To interpret microarray experiments, several ontological analysis tools have been developed. However, current tools are limited to specific organisms.Arabidopsis thaliana, Oryza sativa, Glycine max and Medicago truncatula. An online analysis tool to identify relevant functions is also provided.We developed a bioinformatics system to assign the probe set sequences of any organism to a hierarchical functional classification modelled on KEGG ontology. The GeneBins database currently supports the functional classification of expression data from four Affymetrix arrays; GeneBins provides resources to interpret gene expression results from microarray experiments. It is available at  Microarrays enable us to study the expression of thousands of genes simultaneously, providing a comprehensive overview of the gene activities in a given tissue. A number of ontological tools are now available that support the functional interpretation of gene expression data, through the identification of significant enriched Gene Ontology terms (GO)  associatArabidopsis thaliana, Oryza sativa (rice) and the model legumes Glycine max (soybean) and Medicago truncatula (barrel medic).We propose a new strategy that assigns genes to hierarchical categories (BINs) modelled on the ontology provided by the KEGG database . KEGG isBased on these assignments, we have developed an online tool to identify the significantly over- or under-represented metabolic pathways in a set of sequences using a method based on the hypergeometric distribution, as developed in the BlastSets system . This caIn addition, the classification system provided can also be used in MapMan -13 to diThe GeneBins database is a web-based tool combining a PostgreSQL database management system with a dynamic web interface based on PHP and Perl. Data pre-processing is implemented in Perl and statistical analyses are performed using Perl and the R statistical package .The database contains three components:i. The functional hierarchy (GeneBins structure) consists of two tables; the first table contains the identifiers (BIN codes) and their descriptions (BIN names) and the second contains the hierarchical structure of the classification.ii. The reference databases with identifiers, description and protein sequences from KEGG Orthologous, COG, Swiss-Prot and the reference set of sequences provided by Gene Ontology.iii. The genome arrays containing data from the Affymetrix arrays. Each probe set is described by its identifier, the database from which the sequence used to design the probe set was taken, the accession number and description of a representative sequence, and the consensus sequence spanning from the most 5' to the most 3' probe position in the public Unigene cluster.-8) for each consensus sequence of a given Affymetrix array in each reference database. From these we extracted cross-references to assign the probe set to the corresponding BIN in the GeneBins classification.Probe sets are assigned to the GeneBins hierarchy based on their sequence similarity with amino acid sequences in the reference databases. BINs are linked to these sequences by the cross-references provided by KEGG. We used BLASTX  to find Arabidopsis thaliana, Oryza sativa, Glycine max and Medicago truncatula) are available in the database .It is possible to retrieve the classification of a probe set in a selected genome array by its Affymetrix probe set identifier or by the GenBank accession number of the representative sequence. The results of database queries provide information on the probe set sequence, its position in the functional hierarchy, and the blast matches, as given in Figure GeneBins can be used to identify the functional categories associated with a set of sequences  and thus find the metabolic pathways or other cellular functions up- or down-regulated in microarray experiments. The list of probe set identifiers (Affymetrix probe set identifiers and/or GenBank accession numbers), belonging to a given genome array, can be pasted in a text box or uploaded from a file in the GeneBins website.To provide an overview of the functions affected, a bar plot representing the distribution of the submitted identifiers in the second level of the classification is displayed Figure . Note thTo detect if a certain functional category is statistically over-represented in the selected group of genes, compared to the rest of the genome array, the p-value for all BINs throughout the classification is calculated using the hypergeometric distribution . This p-In addition, to display gene expression data on images representing a functional context of these genes (e.g. metabolic pathways) using MapMan, the complete probe sets classification for each organism can be downloaded in the appropriate MapMan format and in an xml format to be explored locally using any outliner.In the near future, we plan to apply our approach to other Affymetrix arrays. The classification process will be improved by taking into account the domain composition of the proteins. We are currently developing an interface allowing the submission of a set of sequences (e.g. custom DNA microarrays) to be classified automatically.GeneBins provides a hierarchical functional classification, modelled on the KEGG ontology, of probe set sequences of four plant Affymetrix arrays. Based on these assignments, an online analysis tool is available to interpret gene expression results from microarray experiments by identifying the most relevant pathways or functions involved in a submitted list of genes.Access to GeneBins is via a web interface, freely available to all interested users, at It has been tested to work with Safari 2.0, Mozilla Firefox 1.5 and Internet Explorer 6.0 web browsers and does not require any particular plug-in.NG participated in the design, implemented the system and drafted the manuscript with revisions provided by GW. GW conceived and supervised the project. Both authors read and approved the final manuscript.


An analysis of the relationship between duplication events, the time they took place and the expression breadth of the duplicated genes supports the subfunctionalization model, in which expression divergence following gene duplication promotes the retention of a gene in multicellular species. Gene duplications have been hypothesized to be a major factor in enabling the evolution of tissue differentiation. Analyses of the expression profiles of duplicate genes in mammalian tissues have indicated that, with time, the expression patterns of duplicate genes diverge and become more tissue specific. We explored the relationship between duplication events, the time at which they took place, and both the expression breadth of the duplicated genes and the cumulative expression breadth of the gene family to which they belong.We show that only duplicates that arose through post-multicellularity duplication events show a tendency to become more specifically expressed, whereas such a tendency is not observed for duplicates that arose in a unicellular ancestor. Unlike the narrow expression profile of the duplicated genes, the overall expression of gene families tends to maintain a global expression pattern.The work presented here supports the view suggested by the subfunctionalization model, namely that expression divergence in different tissues, following gene duplication, promotes the retention of a gene in the genome of multicellular species. The global expression profile of the gene families suggests division of expression between family members, whose expression becomes specialized. Because specialization of expression is coupled with an increased rate of sequence divergence, it can facilitate the evolution of new, tissue-specific functions. The availability of fully sequenced genomes enables us to explore the links between a species' genotype and its phenotype. For metazoa, one of the most obvious phenotypic characteristics is the appearance of differentiated tissue types. How does the evolution of metazoan genomes relate to tissue differentiation? One aspect of metazoan genome evolution is the emergence of metazoan-specific genes. The contribution of metazoan-specific genes to tissue differentiation was recently demonstrated in a few studies that characterized the tendency of such genes to be specifically expressed in mammalian tissues -3. HowevGene duplication events have been suggested to contribute to the attainment of the complex body organization in metazoan species . A possiBy using expression information from various mouse tissues, we explore several aspects of the relationship between duplication events and specialization of expression that have not yet been characterized. First, we studied the relationship between duplication events and the expression breadth of the duplicated genes. Previous analysis  has showWe identified pairs of close homologous mouse proteins by performing an all-against-all BLAST  search  for the In order to study whether pre-multicellularity and post-multicellularity duplication events lead to different expression breadth in their resulting duplicates, we studied the relationship between expression breadth and number of duplicate pairs in three groups of mouse proteins  was done by comparing the mouse proteins with the proteins of an estimated last unicellular ancestor of mouse. To estimate the proteome of this last unicellular ancestor, we used a combination of the complete protein sequences from a variety of unicellular species . The varied origin of these protein sequences reduces the impact of species-specific gene loss on the classification of preMD/postMD proteins. Proteins with no detectable homologs in any of these unicellular species, or in any other unicellular species out of more than 200 fully sequenced species examined , were classified as metazoan-specific proteins . Because the sequence similarity between duplicate proteins can lead to promiscuity of their reporting probes, we limited the analysis to probes that uniquely report a single sequence . In cases in which a probe set is mapped to several splice variants, only the longest transcript is further analyzed. After this filtration the dataset contained expression information for 4914 mouse proteins. For each protein, we recorded its expression breadth according to the absent/present call in each tissue. In order to avoid re-counting similar tissues, the tissues were grouped into 13 clusters and only a single representative member of each cluster was used for analyses comparing expression breadth. All analyses were repeated using an additional microarray expression dataset from mouse tissues (Novartis GNF1M GeneChip) that will be referred to herein as the additional dataset . The numbers of proteins in the groups analyzed are listed in Table P < 2.2 × 10-16). The expression is plotted against the number of duplicate pairs in Figure A tendency of mammalian genes from large families to be specifically expressed was previously reported . We testP = 1.4 × 10-14) and between rate and the number of duplicate pairs . Both correlations are weaker than the correlation reported here between expression breadth and number of duplicates.A possible explanation for the dependence between the number of duplicate pairs and the expression breadth is the mutual correlation of both factors with the rate of evolution. Several studies show a correlation between the expression breadth of mammalian proteins and their recent rate of evolution, as inferred by comparison with an ortholog from another mammalian species -17. OtheWe wanted to study whether the relationship between expression breadth and number of duplicates can be explained purely in terms of both factors' mutual correlation with the recent rate of evolution. The first step was to estimate the dependence between expression breadth and number of duplicate pairs using a standard contingency table. Next, the contingency table statistic was compared with those formed by randomizing the data such that only the correlation between the expression and number of duplicate pairs that is due to rate is saved. The randomization was performed by grouping the proteins into bins of a similar recent rate of evolution and shuffling the numbers of duplicate pairs within each group, forming a new set of data that has identical correlation between expression and rate as the original data and that retains a similar correlation between number of duplicate pairs and rate. The contingency table statistic for the original data was compared with those of 10,000 sets of randomized data, and it exceeded them all . Therefore, there is a relationship between the number of duplicates and the expression breadth that cannot be explained by the mutual correlation with the recent rate of evolution.Our results indicate that, on average, a duplication event leads to a narrower expression profile Figure . Can theP = 0.51; Figure In order to investigate the effect of pre-multicellularity duplication events on expression breadth, we studied the correlation between expression breadth and the number of duplicate pairs in the preMD subset - a subset of mouse proteins whose duplicates have all arisen through pre-multicellularity duplication events. In agreement with the hypothesis, preMD proteins illustrated no tendency toward an increase in tissue specificity accompanying a rise in the number of duplicate pairs . Although the correlations are not high, and although there is a great variability in the distribution of the data  have arisen through a post-multicellularity duplication event, namely the postMD and the metazoan-specific subsets. In agreement with the predicted tendency of post-multicellularity duplication events to lead to tissue-specific expression, we detect a significant negative correlation between expression breadth and number of duplicate pairs in both subsets .We wished to study the expression breadth of protein families to test whether we can find a correlation not only between expression breadth of an individual protein and its number of duplicate pairs, but also between the size of the family and the cumulative tissue distribution of the entire family . This is unlike the significant negative correlation observed between the expression breadth of an individual protein and its number of duplicate pairs . Therefore, although we have only partial expression information for the large majority of families, our data imply that increasing the size of a family does not affect, on average, the cumulative tissue distribution of a family.We studied the relationship between size and expression breadth in 1249 protein families where expression information was available for at least a single family member. The families vary both in size and in the fraction of the members for which expression information is available. For each protein family a cumulative expression profile was created by summing all tissues in which at least a single family member is expressed Figure . No negaIn Figure Taken together the expression breadth of proteins Figure  with theThe unique physiologic role of each mammalian tissue is determined by the unique composition of the genes expressed in the tissue - the tissue's transcriptome. The transcriptome of each tissue comprises genes that are expressed globally and genes whose expression is limited to a subset of tissues . The ideHere, because of the small number of tissue-specific singleton proteins, we analyzed the tissue distribution of proteins whose expression in our dataset is limited to no more than three tissue clusters, which gave us a dataset of 497 duplicate proteins and 60 singleton proteins. Obviously, it is possible that under different conditions, in a different set of tissues  or during embryonic development, these proteins will be expressed in additional tissues.From the singleton proteins, 25 proteins are classified as pre-metazoan proteins and 32 proteins are classified as metazoan-specific proteins . The tissue distribution, SWISS-PROT  accessioMetazoan-specific proteins are, in many cases, involved in tissue-specific activities -3 and thTissue-specific expression of pre-metazoan singleton proteins is especially interesting. If we assume that such proteins are integral to a biologic process (being singletons) in the unicellular ancestor, the process must therefore be located in a specialized tissue in multicellular species. It may be that some molecules are only required in one tissue, and therefore the enzymes to make them need only to be expressed there. Alternatively, catabolism of some molecules may be restricted to a single tissue, which acts on behalf of the whole organism. Histidase, homogentisicase, and inositol-oxygenase are examples of specifically expressed enzymes . Histidase catalyses the first step in histidine degradation, a process that takes place in liver and skin of mammals . HomogenIn Table The transition from unicellularity to multicellularity can be viewed as a transition from a studio flat to a 'room-differentiated' house. Some of the essential functions from the studio flat will be found in each room . Some functions will become specific to one room type (shower), whereas other functions will have mild adaptations in a few rooms (desk versus dining table). Possibly, some rooms will acquire house-specific functions that cannot be found in the studio flat (conservatory).Characterization of the equivalent molecular differences underlying tissue diversity sheds light on how the evolution of the genome of multicellular species is related to the appearance of cell types. Duplication of genes was long ago suggested to provide a mechanism for tissue differentiation . Here, wSeveral limitations of this analysis must be acknowledged. First, the analysis performed is based only on approximately one-fifth of mouse proteins (proteins that are included in the main expression data), and the expression values are based on a single replicate. However, we were able to repeat the analyses with an additional dataset and obtain compatible results. Second, in both databases the expression data were retrieved from tissues rather than from individual cell types. High coverage, multi-replicate expression data from a wide collection of mammalian cell lines would be ideal for performing such an analysis but is not yet available.The third limitation is that the sequence similarity search used to estimate the phyletic age of a protein might not be sensitive enough to detect distant homologs of fast evolving proteins because their lesser similarity to distant homologs. Fast evolving pre-metazoan proteins can therefore be misclassified as metazoan-specific proteins  of an ancestor gene between its daughter duplicates promotes the retention of a gene in the genome . HoweverHow does the specification of expression lead to the evolution of new, tissue-specific functions? Several lines of evidence indicate that specifically expressed genes diverge at higher rates -17, possopsin gene into two paralogs: c-opsin and r-opsin. The paralogs are found, respectively, in the ciliary and rhabdomeric photoreceptor sister cell types, leading to differences in the light sensitivity of those cells. It has been suggested that this duplication event, which took place in an early metazoan ancestor, had allowed the diversification of these two cell types from a precursor photoreceptor ancestor cell [When does the emergence of a new function define a unique role for a tissue? Few reported examples link specification of function to the emergence of new tissue types. One such example is the duplication of an ancestral tor cell . In mammtor cell . HopefulP < 0.04) and have treated marginal calls as undetected transcripts. In the text, we refer to this dataset as the main dataset.We used microarray data from hybridizations of RNA from mouse tissues to Affymetrix U74Av2 GeneChip using the standard protocol . The data are available from ArrayExpress   . AbsenceThe Absence/presence calls from 22 adult male mouse tissues were used to build 13 tissue clusters by constructing a tree and then cutting it into clusters . The tree was cut at a height that allowed maximal variety of tissue clusters but which clustered together highly similar tissues (such as the two testis samples or different parts of the colon). The tissues and the tissue clusters are listed in .Similarly, the absence/presence calls from 47 adult male mouse tissues were used to build 20 tissue clusters, listed in .In order to avoid re-counting of similar tissues, we used a single representative of each tissue cluster for analyses, comparing the expression breadth of proteins in mouse tissues. The analyses were repeated using different tissue compositions and compatible results are obtained.For both chips , the individual probes' sequences were aligned against all mouse transcripts predicted in the EnsEmbl   will recognize their homologs with a high E value. In order not to classify these proteins as singletons, we applied this self-recognition condition.We conducted an all-against-all BLAST  self-seaE value ≤ 10-10; a mutual coverage of 80% between query and hit; and the proteins were not alternative forms encoded by the same gene. For each protein we counted the number of its duplicate pairs (the number of proteins matching these criteria). When counting the number of pairs, all homologs of the protein that are encoded by the same gene (splice variants) are counted only once because they arose through a common gene duplication event. For example, if protein A has three homologues C, D and E, and C and D are splice variants of the same genes, then protein A is considered to have two duplicate pairs. If protein B is a spice variant of protein A, and it also recognizes proteins C, D and E as homologs, then protein B is also considered to have two duplicate pairs. Proteins A and B are not considered to be duplicate pairs of one another.Two proteins were regarded as duplicates if they met the following criteria: Only proteins classified as either singletons or duplicates using the strict criteria above were further analyzed.Mouse and rat ortholog pairs and their calculated evolutionary rates  were retrieved from EnsEmbl (downloaded 24 April 2005). Those cases in which a mouse protein matched more than a single rat ortholog were discarded from the analysis, unless one of the orthologous pairs was annotated as BRH . Evolutionary rate (dn/ds) values were obtained for 2279 proteins, including 485 singleton proteins (out of 603) and 1794 duplicate proteins (out of 2128) that are represented in the main expression dataset. The median rate for all pairs is 0.08.E value < 10-3 .Mouse proteins were classified as pre-metazoan (descendants from a unicellular ancestor of mouse) or metazoan specific, according to the results of a BLAST search  against Proteins were only assigned to a single category. The classification process is hierarchical: first proteins with hits to more than ten prokaryote species and/or at least a single hit to a nonmetazoan eukaryote are classified as pre-metazoan . Mouse proteins recognizing only metazoan proteins are classified as metazoan specific . A total of 266 mouse proteins for which homologs were not inferred in any of the species (including mouse), probably because of short sequence or low complexity, were not included in any of the categories.We used the Inparanoid program  in orderSaccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli, and Plasmodium falciparum.First, we wished to identify a group of mouse proteins that do not have any duplicates from duplication events that post-date the transition to multicellularity (preMD proteins). Such proteins will therefore not have any in-paralogs when species A is mouse and species B is its last unicellular ancestor. Second, we wanted to identify a group of mouse proteins for which all of their duplicates arose through duplication events that post-date the transition to multicellularity (postMD proteins). Such proteins will therefore have only in-paralogs  when species A is mouse and species B is its last unicellular ancestor. As a reference to the genome of the last unicellular ancestor of mouse, we used the combined sequences of the complete proteomes of The combined unicellular protein sequences , together with the mouse sequences, were used as input for the Inparanoid program. Inparanoid was run with default parameters. The clustering procedure had identified unicellular orthologs for 8305 mouse proteins. Of these, 5886 proteins have mouse in-paralogs and 2419 proteins do not.The Inparanoid program provides predictions for the in-paralogs that a protein has, but not for its out-paralogs. Here, we used Inparanoid clustering only in order to classify a protein as preMD or postMD, but not in order to count its number of duplicate pairs because duplicates, which are out-paralogs, will not be recognized. Because the classification of protein as preMD or postMD was done separately from the count of its duplicate pairs, we filtered the groups to include only proteins for which all of their duplicate pairs are classified into an Inparanoid orthologous group . This filtration omits, for example, proteins that have out-paralogs that, as a result of a species-specific gene loss, were not classified to any orthologous cluster. After the filtration the dataset contained 3920 proteins for which the Inparanoid procedure had identified in-paralogs (out of 5886) and 2231 proteins for which no such in-paralogs were identified (out of 2419). For those 2231 proteins without in-paralogs, we can therefore only identify duplicate pairs that have a different unicellular ortholog (those duplicate pairs are predicted to arise through a pre-speciation duplication event). These 2231 proteins are termed 'preMD proteins' here.From the 3920 proteins with in-paralogs, we filtered out those proteins for which not all of their duplicate pairs are classified into the same Inparanoid orthologous group, leaving 3699 proteins in which all their duplicate pairs are also in-paralogs (they arose through a post-speciation duplication event). These 3699 proteins are termed 'postMD proteins' here.The following additional data are available with the online version of this article. Additional data file Comparison between the main and the additional dataset, tests of the dependence between expression breadth and the number of duplicate pairs in a group of slow evolving metazoan-specific proteins, and figures and tables as described in the manuscript.Click here for file


Chronic pancreatitis is an inflammatory disease which is characterized by an irreversible conversion of pancreatic parenchyma to fibrous tissue. Beside obstructive jaundice and pseudocyst formation, about half of the patients need surgical intervention due to untreatable chronic pain during the course of the disease. In most of the patients with chronic pancreatitis, the head of the pancreas is the trigger of the chronic inflammatory process. Therefore, resection of pancreatic head tissue must be the central part of any surgical intervention. However, it is unclear to which extent the surgical procedure must be radical in order to obtain a favourable outcome for the patients.A single centre randomized controlled, superiority trial to compare two techniques of duodenum preserving pancreatic head resection. Sample size: 65 patients will be included and randomized intraoperatively. Eligibility criteria: All patients with chronic pancreatitis and indication for surgical resection and signed informed consent. Cumulative primary endpoint : duration of surgical procedure, quality of life after one year, duration of intensive care unit stay, duration of hospital stay. Reference treatment: Resection of the pancreatic head with dissection of the pancreas from the portal vein and transsection of the gland (Beger procedure). Intervention: Partial Resection of the pancreatic head without transsection of the organ and visualization of the portal vein (Berne procedure).Duration: September 2003-October 2007.The trial is conducted in compliance with the protocol and in accordance with the moral, ethical, regulatory and scientific principles governing clinical research as set out in the Declaration of Helsinki (1989) and the Good Clinical Practice guideline (GCP). The Center for Clinical Studies of the Department of Surgery Heidelberg is responsible for planning, conducting and final analysis of the trial. Chronic pancreatitis is an inflammatory disease, which is characterized by progressive and irreversible destruction of pancreatic parenchyma and transformation into fibrous tissue. The leading symptom of chronic pancreatitis is chronic pain, which requires surgical intervention in about 50% of the patients .Despite of the fact that the etiopathogenesis of pain in chronic pancreatitis is not fully understood, the concept of a neuroimmune interaction in the head of the pancreas is in accordance with the observation that simple drainage procedures seldom lead to satisfactory pain relief in the long run in patients with an enlarged pancreatic head -6. Only In 1972, H.G. Beger for the first time described the technique of duodenum preserving pancreatectomy in the treatment of patients with chronic pancreatitis  is defined. Groups of 22 patients each would permit the verification of the null-hypothesis with an α-error set at 0.05 and a β-error at 0.10, yielding a power of 90%.To evaluate the true efficacy of the Berne procedure a per protocol analysis is necessary. For the external validity of the study an intention to treat approach for analysis is requested. Due to the nature of complex surgery such as a pancreatic resection it can be expected that one third of all randomised patients will not receive the intended procedure. Therefore at least 65 patients should be randomised.• Patients of any age (>18 ys)• Expected survival time more than 24 months• Informed consent• Participation in another intervention trial that would interfere with the intervention and outcome of this study• Severe psychiatric disorders or neurological diseases• Lack of compliance• Drug and/or alcohol abuse according to local standardsThe final protocol was approved by the independent ethics committee of the University of Heidelberg. The study was registered at Current Clinical Trials (ISRCTN No. 50638764). Patients who are scheduled for duodenum preserving pancreatic head resection had a pre-treatment visit to give the informed consent. During this visit the patient will be screened and informed about the trial. In the course of the conversation, the study procedure, risks, benefits and data management will be clarified in detail.In order to achieve comparable groups for known and unknown risk factors, randomization will be performed. The random allocation sequence was generated by a free available randomization software tool  by a traThe participating surgeons are all experienced in pancreatic surgery. Annually more than 300 resections of the pancreas are performed in Heidelberg. Both procedures used in this trial are supervised by two authors  in order to guarantee correct execution according to the protocol . Concomitant treatment will be equal for both groups including antibiotic prophylaxis, use of sandostatin and anaesthesia.An independent study nurse will document and monitor the procedure in the operating theatre. Blinding in this phase of the trial is not possible due to the research question.Standardized patient interviews will be performed by blinded study nurses 6, 12 and 24 months after surgery, using EORTC QLQ-C30 questionnaire and the disease specific module for pancreatic diseases EORTC QLQ-PAN26 ,20.After a midline laparotomy, wide exposure of the pancreas is obtained by opening of the gastro-colic ligament. The duodenum and the pancreatic head are then mobilized by an extended Kocher maneuver. If there are any doubts on the dignity of the pancreatic head tumour, biopsies will be taken and pathological examination will be performed.After exhibition of the pancreas, the randomisation will be performed using sequentially numbered, opaque, sealed envelopes; according to the allocation, the procedure will be continued.The pancreas is gently lifted and subtle dissection is performed between the mesenteric vein and the body of the pancreas, taking care to avoid injury to the major retropancreatic vessels and smaller side branches. After this step, multiple stay sutures (4/0 PDS) are placed around the parenchyma of the pancreatic head to assign resection margins. This is important to avoid inadvertent injury to the duodenum and assures that a lamella of pancreatic parenchyma along the loop of the duodenum remains and assures blood supply. The subtotal resection of the pancreatic head starts with the transsection of the pancreas above the portal vein proceeding towards the prepapillary common bile duct. In all phases of the intervention meticulous haemostasis is required. Reconstruction is performed with a Roux-en-Y loop of the jejunum with an end-to-side pancreatico-jejunal anastomosis and another side-to-side anastomosis of the jejunal loop with the remaining hollowed pancreatic head.Both the dissection of the pancreatic body from the portal vein and the transsection of the pancreas over the portal vein is not done with the consequence that only one cavity results. Also in this case, multiple stay sutures mark the dissection margin and support blood sparing surgery. After subtotal resection of the pancreatic head, reconstruction is performed in analogy to the original Beger-technique with a Roux-en-Y jejunal loop and side-to-side pancreatico-jejunostomy.Independently of the type of resection (Beger or Berne procedure), in case of stenosis of the intrapancreatic segment of the common bile duct which cannot be decompressed by resecting the circumferential fibrous tissue or in case of accidental opening of the common bile duct during resection, an additional bilio-digestive anastomosis will be performed.The primary endpoint is combined out of four components. In order to adjust for multiple testing a hierarchical model is used.1. Duration of surgical procedure [min]2. Quality of life (EORTC QLQ-C30 questionnaire and the disease specific module for pancreatic diseases EORTC QLQ-PAN26) at 12 months after the intervention3. Duration of stay on the intensive care unit4. Duration of postoperative hospital stayFrequencies of early and late onset complications such as intra- or postoperative bleeding with subsequent need for blood transfusion, pancreatic fistula, postoperative pulmonary complications, wound infections and re-laparotomy; exocrine and endocrine pancreatic function as determined by levels of HbA1c and stool elastase.adverse event (AE) covers any sign, symptom, syndrome, or illness that appears or worsens in a patient during the period of observation in the clinical trial and that may impair the well-being of the patient. The term also covers laboratory findings or results of other diagnostic procedures that are considered to be clinically relevant. A serious adverse event (SAE) is any adverse event that occurs at any time during the period of observation that results in death, is immediately life-threatening, requires or prolongs hospitalization, or results in persistent or significant disability or incapacity.The term AEs will be reported to the principle investigator in regular intervals throughout the study.SAEs which are meet one of definitions of the secondary endpoints are treated as SAEs regarding to documentation, but have not to be reported to the sponsor/principle investigator within 24 h.A flow chart according to the CONSORT statement is included . The basComparisons will be made of the primary endpoints of both intervention groups for all patients included in the study on an intention to treat basis. Furthermore per protocol analysis will be performed as well as including only patients strictly treated according to the study protocol.0,1 no difference of duration of the surgical procedure on the level of 5%). If the null hypothesis of no difference can be rejected the other components can be tested in a hierarchical way as follows   and screData will be recorded in clinical reporting files. Double data entry will be done by two independent staff members and all analysis are performed with SPSS  after plausibility controls and closure of the database.The author(s) declare that they have no competing interests.J.K. and C.M.S participated in the design and coordination of the study and drafted the manuscript. M.N.W., M.E.R., E.G., and M.W.M. participated in the design and coordination and helped to draft the manuscript. U.M. participated in the statistical design and helped to draft the manuscript. H.F. and M.W.B. conceived the study and supervised the coordination of the study. All authors read and approved the final version of the manuscript.


Protein interactions are crucial components of all cellular processes. Recently, high-throughput methods have been developed to obtain a global description of the interactome (the whole network of protein interactions for a given organism). In 2002, the yeast interactome was estimated to contain up to 80,000 potential interactions. This estimate is based on the integration of data sets obtained by various methods . High-throughput methods are known, however, to yield a non-negligible rate of false positives, and to miss a fraction of existing interactions.The interactome can be represented as a graph where nodes correspond with proteins and edges with pairwise interactions. In recent years clustering methods have been developed and applied in order to extract relevant modules from such graphs. These algorithms require the specification of parameters that may drastically affect the results. In this paper we present a comparative assessment of four algorithms: Markov Clustering (MCL), Restricted Neighborhood Search Clustering (RNSC), Super Paramagnetic Clustering (SPC), and Molecular Complex Detection (MCODE).A test graph was built on the basis of 220 complexes annotated in the MIPS database. To evaluate the robustness to false positives and false negatives, we derived 41 altered graphs by randomly removing edges from or adding edges to the test graph in various proportions.Each clustering algorithm was applied to these graphs with various parameter settings, and the clusters were compared with the annotated complexes.We analyzed the sensitivity of the algorithms to the parameters and determined their optimal parameter values.We also evaluated their robustness to alterations of the test graph.We then applied the four algorithms to six graphs obtained from high-throughput experiments and compared the resulting clusters with the annotated complexes.This analysis shows that MCL is remarkably robust to graph alterations. In the tests of robustness, RNSC is more sensitive to edge deletion but less sensitive to the use of suboptimal parameter values. The other two algorithms are clearly weaker under most conditions.The analysis of high-throughput data supports the superiority of MCL for the extraction of complexes from interaction networks. Protein-protein interactions (PPI) play major roles in the cell: transient protein interactions are often involved in post-translational control of protein activity; enzymatic complexes ensure substrate channeling which drastically increases fluxes through metabolic pathways; large protein complexes play essential roles in basal cellular mechanisms such as DNA packaging (histones), transcription (RNA polymerase), replication (DNA polymerase), translation (ribosome), protein degradation (proteasome) ...Various methods have been used to detect PPI. Co-immunoprecipitation, co-sedimentation, and two-hybrid systems have traditionally been used to characterize interactions at the level of a single protein complex. More recently, high-throughput methods have been developed for large-scale detection of pairwise interactions  -3 or mulet al. estimated that data resulting from combined experimental and computational approaches provide clues in favor of approximately 80,000 PPI in the yeast Saccharomyces cerevisiae )In this paper we present a systematic quantitative evaluation of the capability of four clustering methods for inferring protein complexes from a network of pairwise protein interactions. The four methods tested here are Markov Clustering MCL ,36), Res, Res36])The four algorithms tested here rely on distinct approaches for extracting clusters from the graph Table . We giveinflation step is applied to enhance the contrast between regions of strong or weak flow in the graph. The process converges towards a partition of the graph, with a set of high-flow regions (the clusters) separated by boundaries with no flow. The value of the inflation parameter strongly influences the number of clusters.The Markov Cluster algorithm (MCL) ,36 simulThe second algorithm, Restricted Neighborhood Search Clustering (RNSC) ), is a cspin with each node of the graph. Spins belonging to a highly connected region fluctuate in a correlated fashion and nodes with correlated spins are placed in the same cluster. When the temperature increases, the system becomes less stable and the clusters become smaller.The third algorithm, Super Paramagnetic Clustering (SPC)  is a hieThe fourth method, Molecular Complex Detection (MCODE) , detectstest graph. As depicted in Figure From the collection of protein complexes annotated in the MIPS database , we consaltered graphs from the original test graph, by combining addition and removal of edges in various proportions. We refer to altered graphs as Aadd,del, where add and del indicate respectively the percentage of added and deleted edges .In order to evaluate the robustness of the algorithms to missing and false interactions, we generated 41 A100,40, with 100% edge addition and 40% edge removal. Another problem of evaluation is that a certain proportion of interacting proteins can be assigned to the same cluster by chance. In order to estimate the random expectation of correct grouping, we built a random graph by shuffling the edges between nodes of the test graph. With this type of randomization, each node preserves the same number of links as in the original graph.Figure altered random graphs from the random graph, by randomly adding and removing random edges in the same proportions as for the original test graph.We also built 41 permuted clusters of the same sizes as those obtained from the test graph or altered graphs.To each of these 84 graphs , we applied the four algorithms described above, with varying parameter values. As a second way to estimate the random expectation, each clustering result was also randomized so as to obtain a set of complex-wise sensitivity (Sn) represents the coverage of a complex by its best-matching cluster . Reciprocally, the cluster-wise Positive Predictive Value (PPV) measures how well a given cluster predicts its best-matching complex (see the chapter Methods for a detailed description of the matching statistics).The quality of a clustering result was evaluated by comparing each cluster with each annotated complex. The PPV values  and Sn values . The resulting statistics, clustering-wise PPV and clustering-wise Sn, provide complementary and somewhat contradictory information: when the number of clusters decreases, the Sn increases and, in the trivial case where all proteins are grouped in a single cluster, the calculated Sn reaches 1. Reciprocally, the PPV increases with the number of clusters, reaching 1 in the trivial case where each protein is assigned to one separate cluster. In order to integrate the two statistics, we computed a geometrical accuracy (Acc), defined as the geometrical mean of the averaged Sn and PPV values.To estimate the overall correspondence between a clustering result (a set of clusters) and the collection of annotated complexes, we computed the weighted means of all Sn, PPV and Acc and selected the combination of parameters giving maximal accuracy. This analysis revealed that some parameters have a drastic impact on accuracy, whereas others have a limited effect.Each algorithm has one or more parameters that influence properties such as number of clusters, cluster size, and cluster density (number of intra-cluster edges). For each algorithm we measured the impact of the main parameters on Let us illustrate in more detail the procedure of parameter selection with the inflation parameter of the MCL algorithm. With the original test graph, interestingly, the effect of this parameter is barely detectable Figure . Yet thiA100,40  and an increase in PPV (blue curve). These effects are explained by the fact that the number of clusters increases with the inflation parameter .The crucial impact of the inflation parameter becomes obvious when MCL is applied to highly altered graphs. For example, for the altered graph 0 Figure , the incr Figure . The optWe performed the same analysis and selected the optimal parameter values for each one of the 42 graphs , as summarized in Table Note that in the case of the inflation parameter, the most frequent value (1.8) is especially well suited for graphs with a high level of alteration, such as those resulting from high-throughput data. In addition, for the less altered graphs, the accuracy is generally more robust to fluctuations of the inflation  and PPV (ordinate) obtained with the same altered graph as in Figure A100,40). Each dot corresponds to one particular combination of parameter values. This figure shows that the RNSC algorithm is remarkably robust to the choice of parameter values: all the results are grouped in a cloud, with an almost constant PPV (58%) and a restricted range of Sn (between 61% and 87%).For the RNSC algorithm, we tested the impact of 7 parameters on the quality of the clustering. This represents a total of 2,916 combinations of parameter values. Figure The same analysis was carried out for each parameter of each algorithm. The complete tables of optimal values for the 42 graphs using both Accuracy and Separation (see next section) are available as supplementary material see  and 3. TIn this analysis, we chose fixed parameter values for each algorithm Table  and analFigure A100,0. This illustrates the importance of the permutation test: the test makes it possible to estimate the performance of an algorithm in terms of gains relative to the random expectation. We inspected the clustering result in more detail in order to understand why the program can yield high accuracy values even when clusters are permuted. This effect comes from the fact that, under the chosen conditions, SPC yields a huge cluster of 567 proteins, plus a multitude of very small clusters of 1 or 2 proteins. The effect of the huge cluster is to artificially increase the Sn, since a good fraction of each complex is covered by this cluster. Each of the very small clusters yields a high PPV : single-element clusters have by definition a PPV of 1, and 2-member clusters have a minimal PPV of 0.5. This particular distribution of cluster sizes thus creates an artefactual situation by reaching, for two separate reasons, reasonably high scores for both criteria (Sn and PPV).To estimate the random expectation, we performed for each clustering result a permutation test, by shuffling the proteins between clusters. The number of clusters and their respective sizes thus remained unchanged. The geometric accuracy of the permuted clusters is displayed with dotted lines in Figure separation, as the product of the proportion of complex elements found in the cluster by the proportion of cluster elements found in the complex (see Methods for the formula). High separation values indicate a bidirectional correspondence between a cluster and a complex: a maximal value of 1 is reached when a cluster corresponds perfectly with a complex, i.e. when it comprises all of its proteins and nothing more.In order to circumvent this problem, we defined an additional statistic, which we call complex-wise separation indicates how well a given complex is isolated from the other complexes. The maximal value for complex-wise separation is 1. The simplest way to obtain The cluster-wise separation, which indicates how well a given cluster isolates one or several complexes from the other clusters. The maximal value, Similarly, we defined a clustering-wise separation statistic integrates separation values over all complexes and clusters, and indicates the general correspondence between a clustering result and the set of annotated complexes. Separation is particularly relevant to assessing clustering algorithms like MCODE, which permit assigning a protein to multiple clusters. Under some particular parameter combinations, this program tends to yield highly redundant clusters. Table PPV.The Sepcl will be reduced accordingly. Note that the result of Table Cluster-wise separation penalizes this effect by using the marginal sums rather than the cluster size. Thus, if a method generates many redundant clusters, each one intersecting with a given complex, the marginal sum will increase drastically, and Figure In Figure In order to obtain a realistic estimate of algorithm robustness, we thus need to combine edge addition and removal. Figure Figures Saccharomyces cerevisiae. Two of these data sets consist of pairs of interacting proteins detected by the two-hybrid technique published respectively by Uetz et al. [et al. [et al. [et al. [et al. [In the previous chapters our evaluations were based on artificial graphs obtained by adding and removing various proportions of edges to a reference network (the MIPS complexes). The next step was to evaluate the capability of these algorithms to extract relevant information from high-throughput data sets. To this end, we downloaded from the GRID database  six dataz et al.  and Ito  [et al. . The fou [et al. ,6, Ho et [et al. , and Kro [et al.   and sensitivity ("coverage") of the interaction graphs obtained by various high-throughput methods [An important criterion for this analysis is the contrast between the scores reached with the real clustering results and the random expectation estimated with permuted clusters. A look at this contrast already reveals some general characteristics of the data sets. Whatever the clustering method used, the eriments ,2. This  methods , and who methods  shows a  methods ,6,7. We PPV. It is characterized by very weak cluster-wise separation , for example, SPC yields 87 clusters with a mean size of 15.5 but with a median of only 2 proteins per cluster. The mega-cluster includes no less than 1074 proteins and thus comprises most complexes, raising the sensitivity to Sn = 91.8%. The artificial aspect is indicated by the fact that the permuted clusters also reach a very high score Sn = 81.4%. As discussed above, this bias is avoided by the separation statistics, which yield relatively low values for SPC. Similar figures are obtained with the other data sets.SPC is characterized by a high sensitivity and a low For all the data sets, RNSC yields a large number of mini-clusters , plus a few clusters of reasonable size (up to 35 proteins per cluster). It shows a relatively high cluster-wise separation value Figure  but a loAcc, Sep) and also as regards the contrast between real and permuted clusters . The methodology proposed here could be used as well to assess the capabilities of other clustering algorithms with other data sets.We have evaluated the capability of four graph-based clustering algorithms to extract protein complexes from networks of protein-protein interactions. This evaluation has led us to elaborate a testing procedure for the selection of optimal parameters and the analysis of robustness to noise. We have defined new matching statistics called To study the ability of the tested algorithms to extract protein complexes from an interaction network, we built a test graph from the complexes annotated in the MIPS database.In a first step we assessed the impact of the parameters of each algorithm, and determined the optimal values for extracting complexes from an interaction network. This analysis shows that under most conditions, RNSC and MCL outperform MCODE and SPC. RNSC is remarkably robust to variations in the choice of parameters, whereas the other algorithms require appropriate tuning in order to yield relevant results. Secondly we assessed the robustness of these programs to noise and to missing information in the data, by randomly adding and removing edges from the test graph. This analysis clearly revealed differences between the algorithms, highlighting the robustness of MCL, and to a lesser extent RNSC, to graph alterations.We then applied the same four algorithms to interaction networks obtained from six high-throughput studies. This analysis revealed that whatever the algorithm used, some data sets provide insufficient information for extracting the correct protein complexes. An analysis of the more informative data sets confirmed the general superiority of MCL over the three other algorithms tested here.An important limitation of the present evaluation is that it was performed by naive users. Any algorithm is likely to work better in the hands of its own developer than in those of external users. As we did not participate in the development of any of the tested algorithms, our evaluation may underestimate the capabilities of some of the algorithms tested here. An advantage of such an external evaluation, however, is that the evaluators are not biased by better knowledge of one particular algorithm. Consequently, our evaluation might be biased in favour of algorithms which are more user-friendly, or easier to configure. It thus reflects a compromise between algorithm user-friendliness and efficiency.Another limitation is that all of our analyses were performed on unweighted graphs, because our reference graph (the MIPS complexes) does not contain any information that would enable us to assign reliability values (weights) to the edges. It should be mentioned that MCL and SPC can deal with weighted graphs and are likely to give better performances if the weights reflect the reliability of the links between proteins .Saccharomyces cerevisiae [test graph) contains 1,095 polypeptides and 12,261 interactions  and additions . We refer to altered graphs as The random expectation of clustering results was estimated in two ways: with randomized graphs and permuted clusters.randomized graph was obtained by shuffling all the edges of the test graph. This procedure preserves the connectivity of each node while reallocating edges at random. We also generated 41 altered randomized graphs by randomly adding edges to and deleting edges from the random graph in the same proportions as for the test graph. We refer to altered randomized graphs as Radd,del, where add and del indicate respectively the percentage of added and deleted edges.A A set of permuted clusters can be obtained from a clustering result by shuffling the associations between proteins and clusters. This randomization procedure preserves cluster sizes. We applied it to each clustering result obtained with the test graph and the altered graphs.n complexes and m clusters, the contingency table T is a n·m matrix where row i corresponds to the ith annotated complex, and column j to the jth cluster. The value of a cell Ti,j indicates the number of proteins found in common between complex i and cluster j. Note that some proteins belong to several complexes, and that with one one algorithm (MCODE), some proteins may be assigned to multiple clusters or not assigned to any cluster. The marginal sums (per row or per column) of the contingency table thus do not always correspond with complex or cluster sizes. For example, cluster 4 in Table Each clustering result was compared with the annotated complexes by building a contingency table, as schematically exemplified in Table PPV), and accuracy are classically used to measure the correspondence between the result of a classification and a reference. We describe hereafter how these concepts can be adapted to measuring the match between a set of protein complexes and a clustering result. As discussed in the text, these statistics can in some particular cases lead to erroneous interpretations. We thus define an additional statistic, which we call separation.Sensitivity, positive predictive value  indicates the tradeoff between sensitivity and predictive value. It is obtained by computing the geometrical geometrical mean of the Sn and the PPV.The Sn or the PPV metric is low. High accuracy values thus require a high performance for both criteria. It is of particular importance to use the geometric accuracy, as the arithmetic mean would give a false idea of quality in the trivial cases where all proteins are assigned to a single cluster (Sn = 1 ⇒ Accarithm > 0.5) or where, on the contrary, each protein is assigned to a single-element cluster (PPV = 1 ⇒ Accarithm > 0.5).The advantage of taking the geometric rather than arithmetic mean is that it yields a low score when either the The contingency table indicates the absolute frequency of intersections between complexes and clusters. From these values, we derive relative frequencies with respect to the marginal sums, either per row  as the geometrical mean of Sepco and Sepcl.We then compute the R [Clustering was performed on a PC cluster of 40 nodes. Statistical treatments were done and figures made with the freeware statistical package R . GraphicR ,44.Graphs of protein interactions were manipulated using the Java classes developed by the aMAZE group ,46.SB collected the data, built the graphs, tested the algorithms, and ran the evaluation procedures. JvH conceived of the study and participated in its design, coordination, and in defining the matching statistics. Both authors were equally involved in writing the manuscript.Supplementary information about the algorithmsClick here for fileOptimal accuracy parameter valuesClick here for file.Optimal separation parameter values. These files and supplementary figures are also available on Click here for file


Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data. Since its introduction more than 10 years ago [To address the shortcomings of the available software tools and quantification strategies, we modified the classic delta-delta-Ct method to take multiple reference genes and gene specific amplification efficiencies into account, as well as the errors on all measured parameters along the entire calculation track. On top of that, we developed an inter-run calibration algorithm to correct for (often underestimated) run-to-run differences.Our advanced models and algorithms are implemented in qBase, a flexible and open source program for qPCR data management and analysis. Four basic principles were followed during development of the program: the use of correct models and formulas for quantification and error propagation, inclusion of data quality control where required, automation of the workflow as much as possible while retaining flexibility, and user friendliness of operation. Our quantification framework and software fit exactly in current thinking that places emphasis on getting every step of a real-time PCR assay right , especiqBase employs a proven, advanced and universally applicable relative quantification model. An important underlying assumption is that PCR efficiency is assay dependent and sample independent. While this may not be true in every experimental situation, there is currently no consensus on how sample specific PCR efficiencies should be calculated and used for robust quantification. Most evaluation studies attribute a lack of precision to these sample specific efficiency estimation methods. Hence, the gold standard is still the use of a PCR efficiency estimated by a serial dilution series , at least if one aims at accurate and precise quantification. Sample specific PCR efficiency estimation has its usefulness, but currently only for outlier detection -5.Calculation of relative quantities from quantification cycle values requires knowledge of the amplification efficiency of the PCR. As stated above, amplicon specific amplification efficiencies are preferably determined using linear regression  of a serial dilution series with known quantities (either relative or absolute). However, the error on the estimated amplification efficiency is almost never determined, nor taken into account. This error can be calculated using linear regression as well (formulas 2 to 4 and 6), and should subsequently be propagated during conversion of the quantification cycle values to the relative quantities. The formula for the error on the slope provides the mathematical basis to learn how more accurate amplification efficiency estimates can be achieved, that is, by expanding the range of the dilution and including more measurement points.E of the exponential function) and uses a single reference gene for normalization:Methods for the conversion of quantification cycle values  into normalized relative quantities (NRQs) were first reported in 2001. The simplest model described by Livak and Schmittgen  assumes NRQ = 2CtΔΔPfaffl  modifiedThis model constituted an improvement over the classic delta-delta-Ct method, but cannot deal with multiple (f) reference genes, which is required for reliable measurements of subtle expression differences . TherefoThe calculation of relative quantities, normalization and corresponding error propagation is detailed in formulas 7-16.The basic principle of the delta-Cq quantification model is that a difference (delta) in quantification cycle value between two samples  is transformed into relative quantities using the exponential function with the efficiency of the PCR reaction as its base. In principle, any sample can be selected as calibrator, either a real untreated control, or the sample with the highest or lowest expression. In addition, any arbitrary cycle value can be chosen as the calibrator quantification cycle value. The choice of calibrator sample or cycle value does not influence the relative quantification result; while numbers may be different, the actual fold differences between the samples remain identical, so results are fully equivalent and thus only rescaled. However, the choice of calibrator quantification cycle value does have a profound influence on the final error on the relative quantities if the error on the estimated amplification efficiency (see above) is taken into account in the error propagation procedure. To address this issue, we developed an error minimization approach that uses the arithmetic mean quantification cycle value across all samples for a gene within a single run as the calibrator quantification cycle value. As the increase in error is proportional to the difference in quantification cycle between the sample of interest and the calibrator formula 12), the overall final error is minimized if the mean quantification cycle is used as the calibrator quantification cycle value  and coefficients of variation (CV), we calculated these normalization quality parameters for our previously established reference gene expression data matrix obtained for 85 samples belonging to 5 different human tissue groups [in situ calibration); or normalizing against DNA . ClearlTwo different experimental set-ups can be followed in a qPCR relative quantification experiment. According to the preferred sample maximization method, as many samples as possible are analyzed in the same run. This means that different genes (assays) should be analyzed in different runs if not enough free wells are available to analyze the different genes in the same run. In contrast, the gene maximization set-up analyzes multiple genes in the same run, and spreads samples across runs if required Figure . The latWhatever set-up is used, inter-run calibration is required to correct for possible run-to-run variation whenever all samples are not analyzed in the same run. For this purpose, the experimenter needs to analyze so-called inter-run calibrators (IRCs); these are identical samples that are tested in both runs. By measuring the difference in quantification cycle or NRQ between the IRCs in both runs, it is possible to calculate a correction or calibration factor to remove the run-to-run difference, and proceed as if all samples were analyzed in the same run.Inter-run calibration is required because the relationship between quantification cycle value and relative quantity is run dependent due to instrument related variation , data analysis settings (baseline correction and threshold), reagents  and optical properties of plastics. Important to note is that inter-run calibration should be performed on a gene per gene basis. It is not sufficient to determine the quantification cycle or relative quantity relation for one primer pair; the experimenter should do this for all assays.To provide experimental proof of the advantage of sample maximization over gene maximization with respect to reduction in variation, we designed and performed an experiment consisting of five different runs Figure . The resTo our knowledge, there is only one instrument software that can perform such a correction, but the algorithm is based on the Cq values of a single IRC. Although it can be valid to calibrate data based on Cq values, this method has the drawback that the same template dilution needs to be used in all the runs to be calibrated . It is often much more straightforward and easier to calibrate the runs based on the NRQs of the IRCs (formulas 13-16). The quantity  of the calibrating input material is adjusted after normalization. This has the important advantage that independently prepared cDNA of the same RNA source can be used as a calibrator in the different runs . To some extent, even a biological replicate  can be used for inter-run calibration when doing the calibration on the NRQs, provided that the experimenter realizes this introduces some level of biological replicate variation (but still adequately removes inter-run variation). The validity of using independently prepared cDNA as calibrator is demonstrated by the experiment described in Figure It is also advisable to use multiple IRCs. A failed calibrator does not ruin an experiment if two or more are available. In addition, calibration with multiple IRCs gives more precise results with a smaller error. Based on our real calibration experiment, inter-run calibration using a single IRC inherently increases the uncertainty on the relative quantity by about 70% whereas a set of 3 IRCs increases it by only 40% Table . AlthougIt is important to note that formulas 13'-16' can only be used for inter-run calibration if the same set of IRCs is used in all runs to be calibrated. For more complex experimental set-ups (whereby different combinations of IRCs are used in the various runs), advanced inter-run calibration algorithms are currently being developed in our laboratory .The process of inter-run calibration is very analogous to normalization. Normalization removes the sample specific non-biological variation, while inter-run calibration removes the technical run-to-run variation between samples analyzed in different runs. As such, the same formulas can be used to calculate the inter-run calibration factor , and the same quality parameters can be applied to monitor the inter-run calibration process . Calculation of the IRC stability measure allows the evaluation of the quality of the calibration, which depends on the results of the IRCs. Our experiment shows that, with low M values , virtually identical results are obtained for the different selections of IRCs  are stored in the run layer; the experiment layer groups data from different runs that need to be processed and visualized together; and the project layer combines a number of related experiments . This hierarchical structure provides a clear framework to manage qPCR data in a straightforward and simple manner. The qBase Browser window is split into two parts: the bottom of the screen provides an explorer-like window to browse through the data; and the top of the screen contains a separate window displaying the annotation of the selected run, experiment or project. The qBase Browser allows the deletion and addition of projects, experiments and runs. The facility for exporting and importing projects and experiments is a convenient way to exchange data between different qBase users.Each qPCR instrument has its own method of data collection and storage, accompanied by a large heterogeneity in export files with respect to file format, table layout and used terminology. During import into qBase, the different instrument export files are translated into a common internal format. This format contains information on the well name, sample type, sample and gene name, quantification cycle value, starting quantity values (for standards), and the exclusion status. The last field indicates whether the measurement should be excluded from further calculations without actually discarding the measurement.Data can be imported from a number of data formats. Two standards ) and a number of instrument specific formats are supported. The qBase standard consists of a Microsoft Excel table in which the columns correspond to the information that is used internally by qBase. RDML is a universal format under development for the exchange of qPCR data under the form of XML files .The import wizard guides users through the process of data import Figure . To addrThe Analyzer is the data processing module for experiments. It performs relative quantification with proper error propagation along all quantifications, provides a number of quality controls and visualizes NRQs. This process involves several consecutive steps, some of them to be interactively performed by the user, others automatically executed by the program. Users are guided through the analysis by means of a simple workflow scheme in the main screen of the qBase Experiment Analyzer Figure .The first step in the workflow is the (automatic) initialization of an experiment, during which raw data from all individual run files from the same experiment are combined into a single data table. The initialization procedure also generates a non-redundant list of all the samples and genes within the experiment. There are no limits on the number of replicates, genes or samples contained within an experiment, except for those imposed by Excel . The absence of such limitations is a major improvement compared to the existing PCR data analysis tools, which are usually limited to processing data from a single plate or run with a fixed number of sample replicates. In qBase, data points with identical sample and gene names are automatically identified as technical replicates, except when the wells are located in different runs. In the latter case, they are interpreted as IRCs and renamed as such, that is, an appendix is added to indicate the run in which they are analyzed. Within the sample and gene lists on the main screen, a color code is used to label the reference genes and special sample types  layout with well annotation. It allows the modification of gene and sample names, as well as sample types and quantities in individually selected cells or in a range of neighboring cells. Together these tools allow users to review and correct the input annotation.Accurate relative quantification requires appropriate normalization to correct for non-specific experimental variation, such as differences in starting quantity and quality between the samples. The current consensus is that multiple stably expressed reference genes are required for accurate and robust normalization, especially for measuring subtle expression differences. While different tools are available to determine which candidate reference genes are stably expressed  is present for all genes (primer pairs); the quantification cycle values of NTCs are larger than a user defined threshold; the difference in quantification cycle value between samples of interest and NTCs is larger than a user defined threshold; the difference in quantification cycle value between replicated reactions is less than a user defined threshold; and genes are spread over multiple runs .After data quality control, a message box reports all quality issue alerts and the involved data points are color-coded in the data list. This allows users to easily evaluate their data and to select data points for exclusion from analysis without actually removing the data themselves.During initialization, samples are ordered alphanumerically, but the order of the samples can be adjusted in a user defined way. Samples can be re-ordered in the list by using the up and down keyboard arrows or the sample context menu. Samples that do not need to show up in the results can be excluded by using the delete button on the keyboard or the sample context menu. Apart from changing the default sample order and display selection in the Analyzer main screen, this can also be modified in a temporary gene specific manner when reviewing the results (see below).All quantification models transform (logarithm) quantification cycle values into quantities using an exponential function with the efficiency of the PCR reaction as its base. Although these models and derivative formulas have been used for years, no model or software has taken into account the error (uncertainty) on the calculated efficiency. qBase is the first tool that takes the error on the amplification efficiency into account by means of proper error propagation.Within qBase, gene specific amplification efficiencies can be specified in three ways. A default amplification efficiency (and error) can be set to all genes, or it can be provided for each gene individually. In the latter case, the efficiencies and corresponding errors can be simply typed , or calculated from a standard dilution series. qBase provides an interface for the evaluation of standard curves whereby outlier reactions can be removed. Amplification efficiencies are calculated by means of linear regression and can be saved to the gene list, in order to be taken into account during further calculation steps Figure .After raw qPCR data  quality control, reference gene(s) selection and amplification efficiency estimation, qBase can calculate the normalized and rescaled quantities. This process is fully automated and involves the following steps: calculation of the average and the standard deviation of the quantification cycle values for all technical replicates  - the program automatically detects the number of replicates for each sample-gene combination and can deal with a variable number of replicates (formulas 7-8); conversion of quantification cycle values into relative quantities based on the gene specific amplification efficiency (formulas 9-12); calculation of a sample specific normalization factor by taking the geometric mean of the relative quantities of the reference genes (formulas 13-14); normalization of quantities by division by the normalization factor (formulas 15-16); rescaling of the normalized quantities as requested by the user   , the PfaNormalization can be monitored by inspecting the normalization factors for all samples, or by calculating reference gene stability parameters. In an experiment with perfect reference genes, identical sample input amounts of equal quality, the normalization factor should be similar for all samples. Variations indicate unequal starting amounts, PCR problems or unstable reference genes. The qBase normalization factor histogram allows easy identification of these potential problems.One of the unique features of qBase is the option to normalize the relative quantities with multiple reference genes, resulting in more accurate and reliable results. In addition, qBase evaluates the stability of the applied reference genes  by calculating two quality measures: the coefficient of variation of the normalized reference gene expression levels; and the geNorm stability M-value. Both values are only meaningful, or can be calculated only if multiple reference genes are quantified. The lower these quality values, the more stably the reference genes are expressed in the tested samples. Based on our reported data on the expression of 10 candidate reference genes in 85 samples from 13 different human tissues [qBase is especially useful and unique for analysis of experiments containing multiple runs. As users are usually interested in comparing the expression for a given gene between different samples, the sample maximization experimental set-up is the preferred set-up because it minimizes technical (run-to-run) variation between the samples. Nevertheless, the gene maximization set-up is also frequently used. To correct the inter-run variation introduced by this set-up as much as possible, qBase allows runs to be calibrated (on a gene specific basis) using one or multiple IRCs Figure . If no sNormalized and rescaled relative quantities can be presented in three ways: a single-gene histogram, a multi-gene histogram, or a table. The default sample order and sample selection is defined in the main qBase window by editing the sample list. For the single-gene histogram Figure  the defaqBase is freely available for non-commercial research and can be downloaded from the qBase website .For the training of new qBase users we have designed a demo experiment that is explained in detail in a step-by-step tutorial. Demo experiment 1 consists of 4 runs (96-well format) containing 16 samples, 5 standards, and a no template control to be analyzed for 5 genes of interest and 3 reference genes. Demo experiment 2 adds two runs to the initial experiment, expanding it with eight additional samples and three calibrators for inter-run calibration. After training, complete analysis of these six plates can be performed in less than an hour. This includes data import, correction of well annotation, quality control, determination of amplification efficiencies, inter-run calibration, calculations and results interpretation. To our knowledge, there are no other tools available that can perform all these functions. Conventional spreadsheet calculations would take considerably longer, are error prone and do not include quality control.Although qPCR has been around for more than ten years, the employed calculation models are still amenable for improvement. Here we report our advanced, and proven, model for relative quantification that uses gene-specific amplification efficiencies and allows normalization with multiple reference genes. Errors are propagated throughout all calculation steps, and previously ignored errors, such as the uncertainty on the estimated amplification efficiency, are now taken into account. In addition, we developed an inter-run calibration method that allows samples analyzed in different runs to be compared against each other.We implemented these improved and innovative methods in an easy to use, Microsoft Excel based tool for the management and the automated analysis of qPCR data, coined qBase. This freely available software package incorporates several data quality controls and uses an advanced relative quantification model with efficiency correction, multiple reference gene normalization, inter-run calibration and error propagation along each step of the calculations. A configurable graphical results output and the possibility to import and export experiments allow easy results interpretation and data exchange, respectively.As a final comment, we would like to point out that, although our framework and program help management and interpretation of mRNA data, assessment of biological relevance or statistical significance requires the correlation of these mRNA data with protein levels or activity, and the measurement of biological replicates, respectively.According to the Real-time PCR Data Markup Language (RDML) we used the proposed universal terms for the plethora of available descriptions  instead of cycle threshold value (Ct), take off point (TOP) or crossing point (Cp)).Error propagation is performed using the delta method, based on a truncated Taylor series expansion.2 transformed  relative quantity ratio; V, geNorm pairwise variation; M, geNorm stability parameter.N, number of replicates i; g, number of genes j; c, number of IRCs m, m'; r, number of runs l, l'; s, number of samples k; f, number of reference genes p, p'; h, number of standard curve points q with known quantity Q; Cq, quantification cycle; CF, calibration factor; NF, normalization factor; RQ, relative quantity (relative to other samples within the same run for the same gene); NRQ, normalized relative quantity; SE, standard error; IRC, inter-run calibrator; CV, coefficient of variation; A, column matrix in which each element consists of the logA standard curve can be generated from the Cq and quantity values of a dilution series measured for the same amplicon within a single run. The slope and its standard error can be calculated for this curve by means of linear regression:E, and its standard error SE(E) are calculated from these values:The base for exponential amplification jk within a given run l:Calculation of the average Cq value for all replicates of the same gene/sample combination Ejl, with minimization of the overall error:Transformation of mean Cq value into RQ using the gene specific PCR efficiency Cqjkl = Cqreference, jl - Cqjkl     (formula 10)ΔThe procedures for normalization and inter-run calibration are highly analogous and are therefore described in parallel.NF for sample k based on the RQs of the reference genes p.Calculation of the normalization factor CF for gene j in run l based on the NRQs of the IRCs m:Calculation of the calibration factor NRQ, see formula 15)Conversion of RQs into NRQs.Conversion of NRQs into CNRQs:k and a given reference gene p:Calculation of the mean NRQ for all samples CV of a given reference gene p across all samples k:Calculation of the coefficient of variation Calculation of the mean coefficient of variation for all reference genes:Since normalization and inter-run calibration are highly analogous, quality evaluation using the stability parameter M is similar as well. Therefore, both methods are explained in parallel.Agene in which the kth element is the log2 transformed ratio between the relative quantities  of two reference genes p and p' in sample k; matrix Asample is calculated in an analogous manner.Calculation of the s × 1 matrix Airc in which the jth element is the log2 transformed ratio between the NRQs of two IRCs m and m' for the same gene j within a run l; matrix Arun is calculated in an analogous manner:Calculation of the g × 1 matrix Vgene for all samples k and a given reference gene combination . Vgene is the geNorm pairwise variation V for two reference genes.Calculation of the mean log transformed ratio and the standard deviation Virc for all runs l and a given IRC combination  and a given gene j. Vsample and Vrun are calculated similarly from Asample and Arun, respectively:Calculation of the mean log transformed ratio and the standard deviation Mgene of all pairwise variations Vgene of a given reference gene p with all other tested reference genes p'. Mgene represents the geNorm gene stability measure M for a particular reference gene p.Calculation of the arithmetic mean Mirc of all pair wise variations Virc of a given IRC m with all the other IRCs m', for the same gene j. Msample and Mrun are calculated similarly from Vsample and Vrun, respectively:Calculation of the arithmetic mean Calculation of the mean stability measure for all reference genes.Calculation of the mean stability measure for all IRCs:The calculations for Figure For the calculation of the effects of inter-run calibration, NRQ values were retrieved from qBase for runs 1, 2 and 3 independently. Inter-run calibration was performed as described in formulas 13'-16', using one, two or three IRCs . The effect of inter-run calibration with two IRCs was calculated on the three sets of two IRCs . Similarly, the effect of inter-run calibration with one IRC was calculated over all individual IRCs.The increase in error is defined as the ratio of the relative error after and before calibration. The 95% confidence interval (CI) for this increase was calculated on log-transformed ratios. For the investigation of the effect of the selection of (sets of) IRCs from the three available calibrators, CNRQs for the different calibrated data sets were rescaled to allow them to be compared. The fold difference between the data sets was log transformed and a 95% CI was calculated. The effect of calibration with identical or independently prepared cDNA was studied similarly to the effect of the selection of IRCs. The IRC stability measure was calculated as described in formulas 21'-25'.The following additional data are available with the online version of this paper. Additional data file Data and calculations leading to the results presented in Figure 3Click here for fileThe conclusions of these calculations are represented, in part, in Table 2Click here for file