An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error.We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it.Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site. A much more usual approach is to assign an index  that measures the confidence/significance of the hypothesis and let the biologists themselves to establish a cutoff of what they call significant.An important challenge in Serial Analysis of Gene Expression (SAGE)  analysisThis necessity arises because counting sequenced SAGE tags is a process prone to random and systematic errors that affect gene expression abundance estimates. Systematic errors may come from various sources such as GC content bias , sequencIf an Expressed Sequence Tag (EST) library is non-normalized, its counting data, also known as "Digital-Northern", reflects the abundance of genes. Likewise, the Massively Parallel Signature Sequencing (MPSS)  techniquNowadays, the variability in SAGE abundance data is modeled only as due to sampling from sequencing, since almost all statistical procedures are performed after aggregation of observations from various libraries of the same class, creating a "pseudo-library". See -11 for gHere we propose a Bayesian model of mixtures to account for within-class variability as a generalization of the Beta-Binomial model . We alsoi-th library is often modeled as a Bernoulli Process and a fixed unknown tag abundance πi is implicitly assumed. The pdf of the random variable of interest, "expression abundance" π ∈  among all n libraries is unknown, thus each library could be regarded as being created by a realization of π. These features lead naturally to mixture models , turn Eq.1 into the familiar and commonly used binomial distribution (see derivation in the Methods section).The common procedure of merging all observations from libraries of the same class, constructing a "pseudo-library" before statistical inference, is recognized as a particular case of this mixture model: just assume that all libraries have strictly the same abundance, with no biological variability. Mathematically, this is a function with infinite probability density over one single abundance value f(·) as a Beta in Eq.1, yields the so-called Beta-Binomial model (see derivation in the Methods section).We believe that Dirac's Delta is a naive description of real-life SAGE libraries. The Beta distribution is an alternative with non-zero within-class variance to account for intuitively expected biological differences among them. Using θ that describes the random variable π of some fixed gene G, we must decide if there is a difference between A and B classes . We propose to consider genes as being differentially expressed based on non-superposition of the predictive Beta pdfs of both A and B classes. By "predictive" we mean that we use the a posteriori mode in the Beta pdfs. The "non-superposition" intuitive feature is mathematically written as the Bayes Error Rate E  of some gene G is indexed in a model family by means of a parameter vector θ. Therefore, following the usual Bayesian framework, the a posteriori pdf that describes the class is:To start generically, suppose that the probability density function (pdf) for the random variable of interest "expression abundance" X =  is the vector of counts in all n libraries of same class, M =  is the vector of total observations in all n libraries of same class, g(·) is the a priori pdf, and L is the likelihood of each i-th observation. Note that the product of all likelihood functions over all observations is the so-called Likelihood Function.where: posteriori expression anyway.The counting process from automatic sequencing is often modeled as a Binomial. Since the sample size and the stopping rule are not known in advance the model is not strictly Binomial. We do not need the combinatorial constant in the model, but we write it just because it is commonly used and will vanish in f(·) as a Dirac's Delta in Eq.1:Merging all observations from the same class libraries and constructing "pseudo-libraries", with the sum of their components, is the standard procedure to use replicates. Our general model is reduced to this  one if one uses 1{·} is the indicator function.where: Using Eq.4 in Eq.3 yield:θ) = 1, the non-informative uniform a priori distribution.where: g, and the sum of observations is the mathematical translation of "pseudo-libraries" construction.The expert recognizes that f(·) as a Beta in Eq.1 we get the Beta-Binomial model as a particular case of general model:The only published solution that allows non-zero within-class variance in SAGE analysis is the Beta-Binomial model . Using fB(·) is the beta special function, and:where: θ =  as the mean and standard deviation (stdv) of a Beta random variable. We prefer this parameterization of Beta distributions instead of the common  one because: (i) it is much more intuitive to biologists to deal with mean and stdv than with abstract α and β, and (ii) as α, β > 0, the domain Θ = {: 0 ≤ θ1 ≤ 1, 0 ≤ θ22 <θ1 (1- θ1) ≤ 1/4} is bounded and much more amenable to perform the necessary numerical computations.Again, an expert recognizes Using Eq.6 in Eq.3 yield:g is the priori pdf.where: a priori pdf. We use an uniform distribution over Θ. On the other hand, we know in advance that variance of this model cannot be smaller than the variance eventually obtained if we do not consider within-class variability. Even if the within-class variability is very small, it cannot be estimated as being smaller than the simple sampling error because they are inseparable, and sampling error is the lower limit  support, the relevant contribution for this integral is concentrated in a much smaller region. Integrating over the formal limits will cause serious numerical errors, and to avoid this problem we approximate our integration region to an interval delimited by 0.005 and 0.995 quantile of each Beta pdf since the relevant density lie in there.The credibility intervals ("error-bars") for the expression ratio of interesting tags were obtained as described in our recent work . We chosWe have also developed an easy-to-use web-based service that performs all calculations at our server and provides password-protected results. Although desirable, for the sake of automatic web hyperlink with SAGE Genie database, it is not necessary to explicitly identify the tags analyzed but rather any (custom) i.d. string. This could increase privacy or make our web-interface useful for "Digital-Northern", MPSS or any mathematically related problem of mixtures from binomial sampling. Figure The Table For our aims, it is sufficient to focus the analysis at the tag level. Thus, we process the tag counts and let the identification of tag's best gene match as a posterior question that could be carefully done only to really interesting tags. We choose not to process tags whose counts appear only in libraries of one class. It is important to note that all libraries are from bulk material, without cell-lines, and came from patients with similar disease description. The normal libraries came from different normal regions of the brain.We think that this data set is very illustrative since there are biological replicates in the tumor class allowing clear verification of within-class biological variability. On the other hand, taking only one kind of disease, astrocytoma grade III, instead of all brain tumors in the database, leads one to believe that the within-class variability is in fact due to biological diversity of the patients and not due to very distinct molecular profile of distinct brain tumors stored in SAGE Genie's database.in silico comparison is well-suited to demonstrate the necessity of dealing with within-class effect, although it is not our aim here to make a detailed or biological analysis of brain tumor data.Therefore, we believe that this χ2 proportion test and the bayesian Audic-Claverie's method. All these tests were performed using the easy-to-use web-interface IDEG6      (14)where:V is not unrealistic small when Vu is unrealistic small. To fit all these parameters, they used the computationally practical Method of Moments. Once pA, pB, VA and VB are found for classes A and B, these authors test if the proportions are significantly different proposing the use of a tw statistics as following a Student's tdf pdf:The max(·) function assure that SAGE: Serial Analysis of Gene ExpressionMPSS: Massively Parallel Signature SequencingEST: Expressed Sequence Tagpdf: probability density functionGEO: Gene Expression OmnibusRV conceived and executed this work. HB helped with all biological issues. DFCP helped in differential expression detection methods and implemented the on-line web-based tool. CABP helped with Bayesian statistics and proposed the mixture ideas.Results for all evidence measures. This file allows the user to interactively define significance cutoffs for ranked tags. The ranks are based on evidence measures against "no differential expression" hypothesis, i.e., evidences closer to 0 (zero) denote higher confidence in differential expression and closer to 1 (one) denote no evidence of differential expression.Click here for file


Serial Analysis of Gene Expression (SAGE) is a method of large-scale gene expression analysis that has the potential to generate the full list of mRNAs present within a cell population at a given time and their frequency. An essential step in SAGE library analysis is the unambiguous assignment of each 14 bp tag to the transcript from which it was derived. This process, called tag-to-gene mapping, represents a step that has to be improved in the analysis of SAGE libraries. Indeed, the existing web sites providing correspondence between tags and transcripts do not concern all species for which numerous EST and cDNA have already been sequenced. web site.This is the reason why we designed and implemented a freely available tool called Identitag for tag identification that can be used in any species for which transcript sequences are available. Identitag is based on a relational database structure in order to allow rapid and easy storage and updating of data and, most importantly, in order to be able to precisely define identification parameters. This structure can be seen like three interconnected modules : the first one stores virtual tags extracted from a given list of transcript sequences, the second stores experimental tags observed in SAGE experiments, and the third allows the annotation of the transcript sequences used for virtual tag extraction. It therefore connects an observed tag to a virtual tag and to the sequence it comes from, and then to its functional annotation when available. Databases made from different species can be connected according to orthology relationship thus allowing the comparison of SAGE libraries between species. We successfully used Identitag to identify tags from our chicken SAGE libraries and for chicken to human SAGE tags interspecies comparison. Identitag sources are freely available on Identitag is a flexible and powerful tool for tag identification in any single species and for interspecies comparison of SAGE libraries. It opens the way to comparative transcriptomic analysis, an emerging branch of biology. In order to characterize the molecular basis underlying self-renewal versus differentiation decision-making process we investigated the transcriptomic changes of various states related to this process, in two model systems : one derived from chicken and the other from human cells. We decided to use Serial Analysis of Gene Expression (SAGE)  to attaia priori regarding the genes to be studied. It can be used with mRNAs derived from cells of any eukaryotic species. SAGE is based on the isolation of a unique sequence tag from each individual transcript and on serial concatenation of several tags into long DNA molecules. Sequencing of concatemer clones reveals individual tags and allows quantification and identification of transcripts. Tag counts are digitally archived and statistically significant comparisons of expression levels can be made between tag counts derived from different populations of cells.Serial Analysis of Gene Expression is a comprehensive method for analyzing transcriptomes  without any An essential step in SAGE library analysis is the unambiguous assignment of each 14 bp tag to the transcript from which it was derived. This process, called tag-to-gene mapping, represents a step that has yet to be completed in the analysis of SAGE libraries. The automated version of this process mostly involves extracting "virtual tags" from sequence databanks : these virtual tags are predictions of the 14 bp sequences that might be produced by a SAGE experiment. The quality of the databanks from which the virtual tags are extracted represents a limiting step in this process. Ideally, the databanks should represent the complete collection of each and every transcript, fully sequenced and annotated. This clearly has yet to be achieved for most species, and therefore one must use the available information that comes mainly from large EST (Expressed Sequence Tags) projects.Arabidopsis thaliana, Bos taurus, Homo sapiens, Medicago truncatula, Meleagris gallopavo, Mus musculus, Pinus taeda, Rattus norvegicus, Sus scrofa, Triticum aestivum and Vitis vinifera). However, this site doesn't include tag to UniGene mapping for several other species for which numerous EST and cDNA have already been sequenced. This is the reason why we designed and implemented a freely available tool for tag identification that can be used in any species for which transcript sequences are available. It can include both complete cDNAs and EST cluster sequences and allow to interrogate the database according to the source of data, to assess the quality of virtual tags derived from different transcript sequences. In this paper we describe the use of this tool for the chicken  where a large EST sequencing effort was completed )Identitag is a flexible and powerful tool for tag identification in any single species and for interspecies comparison of SAGE libraries. It opens the way to comparative transcriptomic analysis, an emerging branch of biology.Project name: Identitag• Project home page: • Operating system(s): SUN, Linux, Mac OS X• Programming languages: Perl, Bourne Shell, MySQL• License: GNU GPL• CK participated to the design of Identitag, implemented Identitag, and participated to the biological validation of Identitag with SAGE libraries. FD constructed the first two SAGE libraries with which Identitag was tested and participated to the biological validation of Identitag with these data. LD and DM brought their expertise in the orthology area, in order to design the orthology relationship. OG supervised this work. All authors participated in the writing of the manuscript, read and approved the final manuscript.


Learning the exact demographic characteristics of a neighborhood in which a public library serves, assists the collection development librarian in building an appropriate collection. Gathering that demographic information can be a lengthy process, and then formatting the information for the neighborhood in question becomes arduous.As society ages and the methods for health care evolve, people may take charge of their own health. With this prospectus, public libraries should consider creating a consumer health collection to assist the public in their health care needs. Using neighborhood demographic information can inform the collection development librarians as to the dominant age groups, sex, and races within the neighborhood. With this information, appropriate consumer health materials may be assembled in the public library. the data was manipulated with ArcView GIS and queried to produce maps displaying the requested neighborhood demographics to view in respect to libraries.In order to visualize the demographics of a neighborhood, the computer program ArcView GIS (geographic information systems) was used to create maps for specified areas. The neighborhood data was taken from the U.S. Census Department's annual census and library addresses were accumulated through a free database. After downloading the census block information from ArcView GIS produced maps displaying public libraries and requested demographics. After viewing the maps the collection development librarian can see exactly what populations are served by the library and adjust the library's collection accordingly.ArcView GIS can be used to produce maps displaying the communities that libraries serve, spot boundaries, be it "man-made or natural," that exist prohibiting customer service, and assist collection development librarians in justifying their purchases for a dedicated consumer health collection or resources in general. Libraries have the objective to build collections that support the communities they serve. To build a viable collection the collection development librarian, outreach/marketing librarian, and others, must determine exactly what populations reside in the neighborhood, in respect to race, spoken language, educational level, and age groups. One method collection development librarians use to gather demographic information is to physically visit the communities and integrate into the neighborhoods. They may attend events within the community in order to analyze the attendees or walk around the neighborhood to get a feel for the community. Another method for collection development is to perform an informal survey with people that visit the library and learn about their preferences and/or what they like to read or look for on the Internet. Using census information can be a third way to gather community/neighborhood information. The US Census Bureau takes a census of the United States every 10 years and publishes the results on the Internet. Taking the Census information and transforming it into a graphical format provides an objective view of the communities surrounding a library. One way to visualize the census data is through the use of GIS, Geographic Information Systems. GIS are databases arranged by spatial coordinates that, when programmed, can produce maps ."Many disciplines have used GIS and many large academic libraries even support GIS by establishing a GIS department and employing a librarian to specialize in GIS information. In the field of library science articles have been published describing the establishment of GIS departments in libraries and what GIS librarians do -7, but lGovernments use GIS to visualize land use planning, tax appraisal, utility and infrastructure planning and more . BusinesBuilding a collection for a library has become a sophisticated art. Johnson states that "collection building consists of four steps: identifying the relevant items, assessing the item to decide if it is appropriate for the collection and evaluating its quality, deciding to purchase, and preparing an order ." Each sThis paper reports the steps taken to use GIS in assisting a large metropolitan public library system in visualizing neighborhoods that surround branch libraries in order to make an educated decision on whether a dedicated consumer health collection should be established to support the community. The objective of the project was to determine if GIS could be used to improve collection development.After comparing large library systems in major metropolitan areas,  Chicago was selected as a convenient sample to be mapped with GIS because of its size, number of public libraries, their geographic distribution within the urban area, and ease of access to data. Compared to other large cities, the New York Public Library system has 86 libraries and Los Angeles has 67 public libraries . The citWith the selection of the city of Chicago, the next step was to create a map of the metropolitan area. ESRI ArcGIS v9.0 geographic information software (GIS)  was seleIn order to create the city map of Chicago, data files were obtained from the U.S. Census Bureau website. The Census Bureau produces TIGER  Files available for free to the public . These fThe next step required mapping distances between neighborhoods and neighborhood demographics. In order to achieve this, files were obtained from the U.S. Census Bureau website  which di. The output of this search was imported into Excel, cleaned, and converted into a dBase file. The dBase file was imported into ArcView to coordinate the library addresses to the street addresses of the base map. This enabled the software to place an image for each public library in the city map of Chicago.The final piece of information needed to build a map showing neighborhoods in respect to public library locations, was to map the public libraries within Chicago. Because the base map of Cook County was a street map, addresses of public libraries were entered into ArvView to mark all the public libraries within the city of Chicago. The state of Illinois provides a database available on the Internet that permits searching types of libraries and locations of libraries, With the city of Chicago and its public libraries now in a GIS application, the location of libraries was displayed in respect to neighborhoods and distances from library to library . The age ranges presented in the maps varied by query and census information. The groups analyzed were: women, men, white women (group age 40–49), African American women (group age 40–49), Asian . The color schemes used represent the number of people that live within the census block as to the specified GIS query. In Figure In this project ArcGIS was used to build maps of neighborhoods in the city of Chicago, display public libraries and spatially represent distances from libraries to populations. The US Census demographic information was incorporated into ArcGIS and queried to produce 15 different maps. Queries were built to display neighborhood demographics through colors showing the percentages of the population(s). Each query produced a map for a specific age or race. Once the map was produced the public libraries were added to the map and analysis was made regarding collection needs according to the neighborhood population surrounding the library.To decide if a public library should establish a consumer health collection, a map showing the breakdown of women in their 40s was built. As in Figure By creating maps showing the demographic breakdown of the city, a public library can build, adjust, or verify its collection in respect to the neighborhoods it supports . It is eMany studies have been conducted analyzing what people search for on the Internet and who is searching. Studies have found that women in their 40s search for and utilize health information from the Internet more than men -24. UsinUsing ArcGIS can assist in defining the exact make-up of the population that a library serves. By turning the census information into a graphic the library is provided a chance to see how far  their services may reach. Library systems that have branch libraries may have one collection development librarian who buys for each library. By using ArcGIS the central library can analyze the communities surrounding each library to purchase materials appropriately without having to spend time in each library learning the environment. Purchase decisions may now be justified with hard data from ArcGIS. For example, if ArcGIS shows that the largest population surrounding a library speaks a minority language, materials should be purchased in the dominant language to best meet the needs of the community.There is a fairly high learning curve to use ESRI Arcview GIS. There are five programs in ESRI Arcview and each program has separate features that must be used within the specific program to be imported into the primary map. Learning what each program does and then how to incorporate the data into the map requires hours of reading. Acquiring the necessary data to build a map requires background study of the geographic area in order to assure that the map is configured correctly. Upon attaining all necessary data for the map, one must build queries to manipulate the data according to the information need, and then configure the map accordingly.While GIS presents a new technique to use in performing collection development, there was not a way to test the differences from a collection built using GIS information and the current methods of collection development.In addition, research has discovered that knowing the service area radius of the library doesn't mean that people within the service area will actually use or belong to the neighborhood library ,25. WhenWhile GIS can take the census information and demonstrate the population surrounding a library, the census does not describe the specific users of the library . To builBudget constraints will add a need for justification of purchases for a library's collection. Using GIS may provide one method to justify additions to collections. As health care changes and individuals take more control of their health, libraries need to have materials that provide valid information in an age and language appropriate format and be culturally sensitive. Branch libraries respond to the demands of the neighborhood  and knowGIS = geographical information systems


When the number of groups involved is more than two, however, a more general approach is needed.Two major identifiable sources of variation in data derived from the Serial Analysis of Gene Expression (SAGE) are within-library sampling variability and between-library heterogeneity within a group. Most published methods for identifying differential expression focus on just the sampling variability. In recent work, the problem of assessing differential expression between two groups of SAGE libraries has been addressed by introducing a beta-binomial hierarchical model that explicitly deals with both of the above sources of variation. This model leads to a test statistic analogous to a weighted two-sample We describe how logistic regression with overdispersion supplies this generalization, carrying with it the framework for incorporating other covariates into the model as a byproduct. This approach has the advantage that logistic regression routines are available in several common statistical packages.The described method provides an easily implemented tool for analyzing SAGE data that correctly handles multiple types of variation and allows for more flexible modelling. The Serial Analysis of Gene Expression (SAGE) methodology introduced by Velculescu et al.  is a seqBriefly, mRNA transcripts are converted to cDNA and then processed so as to isolate a specific subsequence; starting from the poly-A tail, the subsequence is the 10  or 14 (long SAGE) bp immediately preceding the first occurrence of a cleavage site for a common restriction enzyme. Ideally, this subsequence, or "tag" is sufficiently specific to uniquely identify the mRNA from which it was derived. Tags are sampled, concatenated and sequenced, and a table consisting of the tag sequences and their frequency of occurrence is assembled. The complete table derived from a given biological sample is referred to as a SAGE "library". As most tags are sparse within the entire sample, most libraries contain numbers of tags in the tens of thousands to allow the expression levels to be estimated. Due to the current costs of sequencing, however, the total number of libraries assembled for a given experiment is typically small: often in the single digits and occasionally in the tens.While the type of information, gene expression, being investigated in a SAGE experiment is the same as that in a cDNA or oligonucleotide microarray experiment, there are some qualitative differences in the approaches. First, SAGE uses sequencing as opposed to competitive hybridization. Second, while the expression value reported for an array experiment is a measure of fluourescence and is loosely continuous, SAGE supplies data on gene expression in the form of counts, potentially allowing for a different type of "quantitative" comparison. Third, SAGE is an "open" technology in that it can provide information about all of the genes in the sample. Microarrays, by contrast, are "closed" in that we will only get information about the genes that have been printed on the array.Y, the number of counts of that tag in the library, and n, the total number of tags in the library. In analyzing SAGE data across a series of libraries, interest typically centers on assessing how the underlying true level of gene expression is changing as we move from one library to the next.Mathematically, the information pertaining to the abundance of a particular tag in a sample is summarized in two numbers: Yi} and the set of library sizes {ni}, where the subscript i denotes the specific library. Unless otherwise specified, we will restrict our assessment of differential expression to the case of a single tag. This approach is common to all of the procedures described below. In a real analysis the chosen test is applied to all tags individually and a list of those tags showing differential expression is reported. Different tests will provide altered assessments of significance for individual tags, and hence the list provided will depend on the test employed.When surveyed across a series of libraries, the sufficient statistics containing all of the information about the change in expression for a single tag are the set of counts {Xi describing properties of library i. The most common case involves comparing two groups of libraries, such as cancer and control. In this case the information Xi simply defines which group library i belongs to. If there are more than two groups, Xi can have more levels or can even be vector valued, but as before interest centers on assessing how and whether the expected proportion changes with X.In most problems of interest, there is also covariate information Much work has been done on the problem of comparing expression between two groups. Most of the approaches -9 deal wt-test used to compare two groups of samples in ,ni are the same. While approximate equality may suffice, even this assumption may be questionable for SAGE data, particularly if some of the libraries are drawn from experiments conducted at different times. Williams  should be more than adequate. Second, the presence of overdispersion means that pooling the samples underemphasizes the evidence of a small proportion being supplied by the zero variance of the observed proportions. While we could pursue a more optimal proportion, we choose in this case to simply use the simplistic bound noted above. Here, as the library sizes in the first group are 49610 and 48479, the proportion is 1/(49610 + 48479) and the faked counts are 0.506 = 49610/(49610 + 48479) and 0.494, respectively. Some reformatted results from this fit are shown in Table The second run of the fitting procedure takes the overdispersion parameter as given, and fits the data after replacing the zero proportions in a group with the same small nonzero proportion, giving us a hopefully conservative estimate of the fold change. This type of replacement is commonly used, and is most often justified via the assumption of a vague prior distribution for the proportions, with the point estimate being derived as the posterior mean or mode. A common assumption for a prior in dealing with proportions is the uniform distribution. The posterior mean after 0 successes are observed out of t-value  relies on the approximate normality of the likelihood function in the vicinity of the maximum, and this shape assumption breaks down severely if the number of counts in one group is small. Tests based on changes in the scaled deviance, corresponding to likelihood ratio tests, are better.The results for this fit are ridiculously "insignificant". The problem lies in the fact that the use of a The third run of the fitting procedure fits a simpler submodel, in this case a single proportion for all eight libraries, using the same overdispersion estimate so as to measure the change in deviance. The results of this fit are shown in Table Here, we cannot conclude (given the level of overdispersion) that the difference is real. Note that the degrees of freedom used in the denominator is 5; this follows from the fact that only 6 libraries were used to estimate the overdispersion parameter, and one of those 6 degrees of freedom was needed to estimate the proportion.In general, when any of the groups has very small counts, checking the change in deviance is a good idea.Logistic regression with overdispersion addresses three issues with SAGE data: simultaneously modelling multiple types of variance, dealing with multiple groups at once, and allowing for the incorporation of covariates. This procedure is widely implemented in available software. Further, and most importantly, viewing SAGE data in the logistic regression setting supplies the framework for thinking of models that describe such data.t-tests. The regression setting carries with it other benefits, such as a well-developed body of work regarding model checking, residual analysis, and detection of outliers. For example, the influence of any given library tag count on the overall analysis can be assessed, and methods can be made more robust by bounding these functions so that no single library drives the results.Dealing with multiple types of variance yields significance estimates we believe to be superior to those derived from pooled counts or from There are some areas in which we can identify difficulties and see room for improvement.First, the model that we are using for the error may be improved. For SAGE data, the proportion associated with a specific tag is rarely on the order of a percent, sopi) ≈ log(pi)logit = β0 + β1xi + εi,logit, where "mixed" refers to the fact that we have both fixed effects of interest, the changes with the covariates, and random shocks whose variance needs to be estimated and allowed for. Williams ;p.values <- 2 * pt,fit3$df.residual);}########################################### Next, we deal with three groups##########################################if(0){# We begin by focusing on gains# available when multiple groups are# present, even if the other groups are# not directly part of the contrast of# interest, due to the additional# information that the added groups can# provide about the scale of the# overdispersion.# Here, we use the data from the tag# TGCTGCCTGT, and this time we note that# there are 3 groups of libraries:# normals (libraries 1–2), primary# tumors (libraries 3–4), and cell lines# (libraries 5–8). If we are interested# in the contrast between normals and# primary tumors, we can fit this using# only the data from those two groups,# or using the data from all three.# First, fit the model as if there were# just two groups present.y <- c;n <- c;x <- c;fit1 <- glm ~x,family=binomial);fit2 <- glm.binomial.disp(fit1);# get the correct p-valuesfit2.t.values <- summary(fit2)$coefficients ;fit2.p.values <- 2 * pt, fit2$df.residual);# Next, fit the model assuming that# there are three groups. In this case,# we cannot use a single covariate# vector x, as this is not suited to# indicating 3 or more groups in an# unordered fashion # In general, if we have k groups, we# need to use k-1 covariate vectors.# Here, we use# x1 <- c;# x2 <- c;# The set of all 0s # corresponds to the first group, here# the normals, and the other groups are# defined by which one of the other# covariates is nonzero:# Group 2 (primaries), ,# Group 3 (cell lines), y <- c;n <- c;x1 <- c;x2 <- c;fit3 <- glm ~x1 + x2,family=binomial);fit4 <- glm.binomial.disp(fit3);# get the correct p-valuesfit4.t.values <- summary(fit4)$coefficients ;fit4.p.values <- 2*pt, fit4$df.residual);# The above approach has fit the model# with all of the covariates available,# but in order to perform an analysis of# deviance we want to fit various# submodels using the same estimate of# overdispersion as found here. In this# case, there are 3 submodels:fit5 <- glm ~x1,family=binomial,weights = fit4$disp.weights);fit6 <- glm ~x2,family=binomial,weights = fit4$disp.weights);fit7 <- glm ~1,family=binomial,weights = fit4$disp.weights);# alternatively, the anova function can# be used, but this only considers the# submodels obtained by adding terms# sequentially. Thus, we get the# deviances for beta_0 (the null model),# beta_0 + beta_1 (adding the x1# covariate only), and beta_0 + beta_1 +# beta_2 ;}########################################### Next, we deal with the case of other# covariates, possibly continuous.##########################################if(0){# Here, we are using the counts from the# GCGAAACCCT tag, but we are treating# the 8 libraries as coming from tissue# type 1 (libraries 1–4) and tissue type# 2 (libraries 5–8), with normal tissue# of both types  and# primary tumor of both types . In this hypothetical# example, we are able to partition the# changes into effects associated with# normal/primary differences (x1) or# tissue 1/tissue 2 differences (x2).y <- c;n <- c;x1 <- c;x2 <- c;fit1 <- glm ~x1 + x2,family=binomial);fit2 <- glm.binomial.disp(fit1);# get the correct p-valuesfit2.t.values <- summary(fit2)$coefficients ;fit2.p.values <- 2*pt, fit2$df.residual);# Next, again using the tag as above, we# posit that we also have access to the# levels of a biomarker potentially# predictive of survival, supplied as# the levels of another covariate x3.# The values supplied here were# generated as random draws from a# uniform  distributionx3 <- c;fit3 <- glm ~x1 + x2 + x3,family=binomial);fit4 <- glm.binomial.disp(fit3);# get the correct p-valuesfit4.t.values <- summary(fit4)$coefficients ;fit4.p.values <- 2*pt, fit2$df.residual);}KAB, LD and JSM developed the main ideas and the methodology; LD did most of the coding. CMA supplied SAGE data and provided practical feedback on aspects of earlier approaches found to be wanting, thus guiding further development.


Serial Analysis of Gene Expressions (SAGE) produces gene expression measurements on a discrete scale, due to the finite number of molecules in the sample. This means that part of the variance in SAGE data should be understood as the sampling error in a binomial or Poisson distribution, whereas other variance sources, in particular biological variance, should be modeled using a continuous distribution function, i.e. a prior on the intensity of the Poisson distribution. One challenge is that such a model predicts a large number of genes with zero counts, which cannot be observed.We present a hierarchical Poisson model with a gamma prior and three different algorithms for estimating the parameters in the model. It turns out that the rate parameter in the gamma distribution can be estimated on the basis of a single SAGE library, whereas the estimate of the shape parameter becomes unstable. This means that the number of zero counts cannot be estimated reliably. When a bivariate model is applied to two SAGE libraries, however, the number of predicted zero counts becomes more stable and in approximate agreement with the number of transcripts observed across a large number of experiments. In all the libraries we analyzed there was a small population of very highly expressed tags, typically 1% of the tags, that could not be accounted for by the model. To handle those tags we chose to augment our model with a non-parametric component. We also show some results based on a log-normal distribution instead of the gamma distribution.By modeling SAGE data with a hierarchical Poisson model it is possible to separate the sampling variance from the variance in gene expression. If expression levels are reported at the gene level rather than at the tag level, genes mapped to multiple tags must be kept separate, since their expression levels show a different statistical behavior. A log-normal prior provided a better fit to our data than the gamma prior, but except for a small subpopulation of tags with very high counts, the two priors are similar. The exact magnitude of p is unknown  but is certainly much smaller than 1  as Poisson distributed with intensity λt = pnt.In Serial Analysis of Gene Expression (SAGE), mRNA is extracted from a tissue sample and converted to cDNA, from which oligonucleotides  at specific locations in the cDNA fragments are extracted and amplified using PCR. Those tags are either ten or seventeen bases long, depending on the experimental protocol. Sequencing the PCR product, it is possible to establish the number of copies of each tag extracted. . Idealluznetsov )), whichlambda-values that just happened not to be counted. Those cannot be distinguished from tags that do not exist at all or are never transcribed. The problem of estimating the total number of expressible tags (the size of the transcriptome) was studied by Stern  mod modlambdYt ~ Poisson(λt), λt ~ f     (1)Yt is the observed count for tag t, λt is the "true" expression level of tag t and θ is some parameter in the model. For the prior f we tried a number of candidates . The gamma prior turned out to provide a good fit to the distributions of the tag counts for counts lower than a certain threshold, typically the 98th or 99th percentile. Attempts to model the tag counts above that threshold with a second gamma-distributed component failed, not surprisingly since the number of tags in that range was too small to support meaningful estimates.where α and β are known, the posterior distribution of λt given yt is distributed as Gamma This is convenient because the posterior distribution of γ represents our knowledge of the true gene expression after the SAGE count has been observed. Also, since 1/β is a scale parameter in the Gamma distribution, libraries of different size can be compared. Other things being equal, we expect the estimated value of β to be inversely proportional to the library size.For the purpose of this paper we choose the gamma distribution, whose parameters were estimated with an empirical Bayesian approach . The choY becomes a negative binomial distribution:The marginal distribution of and in particularSince the zero counts are not recorded, the counts of the recorded tags follow a zero-truncated negative binomial distributionα < 1 heuristic methods do not work and ML estimation should be used instead.The zero-truncated negative binomial distribution has been studied by several authors, mainly for modeling group sizes. See Johnson  for an oλt given Yt, one can construct a variance-stabilizing transform for a SAGE library. Also, in order to assess the sensitivity of the SAGE technology with respect to genes with low expression levels, one needs to know the distribution of λ. The idea of applying Poisson models to SAGE data is not new. Cai ) bλ given a count above the threshold. Fortunately, this is not so critical because for a high count the posterior mean will be close to the observed count.As a consequence of this choice, the model does not provide a posterior distribution of λ ~ Gamma and correlation coefficient R. If the total count for the gene is Poisson distributed with intensity λ* ~ Gamma, we haveAnother issue relates to genes mapped to multiple tags. It is reasonable to assume some correlation between two tags representing the same gene. In the human transcriptome map, the counts for those genes were reported as the sum of the tag counts. Suppose a gene is represented by two tags, the count of both being Poisson distributed with intensity and thusα for genes mapped to two or three tags are proportional to the estimated values of α for the genes mapped to a single tag. This suggests that the tag counts have the same distribution, whether they share the gene with other tags or not. The proportionality constant of 2.2 for genes mapped to two tags and 3.5 for genes mapped to three tags correspond to a correlation coefficient of approximately -0.05. This is a surprising result, since we found positive correlation between tags mapping to the same gene in the data used by Cai [This shows that if the counts per gene, rather than the counts per tag, are reported in a data set, genes with different numbers of representing tags should be kept separate. As shown in figure d by Cai . A possiα. More important, the bivariate model has a useful interpretation: the shared gamma process Z is the main effect (gene effect) while the independent gamma process X is the  interaction effect. A further generalization of the bivariate model will be to incorporate multiple interaction effects in a multivariate model, for example a third gamma process related to treatment groups.By modeling two SAGE libraries with a bivariate truncated negative binomial model, it was possible to achieve a more stable estimate of We have made the distinction between truncated models (only positive tags considered) untruncated model (the set of expressible tags assumed to be known). It could be argued, however, that even when zero counts are reported , the untruncated models should allow for a an unknown number of non-recorded tags. Such models are called zero-deflated Poisson models.By the analysis of the HTM data, we have ignored the issue of sequencing errors. Figure λ's across the genes could boost their methods further.SAGE data appear, when sequencing errors are handled properly, to follow a Poisson mixture with a log-normal prior on the Poisson parameter. The gamma prior  provides a good approximation for low counts . Using a bivariate gamma-Poisson model, the transcriptome size can be estimated from the data; alternatively, the list of expressible tags from the Human Transcriptome Map can be used. Whether one prefers the mathematically convenient gamma prior or a log-normal prior traditionally applied to microarray data, and whether one prefers a parametric or non-parametric model for the high-expressed tags, we believe that the Poisson model is useful for analyzing SAGE data because it separates the sample variance in the Poisson process from the biological variance. Vencio  and Cai We used 72 libraries from the Human Transcriptome Map (HTM) , which We also used two of the short-tag libraries from SAGE-genie  , and made use of the S-PLUS option of computing the gradient and Hessian using the double dogleg step(Venables [We augmented this model with a non-parametric component for the high-expressed genes in the same way as the untruncated model described above. We maximized the log-likelihood using a quasi-Newton-Raphson method (S-PLUS function Venables ). As a sα ~ exp(1) and β ~ gamma. In order to quantify the uncertainty on the parameter estimates, we also computed the Bayesian a posteriori distribution of the parameters in the truncated model with non-parametric component using a Markov Chain Monte Carlo (MCMC) algorithm. The MCMC simulations were carried out with WinBugs [For the threshold k for the non-parametric component, the value found by the method of moments (described above) was used . For the prior distribution of the parameters, we used  WinBugs .λ) in two libraries (1 and 2), we assumed the trivariate reduction model :λ1, λ2) = , μ1, μ2 ~ Gamma, τ ~ Gamma     (13)X1 ~ negbinom, X2 ~ negbinom     (15)lambda1 and lambda2 isThe correlation between VAR(λ.) = VAR(μ.) + VAR(τ)     (20)μ1, μ2 and τ are derived from the gamma distribution. For each pair of libraries from the 19 largest libraries from the Human Transcriptome Map, we estimated the parameters in this model. Anticipating that the model would not fit to the frequencies of counts above the threshold found in the univariate model, which varied between 9 and 22, we restricted the analysis to tags with counts below 15 in both libraries. We also fitted an augmented model in which X1 and X2 were allowed to have different values of α. In that model, the marginal distributions of Y1 and Y2 are negative binomial with parameters  and , respectively. Note that it is not expected that α1 is characteristic for library 1 when library 1 is modeled together with different libraries: if library 1 and 2 show a high degree of correlation, ρ will be larger at the expense of α1 and α2where the variances of Raw SAGE data contain a high number of tags with a count of one, many of which, presumably, are artifacts such as sequencing errors. Beissbarth  estimateHTM Human Transcriptome MapSAGE Serial Analysis of Gene ExpressionMCMC Markov Chain Monte CarloBoth authors were involved in the development of the models. HT implemented the models and performed the computations. Both authors contributed to the manuscript.


Five species of the genus Schistosoma, a parasitic trematode flatworm, are causative agents of Schistosomiasis, a disease that is endemic in a large number of developing countries, affecting millions of patients around the world. By using SAGE  we describe here the first large-scale quantitative analysis of the Schistosoma mansoni transcriptome, one of the most epidemiologically relevant species of this genus.After extracting mRNA from pooled male and female adult-worms, a SAGE library was constructed and sequenced, generating 68,238 tags that covered more than 6,000 genes expressed in this developmental stage. An analysis of the ordered tag-list shows the genes of F10 eggshell protein, pol-polyprotein, HSP86, 14-3-3 and a transcript yet to be identified to be the five top most abundant genes in pooled adult worms. Whereas only 8% of the 100 most abundant tags found in adult worms of S. mansoni could not be assigned to transcripts of this parasite, 46.9% of the total ditags could not be mapped, demonstrating that the 3 sequence of most of the rarest transcripts are still to be identified. Mapping of our SAGE tags to S. mansoni genes suggested the occurrence of alternative-polyadenylation in at least 13 gene transcripts. Most of these events seem to shorten the 3 UTR of the mRNAs, which may have consequences over their stability and regulation.SAGE revealed the frequency of expression of the majority of the S. mansoni genes. Transcriptome data suggests that alternative polyadenylation is likely to be used in the control of mRNA stability in this organism. When transcriptome was compared with the proteomic data available, we observed a correlation of about 50%, suggesting that both transcriptional and post-transcriptional regulation are important for determining protein abundance in S. mansoni. The generation of SAGE tags from other life-cycle stages should contribute to reveal the dynamics of gene expression in this important parasite. Quantitative and qualitative transcriptome analyses reveal some of the most important biological aspects of an organism. Transcriptome examination is crucial for the understanding of significant biological processes, allowing the study of transcription/translation relationships, the dynamics of gene expression and, an important feature in parasites, a quantitative evaluation of the expression of genes that are potential targets for drugs or vaccines across diverse life-cycle or developmental stages.S. mansoni has been mainly performed by the partial sequencing of cDNA clones derived from libraries prepared with RNA derived from diverse life-cycle stages of the parasite [Large-scale transcriptome analysis of parasite -4. The lparasite , where wparasite -9 or lifparasite ,11. HoweRattus norvegicus [Saccharomyces cerevisiae [Homo sapiens [Mus musculus [Caenorhabditis elegans [Drosophila melanogaster [Cryptococcus neoformans [Plasmodium falciparum [Giardia lamblia [Toxoplasma gondii [Schistosoma mansoni.Serial Analysis of Gene Expression  is one orvegicus , Saccharrevisiae , Homo sa sapiens , Mus musmusculus , Caenorh elegans , Drosophnogaster , Cryptocoformans  and manylciparum -22 and m lamblia  and Toxoa gondii . Here weS. mansoni were maintained in the laboratory by routine passage through mice and snails and recovered from the porto-mesenteric system by perfusion, after 7 to 8 weeks of infection. Worms were washed in saline solution and stored at -20°C in RNAlater (Ambion) prior to mRNA extraction. Poly-A mRNA was isolated with MACS kit , eluted in 200 μL of DEPC-treated water and treated twice with Promega RQ1 RNAse-free DNAse (1 U/10 μL) for 30 min at 37°C. DNAse was inactivated at 65°C for 10 min. mRNA purity and integrity were checked by RT-PCR using appropriate primer pairs of known genes and also negative controls as described in Verjovski-Almeida et al. [Pooled  adult worms from BH isolate of Sequences from cloning vectors were trimmed and tags were extracted from high-quality segments using Phred . SequencS. mansoni genes was generated in silico after mapping the NlaIII restriction sites (CATG) to the complete set of full-length cDNA sequences from S. mansoni available from GenBank, from the TIGR tentative consensus and the complete set of clusters and singlets generated by our group as part of the S. mansoni transcriptome project [NlaIII restriction site in the transcripts dataset was extracted, thus generating a list of putative S. mansoni tags. These tags were annotated according to the information available for the transcripts from which they were derived. Top priority annotation was given to full-length genes, followed by TIGR consensus and our S. mansoni transcriptome project [A second list, containing putative SAGE tags of  project . Sequenc project . These tS. mansoni transcripts were also screened for putative alternative poly-adenylation sites using SAGE data. For this purpose, the list containing all putative SAGE-tags (adjacent to NlaIII sites) from S. mansoni full-length genes available in GenBank, was cross-referenced with the tag list and the putative tags and ranked according to their position in relation to the 3' end. The most 3' tags, that are more likely to be bona fide tags for the canonical transcripts, were ranked as zero and the remaining tags were organized in ascending order from 3' to 5'. Tags that have rank > 0, a number of counts > 1, and were not followed by a putative site of internal binding of an oligo-dT primer (at least 8 adenines in a window of 10 bases) [Full length 0 bases)  were conEvaluation of positional distribution of SAGE tags and ESTs over S. mansoni full-length cDNAs was carried over a set of 208 genes that were tagged by at least two SAGE tags. Blast analyses showed 26,888 ESTs and 9,589 SAGE tags mapping to these genes, allowing the identification of gene regions covered by these sequences. The mapped coordinates were normalized in terms of relative position of EST over the mRNA and relative coverage over all genes was calculated. This positional distribution was plotted together the distribution of the SAGE tags over the same gene set, where the 0% and 100% are equivalent to 5' and 3' positions of mRNAs, respectively.Functional classification of S. mansoni transcripts was undertaken using the Gene Ontology database. For this, blast analyses of the genes mapped by our SAGE tags were performed against 2,413,334 protein sequences available from Gene Ontology database (02/2007). All ontologies associated to the first hit matched by the query sequence were recovered and then was assumed that S. mansoni gene would have the same functional annotation. Evaluations of function were performed for 3 different classes of abundance including: abundant (represented by more than 500 tags), intermediate (499 to 100 tags) and less abundant (lower than 100 tags).After sequencing and evaluating 5,626 clones of the SAGE library, 4,752 reads (84%) containing 998,200 nucleotides were accepted with the quality criteria adopted. The need for further sequencing was determined by evaluating the frequency of tags that appeared at least twice as a function of total tags sequenced. This curve reached a plateau close to 60,000 tags and suggested coverage of the majority of genes expressed in this developmental stage [S. mansoni adult worms. In fact, 2,886 of these tags found matches in the Schistosome gene index or in the list of S. mansoni transcripts identified by Verjovski-Almeida et al. [The most informative tags are those that appeared at least twice (less likely to contain sequencing artifacts) in the final tag list. These comprised a total of 6,263 distinct tags, which should approximate to the total number of genes expressed in this developmental stage . The lisa et al. , which sS. mansoni transcriptome project [st and 46th most abundant transcripts in adult worms. The most frequent transcript encodes the F10 eggshell protein, followed by a pol-polyprotein transcript, heat shock protein 86 and 14-3-3 protein homolog (see Table S. mansoni gene fragments (contigs and singlets). When only tags that appeared at least twice were considered, 3,347 (53.4%) matched S. mansoni gene fragments. A complete list of all tags, together with their frequency, tag sequences, and respective gene assignments can be found in the supplementary table that accompanies this paper , followed by TIGR consensus sequences and a clustering of the sequences produced by the  project . By usinS. mansoni, blast analyses were performed against 2,413,334 protein sequences available from the Gene Ontology database (Feb/2007). Genes mapped by more than 3 SAGE tags were used as queries. All ontologies associated to the first hit matched by the query sequences were recovered and their functional annotations were given to the respective schistosome gene. In this process, ontologies were assigned to 2,933 genes. Functional classification was then investigated for transcripts distributed in expression classes, according to their tag abundance. We considered that the most abundant functional categories were those containing genes with more than 500 tags; followed by the intermediate (499 to 100 tags) and less abundant classes (lower than 100 tags). This allowed us to describe the most abundant functional classes among the highly expressed, intermediate and lower expressed genes.In order to evaluate the functional categories most abundantly represented in the transcriptome of It can be observed in Figure S. mansoni shows that for some transcripts two or more distinct SAGE tags have been sequenced. These distinct tags were used to investigate alternative poly-adenylation events that may occur in these transcripts. After the analysis of these events, using the criteria described in materials and methods at least 16 alternative poly-adenylation events could be identified in 13 full-length S. mansoni mRNAs  in 35 (9.6%) of them. An extrapolation of this would suggest that the frequency of expression of 90% of the S. mansoni genes expressed in adults could be evaluated by the SAGE approach employed here.The SAGE technique involves generation and sequencing of large numbers of short tags, defined by the occurrence of a recognition site for a type I restriction enzyme in the mRNA . IdeallyS. mansoni Unigene cluster sequences were evaluated, we observed that 2,193 clusters contained ESTs derived from adult worms. Only 169 of these clusters contained full-length sequences. When tags (rank 0 and rank 1) of these 169 clusters were considered, we observed that 132 (78%) were represented in our SAGE tag list. So, this alternative estimate shows that coverage of our SAGE tags was of about 78% of the genes expressed in adult worms. We also noted that 39 UniGene clusters, with no adult-worm derived ESTs in the cluster composition, had their expression confirmed in this stage by our SAGE data.On the other hand, when 8,669 S. mansoni ESTs from the 3' end of the transcripts, for a more complete knowledge of the schistosome transcriptome. This also points to a reduced overlap of the SAGE and available EST data, which will result in a poor coverage of low expressed genes by non-normalized 3' UTR ESTs and in the failure of SAGE-to-transcript assignment.To establish how the transcriptome derived from SAGE and ESTs can be compared to each other, we evaluated the relative distribution of SAGE and EST sequences over a set of 208 worm full-length mRNA sequences available in GenBank. The 208 full-length transcripts are covered by 26,888 ESTs and 9,589 SAGE tags. As expected, 42% of the SAGE tags that map to the set of 208 full-length genes are positioned in the last 20% of the transcripts. On the other hand, only 17% of the ESTs mapped to these genes cover this same 3' portion of the transcripts , this data clearly demonstrates that more 3' sequences from normalized cDNA libraries are required for deciphering the transcriptome of this parasite.Indeed, from the total of 6,263 tags with frequency higher than one, 2,916 (46.6%) found no matches on the transcript databases used. As expected, this failure in finding the correspondent gene for a specific tag was found to be directly related to the low expression of the corresponding transcript, and its reduced coverage by ESTs. In fact this can be used as an indirect measurement of correlation of SAGE and EST coverage. Whereas 96% of the 50 most frequent tags or 92% of top 100 tags could be identified in a transcript, only 53% of all ditags  or only 40% of all 15,655 tags could be assigned to its correspondent gene. As the While the same tag can be mapped to many transcripts (indicating a conservation of a nucleotide motif), we also see that a single transcript might sometimes generate various different tags. This parallels to what happens in proteomic studies when the same protein sometimes generates different spots in a gel. The occurrence of multiple tags deriving from the same transcript could occur by methodological problems  or due to biological features such as splicing variants in the transcript region containing the most 3' tag or as the result of the use of multiple poly-adenylation sites. Whereas the use of SAGE tags to evaluate alternative-splicing is more difficult, the occurrence of alternative poly-adenylation events could be evaluated with less assumptions. In order to reduce the impact of methodological aspects over the determination of alternative poly-adenylation events, we have not considered tags sequenced only once, ambiguous tags (those that could be mapped to different transcripts) or internal tags that appeared before long stretches of A's in the transcript, which could have been used as false polyA tails during the cDNA synthesis step .S. mansoni, suggests that this parasite employs this mechanism for regulating mRNA stability. We should note that the occurrence of partial digestion with NlaIII seems to be rare here, as in our list of 15,655 distinct tags, not a single CATG (the restriction site for NlaIII) could be found.After using the above described filters, consistent events of multiple tags in a single transcript were identified in 13 full length genes. Poly-adenylation events cause a reduction in the transcript size, blocking the transcription of portions of its 3' region, together with the most 3' restriction site of the enzyme used for constructing the SAGE library. The reduction of the 3' UTR observed here, caused by the alternative poly-adenylation was usually accompanied by a removal of a significant portion of the putative ARE transcript repertoire (Adenosine and Uridine-Rich Elements) . AREs arS. mansoni became recently available. Curwen et al. [st to 253th  that could not be evaluated by proteomic analysis [RNA analysis by SAGE enabled the evaluation of genes coding for proteins whose physical-chemical properties impaired their analysis by 2D gel electrophoresis. An example is the determination of transcript abundance of priority vaccine candidates of the World Health Organization , P19 (202nd with 42 tags) and P48 (356th with 26 tags), which advocates their importance in the early-stages of eggshell formation. We should observe that no tags could be identified for egg-secreted proteins (such as ESP3-6 and ESP15), suggesting their expression only in later stages of the eggshell development. The high expression of actin and myosin (heavy and light chains) was also observed, with the identification of their respective genes and gene-paralogs among the top 100 transcripts, reflecting the musculature as one of the major worm tissues. Among the 50 top transcripts, as expected, we observe the high abundance of 12 ribosomal-protein genes as well as genes that encode proteins involved in protein and carbohydrate metabolism. It is also interesting to note the high abundance of the gene that codes for a protein similar to thymosin beta (17th most abundant transcript in adult worms), especially due to its involvement with wound healing [The most abundant tag identified here is 'ACTATTCGGG', a sequence tag that matches diverse isoforms of the gene encoding SmP14, or F10 eggshell protein family. The frequency of this tag strongly suggests that this is the most abundant mRNA species found in adult worms. This abundance is highly significant, especially if we consider the larger biomass of male worms as well as the male bias found in the sex ratio of fections . Indeed, healing , its ant healing -36 and i healing .One of the most notable strengths of the SAGE method is that results from any new experiments are directly comparable to existing databases. SAGE data represent absolute expression levels, based on the digital enumeration of transcript tags in the total transcriptome. This allows the expression level of any gene to be compared with that of any other gene, from among many libraries of different sources and sizes . In thisThe author(s) declare that they have no competing interests.EPBO constructed the SAGE library presented here; RDM was responsible for RNA extraction and PSLO coordinated the bioinformatics analysis of SAGE data. EPBO, AP, RDM, SPG, KAA, CFMM, LCCL, SVA and EDN participated on the sequencing of the library and on the analysis and interpretation of the data; EPBO, PSLO, AP, RDM, DNN, SVA and EDN performed bioinformatics analysis. EPBO, PSLO, SVA and EDN conceived the study and participated on its design and coordination. All authors contributed to the writing of this manuscript and approved its final form.S. mansoni genesAnalysis of positional distribution of ESTs and SAGE tags for a set of 208 full-length . The positional distribution of all ESTs available in GenBank, as well as all SAGE tags from our study was evaluated over a panel of 208 full length S. mansoni genes. Only 17% of the ESTs mappedto 208 full-length transcripts cover the final 20% of the transcripts, while 42% of the generated SAGE tags cover this same region. This shows the reduced overlap of SAGE and ESTs suggests the necessity of generating more S. mansoni ESTs, especially from the 3' end of the transcripts, for a better knowledge of the schistosome transcriptome.Click here for fileSchistosoma mansoni Complete list of SAGE tags. Contains all 15,655 distinct SAGE tags sequenced, together with their frequency (tag count), the accession numbers of the corresponding genes, their relative position on the mRNA, their tag rank and the annotation of the respective gene.Click here for fileFunctional classification of the intermediate and less abundant schistosome transcripts based in Gene Ontology analysis. Functional classification of the most abundant S. mansoni transcripts based in Gene Ontology analysis.Click here for fileProtein and gene rank. Comparison of the top 10 proteins ranked by proteome analysis with the expression rank obtained by SAGE.Click here for file


SAGE has been used widely to study the expression of known transcripts, but much less to annotate new transcribed regions. LongSAGE produces tags that are sufficiently long to be reliably mapped to a whole-genome sequence. Here we used this property to study the position of human LongSAGE tags obtained from all public libraries. We focused mainly on tags that do not map to known transcripts.Using a published error rate in SAGE libraries, we first removed the tags likely to result from sequencing errors. We then observed that an unexpectedly large number of the remaining tags still did not match the genome sequence. Some of these correspond to parts of human mRNAs, such as polyA tails, junctions between two exons and polymorphic regions of transcripts. Another non-negligible proportion can be attributed to contamination by murine transcripts and to residual sequencing errors. After filtering out our data with these screens to ensure that our dataset is highly reliable, we studied the tags that map once to the genome. 31% of these tags correspond to unannotated transcripts. The others map to known transcribed regions, but many of them  are located either in antisense or in new variants of these known transcripts.We performed a comprehensive study of all publicly available human LongSAGE tags, and carefully verified the reliability of these data. We found the potential origin of many tags that did not match the human genome sequence. The properties of the remaining tags imply that the level of sequencing error may have been under-estimated. The frequency of tags matching once the genome sequence but not in an annotated exon suggests that the human transcriptome is much more complex than shown by the current human genome annotations, with many new splicing variants and antisense transcripts. SAGE data is appropriate to map new transcripts to the genome, as demonstrated by the high rate of cross-validation of the corresponding tags using other methods. Serial Analysis of Gene Expression (SAGE)  is a widp = 1 - L)N where L = 14 is the tag length and N = 3.272.204.263 represents the sum of the lengths of the mitochondrial and nuclear genomes. Therefore, p = 0.99). For a LongSAGE tag of 21 bp, this probability of a spurious match is much smaller (p = 0.000744). Therefore the LongSAGE tags are much more specific than 14 bp SAGE tags, even if the specificity of LongSAGE tags is not as high as these theoretical calculations suggest, as nucleotides are not randomly and equally distributed along the genome sequence, and the genome contains many repetitive sequences.The SAGE method consists of sequencing small tags derived from the 3' ends of mRNAs. A crucial step in SAGE analysis is tag identification ,7, or fiA systematic annotation of new transcripts by mapping a library containing 28,000 of these LongSAGE tags to the human genome sequence revealed 15,000 exons that are not currently described, at least half of which belong to novel genes . More rea priori knowing the transcribed sequences. We made a comprehensive study of all tags from all publicly available human LongSAGE libraries deposited in the public Gene Expression Omnibus databank. Most of the studies using 14 bp SAGE tags have focused on the expression of known genes. By contrast, here we concentrated on the tags that have not been generated by known transcripts. Because the main difficulty in estimating the amount of transcription in the human genome seems to be the false positive rate of detection [For this reason, we propose to study this question across the whole human genome using an independent method. We exploited the advantages of the LongSAGE method to study the transcriptome without etection , we firsWe used all the tags available in the public human LongSAGE libraries of the Gene Expression Omnibus database . This coTo be able to predict with sufficient confidence which regions of the genome have generated these SAGE tags, we selected a reliable set of tags from this total dataset. For this purpose, we first considered the tags present only once in our dataset, that have therefore been observed only once in a single SAGE library. Some of these infrequent tags correspond to very weakly expressed transcripts. Others, however, may be incorrect because they have undergone sequencing error(s) during the construction of SAGE libraries. Tags occurring only once represent 13% of the total dataset, and a large proportion (75%) of the different tags. This proportion is not negligible, but as some of these tags, unfortunately, may be incorrect, we checked the reliability of this set of tags before including it in our analysis.As mentioned above, the probability that a 21 bp sequence spuriously matches the human genome sequence is very small : if the subset of tags occuring only once in the total dataset contained many incorrect tags, it should therefore be enriched in unmapped tags. We therefore mapped each tag to the human nuclear and mitochondrial genome sequence, and compared the tags occurring only once in the SAGE libraries and the tags occurring more than once. Among the subset of tags occurring only once, 73% are unmapped. In contrast, in the other pool of tags, significantly less tags (39%) have not been localized.Each transcript generates several tags, that could either be correct or incorrect after sequencing: a large majority of these tags are correct, but a small number are incorrect tags containing one or more sequencing errors. For each incorrect tag present in our dataset, it should be possible to recover somewhere else in our dataset the corresponding correct tag, without sequencing error. Thus, for each tag which is present only once and does not match the genome sequence, we checked whether we could find in our dataset another tag matching the genome sequence and identical to this tag apart from one or two base pairs . For 69% of the unmapped tags occurring only once, we found at least one mapped variant. This frequency drops to 33% in the subset of unmapped tags occurring more than once in our dataset.In conclusion, these results suggest that the subset of tags occurring only once is particularly enriched in incorrect tags resulting from sequencing errors. We have therefore chosen not to include these tags in our analysis.Excluding tags that are present only once does not eliminate all tags containing sequencing errors. Indeed, the same error could occur several times . We tried therefore to eliminate incorrect tags that occur more than once in the libraries.t, we can evaluate the number of variants derived from this tag t by sequencing errors. We identified the set of tags corresponding to all the variants of t (differing by at most two base pairs because of substitution(s), insertion(s) or deletion(s)), and determined for each variant whether it was rare enough to be only due to sequencing error(s). If so, the variant was discarded from the dataset.For this purpose, we implemented the algorithm proposed by Colinge and Feger  . In these sequences, 3% of the tags overlap two exons.Tags that do not map to the genome could correspond to tags overlapping two exons. We computed the expected proportion of such tags using a set of transcripts with reliably annotated exons. For this purpose, we extracted Among the tags from our dataset that do not map to the genome, we found that 1885 different tags overlap two exons, by using Ensembl annotations. These tags correspond to 1% of our initial set of tags. This proportion is slightly lower than the expected value, no doubt because the quality of annotations for all transcripts is not as high as in the set of Refseq transcripts.in silico the LongSAGE tag from 12418 Refseq transcripts  : 6% of these tags extend into the polyA tail.Tags containing part of the polyA tail can also not be mapped to the genome sequence. We computed the expected proportion of such tags using a set of transcripts for which the polyA tail is known. For this, we extracted To estimate the frequency of such tags in our dataset, we mapped these tags to all human ESTs available in dbEST . We alsop that a given tag contains no SNP is p = (1 - 1/1000)21, and the expected proportion of tags with at least one polymorphic site is roughly 2% (1 - p).Unmapped tags may also be due to the presence of a polymorphic region of the genome (Single Nucleotide Polymorphism : SNP), if the allele sequenced in the genome project differs from the allele of the individual used to construct the SAGE library. It has previously been estimated that any two copies of the human genome differ from one another by approximately 0.1% of nucleotide sites  . TherefoWe searched for the presence of such tags among our set of unmapped tags. For this purpose, we used a dataset computed using a previously published method   . Some MEWe considered a tag as a contaminant if it did not map to the human genome, nor to a human mature transcript, it occured only in embryonic stem cell libraries propagated on MEF, and it mapped to the mouse genome. These tags represent a non-negligible proportion (13%) of the tags that do not match the human genome sequence.It is obvious that the percentage of tags that map to the mouse genome varies between embryonic stem cell libraries, revealing different degrees of exclusion of MEF. Our results show that even when libraries have been constructed from carefully dissected material, it is always necessary to filter the tags to exclude tags generated by transcripts present in the remaining MEF.6) as SAGE tags (3.1 × 106). We also tested whether these tags could come from edited mRNAs that are not represented among EST sequences. For this purpose, we examined the transition frequencies when comparing genomic and tag sequences, since the two known families of RNA-editing enzymes in humans perform adenosine to inosine or cytosine to uracil modifications [In total, the origin of 42% of the unmapped tags was explained by one of the situations previously described (29% correspond to a human transcript and 13% correspond to mouse contaminants). However, we could not explain the origin of the remaining unmapped tags. These tags do not belong to any library in particular. The large majority (91%) of these tags correspond to sequences varying by one base from another tag that maps to the genome, and some of these tags could therefore correspond to rare polymorphisms that are not represented by an EST. This is possible, but unlikely to be the main explanation because we studied twice as many ESTs . Because of this, and because our screens exclude many other possible explanations, we think that the most parsimonious explanation for the presence of these tags is that they contain sequencing error(s). We initially used an error rate that was previously published (17.3% of LongSAGE tags contain at least one error ) to remoTags mapping once to the human genome represent nearly half (49%) of the set of different tags. We studied the localization of these tags with respect to known transcripts, to evaluate the amount of transcription inside and outside annotated transcripts.We first studied the tags that are located inside known transcripts, using Ensembl annotations . These aAmong the tags mapping once to the genome, 69% are located in such "extended" transcripts. A more precise description of the tag positions in different parts of these transcripts is displayed in Figure p < 10-16). This means that a large proportion of the genome is transcribed from both strands of the DNA, especially in the 3'end of the transcripts, confirming previous expectations [Arabidopsis thaliana [Even among the tags mapping to annotated transcripts, a non-negligible proportion (32%) maps in antisense compared to the annotated transcribed strand. Such tags have already been highlighted by several previous studies -36. We octations . Among tthaliana , but is thaliana ). For eap(match|erroneous)). Consequently, for each tag from our dataset that maps to the genome (once or more than once), the probability that it is erroneous can be estimated by the following calculation :31% of tags mapping once to the genome  do not correspond to a known transcript. We already have discarded the tags that could have been generated by sequencing errors using a published sequencing error rate in SAGE libraries. However, as we previously mentioned, it is possible that the error rate is higher than anticipated, and that some tags from our dataset still contain sequencing errors. We thus estimated the probability that such tags containing a sequencing error match the human genome sequence. For this purpose, we randomly selected 100,000 tags that match the human genome sequence, and we modified them by introducing "sequencing errors". These errors were randomly attributed, by using the percentage of error for each base we calculated (see methods). We found that 7.6% of these modified tags matched the human genome sequence : this is an estimate of the probability that an erroneous tag maps to the genome (p(erroneous) = 0.173, p(erroneous|match) = 0.020, and even with a higher error rate (p(erroneous) = 0.253), p(erroneous|match) = 0.030 is still low.With a sequencing error rate Because the probability that a mapped tag is erroneous is very small, the majority of the tags mapping once to the genome and outside annotated transcripts should come from unknown transcribed regions. However, it is possible that some of these tags do not belong to new transcripts, because the real 3'UTR may be longer than annotated (even after our extension). We therefore calculated the distance between these tags and the 3' end of the nearest transcript. We observed that very few tags are located in an incompletely annotated 3'UTR (less than 2% of tags that do not correspond to a known transcript are closer than 1000 bp to the nearest transcript).Because the vast majority of tags that do not correspond to an annotated transcript do not originate from an incompletely annotated 3'UTR, we searched for other evidence of transcription in the regions from which these tags originate . We alsAs we mentioned in the introduction, most of the recent work on finding new transcripts in the human genome has been performed using tiling microarrays. We thus compared our SAGE tags (mapping once to the genome but not on known transcripts) with transcribed regions predicted using tiling microarrays. For this comparison, we used the transfrags  recentlConversely, only 0.39% of the transfrags contain one or more of our tags. We propose several explanations for this observation. Nearly half of the transfrags correspond to nonpolyadenylated transcripts  that area priori knowledge of expressed genes. We used all the human LongSAGE libraries available, filtered them to remove tags containing sequencing errors, and systematically mapped these tags to the genome. We particularly concentrated on unexpected localizations, either because the tags did not match the genome sequence, or because they mapped outside known transcripts. We then proposed explanations or hypotheses for the origin of these tags.Using the SAGE method, it is possible to study the transcriptome without any More than one third of the different tags do not map to the human genome. Among them, 42% are part of mRNA sequences but are not found on the human genome because they correspond to polyA tails, junctions between exons, polymorphic sites or contaminant murine transcripts. The other tags are probably due to sequencing error(s). Consequently, the sequencing error rate in these public libraries is probably higher than previously estimated.Half of the different tags map once to the genome, and one quarter of these tags match outside annotated transcripts. This suggests that many transcripts are still to be annotated in the human genome. Because many tags mapping to known transcripts belong either to introns or are aligned in antisense, we suggest that they belong to new variants or antisense mRNAs of these transcripts. Consequently, the human transcriptome seems to be more complex than shown by the current genome annotations, and LongSAGE analysis should help to improve the annotation process., in July 2005.SAGE libraries were downloaded from the NCBI website at the following address: The tags were localized on the human nuclear genome  and mitochondrial genome (Refseq sequence NC_001807) using Megablast . Only maTo discard tags that are likely to have been generated by sequencing errors, we implemented Colinge and Feger's method . This mex, the error rate per base: 0.173 = 1 - (1 - x)17. We obtain x = 0.0111, and therefore the probabilities to find exactly one error in one tag (p1 = 17x(1 - x)16), and exactly two errors in the same tag (p2 = x2(1 - x)15). The probability of finding one or two errors (p1 + p2 = 17.21%) is much greater than the probability of finding more than two errors (p3 = 0.083%). We therefore ignore p3.First, it is necessary to know the probability for one particular tag to be sequenced with an error. It has previously been estimated that the error rate is 17.3% in LongSAGE libraries . This reL be the set of tags in a given library. We can define for each tag t ∈ L one set of tags V1(t) that contain the tags q ∈ L that can be obtained by changing one base of the tag t . Likewise, we define V2(t) as the set of tags q ∈ L that vary from t by two changes. As proposed by [t the average contribution of its neighbors q to the number of occurrences of the tag t. This contribution, ν(t), can be calculated using the following equation:Let posed by , we compocc(q) is the number of occurrences of the tag q, and #Vi(q) the cardinality of Vi(q).where pi/#Vi(q) corresponds to the average contribution of q to each of its neighbors. In other words, each tag will equally contribute to each of its neighbors to increase their number of occurrences.Therefore, ν(t) ≥ occ(t), because these tags may be due to sequencing errors.In each SAGE library, we eliminated all the tags for which If we have not discarded all tags containing sequencing error(s), it is possible that some tags containing sequencing error(s) match the genome sequence. We therefore measured by simulation the probability that these tags containing sequencing error(s) map to the genome.For this purpose, we picked up tags that map once to the genome, and we modified them by introducing "sequencing errors". To obtain tags that could have plausibly been created by sequencing errors, we need to know the probability of finding each base instead of each given base, because of sequencing error (e.g. A changed to a T). By comparing each correct tag (matching once the genome sequence) with incorrect variants of this tag , we obtained a matrix with the relative frequencies of each of the 12 sequencing errors (plus the frequencies of deletions and insertions). We then applied one modification per tag, according to this matrix. Then, we checked whether these modified tags mapped to the genome. Finally, we obtained an estimate of the frequency of tags with a sequencing error that map to the genome.CK and MS designed and performed this study, and wrote the manuscript. LD, OG and DM provided guidance with comments on the study and on the manuscript. All authors read and approved the final manuscript.Characteristics of the LongSAGE libraries analyzed. Characteristics of the LongSAGE libraries analyzed : their identification number in the Gene Expression Omnibus database (GSM), their total number of tags and their title as provided in the Gene Expression Omnibus database.Click here for fileDifferent origins of LongSAGE tags. Classification of the different tags by our filtering process.Click here for fileFrequencies of base changes between a mapped LongSAGE tag and its variants. Since the vast majority of the unmapped tags whose origin could not be explained correspond to sequences varying by one base from another tag that maps to the genome, we investigated if these tags could come from edited mRNA. There are two known families of RNA-editing enzymes in human : the adenosine deaminases acting on RNA (ADAR) which perform adenosine-to-inosine (A-to-I) modifications, and the apoB mRNA-editing catalytic peptide (APOBEC) which induces cytosine to uracile (C-to-U) transformations [rmations . In humarmations . Recent rmations -44. Thesrmations -44. A harmations . We therClick here for file