Evaluating the impact of recommendations based upon health technology assessment (HTA) represents a challenge for both HTA agencies and healthcare policy-makers. Using a psychosocial theoretical framework, this study aimed at exploring the factors affecting physician intention to adopt HTA recommendations. The selected recommendations were prioritisation systems for patients on waiting lists for two surgical procedures: hip and knee replacement and cataract surgery.Determinants of physician intention to use HTA recommendations for patient prioritisation were assessed by a questionnaire based upon the Theory of Interpersonal Behaviour. A total of 96 physicians from two medical specialties  responded to the questionnaire (response rate 44.2%). A multiple analysis of variance (MANOVA) was performed to assess differences between medical specialties on the set of theoretical variables. Given the main effect difference between specialties, two regression models were tested separately to assess the psychosocial determinants of physician intention to use HTA recommendations for the prioritisation of patients on waiting lists for surgical procedures.Factors influencing physician intention to use HTA recommendations differ between groups of specialists. Intention to use the prioritisation system for patients on waiting lists for cataract surgery among ophthalmologists was related to attitude towards the behaviour, social norms, as well as personal normative beliefs. Intention to use HTA recommendations for patient prioritisation for hip and knee replacement among orthopaedic surgeons was explained by: perception of conditions that facilitated the realisation of the behaviour, personal normative beliefs, and habit of using HTA recommendations in clinical work.This study offers a model to assess factors influencing the intention to adopt recommendations from health technology assessment into professional practice. Results identify determinant factors that should be considered in the elaboration of strategies to support the implementation of evidence-based practice, with respect to emerging health technologies and modalities of practice. However, it is important to emphasise that behavioural determinants of evidence-based practice vary according to the specific technology considered. Evidence-based implementation of HTA recommendations, as well as other evidence-based practices, should build on a theoretical understanding of the complex forces that shape the practice of healthcare professionals. In HTerm care .It is generally recognised that there is a gap between the production of scientific evidence and its utilisation to inform decision-making, , and thiAt the health policy level, previous work has reported that HTA recommendations could influence decision-making -11. Accoinstrumental, conceptual or symbolic use in decision-making [Hivon and collaborators have explored end-users' perceptions and use of HTA recommendations . Their fn-making . InstrumAt the healthcare organisations level, the implementation of hospital-based HTA activities could represent a strategy to improve practices . HospitaUntil now, little is known about the implementation of HTA recommendations at the individual level, i.e. in the daily practice of healthcare professionals. However, the literature on physician adoption of scientific evidence and interventions to improve it is extensive . Thus, iIn the field of social psychology, various theories and models have been proposed to understand what influences the adoption of behaviours. Triandis' Theory of Interpersonal Behaviour (TIB)  encompasIntention refers to the individual's motivation regarding the performance of a given behaviour. Facilitating conditions represent perceived factors in the environment that can ease or impede the realisation of a given behaviour. Habit refers to how routine a given behaviour has become, i.e. the frequency of its occurrence. Habit directly influences the behaviour, but can also have an influence on affect. However, this hypothesis was not tested in the present study.A schema adapted from the TIB is presented in Figure Affect represents an emotional state that the performance of a given behaviour evokes for an individual. It is considered as the affective perceived consequences of the behaviour, whereas perceived consequences refer to individual's perception of the instrumental consequences of the behaviour.In the TIB, the behavioural intention is formed by attitudinal as well as normative beliefs. Attitudinal beliefs comprise two dimensions: affect and perceived consequences. Normative beliefs consist of the internalisation by an individual of referent people's or groups' opinions about the realisation of the behaviour, whereas role beliefs reflect the extent to which an individual thinks someone of his or her age, gender, and social position should or should not behave. With respect to the personal normative beliefs, personal norm represents the feeling of personal obligation regarding the performance of a given behaviour, whereas self-identity refers to the degree of congruence between the individual's perception of self and the characteristics associated with the realisation of the behaviour.The TIB also distinguishes between two normative dimensions: social and personal. Social normative beliefs are formed by normative and role beliefs. For the purpose of this study, modifications were brought to the original TIB model. These modifications were consistent with a previous study that has adapted the TIB to understand healthcare professional behaviour . First, professional norm. This variable is related to the integration by the self of the specific normative pressures of one's professional group. The medical profession has a particular culture and sets of norms (e.g. the Hippocratic Oath) that also influence individual physician behaviour [collective self, as proposed by Triandis, which corresponds to the individual's assessment of how she or he should behave given her or his belonging to a specific reference group [Furthermore, in an effort to better adapt the TIB to health professional behaviour, another dimension was added to the personal normative beliefs – the ehaviour . In a prehaviour . This coce group . The proce group .To the best of our knowledge, the TIB has not previously been applied to the study of the adoption of evidence-based recommendations into medical practice. However, this model was successful in explaining a variety of professional behaviours, such as the adoption of information and communication technologies ,25,30,31This study is part of a larger initiative aimed at applying a multi-dimensional theoretical framework to assess the impact of HTA recommendations on decision-making at different levels of the healthcare system. Thus, various methods were used in order to assess factors influencing the uptake of HTA recommendations at the healthcare organisation and clinical decision-making levels. HTA adoption at the organisational level was assessed through a qualitative approach by means of interviews and observations at 15 hospitals of Catalonia. The results of the qualitative study are presented elsewhere ,33.In summary, the qualitative study indicates that factors related to the organisation and financing of the health system influence adoption of HTA recommendations at the hospital level. Furthermore, collaborations between hospitals and the HTA agency favour the integration of recommendations into organisational practices. At the professional level, the high degree of autonomy of medical specialists, the importance of peers and collegial control, and the definition of professional roles and responsibilities influence adoption of HTA recommendations.The present article focuses on the impact of HTA recommendations at the individual level, which has been conceptualised as physician intention to use HTA recommendations to support clinical decision-making. This study was conducted as part of a postdoctoral fellowship (MPG), and the research protocol was approved by Catalonia and Quebec governments. The Catalan Agency for Health Technology Assessment and Research (CAHTAR) also reviewed the research protocol and provided support for the study.Based upon the TIB, this study aimed to answer the following questions:1. Which psychosocial factors from the TIB  significantly explain the intention of physicians to adopt these recommendations into their practice?2. Are the psychosocial factors influencing physicians' intention to adopt HTA recommendations significantly different between the two groups of specialists?3. Do sociodemographic and professional factors  influence physicians' intention to adopt HTA recommendations over and above the psychosocial constructs from the TIB?A consensus was reached with researchers from the CAHTAR to select which recommendations would be investigated. The criteria used in the selection were: 1) publication time sufficient for the HTA recommendation to have been largely disseminated; 2) recommendations representing administrative and clinical health technologies, since the literature reports important variations in factors affecting the adoption of these two types of innovations; and 3) sBoth recommendations proposed a scoring system to assess patient priority on waiting lists for the targeted surgical procedures. The prioritisation systems for cataract surgery and hip and knee replacement were similar, although specific scoring items were used. Their utilisation by physicians practicing in the Catalan network of public hospitals was made mandatory through an instruction issued by the Servei Català de la Salut  in November 2004.emic-etic approach and has been recommended by psychosocial theorists in order to ensure the cultural sensitivity of a study [The field of social psychology has a long tradition in the development of survey instruments based upon theoretical frameworks. In the present study, questionnaire development was based on several theorists' recommendations ,35,36. T a study ,38.First, an open-ended questionnaire was prepared in order to assess the modally salient beliefs in the study population with respect to the behaviour under consideration. Salient beliefs are the first responses to come to a respondent's mind when asking an open-ended question. Therefore, modally salient beliefs are the most frequently reported beliefs regarding the attributes of performing a particular behaviour in the target group . Thus, aCompleted questionnaires were received from five ophthalmologists and seven orthopaedic surgeons. Responses were compiled for each specialty. A content analysis was performed to classify responses into thematic categories. Then the number of responses in each category was compiled, and those having a frequency of two or more were kept as the modally salient beliefs. These salient beliefs were used as the items to assess each theoretical construct of the TIB. A specific questionnaire was developed for each medical specialty, since two distinct recommendations were addressed. However, given the similitude between these recommendations, the two questionnaires used the same items to assess theoretical constructs, thus allowing for the combination of results and comparisons between groups.The first page of the questionnaire presented the study and gave instructions to participants. A sentence indicated that returning the questionnaire implied informed consented to participate in the study. The questionnaire began with a vignette describing a clinical case for which the surgical procedure (cataract surgery or hip and knee replacement) was relevant. By referring to the case presented in the vignette, physicians were asked to answer a total of 30 questions measuring the theoretical constructs of the TIB.It would be easy for me to use CAHTAR's recommendations to support my decision in this case." 1) Totally disagree; 2) Slightly disagree; 3) Neither agree nor disagree; 4) Slightly agree; or 5) Totally agree. The only exception was for the items composing the attitudinal construct that were assessed by means of 5-point bi-polar adjective scales. For example, "For me, using CAHTAR's recommendations to support decision-making in this case would be..." 1) Very foolish; 2) Somewhat foolish; 3) Neither foolish nor wise; 4) Somewhat wise; or 5) Very wise. The number of items used to assess each theoretical construct and their internal consistency are provided in Table Each theoretical item was assessed by a question measured on a five-point Likert scale. For example, to what extent do you agree with the following affirmation – "age group, gender, years of clinical experience, and medical specialty) was collected at the end of the questionnaire. The questionnaire was pre-tested with two physicians of each specialty in order to assess face validity and duration. Subsequently, minor adjustments were done to the wording of some questions. The questionnaire took approximately 15 minutes to complete.Finally, socio-demographic information , as well as large teaching hospitals and smaller general hospitals. Heads of department or service for the targeted specialties  were identified in each hospital as the local collaborators. The principal investigator contacted them by telephone to describe the study and solicit their participation. After receiving consent from all contacted persons, a package containing study questionnaires corresponding to the number of physicians who worked in the service was delivered to the local collaborator in each hospital. The total sample consisted of 217 physicians .First, descriptive analyses of distribution were conducted. Correlations between theoretical variables and between theoretical and external variables were assessed and are reported in Table 2 of the model containing only theoretical variables with the R2 of a model also containing external variables [Second, a comparison between the two groups of specialists was performed on the set of theoretical variables using the multivariate analysis of variance (MANOVA). Given the significant differences between groups, two independent hierarchical regression models were tested in order to assess the determinants of physician intention to use HTA recommendations. The potential impact of external variables  on intention was tested following Pedhazur's recommendation, which consists of comparing the Rariables . No signariables , but no A total of 96 physicians returned completed questionnaires  for a global response rate of 44.2%. Table Table To assess the main effect difference between the two groups, i.e. how they globally differ on the set of theoretical variables, a multivariate analysis of variance (MANOVA) was conducted. This test allows for verifying equality of variances between multiple variables at the same time, without having to adjust for multiple testing. According to Hair et al., a MANOVAThe Hotelling's Trace was used to assess the main effect of medical specialty on the set of theoretical variables, as it has been recommended for two-groups MANOVA.  As shownTherefore, given that intention to use HTA recommendations to support decision-making might have had different determinants within each group of medical specialists, two logistical regression models were tested, including variables from the TIB and external variables.2) in ophthalmologists' intention to use the HTA recommendations to support decision-making. The three determinants explaining this intention were, in order of importance: attitudinal beliefs (β = 0.40), personal normative beliefs (β = 0.36), and social normative beliefs (β = 0.25).Table 2) in orthopaedic surgeons' intention to use the recommendations to support decision-making. The strongest predictors were facilitating conditions (β = 0.39), personal normative beliefs (β = 0.38), and habit (β = 0.25).The final regression model tested to explain the intention to use HTA recommendations for prioritisation of patients on waiting lists for hip and knee replacement is reported in Table This study was the first, to the best of our knowledge, to assess the psychological factors influencing physician intention to use HTA recommendations based upon a recognised theoretical framework. The TIB has been successful in explaining a variety of human behaviours, including the adoption of health technologies among healthcare professionals ,25,30,31A major finding of this study is that intention of physicians to use HTA recommendations in their practice is influenced by a different set of psychosocial factors, depending on the specific context. This difference can either be attributed to the characteristics of the technology targeted in the HTA recommendations, the social and cultural characteristics of the medical specialty, the specific context in which recommendations are implemented, or a combination of these factors. It would be necessary to study the adoption of various HTA recommendations across different medical specialties and contexts in order to verify these hypotheses.Nevertheless, the present study supports the need for mapping interventions to specific population groups in order to improve the adoption of evidence-based practices . A previAmong the factors that were associated with intention to use HTA recommendations to support decision-making, personal normative beliefs were important in both groups of specialists. This variable was formed by three components, namely, personal norm, self-identity, and professional norm. The impact of personal morals or principles on clinical behaviours has been reported in a cross-cultural study of physicians' intention to prescribe hormone therapy . The conThe influence of attitudinal beliefs on the intention to use HTA recommendations was significant only in the ophthalmologists group. Attitude has been found as an important determinant of clinical behaviours in other studies ,53. Howehabit was also associated with the intention to use HTA recommendations among orthopaedic surgeons, which supports the previous hypothesis. It is likely that individual healthcare professionals will tend to adopt evidence-based practice more easily when there is a supportive culture in the working environment. However, it is important to acknowledge a possible threat to professional autonomy when introducing explicit rationing policies, such as prioritisation systems for surgical procedures that can lead to resistance to change [Facilitating conditions, i.e. factors in the environment that support the realisation of the behaviour, were the most influential determinant of intention to use HTA recommendations among orthopaedic surgeons. One plausible explanation is the fact that hip and knee replacements are more complex and costly procedures that require greater resources. Thus, the prioritisation system was endorsed by a majority of the departments as the 'local standard.' Furthermore, the variable o change .Previous studies of the impact of HTA on decision-making at the health policy level recognize the difficulty of measuring how a specific recommendation would inform decision-making on a given topic ,58. AnotAmong the factors that may affect the possibility to generalise the results, it is important to mention a possible participation bias since respondents may have been more knowledgeable and/or interested in the HTA recommendations under study than non-respondents. Unfortunately, contacting non-respondents to assess this potential bias was not feasible since the study was anonymous.The sample size was limited, despite a satisfactory response rate for this specific population. Previous studies usually report lower response rates for mail surveys among physicians ,63. The 2 may reflect a problem of 'over-fitting,' i.e. a perfect but meaningless solution [Given the small sample size, especially in the ophthalmologists group, it is important to use caution when interpreting the results. For undersized samples, the risk of unstable solution is greater when the independent variables are highly correlated. Also, a high Rsolution . To testsolution .2 in the prediction of behavioural intention among healthcare professionals based upon psychosocial theories [2 (.81) also was found, and similar correlations between the independent variables were present [With respect to the possibility of over-fitting, other studies, both with small or larger samples, have reported high Rtheories ,46,65. I present . Thus weA short vignette was used to bring physicians into a decision-making situation for which their intention to refer to the HTA recommendation was assessed. The vignette contained limited information on the clinical case and a hypothetical bias might have been present . HoweverThis study demonstrates the application of a social psychological model to understand the determinants of the adoption of evidence-based practices in healthcare. Of course, this represents only a small portion of the efforts needed to implement evidence-based interventions in order to improve quality in healthcare. Further work should address the translation of knowledge gained from studies on the determinants of healthcare professional behaviours into specific intervention strategies, the successful implementation of these strategies, and the evaluation of their effects on professional behaviours and, ultimately, on the effectiveness of the healthcare system.The author(s) declare that they have no competing interests.ES, JMVP and MPG participated in the design of the study. ES and MPG prepared the study questionnaires. MPG contacted the participants, proceeded to data collection and performed quantitative analyses. ES and JMVP reviewed the findings and a consensus was reached between all authors for data interpretation. MPG prepared a first draft of the manuscript and all authors revised and approved the last version of the manuscript.


In this study, we present a robust and reliable computational method for tag-to-gene assignment in serial analysis of gene expression (SAGE). The method relies on current genome information and annotation, incorporation of several new features, and key improvements over alternative methods, all of which are important to determine gene expression levels more accurately. The method provides a complete annotation of potential virtual SAGE tags within a genome, along with an estimation of their confidence for experimental observation that ranks tags that present multiple matches in the genome.Saccharomyces cerevisiae genome, producing the most thorough and accurate annotation of potential virtual SAGE tags that is available today for this organism. The usefulness of this method is exemplified by the significant reduction of ambiguous cases in existing experimental SAGE data. In addition, we report new insights from the analysis of existing SAGE data. First, we found that experimental SAGE tags mapping onto introns, intron-exon boundaries, and non-coding RNA elements are observed in all available SAGE data. Second, a significant fraction of experimental SAGE tags was found to map onto genomic regions currently annotated as intergenic. Third, a significant number of existing experimental SAGE tags for yeast has been derived from truncated cDNAs, which are synthesized through oligo-d(T) priming to internal poly-(A) regions during reverse transcription.We applied this method to the We conclude that an accurate and unambiguous tag mapping process is essential to increase the quality and the amount of information that can be extracted from SAGE experiments. This is supported by the results obtained here and also by the large impact that the erroneous interpretation of these data could have on downstream applications. Serial Analysis of Gene Expression (SAGE) technology  has beeni.e. determining the UniGene cluster that most likely represents the gene from which the experimental SAGE tag was derived. Each UniGene cluster contains a collection of expressed sequences, which consists of well-characterized mRNA/cDNA sequences and expressed sequence tags (ESTs) that might represent a unique transcript. Unfortunately, this strategy allows only for the partial assignment of tags to transcripts, because the current resources for transcriptome data are incomplete for most species and organisms. Therefore, a significant fraction of the experimentally measured tags remains unidentified. In addition, there are several drawbacks of using this strategy for the mapping of SAGE tags to transcripts. First, a single gene may be represented in several clusters, resulting in ambiguous assignments. Second, EST sequences, which are the major components of the UniGene clusters, have an approximated error rate estimated at 1% (1 in 100 nts), resulting in a tag error assignment rate close to 10% [i.e. hypothetical and unknown genes). For example, SAGE studies in human have shown that 60% of the 14 bp tags do not have any match to sequences in the UniGene clusters [A critical step in the SAGE methodology is the tag mapping process, which refers to the unambiguous assignment of an experimentally measured tag to a given transcript. Currently, the tag mapping process frequently involves the search of the observed tag sequences within the known transcriptome. Commonly employed databases available for tag mapping -10 use Ue to 10% . Third, clusters . The corclusters . Fourth,SAGE can be very efficient for gene discovery and annotation -5. For tIn this work, we designed a bioinformatic method that gives different confidence values to each of the multiple hits in the genome for a tag sequence. Our method allows to fully exploit the abovementioned benefits while using genomic sequences for the tag mapping process in SAGE. The confidence values were assigned according to several parameters that were obtained by the analysis of experimental SAGE tags from previous studies in yeast -17. We dIn this work, we describe a new method, HGA, for tag mapping in SAGE. The method combines existing knowledge of a genome sequence and its current annotation, along with known data from previous SAGE experiments, to increase the accuracy and reduce the ambiguity of the tag mapping process.Saccharomyces cerevisiae. Some of these parameters are highly specific to yeast and may not be as crucial for other organisms, and viceversa.Though this methodology can be applied to any organism, we describe it here with some parameter values that have been specifically tuned for HGA consists of four main steps, which are described below. A detailed flowchart of the HGA method is illustrated in Figure Saccharomyces cerevisiae, its protein tables only specify the coding regions of each gene  and do not contain the assignment of the untranslated regions (UTRs) at the 5' and 3' ends. Therefore it was necessary to assign them. The precise assignment of these regions is particularly relevant in the case of the 3'-UTRs, because it is expected that a significant fraction of experimental SAGE-tags will be obtained from these regions. With better knowledge of the transcriptome, a larger fraction of the UTRs can be accurately assigned. For most model organisms, a large number of expressed sequence tags (ESTs) are available even though only a small fraction of full length cDNAs is known. Therefore, the precise assignment of UTRs for most of the coding genes is not possible. For yeast, about half of the known genes have a predicted 3'-UTR with high confidence. These are mainly due to the identification of downstream polyadenylation signals [The complete genomic sequence of an organism is first searched for occurrences of the recognition site of the anchoring enzyme used in SAGE. The virtual potential SAGE-tags are then extracted by combination with a given tagging enzyme. These potential tags are then compared all-against-all in a pairwise fashion and the frequency of occurrence of each of the potential tags in the genome is determined  of less than 100 nts . At thisAfter the assignment of complete coding transcripts to the genome, the RNA tables are used to map and assign the non-mRNA transcripts (see methods). This feature of the HGA method is new, because previous works in SAGE have not explicitly used the non-coding transcripts to map experimental tags. Though most non-coding transcripts do not contain poly(A) tails, and thus should not be observed in SAGE experiments, a recent study has shown that some ribosomal RNAs are polyadenylated in yeast, even in the absence of a canonical polyadenylation signal . FurtherOnce all transcripts have been assigned, the remaining intergenic regions of the genome are categorized into two types, depending on whether an annotated transcript is present in the complementary strand or not .The structured genome information generated above is crossed against all the potential tags, generating a genome based annotation of virtual SAGE-tags. The resulting virtual tags are categorized into one of seven classes, depending on the genomic position, annotation and frequency of occurrence of each virtual SAGE-tag in the genome Figure , center.de novo. The remaining tags are labelled as 'non-spliced-tags'.As an important complement for this new tag classification scheme, the HGA method also incorporates two additional tag features, which are intended to reduce some potential distortions that can affect the interpretation of SAGE results. First, all continuous stretches of eight or more adenines within each annotated transcript are recorded to account for oligo-dT priming to internal poly(A) regions of RNA molecules during reverse transcription. It has been demonstrated that this process occurs at a high frequency, causing that about 12% of ESTs are truncated due to internal poly(A) priming . TherefoThe resulting tag classification, along with the abovementioned additional tag features, are used to select particular tags from the genome, the occurrences of which are searched for among known experimental SAGE-tags obtained previously and described in the literature for the studied organism. Table To summarize, all the potential tags are assigned with a value according to the genomic regions they map and to their specific features. For example, the tags mapping into a certain transcript will have different values according to the transcript position and the proximity of internal poly(A) sequences, whereas the tags mapping to intergenic regions have a single value.ie. among all occurrences of a tag sequence that is observed multiple times in the genome, there is not a case where a particular virtual tag always has an odds ratio equal or higher than five when compared against all other instances). In these cases, the tags could still be ranked based on the odds ratios that they exhibit, which is provided by the annotation generated by the HGA method. Some examples illustrating how the tag confidence assignment process is carried out by the HGA method are shown in Figure The estimated probability functions described above are then crossed against all virtual genomic tags, to obtain a tag confidence assignment for each potential virtual SAGE-tag in the genome Figure . The oddSaccharomyces cerevisiae , or on the 3'-UTRs (15%) and a small fraction of the tags were found at the 5'-UTRs (5%). As should be expected, these figures correlate with the observed lengths of these elements.The total number of virtual tags shows an inverse linear relationship to its position within the transcript, as expected, based on the fact that position number correlates with distance from the 3'-UTR end, which is directly related to the probability of finding a downstream recognition site for the anchoring enzyme used in SAGE.Only a small fraction of the virtual tags map onto annotated introns (1%) and non-coding RNAs (1%). Very few tags map onto exon-intron boundaries (0.02%), accounting for a total of 13 new tag sequences generated by splicing.When we considered potential unique virtual tag sequences within the genome, most of the results described above remain unchanged  region within a transcript are observed in all SAGE experiments reported. Third, in all experiments, SAGE-tags mapping onto non-coding RNAs are observed. Almost all these cases consist of tags belonging to the class 'non-poly(A)-next' and mapping to the first position within the transcripts, suggesting that typical polyadenylation occurs at the 3' end of these transcripts . Fourth, analogous to what was observed for introns, spliced-tags are observed in all SAGE experiments. This is the first time that experimental SAGE-tags are mapped onto virtual and potential spliced-tags from a genome. Fifth, a significant fraction of experimental SAGE-tags map onto regions in the genome that currently are annotated as intergenic. Though this has already been observed, it must be mentioned that it is for the first time that this analysis is carried out by considering the confidence of the assigned tags, and thus the figures obtained here should be more accurate. These intergenic tags could represent new genes not yet described in yeast. Using the HGA-based annotation they can now be easily ranked according to their estimated confidence, which will facilitate and optimize the experimental planning of the gene discovery process. Finally, a large fraction of the experimental tags that map onto an intergenic region has an annotated transcript on the opposite strand. These tags could correspond either to new genes or to new regulatory elements such as antisense RNA ,23. The i.e. those with several assignments by other authors), independently for each tag class, the obtained figures are highly significant  aSeveral new features that improve the accuracy and completeness of the tag mapping process in SAGE have been incorporated by the HGA method, and are detailed below.First, instead of using only the coding regions of known and hypothetical genes, we assigned, as precisely as possible, the 3' and 5' UTRs, thus generating more accurate putative transcripts. Mature and immature transcripts were generated, by considering exon-intron boundaries, thus keeping and using all the relevant available genomic information and annotation. When no information about UTRs was available for a given gene, we used a fixed maximal length estimated from experimental data. It is noteworthy that most conflicting assignments of unique tags observed between HGA and other authors' assignments resulted from the large length of 3'-UTRs previously used by these authors -17 Tabl. The preSecond, non coding RNAs, in addition to known and hypothetical genes, were also included in the genomic annotation. Though the amount of tags mapping to these transcripts is low Table  and mostS. cerevisiae constitutes one of the best annotated genomes available today. If EST data were used to map experimental SAGE-tags, this information would not be obtained. Hence, a method that considers these elements explicitly in the annotation process would accelerate the discovery of new regulatory elements. The identification of regulatory elements of this kind is important for a complete and accurate interpretation of the gene expression patterns.Third, tags mapping to intergenic regions in the genome, where an annotated transcript is found in the opposite strand, were also considered in the HGA method. These supposedly intergenic tags, if experimentally observed, could account for unknown elements, such as antisense RNA. We showed that a significant fraction of these tags were observed in SAGE experiments with yeast Table , even thFourth, by using genomic information in the tag mapping process, the HGA method identifies tags mapping onto regions where no gene annotations exist in either of the DNA strands. In this work, we demonstrated that a significant fraction of these tags were observed in SAGE experiments with yeasts Table . It is aFifth, the combined use of genomic information along with the generation of new putative splicing tags not explicitly available in the genome sequence, allows a more accurate estimation of tag uniqueness and, therefore, of potentially ambiguous mappings.Sixth, the inclusion of internal poly(A) regions within annotated transcripts as possible reverse transcription initiation sources is another important feature of the HGA method. This was included because recent EST data analyses have shown that a significant fraction of the reverse transcription processes are initiated at internal poly(A) regions of more than 8 consecutive adenines . The resSeventh, the new definition of tag classes considered by the HGA method Table  facilitaFinally, the calculation of tag probabilities from experimental data based on the new tag classification, along with other tag features, allows the HGA method to get the odds or confidence that a tag would be experimentally observed when several instances of a tag sequence are present in the genome. This constitutes the core of the HGA method and one of the most important contributions of this work to reduce the number of unambiguous tag assignments in SAGE. In addition, we also demonstrated that about 20% of the experimental tags mapping onto a transcript are located from the second tag position and above. If this information is not considered in the tag-to-gene mapping process, a substantial fraction of the experimental tags will be missed. Finally, it is important to note that even in those cases where the ambiguity could not be completely removed, the HGA method could reduce the number of possible assignments, thus reducing the overall ambiguity for a particular tag with multiple occurrences in the genome.The score of intergenic tags is strongly dependent on the quality of the genome annotation. In poorly annotated genomes, intergenic tags will have a higher probability of being observed by the HGA methodology. This is a desirable feature for tag probability estimation in the discovery of new genes. In yeast, 11.3% of all experimental SAGE-tags obtained to date and searched against the current annotation of the yeast genome map into an intergenic region, suggesting that new coding or non-coding transcripts are still to be discovered. This figure will be even larger for poorly annotated genomes.Xenopus genome). Therefore, the main problem of using genome sequences for tag-to-gene assignment in long genomes is that with their increased size and complexity tag uniqueness and unambiguous tag-mapping becomes increasingly difficult. It is in these cases that HGA would be most useful, because it will significantly reduce unambiguous tag mappings.In this work, we achieved an 8–10% increase in unambiguous tag assignments when considering all experimental yeast SAGE-tags Table . This imLong-SAGE has been proposed to reduce the ambiguity of tag mapping for large genomes ,25. HoweXenopus tropicalis genome . This genome has 1.5 billion base pairs, half the size of the human genome and 125 times larger than the yeast genome. Our preliminary analysis of all potential tags for X. tropicalis genome showed that tag uniqueness for short (14 nts) virtual tags is around 9.1%, small compared to the 80.6 % of uniqueness for the long (21 nts) virtual tags. When a histogram of occurrence for each tag sequence was constructed, we found that 60% of the virtual short tags have less than 7 matches to the genome and 90% of the tags have less than 20 matches. This low number of genome hits per tag for a significant amount of the potential short tags suggests that the use of HGA tag-mapping should allow proper tag-to-gene assignment when typical SAGE technology with tags of 14 nts is used. More importantly, when we built a virtual reference database for SAGE-tags from X. tropicalis genome using only some of the parameters involved in the HGA method described here, we have found that 40% of the short virtual tags are included in the high confidence class. This indicates that after using the HGA method, a significant fraction of the short tags will be unambiguously mapped with a high confidence to the genome and represents a total potential gain of unambiguous assignments of about 31%. This figure is significantly higher than the 8–10% obtained for yeast, though its estimation for X. tropicalis was based on the virtual genomic tags instead of the experimental SAGE-tags, where this figure will increase, as it was shown for yeast  A significant increase of unambiguous assignments for experimental SAGE-tags in yeast is achieved when using this method Figure .2) Using a genome-based annotation of virtual SAGE-tags like the one described here shows that a significant fraction of experimental SAGE-tags comes from intergenic regions, from partially digested cDNA, from the opposite strand of annotated transcripts, and from truncated cDNAs Figure .3) In all SAGE experiments reported for yeast, tags map onto introns, exon-intron boundaries and onto non-coding RNAs Table .4) In all SAGE experiments reported for yeast, it was observed that the largest fraction of tags map to the coding regions of transcripts and not to the 3' UTR elements Table .Saccharomyces cerevisiae organism was obtained from the July 2005 release available at the Saccharomyces genome database (SGD) web site [The full genome sequence of the web site . The fulweb site .th 2005 release of the genomic annotation available at the SGD web site was used [The July 26was used . This orwas used .subsequence. This process was carried out for the forward and reverse DNA strands, and the results concatenated into a single file. Second, the 14 bp sequences were filtered, selecting only those that matched the pattern CATG at their 5' end. This procedure resulted in a total of 76,516 tags that represent the theoretical product of a complete genome digestion by using a combination of the NlaIII and BsmFI restriction enzymes . In this process, the position and strand where each tag was found in the genome were stored. For this purpose, we set up a new computer program called pattern. Finally, an all-against-all pairwise tag comparison without mismatches was performed and the frequency of occurrence for each tag sequence at the different genomic positions on both strands stored with another computer software: freqtag. All these computer programs for the LINUX operating system are freely available as supplemental material at our web site [Several computer programs in C++ and ANSI C languages were written to perform specific tasks. First, the full DNA sequence of each chromosome in the genome was fragmented in a computer into all possible overlapping oligonucleotide sequences of a length of 14 base pairs (bp) using the computer program web site .ie. the 3'-UTR was defined with a length of zero nts; its end position was therefore assigned to the same position of the end of the annotated ORF). A total of 78 3'-UTRs with a length of zero nts were assigned.Only the records containing the 'ORF' word in the feature type field identifier from the filtered genome annotation table were considered for the annotation and prediction of 5' and 3' UTRs. This restriction yielded a total of 6,591 ORF candidates for the 3' and 5' UTR assignments. The 3'-UTR ends were first assigned to all those cases that cross-matched the previously described annotation . The latie. the 5'-UTR was defined with a length of zero nts and therefore its initial position was assigned to the first nucleotide corresponding to the first codon of the annotated ORF). 265 5'-UTRs with a length of zero nts were assigned.After all 3'-UTR assignments were completed, the 5'-UTRs were assigned as follows: as 5' UTRs of most of yeast ORFs are unknown, a fixed 5'-UTR length of 100 nts was assigned for all those ORFs where the previous annotated transcript was located at a larger distance than 100 nts. In those cases where an ORF was located upstream, then the previously assigned 3'-UTR end position was considered as the end position of the upstream ORF. 100 nts was chosen because more than 95% of the experimental tags that map into the 5'-UTR are observed at an upstream distance from the initial codon of the ORF of less than 100 nts . A totalIn addition to the putative coding transcripts, all non-coding RNAs available in the genomic annotation table were selected. Then, all the virtual genomic SAGE tags were mapped against these annotated elements, based on their genomic positions. Both complete and partial tag matches to a transcript were recorded. A complete match was defined when the virtual tag was totally contained within the transcript. A partial tag match was defined only if the previous condition was not fulfilled and if the most 5' nucleotide was contained within the transcript; otherwise the virtual tag was defined as intergenic. The same criterion described above was used to define those tags mapping to introns. A total of 775 introns are currently annotated within known transcripts in the yeast genome. The virtual tags partially mapping an exon-intron boundary were annotated as such, but in these cases the potential new tags not present in the genomic sequence that could be obtained by the splicing process were also generated and stored into the database. Only 13 potential new tags fulfilling these conditions were generated.All virtual tags defined as intergenic in the previous step were assessed for their occurrence in the opposite strand of an annotated transcript. All virtual tags located within an intergenic region but fully contained in the opposite strand of an annotated transcript were also annotated as such. These tags could be important for the discovery of new interference RNA elements .Virtual tags located within a transcript, but not at the most 3' end position, were observed with a high frequency experimentally due to internal poly(A) priming of oligo-d(T) primer during reverse transcription . TherefoThe class of virtual tags was defined based on three characteristics: 1) the frequency of occurrence of the tag sequence in the genome (whether it is unique or not), 2) the number of annotations for the tag, and 3) the type of annotations of the tag. Using these three distinct features we have defined seven different quality classes for any virtual tag occurring in the genome. The naming and definition of tag classes are described in Table The confidence of virtual tags was defined based on a combination of the tag class, the frequency of occurrence in the genome and the position of mapping within the transcript. All potential tags in the genome were classified as high, low, or undefined confidence. High confidence tags correspond to those that can be unambiguously assigned to a single known gene or to a single intergenic position in the genome. Low confidence tags correspond to those that should not be visible by experiment, because another tag of identical sequence but mapping at a different location in the genome should be. Undefined confidence tags correspond to those that are fuzzy and cannot be assigned clearly to a single gene or intergenic region in the genome. The procedure that we used to define tag confidence is as follows:First, the probability of observation by experiment was calculated for those tags belonging to the class platinum, copper and iron. In the case of the class platinum, the tags were subdivided into two groups depending if the tag was or was not located upstream from an internal poly(A) region. In the case of those tags that were not defined as next to an internal poly(A) site we calculated their frequency of occurrence at different positions in the transcript from the 3'-UTR end, using the currently known experimental SAGE data Figure . To miniie. tags belonging to the silver class), an assessment of the confidence was carried out based on some tag features and on the previously obtained probabilities. The probability of occurrence is obtained from the previous data for all the instances of a particular tag sequence. If an instance of a tag maps to an intergenic region in the genome, then the probability of observing copper and iron tag sequences is used. If an instance of a tag maps into a transcript, then it is first evaluated if it is located upstream from an internal poly(A) region or not. In the case of the former, the probability of a tag mapping upstream from an internal poly(A) region is used; otherwise, the probability is obtained based on the position within the transcript that the tag is found. Once the individual probabilities are obtained for all the instances of the tag sequence, a pairwise comparison table is built, which contains all-against-all odds ratios among the instances. Then, if a single instance of a tag has odd ratios higher than 5.0 against all other instances, this tag is defined as a high confidence tag, and all other instances as low confidence. Otherwise, all tag instances are assigned an undefined confidence.Second, all tags belonging to the classes platinum and copper were defined as high confidence. In the case of non-unique tags in the genome, or in the case of unique tags with multiple annotations (We compiled all experimental information available from SAGE experiments in yeast. These include three independent works -17, accoThe mapping process of experimental SAGE-tags against the virtual library was performed by assigning to the experimental tag the annotation of the high confidence virtual tag, when possible; otherwise, the experimental tag was assigned to multiple transcripts and/or intergenic regions with an undefined confidence.A web server that uses the HGA-based annotation described in this manuscript for the genomic mapping of experimental SAGE tags from yeast or for the exploration of the virtual SAGE-tags on this organism has been implemented and it is freely accessible.Project home page: Operating systems: MacOSX, Linux, WindowsProgramming language: C++, ANSI C, PERL, PHP, MySQLOther requirements: noneLicense: noneAny restrictions to use by non-academics: none


Retroviral insertional mutagenesis screens, which identify genes involved in tumor development in mice, have yielded a substantial number of retroviral integration sites, and this number is expected to grow substantially due to the introduction of high-throughput screening techniques. The data of various retroviral insertional mutagenesis screens are compiled in the publicly available Retroviral Tagged Cancer Gene Database (RTCGD). Integrally analyzing these screens for the presence of common insertion sites  requires an approach that corrects for the increased probability of finding false CISs as the amount of available data increases. Moreover, significance estimates of CISs should be established taking into account both the noise, arising from the random nature of the insertion process, as well as the bias, stemming from preferential insertion sites present in the genome and the data retrieval methodology. We introduce a framework, the kernel convolution (KC) framework, to find CISs in a noisy and biased environment using a predefined significance level while controlling the family-wise error (FWE) . Where previous methods use one, two, or three predetermined fixed scales, our method is capable of operating at any biologically relevant scale. This creates the possibility to analyze the CISs in a scale space by varying the width of the CISs, providing new insights in the behavior of CISs across multiple scales. Our method also features the possibility of including models for background bias. Using simulated data, we evaluate the KC framework using three kernel functions, the Gaussian, triangular, and rectangular kernel function. We applied the Gaussian KC to the data from the combined set of screens in the RTCGD and found that 53% of the CISs do not reach the significance threshold in this combined setting. Still, with the FWE under control, application of our method resulted in the discovery of eight novel CISs, which each have a probability less than 5% of being false detections. A potent method for the identification of novel cancer genes is retroviral insertional mutagenesis. Mice infected with slow transforming retroviruses develop tumors because the virus inserts randomly in their genome and mutates cancer genes. The regions in the genome that are mutated in multiple independent tumors are likely to contain genes involved in tumorigenesis. As the size of these datasets increases, conventional methods to detect these so-called common insertion sites (CISs) no longer suffice, and an approach is required that can control the error independent of the dataset size. The authors introduce a framework that uses a technique called kernel density estimation to find the regions in the genome that show a significant increase in insertion density. This method is implemented over a range of scales, allowing the data to be evaluated at any relevant scale. The authors demonstrate that the framework is capable of compensating for the inherent biases in the data, such as preference for retroviruses to insert near transcriptional start sites. By better balancing the error, they are able to show that from the 361 published CISs, 150 can be identified that have a low probability of being a false detection. In addition, they discover eight novel CISs. In retroviral insertional mutagenesis experiments, genes involved in the development of cancer are identified by determining the loci of viral insertions from tumors induced by retroviruses in mice ,2. AfterA tumor develops when an accumulation of oncogenic insertions causes uncontrolled proliferation of a cell. As a result, the tumor tissue contains many copies of the cell bearing the oncogenic insertions that induced the proliferation, but only a few copies of cells carrying non-oncogenic  insertions. Consequently, when the DNA of the tumor is analyzed, one will encounter the insertion that induced proliferation in larger proportions than insertions that do not. Regions in the genome that are found to carry insertions in multiple independent tumors are called common insertion sites (CISs). As a result, the locations of the CISs are highly correlated with the location of genes involved in tumor development. Cloning the flanking sequences of the inserted virus to determine the insertion loci, and analyzing these data to find significant CISs, therefore enable the discovery of new candidate cancer genes. This is summarized in http://RTCGD.ncifcrf.gov), and contains approximately 4,000 insertions (accessed November 2005). The vast majority of these insertions have been acquired in 20 different screens that each analyzed the insertions using their own definition of a CIS, which sometimes includes manual curation.Over the last few years an extensive amount of insertional mutagenesis data has been published –10. Thespiggy-backing. Due to piggy-backing, it is required that an insertion at a certain locus occurs in more than one tumor, since this greatly reduces the probability of finding a CIS that is not causal for the tumor.Due to noise in the data, not all insertions are informative. In the idealized case, oncogenic insertions are present in every tumor cell , whereas background insertions are only present in a small proportion of the tumor cells. Although this implies that the probability of finding a non-oncogenic insertion is far lower than for an oncogenic insertion, it may still occur that a non-oncogenic insertion is found. This results in non-informative insertions, the noise. Moreover, when a non-oncogenic insertion happens early in the tumor development phase, i.e., co-occurs in the same cell with one or more oncogenic insertions, there will also be many copies of this non-oncogenic insertion in the final tumor. Consequently, the probability of mapping this insertion will increase dramatically. This phenomenon is called A CIS is defined as a region in the genome that has been hit by viral insertions in multiple independent tumors significantly more frequently than expected by chance  evaluates significance at any desired  scale, 2) does so while keeping control of the error (since in the near future a significant increase in these data is expected), and 3) provides the possibility of including a background distribution, enabling compensation for the background bias. In this study, we propose a kernel convolution (KC) framework that meets the criteria outlined above. We apply this framework to the data of all the screens in the RTCGD combined, as if they originated from one screen. This gives us the opportunity to evaluate the method for large amounts of data. Indeed, the method rejects 53% of the CISs that do not reach the significance level in the combined set of screens. While keeping the family-wise error (FWE) under control, the method still revealed eight new CISs that are significant across the different screens. In addition, we provide the nearby putative target genes that may play a role in oncogenesis. Due to its generality, the method can be applied to other types of high-throughput genome-wide data, too; for example, to copy number aberration data or data from insertional mutagenesis screens using transposons .α-level, and the empirical null-distribution of peak heights, which is obtained by random permutation experiments. In addition to a uniform background distribution, this framework allows the inclusion of any other background model in the computation of the null-distribution, in order to compensate for possible background bias. The probability of detecting false CISs is controlled by applying the Bonferroni correction [α-level with the number of peaks in the resulting density estimate. This ensures that the expected error is controlled and is always smaller than the α-level, for any of the scales used. In contrast to previous methods, CISs can now be detected at any desirable  scale, while keeping the error under control.The steps involved in the application of the KC framework can be summarized as follows see  section.rrection , by corrObviously, the choice of the kernel function is an important design parameter in the CIS detection. Various kernel functions  have been proposed for various applications . AlthougSince it is possible for CISs to be present for large scale parameters (broad CISs), but not for small scale parameters (narrow CISs), or vice versa, it is vital to consider the significance of CISs for different scale parameters. We propose a scale space approach in which the scale parameter is varied across a range of values to gain information about the “lifespan” of a CIS, while keeping the FWE at a predefined level. The lifespan is defined as the range of scale parameters for which the CIS is significant (exists). It will be shown that the CISs with a long lifespan  often consist of different narrow CISs that are joined together when increasing the scale parameter.at least one scale parameter.Plotting the CISs versus the scale parameter yields scale space diagrams  false positive (FP), the detection of a CIS at a location other than at the predefined locus; and 4) cross scale FP (csFP), counting an FP at a locus only once even if it occurred across multiple scales.The following evaluation criteria are defined . Interestingly, it is observed that Cdkn2a null mice have a notable bias toward the first and third of these small-scale CISs. This finding suggests that inserts in these CISs functionally differ from those in the other two CISs, either in the expression levels they induce or perhaps via more complex effects on temporal regulation of Myc or the abundance of differentially translated Myc isoforms. As such these tumors might be a useful starting point to examine tumor cells' tolerance to Myc protein in the presence or absence of the Cdkn2a; for instance, with respect to Myc-induced apoptosis/senescence.The added value of breaking a single large-scale CIS for the In  a CIS isα-level. This makes the method suitable for scaling to large datasets.The limitations of the definition from  become eAs expected, the total number of detected CISs is reduced as a consequence of the control of the FWE. The discarded CISs (53%) are not necessarily all false detections; many of them may be screen-specific CISs that consisted of only few insertions and did not reach significance when we integrally analyzed the data. Also, some of the CISs in the RTCGD were found using human interpretation of the insertions. The GKC can also be applied to any relevant subset of the data, although a minimum of approximately 800 insertions is required to reliably estimate a null-distribution within a reasonable timeframe.Additionally, the background bias was removed using the procedure described in the Detection of CISs in large retroviral insertional mutagenesis screens at acceptable false detection rates necessitates correction for multiple testing and renders manual curation of CISs impractical. Current methods do not control the number of falsely detected CISs without changing the scale of the putative CIS, and fail when applied to large datasets. In this paper, this is solved by introducing a KC framework capable of discovering statistically significant CIS, while controlling the FWE for any biologically relevant scale. Because the KC framework controls the error per scale, it is capable of analyzing the data in the scale space, allowing the discovery of narrow as well as broad CISs.We evaluated the performance of the KC framework using three often-used kernel functions: the Gaussian, triangular, and rectangular kernel functions. From the results obtained using artificial data, we conclude that the KC framework is capable of keeping the FWE under the desired error level, for a range of different CISs and scale parameters. The GKC, however, performs most robustly, since it is capable of controlling the error in an unbiased fashion across the scales. This is highly desirable when analyzing the data in a scale space. Additionally, the TKC and GKC show better positional accuracy when compared with the RKC.To test the performance of the method on a large dataset, we used the GKC to integrally analyze the data from the RTCGD. This resulted in the discovery of CISs that are significant across the screens according to a consistent definition, have a low probability of being false detections, and can be analyzed in the scale space. As a consequence, 53% of the CISs previously published in the RTCGD did not reach significance in the combined dataset. Among the discovered CISs are eight novel CISs, of which six could have only been found when we integrally analyzed the data. Three of those might be attributed to the background bias, but this is based on too little evidence. For these novel CISs, the putative targets have been provided.active genes being favored integration targets cannot be incorporated without the data from experiments assessing which genes are active during integration. Mining data from infected cells that did not yet show proliferative selection, independent from the tumor data, and using these in the background bias correction will circumvent these problems. Still, making some assumptions about the background bias model resulted in the rejection of ten csCISs, but verification whether only true hot spots were corrected should be conducted.The KC framework is flexible enough to incorporate a background bias correction. Currently, data to base the background model on is lacking. For instance, the effect of As an additional benefit, the KC framework excels in visualizing the results, allowing the biologist to inspect the smoothed insertion estimate around interesting loci in the genome. Plotting the CISs in the scale space by means of scale space diagrams yield a valuable visualization tool for the biologist, showing the lifespan of CISs across a range of values of the scale parameter. This enabled the detection of screen-specific biases toward small-scale CISs. Together with the insertion locus relative to the neighboring genes, this provides useful information in determining the target of the insertional mutations.Recently, some attention was given to multi-experiment analysis in the detection of significant copy number aberrations across experiments in array-CGH data . The STg:K(·) is a kernel function, nd is the position of the nth insertion, N is the total number of insertions, and G is the genome length. Smoothing the observed insertions in a region around the position of insertion models two important phenomena. First, it models the fact that observing an insertion also contains information about its direct neighborhood. Because there is a limited number of observed insertion positions (data sparseness), it is important to exploit this information. Second, the distance across which insertions can act on a specific target gene is not fixed. There may exist multiple regions of varying widths within or around a gene for which an insertional mutation alters the function of the gene. By smoothing the observed insertions, the joint effect of the insertions on the target gene is incorporated in the model.Convolution of the insertion data with a kernel results in a smoothed estimate of the number of insertions number of insertions, rather than a density, we choose to normalize the kernel function such that: K(0) = 1, rather than the more common normalization: The design of the appropriate kernel function is important. Since we are estimating the y Parzen . Furtherα-level, non-integer estimations of the number of insertions allow a less conservative and more unbiased control of the error across different scales. Nondiscrete estimations of the number of insertions can be obtained by using a descending kernel function  that has its maximum likelihood at the observed insertion. Lastly, smooth kernel functions give more robust local maxima in Nondescending kernels that have sharp flanks , can only result in discrete (or even integer) estimations of the number of insertions. In this study we show that, although the error is controlled to be below the h is the kernel width and tγ and rγ are normalization factors for the width of the kernel function. Because a range of scale parameters is used, the normalization of the width of the kernel functions is not crucial, as long as the range of scale parameters is chosen such that all relevant scales are covered. Here, we choose the normalization factor such that the surface under the kernel functions is equal, that is tγ = rγ = In this study the following well-known kernel functions are used and compared:The null-distribution is estimated by a permutation-based analysis of the insertion data see A. More sHypothesis 1. Null-hypothesis KCμ0 is the mean height of the peaks in the permuted insertion data and μobserved = exceeds the mean height of the peaks in the permuted data (one-tailed test). Significance is guaranteed by thresholding the smoothed estimate of the number of insertions with an amplitude threshold for the peaks. This threshold is established by determining the peak height associated with the α-level in the empirical cumulative distribution function (CDF) calculated on the peaks in the permuted data  is computed. Second  is the mean height of the estimate of the number of insertions in the simulated background data as a function of the genome location g, and μobserved (g) = g) the height of the peak in the smoothed insertion estimate. For the background-corrected significance test, both the mean height of the peaks based on uniform background as well as the mean height of the estimate of the number of insertions in background simulated data needs to be significantly exceeded. Significance is determined from the empirical CDF calculated on all simulation data as a function of the genome location g. We maintain control of the FWE by adjusting the background threshold with the same correction factor as used in the framework without background correction, since again only peaks in the insertion data are considered.Nartificial = 400 insertions is generated on an artificial genome of Gartificial = 2.6 × 108 bp long. The CIS is generated by drawing NCIS insertions from a uniform distribution centered at To simulate the background, a uniform distribution of WCIS is varied from 100 bp to 100 k bp, covering the relevant CIS widths. NCIS is chosen such that the methods operate at a critical point for which some errors are made, enabling good comparison . Additionally, we define the csTPs as observing a TP for at least one scale parameter .To evaluate the accuracy and performance of the methods, some criteria are defined. Using artificial data allows us to evaluate the correctness of a detected CIS, since the actual CIS locus is known. A TP is defined as the true detection of the artificially generated CIS, and is accomplished if the method identifies a CIS that has one of its bounds within the bounds of the artificially generated CIS  is controlled per scale parameter. If the errors made per scale parameter were mutually exclusive, this could result in an undesirably high overall error. To analyze this behavior, the average number of csFPs is computed, which counts an FP at a locus only once even if it occurred across multiple scales.The average number of TPs and FPs are computed by taking the mean across the 500 simulations. Since FPs only occur in the background, distinguishing between the different simulated artificial CISs makes no sense. Therefore, the average number of csFPs is computed by additionally averaging across all different experiments.WCIS + h. If the artificial CIS is detected more than once, the mean of the CIS center positions is taken as the detected position.As a final performance measure, the positional accuracy is evaluated. For this purpose, the deviation of the detected CIS center with respect to the artificial CIS center is normalized to the artificial CIS width plus the scale parameter Figure S1(A) The red insertions are oncogenic insertions, i.e., insertions either activating oncogenes or inactivating tumor suppressor genes, causing uncontrolled proliferation (cell division) of that cell, yielding a tumor. The green insertions are non-oncogenic insertions. Note that in every tumor cell we find this red insertion at exactly the same locus, since these cells are copies.(B) After removal of the tumor from the animal, the tumor cells' DNA is cleaved into small fragments using restriction enzymes. These enzymes cut the DNA at certain nucleotide sequences, resulting in small DNA fragments. An additional property of these enzymes is that they cut exactly within the viral inserts, yielding fragments with viral DNA on one end and host cells' DNA on the other.(C) The histogram shows the abundance of fragments. Because the restriction enzymes cut frequently, many fragments result. However, while only a limited number of insertions is present in each cell, by far most fragments do not contain an insertion. Due to the proliferation in (A), the fragments with oncogenic inserts will be present more often than the fragments containing the non-oncogenic (random) insertions. Note that in reality these abundances are not known.(D) With a polymerase chain reaction (PCR), only the fragments containing a viral insert are amplified (multiplied). There exist various types of PCR reaction that may be used for this purpose, i.e., the linker-mediated PCR  or the i(E) Shows the histogram of the abundances of DNA fragments after the PCR. After PCR amplification, the oncogenic (red) insertions will be more prevalent than others, and should all map to exactly the same locus on the genome (defined as a contig). Due to noise in the following steps (sequencing and mapping), this may differ by several hundred bp. Singletons are sequences that individually map to a certain locus on the genome and ideally consist of the non-oncogenic insertions.(F) Finally, a subset of the fragments is cloned, sequenced, and mapped onto the known Ensembl mouse genome. It is highly probable that an informative (oncogenic) insertion is sequenced and mapped, because the abundance of DNA fragments containing oncogenic insertions is a substantial proportion of the total number of insertions. Thus it might occur that a non-oncogenic insertion is sequenced and mapped, hence the data contains a certain amount of noise.(294 KB EPS)Click here for additional data file.Figure S2 Results for the GKC applied to artificial data. Results for the TKC. Results for the RKC.σCIS.The horizontal solid lines in (A), (C), and (E) show the 5% significance threshold, the dotted lines show the average number of csFPs. The legend shows the different simulated CISs, stating the number of insertions drawn from a normal distribution of standard deviation: (871 KB EPS)Click here for additional data file.Figure S3σ = 1 k bp reaches 100% detection. For all other CISs, some errors are made. The GKC outperforms the RKC and TKC for all simulated CISs.(A) Average number of csTPs per artificially generated CIS. Only the CIS with three insertions with (B) Average deviation of the detected CIS center from the actual simulated CIS center normalized on the simulated CIS width plus the scale parameter under consideration.(380 KB EPS)Click here for additional data file.Figure S4The threshold corrected for multiple testing is given by the horizontal red line. Note that the threshold should change because when the scale parameter increases the number of tests (the number of peaks) decreases. This is not visible in (608 KB EPS)Click here for additional data file.Figure S5The blue line represents the estimation of the number of insertions as a function of position for a certain region. The red line depicts the 0.05 threshold level. In the middle, the CISs are depicted by means of vertical lines. From top to bottom these represent: the CISs for the current scale , the csCISs (cyan), the CISs from the RTCGD (magenta), and the insertions (blue). The genes are not shown on this large scale. At the bottom, the CISs are plotted in the scale space. The vertical axis has a logarithmic scale and indicates the scale for which the CIS was detected .(547 KB EPS)Click here for additional data file.Figure S6CISs that fall within one scale parameter from a TSS are candidates for correction. We clearly see that for small scale parameters only a few of these CISs exist, indicating that it is not justified to correct narrow CISs for background bias.(254 KB EPS)Click here for additional data file.Figure S7A single linkage dendrogram is built from the CIS centers, and thresholded at a linkage distance equal to the highest scale parameter. The mean center position of the CISs within one of the resulting clusters is defined at the locus of the csCIS. Note that a csCIS arises if a CIS is present for at least one scale parameter. In case a CIS is present for only one scale parameter, the csCIS locus is equal to the CIS center position.(239 KB EPS)Click here for additional data file.Table S1NCIS denotes the number of inserts in an artificially generated CIS of width WCIS, in bp. The rate is defined as NCIS / WCIS.(45 KB DOC)Click here for additional data file.


The present study was designed to evaluate the efficacy of an internet-based therapy (Interapy) for Posttraumatic Stress Disorder (PTSD) in a German speaking population. Also, the quality of the online therapeutic relationship, its development and its relevance as potential moderator of the treatment effects was investigated.Ninety-six patients with posttraumatic stress reactions were allocated at random to ten sessions of Internet-based cognitive behavioural therapy (CBT) conducted over a 5-week period or a waiting list control group. Severity of PTSD was the primary outcome. Secondary outcome variables were depression, anxiety, dissociation and physical health. Follow-up assessments were conducted at the end of treatment and 3 months after treatment.d = 1.40). Additionally, patients of the treatment condition showed significantly greater reduction of co-morbid depression and anxiety as compared to the waiting list condition. These effects were sustained during the 3-months follow-up period. High ratings of the therapeutic alliance and low drop-out rates indicated that a positive and stable therapeutic relationship could be established online. Significant improvement of the online working alliance in the course of treatment and a substantial correlation between the quality of the online relationship at the end of treatment and treatment outcome emerged.From baseline to post-treatment assessment, PTSD severity and other psychopathological symptoms were significantly improved for the treatment group (intent-to-treat group × time interaction effect size Interapy proved to be a viable treatment alternative for PTSD with large effect sizes and sustained treatment effects. A stable and positive online therapeutic relationship can be established through the Internet which improved during the treatment process.Australian Clinical Trials Registry ACTRN012606000401550 On the Internet there are thousands of virtual communities that specifically involve issues of substantial personal significance, among them sexual violence, child abuse, loss and, grief, as well as suicide. Typically, these websites provide information and offer forums to share and discuss these experiences. A considerable number of trauma victims use this medium as a way of coping with their experiences. Traumatic experiences are often associated with stigmatization and intense feelings of shame and guilt . In addiThe therapeutic community has only recently discovered the therapeutic potential that the Internet offers [for a review see ]. Lange Furthermore, we were interested in exploring the development and relevance of the online therapeutic relationship. While the treatment rationale of Interapy closely resembles conventional face-to-face CBT approaches with regard to content, the mode of delivery is fundamentally different. In face-to-face treatment therapists and patients see each other, share the same physical space, and are engaged in synchronous verbal and nonverbal communication. Online therapy is based on written (asynchronous) communication, geographical distance and visual anonymity. The therapeutic alliance also known as "working alliance" or "helping alliance" is conceived as an agreement on therapeutic goals and therapeutic tasks. It is also an agreement about the development of bonds of mutual trust, acceptance, and confidence between patient and therapist ,12. The First of all we aimed to examine if the approach introduced by Lange et al.  can be gThe second purpose of this study was to examine the quality of the working alliance, its development through the course of therapy, and whether it moderates the impact of the observed change in symptoms. In accordance with findings on online relationship formation, it was expected that the working alliance would improve during the therapeutic process. Based on prior face-to-face research, it was expected to find significant correlations between patients' ratings of the therapeutic alliance at the end of treatment and treatment outcome. Patients' satisfaction with the online therapeutic contact was explored as an additional indicator of the online therapeutic alliance.Participants were recruited by means of radio and newspaper advertisements as well as advertisements posted on websites for different groups . Recruitment was performed from May to November 2003. Treatment and 3-months follow-up on all participants were completed in May 2004. The treatment approach was approved by the Netherlands Health Care Inspectorate and all participants gave written informed consent. Potential patients browsed through the website, which provided information about a) posttraumatic stress reactions, b) the study and its inclusion criteria, c) the treatment, d) the therapists and supervisors, and e) other treatment alternatives. Applicants were sent screening questionnaires by e-mail. Those who passed the screening were randomly assigned to the cognitive behavioural Interapy treatment group or a waiting list control group (WLC). The waiting list group received treatment after the post-assessment to the Interapy treatment condition. Patients who were excluded from the study were provided with information on where they could receive treatment elsewhere. The treatment lasted five weeks. Assessments were completed at three times .In total, 520 people requested the questionnaires; 171 did not commit themselves to the screening process and 253 were excluded on the basis of the exclusion criteria (see below). Figure Participants were aged between 18 and 68 years, with an average age of 35 years; 90 % were female; 44% had a university degree, and a further 34% had a high school diploma (German Abitur). Forty two percent of the patients reported sudden or violent death of a close person and 32% reported sexual abuse, incest, or rape as traumatic event. On average, the traumatic event had occurred 8 years prior to the therapy (range 2–696 months). Scores on the IES-R indicated that the 96 participants suffered from high levels o distress. The mean scores on the intrusions  and avoidance  subscales were in the upper regions of the norm table for Dutch PTSD patients . Neal etTwo therapists conducted the treatment. Both were female trained clinical psychologists at the doctoral level who had received special training in the application of cognitive behavioural writing assignments for the treatment of PTSD. Their average age was 33 years. The 7-days training was provided by clinical psychologists of Interapy. The therapists participated in weekly supervision sessions.To be included in the study participants had to: 1) have experienced a traumatic event that occurred at least one month prior to treatment and that met the criteria specified in DSM-IV , 2) be 1Applicants were excluded if their score on the SCL-90  exceededDissociative symptoms were tapped using the Somatoform Dissociation Questionnaire (SDQ-5) . The scaRisk of psychosis was measured using the Dutch Screening Device for Psychotic Disorder . This seTo gather miscellaneous information, including drug and alcohol consumption in terms of quality  and frequency of consumption, medications as well as degree of computer and Internet experience a short biographical checklist was administered. Participants were excluded if they indicated heavy alcohol or drug abuse.The revised version of the Impact of Event Scale (IES-R)  was usedThe depression and anxiety subscales of the short form of the SCL-90   were usePhysical and psychological functioning was measured using the 12 item version of the medical Outcome Study Self-report Form (SF-12) .The Working Alliance Inventory (WAI)  was desiIn addition to the WAI, questions concerning patients' satisfaction with the Internet-based contact were asked (e.g. How did you experience the fact being treated through the Internet instead of face-to-face?).Patients were allocated to two weekly 45-minute writing assignments over a five-week period . The therapy consisted of three treatment phases: 1) self-confrontation, 2) cognitive reconstruction, and 3) social sharing. After the fourth writing session, which constituted the end of the first treatment phase, the Working Alliance Questionnaire was administered. The treatment procedure is described in detail by Lange, Schoutrop, Schrieken, and van de Ven  and willAt the beginning of the treatment, participants received psycho-education about the mechanisms of exposure. In the first phase, the therapists helped the patients to focus on the most painful images and thoughts and encouraged the patient to write about them. The patients were instructed to describe the traumatic event thoroughly including their intimate fears and thoughts concerning the traumatic experience. To increase the effect of the exposure, patients were asked to write in the first person and in present tense and to give detailed descriptions of all sensory details they had experienced during the traumatic event including olfactory, visual and auditory stimuli. Participants were explicitly asked not to concentrate on style, grammar, spelling, or the chronological order of their essays. The therapists checked whether patients explicitly addressed the traumatic event as described above. If needed the therapist supported the patient to address the avoided features more forcefully. The following is an example of a writing assignment for essays 3 and 4:"For the next two texts, I would like to ask you to choose one moment of your traumatic event. One moment that you can hardly bear to think about, but that keeps intruding on your thoughts. Write down the most painful memories and emotions you have when you think about it and describe everything that you experience – every feeling, every thought and physical reaction."During the second phase, patients received psycho-education about the principles of cognitive restructuring. The goal of this phase was to form a new perspective on the traumatic event and to regain a sense of control. Participants wrote a supportive letter to an imaginary friend who had been through the same experience. In this letter, the patient was instructed to reflect on the addressee's feelings of guilt and shame, challenge dysfunctional automatic thinking and behaviour patterns, and correct unrealistic assumptions. Furthermore, patients were encouraged to consider potentially positive consequences of the traumatic event for that person's life and the lessons to be learned from it. An example of an instruction for the first two essays in the second phase is as follows:"Imagine you are writing a supportive letter to your friend Hanna, who experienced the same situation as you. Could she have foreseen what happened? Do you think she was responsible for this?"During the third phase, patients received psycho-education about the positive effects of social sharing. In a final letter, they then took symbolic leave of the traumatic event. Patients summarize what has happened to them, reflect on the therapeutic process and describe how they are going to cope now and in the future. Patients could address the letter either to themselves, to a close friend, or another significant person involved in the traumatic event. The letter did not ultimately have to be sent."You wrote that you would like to address the letter to your mother. First, I would like to ask you to describe the circumstances of what happened. Which moments were so important that you would like to tell her about them? What meaning does this experience have in your life. What plans do you have for the future? Who is important in your life and who can support you in the future? It is important to give the past, the present and the future the same weight in this letter."At the beginning of each writing phase, patients proposed individual timetables as to when they planned to write. Halfway through and at the end of each treatment phase, patients received feedback and further writing instructions, which were based on the treatment manual but tailored to patients' specific needs. Important aspects of this feedback were recognition and reinforcement of the patients' independent work, positive feedback and motivation, as well as frequent summaries and encouraging patients to voice questions and doubts.d for repeated measures  will hopefully enhance our understanding of online therapeutic processes. Considering that online therapy is gaining acceptance  and provBSI: Brief Symptom InventoryCBT: cognitive-behavioral therapyDSM-IV: Diagnostic and Statistical Manual of Mental Disorders, Fourth EditionIES-R: revised version of the Impact of Event ScalePTSD: posttraumatic stress disorderSCL-90: Symptom ChecklistSDQ-5: Somatoform Dissociation QuestionnaireSRA: Suicide Risk AssessmentWAI:Working Alliance InventoryWAI-S: short version of the WAIThe author(s) declare that they have no competing interests.CK has made substantial contributions to conception and design, coordination, acquisition of data, analysis and interpretation of data and writing the paper. AM has made substantial contributions to conception and design, analysis and interpretation of data and manuscript revising. Both authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here: