Dietary patterns of adults living in Ouagadougou and their association with overweight

Elodie Becquey
Mathilde Savy
Peggy Danel
Hubert B Dabiré
Sylvestre Tapsoba
Yves Martin-Prével
Dietary patterns of adults living in Ouagadougou and their association with overweight
R E S E A R C H Open Access
Background: Urbanization in developing countries comes along with changes in food habits and living conditions and with an increase in overweight and associated health risks. The objective of the study was to describe dietary patterns of adults in Ouagadougou and to study their relationship with anthropometric status of the subjects. Methods: A qualitative food frequency questionnaire was administered to 1,072 adults living in two contrasted districts of Ouagadougou. Dietary patterns were defined by principal component analysis and described by multivariate analysis. Logistic regression was used to study their association with overweight. Results: The diet was mainly made of cereals, vegetables and fats from vegetable sources. The two first components of the principal component analysis were interpreted respectively as a "snacking" score and as a "modern foods" score. Both scores were positively and independently associated with the economic level of households and with food expenditures (p ≤ 0.001 for both). The "snacking" score was higher for younger people (p = 0.004), for people having a formal occupation (p = 0.006), for those never married (p = 0.005), whereas the "modern foods" score was associated with ethnic group (p = 0.032) and district of residence (p < 0.001). Thirty-six percent of women and 14.5% of men were overweight (Body Mass Index > 25 kg/m 2 ). A higher "modern foods" score was associated with a higher prevalence of overweight when confounding factors were accounted for (OR = 1.19 [95% CI 1.03-1.36]) but there was no relationship between overweight and the "snacking" score. Conclusions: Modernisation of types of foods consumed was associated with the living conditions and the environment and with an increased risk of overweight. This should be accounted for to promote better nutrition and prevent non communicable diseases.

Introduction

Urbanization is a fast-expanding phenomenon in developing countries. Projections are that urban populations in those countries will more than double between 2000 and 2025 [1]. Urbanization is not only synonymous of demographic changes but is also often regarded as a factor of economic and social growth. In particular, urban dwellers have better access to health services, water and sanitation facilities and education, though there are large differences between rich and poor, on the whole [2]. As far as food access is concerned, urban citizens experience less seasonal variations and have a wider range and a better availability of foods than their rural counterparts [2,3]. In low-income countries, urbanization is also often associated with changes in food habits, westernization of dietary patterns, lower levels of physical activity and overall changes in lifestyle [3]. Eating between main meals, buying street foods or processed foods and eating outside the home are frequent practices of urban citizens; also, consumption of meat, fat, salt and sweetened products increases [2][3][4][5][6]. This phenomenon, known as dietary transition, is accompanied by increased rates of overweight and obesity and growing risks of chronic diseases [7,8]. WHO estimated that 79% of the deaths due to non-communicable diseases occurred in developing countries in the early 2000's [9]. This put a strain on health systems which have to cope with these diseases while they still have to address infectious diseases and undernutrition. All developing countries experience dietary and nutritional transitions, at least in urban areas, but at a more or less advanced stage [10,11]. However the association between dietary changes and higher rates of overweight is not fully explained. Changes in food habits, dietary patterns, type of foods eaten and an increase in energy intake are often mentioned but there is no consensus on their respective contribution to overweight [3,12].

In Burkina Faso (West Africa), urban population has been multiplied by seven since 1975 and urban annual growth rate is very high: + 5.15% per year over the 2000-2005 period [13,14]. In 2003, results of a Demographic and Health Survey showed that 33.1% of women living in Ouagadougou, the capital city of Burkina Faso, were overweight or obese, as compared to only 3.8% of their rural counterparts. In this city, however, there were still 12.4% of under-five children who were wasted and 16.1% who were stunted [15]. Food prices, food provisioning and transformation have been studied in Ouagadougou [3,6,16,17] but information is still needed to characterise food habits and dietary patterns and on how they relate to individuals' anthropometric status. Dietary patterns are useful to describe the whole diet, including the potential synergetic effects of foods or nutrients; such an analysis also takes advantage of the collinearity between nutrients or foods when studying the relationships of diet with health outcome [18,19]. As Burkina Faso is still at an early stage of the nutritional transition, information on dietary patterns and their anthropometric outcome is essential to help design intervention strategies aiming at preventing adverse consequences of urbanization.

The objectives of our study were: (i) to identify and describe dietary patterns according to individual characteristics; (ii) to examine their relationship with the anthropometric status of the subjects.

Methods

Sampling

A cross-sectional survey took place in April/May 2005 (dry season) in two districts of Ouagadougou, on a subsample of the population monitored by the Demographic Monitoring System carried out by the Institut Supérieur des Sciences de la Population (ISSP) [Higher Institute of Population Sciences]. These districts have been purposely chosen as different: Wemtenga is a structured district (i.e. a district with amenities, in particular with electricity and water supply) of about 2,500 inhabitants, located near the centre of the town; Taabtenga is a non-structured district (i.e. with no amenities, because of rapid spontaneous urbanization) of about 3,500 inhabitants, located at the periphery of the city. In each district, 300 women and 300 men aged 20 to 65 years were randomly selected from an exhaustive list of inhabitants. Both study areas and sampling technique have already been described elsewhere [20]. The difference in sampling fractions between the two districts was taken into account in all analyses through the attribution of corresponding weights.

Dietary data collection

A qualitative food frequency questionnaire (q-FFQ) was carefully designed, pre-tested on a similar population and then administered to all subjects of the study. This q-FFQ included all locally available foods, identified by their local names: 27 dishes, 9 sauces, 30 snacks and 16 drinks. Interviewees were asked to spontaneously recall all dishes, sauces, snacks and drinks they had consumed over the previous week and to estimate their frequency of consumption. They were then prompted for each item that had not been cited. When items were not consumed during the previous week but were part of the usual diet, and for rarely consumed or seasonal foods, frequencies of consumption per month were estimated and were then turned into frequencies per week over a year. Any food item that was not initially included into the questionnaire but was consumed by a subject during the survey period was then added to the list. The composition of mixed dishes, sauces or snacks was detailed by the interviewees. Then all food items were coded into one or more food groups, using the following list of 22 food groups: cereals, roots/tuber, nuts and seeds, beans and pulses, vitamin A rich fruits and vegetables, other fruits, other vegetables, fatty/processed meats, non fatty meats and poultry, liver, fresh fish, dried fish, eggs, milk/yoghurt, cheese, animal source fats, vegetal source fats, red palm oil, fried foods, sugar/sweetened products, sweetened drinks, alcohol. Frequencies of consumption of each food group per week were finally computed and used in the analyses.

Dietary data analysis

Dietary patterns were determined by principal component analysis (PCA) performed on normalized frequencies of consumption of the 22 food groups (z-scores). The PCA method allows data reduction by identifying, in a multidimensional space, components that summarize the maximum of variability between subjects [21,22]. These components can be interpreted according to initial and supplementary variables that are well correlated with them. The normalized frequencies of consumption of the 27 dishes, 9 sauces, 30 snacks and 16 drinks, as recalled by the q-FFQ, were introduced as supplementary variables (i.e. not taken into account for the computation of the components) to help with the interpretation of results. Only components exhibiting an eigenvalue >2 were considered. This was the case for the two first components. This choice, though somehow arbitrary, was further confirmed by the shape of the screeplot of variance associated with each component, and by their interpretability. As the two first components were easily interpretable, rotation was neither used nor tried [21,23]. Scores of individuals for the two first components of the PCA were used to characterize the two main patterns of food consumption that distinguished people's diets in our sample.

Household and individual characteristics

A section of the questionnaire focused on social, economic and demographic characteristics of households and individuals, and another section focused on dietary habits such as the number of meals per day, snacking habits, eating outside the home.

At the household level, an economic score was constructed by multiple correspondence analysis [24] using the following variables:number of persons per room, quality of the wall and roof materials, electricity, piped water, source of drinking water, type of toilets and shower, waste evacuation, owning of TV set, telephone, refrigerator, video tape recorder and means of transportation. This method projects all subjects on to a multidimensional space according to modalities taken by the variables of interest. Modalities associated with a low economic status (no shower, no kitchen, poor quality roof and walls...) were projected negatively on the first factor and modalities associated with a good economic status (car, shower, many goods, good quality roof and walls etc) were projected positively. The score of every household on the first factor of the multiple correspondence analysis was interpreted as a continuous economic score which has been divided into tertiles to determine low, medium or high economic level.

An individual physical activity score was also computed, based on the frequency of various physical activities: walking, having a physically-demanding profession, practicing a sport, carrying water, crushing cereals, hand-washing clothes, and other physically-demanding activities. Each variable was arbitrarily given a score (0 if never; 1 if sometimes; 2 if often) and the final score was the sum of them (range 0-21). The score has been divided into tertiles to determine low, medium or high physical activity level.

Home food stocks were estimated by the subjects from the quantities of all food items stocked at home (bought at least two days before) and their price. All prices were added up with the help of the field workers to obtain a monetary value of home food stocks.

Anthropometrics

Anthropometric measurement procedure met the WHO recommendations [25]. Measurements were performed at home by field workers. Height was measured to the nearest mm with locally-made devices equipped with height gauges (SECA 206 Bodymeter). Weight (to the nearest 100 g) and body fat percentage (BFP) were measured on scales with a maximum weighing capacity of 130 kg including a foot-to-foot impedance analyser (Bodymaster™, SEB Group, France). The BFP was corrected to take into account the specificity of body composition of African populations [26]. Overweight was defined by a Body Mass Index (BMI) superior to 25 kg/m 2 . Overfatness, or excess of body fat, was defined by a BFP superior to the gender and age-specific cut-offs defined by Gallagher for African-American subjects [27]. Pregnant women (4.6%) and individuals with a physical handicap (1.2%) were excluded from the analyses using anthropometrics.

Data management and analysis

Epi-data software, version 3.1 (The Epi-Data Association, Odense, Demark), was used for data entry, which was made in double to limit data-entry errors. Further data quality checks, data cleaning and statistical analyses were performed with the SAS system, version 9.1 (SAS Institute Inc., Cary, NC, USA). Individuals with incomplete observations for the FFQ (n = 12) and one outlier were excluded from the analysis.

The description of the two dietary patterns according to food habits, food access and socio-demographic characteristics was performed by multivariate linear regression using a manual backward stepwise procedure. The adjusted mean and the standard error of the mean were calculated for each characteristic. Associations between overweight or overfatness and the two dietary patterns were studied by logistic regression, controlling for confounding factors. As the two scores were continuous variables, values of the odds ratios are associated with an increase of one point of the score. The confounding factors were defined as variables that were associated with both anthropometrics and dietary patterns with a type I error of 0.20 and that did not belong to the causal pathway between them. In all other analyses, the level of significance was set at p < 0.05.

Ethics

The study protocol received the approval of the National Committee of Ethics (Ministry of Health) and written informed consent was obtained from all participants.

Results

One thousand and seventy-two individuals aged 15-65 years were surveyed: 276 women and 261 men lived in Wemtenga (structured district) and 281 women and 254 men lived in Taabtenga (unstructured district).

The diet was mainly made of cereals and vegetables (more than 3 times daily) and to a lesser extent of oil from vegetable sources (more than twice daily) (figure 1). Sweetened products, fried foods, non-fatty meats, nuts and seeds and vitamin A-rich fruits and vegetables were also rather frequently consumed, approximately once a day. Other fruits were consumed less frequently (3 times a week). Other animal-source foods were also less frequently consumed particularly dairy products (3 times a week) and eggs (less than once a week).

The two first components of the PCA accounted for 28.5% of the total variance in the data set. The first component, which explained most of the variability between individual dietary consumptions (19% of the total variance), was interpreted as a pattern of snacking: first, almost all food groups and most of foods were positively represented on this first component which means that subjects with high positive scores on the first component tended to consume frequently many sorts of food groups/foods and thus had a high frequency of food intakes; second, food items with the highest scores on this first component were foods commonly consumed outside the main meals (déguè, milk, sweetened drinks, yoghurt, bread, fruits...) (table 1, figure 2. Alternatively to figure 2, see additional file 1, table S1 which presents factor loadings of the supplementary variables on the first two principal components). In this study, snacking will thus be defined as "frequent food consumption outside the main meals". The second component opposed traditional dishes and sauces (tô, okra and kapok sauces) and local snacks (groundnuts, local buns and drinks) to more modern types of foods or preparations (scrambled eggs, chicken, tomato sauce, pastas, cheese, meat, sodas, soup, French dressing, hamburger). This is why the second component was interpreted as a pattern of modernity of the type of foods consumed (regardless food habits). To quantify the two dietary patterns, the loading of individuals on the first component of the PCA was used as a "snacking" score (minimum = -4.74; maximum = 10.12), and the loading on the second component was used as a "modern foods" score (minimum = -4.14; maximum = 8.11).

Regarding food habits and access, subjects declaring the highest occurrences of food intakes between meals and the highest number of meals eaten per day had a significantly higher "snacking" score (table 2). This score was significantly higher when the monetary value of food stocks, the daily household food expenditure per person or the economic score were higher (p ≤ 0.001 for each variable). Subjects working in the formal sector, younger subjects, single subjects, and Muslims had significantly higher "snacking" scores. District of residence, level of education, ethnic group and consumption of dishes outside the home were not associated with the "snacking" score after adjustment for confounding variables.

The "modern foods" score was significantly higher when the number of meals eaten per day was lower and when frequency of food intakes between meals was less frequent (p < 0.001). It was positively associated with the daily household food expense per person (p < 0.001) and the economic score (p = 0.001). Subjects who lived in the structured district, who did not belong to the Islam religion nor to the Mossi ethnic group had a higher "modern foods" score (p < 0.001, p < 0.001 and p = 0.032 respectively). Consumption of dishes outside the home, food stocks, occupation, schooling, age and marital status were not associated with the modern aspect of foods consumed after adjustment for confounding variables.

In our sample, the mean BMI was 24.2 ± 5.0 kg/m 2 among women and 21.8 ± 3.1 kg/m 2 among men; 36.0% of women and 14.5% of men were overweight or obese. The mean BFP was 36.1 ± 8.6% among women and 16.3 ± 5.3% among men; 56.1% of women and 17.3% of men were overfat. The "snacking" score was neither associated with overweight nor with overfatness, after adjustment for confounding factors (table 3). On the other hand, after adjustment for potential confounding factors, an increase of one unit in the "modern foods" score was associated with an increased risk of being overweight (OR = 1.19 [95% CI 1.03-1.36], p = 0.018) or overfat (OR = 1.14 [95% CI 1.00-1.29], p = 0.044). No significant interaction was found between gender and the "modern foods" score or the "snacking" score, neither for the prediction of overweight nor for the prediction of overfatness.

Discussion

In this urban African setting, subjects' diet was based on cereals and vegetables as it is traditionally the case in Burkina Faso. Our data showed that many food groups were rarely eaten, particularly foods from animal sources such as dairy products and eggs. The variety of the diet was rather low, though higher than that of people living in rural Burkina Faso [20]. While the general diet remained very traditional, the principal component analysis revealed that the two dietary patterns responsible for most variability between individual's diets could be interpreted as a "snacking" score (first component of the PCA) and a "modern foods" score (second component of the PCA). These two patterns of food consumption are typically urban. The "modern foods" score was associated to a higher risk of being overweight and overfat whereas the "snacking" score was not.

We have to acknowledge some limitations of our study. First, the sample was not representative of the whole population of Ouagadougou. However, we compared socio-demographic and economic data of the two districts already available from the Demographic and Health Monitoring System (DHMS) to the same data from the representative sample of Ouagadougou used by the most recent DHS (2003) [28]. When selecting an equal number of subjects from each district, the mean values obtained matched correctly, suggesting that our sample covered a large social and demographic diversity of individuals living in the city. Hence, we assume that our sample also caught a maximum of variety in diets and food patterns that should not be dramatically different from those observed in the whole population of Ouagadougou. Second, our q-FFQ was pre-tested in a population similar to our study sample but was not validated against another dietary method or biomarkers. However, the purpose of this study was to explore food consumption in Ouagadougou in order to draw a very first picture of diets to fill a gap of knowledge in such a setting. The pre-test revealed that our q-FFQ was acceptable and understandable by the subjects. In addition, the food grouping used had been proved to be associated with several socio-economic and demographic characteristics of women of the same sample [20]. It is also often argued that the lack of quantification of food intake is a major limit of the FFQ method. Nevertheless, FFQ has been identified a quick and valid tool to assess dietary consumption that, when combined with factor analysis, allows studying dietary patterns in relation to health outcomes, even without adjustment on energy intake or portion size [18,29]. Given the challenge of estimating portion sizes in such a setting and given the explorative purpose of the study, development of a semi-quantitative version of our questionnaire was not considered. In addition, our q-FFQ was designed to minimise some other sources of bias by means such as the accuracy of the list of foods, the aggregation of frequencies from single items or the frequency options which could be expressed weekly or monthly [30]. The semi-open interview method allowed staying close to what subjects really ate and allowed including all sort of foods even if very specific. Finally, in order to account for the two major co-factors of dietary intake, namely households' economic level and individuals' physical activity, we relied on proxy scores that were not validated as such. However, the economic score was constructed using a standard method recommended by several authors [31][32][33]. As far as the physical activity score is concerned, we ensured that our proxy was significantly and independently associated with overweight and overfatness, in the expected direction, in the models  studying the relationships of these variables with both the "snacking" and the "modern foods" scores (results not shown).

A-posteriori methods to derive dietary patterns have some limitations: first, researchers have to take quite subjective decisions at various stages of the process; second, results are very population-specific and not reproducible across different sample [21,34]. However, these methods are very useful to summarize behaviours from many variables in a population where no previous knowledge exists; hence the choice of PCA to perform dietary pattern analysis in our sample, which was driven by the fact that very few data are available about food consumption patterns in Ouagadougou. It was therefore difficult to apply an a-priori method such as the use of pre-defined indices. Indeed, these types of indices are based on existing knowledge about relationship between a type of diet and health outcomes and must also be adapted to the specific population behaviours [19]. To our knowledge, such an index has never been validated in a Sahelian urban area. Among a-posteriori methods, we chose PCA over cluster analysis to insist on similarities rather than on differences between diets. However, both methods have proved to reveal similar dietary patterns [35].

Our results showed interesting relationships between the living conditions of individuals and the level of the two scores characterizing their diet. First, snack consumption was associated with the less structured living conditions such as those of younger and single subjects: older and married subjects are more likely to have an organised life with planned meals. Likewise, working people, especially those working in the formal sector, ate snack foods more frequently, probably due to the little time available and the organisation of the day outside their home. Second, while the district of residence was not associated with the "snacking" score, it was associated with the "modern foods" score. These results mean that snacking was encountered in both districts but types of snack were different: traditional snacks in the non structured district and more modern snacks in the structured one. This highlights the role of environment and food availability in the choice of foods consumed, as described by many authors [36,37]. Likewise, ethnicity and religion had an impact on the modernity of foods consumed: subjects from the native ethnic group (Mossi) had more traditional diets than the non native, as well as Muslims as compared to other religions. Finally, the economic level was positively associated with both scores, suggesting that the poorest people can only afford traditional and staple diets. This interpretation is reinforced by the positive relationship between the two scores and the household food expenditure: whatever the household's economic level, snacking or eating modern foods is more costly.

Although definitions and interpretation of traditionalism, modernity or snacking vary quite a lot across dietary pattern studies, other studies described a pattern close to our description of the traditional/modern foods pattern, while very few studies identified a snacking pattern. In a review summarizing 30 studies that reported dietary patterns derived from factor analysis [29], 19 of them identified either a traditional or a western pattern, and sometimes both. Healthier patterns (i.e. not the western pattern) were associated with higher income,  Table 3 association between the "snacking" score and the "modern foods" score and overweight and overfatness (a) N "snacking" score "modern foods" score education and age, but we have to acknowledge that most of these studies were performed in developed countries. Associations between dietary patterns and living conditions or socio-demographic characteristics appear to be context-dependent. In the same review, only one out of the 30 studies identified a snacking pattern, but it was amongst young children living in a geographically and culturally different area compared to our study area [38]. Recent studies describing diets derived from data-driven methodologies and performed in West Africa are scarce. No other study using PCA to derive dietary patterns could be found in similar populations. The closest study we identified was conducted in urban Benin and authors derived diets from cluster analysis [39]. Its results were in line with ours: the traditional diet in Cotonou was characterised by high intakes of cereals and the transitional diet by high intakes of bread and pastas, animal source foods, milk products, fats and sweets. This is quite similar to the description of the "modern foods" score we identified in Ouagadougou. Also, a positive relationship between socio-economic score and modernity of food intake was identified in Cotonou, as in Ouagadougou.
OR (b
One other important result of our study is that the "modern foods" score was positively associated with overweight and with overfatness. Similar results were obtained with BMI and BFP used as continuous variables (results not shown). This is in line with the concept of dietary transition described by the literature, and suggests that in Ouagadougou, changes in types of food eaten would be one explanation among others for the nutrition transition. Many other studies, in different settings, focused on the relationship between dietary patterns and anthropometric status. A review reported that 19 studies out of 30 showed a relationship between western food patterns and overweight [12]. In urban Brazil, another study demonstrated that a "traditional" diet, constructed by factor analysis, was associated with a lower risk of overweight and obesity [40].

In Ouagadougou, more in-depth investigation is still required to identify what exactly in the consumption of modern types of foods leads to increased overweight. Possible explanations are quantities consumed, food habits, macronutrient balance or energetic content of modern types of foods. To investigate all these possible explanations, a quantitative study describing the nutrient content of modern foods is required. The assessment of their energy and micronutrient densities would help to clarify the relationship between the "modern foods" pattern and overweight. Since the "modern foods" score was negatively associated with the number of main meals in the day and with the frequency of food consumption in between, eating more modern types of foods does not seem to be explained by a multiplication of food intake occasions. This is in line with the fact that the "snacking" score, which is independent of the "modern foods" score by construction, was not associated with an increased risk of overweight. Alternate hypothesis would be that quantities consumed at each food intake are more substantial and/or that most of modern foods are energy-dense, leading to unbalanced diet. The latter hypothesis is supported by a study conducted in Ouagadougou, which showed that obesity was associated with a higher consumption of micronutrient-, protein-, and sugar-rich foods [41]. An interesting hypothesis was also suggested from the study in Cotonou [39]. The authors explained that the transitional diet was not a drastic replacement of traditional foods by western ones, but more the result of adding western foods to a traditional diet. This scenario could be transposed to Ouagadougou and could therefore explain part of the phenomenon.

Conclusions

Our study showed that in a setting where dietary patterns remain largely traditional, there was some evidence of a higher risk of being overweight and overfat associated with consumption of modern types of foods but not with snacking. While consumption of modern foods, known to be more frequent in urban settings, appeared to be still limited in our sample, it must be noticed that another study in Ouagadougou identified worrying rates of non communicable food related diseases [41,42]. This highlights the relevance of studying food patterns in such a context. Such studies may help implementing appropriate nutritional policies and programs to address the double burden of malnutrition, especially in West-African countries which are still at an early stage of their nutritional transition.

Additional file 1: Factor loadings of the food items (supplementary variables) on the first two principal components identified. Table  alternative to figure 2 presenting the factor loadings of the food items on the first two principal components. Click here for file [ http://www.biomedcentral.com/content/supplementary/1475-2891-9-13-S1.PDF ]

Figure 1
1Mean weekly frequency of consumption of the 22 food sub-groups.

Figure 2
2Plot of the factor loadings of food groups (active variables) and food items (supplementary variables). Active and supplementary variables are plotted on the first two components of the PCA. All active variables (food groups) appear in green and capital letters. Supplementary variables (food items) appear in orange and small letters. For clarity, only well represented food items, i.e. items whose sum of cos 2 on the first two components was superior to the mean, are named in the graph. Other food items were not informative for interpretation and were not labelled.

Table 1
1Factor loadings of the food groups on the first two principal components identified (a) (a) loadings for variables whose contribution to the variance of the component is superior to the mean are shown in boldFood groups
Snacking
Modernity

fried foods
0.72
-0.25

vegetable source fats
0.67
0.02

sugar/sweetened products
0.63
-0.05
Cereals
0.60
-0.47

other vegetables
0.60
0.18

sweetened drinks
0.58
-0.09

milk/yoghurt
0.54
0.14

non fatty meats and poultry
0.52
0.47

fresh fish
0.51
-0.18

roots and tubers
0.48
0.30

Eggs
0.40
0.47
other fruits
0.36
-0.23

nuts and seeds
0.30
-0.47

beans and pulses
0.27
-0.52

red palm oil
0.20
0.16

Cheese
0.18
0.30

fatty/processed meats
0.17
0.43

vitamin A-rich fruits and vegetables
0.15
-0.37

animal source fats
0.13
-0.09
Liver
0.08
0.16

Alcohol
0.07
0.15

dried fish
-0.38
-0.30

Table 2
2Characteristics associated with the "snacking" score and with the "modern foods" score (a)(b)%
Adjusted mean ± SEM (c)
of the "snacking" score

p
Adjusted mean ± SEM (c)
of the "modern foods" score

p

usual number of meals eaten per day

one or two
44
-0.40 ± 0.09
<0.001
0.36 ± 0.06
<0.001
three or more
56
0.24 ± 0.08
-0.33 ± 0.05

consumption of foods between meals

<2 times a week
29
-0.82 ± 0.11
<0.001
0.24 ± 0.07
<0.001

2 to 6 times a week
44
0.02 ± 0.09
-0.17 ± 0.05

every day
26
0.79 ± 0.12
-0.12 ± 0.07

consumption of dishes outside the home

never
22
-0.13 ± 0.13
0.068
once a week or less
20
-0.32 ± 0.13

2 to 6 times a week
15
0.09 ± 0.15

every day
44
0.10 ± 0.09

estimation of the monetary value of food stocks*

no stocks
36
-0.31 ± 0.10
<0.001
-0.06 ± 0.06
0.059

]0 -10,000] cfa
13
-0.10 ± 0.16
0.18 ± 0.10

]10.000 -20,000] cfa
25
-0.16 ± 0.11
-0.16 ± 0.07

]20.000 -30,000] cfa
17
0.26 ± 0.14
-0.02 ± 0.09
more than 30,000 cfa
9
0.87 ± 0.19
0.11 ± 0.12

daily household food expenditure per person*

[0 -100[cfa
31
-0.33 ± 0.10
<0.001
-0.24 ± 0.07
<0.001

[100 -200[cfa
39
-0.10 ± 0.09
-0.05 ± 0.06

[200 -500[cfa
23
0.23 ± 0.12
0.15 ± 0.08

500 cfa and more
7
0.81 ± 0.23
0.36 ± 0.14

economic score
low
39
-0.29 ± 0.10
0.001
-0.28 ± 0.08
0.001

medium
34
-0.07 ± 0.10
-0.02 ± 0.06

high
27
0.37 ± 0.12
0.30 ± 0.11

district

non structured
58
-0.25 ± 0.06
<0.001

structured
42
0.30 ± 0.09

occupation

none
36
-0.15 ± 0.10
0.006
informal sector (d)
49
-0.10 ± 0.08

formal sector
15
0.43 ± 0.16

ever attended school

no
43
-0.12 ± 0.06
0.090

yes
57
0.03 ± 0.05

Age

< 25 y
24
0.26 ± 0.13
0.004
25-29 y
25
0.03 ± 0.12

30-39 y
28
0.01 ± 0.11

40 y and more
23
-0.47 ± 0.13

marital status

single
37
0.28 ± 0.11
0.005
0.08 ± 0.07
0.092

couple
60
-0.22 ± 0.08
-0.10 ± 0.05

widowed/divorced
3
-0.29 ± 0.34
-0.12 ± 0.21

religion
muslims
61
0.11 ± 0.07
0.007
-0.16 ± 0.05
<0.001

catholics
33
-0.27 ± 0.10
0.17 ± 0.06

Others
6
-0.26 ± 0.24
0.12 ± 0.15

Becquey et al. Nutrition Journal 2010, 9:13
http://www.nutritionj.com/content/9/1/13

Table 2 :
2Characteristics associated with the "snacking" score and with the "modern foods" score (a)(b) (Continued)ethnic group

mossi
67
-0.09 ± 0.04
0.032

other than mossi
33
0.08 ± 0.06

(a) results of multivariate analyses. Variables with p > 0.10 were not included in the model and not reported in the table. thus appear as blank cells
(b) characteristics tested but not significantly associated with the one or the other score: gender, rural/urban place of birth, supplementary income
(c) SEM: standard error of the mean
(d) occupations within the informal sector can be various, but their common point is that they are not declared and thus neither taxed nor monitored by the
government. This often corresponds to less stable occupation than within the formal sector
* equivalent in local currency (cfa Franc). 1 Euro = 656 cfa Francs

OR: Odds Ratio associated with an increase of one point of the score (c) adjusted for the following variables: age, gender, physical activity, ethnicity, rural/urban place of birth, marital status, economic level, professional occupation, schooling and district. The interaction score*gender was not included in the final model because it was not significant) for overweight
unadjusted
1060
1.01 [0.94-1.09] (p = 0.703)
1.19 [1.08-1.32] (p <0.001)

adjusted (c)
867
1.04 [0.95-1.13] (p = 0.424)
1.19 [1.03-1.36] (p = 0.018)

OR (b) for overfatness
unadjusted
1060
1.03 [0.96-1.10] (p = 0.340)
1.11 [1.02-1.22] (p = 0.015)

adjusted (c)
911
1.03 [0.95-1.12] (p = 0.483)
1.14 [1.00-1.29] (p = 0.044)

(a) multiple logistic regression model
(b)
AcknowledgementsThis work was supported by grants from the Institut de Recherche pour le Développement (IRD, France). The first author received a research allowance from the French Ministry of Research through the Doctoral School 393 of Pierre and Marie Curie University (UPMC-Paris VI, France).Authors' contributions Y.M-P., M.S., S.T. and H.D. designed the study. M.S., P.D. and Y.M-P. collected the data. E.B. and Y.M-P. performed the data analysis and drafted the manuscript. All authors participated in the interpretation and discussion of the results and critically revised the manuscript. All authors read and approved the final manuscript.Competing interestsThe authors declare that they have no competing interests.
Brockerhoff : An urbanizing world -Brief 2. Achieving urban food and nutrition security in the developing world -2020 Focus 3. Washington, D.CBrockerhoff : An urbanizing world -Brief 2. Achieving urban food and nutrition security in the developing world -2020 Focus 3 Washington, D.C.: IFPRI 2000.

Features of urban food and nutrition security and considerations for successful urban programming. Globalization of food systems in developing countries: impact on food security and nutrition Rome: FAO. M T Ruel, J L Garrett, Ruel MT, Garrett JL: Features of urban food and nutrition security and considerations for successful urban programming. Globalization of food systems in developing countries: impact on food security and nutrition Rome: FAO 2004, 27-54.

La transition nutritionnelle, l'alimentation et les villes dans les pays en développement. B Maire, F Delpeuch, Cahiers Agricultures. 13Maire B, Delpeuch F: La transition nutritionnelle, l'alimentation et les villes dans les pays en développement. Cahiers Agricultures 2004, 13:23-30.

Some urban facts of life: Implications for research and policy. M T Ruel, L Haddad, J L Garrett, World Development. 2711Ruel MT, Haddad L, Garrett JL: Some urban facts of life: Implications for research and policy. World Development 1999, 27(11):1917-1938.

Globalization of food systems in developing countries: a synthesis of country case studies. Globalization of food systems in developing countries: impact on food security and nutrition Rome: FAO. G Kennedy, G Nantel, P Shetty, Kennedy G, Nantel G, Shetty P: Globalization of food systems in developing countries: a synthesis of country case studies. Globalization of food systems in developing countries: impact on food security and nutrition Rome: FAO 2004, 1-25.

Agroalimentaire et lutte contre la pauvreté en Afrique subsaharienne. C Broutin, N Bricas, Editions du Gret. ParisBroutin C, Bricas N: Agroalimentaire et lutte contre la pauvreté en Afrique subsaharienne. Paris: Editions du Gret 2006.

Overweight exceeds underweight among women in most developing countries. M A Mendez, C A Monteiro, B M Popkin, Am J Clin Nutr. 813Mendez MA, Monteiro CA, Popkin BM: Overweight exceeds underweight among women in most developing countries. Am J Clin Nutr 2005, 81(3):714-721.

The nutrition transition: worldwide obesity dynamics and their determinants. B M Popkin, P Gordon-Larsen, Int J Obesity. 28Popkin BM, Gordon-Larsen P: The nutrition transition: worldwide obesity dynamics and their determinants. Int J Obesity 2004, 28:S2-S9.

WHO: Diet, nutrition and the prevention of chronic diseases -Report of the joint WHO/FAO expert consultation. GenevaWHO: Diet, nutrition and the prevention of chronic diseases -Report of the joint WHO/FAO expert consultation. Geneva: WHO 2003.

Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. B M Popkin, Am J Clin Nutr. 842Popkin BM: Global nutrition dynamics: the world is shifting rapidly toward a diet linked with noncommunicable diseases. Am J Clin Nutr 2006, 84(2):289-298.

Symposium: Obesity in developing countries: Biological and ecological factors -Introduction. B Caballero, J Nutr. 1313Caballero B: Symposium: Obesity in developing countries: Biological and ecological factors -Introduction. J Nutr 2001, 131(3):866s-870s.

Food intake patterns and body mass index in observational studies. P Togo, M Osler, T I Sorensen, B L Heitmann, Int J Obes Relat Metab Disord. 2512Togo P, Osler M, Sorensen TI, Heitmann BL: Food intake patterns and body mass index in observational studies. Int J Obes Relat Metab Disord 2001, 25(12):1741-1751.

World Urbanization Prospects: The 2007 Revision Population Database. World Urbanization Prospects: The 2007 Revision Population Database. [http://esa.un.org/unup].

Ouagadougou: Ministère de l'économie et du développement, Comité National du recensement et Bureau central du recensement. INSD: Résultats préliminaires du Recensement général de la population et de l'habitation de. INSD: Résultats préliminaires du Recensement général de la population et de l'habitation de 2006. Ouagadougou: Ministère de l'économie et du développement, Comité National du recensement et Bureau central du recensement 2007.

Enquête Démographique et de Santé rapport final Institut National de la Statistique et de la Démographie. S Mariko, Chapitre 9 -Allaitement et état nutritionnel. Ouagadougou, Burkina Faso; Calverton, Maryland, USAMinistère de l'économie et du DéveloppementMariko S: Chapitre 9 -Allaitement et état nutritionnel. Enquête Démographique et de Santé rapport final Institut National de la Statistique et de la Démographie, Ministère de l'économie et du Développement, Ouagadougou, Burkina Faso, et ORC Macro, Calverton, Maryland, USA 2003, 145-172.

Y Konkobo, A Karimou, S Kabore, K Diasso, Food practices in Ouagadougou, Burkina Faso -Cereals, legumes, tubers and vegetables. MontpellierKonkobo Y, Karimou A, Kabore S, Diasso K: Food practices in Ouagadougou, Burkina Faso -Cereals, legumes, tubers and vegetables. Montpellier: CIRAD 2002.

Montpellier: CIRAD-SAR. E Cheyns, Les pratiques d'approvisionnement alimentaire des consommateurs de Ouagadougou (Burkina). Cheyns E: Les pratiques d'approvisionnement alimentaire des consommateurs de Ouagadougou (Burkina). Montpellier: CIRAD-SAR 1999.

Importance of understanding food consumption patterns. H Barkoukis, J Am Diet Assoc. 1072Barkoukis H: Importance of understanding food consumption patterns. J Am Diet Assoc 2007, 107(2):234-236.

Panagiotakos D: a-priori versus a-posteriori methods in dietary pattern analysis: a review in nutrition epidemiology. Nutrition Bulletin. 334Panagiotakos D: a-priori versus a-posteriori methods in dietary pattern analysis: a review in nutrition epidemiology. Nutrition Bulletin 2008, 33(4):311-315.

Are dietary diversity scores related to the socio-economic and anthropometric status of women living in an urban area in Burkina Faso? Public Health Nutr. M Savy, Y Martin-Prevel, P Danel, P Traissac, H Dabire, F Delpeuch, 11Savy M, Martin-Prevel Y, Danel P, Traissac P, Dabire H, Delpeuch F: Are dietary diversity scores related to the socio-economic and anthropometric status of women living in an urban area in Burkina Faso? Public Health Nutr 2008, 11(2):132-141.

Invited commentary: Factor analysis and the search for objectivity. M E Martinez, J R Marshall, L Sechrest, Am J Epidemiol. 1481Martinez ME, Marshall JR, Sechrest L: Invited commentary: Factor analysis and the search for objectivity. Am J Epidemiol 1998, 148(1):17-19.

Principal component analysis. A step-by-step approach to using the SAS System for factor analysis and structural equation modeling Cary. L Hatcher, SAS Institute IncNCHatcher L: Principal component analysis. A step-by-step approach to using the SAS System for factor analysis and structural equation modeling Cary, NC: SAS Institute Inc 1994, 1-127.

H Abdi, Factor rotations in factor analyses. Encyclopedia for research methods for the social sciences Thousand Oaks, CA: SageLewis-beck M, Bryman A, Futing T 2003. Abdi H: Factor rotations in factor analyses. Encyclopedia for research methods for the social sciences Thousand Oaks, CA: SageLewis-beck M, Bryman A, Futing T 2003, 978-982.

Multiple correspondence analysis. Encyclopedia of measurement and statistics Thousand Oaks, CA: SageSalkind NJ. H Abdi, D Valentin, Abdi H, Valentin D: Multiple correspondence analysis. Encyclopedia of measurement and statistics Thousand Oaks, CA: SageSalkind NJ 2007, 651-657.

WHO: Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser. 854WHO: Physical status: the use and interpretation of anthropometry. Report of a WHO Expert Committee. World Health Organ Tech Rep Ser 1995, 854:1-452.

Comparison of foot-tofoot impedance with air-displacement plethysmography to evaluate body composition in African women. A Gartner, A Dioum, B Maire, F Delpeuch, Y Schutz, J Body Comp Res. 14Gartner A, Dioum A, Maire B, Delpeuch F, Schutz Y: Comparison of foot-to- foot impedance with air-displacement plethysmography to evaluate body composition in African women. J Body Comp Res 2004, 1(4):137-145.

Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. D Gallagher, S B Heymsfield, M Heo, S A Jebb, P R Murgatroyd, Y Sakamoto, Am J Clin Nutr. 723Gallagher D, Heymsfield SB, Heo M, Jebb SA, Murgatroyd PR, Sakamoto Y: Healthy percentage body fat ranges: an approach for developing guidelines based on body mass index. Am J Clin Nutr 2000, 72(3):694-701.

Enquête Démographique et de Santé rapport final Institut National de la Statistique et de la Démographie. A Toe-Foro, et ORC Macro. Chapitre 2 -Caractéristiques des ménages. Ministère de l'économie et du DéveloppementToe-Foro A: Chapitre 2 -Caractéristiques des ménages. Enquête Démographique et de Santé rapport final Institut National de la Statistique et de la Démographie, Ministère de l'économie et du Développement, Ouagadougou, Burkina Faso, et ORC Macro, Calverton, Maryland, USA 2003, 11-23.

Dietary patterns and health outcomes. A K Kant, J Am Diet Assoc. 1044Kant AK: Dietary patterns and health outcomes. J Am Diet Assoc 2004, 104(4):615-635.

Warm D: Development, validation and utilisation of food-frequency questionnaires -a review. J Cade, R Thompson, V Burley, Public Health Nutr. 54Cade J, Thompson R, Burley V, Warm D: Development, validation and utilisation of food-frequency questionnaires -a review. Public Health Nutr 2002, 5(4):567-587.

Building a synthetic index of the households' economic level in nutritional surveys. Application examples in the Congo. P Traissac, F Delpeuch, Maire B Martin-Prevel, Y Cornu, A Trèche, S , Rev Epidemiol Sante Publique. 45Traissac P, Delpeuch F, Maire B, Martin-Prevel Y, Cornu A, Trèche S: Building a synthetic index of the households' economic level in nutritional surveys. Application examples in the Congo. Rev Epidemiol Sante Publique 1997, 45:114-115.

Construction of a socio-economic index to facilitate analysis of health data in developing countries. I Cortinovis, V Vella, J Ndiku, Soc Sci Med. 368Cortinovis I, Vella V, Ndiku J: Construction of a socio-economic index to facilitate analysis of health data in developing countries. Soc Sci Med 1993, 36(8):1087-1097.

Assets as a measure of household welfare in developing countries. D Sahn, D Stifel, n°00-11Center for Social Development33St. Louis, Missouri; Washington University in St. LouisWorking PaperSahn D, Stifel D: Assets as a measure of household welfare in developing countries. Working Paper n°00-11 St. Louis, Missouri: Center for Social Development, Washington University in St. Louis 2000, 33.

Are dietary patterns useful for understanding the role of diet in chronic disease?. P F Jacques, K L Tucker, Am J Clin Nutr. 731Jacques PF, Tucker KL: Are dietary patterns useful for understanding the role of diet in chronic disease? Am J Clin Nutr 2001, 73(1):1-2.

Empirically derived eating patterns using factor or cluster analysis: a review. P K Newby, K L Tucker, Nutr Rev. 625Newby PK, Tucker KL: Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 2004, 62(5):177-203.

Associations of the local food environment with diet quality -A comparison of assessments based on surveys and geographic information systems. L V Moore, Avd Roux, J A Nettleton, D R Jacobs, Am J Epidemiol. 1678Moore LV, Roux AVD, Nettleton JA, Jacobs DR: Associations of the local food environment with diet quality -A comparison of assessments based on surveys and geographic information systems. Am J Epidemiol 2008, 167(8):917-924.

The contextual effect of the local food environment on residents' diets: The atherosclerosis risk in communities study. K Morland, S Wing, A D Roux, Am J Public Health. 9211Morland K, Wing S, Roux AD: The contextual effect of the local food environment on residents' diets: The atherosclerosis risk in communities study. Am J Public Health 2002, 92(11):1761-1767.

Multivariate analysis of diet among threeyear-old children and associations with socio-demographic characteristics. K North, P Emmett, A S Team, Eur J Clin Nutr. 541North K, Emmett P, Team AS: Multivariate analysis of diet among three- year-old children and associations with socio-demographic characteristics. Eur J Clin Nutr 2000, 54(1):73-80.

Dietary patterns of urban adults in Benin: relationship with overall diet quality and socio-demographic characteristics. R Sodjinou, V Agueh, B Fayomi, H Delisle, Eur J Clin Nutr. 632Sodjinou R, Agueh V, Fayomi B, Delisle H: Dietary patterns of urban adults in Benin: relationship with overall diet quality and socio-demographic characteristics. Eur J Clin Nutr 2009, 63(2):222-228.

Sichieri R: Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obes Res. 101Sichieri R: Dietary patterns and their associations with obesity in the Brazilian city of Rio de Janeiro. Obes Res 2002, 10(1):42-48.

Socio-spatial disparities of obesity among adults in the urban setting of Ouagadougou. H Z Ouedraogo, F Fournet, Y Martin-Prevel, J Gary, M C Henry, G Salem, Public Health Nutr. 1112Ouedraogo HZ, Fournet F, Martin-Prevel Y, Gary J, Henry MC, Salem G: Socio-spatial disparities of obesity among adults in the urban setting of Ouagadougou, Burkina Faso. Public Health Nutr 2008, 11(12):1280-1287.

Hypertension, urbanization, social and spatial disparities: a cross-sectional populationbased survey in a West African urban environment. A Niakara, F Fournet, J Gary, M Harang, L V Nebie, G Salem, 10.1186/1475-2891-9-13Trans R Soc Trop Med Hyg. 10111Niakara A, Fournet F, Gary J, Harang M, Nebie LV, Salem G: Hypertension, urbanization, social and spatial disparities: a cross-sectional population- based survey in a West African urban environment (Ouagadougou, Burkina Faso). Trans R Soc Trop Med Hyg 2007, 101(11):1136-1142. doi:10.1186/1475-2891-9-13

Dietary patterns of adults living in Ouagadougou and their association with overweight. Becquey, Nutrition Journal. 913Cite this article asCite this article as: Becquey et al.: Dietary patterns of adults living in Ouagadougou and their association with overweight. Nutrition Journal 2010 9:13.

. Becquey, Nutrition Journal. 913Becquey et al. Nutrition Journal 2010, 9:13 http://www.nutritionj.com/content/9/1/13


TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions

Daehwan Kim
Geo Pertea
Cole Trapnell
Harold Pimentel
Ryan Kelley
Steven L Salzberg
TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions
M E T H O D Open Access
TopHat is a popular spliced aligner for RNA-sequence (RNA-seq) experiments. In this paper, we describe TopHat2, which incorporates many significant enhancements to TopHat. TopHat2 can align reads of various lengths produced by the latest sequencing technologies, while allowing for variable-length indels with respect to the reference genome. In addition to de novo spliced alignment, TopHat2 can align reads across fusion breaks, which can occur after genomic translocations. TopHat2 combines the ability to identify novel splice sites with direct mapping to known transcripts, producing sensitive and accurate alignments, even for highly repetitive genomes or in the presence of pseudogenes. TopHat2 is available at

Background RNA-sequencing technologies [1], which sequence the RNA molecules being transcribed in cells, allow exploration of the process of transcription in exquisite detail. One of the primary goals of RNA-sequencing analysis software is to reconstruct the full set of transcripts (isoforms) of genes that were present in the original cells. In addition to the transcript structures, experimenters need to estimate the expression levels for all transcripts. The first step in the analysis process is to map the RNA-sequence (RNAseq) reads against the reference genome, which provides the location from which the reads originated. In contrast to DNA-sequence alignment, RNA-seq mapping algorithms have two additional challenges. First, because genes in eukaryotic genomes contain introns, and because reads sequenced from mature mRNA transcripts do not include these introns, any RNA-seq alignment program must be able to handle gapped (or spliced) alignment with very large gaps. In mammalian genomes, introns span a very wide range of lengths, typically from 50 to 100,000 bases, which the alignment algorithm must accommodate. Second, the presence of processed pseudogenes, from which some or all introns have been removed, may cause many exon-spanning reads to map incorrectly. This is particularly acute for the human genome, which contains over 14,000 pseudogenes [2].

In the most recent Ensembl GRCh37 gene annotations, the average length of a mature mRNA transcript in the human genome is 2,227 bp long, and the average exon length is 235 bp. The average number of exons per transcript is 9.5. Assuming that sequencing reads are uniformly distributed along a transcript [3], we would expect 33 to 38% of 100 bp reads from an RNAseq experiment to span two or more exons. Note that this proportion increases significantly as read length increases from 50 to 150 bp (see Additional file 1 for more details).

More important for the alignment problem is that around 20% of junction-spanning reads extend by 10 bp or less into one of the exons they span. These small 'anchors' make it extremely difficult for alignment software to map reads accurately, particularly if the algorithm relies (as most do) on an initial mapping of fixed-length k-mers to the genome. This initial mapping, using exact matches of k-mers, is crucial for narrowing down the search space into small local regions in which a read is likely to align. If a read extends only a few bases into one of two adjacent exons, then it often happens that the read will align equally well, but incorrectly, with the sequence of the intervening intron. For example, as illustrated in Figure 1, suppose that read r spans exons e 1 and e 2 , extending only four bases into e 2 . Suppose also that that e 2 begins with GTXX, and the intervening intron also begins with GTXX, where X stands for any of A, G, C, and T. Then r might align perfectly to e 1 and the first four bases of the intron, and the alignment algorithm will fail to find the spliced alignment of r.

In order to handle this problem, the software TopHat2 uses a two-step procedure. First, similar to TopHat1 [4], it detects potential splice sites for introns (detailed further in Methods). It uses these candidate splice sites in a subsequent step to correctly align multiexon-spanning reads. Some RNA-seq aligners, including GSNAP [5], RUM [6], and STAR [7], map reads independently of the alignments of other reads, which may explain their lower sensitivity for these spliced reads (see Results and discussion). MapSplice [8] uses a two-step approach similar to TopHat2.

RNA-seq read alignment is further complicated by the presence of processed pseudogenes in the reference genome. Pseudogenes often have highly similar sequences to functional, intron-containing genes. In most cases, the pseudogene versions are not transcribed [9], although this suggestion has recently been disputed [10]. The crucial problem for alignment is that reads spanning multiple exons can be mapped perfectly or near-perfectly to the pseudogene version of a functional gene. For example, suppose a read r spans two exons of a given gene. If the aligner tries to align the read globally (end to end), then it will find an alignment to the pseudogene copy ( Figure 1). If the spliced alignment phase, which usually occurs later, does not attempt to realign r, then the pseudogene copy will 'absorb' all reads spanning the splice sites for that gene. TopHat2 can feed r into the spliced alignment phase even when r has been aligned end to end, allowing it to circumvent this problem (see Methods and Results and discussion sections).

We also note that, in our analysis of RNA-seq reads from multiple human samples [11,12], genes with Figure 1 Two possible incorrect alignments of spliced reads. 1) A read extending a few bases into the flanking exon can be aligned to the intron instead of the exon. 2) A read spanning multiple exons from genes with processed pseudogene copies can be aligned to the pseudogene copies instead of the gene from which it originates.

processed pseudogenes seem to be expressed at higher levels compared with other genes (see Results and discussion). Although this observation has not been explored thoroughly, a plausible explanation is that genes with higher levels of expression may, over the course of evolution, have had an increased chance of being picked up by transposons and re-integrated into the genome, creating pseudogene copies.

Concerning the human genome, for which there are relatively comprehensive annotations of protein-coding genes, the annotations can be used to map reads more accurately by aligning the reads preferentially to real genes rather than pseudogenes. GSNAP [5] and STAR [7] also make use of annotation, although they use it in a more limited fashion in order to detect splice sites. TopHat2 can use the full-length transcripts defined by annotations during its initial mapping phase, which produces significant gains in sensitivity and accuracy (see Results and discussion).

Transcripts from a target genome may differ substantially from the reference genome, possibly containing insertions, deletions, and other structural variations [13,14]. For such regions, previous spliced alignment programs (including the original TopHat) sometimes fail to find a proper alignment. In TopHat2, we implemented new procedures to ensure that reads are aligned with true insertions and deletions (indels). Indels due to sequencing errors will be discovered by Bowtie2 [15], the underlying mapping engine of TopHat2, which can detect short indels very efficiently. In addition, very large deletions, inversions on the same chromosome, and translocations involving different chromosomes are detected by the TopHat-Fusion algorithms [16], which are now incorporated into TopHat2 and available by a simple command-line switch.

TopHat2 also includes new algorithms to handle more diverse types of sequencing data. This includes the ability to handle reads generated by ABI SOLiD technology (Life Technologies, Carlsbad, CA, USA) using its 'color space' representation. To accomplish this, TopHat2 uses a reference genome translated entirely into color space in order to take advantage of the error-correction capability of that format. TopHat2 also handles datasets in which the reads have variable lengths, allowing the experimenter to merge datasets from multiple sequencing runs with different lengths.

Results and discussion

TopHat2 can use either Bowtie [17] or Bowtie2 [15] as its core read-alignment engine. TopHat2 has its own indel-finding algorithm, which enhances indel-finding ability of Bowtie2 in the context of spliced alignments. In order to evaluate TopHat2 and compare it with other methods, we ran multiple computational experiments using both real and simulated RNA-seq data.

For the simulations, we created multiple sets of 40,000,000 paired-end reads, 100 bp in length, from the entire human genome (release GRCh37). Instead of trying to precisely mimic real RNA-seq experiments, which may not be possible in any practical sense, we generated data with relatively simple settings and expression levels, calculated using a model from the Flux Simulator system [18], as follows. For the first test set, we generated reads from the known transcripts on the entire human genome without introducing any mismatches or indels. We then generated additional datasets, in which we included 1) insertions and deletions into the known transcripts at random locations, and 2) insertions and deletions in the reads themselves to mimic sequencing errors (see Additional file 1). Each of these experimental errors was introduced to test different capabilities of TopHat2 and other RNA-seq aligners. Following the simulations, we evaluated the programs using a recent, real RNA-seq dataset.

Alignments of simulated reads (error-free)

We generated 40,000,000 paired-end reads and performed two sets of experiments, using: 1) 20,000,000 'left' reads from the paired-end dataset ( Table 1) and 2) 20,000,000 pairs of reads (Table 2). Reads spanning multiple exons are called junction reads, and our single-end data contained 6,862,278 such reads (34.3%). The most challenging alignments are those for which a junction read extends by 10 bp or less into one of the exons, which we call short-anchored reads; 1,448,022 of the single-end reads (7.2%) fell into this category. We report the accuracy junction reads and short-anchored reads separately (Table 1, Table 2).

We also tested 20,000,000 read pairs (40,000,000 reads), of which 9,491,394 (47.5%) had at least one read spanning multiple exons; 2,702,624 of these pairs (13.5%) had at least one short-anchored read that extended by10 bp or less into one of its exons. Table 2 shows the results of mapping these reads with TopHat2 and other programs.

As shown in Table 1, TopHat2 correctly aligned more than 98% of the reads, which was higher than with any of the other methods, whose accuracy ranged from 88 to 97%. The difference was more pronounced for junction reads, with TopHat2 being able to align more than 94% of the reads, whereas the other methods ranged in accuracy from 65 to 92%. GSNAP, RUM, and STAR had particular difficulty aligning short-anchored reads, aligning only 26%, 8.6%, and 3.5%, respectively, while MapSplice performed considerably better, aligning 75.6% of these reads. By contrast, using Bowtie1 as its main aligner, TopHat2 aligned 93.7% of the short-anchored reads (Table 1). Both TopHat2 and MapSplice use a two-step algorithm, first detecting potential splice sites, and then using these sites to map reads. This two-step method may explain their superior performance at mapping reads with short anchors.

The results for the paired reads (Table 2) were similar to those for the unpaired reads. TopHat2 aligned the highest percentage of reads (96.7%), followed by MapSplice (92%), with the other methods ranging from 79 to 88%. The difference widened again for junction reads, with TopHat2 aligning 93%, followed by MapSplice (86%), GSNAP (76%), STAR (69%), and RUM (56%). Most striking of all was the performance on shortanchored reads, which most of the methods had great difficulty aligning correctly; TopHat2 aligned 90% of these, MapSplice aligned 72%, and the other methods aligned only 3 to 22%.

We assessed alignment rates for reads, junction reads, and junction reads with small anchors for a variety of read lengths (50, 100, 150, and 200 bp) (see Additional file 1, Figure S1). TopHat2 consistently outperformed all the other aligners for each read length. Comparing the alignment performance for junction reads with one to three mismatches, TopHat2 and MapSplice showed the highest recall rates (see Additional file 1, Table S2).

Alignments of simulated reads with short indels

We tested the spliced alignment programs using reads with small indels (1 to 3 bp), using two sets of simulated reads: (1) true indels, in which the transcripts were modified by inserting or deleting one to three bases at random locations; and (2) indels caused by sequencing errors, in which indels are randomly inserted into the reads. As before, all transcripts were simulated from known genes from the entire human genome. We intentionally used a relatively high rate of indels to test the mapping capabilities of the programs in the presence of these types of mutations.

For single-end reads, RUM, GSNAP, and TopHat2 performed similarly, with 69 to 82% accuracy (recall) rates for true indels and 62 to 83% for reads with indel-sequencing errors ( Table 3, Table 4). STAR and MapSplice showed relatively lower recall rates for both datasets. Note that when used with the original Bowtie program (a non-  gapped aligner), TopHat2 was able to map 'true' indel reads using its own indel-finding algorithms. For paired-end reads with indels, GSNAP had the highest rate of correct alignments (77%), followed by TopHat2 (60 to 69%), RUM (60 to 61%), and STAR (53 to 54%). MapSplice showed the lowest accuracy for both single-end and paired-end reads.

We defined boundary indels as those within 25 bp of a splice site. We separately computed the accuracy on all reads with boundary indels ( Table 3, Table 4).

Alignment of a large set of real RNA-seq reads

Any test of alignment algorithms should use real data to provide a measure of the likely performance in practice.

For these experiments, we used a recently released set of RNA-seq reads gathered across a time-course experiment reported by Chen et al. ( [11]; GEO accession number: GSM818582). These data comprise 130,705,578 million paired-end reads in 65,352,789 pairs. All reads are 101 bp in length.

Because we did not know the true alignments for this RNA-seq dataset, we used the following objective criteria to evaluate each program: 1) the cumulative number of alignments with edit distances of 0, 1, 2, and 3 for each read; and 2) the cumulative number of spliced alignments that agree with the annotation for the corresponding human genes, taken from the Ensembl GRCh37 release of the human genome.

For each program, we aligned the paired-end reads with and without the known gene annotations, where possible. RUM is designed to run with these annotations, whereas MapSplice maps strictly without them. We then evaluated the mapping results in terms of the number of read or paired-read mappings.

TopHat2 consists of three mapping steps: 1) transcriptome mapping, which is used only when annotation is provided; 2) genome mapping; and 3) spliced mapping (see Methods for details). TopHat2 uses a remapping edit distance threshold t, specified by the user, as follows. If a read aligns to the transcriptome in step 1) with an edit  distance of less than t, TopHat2 will not remap the read in subsequent steps. Otherwise, TopHat2 will try to realign the read in steps 2) and 3), and then, depending on the resulting edit distance, it will use the read to detect novel splice sites. A setting of t = 0 means that TopHat2 will realign every read in all three steps. When we used t = 0 ( Figure 2: 'TopHat2 realignment 0') on the real data, we consistently obtained better mapping results in terms of edit distance and the number of alignments corresponding to known splice sites (Figures 2; Figure 3; see Additional file 1, Figures S3-S4) for read and pair alignments, respectively (see Additional file 1, Tables S5-S6 and Tables S10-S11). Figure 2 shows the alignment performance for each program both with and without using annotations, where all the programs were configured to report alignments with edit distances of up to 3 (and more in some programs). We compared the de novo alignments of reads for edit distances of 0, 1, 2, and 3. As expected, all programs found more alignments as the maximum permissible edit distance increased. For an edit distance of 0, which allows only perfect matches, TopHat2 mapped noticeably fewer reads without its new realignment function than it did with the function. This occurs because TopHat2 first aligns reads end to end with Bowtie2 before trying spliced alignments. Thus if a read is aligned end to end with, for example, one to three mismatches, then without the realignment function, TopHat2 accepts that alignment and may miss a spliced alignment with fewer mismatches.

By contrast, TopHat2 with t = 0 mapped the largest number of reads for all edit distances, followed in most cases by GSNAP. Note that for alignments with an edit distance of up to 3, TopHat2 without realignment discovered almost as many alignments as GSNAP.

When alignment methods were run with the assistance of gene annotations (Figure 2, right panel), the results were somewhat better than the de novo alignments. TopHat2 with or without realignment produced the highest number of mappings, followed by GSNAP, RUM, and STAR. The realignment procedure gave a much smaller advantage to TopHat2 in these experiments.

One way to estimate the accuracy of mappings is to compare alignments to known splice sites. We compared all aligners on only those reads that required splitting, counting how many known (Figure 3, left) and known plus novel (Figure 3, right) splice sites they identified. For de novo alignment, TopHat2 with realignment had the highest sensitivity, followed by MapSplice. Consistent with our tests on simulated reads, GSNAP and STAR showed relatively lower alignment rates. When using annotation, TopHat2 without realignment showed the highest mapping rate, slightly outperforming TopHat2 with realignment. GSNAP and STAR, which performed less well, map reads against substrings containing splice sites rather than whole transcripts. Direct mapping against whole transcripts, as TopHat2 does, worked well, especially when mapping reads spanning small exons, where a single read might span more than two exons.

Based on these results, we suggest two alternative strategies for alignment with TopHat2. First, if gene annotations are available, as they are for the human genome and some model organisms, then these annotations should be used with TopHat2, even without realignment. Alternatively, if annotations are unavailable or incomplete, then we recommend using TopHat2 with its realignment algorithm to produce the most complete set of alignments.

The run time and the peak memory usage of the programs used in this study varied greatly. We compared performance on all programs using the Chen et al. data [11] of 130 million reads (see Additional file 1, Table S8). Overall, STAR is much faster (32 minutes) than the other programs, which required from 8 to 55 hours. However, STAR requires a large amount of real memory, at least 28 GB, whereas most of the other programs required less than 8 GB.

Effects of pseudogenes on RNA-seq mapping

The Ensembl gene annotations (release 66) contain 32,439 genes, including non-coding RNA genes, and over 14,000 pseudogenes. Of the real genes, we found that 872 (2.7%) genes had pseudogene copies; that is, at least one transcript (or isoform) can be aligned to a pseudogene with at least 80% identity across the full length of the transcript. Using data from the Chen et al. study [11] and from the Illumina Body Map project [12], we found that genes with pseudogene copies seem to have higher expression levels than those without pseudogene copies. Table 5 shows the proportion of reads mapping to genes with pseudogenes, using both the raw count and a normalized count divided by the length of the transcript. Although only 2.7% of genes have pseudogene copies, these genes account for 22.5% (non-normalized) or 26.9% (normalized) of the RNA-seq reads in the Chen et al. data. In the RNA-seq experiments from the Illumina Body Map (the white blood sample only), we saw 19.1% (normalized) of reads mapping to genes with pseudogenes (see Additional file 1; Table S12). From both RNA-seq experiments, we noted that genes with multiple pseudogene copies were more abundantly expressed than those with a single pseudogene copy. We ran a similar analysis looking only at the 20,417 protein-coding genes in Ensembl, with similar results: 22% of read pairs (26 times the number expected) mapped to genes with processed pseudogenes (see Additional file 1; Table S13). Figure 4 shows various mapping results from TopHat2 with and without realignments at various edit distances.  Figure 2 The number of read alignments from TopHat2, GSNAP, RUM, MapSplice, and STAR. Tthe RNA-seq reads are from Chen et al. [11]. TopHat2 was run with and without realignment (realignment edit distance of 0). TopHat2, GSNAP, and STAR were run in both de novo and gene-mapping modes, while MapSplice was run only in de novo mode and RUM was run only in gene-mapping mode. The number of alignments at each edit distance is cumulative; for instance, the number of alignments at an edit distance of 2 includes all the alignments with edit distance of 0, 1, or 2. TopHat2 realignment 0 • Alignments whose splice sites correspond to gene annotation All spliced alignments including novel splice sites Figure 3 The number of spliced-read alignments from TopHat2, GSNAP, RUM, MapSplice, and STAR. The RNA-seq reads are from Chen et al. [11]. TopHat2, GSNAP, and STAR were run in both de novo and gene-mapping modes while MapSplice was run only in de novo mode and RUM was run only in gene-mapping mode. For each mapping mode, the two panels on the left show the number of spliced alignments whose splice sites were found in the gene annotations, and the two panels on the right show the number of all spliced alignments including novel splice sites.

As we allowed TopHat2 to realign more reads, it found the spliced alignments that were otherwise hidden by pseudogene alignments. This in turn substantially increased its mapping rates for known splice sites.

The completeness of human gene annotations

Using the de novo mapping mode in TopHat2, GSNAP, MapSplice, and STAR, we looked at how many spliced alignments were found in the Ensembl annotations. The proportions of spliced mappings to known splice sites were 88 to 90%, 97%, 96%, and 83 to 93% in TopHat2, GSNAP, STAR, and MapSplice, respectively ( Figure 5). Although this analysis considered only the RNA-seq data from Chen et al. [11], the TopHat2 result suggests that many additional spliced alignments, up to 12%, might remain to be discovered. Most of the novel splicing events in these alignments were supported by 10 or more reads that extended for 50 or more bases on each side.

Conclusions

Discovery of new genes and transcripts is a major objective in many RNA-seq experiments. Deep RNA-seq experiments continue to uncover previously unseen elements of the transcriptome, even in well-studied organisms. Mapping reads to the genome is a core step in such screens, and the accuracy of mapping software can determine the accuracy of downstream steps such as gene and transcript discovery or expression quantification. We have described TopHat2, which provides major accuracy improvements over previous versions and over other RNA-seq mapping tools. Because TopHat2 is built around Bowtie2, it can now align reads across small indels with high accuracy, a feature crucial for studies assessing the effects of genetic mutations on gene and transcript expression. TopHat2 is engineered to work well with a wide range of RNA-seq experimental designs, and it is optimized for the widely available long paired-end reads. These reads pose new challenges because they can span multiple splice sites rather than just one or two; we estimate that nearly half of reads 150 bp long will span two or more human exons. The algorithmic improvements in TopHat2 address this challenge, maintaining both accuracy and speed. Other refinements to the algorithm increase accuracy for reads that span a junction with only a small (≤ 10 bp) overhang, reducing errors in downstream transcript assembly using tools such as Cufflinks. TopHat2 also makes powerful use of available gene annotations, which allow it to avoid erroneously mapping reads to pseudogenes, and generally improve its overall alignment accuracy. Annotation also allows TopHat2 to better align reads that cover microexons, non-canonical splice sites, and other 'unusual' features of eukaryotic transcriptomes.

We have shown that TopHat2 performs well over a wide range of read lengths, making it a good fit for most RNA-seq experimental designs. This scalability suggests that as read lengths grow, TopHat2 will continue to report accurate, sensitive alignment results and allow for robust downstream analysis. We believe that TopHat2 reports more accurate alignments than competing tools, using fewer computational resources. RNAseq experiments are becoming increasingly common and are now routinely used by many biologists. We expect that TopHat2 will provide these scientists with accurate results for use with expression analysis, gene discovery, and many other applications.

Methods

Given RNA-seq reads as input, TopHat2 begins by mapping reads against the known transcriptome, if an annotation file is provided. This transcriptome mapping improves the overall sensitivity and accuracy of the mapping. It also gives the whole pipeline a significant Using Bowtie2, we aligned RNA-seq paired-end reads to 32,439 annotated genes. b Number of pseudogene copies of a gene. The first row shows genes that have just one pseudogene, followed by rows for genes with two, three, four, and at least five pseudogene copies.

c Number of genes with the specified number of pseudogene copies; for example, 553 genes (1.7% of all genes) have one pseudogene copy. d Percentage of read pairs that were mapped to genes with pseudogene copies. e Ratio of columns 3 and 2. f These two columns were similarly defined using a normalized count, where the number of reads mapping to each gene was normalized to account for gene length. The differences in the numbers of alignments at edit distance of 0 are mostly explained by the differences in the numbers of spliced alignments at edit distance of 0 because many spliced reads are incorrectly aligned to processed pseudogenes with a few mismatches. Figure 4 The number of read and spliced-read alignments from TopHat2, using different realignment edit distances and no realignment. Edit distances of 0, 1, and 2 were used. As TopHat2 allows more realignment from no realignment to 2 to 1 to 0, the number of read alignments and spliced-read alignments increases, so that the differences in the numbers of read alignments from TopHat run with different realignment edit distance are mostly explained by the increase in the number of spliced-read alignments. speed increase, owing to the much smaller size of the transcriptome compared with that of the genome (see Figure 6).

After the transcriptome-mapping step, some reads remain unmapped because they are derived from unknown transcripts not present in the annotation, or because they contain many miscalled bases. In addition, there may be poorly aligned reads that have been mapped to the wrong location. TopHat2 aligns these unmapped or potentially misaligned reads against the genome ( Figure 6, step 2). Any reads contained entirely within exons will be mapped, whereas other spanning introns may not be.

Using unmapped reads from step 2, TopHat2 tries to find novel splice sites that are based on known junction signals (GT-AG, GC-AG, and AT-AC). TopHat2 also provides an option to allow users to remap some of the mapped reads, depending on the edit distance values of these reads; that is, those reads whose edit distance is  Figure 5 The number of spliced-read alignments from TopHat2, GSNAP, STAR, and MapSplice without using gene annotation. The number of read alignments whose splice sites were found in the gene annotations are shown in brown, and the number of all spliced-read alignments including novel splice sites are shown in green.

greater than or equal to a user-provided threshold will be treated as unmapped reads. To accomplish this, the unmapped reads (and previously mapped reads with low alignment scores) are split into smaller non-overlapping segments (25 bp each by default) which are then aligned against the genome ( Figure 6, step 3). Tophat2 examines any cases in which the left and right segments of the same read are mapped within a user-defined maximum intron size (usually between 50 and 100,000 bp). When this pattern is detected, TopHat2 re-aligns the entire read sequence to that genomic region in order to identify the most likely locations of the splice sites ( Figure 6). Using a similar approach, indels and fusion breakpoints are also detected in this step. The genomic sequences flanking these splice sites are concatenated, and the resulting spliced sequences are collected as a set of potential transcript fragments. Any reads not mapped in the previous stages (or mapped very poorly) are then re-aligned with Bowtie2 [15] against this novel transcriptome.

After these steps, some of the reads may have been aligned incorrectly by extending an exonic alignment a few bases into the adjacent intron ( Figure 1; Figure 6, steps 3 to 5). TopHat2 checks if such alignments extend into the introns identified in the split-alignment phase; if so, it can realign these reads to the adjacent exons instead.

In the final stage, TopHat2 divides the reads into those with unique alignments and those with multiple alignments. For the multi-mapped reads, TopHat2 gathers statistical information (for example, the number of supporting reads) about the relevant splice junctions, insertions, and deletions, which it uses to recalculate the alignment score for each read. Based on these new alignment scores, TopHat2 reports the most likely alignment locations for such multi-mapped reads.

For paired-end reads, TopHat2 processes the two reads separately through the same mapping stages described above. In the final stage, the independently aligned reads are analyzed together to produce paired alignments, taking into consideration additional factors including fragment length and orientation.

For the experiments described in this study, the program version numbers were: TopHat2 2.0.8, TopHat1  Authors' contributions DK, SLS, GP, and CT performed the analysis and discussed the results of TopHat2. DK, GP, and CT were mainly responsible for implementing TopHat2. HP implemented the transcriptome-mapping algorithms, with help from GP and DK. RK implemented the indel-alignment algorithms, with help from DK. DK, SLS, GP, and CT wrote the manuscript. All authors read and approved the final manuscript.

Figure 6
6TopHat2 pipeline. Details are given in the main text.

: Base pair; indel: Insertion or deletion; RNA-seq: RNA sequence;

Table 2
2Performance of TopHat2 and other spliced aligners on a set of 20 million pairs of 100-bp reads, simulated based on transcripts from the entire human genome.Program
No. of mapped
pairs

Correctly mapped
pairs, %

Incorrectly mapped
pairs, %

Unmapped
pairs, %

Correct junction
pairs, % a

Correct short-anchored
pairs, % b

TopHat2 +
Bowtie1

19,683,426
96.70
1.72
1.58
93.31
90.09

TopHat2 +
Bowtie2

19,686,006
96.19
2.24
1.57
92.03
85.88

TopHat1.14
19,219,055
89.57
6.53
3.90
78.36
40.39

GSNAP
19,999,867
88.84
11.16
0.00
76.55
22.87

RUM
19,869,579
79.07
20.28
0.65
56.28
8.42

MapSplice
19,342,087
92.03
4.68
3.29
86.53
72.48

STAR
19,951,620
85.21
14.55
0.24
68.94
3.16

a

There were 9,491,394 pairs of reads classified as junction pairs.
b There were 2,702,624 pairs containing short-anchored reads.

Table 1
1Performance of TopHat2 and other spliced aligners on a set of 20 million 100-bp, single-end reads, simulated based on transcripts from the entire human genome.There were 6,862,278 reads spanning one or more splice junctions; the alignment accuracy of junction reads refers to this set. b There were 1,448,022 reads extending 10 bp or less into one exon; the alignment accuracy of the short-anchored reads is based on these alignments.Program
No. of mapped
reads

Correctly mapped
reads, %

Incorrectly mapped
reads, %

Unmapped
reads, %

Correct junction
reads, % a

Correct short-anchored
reads, % b

TopHat2 +
Bowtie1

19,826,638
98.31
0.82
0.87
95.28
93.69

TopHat2 +
Bowtie2

19,826,673
98.03
1.10
0.87
94.28
89.67

TopHat1.14
19,616,874
94.64
3.45
1.91
84.44
44.08

GSNAP
19,997,255
94.21
5.77
0.02
83.15
26.01

RUM
19,555,823
88.11
9.67
2.22
65.35
8.59

MapSplice
19,872,372
97.28
2.08
0.64
92.09
75.57

STAR
19,087,508
92.14
3.30
4.56
77.17
3.54

a

Table 3
3Performance of TopHat2 and other spliced aligners on single-end reads containing insertions and deletions (indels) of 1 to 3 bp.The number of reads containing each type of error is indicated in the column header. b Boundary indels occur within 25 bp of an exon boundary. Percentages refer only to the reads of each type, not to the entire dataset.Reads with true indels (1,428,499) a
Reads with sequencing-error indels (1,525,657) a

Program Accuracy,
%

Accuracy on 351,465 reads with boundary indels,
% b

Accuracy,
%

Accuracy on 357,334 reads with boundary
indels, % b

TopHat2
+
Bowtie1

70.9
16.8
12.1
2.8

TopHat2
+
Bowtie2

63.7
25.2
62.6
21.2

GSNAP
82.7
71.9
83.1
71.8

RUM
69.4
43.0
70.3
45.4

MapSplice 27.3
3.7
27.5
3.8

STAR
46.6
16.9
47.7
17.1

a

Table 4
4Performance of TopHat2 and other spliced aligners on paired reads in which at least one of the reads contained insertions and deletions (indels) of 1 to 3 bp.The number of pairs containing each type of error is indicated in the column header. b Boundary indels occur within 25 bp of an exon boundary. Percentages refer only to the pairs of each type, not to the entire dataset.Pairs with true indels (2,754,313) a
Pairs with sequencing-error indels (2,934,043) a

Program
Accuracy,
%

Accuracy on 685,937 pairs with boundary
indels, % b

Accuracy,
%

Accuracy on 695,771 pairs with boundary
indels, % b

TopHat2
+ Bowtie1

69.8
16.3
14.0
3.1

TopHat2 +
Bowtie2

62.3
24.0
60.8
19.8

GSNAP
77.0
63.8
77.8
64.8

RUM
60.3
34.3
61.3
36.0

MapSplice
25.5
3.4
25.0
3.2

STAR
53.4
19.2
54.9
21.4

a

Table 5
5Expression levels of genes with pseudogene copies from Chen et al.[11]. aNumber of pseudogene copies b Gene with pseudogene copies (%) c Pair
count, % d

Ratio e
Normalized count, % f Normalized ratio f

1
553 (1.7%)
6.85
× 4.02
9.37
× 5.49

2
113 (0.4)
5.15
× 14.79 5.20
× 14.93

3
49 (0.2)
1.27
× 8.38
1.96
× 12.99

4
27 (0.1)
2.27
× 27.32 2.28
× 27.35

≥ 5
130 (0.4)
6.91
× 17.24 8.08
× 20.16

Total (≥ 1)
872/32,439 (2.7)
22.45
× 8.35
26.88
× 10.00

a

1.1.4, GSNAP 2013-01-23, RUM 1.12_01, MapSplice 1.15.2, and STAR 2.3.0e. For the specific parameters for each program, see Additional file 1, Table S9, and for the source code of TopHat 2.0.8, see Additional file 2.
Kim et al. Genome Biology 2013, 14:R36 http://genomebiology.com/2013/14/4/R36
AcknowledgementsWe thank Lior Patcher and Adam Roberts for their invaluable contributions to our discussions on the TopHat2 pipeline. This work is supported in part by the National Human Genome Research Institute (NIH) under grants R01-HG006102 and R01-HG006677.
Mapping and quantifying mammalian transcriptomes by RNA-Seq. A Mortazavi, B A Williams, K Mccue, L Schaeffer, B Wold, Nat Methods. 5Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B: Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 2008, 5:621-628.

B Pei, C Sisu, A Frankish, C Howald, L Habegger, X J Mu, R Harte, S Balasubramanian, A Tanzer, M Diekhans, A Reymond, T J Hubbard, J Harrow, M B Gerstein, The GENCODE pseudogene resource. 1351Pei B, Sisu C, Frankish A, Howald C, Habegger L, Mu XJ, Harte R, Balasubramanian S, Tanzer A, Diekhans M, Reymond A, Hubbard TJ, Harrow J, Gerstein MB: The GENCODE pseudogene resource. Genome Biol 2012, 13:R51.

Improving RNA-Seq expression estimates by correcting for fragment bias. A Roberts, C Trapnell, J Donaghey, J L Rinn, L Pachter, Genome Biol. 1222Roberts A, Trapnell C, Donaghey J, Rinn JL, Pachter L: Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol 2011, 12:R22.

TopHat: discovering splice junctions with RNA-Seq. C Trapnell, L Pachter, S L Salzberg, Bioinformatics. 25Trapnell C, Pachter L, Salzberg SL: TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 2009, 25:1105-1111.

Fast and SNP-tolerant detection of complex variants and splicing in short reads. T D Wu, S Nacu, Bioinformatics. 26Wu TD, Nacu S: Fast and SNP-tolerant detection of complex variants and splicing in short reads. Bioinformatics 2010, 26:873-881.

Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM). G R Grant, M H Farkas, A D Pizarro, N F Lahens, J Schug, B P Brunk, C J Stoeckert, J B Hogenesch, E A Pierce, Bioinformatics. 27Grant GR, Farkas MH, Pizarro AD, Lahens NF, Schug J, Brunk BP, Stoeckert CJ, Hogenesch JB, Pierce EA: Comparative analysis of RNA-Seq alignment algorithms and the RNA-Seq unified mapper (RUM). Bioinformatics 2011, 27:2518-2528.

STAR: ultrafast universal RNA-seq aligner. A Dobin, C A Davis, F Schlesinger, J Drenkow, C Zaleski, S Jha, P Batut, M Chaisson, T R Gingeras, Bioinformatics. 29Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, Batut P, Chaisson M, Gingeras TR: STAR: ultrafast universal RNA-seq aligner. Bioinformatics 2013, 29:15-21.

MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. K Wang, D Singh, Z Zeng, S J Coleman, Y Huang, G L Savich, X He, P Mieczkowski, S A Grimm, C M Perou, J N Macleod, D Y Chiang, J F Prins, J Liu, Nucleic Acids Res. 38178Wang K, Singh D, Zeng Z, Coleman SJ, Huang Y, Savich GL, He X, Mieczkowski P, Grimm SA, Perou CM, MacLeod JN, Chiang DY, Prins JF, Liu J: MapSplice: accurate mapping of RNA-seq reads for splice junction discovery. Nucleic Acids Res 2010, 38:e178.

Millions of years of evolution preserved: a comprehensive catalog of the processed pseudogenes in the human genome. Z Zhang, P M Harrison, Y Liu, M Gerstein, Genome Res. 13Zhang Z, Harrison PM, Liu Y, Gerstein M: Millions of years of evolution preserved: a comprehensive catalog of the processed pseudogenes in the human genome. Genome Res 2003, 13:2541-2558.

Expressed pseudogenes in the transcriptional landscape of human cancers. S Kalyana-Sundaram, C Kumar-Sinha, S Shankar, D R Robinson, Y M Wu, X Cao, I A Asangani, V Kothari, J R Prensner, R J Lonigro, M K Iyer, T Barrette, A Shanmugam, S M Dhanasekaran, N Palanisamy, A M Chinnaiyan, Cell. 149Kalyana-Sundaram S, Kumar-Sinha C, Shankar S, Robinson DR, Wu YM, Cao X, Asangani IA, Kothari V, Prensner JR, Lonigro RJ, Iyer MK, Barrette T, Shanmugam A, Dhanasekaran SM, Palanisamy N, Chinnaiyan AM: Expressed pseudogenes in the transcriptional landscape of human cancers. Cell 2012, 149:1622-1634.

Personal omics profiling reveals dynamic molecular and medical phenotypes. R Chen, G I Mias, J Li-Pook-Than, L Jiang, H Y Lam, E Miriami, K J Karczewski, M Hariharan, F E Dewey, Y Cheng, M J Clark, H Im, L Habegger, S Balasubramanian, M O'huallachain, J T Dudley, S Hillenmeyer, R Haraksingh, D Sharon, G Euskirchen, P Lacroute, K Bettinger, A P Boyle, M Kasowski, F Grubert, S Seki, M Garcia, M Whirl-Carrillo, M Gallardo, M A Blasco, Cell. 148Chen R, Mias GI, Li-Pook-Than J, Jiang L, Lam HY, Miriami E, Karczewski KJ, Hariharan M, Dewey FE, Cheng Y, Clark MJ, Im H, Habegger L, Balasubramanian S, O'Huallachain M, Dudley JT, Hillenmeyer S, Haraksingh R, Sharon D, Euskirchen G, Lacroute P, Bettinger K, Boyle AP, Kasowski M, Grubert F, Seki S, Garcia M, Whirl-Carrillo M, Gallardo M, Blasco MA, et al: Personal omics profiling reveals dynamic molecular and medical phenotypes. Cell 2012, 148:1293-1307.

The Illumina Body Map 2.0 data. The Illumina Body Map 2.0 data.. [http://www.ebi.ac.uk/arrayexpress/ browse.html?keywords=E-MTAB-513&expandefo=on].

Mobile elements create structural variation: analysis of a complete human genome. J Xing, Y Zhang, K Han, A H Salem, S K Sen, C D Huff, Q Zhou, E F Kirkness, S Levy, M A Batzer, L B Jorde, Genome Res. 19Xing J, Zhang Y, Han K, Salem AH, Sen SK, Huff CD, Zhou Q, Kirkness EF, Levy S, Batzer MA, Jorde LB: Mobile elements create structural variation: analysis of a complete human genome. Genome Res 2009, 19:1516-1526.

The diploid genome sequence of an individual human. S Levy, G Sutton, P C Ng, L Feuk, A L Halpern, B P Walenz, N Axelrod, J Huang, E F Kirkness, G Denisov, Y Lin, J R Macdonald, A W Pang, M Shago, T B Stockwell, A Tsiamouri, V Bafna, V Bansal, S A Kravitz, D A Busam, K Y Beeson, T C Mcintosh, K A Remington, J F Abril, J Gill, J Borman, Y H Rogers, M E Frazier, S W Scherer, R L Strausberg, PLoS Biol. 5254Levy S, Sutton G, Ng PC, Feuk L, Halpern AL, Walenz BP, Axelrod N, Huang J, Kirkness EF, Denisov G, Lin Y, MacDonald JR, Pang AW, Shago M, Stockwell TB, Tsiamouri A, Bafna V, Bansal V, Kravitz SA, Busam DA, Beeson KY, McIntosh TC, Remington KA, Abril JF, Gill J, Borman J, Rogers YH, Frazier ME, Scherer SW, Strausberg RL, et al: The diploid genome sequence of an individual human. PLoS Biol 2007, 5:e254.

Fast gapped-read alignment with Bowtie 2. B Langmead, S L Salzberg, Nat Methods. 9Langmead B, Salzberg SL: Fast gapped-read alignment with Bowtie 2. Nat Methods 2012, 9:357-359.

TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. D Kim, S L Salzberg, Genome Biol. 1272Kim D, Salzberg SL: TopHat-Fusion: an algorithm for discovery of novel fusion transcripts. Genome Biol 2011, 12:R72.

Ultrafast and memoryefficient alignment of short DNA sequences to the human genome. B Langmead, C Trapnell, M Pop, S L Salzberg, Langmead B, Trapnell C, Pop M, Salzberg SL: Ultrafast and memory- efficient alignment of short DNA sequences to the human genome.

. Genome Biol. 1025Genome Biol 2009, 10:R25.

Modelling and simulating generic RNA-Seq experiments with the flux simulator. T Griebel, B Zacher, P Ribeca, E Raineri, V Lacroix, R Guigo, M Sammeth, 10.1186/gb-2013-14-4-r36Nucleic acids Res. Griebel T, Zacher B, Ribeca P, Raineri E, Lacroix V, Guigo R, Sammeth M: Modelling and simulating generic RNA-Seq experiments with the flux simulator. Nucleic acids Res 2012. doi:10.1186/gb-2013-14-4-r36

TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Kim, Genome Biology. 1436Cite this article asCite this article as: Kim et al.: TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology 2013 14:R36.

. Kim, Genome Biology. 1436Kim et al. Genome Biology 2013, 14:R36