An important goal of DNA microarray research is to develop tools to diagnose cancer more accurately based on the genetic profile of a tumor. There are several existing techniques in the literature for performing this type of diagnosis. Unfortunately, most of these techniques assume that different subtypes of cancer are already known to exist. Their utility is limited when such subtypes have not been previously identified. Although methods for identifying such subtypes exist, these methods do not work well for all datasets. It would be desirable to develop a procedure to find such subtypes that is applicable in a wide variety of circumstances. Even if no information is known about possible subtypes of a certain form of cancer, clinical information about the patients, such as their survival time, is often available. In this study, we develop some procedures that utilize both the gene expression data and the clinical data to identify subtypes of cancer and use this knowledge to diagnose future patients. These procedures were successfully applied to several publicly available datasets. We present diagnostic procedures that accurately predict the survival of future patients based on the gene expression profile and survival times of previous patients. This has the potential to be a powerful tool for diagnosing and treating cancer. Procedures that utilize both gene expression data and clinical data to identify subtypes of cancer can provide more accurate prognoses When a patient is diagnosed with cancer, various clinical parameters are used to assess the patient's risk profile. However, patients with a similar prognosis frequently respond very differently to the same treatment. This may occur because two apparently similar tumors are actually completely different diseases at the molecular level .The main example discussed in this paper concerns diffuse large B-cell lymphoma (DLBCL). This is the most common type of lymphoma in adults, and it can be treated by chemotherapy in only approximately 40% of patients . SeveralIf different subtypes of cancer are known to exist, there are a variety of existing techniques that can be used to identify which subtype is present in a given patient . HoweverThere are two main approaches in the literature to identify such subtypes. One approach uses unsupervised learning techniques, such as hierarchical clustering, to identify patient subgroups. This type of procedure is called “unsupervised” since it does not use any of the clinical information about the patient. The subgroups are identified using only the gene expression data.  For an overview of unsupervised learning techniques, see Hierarchical clustering  has succThe second approach to identifying subtypes of cancer is based exclusively on the clinical data. For example, patients can be assigned to a “low-risk” or a “high-risk” subgroup based on whether they were still alive or whether their tumor had metastasized after a certain amount of time. This approach has also been used successfully to develop procedures to diagnose patients .However, by dividing the patients into subgroups based on their survival times, the resulting subgroups may not be biologically meaningful. Suppose, for example, that there are two tumor cell types. Suppose further that patients with cell type 2 live slightly longer than patients with cell type 1 but that there is considerable overlap between the two groups . Assume To overcome these difficulties, we propose a novel procedure that combines both the gene expression data and the clinical data to identify cancer subtypes. The crux of the idea is to use the clinical data to identify a list of genes that correlate with the clinical variable of interest and then apply unsupervised clustering techniques to this subset of the genes.For instance, in many studies, the survival times of the patients are known even though no tumor subtypes have been identified . We can Once such a list of significant genes is compiled, there are several methods we can use to identify clinical subgroups. We can apply clustering techniques to identify subgroups of patients with similar expression profiles. Once such subgroups are identified, we can apply existing supervised learning techniques to classify future patients into the appropriate subgroup. In this study, we will use the “nearest shrunken centroids” procedure of Sometimes, however, a continuous predictor of survival is desired. We also describe a supervised version of principal components analysis that can be used to calculate a continuous risk score for a given patient and identify subtypes of cancer. The resulting predictor performs very well when applied to several published datasets.These two methods will produce satisfactory results in most datasets. However, we will describe some variations of these methods that can sometimes improve their performance. When we cluster a dataset using only a subset of the genes, it is important that we choose the correct subset of genes. Choosing the genes with the largest Cox scores is generally a good strategy, but this procedure sometimes selects some spurious genes. We will show that one can use partial least squares (PLS) to compute a “corrected” Cox score. Selecting the genes with the largest “corrected” Cox scores can produce better clusters than selecting genes with largest raw Cox scores. Additionally, we will describe two other continuous predictors of survival that we will call β˜ and γ^. For some problems, they are better predictors than the continuous predictor based on supervised principal components . These mThere are also related methods for predicting the survival of cancer patients using gene expression data. Moreover, in many applications, we would like to identify which genes are the best predictors of survival. These genes could be analyzed in the laboratory to attempt to discover how they influence survival. They could also be used to develop a diagnostic test based on immunostaining or reverse transcriptase PCR. For these applications, it is important to have a predictor of survival that is based on a small subset of the genes. This is another important advantage of our methods over existing methods.p genes for each patient. (Note that 𝓃 ≫ p.) We assume that there are several different types (classes) of cancer, each of which responds differently to treatment, and each of which is distinct at the molecular level. Therefore, given a set of 𝓃 patients with different classes of cancer, we wish to train a classifier that can diagnose which type of cancer a future patient has, given the expression levels of the patient's p genes. We will show that it is possible to identify such subgroups using the semi-supervised learning techniques described in the previous paragraph, and that identification of such subgroups can enable us to predict the clinical outcome of cancer more accurately.Our goal is to identify subtypes of cancer that are both clinically relevant and biologically meaningful. Suppose that we have 𝓃 patients, and we measure the expression level of As noted in the Introduction, we needed to assign each patient to a subgroup before we could apply nearest shrunken centroids. First, we applied an unsupervised 2-means clustering procedure to the DLBCL data of p-value of 0.416. Thus, conventional clustering techniques failed to identify subgroups that differed with respect to their survival times. Subgroups identified using hierarchical clustering also did not differ with respect to survival (data not shown).We compared the survival times of the two subgroups using a log-rank test. The log-rank test statistic was 0.7, with a corresponding p-value of the log-rank test was 0.03.We assigned each patient in the training data to either a “low-risk” or “high-risk” subgroup based on their survival time . A plot of the two resulting survival curves is shown in Recall that when we performed 2-means clustering on the patients in the test data using all 7,399 genes and used a log-rank test to compare the survival times of the patients in the two resulting clusters, the result was not significant. To test our new clustering method, we calculated the Cox scores of all 7,399 genes based on the 160 training observations and ranked the genes from largest to smallest based on their absolute Cox scores. We then clustered the 80 test observations using only the 25 top-scoring genes. This time, the log-rank statistic comparing the survival times of the two clusters was highly significant . This predictor is stronger than the discrete predictor shown in R2 = 0.08, likelihood ratio test statistic = 6.7, 1 d.f., p = 0.00966).Thus far, all of our examples have been based on the DLBCL data of Unfortunately, the expression levels of only 70 genes were available for the 292 patient dataset, making it difficult to test our methodology. However, we were able to apply our supervised principal components method. The expression levels of approximately 25,000 genes were available for the earlier study (consisting of 78 patients). After applying crossvalidation, we selected a model consisting of eight genes, five of which were included among the 70 genes in the larger dataset. Thus, we fit a supervised principal components model using these five genes and applied it to the dataset of 292 patients.R2 statistic for each model. R2 measures the percentage of the variation in survival time that is explained by the model. Thus, when comparing models, one would prefer the model with the larger R2 statistic.) We see that our supervised principal components method produced a stronger predictor of metastasis than the procedure described in The results are shown in R2 calculated using all 292 patients are inflated, since part of the dataset used to validate the model was also used to train the model. We include these results merely to demonstrate the greater predictive power of our methodology. Moreover, we repeated these calculations using only the 234 patients that were not included in the earlier study to ensure that our results were still valid.).We applied the methods discussed above to identify these subgroups  for the simulated dataset. This simulation was repeated ten times. The results are shown in R2 was zero for each iteration. If we had chosen a smaller value of the tuning parameter Δ, the procedure would have performed better, although not significantly better.) The continuous predictor based on supervised principal components performed nearly as well as the methods based on semi-supervised clustering.In the first simulation, we found that the fully supervised and the fully unsupervised methods produced much worse results than the semi-supervised methods. , so a diagnosis based on this procedure is likely to be inaccurate.Supervised clustering methods can overcome these problems. We have seen that if we selected significant genes prior to clustering the data, we could identify clusters that were clinically relevant. We have also seen how knowledge of these clusters could be used to diagnose future patients.This supervised clustering methodology is a useful prognostic tool. It is also easy to interpret. However, it has certain shortcomings as well. Recall our conceptual model shown in X as a predictor of patient survival. The chosen subset contained the genes with the largest Cox scores. This method could also be used to detect cancer subtypes, since the principal components will presumably capture the variation that exists between subtypes. It is also capable of identifying variation within these subtypes, which, as discussed above, cannot be identified using supervised clustering. We showed that this procedure could produce a stronger predictor of survival than the discrete predictor based on supervised clustering.One possible such predictor is our supervised principal components procedure. This procedure used the principal components of a subset of the expression matrix p = 0.05) for all four datasets, which was not true for any of the other methods. Finally, if we consider only discrete predictors of survival, our semi-supervised clustering methods performed better than the other models on at least three of the four datasets.We compared our methods to several previously published methods for predicting survival based on microarray data. In general, our methods performed significantly better than these existing methods. In particular, our supervised principal components method gave the best results on three of the four datasets.  Furthermore, each of our proposed methods was a significant predictor of survival  for a given patient is essential to treat cancer successfully. If the risk of metastasis is high, the cancer must be treated aggressively; if the risk is low, milder forms of treatment can be used. Using DNA microarrays, researchers have successfully identified subtypes of cancer that can be used to assess a patient's risk profile. Our results show that semi-supervised learning methods can identify these subtypes of cancer and predict patient survival better than existing methods. Thus, we believe they can be a powerful tool for diagnosing and treating cancer and other genetic diseases.The nearest shrunken centroids procedure calculates the mean expression of each gene within each class. Then it shrinks these centroids toward the overall mean for that gene by a fixed quantity, Δ. Diagonal linear discriminant analysis (LDA) is then applied to the genes that survive the thresholding. Details are given in We created two classes by cutting the survival times at the median survival time (2.8 y). Any patient who lived longer than 2.8 y was considered to be a “low-risk” patient, and any patient that lived less than 2.8 y was considered to be a “high-risk” patient. In this manner, we assigned a class label to each observation in the training data.Unfortunately, many of the patients' survival times were censored, meaning that the individual left the study before the study was completed. When this occurs, we do not know how long the patient survived; we only know how long the patient remained in the study prior to being lost to follow-up.If an observation is censored, we may not know to which class it belongs. For example, suppose that the median survival time is 2.8 y, but that a patient left the study after 1.7 y. If the patient died in the interval between 1.7 y and 2.8 y, then the patient should be assigned to the “high-risk” group. Otherwise, the patient should be assigned to the “low-risk” group. However, there is no way to determine which possibility is correct.T denote the survival time of this patient. Then, using the Kaplan-Meier curve, we can estimate p(T>50) and p(T>20). Then we can estimate p(T>50|T>20) as follows:Based on the Kaplan-Meier survival curve for all the patients, we can estimate the probability that a censored case survives a specified length of time . For exaand, of course,In this manner, we can estimate the probability that each censored observation belongs to the “low-risk” and “high-risk” classes, respectively.However, it is still unclear how we would train our classifier based on this information. Nearest shrunken centroids is a modified version of LDA. It is described in detail in xi}yi}g represent the number of discrete classes to which the yis may belong. (If we are dividing the training data into “low-risk” and “high-risk” patients, then g = 2.) When we perform LDA when all of the yis are known, the problem is to fit the mixture modelLet {i is a Gaussian density function, and the θis correspond to the mean of the observations in each class. The πis correspond to “prior” probabilities that an observation belongs to class 𝒾.) In this case, we must fit this model on the basis of the classified (uncensored) training data, which we denote by t, and the unclassified (censored) feature vectors x𝒿 ( 𝒿 = 𝓃+1, …,𝓃+𝓂), which we denote by t𝓊. (Also, note that Φ = (π′,θ′)′ denotes the vector of all unknown parameters.) as missing data. It turns out to be very simple in the case of LDA. The E-step is effected here simply by replacing each unobserved indicator variable 𝓏ij by its expectation conditional on xj. That is, 𝓏ij is replaced by the estimate of the posterior probability that the 𝒿th entity with feature vector xj belongs to Gi(𝒾 = 1, …,G, 𝒿 = n + 1, …,n+m)  . We takei and μi in the M-step are equally simple:The estimates of πandwherei is the posterior probability that the 𝒿th entity with feature vector xj belongs to Gi, or, in other words,In these expressions, τWe continue these imputations until the algorithm converges. In practice, one imputation seems to be sufficient for most problems, since each imputation is computationally intensive, and additional imputations did not seem to change the results significantly.We calculated the Cox scores of each gene based on the 160 training observations, and obtained a list of the most significant genes. Then we performed 2-means clustering on these 160 observations using the genes with the largest absolute Cox scores and obtained two subgroups. We repeated this procedure multiple times with different numbers of genes. For each such clustering, we trained a nearest shrunken centroid classifier to assign future patients to one subgroup or the other and examined the crossvalidation error rate.G of possible values of Γ. (2) Let pmin = 1 and emin = 1. (3) For each Γ in G, do the following: (4) Perform k-means clustering using only those genes with absolute Cox scores greater than Γ. (5) Perform a log-rank test to test the hypothesis that the k clusters have different survival rates. Call the p-value of this test p. (6) If p≥pmin, then return to step 3. (7) Fit a nearest shrunken centroids model based on the clusters obtained in step 3. Calculate the minimum crossvalidation error rate across all values of the shrinkage parameter, and call it e. (8) If e<emin, then let Γbest = Γ, and return to step 3. Otherwise return to step 3 without changing the value of Γbest. The optimal value of Γ is taken to be the value of Γbest when this procedure terminates.The problem of choosing the number of genes on which to perform the clustering is more complicated than it appears. The obvious way to choose the optimal number of genes on which to cluster is to simply minimize the crossvalidation error rate of the nearest shrunken centroids model based on the clustering. This works up to a certain point. It is possible that the clustering procedure will identify a cluster that is unrelated to survival.  Thus, we needed to build a safeguard against this possibility into our procedure. After performing clustering based on a given set of high-scoring genes, we performed a log-rank test to determine if the resulting clusters differed with respect to survival. If they did not, the clustering was discarded without further analysis. An outline of the procedure follows: (1) Choose a set Several comments about this procedure are in order. First, note that we did not recalculate the Cox scores at each fold of the crossvalidation procedure. We calculated them only once, using all of the patients in the dataset. There are several reasons for doing this. Recalculating the Cox scores at each fold would be extremely expensive computationally. Moreover, we found that the Cox score of a given gene varied depending on the number of patients (and which patients) we included in the model. Thus, if a given value of Γ produced a low crossvalidation error rate, there was no guarantee that a model based on the full dataset using this value of Γ would produce good results, since the model based on the full dataset may use a different list of genes. Other studies have found that using the entire dataset to produce a “significant gene list” prior to performing crossvalidation can produce more accurate predictions .G was left unspecified in the procedure. The choice of which (and how many) possible values of Γ to include in G depends on the problem at hand, as well as the computational power available. As a default, we recommend trying 100 evenly spaced values of Γ between the 90th percentile of the Cox scores and the maximum of the Cox scores. However, the optimal Γbest varies greatly from dataset to dataset, so we recommend trying several different forms of G if adequate computing power exists.Also, the set p-value of the log-rank test after performing the original clustering, we insisted not only that the p-value be significant, but also that it be lower than the best p-value obtained thus far. The reasons for this are twofold. First, experience suggests that if a given set of genes produces a good clustering on the training data , then it is likely to produce a good clustering on the test data.  Moreover, this speeds up the algorithm substantially. Calculating the nearest shrunken centroids crossvalidation error rate for a given clustering is the slowest part of the procedure; the time required to perform the clustering and calculate the log-rank statistic is insignificant in comparison. Thus, by only considering clusterings which produce a log-rank statistic with a small p-value, we allow the set G to be much larger than would be feasible otherwise.Furthermore, note that when we calculated the k was unspecified in the procedure. We have experimented with some algorithms to choose the value of k automatically, but without success. If possible, we recommend that the value of k be chosen based on prior biological knowledge.  If this is not possible, we recommend trying several different small values of k and choosing the one that gives the best results. Finally, the number of clusters X be the p×n matrix of expression values, for p genes and n patients. Let xij denote the expression level of the 𝒾th gene in the 𝒿th patient. Assume that each patient has one of two possible underlying tumor types. Without loss of generality, assume that patients 1, …,m have tumor type 1, and that patients m + 1,…,n have tumor type 2. Then assume that the genetic profiles of the two tumor types are distinct from one another, which is equivalent to assuming that the joint distribution of (xj1, …,xpj) is different for 1 ≤ 𝒿 ≤ 𝓂 than it is for 𝓂 + 1 ≤ 𝒿 ≤ 𝓃. Thus, if we choose constants {ai}jxija will be different for 1 ≤ 𝒿 ≤ 𝓂 than it is for 𝓂 + 1 ≤ 𝒿 ≤ 𝓃.  Hence, assuming that variation in gene expression accounts for variation in survival, we would expect that TXu1 captures a large percentage of the variation in survival.  For each Γ in G, split the training data into k random partitions . For most problems (and for the rest of this discussion), we can let k = 10. (3) For each crossvalidation fold, take a singular value decomposition of X, leaving out one of the 10 partitions for validation purposes. Use only those genes with absolute Cox scores greater than Γ. (4) Calculate υ^ for the 10% of the data that was withheld, as described above. (5) Fit a Cox proportional hazards model to υ^, and calculate the chi-square statistic for the log-rank test associated with this model. Denote the chi-square statistic for the 𝒾th crossvalidation fold by 𝓌i. (6) Average the 𝓌is over the 10 crossvalidation folds. Call this average 𝓌Γ. (7) If 𝓌Γ is greater than the value of 𝓌Γ∗, then let Γ∗ = Γ and 𝓌Γ∗ = 𝓌Γ. (8) Return to step 2. The set G is left unspecified in the procedure. As a default, we recommend trying 30 evenly spaced values of Γ between the 90th percentile of the Cox scores and the maximum of the Cox scores, although this recommendation is somewhat arbitrary.To choose the optimal value of Γ, we employ the following procedure: (1) Choose a set V (rather than simply taking the first column of V). Suppose we wish to find a predictor based on the first k columns of V. We can perform the following procedure: (1) Let X denote the training data. Take the singular value decomposition of X = TUDV as described above (after selecting an appropriate subset of the genes). (2) Fit a Cox proportional hazards model using the first k columns of V as predictors. (3) Calculate the matrix V^I for the test data using k columns of V^I using the Cox regression coefficients obtained in step 2. Use the resulting sum as a continuous predictor of survival.In some cases, we can improve the predictive power of our model by taking a linear combination of several columns of http://cran.r-project.org/. R source code for the procedures described in this paper are available from the authors upon request .Click here for additional data file.Data S2This file contains R functions for implementing the procedures we have described in our study.(6 KB TXT).Click here for additional data file.Data S3This file contains the R source code that we used to perform the first simulation study in our paper.(31 KB TXT).Click here for additional data file.Data S4This file contains the R source code that we used to perform the second simulation study in our paper.(39 KB TXT).Click here for additional data file.Dataset S1The gene expression data for the breast cancer study of (2.9 MB CSV).Click here for additional data file.Dataset S2The names of each of the 4,751 genes in the study of (74 KB CSV).Click here for additional data file.Dataset S3The clinical data for the study of (1 KB CSV).Click here for additional data file.Dataset S4The gene expression data for the 70 genes in the breast cancer study of (141 KB CSV).Click here for additional data file.Dataset S5The names of the 70 genes in the study of (1 KB CSV).Click here for additional data file.Dataset S6A single column that is 1 if the patient was included in the earlier study (that of (1 KB CSV).Click here for additional data file.Dataset S7The clinical data for the study of (5 KB CSV).Click here for additional data file.Dataset S8The gene expression data for the DLBCL study of (24.38 MB CSV).Click here for additional data file.Dataset S9The clinical data for the study of (2 KB CSV).Click here for additional data file.Dataset S10This is the gene expression data for the lung cancer dataset of (5.39 MB TXT).Click here for additional data file.Dataset S11This is the clinical data for the lung cancer dataset of (1 KB TXT).Click here for additional data file.Dataset S12This is the gene expression data for the AML dataset of (9.96 MB TXT).Click here for additional data file.Dataset S13This is the clinical data for the AML dataset of (1 KB TXT).Click here for additional data file.Figure S1(8.26 MB TIFF).Click here for additional data file.Figure S2(8.33 MB TIFF).Click here for additional data file.Figure S3(8.42 MB TIFF).Click here for additional data file.Protocol S1(28 KB TEX).Click here for additional data file.


There is great variation in the Accident and Emergency workload and location of Urology services in UK hospitals. This study investigated the relationship of the initial management of acute renal colic with the department workload plus local facilities including location of X-ray and urology services in UK Accident and Emergency (A&E) departments.small); 30,000 to 50,000 (medium); 50,000 to 80,000 (large) and >80,000 (very large) patients per year. One third of departments were selected in each group leading to a sample size of 106. A questionnaire was administered. Associations between categorical variables were investigated using the chi-squared test and when not valid, Fisher's Exact test was employed. Differences between groups in ordinal variables were investigated using the Mann-Whitney test.A&E departments in each of the 11 UK Deanery regions were stratified based on departmental workload, namely <30,000 (P = 0.003); with 57.1% of the small departments performing none and at least 82.8% of units in the other categories performing at least one. Of those departments with X-ray facilities in or adjacent to the department, 63% performed an intravenous urography (IVU) compared to 25% of those departments without (P = 0.026). Of those departments with on-site urology services, 86% performed at least one radiological investigation compared to 52% of units without such services (P = 0.001). Department workload was associated with the first choice analgesia  (P = 0.011). Of the small departments, 64.3% used NSAIDs, 21.4% used parenteral opiates and 14.3% used neither. In comparison, NSAIDS were used by at least 87%, and opiates by at most 12.5% of units in each of the other three categories of department workload.All questionnaires were returned. Twenty-nine units (27.4%) did not perform any radiological investigation on renal colic patients. The number of radiological investigations that were available to departments was associated with workload (Over a quarter of UK A&E departments did not perform any radiological investigations and some departments do not even offer renal colic patients any analgesia. Patient management was associated with departmental workload, location of X-ray and Urology services. National guidelines are needed to ensure optimum care for all patients. Upon presentation to the A&E department, suspected acute renal colic patients must have a clinical examination and radiological investigations to confirm the diagnosis. Without radiological investigations, life-threatening conditions such as abdominal aortic aneurysm and ectopic pregnancy may be misdiagnosed as renal colic. However, a delay in the diagnosis is possible as the facilities needed for the diagnosis are sometimes not based in the same hospital as the A&E department. In the UK, great variation exists between Accident and Emergency services in their workload as measured by the number of new patients seen per year. Larger Small- less than 30,000, Medium- 30,000 to 50,000, Large -50,000 to 80,000 and Very Large-more than 80,000. For each of the four-workload categories in each of the 11 Deanery regions, every third unit was selected resulting in a total of 106 (34.1%) departments.The handbook of the British Association for Accident and Emergency lists a total of 311 A&E departments in the UK[A questionnaire was administered by post to the 106 departments (see Appendix 1) requesting details about the location of X-ray, location of Urology services plus current practice in the investigation and management of pain in acute renal colic patients. A covering letter was included indicating that the purpose of the survey was to collect information about practice when patients present to the A&E department and not their subsequent management. The most senior medical member of each department was invited to complete the questionnaire. Over a period of nine months, each of the 106 departments returned a completed questionnaire. A total of 35 departments did not respond initially and they were sent a second questionnaire by post as a reminder. Ten departments did not respond to the second questionnaire and these were followed up with a telephone call. Consultants completed the questionnaire in 74.5% (n = 79) of units. Middle grade doctors who had been in post for at least six months completed the remaining 27 (25.5%) questionnaires.2) was used to investigate the following associations: a) the location of X-ray and intravenous urography; b) location of Urology services and total number of investigations performed and c) categorised departmental workload with the investigations performed and also the analgesics used. When the Chi-squared test was invalid, Fisher's Exact test (test statistic denoted by FI) was employed. The Chi-Squared was considered to be invalid if more than 20% of the cells had an expected value less than five or if one of the cells had an expected value less than one [2) [The Chi-squared test (test statistic denoted by χ one [2) . DegreesP = 0.026].Of the 106 departments, a total of 94 (88.7%) had X-ray facilities located in the department. A greater proportion of those departments that have X-ray facilities within their premises used the Intra-Venous Urogram (IVU) option compared to those departments without these facilities [n = 59 (62.5%) versus n = 3 (25%); FI = 6.03, df = 1, P = 0.001)  departments. The total number of radiological investigations [IVU, Ultrasound Scan (USS) or Computed Tomogram (CT)] that were available to units was categorised as none, one and two or more. Those departments that had urology on-site had more radiological options available than those without P = 0.00  .The relationship between department workload and the average number of films used when an IVU was performed is shown in Table P = 0.089). However, at least half of the large and the very large units used USS compared to less than 30% of the departments in the small and medium sized categories  (see Table very large departments, 37.5% (n = 3) could perform a CT scan compared to less than 10% of the units in each of the small, medium and large categories.The relationship between department workload and if a CT scan was available just missed statistical significance (P = 0.003)  of the very large departments had at least two options available.A total of 29 units (27.4%) did not perform any radiological investigations. The relationship between the total number of investigations available and department workload was statistically significant (P = 0.011)  of the small departments and one large department; one of these small units plus the large department reported using codydramol (a combination of paracetamol with dihydrocodeine). Of the 106 departments, 91 (85.8%) used NSAIDs including 86 (81.1%) – diclofenac and five (4.7%)- ketorolac as the first choice analgesia. Of the 86 departments that used diclofenac, 68 (79.1%) routinely used the intra-muscular route, 17 (19.7%) the rectal route and one (1.2%) administered it orally.There was a statistically significant relationship between department workload and the first choice analgesia: either NSAIDs (Diclofenac or Ketorolac) or parenteral opiates  did not perform any radiological investigation  did not perform any radiological investigation. The concern is greatest for those departments in the The first choice analgesic used by most units is NSAIDS in keeping with the literature; more departments, however, need to adopt the use of the rectal route for diclofenac in order to avoid the potential complication of the intra-muscular route. The low percentage of departments using parenteral opiates as first-choice analgesic is encouraging as parenteral opiates are better used as second choice in view of the unavoidable delay that occurs before their administration.The practice in over a quarter of A&E departments in the UK is below standard. There is significant association with departmental workload and location of services such as radiology and Urology relative to A&E. We suggest that national guidelines be developed for the management of acute renal colic in A&E departments to ensure optimum care for all patients. Subsequent to the implementation of any guidelines, we suggest that UK practice is regularly reviewed.The author(s) declare that they have no competing interests.TAL undertook the literature search, designed the questionnaire, participated in the collection and analysis of data, and wrote the paper. PMS undertook the statistical analysis and contributed to the writing of the paper. NN initiated the research, participated in data collection and contributed to the paper. CS performed the initial stratification and selection of units for the study. NP participated in data collection.1. Name of hospital: ____________________________________________2. Where is X-ray located? Within or adjacent to A&E □Distant □3. Where are urology services located? Same Site as A&E □Separate Site from A&E □4. Are the following investigations performed on suspected cases of renal colic?a) Urinalysis No □ Yes □b) IVU No □ Yes □If IVU is performed, please indicate how many films are used □c) CT No □ Yes □d) USS No □ Yes □e) Nuclear Medicine No □ Yes □5. Which of the following analgesics are given on presentation?a) Codydramol Other Oral No □ Yes □b) NSAIDS No □ Yes □If NSAIDS used; which one: (indicate route below)i. Intra-muscular □ii. Oral □iii. Rectal □iv. Intra-venous □c) Parenteral opiate No □ Yes □If parenteral opiates are used then please indicate if first or second choice:i. First choice □ii. Second choice □The pre-publication history for this paper can be accessed here:


Celiac disease is a small intestinal inflammatory disorder characterized by malabsorption, nutrient deficiency, and a range of clinical manifestations. It is caused by an inappropriate immune response to dietary gluten and is treated with a gluten-free diet. Recent feeding studies have indicated oats to be safe for celiac disease patients, and oats are now often included in the celiac disease diet. This study aimed to investigate whether oat intolerance exists in celiac disease and to characterize the cells and processes underlying this intolerance.We selected for study nine adults with celiac disease who had a history of oats exposure. Four of the patients had clinical symptoms on an oats-containing diet, and three of these four patients had intestinal inflammation typical of celiac disease at the time of oats exposure. We established oats-avenin-specific and -reactive intestinal T-cell lines from these three patients, as well as from two other patients who appeared to tolerate oats. The avenin-reactive T-cell lines recognized avenin peptides in the context of HLA-DQ2. These peptides have sequences rich in proline and glutamine residues closely resembling wheat gluten epitopes. Deamidation (glutamine→glutamic acid conversion) by tissue transglutaminase was involved in the avenin epitope formation.We conclude that some celiac disease patients have avenin-reactive mucosal T-cells that can cause mucosal inflammation. Oat intolerance may be a reason for villous atrophy and inflammation in patients with celiac disease who are eating oats but otherwise are adhering to a strict gluten-free diet. Clinical follow-up of celiac disease patients eating oats is advisable. Are oats safe for patients with celiac disease? Some patients studied here have an immune response to the oat protein avenin and show disease symptoms Celiac disease is a chronic inflammatory condition caused by an inappropriate immune response of intestinal T-cells reactive to gluten proteins of wheat and similar prolamin proteins of related cereals . The majOats have traditionally been excluded from the gluten-free diet. Several feeding studies, however, have indicated that patients with celiac disease and dermatitis herpetiformis tolerate oats without signs of intestinal inflammation –9. Of noIt remains to be proven that all celiac disease patients tolerate oats following long-term exposure. A recent study of 39 Finnish patients randomized to eat a gluten-free diet with 50 g of oats daily or a standard gluten-free diet for 1 y reported more intestinal symptoms and more gut inflammation in the group of patients eating oats, although the mucosal integrity was not disturbed . In an oWe studied nine adults with celiac disease who had a history of exposure to pure oats. The oats were derived from a quality-controlled production line and were shown to be free from contamination of other cereals as described elsewhere . The selWe took small intestinal biopsies from the horizontal part of the duodenum by gastroduodenoscopy using an Olympus (Tokyo, Japan) GIF-IT140 scope and scored them according to the modified Marsh criteria . Intraep4HCO3 and digested with chymotrypsin. Gluten and gliadin (ethanol-soluble proteins of gluten) were isolated from household wheat flour and digested with chymotrypsin as described [Oat grains  were ground and the flour was washed twice with water-saturated 1-butanol. The pellet was dissolved in 45% ethanol overnight and centrifuged. The avenin fraction was precipitated from the supernatant by adding two volumes of 1.5 M NaCl. The precipitate was dissolved either in 0.01 M acetic acid (pH 1.8) and digested with pepsin and subsequently trypsin (pH 7.8) or in 2 M urea /0.01 M NHescribed .Avenin peptides were synthesized on a robotic system (Syro MultiSynTech, Bochum, Germany) using Fmoc/OtBu chemistry and 2-chlorotrityl resin . The identity of the peptides was confirmed by electrospray mass spectrometry, and purity was analyzed by reverse-phase HPLC.2, respectively, at 37 °C for 2 h.Treatment of the peptides with guinea pig  and human recombinant TG2 was performed in the presence of 1 and 2 mM CaClThe generation of T-cell lines, T-cell cloning, and T-cell proliferation assays were performed as described elsewhere . Single A pepsin-trypsin digest of avenin was separated by gel filtration , and a fraction containing T-cell stimulatory fragments was further separated by reverse-phase HPLC  using an acetonitrile gradient from 5% to 40% with 1%/min and from 40% to 64% with 3%/min (flow rate 0.9 ml/min, containing 0.05% trifluoroacetic acid).http://www.matrixscience.com).Electrospray ionization tandem mass spectrometry was performed on a quadrupole time-of-flight hybrid mass spectrometer (Micromass, Manchester, England). For spraying, needles were typically held at 900 V towards a skimmer cone (40 V). In collision-induced dissociation of selected peptide ions , the generated characteristic b- and y-type fragment ions  were detNine adults with celiac disease who had a history of exposure to oats assessed to be free from contamination of other cereals were studied. In some cases they came for gastroduodenoscopy for clinical reasons, in other cases, they agreed to come for research reasons. The characteristics of the patients are given in Responses to TG2-treated avenin were detected in the polyclonal T-cell lines derived from the avenin-challenged biopsies from all three patients who had clinical and histopathological signs of oat intolerance . IntestiTo identify the T-cell stimulatory peptides, we initially studied an avenin-specific T-cell line TCL CD422.2.4) isolated from the oat-intolerant patient CD422. The T-cells weakly recognized one gel filtration fraction (#25) of a pepsin-trypsin digest of avenin. This fraction was further separated by reverse-phase HPLC, and retested for T-cell recognition A and 2B.2.2.4 isoFour avenin peptides  and interferes with absorption of nutrients from food. Patients with celiac disease do not tolerate a protein called gluten, which is found in wheat, rye, and barley. When people with celiac disease eat foods containing gluten, their immune system responds by damaging the small intestine. The disease is quite serious in some patients, but eating a strictly gluten-free diet can eliminate all of the symptoms. Unfortunately, wheat, barley, and rye products like flour are found in many common foods, and patients have to avoid them for the rest of their lives. Previous studies suggested that oats were safe for patients with celiac disease, and as a result, they often form part of a gluten-free diet.Contrary to other studies, this one demonstrates that oats intolerance does exist in some patients with celiac disease. These patients have an immune reaction to oats that is similar to the reaction most celiac disease patients have to wheat, barley, and rye.It appears that oats are not safe for all patients with celiac disease. Patients who eat oats as part of a gluten-free diet should discuss their diet and any symptoms with their doctors; doctors should keep in mind that patients might develop symptoms when they eat oats.The researchers studied only a small number of patients, and this study cannot tell us how common oats intolerance is among celiac disease patients.http://digestive.niddk.nih.gov/ddiseases/pubs/celiac/US National Institutes of Diabetes, Digestive, and Kidney Disorders: http://www.celiac.org/Celiac Disease Foundation: http://www.gluten.net/The Gluten Intolerance Group: http://www.celiac.com/The Celiac Disease Foundation:


In free community science, where large numbers of scientists participate as volunteers in a single project, the ideal of scientific cooperation finds a new expression. Free community science was inspired by the free software movement, which itself was inspired by the application of the ideal of scientific cooperation, as it was applied to software development by the operating system developers of the Massachusetts Institute of Technology Artificial Intelligence Lab in the 1970s. This ideal has suffered for two decades from corporate pressure to privatize science, so it is very gratifying to see that the free software movement can today help reinvigorate the principle that inspired it.The ideal of scientific cooperation goes beyond the conduct of individual projects. Scientific cooperation is also being reinvigorated today through the open-access movement, which promotes the public's freedom to redistribute scientific and scholarly articles. In the age of the computer networks, the best way to disseminate scientific writing is by making it freely accessible to all and letting everyone redistribute it. I give a vote of thanks to the Public Library of Science for leading the campaign that is now gaining momentum. When research funding agencies pressure journals to allow free redistribution of new articles they fund, they should apply this demand to the old articles “owned” by the same publishers—not just to papers published starting today.Journal editors can promote scientific cooperation by adopting standards requiring internet publication of the supporting data and software for the articles they publish. The software and the data will be useful for other research. Moreover, research carried out using software cannot be checked or evaluated properly by other scientists unless they can read the source code that was used.A significant impediment to publication and cooperation comes from university patent policies. Many universities hope to strike it rich with patents, but this is as foolish as playing the lottery, since most “technology licensing offices” don't even cover their operating costs. Like the Red Queen, these universities are running hard to stay in the same place. Society should recognize that funding university research through patents is folly, and should fund it directly, as in the past. Meanwhile, laws that encourage universities to seek patents at the expense of cooperation in research should be changed.Another impediment comes from strings attached to corporate research funding. Universities or their public funding agencies should ensure private sponsors cannot block research they do not like. These sponsors must never have the power to veto or delay publication of results—or to intimidate the researchers. Thus, sponsors whose interests could be hurt by publication of certain possible results must never be in a position to cut the funding for a specific research group.The free software movement, the free redistribution policy of this journal, and the practice of free community science for developing diagnostic disease classifications  are all


A combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts, providing the first experiment-driven annotation of the human genome. Computational and microarray-based experimental approaches were used to generate a comprehensive transcript index for the human genome. Oligonucleotide probes designed from approximately 50,000 known and predicted transcript sequences from the human genome were used to survey transcription from a diverse set of 60 tissues and cell lines using ink-jet microarrays. Further, expression activity over at least six conditions was more generally assessed using genomic tiling arrays consisting of probes tiled through a repeat-masked version of the genomic sequence making up chromosomes 20 and 22.The combination of microarray data with extensive genome annotations resulted in a set of 28,456 experimentally supported transcripts. This set of high-confidence transcripts represents the first experimentally driven annotation of the human genome. In addition, the results from genomic tiling suggest that a large amount of transcription exists outside of annotated regions of the genome and serves as an example of how this activity could be measured on a genome-wide scale.These data represent one of the most comprehensive assessments of transcriptional activity in the human genome and provide an atlas of human gene expression over a unique set of gene predictions. Before the annotation of the human genome is considered complete, however, the previously unannotated transcriptional activity throughout the genome must be fully characterized. The completion of the sequencing of the human, mouse and other genomes has enabled efforts to extensively annotate these genomes using a combination of computational and experimental approaches. Generating a comprehensive list of transcripts coupled with basic information on where the different transcripts are expressed is an important first step towards annotating a genome once it has been fully sequenced. The task of identifying the transcribed regions of a sequenced genome is complicated by the fact that transcripts are composed of multiple short exons that are distributed over much larger regions of genomic DNA. This challenge is underscored by the widely divergent predictions of the number of genes in the human genome. For example, direct clustering of human expressed sequence tag (EST) sequences has predicted as many as 120,000 genes , and a het al. [Several recent studies have highlighted the limitations of relying solely on computational approaches to identify genes in the draft of the human genome -13. Furtet al.  also indet al. . They deab initio prediction programs operate by recognizing coding potential in stretches of genomic sequence, where the recognition capability of these programs depends on a training set of known coding regions [ab initio prediction programs or assembled from EST data are also inaccurate or incomplete much of the time [ab initio prediction programs perform well at identifying known genes, predictions that do not use existing expressed sequence and protein data often miss exons, incorrectly identify exon boundaries, and fail to accurately detect the 3' and 5' untranslated regions UTRs [Most current methods in widespread use for identifying novel genes in genomic sequence depend on sequence similarity to expressed sequence and protein data. For example,  regions . Therefothe time -12. Whilons UTRs . Similarons UTRs . These dons UTRs , but cloons UTRs .Arabidopsis thaliana and Escherichia coli genomes. The recent sequencing and analysis of the mouse genome indicates extensive homology between intergenic regions of the human and mouse genomes, further highlighting the potential for other classes of transcribed regions [Recently, several groups have used microarrays to test computational gene predictions experimentally and to tile across genomic sequence to discover the transcribed regions in the human and other genomes -12,15-17 regions . Interes regions .In the study reported here, we describe hybridization results generated from two large microarray-based gene-expression experiments involving predicted transcript arrays spanning the entire human genome and a comprehensive set of genomic tiling arrays for human chromosomes 20 and 22. mRNA samples collected from a diversity of conditions were amplified using a strand-specific labeling protocol that was optimized to generate full-length copies of the transcripts. Analyses of the resulting hybridization data from both sets of arrays revealed widespread transcriptional activity in both known or high-confidence predicted genes, as well as regions outside current annotations. The results from this analysis are summarized with respect to published genes on chromosomes 20 and 22 in addition to our own extensive set of genome alignments and gene predictions. Combining computational and experimental approaches has allowed us to generate a comprehensive transcript index for the human genome, which has been a valuable resource for guiding our array design and full-length cloning efforts. In addition, the expression data from the 60 conditions provides a comprehensive atlas of human gene expression over a unique set of gene predictions .ab initio gene-finding algorithms tend to have a high false-positive rate when applied at a low-stringency setting to cast as broad a discovery net as possible. Second, gene-finding algorithms are trained on known protein-coding genes, which may limit their ability to detect truly novel classes of transcribed sequences.Figure The second step towards the CTI is the use of two different types of microarrays to address these limitations Figure . First, ab initio gene predictions. The process resulted in mapping 91% of the well characterized genes found in the RefSeq database [To generate the PTI, three distinct computational analysis steps were executed in parallel: predictions based on similarity to expressed sequences from human and mouse; predictions based on similarity to all known proteins; and database , a percedatabase ,3. The mab initio predictions. The categories, in decreasing order of support, are as follows: (1) known genes, taken as the set of 11,214 human genes represented in the RefSeq database when the arrays were designed; (2) ab initio gene models with expressed sequence and protein support; (3) ab initio gene models with expressed sequence support; (4) ab initio gene models with protein support; (5) alignments of expressed sequence and protein data; (6) alignments of expressed sequence data, requiring at least two overlapping expressed sequences; (7) ab initio gene models with no expressed sequence or protein support; and (8) alignments of protein data. Because of the limitations discussed in the previous section, we considered predictions with a single line of evidence (categories 6-8) as low confidence.All locus projections were classified into one of eight categories on the basis of the level of underlying evidence from expressed sequence similarity, protein similarity and Table ab initio gene-finding algorithms [We previously described a high-throughput, experimental procedure to validate predicted exons and assemble exons into genes by using co-regulated expression over a diversity of conditions . Here wegorithms ,25,26 agWe used an enhanced version of a previously described gene-detection algorithm to analyze the predicted transcript array dataset . BasicalThe sensitivity of our procedure was assessed by computing the EVG detection rate for those Sanger genes that overlap predictions (locus projections) represented in our PTI Table . The aveThis 20% false-negative rate is the result of a complex mixture of issues, including limitations in our EVG-detection algorithm, limitations in the probe design step, lack of expression in the conditions profiled, and/or alternative splicing events. While the EVG-detection algorithm provides an efficient method to assemble probes into transcript units, the detection capabilities of this model could be expected to improve as the number of samples and the number of probes targeting any given transcript increases. The use of four probes per predicted transcript was determined to be sufficient for detection of most transcripts, as supported by the overall detection rate of known genes, although in many cases the probe design step was limited by our ability to find four high-quality probes per transcript. For many transcripts, there were not four nonoverlapping probes predicted to have good hybridization characteristics for the microarray experiment carried out here. The 60 samples were chosen to represent a broad array of tissue types, as an exhaustive list of human tissues is impossible to obtain. Because no replicate tissues/cell lines were run for any of the 60 chosen samples, we relied on the replication inherent in monitoring the same transcripts over 60 different conditions. In this case, genes expressed in multiple samples provide the replication necessary to increase our confidence in the detections. However, there are clear limitations in not replicating tissues/cell lines, as genes may be expressed in only a single condition or may be switched on only under certain physiological conditions or only during a certain stages of development. In such cases, we would have reduced power to detect these genes.Genes in the lower-confidence categories of our PTI annotations, which are not typically considered genes by Sanger, were detected at a significantly reduced rate. Interestingly, of the 337 (188 +149) higher-confidence transcripts on chromosomes 20 and 22 that did not intersect with Sanger genes, 47 (or 14%) were detected as EVGs ).However, before we can make claims to the discovery potential for this method over the entire genome, we need to assess the false-positive detection rates. To this end, we defined as false positives all detections made in regions with support by only a single gene model that fell outside Sanger-annotated genes on chromosomes 20 and 22. Applying this definition over all transcripts in our PTI leads to a false-positive rate of 3% (11 out of 406). Because we cannot exclude the possibility that some of the transcripts supported by a single gene model represent real genes, we consider this false-detection rate as an upper bound on the actual false-positive rate. Accepting that the Sanger annotations represent the gold standard for chromosome 22, we detected 70% of all Sanger-annotated genes, while only 4% of the chromosome 22 locus projections that did not intersect Sanger genes were detected by our procedure, highlighting the sensitivity and specificity of this approach. In addition, the enrichment for EVG detections in Sanger genes versus the non-Sanger PTI on chromosomes 20 and 22 was extremely significant with a ab initio gene predictions ever undertaken. We can use the false-positive and negative rates derived above to assess the discovery potential on that part of the genome that has not been as extensively characterized as chromosomes 20 and 22. First, we note that our detection rates over the genome were similar to that given for chromosomes 20 and 22. That is, 75% of the category 1 genes (RefSeq genes) were detected over the entire genome, compared to 80% for chromosomes 20 and 22. In total, 15,642 genes in the PTI were experimentally validated using this array-based approach. Assuming the false-positive rate of 3% defined above and a conservative false-negative rate of 30%, defined as the percentage of Sanger genes we failed to detect on chromosomes 20 and 22, these data suggest there are close to 21,675 potential coding genes represented in our PTI set. Because our PTI misses close to 10% of the Sanger genes, we corrected this number for those genes not represented in this set and provide an estimate of the total number of protein-coding genes in the human genome supported by our data to be approximately 25,000. This number is consistent with estimates given in the current release (22.34d.1) of the Ensembl database [Summarizing EVG data over the entire genome and assessing the discovery potential. The last column of Table database ,29.However, we caution that the estimate provided is based solely on the data described here, and that orthogonal sources of data  continueab initio prediction). These 2,093 transcripts represent a rich source of potential discoveries in our PTI. To assess the potential biological functions of this novel gene set, we annotated translations of this set by searching the domains represented in the Protein Families database (Pfam) [From Table e (Pfam) . The seae (Pfam)  codes aset al. [Because multiple probes in each of the approximate 50,000 predicted genes in the human genome have been monitored over 60 different tissues and cell lines, the EVG data represent a significant atlas of human gene expression that is now publicly available . For eacet al.  to proviTo complement the use of PTI arrays, we constructed a set of genome tiling arrays comprised of 60 mer oligonucleotide probes tiled in 30 base-pair steps through both strands of human chromosomes 20 and 22. Repetitive sequences identified by RepeatMasker were ignored for probe design. These genome tiling arrays allow for an unbiased view of the transcriptional activity outside of known and predicted genes on these two chromosomes. mRNA from six (chromosome 20) or eight (chromosome 22) conditions was amplified and hybridized to the tiling arrays .Figure e Figure , the tilEWSR1 gene. In contrast to the first example, this gene has intense transcriptional activity outside of the annotated exons. Specifically, the EWSR1 gene has 43 potentially false-positive calls out of 203 intron probes. However, the EST data and alternative splicing predictions strongly suggest that these probes represent biologically relevant transcriptional activity. As with the KDELR3 gene, EWRS1 is annotated by RefSeq as having two transcripts: NM_005243 and NM_013986. The Acembly predictions identify four additional alternative splice forms; most noteworthy among these are EWSR1.b and EWSR.g, shown in Figure EWSR1 gene that essentially divide the largest transcript into two transcripts, suggesting that multiple promoter and transcription-stop signals are present in this gene. The tiling data depicted in Figure EWSR.b and EWSR.g predicted alternative splice forms, providing experimental support that these predictions represent actual isoforms of this gene. In fact, these data may provide a more accurate representation of the putative structure of this gene, as they support multiple alternatively spliced transcripts in this gene, beyond what has already been annotated in the RefSeq database. In all, 5% of the probes detected as expressed in intronic sequence mapped to predicted alternative splice forms. Given the extent of alternative splicing that is yet to be characterized [Figure cterized , we belip-value for this enrichment using the Fisher exact test is less than 10-15). To estimate the upper bound of false-positive calls, we counted as false-positive events each probe identified as expressed by the detection process, but falling within an annotated intron of the RefSeq genes we detected as expressed. This resulted in an estimated false-positive rate of 1.3%.Our genome tiling arrays consisted of 2,119,794 and 1,201,632 probes for chromosomes 20 and 22, respectively. Of these, 1,615,034 probes fell into Sanger gene regions, with 239,542 probes actually overlapping Sanger exons. Under stringent criteria 64,241 probes were detected as expressed, with 34,245 of these falling within Sanger exons, 18,551 falling within Sanger introns, and 15,835 probes falling completely outside all Sanger annotations. This widespread transcriptional activity outside annotated regions of the human genome is consistent with other reports from multiple species ,12,15,16As indicated in Figure For those probes falling outside all Sanger genes, we again made use of our custom genome annotations to help interpret the extent of transcriptional activity in these regions. Table One further advantage of the tiling data is that they can be used to discriminate between transcribed and non-transcribed sequences conserved between human and mouse, or between any other pair of species. Figure ab initio methods, sequencing of EST libraries, full-length gene cloning projects, and comparative analyses between fully sequenced genomes of different species. However, we are still a long way from having a comprehensive set of annotations for the human and other genomes. There is need for new high-throughput experimental approaches to accelerate the process of annotating sequenced genomes in a comprehensive and accurate fashion. Toward this goal, we have used two microarray-based experimental approaches to provide evidence of widespread transcription activity outside of any known or predicted genes in the human genome. We have also provided experimental support for many ab initio predicted genes that have no other or minimal experimental sequence support, suggesting a small but significant class of genes that have evaded all other forms of experimental detection. Similar identifications have been made recently in the first extensive comparative analysis between mouse and human genomes [A complete understanding of the human genome will only come after all genes have been identified and the functions of those genes have been determined. There has been much recent progress in defining the human transcriptome with  genomes . Despite genomes ,40. FinaWe have used the expression data for the approximate 50,000 predicted transcripts hybridized to 60 diverse conditions in combination with genomic tiling data to generate a CTI containing 28,456 experimentally supported transcripts. The transcripts represented in the CTI include all computational predictions with two or more lines of evidence from our PTI , in addition to the overlapping set of 15,642 transcripts detected as EVGs. This resulting comprehensive list of known and predicted transcripts provides the starting point for large-scale systematic studies to determine the biological function of genes in both normal and disease states. The primary goal of the CTI is to allow researchers to focus experimental efforts on a comprehensive set of genes that are likely to be real.It is of note that between the time the predicted transcript arrays were designed and annotated using the custom genome annotations described above, and the time this work was published, more than 6,000 genes were added to the RefSeq collection. These newer RefSeq genes were represented by 5,100 locus projections in our original PTI that were not classified in the RefSeq category. Interestingly, 4,212 were detected as EVGs in the present analysis and had already been included in our CTI, a validation rate slightly greater than 82%. Only 19% of the non-RefSeq genes in our PTI had been detected as EVGs  and a mouse gene index (MGI). Clustering and alignment were performed with the DoubleTwist Clustering and Alignment Tools (CAT) . Input d-20 for human sequences and 10-8 for mouse sequences. For human UniGene and HGI, we refined only those BLAST hits where the target sequence showed greater than or equal to 92% identity to the genomic sequence over 75 bp. For human RefSeq, we refined hits with greater than or equal to 95% identity, and for MGI, RefSeq, and UniGene, we refined hits with greater than or equal to 80% identity. These thresholds were empirically determined to provide good sensitivity in aligning most sequences to the genome while limiting multiple alignments past those expected from paralogs present in the human genome. In all cases percent identity was measured over 75 bp. Individual sim4 exons of questionable confidence were then removed on the basis of percent identity and length thresholds. All sequence databases were downloaded from GenBank August, 2001.Human and mouse UniGene and RefSeq, MGI, and HGI sequences were aligned with the genome first by BLASTN 2.2.1 , followe-5. Adjacent protein alignments from a single protein were grouped together as a prediction whenever the protein sequence coordinates of the alignments were consistent in direction and did not significantly overlap.The GenBank nonredundant protein database (downloaded 25 August 2001) was aligned to the genomic sequence with BLASTX 2.2.1  using anab initio gene-prediction algorithms were run independently across the entire genome assembly to augment alignment-based gene identification methods. GrailEXP 4.0, GENSCAN 1.0, and FGENESH version 1.c were run with default parameters for human sequence. GrailEXP used expressed sequence evidence from RefSeq, UniGene and DoubleTwist HGI to refine gene predictions. FGENESH+ was run with protein sequences from BLASTX with E-score lower than 10-5. When multiple protein alignments overlapped, all overlapping protein sequences were clustered with BLASTClust [GrailEXP 4.0 , GENSCANASTClust  and the Locus projections contained the union of all exons from all overlapping predictions in a contiguous region of the chromosome that were derived from sequence alignments or gene-finding algorithms. Predictions to a given strand of the genomic sequence that overlapped by even a single nucleotide were grouped into a single locus projection (antisense transcripts were not considered in defining the locus projections). The criteria for grouping predictions were intentionally kept loose, given that the intent was to include as many potential exons as possible in a given genomic region, and then use the experimental microarray-based approach to elucidate the actual gene structure. These merged overlapping predictions defined the 5' and 3' ends of the locus projections. Overlapping predicted exons were merged to form an exon prediction of maximal extent. Low-quality predicted exons from sim4 alignments that contained a high percentage of A or T were removed. We also removed sim4-predicted exons that overlapped two or more predicted exons from another sim4 alignment. Additionally, 3' sim4 and 3' or 5' FGENESH+ predicted exons that were short and/or distant from internal predicted exons were removed. Finally, locus projections that contained mRNAs from RefSeq were split at the 5' end of the RefSeq sequence.ab initio gene model.Locus projections supported by expressed sequences alone could be portions of 3' or 5' UTRs of genes included in the other gene-prediction categories described in the text. To minimize the consequences of this potential artifact, we used a UTR filter to exclude locus projections from the expressed sequence alone category that were within 20 kb of a locus projection supported by an ab initio; expressed sequence + ab initio; protein + ab initio; expressed sequence + protein; ab initio alone; protein alone; expressed sequence alone. FGENESH+ predictions were counted as protein + ab initio. For the ab initio category, predictions from at least two of FGENESH, GENSCAN and GrailEXP were required to overlap in at least one exon to be merged.All data were loaded into a relational database to count and categorize locus projections. At least one type of evidence was assigned to each predicted exon for each locus projection. Multiple types of evidence were assigned to a merged predicted exon if there was overlap between predicted exons of different types for at least 1% of the length of the merged exon prediction. One of the eight evidence categories discussed in the text was assigned to each exon on the basis of the combination of types of evidence. Locus projections inherited the highest-ranking evidence category of their constituent exons. Evidence categories were ranked in the following order: Refseq (highest); expressed sequence + protein + Escherichia coli contamination and human non-coding RNA and mitochrondrial DNA contamination using Scylla (Paracel). For genomic tiling arrays, 60 mer probes were then selected from unmasked regions of both forward and reverse complement strands at uniform 30-base intervals. For predicted transcript arrays, up to four oligonucleotide probes were selected from the unmasked regions of each transcript using a multistep process.Input sequences for probe selection were masked for vector, interspersed repeats, simple repeats, poly(A) tails, Saccharomyces cerevisiae were added to the 3' ends of probes shorter than 60 nucleotides so that they had a total length of 60 bases when printed onto the arrays.The first step in the probe-selection process was the generation of a pool of candidate probes 60 nucleotides long (60 mers), where each probe was required to fall entirely within an exon from the set of exons under consideration. If there were fewer than four 60 mers then all 50 mers were considered as well. If there were fewer than four 50 mers or 60 mers then all 40 mers were considered, and so on. Stilts composed of sequence from The second step in the probe-selection process was the classification and reduction of the probe pool on the basis of base composition and related filters. Probes were sorted into four classes on the basis of several criteria, including A, G, C and T content, GC content, the length of the longest homopolymeric run and the number of A residues at the 5' end. For example, a probe had to have GC content between 35 and 45% to be in class 1, between 15 and 55% to be in class 2, and between 10 and 60% to be in class 3. After all classifications were made, probes from lower-quality classes were discarded, keeping the number of probes per gene greater than 15. In cases where a pair of probes was overlapping by more than 50 bases, only a single probe was chosen.The final step in the probe-selection process identified probes with minimal overlap, and predicted cross-hybridization and desirable positions in the transcript sequence. Cross-hybridization prediction was based on BLAST searching of the full collection of transcript sequences . Probes All arrays included a set of standard control probes which were used for image processing and quality control. Each array also included 30 randomly distributed copies of each of 51 negative-control probes. These probes were selected for their low intensities in previous human hybridizations. The negative controls local to each experimental probe were used for background correction. Non-control probes were added to each array such that all probes for a given input sequence were grouped together and ordered by their position on the sequence.Hybridization material was generated through a random-priming amplification procedure using primers with a random sequence at the 3' end and fixed motif at the 5' end. This amplification procedure has been fully described  and has 10 (expression ratio) where the expression ratio was taken to be the ratio between normalized, background-corrected intensity values for the two channels (red and green) for each spot on the predicted transcript arrays. An independent normalization routine was carried out on the tiling data as described [Array images were processed as described  to obtaiescribed  to correProbes from each computationally determined locus were analyzed for coordinated expression over 60 tissues by adapting an additive, probe-specific model initially developed to estimate gene expression indices . The modyij = μ + φj + θi + εj,yij represent the mlratio measurements for sample pair i and probe j in the current transcriptional model, μ is the grand mean term, φi is the probe-specific term for probe j in the model, θi is the sample-specific term for sample i, and εj is the probe-specific error term, which is taken to be normally distributed with mean 0 and variance . Given the above representation for an observed mlratio value, the likelihood for a single probe over N condition pairs is simplywhere the M probes comprising the current model:From this, the likelihood for a given transcriptional model, where a transcriptional model in this context is defined as a set of probes that are adjacent to one another in the genomic sequence and that co-regulate over a number of conditions, is easily seen to be the product of the individual probe likelihoods defined above over the The maximum likelihood estimates for the parameters of this model are obtained using standard optimization techniques.ab initio prediction that fell outside annotated Sanger genes on chromosomes 20 and 22. False negatives were defined as Sanger genes on chromosome 20 and 22 that were not detected. Probe sets with a maximum likelihood statistic greater than 100 and an r2 value for fit of data to the model greater than 0.8 were considered high-confidence candidates for EVGs.With the likelihood model described above, probe groups making up a transcriptional model were formed by iteratively considering whether neighboring probes (within a PTI member based on genomic location) of a given probe improved the fit of the model just described. This was determined by examining the likelihood ratio statistics between the current, best transcriptional model with or without an additional probe included in the model. Thresholds for the likelihood ratio test statistic and the different model parameters were empirically determined to minimize false-positive and false-negative rates. False positives were estimated by the detection of PTI members supported by only a single p-value < 0.01) in at least 10% of the samples; or significantly differentially expressed  in at least 10% of the condition pairs, were considered validated.For each high-confidence EVG candidate, probes were further assessed by considering the number of conditions in which the absolute intensity of the probe was seen to be significantly above background, and the number of times the probe was seen significantly differentially expressed. Candidate EVGs with at least one probe that was: significantly above background .Hidden Markov model Pfam (HMMPfam) domain predictions were run on six-frame translations of the PTIs using the HFRAME software from Paracel with an E-value cutoff of 0.01 and frameshift penalty of -12. Information on Pfam  domains The following additional data is available with the online version of this paper and at . AdditioA complete list of 48,614 transcripts in the PTI that were represented on the set of predicted transcript arraysClick here for additional data fileA complete list of 60 tissues and cell lines hybridized to the predicted transcript arraysClick here for additional data fileA list of six tissues and cell lines hybridized to the chromosome 20 genomic tiling arraysClick here for additional data fileThe eight tissues and cell lines hybridized to the chromosome 22 genomic tiling arraysClick here for additional data fileA comparison of EVG predictions with RefSeq sequencesPClick here for additional data file