The Internet provides a new meeting ground, especially for gay men, that did not exist in the early 1990s. Several studies have found increased levels of high risk sexual behaviour and sexually transmissible infections (STI) among gay men who seek sex on the Internet, although the underlying processes are not fully understood. Research funded by the UK Medical Research Council (2002–2004) provided the opportunity to consider whether the Internet represents a new sexual risk environment for gay and bisexual men living in London.The objectives of the Internet and HIV study are to: (i) measure the extent to which gay men living in London seek sexual partners on the Internet; (ii) compare the characteristics of London gay men who do and do not seek sex on the Internet; (iii) examine whether sex with Internet-partners is less safe than with other sexual partners; (iv) compare use of the Internet with other venues where men meet sexual partners; (v) establish whether gay men use the Internet to actively seek partners for unprotected anal intercourse; (vi) determine the potential for using the Internet for HIV prevention. These objectives have been explored using quantitative and qualitative research methods in four samples of London gay men recruited and interviewed both online and offline. The four samples were: (i) gay men recruited through Internet chat rooms and profiles; (ii) HIV positive gay men attending an NHS hospital outpatients clinic; (iii) gay men seeking an HIV test in an NHS HIV testing or sexual health clinic; (iv) gay men recruited in the community.Quantitative data were collected by means of confidential, anonymous self-administered questionnaires (n>4000) completed on-line by the Internet sample. Qualitative data were collected by means of one-to-one interviews (n = 128) conducted either face-to-face or on-line.The strength of the Internet and HIV study is its methodological plurality, drawing on both qualitative and quantitative research among online and offline samples, as well as taking advantage of recent advances in web survey design. The study's findings will help us better understand the role of the Internet in relation to gay men's sexual practice Several studies have found elevated levels of high risk sexual behaviour among people who seek – and meet – sexual partners through the Internet -7 In a sA San Francisco study also found that gay men were more likely than heterosexual men and women to use the Internet to meet sexual partners . Around Similar reports have also emerged from European studies. An investigation of gay men in London gyms found that in the year 2000 over one-third of those with Internet access had used it to look for sex . When suThe association between seeking sex on the Internet and high risk sexual behaviour raises a number of important, as yet unanswered questions. Does the excess risk for HIV and STI occur with sexual partners whom men actually meet through the Internet? Does the association reflect the fact that high risk men are selectively using the Internet to look for sex? Or does the Internet in some way facilitate high risk behaviour? Is the Internet attracting a constituency of men who have little contact with the established gay scene or health promotion agencies ? For exaTo address these questions we have undertaken research funded for two years (2002–2004) by the UK Medical Research Council and the Department of Health. This has been conducted by researchers at City University London in collaboration with colleagues at the MRC Social and Public Health Sciences Unit Glasgow and University College London (see appendix 1).The core research question is: Does the Internet represent a new sexual risk environment for gay/bisexual men living in London?" (referred to as "gay men" throughout the rest of this paper). And if so, what are the underlying processes? The research focuses on gay men living in London since the incidence and prevalence of HIV infection among gay men in London is higher than elsewhere in the UK -13.• measure the extent to which London gay men seek sexual partners on the Internet• compare the characteristics of gay men who do and do not seek sex on the Internet• examine whether sex with Internet-partners is less safe than with other sexual partners• compare use of the Internet with other venues such as saunas and backrooms• establish whether gay men use the Internet to actively seek partners for unprotected anal intercourse• determine the potential for using the Internet for HIV preventionThese objectives have been explored using both quantitative and qualitative research methods. While quantitative research methods can provide data on a range of outcomes they can rarely offer insight into underlying processes. Qualitative research, on the other hand, illuminates our understanding of human behaviour but is limited in the extent to which findings can be generalized. By adopting methodological plurality and employing both quantitative and qualitative methods, we will be able to build on the strengths of both approaches within an integrated research programmeIn desribing the design and methods of the Internet and HIV study we focus on sampling strategy, recruitment into the study, data collection and data analysis.Research among hard-to-reach groups such as gay men is usually based on convenience rather than probability samples . For exaConsequently, we recruited gay men from four different sources – one online, three offline. Each sample has specific features which are pertinent to the research question.HIV positive men are central to any research concerning HIV risk, transmission and prevention -21. FurtMost people seeking an HIV test have been at risk of HIV infection. Furthermore, HIV negative gay men with a history of multiple repeat testing report elevated levels of high risk sexual behaviour . This grSurveying gay men in the community allows us to examine the extent to which they use the Internet for seeking sexual partners and the associated risks. Previous research has shown that gay men surveyed in central London gyms are broadly representative of men "on the scene" in London ie men who go to gay bars, clubs and other venues . HoweverMen who use Internet chat rooms and profiles to seek sex with other men are clearly of central importance to this research project. The Internet may attract a constituency of men who would not otherwise be included in behavioural surveys among gay men ie men who do not go to gay bars, clubs or other venues and men who have not been tested for HIV . CompariBased on previous research conducted among gay men in both clinic and community settings ,8,22,23 For the qualitative arm of the study, we recruited at least 20 men from each of the four samples to allow us to derive accounts of Internet dating and sexual practice from a diverse range of men, selected purposively according to age, education, employment, HIV status and use of the Internet for seeking sex.Men diagnosed with HIV infection attending an outpatient treatments clinic at the Royal Free Hampstead NHS Trust hospital, London over an 8 month period (October 2002–May 2003) were invited to participate in the research. Patients with a limited command of English were ineligible for the study as were those who were too ill to complete a questionnaire. Eligible patients were approached in the clinic's waiting area by a trained member of the research team who discussed the project with them. Patients were provided with written information about the research, contact details of the research team as well as helpline numbers. Once they had provided written consent, respondents were asked to complete a pen-and-paper questionnaire in the clinic and return it in a sealed envelope to the team member (further information about the questionnaire in Research Methods below). Some patients were only in the waiting area for a short time so there wasn't an opportunity to invite them to take part in the study.Over the eight month survey period (October 2002–May 2003), 1001 individual male patients attended the clinic of whom 939 were deemed eligible for the study. Of those who were eligible, 864 were asked to complete a questionnaire and 620 did so. The response rate was 72% of men who were offered a questionnaire and 66% of all eligible men who attended the clinic. Of the 620 men who completed a questionnaire, 542 described their sexual orientation as gay or bisexual or had had sex with another man in the previous year. Of these, 523 men provided sufficient information to be included in the quantitative sample  (further information about the one-to-one interviews in Research Methods below). If they agreed, the researcher contacted them to arrange a time for the interview. This could be in the researcher's office at City University London, in the hospital or at the respondent's home. In this way 20 HIV positive gay men were recruited for one-to-one interviews as part of the qualitative arm of the study table .People seeking an HIV test at the same-day HIV testing clinic, Royal Free Hampstead NHS Trust Hospital over a 13 month period (October 2002–November 2003) were invited to take part in the study. Based on previous research in the HIV testing clinic, we estimated that this would generate a sample of approximately 500 gay men, 500 heterosexual men and 500 heterosexual women . AlthougUsing a similar strategy to the outpatient HIV treatments clinic, everyone seeking an HIV test was asked to participate in the research. Those who agreed completed a detailed, self-administered pen-and-paper questionnaire, after providing written consent, while they were waiting for their pre-test counselling. People with limited command of English were deemed ineligible for the study as were those who were too ill, too young (under 18 years) or too anxious to complete a questionnaire. Those attending the testing clinic on more than one occasion during the survey period were asked to only complete the questionnaire once.Over the 13 month survey period (October 2002–November 2003), 1889 individuals came to the Royal Free clinic for an HIV test of whom 1753 were eligible for the study. Of those who were eligible, 1640 were asked to complete a questionnaire and 1230 did so. The response rate was 75% of people who were offered a questionnaire and 70% of all eligible persons who attended the clinic. Of the 1230 people who completed a questionnaire, 345 described their sexual orientation as gay or bisexual, 435 as heterosexual male and 450 as heterosexual female; 334 gay/bisexual men provided sufficient information to be included in the quantitative sample.Gay men who completed a questionnaire were asked if they would be willing to have an in-depth, face-to-face interview with the qualitative researcher (MD). If they agreed, the researcher contacted them to arrange a time for the interview as described above. In this way, 16 gay men seeking an HIV test at the Royal Free were recruited for one-to-one interviews for the qualitative arm. Heterosexual men and women who completed the questionnaire were not asked to have a one-to-one interview.Previous research suggested that the majority of clinic attenders would test HIV negative while approximately 6% of gay men were expected to test HIV positive. It was considered inappropriate to ask men who had just been diagnosed HIV positive if they would be willing to have a face-to-face interview. Consequently, the qualitative sample from the HIV testing clinic solely comprised men who had tested negative. Anyone who volunteered for an interview who subsequently received a positive test result was not included in the qualitative sample.After 6 months it became apparent that while the response rate at the Royal Free HIV testing clinic was high the number of gay men seeking a test was not as large as had been expected. We decided therefore to extend recruitment to men seeking an HIV test at a sexual health clinic specifically for gay men at Barts and the London NHS Trust hospital, London. All men attending this clinic are gay.Using a similar strategy to the one developed at the Royal Free, everyone attending the gay men's sexual health clinic at Barts and the London hospital between June and November 2003 was invited to participate in the research. Those who agreed completed a self administered questionnaire while they were waiting in the clinic for their appointment. Although all men were asked to complete a questionnaire, only men seeking an HIV test were eligible for inclusion in the Internet and HIV quantitative sample.Over the 6 month survey period at the Barts and the London sexual health clinic (June – November 2003), 211 gay men came for an appointment of whom 209 were eligible for the study. Of those who were eligible, 198 were asked to complete a questionnaire and 156 did so. The response rate was 79% of men who were offered a questionnaire and 75% of all eligible men attending the clinic.Of the 156 gay men who completed a questionnaire at Barts and the London, 70 were seeking an HIV test and provided sufficient information to included in the quantitative sample for the study. These men were also asked if they were willing to have a one-to-one interview with the qualitative researcher. Four men were recruited in this way for the qualitative arm of the study.Combining the data from the two clinics, 404 gay men seeking an HIV test were recruited for the quantitative arm of the study between October 2002–November 2003, of whom 20 agreed to have a one-to-one interview for the qualitative arm table . In addiPrevious research has shown that gyms in central London provide a suitable environment for undertaking detailed behavioural research among gay men at risk of HIV infection ,17,24,25In the mixed gyms the number of questionnaires handed out to gay men was estimated by multiplying the total number of questionnaires distributed in the gym by the proportion of male members who were gay (according to the managers' estimates). To calculate the response rates we divided the number of questionnaires returned by gay men (as indicated on the questionnaire) by the estimated number of questionnaires handed out to gay men, as described above. In 2002, 921 gay men completed the questionnaire while in 2003, 550 men did so. Those men who provided information on their HIV status  were included in the quantitative sample . If they agreed, the researcher contacted them to arrange a time for the interview as described above. Some interviewees were recruited through snowballing. In this way, 23 men were recruited from the gyms for the qualitative arm of the study table .)or gay.com  were invited to take part in the study. Gaydar and gay.com are two of the UK's most popular websites for gay men . Over a four week period in May-June each year, a series of pop-ups and banners advertised the research project in UK chatrooms and on profiles pages. Clicking on a popup or banner took men to the homepage of the online questionnaire. Men who agreed to complete the questionnaire then did so after providing informed consent – all online.In both 2002 and 2003, men using UK chat rooms or personal profiles on gaydar  . Those who agreed were asked to send an email to the researcher who then contacted them, also by email, to provide further information about the study and to arrange a time for the interview as described above. Interviews were either conducted online or face-to-face (see Research methods below). Of the London men who completed a questionnaire online in 2002 or 2003, 65 went on to have a one-to-one interview as part of the qualitative arm table ; 30 men The questionnaires sought detailed information on the men's socio-demographic characteristics , sexual orientation, HIV test history (date and result of last test), history of STIs, access to and use of the Internet, seeking sex on the Internet, use of other venues , as well as sexual risk behaviour in the previous 3 months, differentiating between Internet- and other sex partners. Unprotected anal intercourse (UAI) in the previous 3 months was categorized according to type  and HIV status of partner. Detailed information was collected about sexual behaviour with partners met through the Internet and with partners met elsewhere in order to compare the level and nature of risk with Internet and other sexual partners. Data were also collected on potential confounding factors such as recreational drug use, alcohol consumption, relationships, attitudes towards new treatments for HIV and mental health. Standard and validated questionnaire items were used extensively (copies of the questionnaires are available from JE).Core questions, included in the questionnaires for all four samples, were worded identically to ensure direct comparability between the different groups. In addition, some questions specific to each group were included eg, detailed questions on HIV test history and reasons for testing for people seeking an HIV test; questions on HIV medication, CD4 and viral load for HV positive men. The questionnaires were piloted both online and offline among gay men at the developmental stage of the study and revised in the light of any feedback and comments.People recruited offline, in clinics or gyms, completed a pen-and-paper questionnaire. All questionnaires were confidential and anonymous. They contained no information that would allow an individual respondent to be identified. For the HIV positive men, information on their most recent viral load and CD4 count was abstracted from hospital records and linked to their questionnaire without breaching confidentiality. The HIV test result of those seeking an HIV test was linked to their questionnaire, again without breaching confidentiality. Once the questionnaire and clinic data had been linked in the database individual identifiers  were removed and destroyed to ensure anonymity.) working in close collaboration with the research team. Because of their technical expertise and capacity, gaydar hosted the questionnaire on their server. All questionnaires were confidential and anonymous. Identifiers such as IP addresses were removed from the questionnaires completed online before the data were downloaded to a database.Men recruited through Internet chatrooms and profiles completed the questionnaire online. The online questionnaire was constructed by a computer programmer at gaydar (Within each sample men were asked (a) to complete only one questionnaire and (b) whether they had been in any of the other samples. For example, men who completed the questionnaire online were asked whether they had also completed a questionnaire in the gyms or clinics, etc.Qualitative data were collected by means of one-to-one, in-depth interviews conducted face-to-face (n = 93) or on-line (n = 35)The interviewees were volunteers from the quantitative samples recruited in the HIV treatments clinic, HIV testing clinic, gyms or online as described above. They provided written consent for the one-to-one interview which lasted between 50 and 90 minutes and was audio-taped for transcription. The interviews were generally conducted in the research office at City University London but occasionally at the interviewee's home or in the clinic. Confidentiality was provided in two ways; first, the interviewee's contact details were not linked to the interview transcript and secondly personal identifiers such as the person's name, where he was born, lived or worked were removed from the transcripts. Once all the data had been transcribed and entered into the database, individual identifiers (eg interviewees' contact details) were destroyed.online quantitative samples. They provided informed consent by email. Interviews were prearranged and conducted in a private room on  or  which only the interviewer and interviewee could enter. The interviews were synchronous and conducted entirely through text generated as online chat. Each interview lasted between 50 and 90 minutes at the end of which the interview text was copied and pasted into a Word document. Confidentiality was provided in the same manner as the face-to-face interviews..The interviewees were volunteers from the Interviews were conducted in two phases. Phase one (n = 24 interviews) focused on how gay men used the Internet for sexual partnering while phase two (n = 104) focused on risk behaviour related to the Internet.phase one were based on a topic guide comprising questions about age, residence, schooling, employment, relationship status and HIV testing. Social and sexual lifestyles were explored in depth paying particular attention to the role of the Internet. The topic guide included seeking sexual partners, preferences for different ways of meeting partners (including the Internet), Internet experience and skills, learning how to use the Internet for sexual partnering, online communication skills, other uses of the Internet and related media, a recent sexual episode and its relationship with the Internet.The interviews for The topic guide for phase two comprised social background; sexual lifestyle and role of the Internet; risk episodes including a description of anal sex with a condom and without a condom with Internet and non-Internet partners; HIV testing (where relevant); general discussion of Internet experience and web profiles; safer sex ; and sources of knowledge and skills about risk reduction . For HIV positive men the topic guide also covered their experience of HIV treatment and care  and the relationship between treatments and risk ;The research protocol was approved by the following ethics committees: Royal Free Hospital and Medical School Local Research Ethics Committee, the East London and The City Research Ethics Committee and City University London Research Ethics Committee.Over 4000 London gay men were recruited for the quantitative arm of the study in 2002 and 2003 from the HIV treatments and testing clinics, gyms and through the Internet. Of these, a subset of 128 men were interviewed one-to-one for the qualitative arm . On the other hand, men surveyed online were more likely to have used the Internet to look for sex (approximately 90%) than men who surveyed in the clinics or gyms (40–50%). The differences between the samples will be explored further in future analyses and subject to formal statistical testing. For the most part the four samples were independent; only a minority of men said they had completed a questionnaire in more than one recruitment site (eg in a clinic and in a gym); range 2–10%.Data analysis, using standard statistical packages, will allow us to examine; whether sex with Internet partners is of higher risk than with other men  and the extent to which Internet sex seekers were specifically looking for unprotected anal intercourse either online or offline. The characteristics of those who have and have not used the Internet to find a sexual partner will be compared to explore whether selected groups of men  seek sex on the Internet. The importance of the Internet in relation to other venues will be examined. The samples, ranging in size from 404 to 1218, are sufficiently large to allow us to detect statistically significant differences between and within the different groups at the 5% level of significance.surveyed in the community (eg in Scotland). The data collected online will also allow us to examine patterns of Internet use in a sample.that covers the whole of the UK.All analyses will be conducted for HIV positive, negative and never-tested men separately and comparisons will be made within and between the four samples. Comparison of those recruited through the Internet with the community and clinic samples will indicate whether the Internet attracts men who would otherwise be hard to reach for health promotion and HIV prevention. The data collected online from men living in the UK but outside London will provide opportunities for further analysis. For example their characteristics and behaviours can be compared with those of the London men surveyed online as well as with other men living outside London A PhD studentship, funded separately by the UK Economic and Social Research Council (ESRC), will allow us to examine methodological issues around using the Internet for data collection and research , such asIn addition, data collected in the HIV testing clinic will also allow us to explore Internet sex-seeking and risk behaviours among heterosexual men and women.The interview transcripts from both the face-to-face and online interviews were coded and analysed using Nvivo. The background characteristics of the men interviewed for the qualitative arm of the study are summarized in table Interviewees were recruited for qualitative interviews once they had completed a behavioural questionnaire for the quantitative arm of the study. Additional purposive criteria were adopted to ensure the sample included men from a range of age groups; of different HIV status ; of varying educational attainment; both employed and unemployed; and who reported differential use of the Internet for sexual purposes. Online, as well as face-to-face (FTF) interviews were conducted to encourage the participation of gay men who used the Internet to look for sex. This combination of matching the quantitative arm in terms of recruitment sites, together with additional purposive criteria and two interviewing methods resulted in 128 qualitative interviews with a diverse range of gay/bisexual men. All interviewees lived in the greater London area.For the qualitative arm of the study, we addressed analytic bias using guidance provided by Barbour , Kvale  and Popa• Iteration• Team analysis• TransparencyWe conducted a mapping phase to provide an empirical context for our thinking about the Internet-related sexuality of gay men and to orient further research in terms of the sampling and topic guides. We also adopted the practice of constant comparison derived from grounded theory as another form of iteration.The research team was involved in a cycle of reflection on the data as they were generated and analysed. In the first meeting the team reviewed a transcript to identify possible themes for analysis. Notes and the transcription of the meeting contributed to the formulation of themes. At an intermediate stage the team was asked to apply the thematic framework to other transcripts. This helped to assess the utility of the framework and elaborate possible new themes. A second meeting was then held where the team reviewed the coding scheme for the qualitative research and its application to the entire data set. This process was conducted twice – first for data generated during phase 1, then for data collected in phase 2.We documented how the data were generated, catalogued and analysed to make it open to observation.The one-to-one interviews will be analysed for recurring and contradictory themes relating to the core research questions. Phase one will help orientate the qualitative research around the Internet and risk. Phase two will provide information about the range of sexual experiences linked to the Internet as well as how people communicate about sex and risk through the Internet. The qualitative interviews will allow for greater interrogation than is permitted by self-completed questionnaire alone. A number of issues will be examined including personal experiences of using the Internet for sex, partner selection, the context in which unsafe sex occurs, the emergence of social and sexual networks via the Internet and the value or meaning placed on unsafe sex with casual partners met in different settings including the Internet.Does the Internet represent a new sexual risk environment for gay men? To answer this question, we have employed a range of quantitative and qualitative research methods in online and offline samples of London gay men. The data we collect will allow us to explore in depth the association between seeking sex on the Internet and high risk behaviour and also consider the underlying processes.One of the strengths of the Internet and HIV study is its methodological plurality. Detailed behavioural data have been collected in the quantitative arm of the study from a large number of men recruited in community and clinic settings as well as through the Internet. Questionnaires were completed online by the Internet sample. On the other hand, qualitative interviews will allow for a greater understanding of the context in which unsafe sex occurs and the processes that underlie the behavioural patterns seen in the quantitative analysis. By using both quantitative and qualitative methods, we hope to garner the best that both approaches can offer within an integrated research programmeSampling men online has allowed us to develop innovative research methods for both the quantitative and qualitative arms of the study by taking advantage of recent advances in web-based data collection. The online questionnaire provided opportunities for innovation with respect to its design, format of the questions and data entry. Conducting one-to-one interviews online has opened up new ways of undertaking qualitative research. Ours is one of a small number of studies which have explored this approach to qualitative interviewing . In addiThe Internet undoubtedly offers enormous potential for HIV prevention and sexual health promotion ,35. Our None declared.JE, GH and LS conceived the study; all authors participated in its design; JE was responsible for overall project management; GB was responsible for conducting the quantitative arm of study; MD was responsible for the qualitative arm; JE drafted the manuscript with input from GB and MD. All authors read, revised and approved the final manuscript.The Internet and HIV study: MRC-funded researchCity University London, Institute of Health Sciences, St Bartholomew School of Nursing and MidwiferyProfessor Jonathan Elford, principal investigatorGraham Bolding, research fellow (quantitative arm)Mark Davis, research fellow MRC Social and Public Health Sciences Research Unit, GlasgowProfessor Graham Hart, co-investigatorRoyal Free and University College Medical School, LondonProfessor Lorraine Sherr, co-investigatorCollaborators, , Royal Free Hampstead NHS Trust Hospital London, Barts and the London NHS Trust Hospital, Camden Primary Care Trust, London, Central London gyms.The research was funded from 2002–2004 by the UK Medical Research Council and the Department of Health as part of its AIDS Epidemiological Research Programme Further information about the project can be found on our website at The Internet and research methodology: ESRC-funded PhD studentshipCity University London, School of Social Sciences and Institute of Health Sciences, St Bartholomew School of Nursing and MidwiferyAlison Evans PhD candidateSchool of Social Sciences and Institute of Health Sciences, St Bartholomew School of Nursing and MidwiferyProfessor Dick Wiggins first supervisor,School of Social Sciences, Department of SociologyProfessor Jonathan Elford second supervisor,Institute of Health Sciences, St Bartholomew School of Nursing and MidwiferyAdvisory BoardCity University LondonGraham Bolding, Mark Davis, Professor Jonathan Elford, Professor Roger Jowell, Professor Dick WigginsMRC Social and Public Health Sciences Research Unit, GlasgowProfessor Graham HartRoyal Free and University College Medical School, LondonProfessor Lorraine SherrFurther information can be found at The pre-publication history for this paper can be accessed here:


Arabidopsis.Microarray analysis and the 'InterAct class' method were used to study interactions between carbon and nitrogen signaling in  Carbon and nitrogen are two signals that influence plant growth and development. It is known that carbon- and nitrogen-signaling pathways influence one another to affect gene expression, but little is known about which genes are regulated by interactions between carbon and nitrogen signaling or the mechanisms by which the different pathways interact.Arabidopsis thaliana. An informatic analysis using InterAct Class enabled us to classify genes on the basis of their responses to carbon or nitrogen treatments. This analysis provides in vivo evidence supporting the hypothesis that plants have a carbon/nitrogen (CN)-sensing/regulatory mechanism, as we have identified over 300 genes whose response to combined CN treatment is different from that expected from expression values due to carbon and nitrogen treatments separately. Metabolism, energy and protein synthesis were found to be significantly affected by interactions between carbon and nitrogen signaling. Identified putative cis-acting regulatory elements involved in mediating CN-responsive gene expression suggest multiple mechanisms for CN responsiveness. One mechanism invokes the existence of a single CN-responsive cis element, while another invokes the existence of cis elements that promote nitrogen-responsive gene expression only when present in combination with a carbon-responsive cis element.Microarray analysis was used to study global changes in mRNA levels due to carbon and nitrogen in This study has allowed us to identify genes and processes regulated by interactions between carbon and nitrogen signaling and take a first step in uncovering how carbon- and nitrogen-signaling pathways interact to regulate transcription. Carbon and nitrogen are two major macronutrients required for plant growth and development. Specific carbon and nitrogen metabolites act as signals to regulate the transcription of genes encoding enzymes involved in many essential processes, including photosynthesis, carbon metabolism, nitrogen metabolism, and resource allocation -5. For ecis elements that have been shown to be carbon-responsive cis elements (C-elements) and none has been identified from studies in Arabidopsis thaliana. Although much less is known concerning the mechanisms controlling nitrogen signaling, microarray analysis has been used to identify nitrogen-responsive genes [A. thaliana [In plants, there are multiple carbon-responsive signaling pathways -13, and ve genes ,18. It hthaliana , but a gA. thaliana, including the identification of a putative cis regulatory element that is responsive to either light or carbon signals [cis elements that may be responsible for coordinating a gene's responses to both these signaling pathways.Previously, microarrays were used to identify genes and biological processes regulated by interactions between carbon and light signaling in  signals . In thisArabidopsis. We predicted six general models that could describe the possible modes of gene regulation due to carbon, nitrogen and CN together. Three of these models do not involve interactions between carbon and nitrogen signaling. The 'No effect' model includes genes not regulated by carbon, nitrogen and/or CN. The 'C-only' model includes genes regulated only by carbon. Finally, the 'N-only' model includes genes regulated only by nitrogen. Three additional models are needed to describe the regulation of genes affected by interactions between carbon and nitrogen signaling  depicts a gene X, for which regulation requires carbon and nitrogen, and neither carbon alone nor nitrogen alone has an effect. Model 3 (CN dependent/independent) incorporates both an independent and a dependent component to the interactions of carbon and nitrogen signaling. For gene Y, carbon alone has an independent inductive effect, while nitrogen has a carbon-dependent effect as it can enhance the effect of carbon, but has no effect on its own (Model 3 CN-enhanced). For gene Z, nitrogen alone has an independent inductive effect, while carbon has a nitrogen-dependent effect. These general models can be broken down into more descriptive sub-models. For example, Model 2 can be broken into two sub-models for which CN results in either an inductive or repressive effect.The goal of this study was to use a genomic approach to test the hypothesis that carbon and nitrogen signaling pathways interact to regulate the expression of genes in g Figure . Model 1in vivo significance of the above models, a microarray analysis of RNA from plants treated transiently with distinct carbon and nitrogen treatments was carried out, and the results were analyzed to determine the carbon and nitrogen regulation of different genes. For this study, we analyzed RNA isolated from Arabidopsis seedlings exposed to four different transient carbon and/or nitrogen treatments   Figure  using AfW, X, Y and Z) were classified by InterAct Class .InterAct Class is a ranking system used to qualitatively compare gene-expression profiles across multiple treatments. For each gene, each treatment is assigned a value representing the effect of the treatment on the expression of that gene. Treatments that result in repression of a gene are assigned a negative number, treatments that do not significantly affect a gene are assigned zero, and treatments that cause induction are assigned a positive number. If more than one treatment causes induction or repression, the treatments are ranked so that the treatment that causes the most induction or repression will be assigned the number furthest from zero. The four hypothetical genes in Figure s Figure , demonstOut of 23,000 genes on the Affymetrix chip, 3,652 passed our stringent filtering criteria for reproducibility among treatment replicates and were assigned an InterAct class. Our subsequent analysis of the expression patterns of these 3,652 genes validated the existence of 60 different InterAct classes Table . For exaThe InterAct classes were assigned to one of the six general models. To identify biological processes that contain a significant number of genes regulated by carbon, nitrogen and/or CN, we determined which Munich Information Center for Protein Sequences (MIPS) functional categories (funcats) ,22 were p-value = 6.0 × 10-4). Therefore, the metabolism funcat is under-represented in the No effect model, showing that metabolism displays significant regulation by carbon and/or nitrogen. This analysis revealed several primary funcats  that are significantly under-represented in the No effect model  of the genes assigned to an InterAct class and a funcat are assigned to the No effect model Table . This pep-value, and the funcat analysis extended to every sub-model and every funcat).For the funcats that are under-represented in the No effect model, this type of analysis was extended to examine the regulation of these funcats in all of the sub-models. This analysis enabled us to determine into which sub-models the genes from these funcats fell and to determine whether the genes in these funcats are under- and over-represented (-S and +S respectively) in these sub-models Table    Table . These gcis search analysis. Four nuclear genes encoding ribosomal proteins predicted to be localized to the mitochondria [cis-regulatory elements. Over-represented motifs in the promoters of the four protein synthesis genes or the three ferredoxin metabolism genes were identified using AlignAce [As we found that genes in the funcat protein synthesis are over-represented in Model 3 CN-enhanced  on the transcription of a gene  that responds to carbon alone. Such carbon-responsive cis elements (C-elements) can be identified because they should also be over-represented in the promoters of genes that are induced by carbon alone (the C-only inductive model). From this analysis, a number of the AlignAce motifs identified from the ferredoxin metabolism and protein synthesis genes in the Model 3 CN-enhanced were also shown to be associated with C-only inductive model genes (Table cis element) that responds specifically to a CN-signaling pathway  exists, it would be predicted to be over-represented in the promoters of genes in Model 3 CN-enhanced, but would not be over-represented in the C-only inductive model. Two of the AlignAce motifs fit this pattern . Another prediction is that if CN1 and CN2 regulate gene expression, biologically related genes might also contain CN1 and CN2. Interestingly, ferredoxin-dependent nitrite reductase (At2g15620) contains three copies of CN1 and one copy of CN2 in its promoter. This gene is in Model 3 CN-enhanced (InterAct class 1021), its protein product is localized to the chloroplast [p-value = 0.0364), but it would not be unlikely to find one copy of CN2 . In addition, a total of four copies of CN1 and CN2 was very unlikely  in any combination .If CN1 and CN2 regulate gene expression, they might be expected to be evolutionarily conserved. Unfortunately, oroplast  and its oroplast , suggestA. thaliana has only one copy of ferredoxin-dependent nitrite reductase, we searched the O. sativa genome sequence for ferredoxin-dependent nitrite reductase genes. Again, we found only one gene [A. thaliana ferredoxin-dependent nitrite reductase gene and the O. sativa gene for an alignment. Despite this lack of similarity, we tested for the presence of CN1 and CN2 in the promoter of this gene; three copies of CN1  and one copy of CN2  were found. Again, it was very unlikely that a total of four copies of CN1 and CN2  would occur in the promoter sequence.As one gene . BLAST [one gene  did not cis element that alone has little or no effect on gene regulation, but when present in combination with a C-element, enhances the induction caused by carbon and is dependent on a carbon-responsive transcription factor . To identify NDEs, our strategy for cis element identification was modified. NDEs would be expected to be over-represented in the promoters of Model 3 CN-enhanced genes, but only when present in combination with a separate C-element, as both elements are required to give the enhanced expression due to CN. However, some of the AlignAce motifs are potentially involved in regulating expression due to the carbon treatment in cooperation with the already identified C-elements. These cis elements would be similar to NDEs as they would be over-represented in genes induced by carbon in combination with the already identified C-elements. As these motifs are not NDEs, we sought to identify them and remove them from the analysis. AlignAce motifs were tested to determine whether they are over-represented in the promoters of genes whose promoters contain any of the C-elements and are in the C-only inductive model. Those that were found to be over-represented were eliminated from further analysis because these motifs are potentially involved in carbon regulation and are not NDEs. Next, the remaining 33 AlignAce motifs were tested to determine if any are NDEs by determining whether they are over-represented in combination with a C-element within the promoters of the Model 3 CN-enhanced genes. Seven of the potential NDEs are over-represented  with at least one C-element in the promoters of the Model 3 CN-enhanced genes, resulting in 12 significant combinations between putative NDEs and C-elements .A second mechanism by which the expression due to CN could be greater than C+N could involve a nitrogen-responsive r Figure . Other r another . In the p-value < 0.005).To determine if this approach resulted in an enrichment of NDEs, the promoter sequence of each gene was randomized, and the same test was performed. This enabled us to determine whether the remaining 33 AlignAce motifs were over-represented in combination with each C-element in the randomized promoters of the Model 3 CN-enhanced genes. Sets of the randomized promoters (200 sets) were tested, and none of them had as many significant pairs of potential nitrogen-dependent enhancers of carbon regulation and C-elements than the 12 found in the actual promoters. This randomization proves that our approach successfully enriched for NDEs in the actual promoters of the Model 3 CN-enhanced genes and that all the observed significant combinations cannot be due to false positives . Two NDEs (N1 and N2) were found to be associated with C-elements  to regulate gene expression in response to CN, then genes that contain both motifs and are in Model 3 CN-enhanced should be misrepresented in certain functional groups as these genes are truly co-regulated. This misrepresentation should occur not only with respect to the genome, but also with respect to the genes in Model 3 CN-enhanced. This result is expected because these genes are more closely related to each other than to the other genes in Model 3 CN-enhanced, and because their CN regulation is the result of the action of the same transcription factor(s). Funcat analysis was used to determine if any functional categories were misrepresented in the genes whose promoters contain N1C6, N1C7, N2C3, N2C6, N2C7 or N2C10 and are in Model 3 CN-enhanced. As the genes used to derive most of the pertinent  and C10 A. thaliana [This report contains the one of the first genome-wide investigations of carbon- and nitrogen-signaling interactions in thaliana . While tBiological processes containing genes that respond significantly to carbon, nitrogen and/or CN were initially identified by finding MIPS funcats ,22 that To gain a better understanding of how the metabolism, energy and protein synthesis funcats are regulated by carbon and/or nitrogen, the sub-models in which they are misrepresented were identified Table . This anASN1 (At3g47340), which has been proposed to be involved in producing asparagine for the transport of nitrogen when carbon levels are low and has been shown to be repressed by carbon [GDH1 (At5g18170), which has been proposed to be involved in ammonia assimilation when ammonia levels are high, is repressed by carbon, and induced by nitrogen [ASN1 is induced because it is involved in shifting the excess nitrogen to asparagine, as asparagine is an efficient way to store and transport nitrogen with respect to carbon [ASN1 is repressed. The regulation of these genes demonstrates the exquisite control of metabolic genes required to balance carbon and nitrogen availability.Surprisingly, metabolism is over-represented in Model 3 CN-suppressed, indicating that many of the genes involved in metabolism show less expression due to CN than expected. The majority of the genes (28 out of 34) were repressed by carbon, induced by nitrogen and repressed by CN, and were assigned to InterAct classes such as -21-2-1 . Several of these genes encode enzymes involved in the catabolism of complex carbohydrates, including β-fructofuranosidase (At1g12240), β-amylase (At3g23920) and β-glucosidase (At3g60130 and At3g60140). y carbon , was assnitrogen , and wasOur studies also showed that protein synthesis is one of the processes most affected by the interactions between carbon and nitrogen signaling Table . In addiTrichoderma hamatum, the gene for ribosomal protein L36 is regulated by interactions between carbon and nitrogen, as it is induced only by CN, and not by carbon or nitrogen alone [Little work has been done on the transcriptional control of protein synthesis by carbon and/or nitrogen signaling in plants. However, it has been shown in yeast that genes encoding ribosomal proteins are induced by nitrogen in the presence of carbon; whether this induction by nitrogen requires carbon to be present was not addressed in the yeast study . Furthercis-regulatory mechanisms involved in carbon and nitrogen signaling interactions. We hypothesized that there could be two general transcriptional mechanisms that would result in the expression due to CN being greater than that due to C+N . Since CN1 and CN2 , and while three copies in the promoter of the O. sativa gene did not reach the 0.05 cutoff, this might represent some small change in the specificity of the regulating factor between O. sativa and A. thaliana. The failure of BLAST to detect any similarity between the promoters of these two genes suggests that their transcriptional regulators share very little sequence specificity, so a slight change in specificity is not unexpected. The same analysis suggests that CN2 is a false positive because it is not over-represented in the promoters of ferredoxin-dependent nitrite reductase genes. However, we cannot rule out the possibility that the combination of CN1 and CN2 is what is important in regulating these genes, as having a total of four copies of CN1 and CN2 is unlikely in the promoters of both genes. One possibility is that there is a positional relationship between the copies of CN1 and CN2 that is important. From a quick visual inspection, there does not appear to be a conserved relationship between the three copies of CN1 and one copy of CN2 in the A. thaliana and O. sativa promoters. These issues will have to be resolved by further experimental work; however, these results do suggest that ferredoxin, ferredoxin reductase and ferredoxin-dependent nitrite reductase are co-regulated by carbon and nitrogen due to CN1 and/or CN2. CN1 and/or CN2 therefore might act to link nitrogen reduction and energy metabolism.Further analysis suggests that CN1 is involved in regulating the expression of ferredoxin-dependent nitrite reductase. Finding three copies of CN1 in the promoter of the Our analysis found CN-elements in the promoters of the ferredoxin-related genes Table , but notcis elements that are responsive to carbon and nitrogen signaling interactions. It is noteworthy that the putative CN-elements and NDEs represent cis elements that have not been previously identified and as such may represent novel components of the CN regulatory circuit. Further study of the identified genes and cis elements is required to bring about a complete understanding of interactions between carbon and nitrogen signaling.This analysis represents a first step in understanding how carbon and nitrogen signaling interact to control gene expression and has identified genes and putative Arabidopsis thaliana seeds of the Columbia ecotype were surface-sterilized and plated on designated media and vernalized for 48 h at 8°C. Plants were grown semi-hydroponically under 16-h-light (70 E/m2/sec)/8-h-dark cycles at a constant temperature of 23°C on basal Murashige and Skoog (MS) medium (Life Technologies) supplemented with 2 mM KNO3, 2 mM NH4NO3 and 30 mM sucrose [3 and NH4NO3) or carbon (sucrose) and dark-adapted for 48 h. To perform specific metabolic treatments, 25 dark-adapted seedlings were transferred to fresh MS medium containing 0% or 1% (w/v) sucrose and/or 2 mM KNO3 and 2 mM NH4NO3 or no nitrogen, and illuminated with white light for an additional 8 h (70 E/m2/s1). Following these transient carbon and nitrogen treatments, whole seedlings were harvested, immediately frozen in liquid nitrogen, and stored at -80°C before RNA extraction. sucrose . Two-weeArabidopsis ATH1 Target (Affymetrix). Washing, staining and scanning were carried out as recommended by the Affymetrix instruction manual. Expression analysis was performed with the Affymetrix Microarray Suite software (version 5.0) set at default values with a target intensity set to 150. Three biological replicates for each treatment were carried out.RNA was isolated from whole seedlings using a phenol extraction protocol as previously described . Double-Only Affymetrix probes representing genes that were deemed to be expressed in all treatments and replicates were used for subsequent analysis by InterAct Class ,20. For p-values were calculated for the MIPS functional categories (funcats) [n; p was the number of genes assigned to the specific model being analyzed divided by the number of genes assigned to an InterAct class and funcat; k was the number of genes in the funcat being analyzed and assigned to the model being analyzed. This analysis, with the baseline being all the genes assigned an InterAct class, accounts for any biases that may have been caused by discarding all the absent genes. The one-tailed p-value was considered when the Poisson approximation of binomial probabilities was used. For the binomial-ratio and the exact binomial probability test, the p-value for k or more out of n was used.funcats) ,22 analyfuncats) . Brieflyn was the number of genes in Model 3 CN-enhanced (the child list), k was the number of genes in the pathway being analyzed and in the child list, and p was the number of genes in the pathway being analyzed and in the parent list divided by the number of genes in the parent list. We limited our search to pathways that contained more than two genes in the Model 3 CN-enhanced list. To identify cis-regulatory elements involved in regulating genes in Model 3 CN-enhanced and protein synthesis, we used genes involved in protein synthesis that were assigned Model 3 CN-enhanced, to drive the cis search: At1g07070 , At2g36620 , At5g07090 , and At5g58420 .Pathways whose genes are over-represented in Model 3 CN-enhanced were identified using the informatic tool PathExplore  functionA. thaliana promoters for every gene [n, p is the number of genes in the sub-model  divided by the total number of genes assigned an InterAct class, and k is the number of genes whose promoters contain the AlignAce motif being analyzed (in a specific copy number) and that is in the particular sub-model being tested. A p-value was only calculated if k is greater than nine. In each case, the lowest p-value is given. Cis elements over-represented in the C-only inductive model are considered to be putative C-elements, and cis elements that are over-represented in the promoters of Model 3 CN-enhanced genes and are not over-represented in the promoters of C-only inductive genes, are considered to be putative CN-elements  and that are in the C-only inductive model was used as k. In this example, the analysis will determine if the genes that contain the second motif and the C-element being analyzed are over-represented in the C-only inductive model compared to the genes that just contain the C-element. The same approach was used to identify NDEs as described below.To identify interacting elements, a similar analysis was used. For example, to identify motifs interacting with a C-element Table  in regulp-value less than 0.05 is obtained, the C-element and potential NDE are a significant combination and are likely to regulate carbon and nitrogen interactions. As each motif is tested with each of the 11 C-elements, two steps were taken to control for the multiple tests. First, single strands of the promoter sequences of the A. thaliana genes were randomized 200 times, the reverse complement of the randomized strand was determined, and the number of times the 33 remaining AlignAce motifs were found to be over-represented  with the C-elements was determined and compared to the number of significant combinations  between the 33 remaining motifs and the C-elements when the actual promoters were used. In no set of the randomized promoters were the potential NDEs found to form more significant combinations with the 11 C-elements than the actual promoter sequences . In the second control step, the number of significant combinations that each of the 33 remaining AlignAce motifs was involved in was determined and compared to the number of significant combinations found with the 200 sets of randomized promoters. For one motif, if one random set is significant with as many C-elements as the real promoters the p-value would be 0.005 (1/200).The 33 motifs ) that are not N-, CN- or C-associated elements were tested to determine whether they are potential NDEs. They were tested to see whether genes whose promoters contained these motifs plus a C-element Table  are overp-value )The promoter for At2g15620 was extracted from RSA tools ,40. The O. sativa sequence. Only one hit was found. This gene is annotated as a ferredoxin-dependent nitrate reductase [The sequence database was searched using BLAST  for a geeductase . The 1,0cis elements was performed similarly to the approach described above. Briefly, the number of genes assigned to the funcat being analyzed and Model 3 CN-enhanced was used as n; p was the number of genes assigned to Model 3 CN-enhanced and the funcat being analyzed divided by the number of genes assigned to Model 3 CN-enhanced and a funcat; k was the number of genes in the funcat being analyzed that was assigned to the Model 3 CN-enhanced category and containing the combination of C- and N-element being analyzed.Funcat analysis of the genes whose promoters contain specific Statistical significance of localization was calculated similarly. The only difference being that instead of genes assigned a funcat, genes whose protein products are predicted to be localized in the compartment being analyzed were used. Predicted protein localizations were extracted from the TAIR web page .The following additional data are available with the online version of this paper: Additional data file A table listing the Affymetrix probe ID, gene, and InterAct class for all the Affymetrix probes assigned an InterAct classClick here for additional data fileThe data from 12 Affymetrix microarray chips used in this studyClick here for additional data file


Every multicellular organism depends on the coordinated actions of a multitude of cell types, each designed to carry out specific jobs. In vertebrates, for example, the task of ferrying oxygen to organs, tissues, and cells throughout the body is shouldered by red blood cells. These cells must develop early in the embryo to nourish the body and must be maintained at the right levels throughout adulthood to keep the organism healthy.Specialized red blood cells arise from undifferentiated stem cells in a developmental process called hematopoiesis. All vertebrates have the same basic plan for hematopoiesis during their development, with one group of stem cells producing embryonic blood cells and, later, another group in a different part of the body making adult blood cells. Such developmental programs are tightly orchestrated by suites of transcription factors—proteins that turn genes on and off.But which transcription factors act—and how and where they act—is still largely unknown. Scientists and doctors alike want to better understand differentiation in order to grasp not only how it proceeds normally but also how it can go wrong and lead to diseases, such as leukemia, in which white blood cells become cancerous, and aplastic anemia, in which bone marrow stem cells make too few red blood cells.To discover the genes involved in hematopoiesis, Leonard Zon's group at Children's Hospital, Boston, and their collaborators exposed zebrafish to mutagens and then studied the individuals that developed relevant disorders. In 1996, the group produced three zebrafish lines with embryos lacking fully differentiated red blood cells; since these fish also had especially shimmery tails, the lines were named moonshine.moonshine (mon) gene, it turns out, encodes a transcription factor with wide effects on the embryo's mesoderm, the set of cells that eventually form the circulatory system, muscles, and skeleton. The researchers found that all three lines of mutant zebrafish had mutations in the mon gene.Now Zon and his colleagues have identified the mutant gene responsible for the moonshine zebrafish lines' peculiar traits. The In the mutant fish, stem cells that produce red blood cells were present initially, but the defective moonshine protein was unable to keep the blood cells alive. These blood cells underwent apoptosis, or programmed cell death, leaving the fish unable to make red blood cells, and they died at about two weeks old.mon gene is most similar to the human and mouse Tif1-gamma, one of a family of proteins known to link DNAbinding proteins with other factors that activate or repress gene activity. Using a DNA analysis technique in mouse cell cultures, the researchers found Tif1-gamma in nuclear bodies, multi-protein complexes in cell nuclei that help regulate gene expression. But the Tif1-gamma nuclear bodies didn't fit into any known class of such complexes, so it's an open question how Tif1-gamma acts and what other proteins help regulate it. Finding a transcription factor such as Tif1-gamma involved in early cell specialization opens a door to a suite of studies on the targets of the transcription factor, as well as other genes that act along with mon to affect red blood cell development.The researchers found that the protein encoded by the


The Generalized Hidden Markov Model (GHMM) has proven a useful framework for the task of computational gene prediction in eukaryotic genomes, due to its flexibility and probabilistic underpinnings. As the focus of the gene finding community shifts toward the use of homology information to improve prediction accuracy, extensions to the basic GHMM model are being explored as possible ways to integrate this homology information into the prediction process. Particularly prominent among these extensions are those techniques which call for the simultaneous prediction of genes in two or more genomes at once, thereby increasing significantly the computational cost of prediction and highlighting the importance of speed and memory efficiency in the implementation of the underlying GHMM algorithms. Unfortunately, the task of implementing an efficient GHMM-based gene finder is already a nontrivial one, and it can be expected that this task will only grow more onerous as our models increase in complexity.As a first step toward addressing the implementation challenges of these next-generation systems, we describe in detail two software architectures for GHMM-based gene finders, one comprising the common array-based approach, and the other a highly optimized algorithm which requires significantly less memory while achieving virtually identical speed. We then show how both of these architectures can be accelerated by a factor of two by optimizing their content sensors. We finish with a brief illustration of the impact these optimizations have had on the feasibility of our new homology-based gene finder, TWAIN.In describing a number of optimizations for GHMM-based gene finders and making available two complete open-source software systems embodying these methods, it is our hope that others will be more enabled to explore promising extensions to the GHMM framework, thereby improving the state-of-the-art in gene prediction techniques. In practice, all of the probabilities described in all of these models are represented in log space , so that products of probabilities can be replaced with sums of their logs.where nth-order Markov chain M for state qi would evaluate the probability P of a putative feature Si according to:The simplest variable-length model used in practice is the Markov chain. An xj is the jth nucleotide in the sequence of the putative feature, di is the length of that feature, and PM(xj|xj-n..xj-1) is the probability of nucleotide xj conditional on the identities of its n predecessor nucleotides, according to content model M. As with the fixed-length model described above, this computation is typically done in log space.where In scoring the signals and content regions of a putative gene parse, it will be important for us to carefully differentiate between the nucleotides which are scored by a signal sensor and those which are scored by a content sensor in a putative parse. As shown in Figure The first step of the PSA algorithm is to compute a prefix sum array for each content sensor. For noncoding states (introns and intergenic) this can be formalized as shown in Figure phase 0, phase 1, and phase 2. We employ three Markov chains, M0, M1, and M2, corresponding to these three phases. Together, these three chains constitute a three-periodic Markov chain, M{0,1,2}. Exon states then require three arrays, each of which can be initialized using the procedure shown in Figure In the case of exon states, it is important to capture the different statistical properties present in the three codon positions, referred to as i, 0, αi, 1, and αi, 2 for an exon state qi as follows:In this way, we can initialize the three arrays αfor ω ← 0 to 2 doi,ω, S, M{0, 1, 2},ω) ;   init_phased.The individual chains RS = log P(xh..xh+n-1|θ), where h is the position of the beginning of the window and n is the window length. If signal thresholding is desired, RS can be compared to a pre-specified threshold and those locations scoring below the threshold can be eliminated from consideration as putative signals.Once the prefix sum arrays have been initialized for all variable-duration states, we make another left-to-right pass over the input sequence to look for all possible matches to the fixed-length states, via the signal sensors. In general, a signal sensor θ models the statistical biases of nucleotides at fixed positions surrounding a signal of a given type, such as a start codon. Whenever an appropriate consensus is encountered (such as ATG for the start codon sensor), the signal sensor's fixed-length window is superimposed around the putative signal  and evaluated to produce a logarithmic signal score signal queue for consideration later as possible predecessors of subsequent signals in a putative gene model. As each new signal is encountered, the optimal predecessors for the signal are selected from among the current contents of the signal queues, using a scoring function described below. In the example (forward strand) GHMM depicted in Figure The remaining candidates for signals of each type are then inserted into a type-specific ATG→TAGATG→GTGT→AGAG→GTAG→TAGTAG→ATGPt(qi|qi-1), which is included in the scoring of a possible predecessor; this probability can be accessed quickly by indexing into a two-dimensional array. The logarithmic transition score will be denoted RT = log Pt(qi|qi-1).Associated with each of these patterns is a transition probability, Pd(di|qi) for distance (=duration) di and signal type (=state) qi. This probability can usually be obtained relatively quickly, depending on the representation of the duration distributions. If the distributions have been fitted to a curve with a simple algebraic formula, then evaluation of the formula is typically a constant-time operation. If a histogram is instead maintained, then a binary search is typically required to find the histogram interval containing the given distance. We denote the logarithmic duration score RD = log Pd(di|qi:j) where di is the length of the content region delimited by signals qi and qj, and qi:j is the variable-length state corresponding to that content region.The distance from a prospective predecessor to the current signal is also included in the evaluation in the form of Pe. For a fixed-length state, this is simply the score produced by the signal sensor. For a variable-length state qi, Pe can be evaluated very quickly by indexing into the prefix sum array αi,γ for state qi and phase γ at the appropriate indices for the two signals and simply performing subtraction:Following Equation 2, the final component of the scoring function is the emission probability RC ← αi,γ [wpos(scur) - 1] - αi,γ [wpos(spred) + wlen(spred) - 1],     (5)wpos(s) is the 0-based position (within the full input sequence) of the first nucleotide in the context window for signal s, wlen(s) is the length of the context window for signal s, and spred and scur are the predecessor and current signals, respectively. In the case of coding features, γ is the phase of the array and ω = (γ + pos(scur))mod3 is the phase of scur, for pos(scur) the position of the leftmost consensus base of scur. For reverse-strand features, since the prefix sum arrays tabulate their sums from the right instead of the left, the subtraction must be reversed:where RC ← αi,γ[wpos(spred) + wlen(spred)] - αi,γ[wpos(scur)],     (6)L - pos(scur) - 1)mod3, for L the sequence length. For noncoding features, the phases can be ignored when computing RC, since there is only one array per noncoding state.and ω =  denotes the logarithmic inductive score for signal si in phase γi. For forward-strand coding features, the phases γi and γj are related by:for current signal i = (γj - Δ)mod3,     (8)γfor Δ the putative exon length, or, equivalently,j = (γi + Δ)mod3.     (9)γi = γj. For intergenic features, the phase will always be 0 for a forward strand signal and 2 for a reverse strand signal .These relations can be converted to the reverse strand by swapping + and -. For introns, γsj. This scoring function is evaluated for all appropriate predecessor signals, which are readily available in one or more queues, as mentioned above. A pointer called a trellis link is then created, pointing from the current signal to its optimal predecessor. In the case of those signals that can terminate an exon or an intron, three optimal predecessors must be retained, one for each phase. The inductive score RI of the new signal sj is then initialized from the selected predecessor si as follows:The result of Equation 7 is the optimal predecessor for signal RI ← RI + RT + RD + RC + RS(sj),     (10)RS(sj) is the logarithmic score produced by the signal sensor for signal sj.where in-phase  stop codons are generally not permitted in coding exons; for this reason, any potential stop codon  will eclipse any preceding start codon or acceptor site  in the corresponding phase. The algorithm shown in Figure A final step to be performed at each position along the input sequence is to drop from each queue any signal that has been rendered unreachable from all subsequent positions due to intervening stop codons. Except for the final stop codon of a gene, For the reverse strand, line 3 of eclipse should be changed to:mod3;ω ← (p-pos(s)-len(s) - 1) mod3 we mean the positive remainder after division of x by 3; in some programming languages (such as C/C++), a negative remainder may be returned, in which case 3 should be added to the result.where len(s) is the length of the consensus sequence for signal s . Note that by xA special case of eclipsing which is not handled by eclipse is that which occurs when a stop codon straddles an intron; this can be handled fairly simply by checking for such when considering each donor signal as a prospective predecessor for an acceptor signal (or vice-versa on the reverse strand). As each predecessor is evaluated, the bases immediately before the donor and immediately following the acceptor are examined, and if a stop codon is formed, the predecessor is no longer considered eligible for selection in the corresponding phase.As shown in Figure In the case of noncoding queues , the assumption that noncoding features follow a geometric  distribution allows us to limit these queues to a single element (per phase), because once a noncoding predecessor has been selected in a given phase, no other noncoding predecessor which has already been compared to the selected predecessor can ever become more attractive by virtue of its transition probability , its duration probability (since the geometric distribution ensures that their respective duration probabilities decrease at the same rate), nor its sequence probability .L) for an input sequence of length L and a GHMM with a fixed number of states. It will be seen that the traceback procedure described below also requires time O(L), and so this is the time complexity of the PSA decoding algorithm for normal eukaryotic genomes .Because the coding and noncoding queues are effectively limited to a constant load (as argued above), the expected processing time at each nucleotide is O(1) in practice and therefore the entire algorithm up to this point requires time O must be instantiated at either terminus of the sequence . Those at the left terminus will have been entered into the appropriate queues at the very start of the algorithm as prospective targets for the first trellis links (and having inductive scores RI = 0), and those at the right terminus are the last signals to be evaluated and linked into the trellis. The highest scoring of these right terminal anchor signals is selected (in its highest-scoring phase) as the starting point for the traceback procedure. Traceback consists merely of following the trellis links backward while adjusting for phase changes across exons, as shown in Figure Once the end of the sequence is reached, the optimal parse φ can be reconstructed by tracing back through the trellis links. In order for this to be done, a set of virtual, Modifications to Figure O(L|Q|) for sequence length L and variable-duration state set Q. If, for example, array elements are 8-byte double-precision floating point numbers, then the GHMM depicted in Figure It should be clear from the foregoing that the space requirements of the PSA decoding algorithm are The memory requirements can be reduced in several ways. First, Markov chains can be shared by similar states. For example, the intron and intergenic states can share a single Markov chain trained on pooled noncoding DNA, and all the exon states can use the same three-periodic Markov chain trained on pooled coding DNA. To our knowledge, the extent to which this optimization affects the accuracy of the resulting gene finder has not been systematically investigated, though it is commonly used in practice. Second, the models for exons can be modified so as to utilize likelihood ratios instead of probabilities. If the models for exons are re-parameterized to compute:and the noncoding models are modified to compute:L|Q|). An additional consideration is that the log-likelihood strategy makes unavailable (or at least inseparable) the raw coding and noncoding scores, which might be desired later for some unforeseen application.then the latter can be seen to be unnecessary, since it will always evaluate to 1. Such a modification is valid and will have no effect on the mathematical structure of the optimization problem given in Equation 2 as long as the denominator is evaluated using a Markov chain or other multiplicative model, since the effect of the denominator on inductive scores will then be constant across all possible predecessors for any given signal. Using such ratios allows us to skip the evaluation of all noncoding states, so that the number of prefix sum arrays required for a double-stranded version of the GHMM in Figure A third method of reducing the memory requirements is to eliminate the prefix sum arrays altogether, resulting in what we call the Dynamic Score Propagation (DSP) algorithm.L+|Q|), where the constant factor associated with the L term is small, reflecting only the number of signals per nucleotide emitted by the signal sensors, as well as the memory required to store the sequence itself.Informally, the DSP algorithm is similar to the PSA algorithm except that rather than storing all nucleotide scores for all content sensors in a set of prefix sum arrays, we instead store only the specific elements of those arrays that are needed for assessing prospective predecessors during the trellis formation. Associated with each signal is a "propagator" variable which represents the log probability of the highest-scoring partial parse up to and including this signal. As processing proceeds left-to-right along the sequence, these propagators are updated so as to extend these partial parses up to the current position. In this way, the inductive score of each signal is incrementally propagated up to each potential successor signal that is encountered during processing; when a signal is eclipsed in all phases by stop codons , propagation of that signal's inductive score halts, since further updates would be useless beyond that point. Because no prefix sum arrays are allocated, and because the signal queues are effectively limited in size (as argued previously), the expected memory requirements of DSP will be seen to be O, the signal will have one propagator per queue, but it will be clear from the context to which propagator we refer. Each queue will also have a propagator associated with it, though for the sake of reducing ambiguity we will refer to these as accumulators and represent them with the symbol α. The purpose of the accumulators is to reduce the number of updates to individual signal propagators; otherwise, every signal propagator in every queue would need to be updated at every position in the input sequence. The accumulator for a given queue will accumulate additions to be made to the propagators of the signals currently in the queue. The update of signal propagators from their queue's accumulator is delayed as long as possible, as described below. Accumulator scores are initialized to zero, as are the propagator scores for the left terminus anchor signals; the general case of propagator initialization will be described shortly.Each signal Updating of a propagator π from an accumulator α is simple in the case of a noncoding queue:0≤ω≤2 π[ω] ← π[ω] + α[0].     (13)∀s associated with the propagator π, in order to synchronize the periodic association between phase and array index:For coding queues, the update must take into account the location of the signal 0≤ω≤2 π[ω] ← π[ω] + α[(ω - pos(s) - len(s))mod3],     (14)∀or, on the reverse strand:0≤ω≤2 π[ω] ← π[ω] + α[(ω + pos(s) + len(s))mod3].     (15)∀M, a coding accumulator can be updated according to the rule:Given a content sensor 0≤ω≤2 α[ω] ← α[ω] + log PM[(ω+f)mod3](xf),     (16)∀or, on the reverse strand:0≤ω≤2 α[ω] ← α[ω] + log PW[(ω-f)mod3](xf),     (17)∀f is the position of the current nucleotide xf, PM[ω](xf) is the probability assigned to xf by the content sensor M in phase ω, and W is the reverse-complementary model to M which computes the probability of its parameter on the opposite strand and taking contexts from the right rather than from the left. This update occurs once at each position along the input sequence. Use of f provides an absolute frame of reference when updating the accumulator. This is necessary because the accumulator for a queue has no intrinsic notion of phase: unlike an individual signal, a queue is not rooted at any particular location relative to the sequence.where th element of the accumulator must be updated:For noncoding queues, only the 0log PM(xf).     (18)α[0] ← α[0] + si denote the predecessor under consideration and sj the new signal. Denote by Δ the length of the putative exon. Then on the forward strand, we can compare predecessors with respect to phase ω via the scoring function RCI + RD + RT, where RD and RT are the duration and transition scores described earlier and RCI includes the content score and the inductive score from the previous signal:All that remains is to specify the rule for selecting an optimal predecessor and using it to initialize a new signal's propagator. We first consider new signals which terminate a putative exon. Let 0≤ω≤2 RCI ← πi[(ω - Δ)mod3].     (19)∀On the reverse strand we have:0≤ω≤2 RCI ← πi [(ω + Δ)mod3].     (20)∀For introns it is still necessary to separate the three phase-specific scores to avoid greedy behavior, though the phase does not change across an intron, so no Δ term is necessary:0≤ω≤2 RCI ← πi[ω].     (21)∀When the preceding feature is intergenic we need only refer to phase zero of the preceding stop codon:RCI ← πi[0],     (22)or, on the reverse strand, phase 2 of the preceding start codon (since the leftmost base of the reverse-strand start codon will reside in phase 2).RCI + RD + RT is selected with respect to a given phase ω, the appropriate element of the new signal's propagator can be initialized directly:Once an optimal predecessor with score j[ω] ← RCI + RD + RT + RS(sj),     (23)πRS(sj) = P(context(sj)|θj) is the score assigned to the context window of the new signal sj by the appropriate signal sensor θj. An exception to Equation 23 occurs when ω is not a valid phase for signal sj , in which case we instead set πj[ω] to -∞.where One final complication arises from the fact that the algorithm, as we have presented it, does not permit adjacent signals in a prospective parse to have overlapping signal sensor windows; to allow such would be to permit double-counting of nucleotide probabilities, thereby biasing the probabilistic scoring function. It is a simple matter to reformulate the algorithm so that signal sensors score only the two or three consensus nucleotides of the signals under consideration; this would allow adjacent signals in a prospective parse to be as close as possible without actually overlapping . However, doing so might be expected to decrease gene finder accuracy, for two reasons: (1) statistical biases occurring at fixed positions relative to signals of a given type can in general be better exploited by a signal sensor specifically trained on such positions than by a content sensor trained on data pooled from many positions at variable distances from the signal, and (2) in the case of Markov chains and Interpolated Markov Models, probability estimates for nucleotides immediately following a signal can be inadvertently conditioned on the few trailing nucleotides of the preceding feature (assuming the chain has a sufficiently high order), even though the models are typically not trained accordingly. For these reasons, we prefer to use signal sensors which impose a moderate margin around their respective signals, both to detect any biologically relevant biases which might exist within those margins, and to ensure that content sensors condition their probabilities only on nucleotides within the same feature.Given the foregoing, it is necessary to utilize a separate "holding queue" for signals which have recently been detected by their signal sensors but which have context windows still overlapping the current position in the DSP algorithm. The reason for this is that propagator updates via Equations 13–15 must not be applied to signals having context windows overlapping any nucleotides already accounted for in the accumulator scores, since to do so would be to double-count probabilities. It is therefore necessary to observe the following discipline.Gi there must be a separate holding queue, Hi. When a signal is instantiated by a signal sensor it is added to the appropriate Hi rather than to Gi. As the algorithm advances along the sequence, at each new position we must examine the contents of each holding queue Hi to identify any signal having a context window which has now passed completely to the left of the current position. If one or more such signals are identified, then we first update the propagators of all the signals in the main queue Gi using Equations 13–15, then zero-out the values of the accumulator αi for that queue, and then allow the recently passed signals to graduate from Hi to Gi. Observe that at this point all the signals in Gi have in their propagators scores which have effectively been propagated up to the same point in the sequence, and that point is immediately left of the current position; this invariant is necessary for the proper operation of the algorithm. All content sensors are then evaluated at the current position and their resulting single-nucleotide scores are used to update the accumulators for their respective queues. Finally, whenever it becomes necessary to evaluate the signals in some queue Gi as possible predecessors of a new signal, we must first update the propagators of all the elements of Gi as described above, so that the comparison will be based on fully propagated scores.Associated with each signal queue We now give a proof that DSP is mathematically equivalent to PSA, since it may not be entirely obvious from the foregoing description. We will consider only the forward strand cases; the proof for the reverse strand cases can be derived by a series of trivial substitutions in the proof below.j[ω] for signal sj is initialized to the PSA inductive score RI. For the basis step, recall that the left terminus anchor signals were initialized to have zero scores in both PSA and DSP, regardless of whether a given signal began a coding or noncoding feature. In the case of coding features, substituting Equation 19 into Equation 23 yields:To begin, we show by induction that the signal propagator πj[ω] ← πi[(ω - Δ)mod3] + RD + RT + RS(sj).     (24)πj[ω] = RI only if:According to Equation 10, this initialization will result in πi[(ω - Δ)mod3] = RI + RC,     (25)πi = (ω - Δ)mod3 according to Equation 8. At the time that signal sj is instantiated by its signal sensor, πi has been propagated up to e = wpos(sj) - 1, the nucleotide just before the leftmost position of the context window for sj. By the inductive hypothesis, πi[γi] was initialized to RI. This initialization occurred at the time when the current DSP position was at the beginning of the predecessor's context window. Note, however, that πi effectively began receiving updates at position b = wpos(si) + wlen(si), the position immediately following the end of the signal's context window, at which point si graduated from its holding queue. Thus, πi[γi] will have accumulated content scores for positions b through e, inclusive. In order to establish Equation 25, we need to show that these accumulations sum to precisely RC.where γM:Substituting Equation 16 into Equation 14 we get the following formula describing propagator updates as if they came directly from content sensor 0≤ω≤2 π[ω] ← π[ω] + log PM[(ω+Δ)mod3](xf),     (26)∀f-(pos(si) + len(si)) is the distance between the rightmost end of signal si and the current position f in the DSP algorithm. Let us introduce the notation:where Δ = i,j,ω) = ∑k = i..jlog PM[(ω+k)mod3](xk).     (27)F - len(si)); this can be verified by expanding this expression via Equation 27 and observing that the result equals a summation of the log term in Equation 26 over f = b to e. Looking at init_phased, it should be obvious that the effect of lines 5 and 8 will be that:Using this notation, πi,γ [h] = ∑k = 0..hlog PM [(k+γ)mod3](xk) = F.     (28)αi[γi] has accumulated RC is therefore equivalent to:According to Equation 5, showing that πF = F - 1,γ) - F + wlen(si) - 1,γ),     (29)i - pos(si) - len(si) and γ = ω - pos(sj). Equivalently:where ψ = γF = F - F.     (30)mod3), observe that pos(sj) - (pos(si) + len(si)) = Δ, the length of the putative exon , and further that γi - ω ≡ -Δ(mod3) according to Equation 8, so that ψ - γ ≡ Δ-Δ ≡ 0(mod3). Thus, Equation 30 is equivalent to:To see that ψ ≡ γ = F - F,     (31)sj is initialized to the PSA inductive score RI, and thus establishes the inductive step of the proof in the case of coding features.which can be established as a tautology by simple algebra after expansion with Equation 27. This shows that the signal propagator for signal To see that the above arguments also hold for noncoding features, note that Equation 21 simplifies Equation 25 to:i[ω] = RI + RC,     (32)πthat Equations 13 and 18 combine to simplify Equation 26 to:0≤ω≤2 π[ω] ← π[ω] + log PM(xf),     (33)∀and that lines 4 and 6 of init_nonphased cause:i[h] = ∑k = 0..hlog PM(xk) = FNC,     (34)αFNC = ∑k = i..jlog PM(xk). We can thus reformulate Equation 29 as:for FNC = FNC - 1) - FNC + wlen(spred) - 1),     (35)or, equivalently:FNC = FNC - FNC,     (36)which is again a tautology. In the interests of brevity, we leave it up to the reader to verify that the above arguments still apply when the noncoding features are intergenic, thereby invoking Equation 22 rather than Equation 21 in formulating Equation 31.RCI + RD + RT) criterion of DSP as long as RCI = RC + RI at the time the optimal predecessor is selected, which we have in fact already shown by establishing Equation 25.To see that the selection of optimal predecessors is also performed identically in the two algorithms, note that the PSA criterion given in Equation 7 is equivalent to the argmax with the sequence xj-n..xj. Although hash tables provide a relatively efficient solution for this task, they are wasteful in the sense that as we evaluate the chain on successive nucleotides in a sequence, we repeatedly manipulate preceding nucleotides in forming successive substrings to be indexed into the hash table.Markov chains are typically implemented in GHMM-based gene finders using hash tables, due to the simplicity of such an implementation. Thus, for a given Markov chain n+1 or less, and where the state having label xj-n..xj emits the probability PM(xj|xj-n..xj-1), for nth-order Markov chain M. In this way, the transition probabilities of the Markov chain become the state emissions of the FSM. During a single left-to-right scan of a sequence, each base requires only a single two-dimensional array indexing operation to access the desired probability, and a single integer value store operation to remember the identity of the new state. When compared to the typical regime of arithmetic and bit-shift operations over an (n+1)-element string that would be required for a typical hash function, the difference can be significant.A much faster (and much more elegant) solution is to employ a Finite State Machine (FSM) in which states exist for all possible sequences of length Implementing this optimization is fairly straightforward, both for conventional Markov chains and for Interpolated Markov Models, whether homogeneous or three-periodic. Central to the method is a means of mapping between state labels and integer state identifiers for use in indexing into the transition table. The base-4 number system can be utilized for this purpose, assuming a nucleotide mapping such as ∇ = {A↔0, C↔1, G↔2, T↔3}. To account for lower-order states, define:L. Converting a string S = x0..xL-1 to base-4 can be accomplished as follows:which gives the total number of strings of length less than S can be mapped to a state index using:Now a string state(S) = B(|S|) + λ(S),     (39)S| denotes the length of S.where |nth-order Markov chain in hash table format, the FSM state emissions can be initialized by indexing state labels into the hash table to obtain the Markov chain transition probabilities. The transition table can be initialized fairly simply by noting that the successor of state x0..xL-1 upon seeing symbol s is x1..xL-1s if L = n + 1, or x0..xL-1s for L <n + 1. A model for the reverse strand can be handled by applying this scheme in reverse, so that the state with label xj-n..xj emits the probability PM(xj-n|xj-n+1..xj), and the lower-order states are reserved for the end of the sequence rather than the beginning.Given this integer↔label mapping and an Table not use the C++ Standard Template Library (STL) implementations of string and hash, due to efficiency concerns regarding the re-copying of string arguments to the hash function. Our custom string hashing implementation was found to be much faster than the STL implementation (data not shown). Accordingly, one can expect an FSM implementation to show even greater gains as compared to an STL-based hashing implementation.Table We utilized our DSP-based gene finder TIGRscan  in the cAspergillus fumigatus and A. nidulans) while consuming under 50 Mb of RAM, whereas an earlier prototype of this system applied to the same input data routinely exhausted all available memory on a computer with 1 Gb of RAM. We are hopeful that through the use of optimizations such as those described here we will be able to apply TWAIN to other pairs of genomes with longer genes, and possibly extend the program to handle more than two species simultaneously.As a result of these and other optimizations  we were able to apply TWAIN's gene prediction component to a pair of fungal genomes : Linux/UNIX* Programming language: C/C++* Other requirements: compiled using gcc 3.3.3* License: Artistic License, see * Any restrictions to use by non-academics: terms of Artistic License* The DSP algorithm was devised by WHM, who also performed the computational experiments and wrote the manuscript. The PSA gene finder GlimmerHMM was implemented by MP. MP, ALD, and SLS provided detailed insights into the PSA architecture and provided valuable comments on the manuscript.