CML and PCa were the most closely related, the second were T2DM and PCa, and T2DM and PCa were the least related pair in our study. Our study provides a new avenue for further studies on T2DM and cancers, which may promote the discovery and development of novel therapeutic and can be used to treat multiple diseases. According to the International Diabetes Federation (IDF), more than 640 million people worldwide will suffer from diabetes by 2040.Finding gene‐disease and disease‐disease associations play important roles in the biomedical area, and many prioritization methods have been proposed for this goal.In our study, firstly, we obtained a list of disease‐related genes form the Disease‐Connect website and we constructed disease networks by Agilent Literature Search software. Then, the disease networks were divided into modules by MCODE. GO enrichment analysis was performed to identify the significant functions and pathways of overlapping modules in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. Based on molecular network construction and enrichment analysis, our study aimed to systemically explore the molecular associations among T2DM, CML, and PCa, which provided a novel and feasible idea for the study the association in T2DM and different cancers.22.1https://disease-connect.org/), a freely available web database for mechanism‐based disease‐disease connections. We analyzed the functions of the disease‐related genes by using the Database for Annotation, Visualization and Integrated Discovery (DAVID) (https://david.abcc.ncifcrf.gov/), which provides a comprehensive set of functional annotation tools for understanding the biological significances of many genes.P < 0.05 was used as the cutoff criterion for GO and KEGG pathway enrichment analyses, and P‐values were ranked.The terms “type 2 diabetes mellitus (T2DM),” “chronic myeloid leukemia (CML),” and “malignant prostate cancer (PCa)” were entered into the “Disease View” search box of the Disease‐Connect website (2.2https://www.cytoscape.org/), and an overview network of gene/protein associations was obtained. Cytoscape is an open source tool for visualizing complex networks and integrating biomolecular interaction networks, especially for large data sets of gene‐gene, gene‐protein, and protein‐protein interactions.Then, the disease‐related genes were submitted to Agilent Literature Search software, version 3.20 (2.3https://www.cytoscape.org) to visualize the three disease‐related networks and analyze the properties of these networks. Network parameters  were calculated and illustrated.We used the Cytoscape software version 3.20 (2.4https://baderlab.org/Software/MCODE) were performed for network module division.K = 2, Node Score Threshold = 0.2, Max. Depth = 100) were used as the criteria for network module screening.After constructing the networks for the three diseases, centrality analysis and a complex molecular algorithm MCODE 1.32 , we obtained 233 T2DM‐related, 733 CML‐related, and 511 PCa‐related genes Table . SGCD an0, 2016, 3.2All of the 233 T2DM‐related genes, 733 CML‐related genes, and 511 PCa‐related genes were submitted to DAVID for GO functional enrichment analysis. And we obtained the biological functions and pathways of these disease‐related genes. A total of 43 biological functions and 12 KEGG pathways were identified for the T2DM‐related genes, 174 biological functions and 34 KEGG pathways for the CML‐related genes, and 97 biological functions and 18 KEGG pathways for the PCa‐related genes, respectively Figure  and S4.In T2DM, insulin process (13.95%), transport process (9.3%), glucose process (9.3%), development process (9.3%), cell differentiation process (6.98%), and metabolic process (6.98%) were the main functional biological processes  were found among T2DM, CML, and PCa Table . Four ovNo overlapping pathways were identified for the three diseases. FoxO signaling pathway was the overlapping pathway between T2DM and CML; nine overlapping pathways were found between CML and PCa, mainly related to cancer and cell adhesion Table . And the3.3The disease‐related genes were submitted to the Agilent Literature Search 3.2, and we obtained the T2DM‐related  of the three diseases, we identified the modules for RND3 and SGCD by MCODE software, respectively. Functional analysis of the RND3 and SGCD modules by GO annotation was shown in Figure 3.5.2Proteolysis, transcription from RNA polymerase II promoter, cullin deneddylation, negative regulation of cell proliferation, nucleotide‐excision repair, DNA damage recognition, and neurogenesis were obtained by the GO functional enrichment analysis for the five overlapping modules between T2DM and CML Table .3.5.3Regulation of ion transmembrane transport, white fat cell differentiation, viral genome replication, and membrane depolarization during action potential were the statistically significant functions of the five overlapping modules between CML and PCa Table .4In our study, several new common biological backgrounds and overlapping genes of T2DM, CML, and PCa were discovered by network and enrichment analysis. In order to reveal and verify the genetic connections among T2DM, CML, and PCa, literature‐based text mining was performed. We discovered that T2DM and cancers have a certain molecular association at gene, pathway, biological process, and module levels according to network and enrichment analysis. More than 75% of the overlapping genes were found to be consistent with the findings of previous studies Figure B. HematoRND3 and SGCD were the overlapping genes of the three diseases, which has also been reported in previous studies.Tyrosine kinase inhibitors (TKIs), such as imatinib and dasatinib, have been proven to be effective in the treatment of CML and PCa, and in most situations can improve the pre‐existing diabetes or lower the average blood glucose levels to a certain degree.According to our findings, CML and PCa were the most closely related, with 34 overlapping genes, five overlapping modules, 27 overlapping biological functions and nine overlapping pathways. For example, development, cell proliferation, and cell adhesion processes were the main biological processes both in CML and PCa. Cancer, cell adhesion, and PI3K‐Akt signaling pathways were the common pathways in CML and PCa. Prior studies have revealed that NF‐κB, FAK, JAK1/2, ETK, JNK, MAPK, and PI3K‐Akt signaling pathways were involved in processes: cell growth, migration, invasion, angiogenesis, and apoptosis in CML and PCa Figure B.26, 30,As for the relationship between T2DM and CML, 13 overlapping genes, one overlapping modules, four overlapping biological functions and one overlapping pathway  were found. It has been shown that response to glucose process was related to CML.And T2DM and PCa were the least related pair in our study, with only six overlapping genes, five overlapping modules, and one overlapping biological function (endocrine pancreas development). Endocrine pancreas development has been associated with the risk of prostate cancer as well as insulin release from β cells in patients with T2DM.In summary, SGCD and RND3 were the main gene‐to‐gene relationship among T2DM, CML, and PCa; apoptosis, development, and transcription from RNA polymerase II promote processes were the main functional connections among T2DM, CML, and PCa by network enrichment analysis. There is a “scalene” relationship among T2DM, CML, and PCa at gene, pathway, biological process, and module levels: CML and PCa were the most closely related, the second were T2DM and PCa, and T2DM and PCa were the least related pair in our study Figure A. Our sthttps://disease-connect.org/) was on 31 December 2015, which may lead to incomplete data on disease‐related genes, and this needs to be addressed in our further studies.We also acknowledge some limitations of the present study. The last update of the database (Disease‐Connect website, The authors declare that they have no competing financial interests. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Prioritization of genomic data has become a useful tool for uncovering the phenotypic effect of genetic variations (e.g. copy number variations or CNV) and disease mechanisms. Due to the complexity, brain disorders represent a major focus of genomic research aimed at revealing pathologic significance of genomic changes leading to brain dysfunction. Here, we propose a “CNV data laundering” algorithm based on filtering and prioritizing of genomic pathways retrieved from available databases for uncovering altered molecular pathways in brain disorders. The algorithm comprises seven consecutive steps of processing individual CNV data sets. First, the data are compared to in-house and web databases to discriminate recurrent non-pathogenic variants. Second, the CNV pool is confined to the genes predominantly expressed in the brain. Third, intergenic interactions are used for filtering causative CNV. Fourth, a network of interconnected elements specific for an individual genome variation set is created. Fifth, ontologic data (pathways/functions) are attributed to clusters of network elements. Sixth, the pathways are prioritized according to the significance of elements affected by CNV. Seventh, prioritized pathways are clustered according to the ontologies.The algorithm was applied to 191 CNV data sets obtained from children with brain disorders  by SNP array molecular karyotyping. “CNV data laundering” has identified 13 pathway clusters (39 processes/475 genes) implicated in the phenotypic manifestations.Elucidating altered molecular pathways in brain disorders, the algorithm may be used for uncovering disease mechanisms and genotype-phenotype correlations. These opportunities are strongly required for developing therapeutic strategies in devastating neuropsychiatric diseases. Brain disorders frequently result from genomic variations altering a variety of molecular and cellular pathways . Due to We propose a CNV prioritization algorithm — “data laundering” — suitable both for diagnostic and basic research. The algorithm is based on an idea that brain diseases result from genomic alterations affecting directly the brain , 14 and,First, a pool of CNVs is obtained by molecular karyotyping. At this stage, CNVs are checked for recurrence by in-house and web databases. In-house databases of genomic variants obtained by similar microarray types are applied to spot recurrent aberrations. It is to note that the indexation of CNV in a Database of Genomic Variants or any other database dedicated to non-pathogenic genome variations is not a criterion for the exclusion at following stages. Further, the localization and ontology of CNV genes  are obtained. At this stage, genes lacking appropriate ontology, CNV encompassing introns, recurrent/non-pathogenic CNV are excluded from further analysis. It is worth mentioning that here, CNV are defined as copy number DNA gains/losses < 500 kbp.Secondly, the genes are in silico analyzed in terms of the expression in the central nervous system. As brain pathology is suggested to be mainly associated with neurobehavioral phenotypes, it is recommended to proceed to the next step with a pool of genes highly expressed in the brain.Third step is referred to as retrieving gene-gene interactions. Considering the differences in databases, it is suggested to use several resources . Here, we have merged data from NCBI gene, BioGRID and STRING.During the fourth step, the gene list is evaluated for uncovering interactions and interaction enriched gene clusters (sets of interacting genes). Further, only large groups of interacting genes are analyzed, leaving aside small clusters of interacting elements. This criterion is based on a hypothesis that highly interacting genes (proteins) are more likely to be involved in the same processes or influence a disease with similar symptoms .Fifth, the pathway lists are obtained for the set of interacting genes. During database selection, one should consider such parameters as the nature and curability of pathway data. Here, Gene Ontology (GO), KEGG, Reactome, NCBI Biosystems were used.PP):IPP — index of pathway prioritization; NCNV genes— number of CNV genes in a pathway found in molecularly karyotyped cohort; Npathway genes — total number of pathway genes. If the IPP is higher than average , the pathway is prioritized.Sixth, to process the pathway lists, we introduce a parameter (prioritization criterion) to determine significantly enriched pathways. To calculate the parameter, a total number of genes for each pathway are obtained. Pathways, in which less than 25 genes are affected by CNVs, are excluded. The remaining pathways are ranked using the index of pathway prioritization  . Thus, tUsing the algorithm and Affymetrix CytoScan HD microarray, we analyzed 191 genomes  of children with ID, ASD and congenital abnormalities without gross chromosomal and genomic rearrangements (i.e only the CNVs less than 500 kbp in size were included). The raw results of the algorithm processing are shown in Fig. PP, which allowed us to obtain enriched pathways for each database: KEGG — 1, REACTOME — 11, Biosystems — 0, GO — 27. Pathway clustering was performed according to pathway ontologies.We obtained a set of 741 genes affected by pathogenic or likely pathogenic CNVs. “Expression filtering” allowed to select 307 genes highly expressed in the brain. Cross-checking interactions of these genes (step 3) was used for building an interactome (step 4) encompassing 3156 genes. These genes were involved in 302, 978, 3380, and 2350 pathways, according to KEGG, REACTOME, Biosystems, and GO, respectively. For each pathway, we calculated IThe application of CNV prioritization or “data laundering” algorithm yielded 39 genomic networks (pathways) forming 13 clusters of processes, involving 475 genes. These pathway clusters were as follows: neurodegenerative diseases, proteasome, signaling by ERBB4, transcription regulation, regulation of TP53, signaling by NOTCH, senescence, mitosis, DNA repair, vesicles functioning, actin functioning, macromolecular interactions, B cells functioning  [SNAP25 was associated with hyperactivity in ASD. In high-functioning autism, increased syntaxin 1A expression was observed. Various studies showed that the reduced expression in the anterior part of the cingulate gyrus was observed in patients with ASD [SHANK3, GIT1, DISC1, SRGAP3, OPHN1, LIMK1, NRG1, CYFIP1, SYNGAP1, KALRN, NCKAP1 and CNKSR2 regulate upward signaling that stimulate the dynamics of the actin cytoskeleton in dendritic spines [According to the value of diseases . Neuroderansport . The p53ransport . Disruptransport . The proodermia) . Additiowith ASD . Notch swith ASD . The dysc spines .What are the pathways disrupted by CNV genes?Do these pathways merge into a single global cluster reflecting a specific cellular/molecular process?Currently, individual genome analysis obtains big data which are to be processed for basic, diagnostic and therapeutic purposes. Molecular karyotyping detects CNVs, which may output candidate gene and pathway lists. To discover genetic basis of an individual’s phenotype or pathways to a disorder, it appears necessary to answer two questions:Application of bioinformatic strategies similar to the data laundering algorithm is able to answer these questions. It is necessary to stress that data analysis requires tools considering multiple factors and a theoretic background. Pathway clustering represents a promising bioinformatic tool, enabling the development of therapeutic strategies based on a molecular mechanism . SimilarThe algorithm has been applied to a cohort of 191 children with ID, ASD and congenital abnormalities, yielding 13 pathway clusters potentially associated with brain disorders: neurodegenerative diseases, proteasome, signaling by ERBB4, transcription regulation, regulation of TP53, signaling by NOTCH, senescence, mitosis, DNA repair, vesicles functioning, actin functioning, macromolecular interactions, B cells functioning. Thus, the data laundering algorithm using CNV data allows obtaining clusters of candidate (disease-associated) processes. This algorithm is important for further basic and diagnostic research. Moreover, the application of the algorithm to molecular diagnostics of genomic pathology makes it possible to expand our knowledge about disease mechanisms in individual cases. Our findings may have importance for the development of therapeutic strategies and relevant psychological intervention for genetically determined ID and ASD cases caused by CNVs –28. Mole