Camille Vindras-Fouillet

Olivier Ranke
email: olivier.ranke@orange.fr

Jean-Pierre Anglade
email: anglade.jean-pierre@wanadoo.fr

Bruno Taupier-Letage
email: bruno.taupier-letage@itab.asso.fr

Chable Véronique
email: veronique.chable@rennes.inra.fr

Isabelle Goldringer
email: isabelle.goldringer@moulon.inra.fr

Sensory Analyses and Nutritional Qualities of Hand-Made Breads with Organic Grown Wheat Bread Populations

Keywords: Organic Farming, Sensory Analysis, Bread Wheat, Population Varieties, Nutritional Analysis

Population varieties can meet the needs of organic farming as they are composed of diverse genotypes and have nutritional and sensory characteristics of interest. Their intra-specific genetic variability helps to adapt to the diversity of soil and climate conditions, management practices and needs. Moreover, an integrated organic bread sector has emerged willing to use more wheat populations. To explore sensory and nutritional potential of bread wheat populations, hedonic tests, sensory profile and nutritional analyses were implemented on eight wheat population varieties and one modern variety. Hedonic tests revealed consensus among consumer when ranking according to specific sensory characteristics and showed preferences for red wheat breads. Descriptive sensory test showed significant differences between genotypes on 6 out of 11 descriptors and confirmed the sensory specificity of red wheat accessions. Nutritional analyses showed differences especially on minerals content. Sensory differences between breads from different genotypes can be perceived both by trained and untrained panels as suggested by results. Moreover, red accessions showed specific sensory characteristics which are well-perceived by the two panels. Breeding effort should be maintained for high diverse genotypes with improved sensory and nutritional qualities adapted to organic farming conditions. Indicators like kernel colour should be sought to help integrate such criteria.

Introduction

Over the second half of the 20th century, plant breeding has developed varieties adapted to high input farming systems and industrial baking, resulting in the replacement of local varieties, which were potentially adapted to the specific soil and climate conditions of each region and had potentially good bread making properties [START_REF] Bordes | Agronomic Characteristics, Grain Quality and Flour Rheology of 372 Bread Wheats in a Worldwide Core Collection[END_REF]. Recently, however, agricultural practices more environment-friendly such as organic farming have developed leading to an increased diversity in agronomic, soil and ecological conditions. This, together with the growing demand for diversification in terms of product quality, has generated a need for new varieties, general and specific, which are barely met by the standard model of variety innovation.

Breeding for organic farming should take several criteria into account: adaptation to the local soil and climatic conditions and to pest and pathogen pressures, weed competition, conformity to the organic specification and specifically growing without chemical inputs and pesticides, no genetic modification, acceptable economic profitability and a high level of nutritional and gustatory quality [START_REF] Lammerts Van Bueren | The Need to Breed Crop Varieties Suitable for Organic Farming, Using Wheat, Tomato and Broccoli as Examples: A Review[END_REF]. Modern varieties have been bred to provide high yield under high input agricultural conditions and thus they might lack the robustness and quality characteristics that are required for organic agriculture. Up to now, wheat sensory properties have not been systematically included as qualities parameters in the selection of new wheat varieties. However, the availability of suitable wheat varieties is critical for the development of organic agriculture able to meet demands in terms of nutritional and sensory quality.

Sensory properties are usually measured through sensory methods (descriptive analyses) as instrumental techniques are still limited to predict human perception [START_REF] Torri | Sensory Test vs. Electronic Nose and/or Image Analysis of Whole Bread Produced with Old and Modern Wheat Varieties Adjuvanted by Means of the Mycorrhizal Factor[END_REF]. The influence of farming system, milling and baking technique including flour type and fermentation [START_REF] Katina | Optimization of Sourdough Process for Improved Sensory Profile and Texture of Wheat Bread[END_REF] [5] and genetic structure [START_REF] Ploeger | Sensory Evaluation of Processed Wheat from a Defined Field-Trial (Quality Low Input Food)[END_REF] [START_REF] Starr | Sensory Profiles of Cooked Grains from Wheat Species and Varieties[END_REF] on bread sensory properties have been investigated. In order of importance the identified factors are milling technique, baking practice (fermentation type and time, kneading intensity, etc.), genetic structure and finally farming system. To summarize, it seems that stone milled breads, wholegrain breads and organic wheat breads have specific sensory properties which are thought to be due to bran compared to conventional bread or white pan bread [START_REF] Khilberg | Sensory Qualities of Whole Wheat Pan Bread-Influence of Farming System, Milling and Baking Technique[END_REF]. They showed more intense aromas such as roasted cereals, bitterness or a crusty texture.

This brought to light the close linked between nutritional and sensory qualities of wheat bread. Specific nutritional quality of wheat bread can be defined by a complex set of variables. The quantity and quality of grain protein is a first parameter that defines the textural properties of the bread. Among wheat protein the gliadin is implicated in the dough extensibility. Moreover gliadin is suspected to play a role in gluten sensitivity by acting as inhibitors of alpha-amylase activity in some animals [START_REF] Štepánková | Experimentally Induced Gluten Enteropathy and Protective Effect of Epidermal Growth Factor in Artificially Fed Neonatal Rats[END_REF]. The pro-vitamin and vitamin content are vital nutrient and their supplementation are important for the treatment of certain health problems. The "antioxidant" hypotheseis that Vitamin E, carotenoids and other antioxidant micronutrients afford protection against chronic disease by decreasing oxidative damage [START_REF] Meydani | Antioxidants in the Prevention of Chronic Diseases[END_REF]. The predominant carotenoid in wheat is lutein and is suspected to reduce the oxidative damage link to the aging process [START_REF] Leenhardt | Genetic Variability of Carotenoid Concentration, and Lipoxygenase and Peroxidase Activities among Cultivated Wheat Species and Bread Wheat Varieties[END_REF]. Moreover, carotenoid content may affect bread flavour by limiting the development of hexanal, a compound responsible for flavour problems [START_REF]Supplément technique INBP n 83-Le goût du pain[END_REF]. Minerals content is another source of health benefits. Nevertheless, the availability of minerals depends on phytate content which had a decalcifying effect. On a sensory point of view, alkaline metal such as potassium or alkaline earth such as magnesium may contribute to the taste of wheat bread [START_REF] Fujino | Other Grain Components[END_REF]. Finally the different categories of dietary fibres have shown to benefit diabetes, blood cholesterol level, body weight control and reduction of the risk of coronary heart diseases. Moreover some fibres as pentosan are implicated in the textural properties of bread wheat.

Farmer-bakers in the "Réseau Semences Paysannes" (RSP) network (a network of more than 70 associations aimed at the maintenance of cultivated biodiversity) have identified so called "landraces" (local varieties released before 1840s) and varieties from ancient breeding (varieties that have been released between 1840 and 1960) as a potential source of genetic diversity to breed wheat varieties adapted to their practices and needs. Those two types of varieties have been created before the genetic bottleneck associated with the emergence of "formal breeding" (breeding for pure lines) [START_REF] Roussel | SSR Allelic Diversity Changes in 480 European Bread Wheat Varieites Released from 1840 to 2000[END_REF]. The genetic variability of population varieties can be expected to improve resilience because of their great adaptation capacity. Moreover, a significant historic evolution on nutritional composition of wheat grain has been observed between pure line varieties and population varieties which suggest that there is a potential for aroma and flavour diversity in population varieties [START_REF] Roussel | SSR Allelic Diversity Changes in 480 European Bread Wheat Varieites Released from 1840 to 2000[END_REF]. The farmerbakers from RSP have started to reproduce some of them and are interested in testing their processing in bread.

In this context, a French interdisciplinary research programme, PICRI (Partnership between Institutions and Citizen for Research and Innovation), was launched in 2007 to develop participatory breeding and management methods based on the broad diversity of landraces and historic varieties and to characterize their variability for hedonic, sensory and nutritional quality.

The objective of this work was to assess sensory quality of different wheat varieties and mixtures of varieties and correlate with nutritional analyses of the same sample. Moreover, liking tests have been implemented to check if sensory differences should be perceived and appreciated by consumers as no study on hedonic test on landraces exists [START_REF] Torri | Sensory Test vs. Electronic Nose and/or Image Analysis of Whole Bread Produced with Old and Modern Wheat Varieties Adjuvanted by Means of the Mycorrhizal Factor[END_REF]. Such information is expected to support the development of a greater diversity of wheat relevant to organic wheat production within an agronomic and social context at a local scale.

Materials and Methods

Wheat Samples and Milling

Nine landraces and historic varieties and one pure line variety used as a control sample were grown in an experimental field under organic agriculture management in La Bergerie de Villarceau near Paris in France (49.11; 1.718). The wheat grown in 2007-2008 has been used for hedonic assessment whereas the next year's crops (2008)(2009) were studied for their sensory and nutritional properties. Trials were repeated three times on plot of 120 m 2 . Phenotypic measures (not shown in this article) included yield have been realised in order to better characterised genotypes and help breeding process. Grains from replication were pooled before being cleaned and stored in a dry room. After four-month storage, 500 g of each pooled grain sample were sent to the laboratory for nutritional analyses. At the same time, wheat flour samples were obtained from the stone milling (T80 flour type) of grain samples. The samples evaluated in the different tests are summarized in Table 1.

Descriptive Sensory Analysis

Bread was made by Pierre Delton, a local baker, one day before the sensory evaluation. The same bread dough formula was adopted (water: 70% of the flour weight; sourdough: 15%; salt: 2.7%) and the same bread making process was followed: hand-kneading (flour, water, sourdough, salt) for 25 min; left to rest at 27˚C for 4 h; forming (1 kg size loaf); rising at 29˚C for 3 h, etching of the surface of the bread; baking in a simultaneous way (45 min at 240˚C). Breads were sliced (slices 13 mm thick) ten minute before tasting. Each slice was cut into 5 pieces with each piece including both crust and crumb in order to evaluate the overall sensory characteristics.

Sensory profile training and evaluation took place in a meeting room at the INRA laboratory, Le Moulon. The panel was composed of farmers, bakers, researchers and technicians involved in the project, along with volunteers of the "Nature & Progrès" association.

An initiation to sensory analysis was carried out at the beginning of the project on a large number of breads produced using endogenous sourdough. This provided diverse product qualities for panellist taste references. A first set of sensory attributes was developed. Two training days at an interval of one week completed the initiation Table 1. List of wheat samples evaluated in the three experiments (the "X" indicate that the variety (column) has been evaluated in the test correspondant to the line).

Rouge de Bordeaux

Rouge du Roc

Odessa Mélange de James

Chiddamd' automne 20% Ile de France + 80% Gâtine

Touzelle Talisman Dattel Renan course. One training day consisted in evaluating a set of loaves based on the descriptor list that was produced during the initiation course and then reduced during the training course. Each of the two training days comprised four sessions lasting one and a half hours. The same samples were evaluated during the sessions of one day. Panellists received feedback on their performance after each session in order to improve and standardise their discriminatory ability as a panel and to reach consensus. The panel agreed unanimously on 11 attributes to constitute a descriptive profile for the bread: appearance (crust colour, crumb colour, air cell size), aroma (wheat bread, roasting cereals), taste (saltiness, bitterness, acidity), texture (crispiness, smoothness) and long lasting taste. A detailed definition was established for each sensory attribute (Table 2). Intensity of the sensory attributes was rated on a discontinuous, structured scale from 0 (very low) to 7 (high).

Two evaluation sessions on a set of 6 bread samples were then organised in order to provide replication and to assess the panel's performance. The design was complete and balanced for the rank and carry-over effects. A random, three-digit code was assigned to each sample.

Nutritional Analyses

Nutritional analyses have been subcontracted to an independent laboratory, Agrobio Rennes (Rennes, France). Grain was stored in an air conditioned-room at 22˚C between reception and analysis. All samples were then milled in a ball mill just before analysis. The objective of this work was first to characterise genotypes on certain variables linked to nutritional quality of wheat grain. Then correlations between sensory and nutritional variables were studied as the same samples have been evaluated.

A set of ten wheat grain samples was analysed for nutrient content. The choice of nutrient measurements was based on their interest in health as well as technological properties. The following nutrient contents were measured by the Agrobio laboratory in Rennes: protein content, gliadin content, mineral content (MG, Fe, Zn, Se) carotenoid content (lutein, alpha-carotenoid and beta-carotenoid), vitamin B1 and vitamin E content, phytic acid content and soluble and insoluble fibre content. Protein content was measured using the Kjeldahl method, a method for determining the quantity of nitrogen in chemical substances developed by Johan Kjeldahlin (1883). Gliadin content has been measure using ELISA (Enzyme-linked immunosorbent assay) method. The nutritional density was assessed through the mineral content. Mineral content was analysed by Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES). ICP-AES is a multi-element method with very good detection power and offering reliable and rapid determination of the analytes. To estimate the availability of minerals, phytic acid content has been measured by titration and compared with the total minerals content (Mg, Zn, Se, Fe). Carotenoid content (lutein, alpha-carotenoid and beta-carotenoid), vitamin B1 and vitamin E content have been measured using High Performance Liquid Chromatography (HPLC). HPLC is the most frequently used Table 2. Sensory attributes, description and results of sensory profiles (NS: Non Significant).

Sensory attributes Description p-Value

Crust colour intensity Degree of perceived brown colour characterising the crust (from low (0) to high (7) intensity) 0.002439

Crumb colour intensity Degree of perceived brown colour characterising the crumb (from low (0) to high [START_REF] Starr | Sensory Profiles of Cooked Grains from Wheat Species and Varieties[END_REF] Finally soluble and insoluble fibres, implicated in textural properties and health benefits properties (lipolytic activities) has been measured using the enzymatic-gravimetric methods with phosphate buffer and three enzyme (heat stable a-amylase, protease and amyloglucosidase). The results are the means of ten repeated measurements.

Liking Tests

For each test the bread was made by one of two local bakers Pierre Delton (Le Pain de Pierre) and Lafuette (Mme VIGIER, Montigny le Bretonneux) one day before the sensory evaluation. It was baked according to their own practices in order to be as close as possible to reality. The questionnaire was implemented at three consumer-oriented events over a two year period. According to the norm defined by the French standardisation association, AFNOR (1988), at least 60 consumers should be interviewed for hedonic tests to obtain significant and reliable results. Finally, as different breads were tasted at each event, the results from one event could not be compared with those from another. For greater clarity and robustness, the study presents the results of the hedonic test implemented at the "fête de l'Humanité" in Paris 2009 and the "Fête du Parc Naturel du Vexin", Jambville 2010, where a sufficient number of respondents were registered (respectively 72 and 169).

The questionnaire asked consumers to rank their preferences on the following sensory traits: external appearance, crumb appearance, aroma, taste, smoothness, chewing and overall preference. A descriptive test was proposed to consumer in order to assess consumers' ability to explain their preferences.

Statistical Analyses

Sensory Profile

Analysis of variance (ANOVA) of the descriptive sensory data was performed using the Sensomine R package with panellists as repeated measures. Significant descriptors were selected at p < 0.05 using the following ANOVA model: Y = μ + judge + product + session + product * judge + product * session + judge * session + ε. The panel's power of discrimination was evaluated using the significant effect of product factor. The consensus between panellists has been assessed through the non-significant effect of panellist factor and the product by panellist interaction. Finally repeatability has been measured through the non-significant effect of product by session interaction.

A multi-dimensional profile was then drawn up to view the products' sensory differences.

Nutritional Characterisation

Principal Component Analysis was performed in order to visualise the differences between samples on the nine nutritional descriptors and identified correlated variables. Six of the samples have been assessed on a sensory and nutritional point of view. To explore the link between sensory and nutritional properties a Multiple Factorial Analyses has been run on the two data type as groups.

Liking Tests

A Friedman rank sum test was performed for each descriptor on individual hedonic classification in order to determine the existence of a consensus among consumer and characterised the preferences of group of consensual consumer.

To test the capacity of consumers to perceive differences between breads from diverse genotypes, a PCA was run on the means score of the consumer sensory descriptive data for each product and each descriptor.

Results and Discussion

Sensory Profile

Of the 11 sensory descriptors, the following 6 attributes were significant in discriminating the products: crust colour, crumb colour, air cell size, roasted aroma, crustiness, wheat grain aroma and long lasting taste (Table 2). There was a consensus on the ranking of the products by the panel (as the panel effect was non-significant) but not on the scaling of the intensity. The panel showed good repeatability and could discriminate for the following descriptors (non-significant product by session interaction effect): crust colour, air cell size, crustiness and long lasting taste. The results could be interpreted as a consensus was reached by the panel. A few more sessions would improve the reliability of the results. In order to facilitate visualisation of the discrimination between products, the ellipses given by the panellipse function of the Sensomine R package are provided (Figure 1(a)). Each ellipse was built as a trust-worthy index (for each product, the 95% closest point of the 500 virtual panels placed this product inside the ellipse). Therefore, when two ellipsesare disjointed, this means that the products were significantly different. The first Principal Component (PC1) explained 55% of data variability and the second Principal Component (PC2) explained 25% of the variation, giving a total explained variance of 80% for the first two principal components (Figure 1). The six discriminant descriptors are well represented on the first plan. Sensory characteristics are of increasing intensity from the left to the right side.

On the right side, breads made from "Odessa" and "Mélange de James" were characterised by the brown colour of their crumb and crust and by its intense roasted aroma (Figure 1(b)). On the left side, "Rouge de Bordeaux" and "Renan" were assessed with low scores for the same attributes. The second dimension oppose dacidic breads (lower right-hand corner) and breads with a long lasting flavour (at the top). Breads characterised by acidic notes were made using "Mélange de James" and "Odessa". Breads with a long lasting flavour were made from "Rouge de Bordeaux" and "Mélange IdF". The bread made with the red variety "Rouge de Bordeaux" was significantly different from the "Renan", "Melange de James" and "Odessa" and was characterised by its long lasting taste. This may be due to the lute in content. "Renan" was the only one that showed no specific sensory characteristics.

Nutritional Analyses

The nutritional composition was examined on all wheat samples (Table 3). Alpha-carotenoid and beta-carotenoid have not been detected in all wheat samples so that they do not figure on the table.

The two first axes of the PCA accounted for 65% of the variability (Figure 2). The first component separated the wheat grain with high vitamin B1, minerals (iron, magnesium) and total nitrogenous concentration from the light content (Figure 2(b)). The second component opposed wheat grain with high phytate content from grain with low phytate content. The results showed two groups of varieties with specific characteristics.

The first group composed of the varieties "Blé de Gâtines", "Odessa", "Renan" and "Rouge de Bordeaux" was characterised by a higher content of phytate, soluble fibre and vitamin E and by a low concentration of lutein and insoluble fibre (Figure 2(a)). This group showed a low mineral content which availability might be limited by the high phytate content as the ratio minerals/phytate is close to zero (Table 3). The second group was composed of the following varieties: "Touzelle", "Chiddamd' Automne" and "Mélange Île de France". This group was opposed to the first one because of its high lutein and insoluble fibre content and low phytate content. Minerals were in higher proportions and should be more available with regards to phytate concentration.

"Mélange de James" and "Dattel" did not show any specific pattern on the set of measurement scarried out and were not well differentiated on the first two axis of the PCA. Finally, the "Rouge du Roc" variety was characterised by a high content in iron, selenium, magnesium and total nitrogenous matter and a low vitamin E concentration. These minerals were available thanks to the low phytate content (ratio minerals/phytate of 34). This wheat variety has more insoluble than soluble fibre.

Based on these results, it seems surprising that "Rouge du Roc", which was derived a few years ago by the selectionby a farmer of a single plant with awns within a field of "Rouge de Bordeaux", showed a drastically different nutritional composition, particularly in terms of iron, vitamin and nitrogen content. Indeed, genetic diversity of "Rouge du Roc" has been assessed using microsatellite molecular markers and was somewhat different from that of "Rouge de Bordeaux" indicating that the selection of a few spikes within the large variability of "Rouge de Bordeaux" led to a drastic shift in the genetic composition of the "Rouge du Roc" population [START_REF] Thomas | On-Farm Dynamic Management of Genetic Diversity: The Impact of Seed Diffusions and Seed Saving Practices on a Population-Variety of Bread Wheat: On-Farm Crop Metapopulation of Bread Wheat[END_REF].

Whereas lutein content [START_REF] Leenhardt | Genetic Variability of Carotenoid Concentration, and Lipoxygenase and Peroxidase Activities among Cultivated Wheat Species and Bread Wheat Varieties[END_REF] was found around 1.5 µg/g on varietal mixes with HPLC, our data show concentration lower than 0.1 µg/g. Another study on the impact of genotype and environment factor on antioxidant Table 3. Nutritional analyses of 9 old varieties and 1 pure line. Some samples should not be assesed on gliadin content because of the presence of weevil (NA data). Numbers indicate the group identified by HAC. properties revealed concentration of lutein between 0.10 and 0.69 [START_REF] Lv | Effect of Genotype, Environment, and Their Interaction on Phytochemical Compositions and Antioxidant Properties of Soft Winter Wheat Flour[END_REF] which is still higher than values obtains in this study. There is much debate about the reliability of indirect measurement techniques, such as HPLC. For instance, when HPLC was compared with direct measurements of total carotenoid content in pastas using photo acoustic (PA) spectroscopy and colorimetry [START_REF] Dóka | Exploiting Direct and Indirect Methods for the Estimation of the Total Carotenoid Concentration in Dried Pastas[END_REF], HPLC was able to detect total carotenoid concentration in only six wheat samples among the 13 samples analysed, whereas PA spectroscopy and colorimetry showed measurable signals for all 13 samples. Indeed, the milling process, which is necessary for HPLC analysis, is known to affect wheat quality. In particular, violent milling might destroy carotenoid pigments. Incomplete extraction and insufficient concentration before analysis are also potential reasons for the absence of HPLC peaks. Here, almost all the samples show alpha-carotene and beta-carotenenot detectable. Moreover, indirect measurement techniques, such HPLC, are costly and time consuming methods, and are not well suited to large scale varietal screening. Direct measurement should therefore be preferred for this purpose [START_REF] Leenhardt | Genetic Variability of Carotenoid Concentration, and Lipoxygenase and Peroxidase Activities among Cultivated Wheat Species and Bread Wheat Varieties[END_REF].

Consumer Preferences

Among the 72 respondents at the "Fête de l'Humanité" in Paris, 2009, 51% of consumers ranked breads made from the variety "Rouge de Bordeaux" highest. Some sensory characteristics could be linked to overall preferences (Figure 3): the "slight bitterness", characteristic of the "Rouge de Bordeaux" variety, or the sheen of the "Touzelle" bread. Result analyses show two consumers' groups which were opposed on the bitterness appreciation. The first group seemed to prefer "Touzelle" and "Rouge de Bordeaux" breads for their dense texture and their sheen. They preferred bread with an intense roasted aroma and dark crumb. The second consumer group favoured the bitterness of Talisman's wheat bread.

The bread made from the "Rouge du Roc" variety was preferred at "Jambville 2010", where 44% of consumers ranked it highest. Consumers preferred the external appearance of the "Mix of Ile de France and Gâtine" wheat bread but preferred the taste and the textural properties of "Rouge du Roc" wheat bread.

Overall Discussion

The two first component of the MFA accounted for about 60% of the variability (Figure 4). The first dimension is defined by the minerals content (Mg, Zn, Se) with genotypes with high value of Mg and Zn on the left side ("Rouge de Bordeaux", "Renan") and genotype with high value of Se on the right side ("Mélange Idf", "Mélange de James") (Figure 4(a)).

The lutein content contributed to this first axes and was correlated with the roasted aroma, the sensory descriptors the most represented on the first dimension (Table 4). The second axes revealed a strong correlation between the sensory attribute "smoothy" and the insoluble fibres content. However, we cannot conclude on the link between nutritional and sensory characteristics from those results as only 6 genotypes have been investigate. The two accessions with red grain ("Rouge de Bordeaux" and "Rouge du Roc") were perceived as having a long lasting flavour by consumers at the two events and by a trained panel. Nutritional analysis showed specific nutrient content for "Rouge du Roc" with high iron and magnesium content. "Odessa" showed a weak aroma for most of the sensory descriptors when evaluated by consumers but not for the trained panel. Nevertheless, variations in cooking intensity between loaves evaluated by the trained panel were reported and this could explain the more intense roasted aroma of "Odessa". Consistently, "Odessa" showed a low level of minerals in the nutritional evaluation.

Dark (or red) grain varieties were characterized by their high vanillic and ferullic acid content, which is possibly linked to the vanilla odour [START_REF] Liu | Comparison of Antioxidant Activities of Different Coloured Wheat Grains and Analysis of Phenolic Compounds[END_REF]. Dark grain colour is due to anthocyanin stored in the pericarp and the testa [START_REF] Guo | Segregation Ratios of Colored Grains in Crossed Wheat[END_REF] [START_REF] Zeven | Wheats with Purple and Blue Grains: A Review[END_REF]. Anthocyanins are known to affect sensory characteristics. For example, they have been found positively correlated to astringency in wine grapes [START_REF] Brossaud | Bitterness and Astringency of Grape and Wine Polyphenols[END_REF]. These results show that consumers can perceive sensory differences that might be associated to dark and light coloured wheat grain. Breeding for high phytonutrients content appears possible as a highly significant genetic variability and a low genotype by environment interaction for magnesium content for a large set of varieties have been shown [START_REF] Oury | Genetic Variability and Stability of Grain Magnesium, Zinc and Iron Concentrations in Bread Wheat[END_REF]. The rate for modern varieties ranged from 600 to 1400 ppm whereas some exotic genotypes showed a higher content of 1890 ppm. Zinc content also showed a large genetic effect but genotype by environment interaction was more significant. The nutritional quality of organic wheat bread from diverse origins such as Einkorn or Emmer have been studied and showed a higher content of mineral and phytonutrients than bread wheat varieties [START_REF] Hussain | Quality of Organically Produced Wheat from Diverse Origin[END_REF]. Moreover, significant variation in the total carotenoid content on 48 bread wheat varieties have been found and suggested the possibility of increasing the nutritional value of bread products by selection [START_REF] Leenhardt | Genetic Variability of Carotenoid Concentration, and Lipoxygenase and Peroxidase Activities among Cultivated Wheat Species and Bread Wheat Varieties[END_REF].

The impact of genetic diversity on sensory properties has been assessed on cooked grain [START_REF] Starr | Sensory Profiles of Cooked Grains from Wheat Species and Varieties[END_REF] [23], flour [START_REF] Czerny | Important Aroma Compounds in Freshly Ground Wholemeal and White Wheat Flour-Identification and Quantitative Changes during Sourdough Fermentation[END_REF] and bread [START_REF] Brabant | Influence de la variété de blésur le goût du pain[END_REF]. The aromatic potential represented by wheat diversity has been studied by implementing a sensory profile on cooked wheat grain from cultivars of different wheat species such as Einkorn, Emmer and Spelt and from bread wheat cultivars [START_REF] Løje | Chemical Composition, Functional Properties and Sensory Profiling of Einkorn (Triticum monococcum L.)[END_REF]. The Einkorn sample showed significant differences on "oat/oatmeal porridge" and "sweet" attributes. In the case of bread, assessing the sensory differences should be carried out after the flour process.

When studying the sensory properties of breads from modern varieties of white flour type (T55), only very slight differences between varieties were found [START_REF] Brabant | Influence de la variété de blésur le goût du pain[END_REF]. This might be due to the choice of varieties which were genetically related and to the flour type, as highlighted in a study on aroma compounds on wholemeal and white wheat flour [START_REF] Czerny | Important Aroma Compounds in Freshly Ground Wholemeal and White Wheat Flour-Identification and Quantitative Changes during Sourdough Fermentation[END_REF]. This study showed that the wheat flour and the type of flour should be the main factors of wheat bread aroma production and showed a more intense aroma for whole wheat flour. Since bran is characterised by its mineral and phytonutrient content, one hypothesis is that sensory characteristics are related to micronutrients [START_REF] Fujino | Other Grain Components[END_REF]. Alkaline metals, such as potassium, or alkaline earths, such as calcium and magnesium, can be found in in the form of phosphates, carbonates, chlorides and sulphates and may contribute to flavour. Another potential source of flavour variability in wheat breads is lutein, which should affect flavour secondary by limiting the development of hexanal, a compound responsible for flavour and odour problems [START_REF]Supplément technique INBP n 83-Le goût du pain[END_REF]. The nutritional improvement of bread wheat should therefore contribute to the improvement of the sensory quality of bread.

On a sensory methodology point of view, the use of a consumer panel to objectively evaluate sensory quality should be discussed as it is not common in sensory analysis field. The descriptive consumer test showed the consumers' ability to explain their preferences consistently as they were able to differentiate red accessions at each event. Sensory profile obtained from consumers can show consensus and reproducibility with a large number of consumers [START_REF] Husson | Which Value Can Be Granted to Sensory Profiles Given by Consumers? Methodology and Results[END_REF]. Developing a specific descriptive sensory test for use with consumers will be of interest since such a test would offer a quicker and less expensive option than conventional profiling with a trained panel. Moreover, it is a way of bringing consumers closer to the farmers and the agricultural sector and of making them aware of the issues of agricultural production. Such methods are being developed, one of the most promising being napping. It relies on identification of the sensory distances perceived and offers greater flexibility [START_REF] Perrin | Comparison of Three Sensory Methods for Use with the Napping ® Procedure: Case of Ten Wines from Loire Valley[END_REF].

Conclusions

The evaluation of the sensory impact of wheat varieties on bread is a complex task as all the varieties studied have to be grown in the same location, in the same year and under the same farming practices to eliminate environmental effects. Since the baking process has a great impact on the sensory qualities, bread from different varieties must be baked by the same baker in a single batch. This study focuses on the sensory characteristics of a small set of population varieties but further investigations are needed on individual impact and interaction of bread main parameters.

Differences among varieties were perceived even after flour processing by both experts and consumers. It seems that nutritional and sensory value of wheat bread could be improved by selecting specific genotypes.

A large number of landraces and historic varieties can be obtained from the national and international gene banks [START_REF] Roussel | SSR Allelic Diversity Changes in 480 European Bread Wheat Varieites Released from 1840 to 2000[END_REF]. Some of them, including "Rouge de Bordeaux", "Rouge du Roc", "Touzelle", "Mélange de James", and several others that were tested in this study, have been grown, selected and multiplicated by farmers for several years to adapt them to their environments and agricultural practices. They are now cultivated on larger plots and assessed in participatory plant breeding programs [START_REF] Rivière | Response to Farmer Mass Selection in Early Generation Progeny of Bread Wheat Landrace Crosses[END_REF]. There is a strong demand for more information on their baking, sensory and nutritional qualities.

This study paves the way for a number of new initiatives in France that experiment the sensory qualities of breads obtained from a large range of wheat varieties. A database including genotype by environment analyses is needed to help breeding efforts for quality.

Figure 1 .

 1 Figure 1. (a) Confidence ellipse of the mean point of the PCA model of the trained panel sensory evaluation of 6 wheat varieties; and (b) variable factor map.

Figure 2 .

 2 Figure 2. (a) Individual factor map of the PCA model of nutritional analyses and (b) correlation circle.

Figure 3 .

 3 Figure 3. (a) Confidence ellipse of the mean point of the PCA model of the consumer sensory evaluation of 3 wheat varieties at the "Fête de l'Humanité 2009" event and (b) variable factor map, (c) PCA model of the consumer sensory evaluation of 3 wheat varieties at the "Fête du Parc Naturel Regional du Véxin 2010" event and (d) variable factor map.

Figure 4 .

 4 Figure 4. (a) Individual factor map of the MFA on nutritional and sensory data and (b) correlation circle.

Total nitrogenousmatter Gliadin (g/100g) Magnesium (mg/100g) Iron (mg/100g) Zinc (mg/100g) Selenium (mg/100g) Lutein (mg/100g) Vitamin B1 (mg/100g) Vitamin E (mg/100g) Phytate (mg/100g) Soluble fibres (g/100g) Insolubles fibres (g/100g) Mélange de

	James	10.98	NA	119.3	2.8	3.4	4.4	0.04	0.29	3.15	1150	2.8	11.8
	Touzelle (2)	10.06	NA	124.9	2.5	3.3	3.2	0.07	0.3	2.75	873	1.3	10.4
	Rouge de Roc	13.57	5.05	157.5	3.9	4.9	12.4	0.04	0.39	2.25	1097	1.3	9.7
	Chiddam
	d'automne	10.83	4.72	130.7	3.1	3.7	6.7	0.05	0.29	3.13	1021	0.7	14.3
	(2)
	Ile de France (2)	11.09	5.43	122.9	3	3.2	4.3	0.09	0.33	3.22	959	2.5	10.8
	Blé de Gâtine (1)	11.33	6.42	119.3	2.8	4.6	8.5	0.03	0.3	3.95	1370	2.6	10.1
	Renan (1)	11.59	NA	134.8	2.8	4.9	0.017	0.04	0.31	4.03	1216	2.2	11.1
	Rouge de Bordeaux (1)	11.27	5.36	141.8	3	5	0.006	0.04	0.31	2.36	1301	3.6	11.6
	Dattel	12.3	NA	128.3	3.3	4.1	10.6	0.05	0.34	3.27	1225	3.6	9.2
	Odessa (1)	10.67	5.33	136.4	3	4.9	0.011	0.04	0.35	4.02	1245	2.6	8.6

Table 4 .

 4 Contribution, correlation and p-value of the AFM on nutritional and sensory data as groups (NS: Non Significant).

	Variable		Dim1			Dim2
		Contribution	Correlation	p-Value	Contribution	Correlation	p-Value
	Air size area	3.80	0.52	NS	9.93	-0.68	NS
	Salty	5.38	0.62	NS	8.53	-0.63	NS
	Acid	3.19	0.47	NS	6.91	-0.572	NS
	Smoothy	0.36	-0.16	NS	15.66	0.86	**
	Roasted aroma	8.61	0.78	NS	1.91	0.30	NS
	Sweety	0.87	-0.24	NS	0.001	-0.007	NS
	Crusty	8.85	0.795	NS	0.003	0.013	NS
	Crust colour	4.12	0.54	NS	3.18	0.39	NS
	Wheat grain aroma	2.96	0.46	NS	4.64	0.47	NS
	Long lasting taste	5.36	0.62	NS	11.41	0.74	NS
	Crumb colour	7.70	0.74	NS	0.51	-0.16	NS
	Total nitrogenous matter	2.68	-0.49	NS	5.90	0.59	NS
	Magnesium	10.04	-0.95	**	0.85	-0.22	NS
	Iron	1.53	-0.37	NS	1.00	0.24	NS
	Zinc	9.51	-0.92	**	1.08	-0.25	NS
	Selenium	9.07	0.90	**	2.38	0.38	NS
	Lutein	4.90	0.66	NS	0.48	0.17	NS
	Vitamin B1	0.15	-0.11	NS	4.11	-0.49	NS
	Vitamin E	0.04	0.06	NS	3.88	-0.48	NS
	Phytate	7.27	-0.81	NS	0.02	0.03	NS
	Soluble fibre	3.54	-0.56	NS	4.07	0.49	NS
	Insoluble fibre	0.04	-0.06	NS	13.55	0.90	**

(modern variety) Consumer test X X X X X X X X Sensory profile X X X X X X Nutritional analyses X X X X X X X X X

Acknowledgements

The authors would like to thank the members of "Nature & Progrès" for their contribution to the implementation of consumer tests and specially Adèle Hospital who participate to all the consumer tests, our baker partners for their involvement in the experiments. This work has been conducted with the financial support of the Région Ile de France (Partenariat Institutions Citoyens pour la Recherche et l'Innovation, PICRI 2007-2011).


Nicolas Kaspric
email: nicolas.kaspric@clermont.inra.fr

Matthieu Reichstadt

Brigitte Picard

Jérémy Tournayre

Muriel Bonnet
email: muriel.bonnet@clermont.inra.fr

Protein Function Easily Investigated by Genomics Data Mining Using the ProteINSIDE Online Tool

Keywords: Online tool, workflow, protein function, protein interaction, protein secretion, gene ontology, networks, ruminant, genomics

Nowadays, genomic and proteomic studies produce vast amounts of data. To get the biological meaning of these data and to generate testable new hypothesis, scientists must use several tools often not designed for ruminant studies. Here we present ProteINSIDE: an online tool to analyse lists of protein or gene identifiers from well-annotated species (human, rat, and mouse) and ruminants (cow, sheep, and goat). The aims of ProteINSIDE modules are to gather biological information stores in well-updated public databases, to proceed to annotations according to the Gene Ontology consortium, to predict potentially secreted proteins, and to search for proteins interactions. ProteINSIDE provides results from several software and databases in a single query. From a list of identifiers, ProteINSIDE uses orthologs or homologs to extend analyses and biological information retrieval. As a tutorial, we presented how to launch, to recover, to view, and to interpret the results provided by the two types of analysis available with ProteINSIDE (basic and custom analyses). ProteINSIDE is freely available using an internet browser at www.proteinside.org. The results of this article are provided on the home page of ProteINSIDE website as the example of an analysis result.

INTRODUCTION

Given the increasing amount of genomic and proteomic data produced even in ruminants [START_REF] Chaze | Proteome dynamics during contractile and metabolic differentiation of bovine foetal muscle[END_REF][START_REF] Picard | Quest for Novel Muscle Pathway Biomarkers by Proteomics in Beef Production[END_REF][START_REF] Taga | Cellular and molecular large-scale features of fetal adipose tissue: is bovine perirenal adipose tissue brown?[END_REF], there is a challenge for the bioinformatic data processing, which has not yet been completely solved [START_REF] Schmidt | Bioinformatic analysis of proteomics data[END_REF]. Such bioinformatic data processing has to proceed to data gathering and database searching in order to produce a functional interpretation of large datasets. For this purpose, workflows integrating several bioinformatics analyses are now available [START_REF] Wong | IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks[END_REF][START_REF] Pache | NetAligner--a network alignment server to compare complexes, pathways and whole interactomes[END_REF][START_REF] Renaud | DroPNet: a web portal for integrated analysis of Drosophila protein-protein interaction networks[END_REF][START_REF] Tuncbag | SteinerNet: a web server for integrating 'omic' data to discover hidden components of response pathways[END_REF] and were developed to mine dataset from specific species (BioMyn [START_REF] Ramirez | Novel search method for the discovery of functional relationships[END_REF] for human, DroPNet [START_REF] Renaud | DroPNet: a web portal for integrated analysis of Drosophila protein-protein interaction networks[END_REF] for Drosophila, TAIR [START_REF] Lamesch | The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools[END_REF] for Arabidopsis thaliana, EcoCyc [START_REF] Keseler | EcoCyc: fusing model organism databases with systems biology[END_REF] for Escherichia coli …) or to identify candidate genes related to diseases as ToppGene [START_REF] Chen | ToppGene Suite for gene list enrichment analysis and candidate gene prioritization[END_REF] or NetPath [START_REF] Kandasamy | NetPath: a public resource of curated signal transduction pathways[END_REF]. The few workflows currently used for the bioinformatics data processing of ruminant datasets are multispecies. Consequently, the data source of the results proposed is not available because of the privacy of databases (as the licensed software Pathway Studio [START_REF] Nikitin | Pathway studio--the analysis and navigation of molecular networks[END_REF] or Ingenuity Pathway Analysis (www.ingenuity.com; Redwood City, CA, USA). An alternative for scientists working with ruminant datasets is to use dedicated and complementary bioinformatics tools implemented as web services. These tools are dedicated to one type of analysis, as for example the annotation according to the Gene Ontology (GO) [START_REF] Ashburner | Gene ontology: tool for the unification of biology. The Gene Ontology Consortium[END_REF], the prediction of signal peptide to identify putative secreted proteins [START_REF] Petersen | SignalP 4.0: discriminating signal peptides from transmembrane regions[END_REF], or the molecular interactions identification [START_REF] Aranda | PSICQUIC and PSISCORE: accessing and scoring molecular interactions[END_REF] and visualization as networks [START_REF] Lopes | Cytoscape Web: an interactive web-based network browser[END_REF][START_REF] Smoot | Cytoscape 2.8: new features for data integration and network visualization[END_REF]. Whatever the analysis carried out, the first step is to connect a protein name to a unique identifier (ID). Conversely to gene names that have been standardized, protein names or IDs can differ between databases or tools, especially for ruminant data that remains to be largely curated in most of databases. Thus, the use of several bioinformatics tools to mine ruminant datasets leads to a substantial loss of information and time.

A strategy to perform a systematic and integrative analysis of biological protein information from ruminant datasets is to develop an online workflow that integrates several analysis steps in one package and from a unique ID. Thus, we propose ProteINSIDE [START_REF] Kaspric | Protein function easily investigated by genomics data mining using the ProteINSIDE web service[END_REF], a web service dedicated to a systematic and integrative analysis of protein's biological information from ruminant datasets. Unlike human, mouse or rat, ruminant species are less annotated and protein sequences or information are not always curated. Often, scientists working with ruminant use orthologs or homologs with the aim to increase the meaningful biological contexts for proteins thanks to knowledge available in well-annotated species. Thus, ProteINSIDE was designed to run using lists of protein or gene IDs from 6 species (cow, sheep, goat, human, rat, and mouse) to annotate biological and molecular functions and cellular location, predict secreted proteins, search for interactions between proteins within and/or outside a dataset. The objective of this article is to propose a tutorial to use ProteINSIDE and interpret generated results.

METHODS

This

section lists necessary equipment, ProteINSIDE resources and describes the dataset used to assess the functionalities of the tool.

ProteINSIDE's features

ProteINSIDE is an online workflow with an interface devoted to accessible and fully customisable analyses from lists of protein or gene IDs. Registered users have access to an analyses manager to run and save analysis, and visualise the results. Unregistered users can use ProteINSIDE, but there is no analyses manager and analyses are deleted each month. ProteINSIDE is divided into three parts: the workflow, the database, and the web interface (Figure 1).

The web interface, designed to easily use ProteINSIDE, helps the user to create the analyses, to have access to the results thanks to a balance between technical functionalities and visual elements, and to inform about updates (Figure 1). ProteINSIDE proposed two types of analysis to be launched: the basic analysis (automatic settings) and the custom analysis (user's settings). There is also a pre-set analysis for registered users only who want to make a new analysis with settings of a previous analysis. The basic analysis performs a:  Functional annotation using GO terms by querying QuickGO database [START_REF] Binns | QuickGO: a web-based tool for Gene Ontology searching[END_REF] without electronic annotation.  Prediction of secreted proteins using SignalP [START_REF] Petersen | SignalP 4.0: discriminating signal peptides from transmembrane regions[END_REF] and TargetP [START_REF] Emanuelsson | Predicting subcellular localization of proteins based on their N-terminal amino acid sequence[END_REF] software. We improve the prediction by giving GO terms related to the cellular location of the protein and the processes of secretion.  Search of proteins interactions curated and listed in IntAct [START_REF] Orchard | The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases[END_REF], UniProt [START_REF] Uniprot | Activities at the Universal Protein Resource (UniProt)[END_REF], and BioGrid [START_REF] Chatr-Aryamontri | The BioGRID interaction database: 2015 update[END_REF] databases.

The custom analysis performs programs and their options that have to be selected by the user in order to:

 Perform a functional annotation using GO terms from QuickGO, with the options to select also electronic annotations (predicted and scripted annotations), and to generate a GOTree view of linked GO terms (pathways of functional annotation).  Predict secreted proteins with the option to increase the software's sensitivity of prediction and by this way to increase the number of predicted proteins, however with a higher number of false positive results.  Search for protein interaction within (core network) and outside (extended network) the uploaded dataset. Options propose to select interactions stored within 1 to 31 databases gathered by the PSICQUIC website [START_REF] Aranda | PSICQUIC and PSISCORE: accessing and scoring molecular interactions[END_REF]. User can select the databases depending on the type of interactions (PPi, Nucleic acid-Protein interaction (NPi), and Smallmolecule-Protein interaction (SPi)) and the data (curated, predicted, curated according to the IMEx project [START_REF] Orchard | Protein interaction data curation: the International Molecular Exchange (IMEx) consortium[END_REF] or the MIMIx curation [START_REF] Orchard | The minimum information required for reporting a molecular interaction experiment (MIMIx)[END_REF]). PSICQUIC service or some databases could be offline, that's why the status of each website is indicated in the table.

To submit an analysis, users either directly paste a list of IDs or upload a file of IDs. Inputs can be protein (e.g., ADIPO_HUMAN) or gene (e.g., ADIPO or gi|62022275) ID, or protein accession numbers (e.g., Q15848) from six species: cow, human, rat, mouse, sheep, and goat (Figure 2). A new analysis is run directly or is placed on a waiting list if the workflow is overloaded. Uploaded data and results remain confidential. In addition to the web interface, ProteINSIDE is composed of a database and a workflow.

The database (invisible to any user) collects and stores the information required for the proper functioning of ProteINSIDE. It stores analysis settings and results to reduce server load (Figure 1). The database stores also a gathering of biological information from the NCBI [START_REF] Coordinators | Database resources of the National Center for Biotechnology Information[END_REF] (Gene, Protein, and HomoloGene for known orthologous proteins between the 6 species) and UniProt [START_REF] Uniprot | Activities at the Universal Protein Resource (UniProt)[END_REF] databases (for the ID Mapping module), and QuickGO [START_REF] Binns | QuickGO: a web-based tool for Gene Ontology searching[END_REF] and AmiGO [START_REF] Carbon | AmiGO: online access to ontology and annotation data[END_REF] (for the GO annotation module). A script updates automatically and monthly the database by extracting IDs, homologs, biological function, FASTA sequence, and other information from the latest releases of these databases.

The workflow uses uploaded data. It is a combination of Perl and R scripts to query databases, recover protein data, perform calculations and run algorithms for signal peptide predictions and network visualisation (Figure 1). The workflow is invisible to any user. The workflow is composed of 4 parts: the "ID Mapping", the search of annotations according to GO, the prediction of secreted proteins, and the search of protein-protein interactions (PPi). The workflow always starts by the ID Mapping program which searches the biological information available for each protein or gene of the input within the ProteINSIDE database. Gathered biological information is required to run the 3 other modules of the workflow: "Gene Ontology", "Secreted Proteins", and "Protein Interaction" (described in the "Results" section of this article). The GO program queries QuickGO and ProteINSIDE's databases to perform the functional annotation. The GO program analyses over-and under-represented terms to highlight the most relevant GO terms related to the input. These statistical calculations are made with an R script performing a Fisher´s exact test (functional enrichment first proposed by FatiGO [START_REF] Al-Shahrour | FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes[END_REF]) and the resulting p-value is corrected or not by the Benjamini & Hochberg (BH) test [START_REF] Benjamini | Controlling the False Discovery Rate -a Practical and Powerful Approach to Multiple Testing[END_REF]. The prediction of secreted proteins is made using a local version of SignalP (version 4.1) that looks for a signal peptide on amino acid sequence of each protein [START_REF] Petersen | SignalP 4.0: discriminating signal peptides from transmembrane regions[END_REF] (cutoff of 0.45 and 0.34 for SignalP prediction, in the basic and custom analysis with the sensitive option selected, respectively; for more information see the tutorials of SignalP a . To ascertain that proteins are secreted, ProteINSIDE uses TargetP [START_REF] Emanuelsson | Predicting subcellular localization of proteins based on their N-terminal amino acid sequence[END_REF] (version 1.1) to predict the cellular location of each protein. ProteINSIDE uses a pre-set cutoff option to get a significant prediction (higher than 95%) according to TargetP instructions b . Protein interactions are searched using PSICQUIC service [START_REF] Aranda | PSICQUIC and PSISCORE: accessing and scoring molecular interactions[END_REF] and statistical calculations are made with an R script and the package "tnet" [START_REF] Tore | Structure and Evolution of Weighted Networks[END_REF]. ProteINSIDE performs sequence alignment thanks to a local version of NCBI BlastP [START_REF] Altschul | Basic local alignment search tool[END_REF] against UniProt/Swissprot databases [START_REF] Uniprot | Activities at the Universal Protein Resource (UniProt)[END_REF][START_REF] Artimo | ExPASy: SIB bioinformatics resource portal[END_REF]. Lastly and as an additional valuable tool, ProteINSIDE lists in one table all known IDs for an input of proteins or genes thanks to the ProteCONVERT module. This list is the result of a search and of a gathering of IDs thank to the ProteINSIDE biological database. Only registered users have access to the ProteCONVERT module. First, enter a name for the analysis and select the species of study. There are two ways to submit a protein or gene list; you can use an input file or directly paste your IDs. The input file must be less than 250 kb and the file format must be specified. There is also a "Sample" button that loads parameters for an example analysis. Once everything is filled, click on the button "Run the job" to submit.

Implementation

ProteINSIDE is freely available online at www.proteinside.org and doesn't require an installation on a computer. ProteINSIDE is completely adapted for any internet browser and tablet. We recommend multiprocessors computer with at least 2 GB of ram to get better performances for huge network visualization and filtering.

The web interface is programmed in PHP, HTML, and JavaScript. The workflow has been completely programmed in Perl (version 5.10.1; CPAN modules (Comprehensive Perl Archive Network) and BioPerl [START_REF] Stajich | The Bioperl toolkit: Perl modules for the life sciences[END_REF]) and R scripts (version 3.0.1). The database was made in MySQL (version 5.5) (Figure 1).

Sample dataset

We have created a dataset to assess ProteINSIDE performances. This dataset is composed of the UniProt accession numbers of 133 proteins (Table 1): 34 proteins related to the glycolysis cycle, 11 proteins from the respiratory chain, 5 proteins from the tricarboxylic acid cycle (TCA), 79 hormones or secreted proteins, and proteins with very specific functions unrelated to the others. We also included a duplicated ID among proteins of the glycolysis to verify its recognition by ProteINSIDE. ProteINSIDE is able to detect duplicate protein even if the IDs are different: a Gene Name, a UniProt accession number, and a Gene Identifier related to a same protein will be taken into account as a single protein.

We have created this dataset on bovine species, but the number of annotations and PPi weren't sufficient for a complete overview of the functionalities of ProteINSIDE. Then, we used the same proteins using human IDs to test ProteINSIDE with the basic and the custom analyses (Table 1).

RESULTS

Here we present how to run a basic or custom analysis and how to view the results. We explain how to interpret the results and we discuss the relevance of biological information extracted by ProteINSIDE for our sample dataset of 133 proteins.

Setting up a Basic Analysis: a standard overview of a dataset

ProteINSIDE performs a basic analysis (in which settings are locked and the workflow provides GO terms, list of putative secreted proteins, and PPi data from IntAct [START_REF] Kerrien | The IntAct molecular interaction database in 2012[END_REF], UniProt [START_REF] Uniprot | Activities at the Universal Protein Resource (UniProt)[END_REF], and BioGrid [START_REF] Chatr-Aryamontri | The BioGRID interaction database: 2013 update[END_REF] databases). A basic analysis gives a complete overview of a dataset. To set up a basic analysis, user has to follow these steps (Figure 2):  Click on "Basic Analysis" menu on the homepage of ProteINSIDE  Fill in "the job name" box  Select the species of study (the same species as the uploaded IDs)  Upload an input file or directly paste IDs  Click on the "Run the job" button to submit a new analysis

The analysis status is indicated by the colour of a button: red for "analysis on the waiting list", yellow for "the analysis is running" and green "analysis done". The blue globe is the link to access to the online results:

 Click on the blue globe button to view the results (or use the trash to delete them)  Visualise the results summary produced by the four modules of analysis on the first default page (entitled "Results Summary", Figure 3)  Navigate between the four module's results pages by clicking on the module's name on the toolbar menu. For our sample dataset, the "Results Summary" page reported that all 133 proteins were recognized by ProteINSIDE and the protein in duplicate was identified and excluded from the analysis (Figure 3). Thus, 132 proteins were submitted to the four modules of analysis.

Analyses and data

The "ID Mapping's" module aimed to retrieve and gather basic biological knowledge, results are directly viewed on the "ID Resume" web page of ProteINSIDE. This module compares each submitted IDs to the database of ProteINSIDE to ascertain a match with genes or proteins from human, rat, mouse, cow, sheep or goat species. The local biological database of ProteINSIDE is a combination of NCBI Gene/Protein, NCBI HomoloGene [START_REF] Coordinators | Database resources of the National Center for Biotechnology Information[END_REF], and UniProt [START_REF] Uniprot | Activities at the Universal Protein Resource (UniProt)[END_REF] databases. These databases were chosen because data are easily extractable, curated and daily updated. For each uploaded ID, ProteINSIDE obtains and summarises as a downloadable table (Figure 4): gene or protein ID, gene or protein name, a summary of protein function, gene chromosomal location, and information on tissue expression and cellular location. The module also recovers the protein sequence of each input ID. Each protein and gene ID listed on this web page are linked to corresponding UniProt and NCBI web pages. FASTA amino acid sequences of each input are also downloadable.

The module dedicated to the functional annotation according to the GO consortium, produces results that are viewed on the "GO" web page of ProteINSIDE. ProteINSIDE imports GO terms by querying the QuickGO database [START_REF] Huntley | QuickGO: a user tutorial for the webbased Gene Ontology browser[END_REF]. QuickGO was chosen because of its daily update, accessibility, and performances. ProteINSIDE only imports GO terms that have been selected by evidence codes (GO Inferred from Electronic Annotation codes (IEA) are excluded by the basic analysis) and confirmed by curators. The GO script of ProteINSIDE analyses over-and under-represented terms to identify the most relevant and the most specific terms associated with the uploaded list. Within a GO, ProteINSIDE compares the number of genes or proteins from the dataset to the total number of gene products (for a species) declared in the AmiGO database [START_REF] Carbon | AmiGO: online access to ontology and annotation data[END_REF] to provide a coverage frequency, and thus, to identify the most representative pathways associated to a dataset. The result is viewed on the "GO" web page of ProteINSIDE as tables and diagrams. Three tables (Figure 5) report the GO terms that annotated two or more proteins (Figure 5-B), the GO terms that annotate one protein (Figure 6-C), as well as all GO terms for a protein (Figure 5-D). Each annotation is informed with an evidence code (that reflects the type of experimental evidence or analysis to describe an annotation between a GO term and a gene product) and the database source. Tables are automatically sorted by the best enrichment p-value to help the user to view the most significant GO terms related to a dataset. Tables can also be sorted by ontology group, p-value range for enrichment, GO term description, gene name or any input IDs (Figure 5B). From the sample dataset of 132 proteins, ProteINSIDE annotated 128 proteins with 624 GO terms. The most significant enriched GO terms is "hormone activity" (that annotated 31 proteins over the 79 expected; not shown) and "glycolytic process" (that annotated 27 proteins over the 33 expected; Table 1). The low number of annotated proteins may be related to our choice to use only GO terms that have been confirmed by curator in the basic analysis. This means that the basic analysis doesn't use the annotation with IEA (Inferred by Electronic Annotation) evidence code. However, the option to use IEA is provided in the custom analysis to extend the annotations. The module that aims to predict potentially secreted proteins provides results on the "Secreted Proteins" web page of ProteINSIDE (Figure 6). To identify proteins that are putatively secreted, ProteINSIDE first predicts the presence of a signal peptide on a protein sequence (imported by the "ID Mapping" module) through a local version of the SignalP tool [START_REF] Petersen | SignalP 4.0: discriminating signal peptides from transmembrane regions[END_REF]. SignalP was chosen because of its high prediction score in comparison with other available tools [START_REF] Emanuelsson | Locating proteins in the cell using TargetP, SignalP and related tools[END_REF][START_REF] Caccia | Bioinformatics tools for secretome analysis[END_REF]. To ascertain the prediction, a local version of TargetP software [START_REF] Emanuelsson | Predicting subcellular localization of proteins based on their N-terminal amino acid sequence[END_REF] predicts the subcellular location of the proteins. ProteINSIDE also checks the subcellular location of the protein using UniProt source to confirm TargetP results. As a final verification step, ProteINSIDE selects the GO terms related to secretory pathways for each SignalP prediction. For this purpose, we have selected about 1,000 GO terms related to secretion (monthly updated) as for example: secretion, vesicle, or extracellular region. This four-step analysis improves the reliability of proteins proposed to be secreted thanks to a signal peptide and to our knowledge is unique to ProteINSIDE [START_REF] Caccia | Bioinformatics tools for secretome analysis[END_REF]. However, proteins are also secreted by pathways that do not involve signal peptide such as: endosomal recycling, plasma membrane transporter, membrane flip-flop, and membrane blebbing including the formation of vesicles or exosomes [START_REF] Nickel | The mystery of nonclassical protein secretion. A current view on cargo proteins and potential export routes[END_REF]. Thus, ProteINSIDE was designed to predict the proteins secreted by other pathways, by gathering the data of subcellular location provided by UniProt, GO terms, and TargetP results (Figure 6-B). From our sample dataset of 132 proteins, ProteINSIDE has predicted 85 proteins as potentially secreted outside the cell by a signal peptide, among them 78 over the 79 proteins that were expected (Table 1). This lack of perfect prediction can be explained by the false positive and false negative prediction rates of SignalP, as already evaluated by Petersen et al. (Supplementary materials and methods of [START_REF] Petersen | SignalP 4.0: discriminating signal peptides from transmembrane regions[END_REF]). Over the 85 predicted secreted proteins, 65 were also annotated by GO terms related to the secretion. The subcellular locations of 81 proteins were both confirmed by TargetP and UniProt source. Additionally, 30 proteins were predicted to be secreted without signal peptide.

The fourth module is dedicated to PPi analysis and results are viewed on "Protein Interaction" web page of ProteINSIDE. PPi identification and visualisation within a network conveyed how various genes or proteins contribute to cellular or metabolic processes. ProteINSIDE uses the PSICQUIC service [START_REF] Aranda | PSICQUIC and PSISCORE: accessing and scoring molecular interactions[END_REF] to identify PPi and imports PPi identified by their "interaction detection methods" with experimental proofs and confirmed by curator. The basic analysis identifies PPi within the uploaded dataset (core network) using the preselected databases IntAct, UniProt, and BioGrid. These PPi databases were chosen as a default option because there are daily updated and reviewed by curators as well as by the curation processes of the IMEx project (that ensures reliable interactions data using experts and curation rules shared between many interaction databases [START_REF] Orchard | Protein interaction data curation: the International Molecular Exchange (IMEx) consortium[END_REF]) or MIMIx (a guideline of the minimum information required for reporting a molecular interaction experiment, thus advising the user on how to use the interaction data [START_REF] Orchard | The minimum information required for reporting a molecular interaction experiment (MIMIx)[END_REF]). Moreover, BioGrid is the biggest PPi database that has its own curation workflow (more than 740000 curated PPi) and is not a partner of IMex curation program. IntAct is another big PPi database with more than 380000 PPi currated according to IMex and MIMIx curation rules and that are often listed in several databases. UniProt is a major database dedicated to the study of proteins. Thus, it possesses its own curated PPi but in lesser amounts compared to the two other specialized databases (less than 13 000 PPI; UniProt is a partner of IMEx project). By using 3 databases as a default option, the aim of ProteINSIDE is to favour the use of multiple PPi databases in order to improve the PPi data gathering [START_REF] Martha | Constructing a robust protein-protein interaction network by integrating multiple public databases[END_REF]. These 3 PPi databases ensure the good recovery of known interactions for an overview of interactions within or/and outside of a new dataset. Then, ProteINSIDE lists pairs of proteins known to interact between each other in a downloadable table (Figure 7) and constructs a network (Figure 8) using the PPi identified within the uploaded list. The dynamic network is available by using the "Cytoscape" button on the "Protein Interaction" web page ("Dynamic Cytoscape view of PPi", Figure 7-A). Within the network, edges are experimental detection methods used to identify the PPi. Consequently, several edges may link two proteins. Network can be sorted by the number of interactions by node, the proximity of a node to other nodes (closeness centrality; CC) and the shortest paths between nodes (betweenness centrality; BC) (Figure 8-A). These centralities criteria were already proven to be efficient to select key nodes/proteins within a pathway [START_REF] Hwang | A protein interaction network associated with asthma[END_REF]. From our sample dataset of 132 proteins, ProteINSIDE has identified 29 PPi that involved 28 different proteins (Figure 7-B). As expected from our small dataset, ProteINSIDE linked, within sub networks, proteins involved in glycolysis, TCA or respiratory chain as protein complexes (partially on Figure 8-B).

Setting up a Custom Analysis: an addedvalue provided by the extension of the analysis

We made a custom analysis using the same major settings as for the basic analysis with additional options (GO network, GO electronic annotations, and extension of PPi to proteins outside of the dataset in the same species, extended network). To set up the custom analysis, user has to follow these steps (also explained by Figure 2):  Click on the "Custom Analysis" menu on the homepage of ProteINSIDE  Fill in "the job name" box  Select the species of study (the same species as the uploaded IDs)  Upload an input file or directly paste IDs Then, user has to select the settings of either all or only one module of analysis on the section "4" of the page, by following these steps (Figure 9): B andC) GO terms are also sorted as two dynamic tables (a table for GO terms that annotate more than one protein on the dataset -B, and a second table for the GO terms with a single protein annotated -C). Tables can be sorted by GO term, function, protein name or ID, gene name, number of annotations, annotation frequency or annotation enrichment. (D) A third table lists all GO codes for a given protein. Users can move the cursor over a protein to be informed about the evidence code and the database source of the GO annotation (B; where IDA means "Inferred from Direct Assay").

 Select Gene Ontology module

o Select "GO electronic annotation (IEA)" if you aim to use GO annotation inferred from electronic annotation. o Select "Gene Ontology Tree network" to view linked GO terms  Select Signal peptide module to use the basic cutoff value of prediction  Select Protein -protein interaction module o Select "Protein -protein interaction custom analysis" o Select "Extend PPi research using protein outside of the dataset", if wanted o Select "Human species" to analyse PPi using data available in Human, for example o Select either the 3 most used databases (IntAct, UniProt, and BioGrid as used in the basic analysis) or from 1 to 31 databases (PPi are daily updated in database)

Alternatively, user can load automatically the same settings as those already used in a previous custom analysis, by clicking on the "pre-set" button.

At the completion of the custom analysis, the "ID Resume" web page provides the same information than the basic analysis (Figure 3).

Within the GO module, the choice to use the electronic annotation option has increased both the number of annotated proteins (132 rather than 128 without IEA in the basic analysis) and the number of annotations by around 40% (1080 unique GO terms rather than 624 in the basic analysis). Thanks to IEA option, ProteINSIDE correctly retrieved the 33 expected proteins related the glycolytic process and the 79 proteins related to a hormone activity (Table 1). The GOTree network linked 570 GO terms. A link between 2 terms is represented by an "is_a" relation: "Diuretic hormone activity" linked to "Hormone activity" means that "Diuretic hormone activity" is a "Hormone activity" pathway. The network can be sorted by ontology group, by p-value range (to select and to link only the most enriched GO terms), by the number of directly linked terms or also by the number of GO terms linked together (to select group of GO terms involved in the same biological function). From our sample dataset, we have chosen to illustrate the GO tree of the "Molecular Function" group (Figure 10). In this visualisation, squares with dark red colour were GO terms which have annotated the highest number of proteins. Among them and as expected the GO:0005179 with the best p-value and the darkest red colour was "Hormone activity", in agreement with the over representation of hormones in our sample dataset.

The "Secreted Proteins" module has predicted the same 85 proteins as the basic analysis as being secreted. By comparison with the basic analysis, the use of IEA option has allowed to confirm this prediction for 82 proteins that were also annotated with GO terms related to a "secretion" function.

By comparison with the basic analysis, the settings selected within the "Protein Interaction" module provided PPi within the dataset (between proteins of the dataset, as the basic analysis) and PPi between proteins from the dataset and outside of the dataset. For the extended network, ProteINSIDE retrieved 688 PPi made by 500 proteins. Among them, 61 proteins were from our uploaded sample dataset. By using PPi outside of the dataset in Human species, we got 95% more PPi that involved 60% more proteins from the sample dataset than the PPi recovered with the basic analysis. The extended network (Figure 11) highlighted major subnetworks related to the respiratory chain (Figure 11-A), hormone activity such as signalization pathways of adipokines (Figure 11-B), growth hormone (Figure 11-C), thyroid hormones (Figure 11-D), glycolysis (not highlighted), and carbohydrate metabolism (not highlighted). This is consistent with the over selection of proteins from glycolysis, TCA or hormones or adipokines. Betweenness and closeness centralities were used to sort the most central proteins of this extended network (Figure 11-E). By this way, we identified 22 highly central proteins, 13 of them coming from the uploaded sample dataset and involved in respiratory chain and glycolysis as protein complexes.

DISCUSSION

Currently, most genomic and proteomic studies increasingly generate data which have to be gathered, filtered, and analysed using one or more softwares [START_REF] Blake | Beyond the data deluge: data integration and bio-ontologies[END_REF][START_REF] Howe | Big data: The future of biocuration[END_REF][START_REF] Gobeill | Managing the data deluge: data-driven GO category assignment improves while complexity of functional annotation increases[END_REF]. The major and widely used strategies to systematically study proteins [START_REF] Carnielli | Functional annotation and biological interpretation of proteomics data[END_REF] and genes [START_REF] Huang Da | Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists[END_REF] in a cell are based on functional annotation, proteins interactions and pathways analysis. The literature describes many tools for genomic and proteomic data analysis [START_REF] Schmidt | Bioinformatic analysis of proteomics data[END_REF]. Scientists have to select appropriate tools among those for either the GO annotation [START_REF] Ashburner | Gene ontology: tool for the unification of biology. The Gene Ontology Consortium[END_REF][START_REF] Al-Shahrour | FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes[END_REF][START_REF] Maere | BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks[END_REF][START_REF] Binns | QuickGO: a web-based tool for Gene Ontology searching[END_REF][START_REF] Carbon | AmiGO: online access to ontology and annotation data[END_REF][START_REF] Eden | GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists[END_REF], the prediction of secreted proteins [START_REF] Rice | EMBOSS: the European Molecular Biology Open Software Suite[END_REF][START_REF] Hiller | PrediSi: prediction of signal peptides and their cleavage positions[END_REF][START_REF] Emanuelsson | Locating proteins in the cell using TargetP, SignalP and related tools[END_REF][START_REF] Kall | Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server[END_REF], or the search of protein -protein interactions [START_REF] Hernandez-Toro | APID2NET: unified interactome graphic analyzer[END_REF][START_REF] Tarcea | Michigan molecular interactions r2: from interacting proteins to pathways[END_REF][START_REF] Kerrien | The IntAct molecular interaction database in 2012[END_REF][START_REF] Licata | MINT, the molecular interaction database: 2012 update[END_REF][START_REF] Chatr-Aryamontri | The BioGRID interaction database: 2013 update[END_REF][START_REF] Franceschini | STRING v9.1: proteinprotein interaction networks, with increased coverage and integration[END_REF][START_REF] Zhang | PrePPI: a structure-informed database of proteinprotein interactions[END_REF].

ProteINSIDE is not just an additional resource since it was designed to provide efficient and original strategies to run in a single query, biological knowledge gathering, GO terms annotation, secreted protein prediction, and protein interaction. The DAVID [START_REF] Huang | Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources[END_REF], ToppGene [START_REF] Chen | ToppGene Suite for gene list enrichment analysis and candidate gene prioritization[END_REF] or Babelomics [START_REF] Medina | Babelomics: an integrative platform for the analysis of transcriptomics, proteomics and genomic data with advanced functional profiling[END_REF] software resources are often mentioned for the biological knowledge gathering, functional annotation using GO terms or searches for proteins interactions. By comparison to these tools, added-values of ProteINSIDE have to be highlighted.

ProteINSIDE provides a functional annotation using a monthly updated GO terms database and enrichment calculation. Indeed, the list of GO terms is in constant evolution and GO terms could become redundant or obsolete the next month [START_REF] Ashburner | Gene ontology: tool for the unification of biology. The Gene Ontology Consortium[END_REF]. This could induce bad information in the results of an analysis if the database is not often updated. Each result of the annotation is easily readable thanks to dynamic tables and diagrams which can be sorted with many options and can be downloaded to work offline. The GO tree visualization of the most often associated GO terms with a list of IDs, is another added-value of ProteINSIDE. Tree networks of GO terms are also done by AmiGO or QuickGO to get an ancestor chart of a single term. However, ProteINSIDE is the only tool which highlights biological pathways of a dataset using linked GO terms and their representativeness rate (using p-values and number of annotations). This network visualization is also easy to use thanks to the friendly user interface that gives access to the sort options. For the PPi research and visualization, ProteINSIDE uses only interactions that are based on experimental observations. The drawback is that the number of PPi identified by ProteINSIDE could be lower than those proposed by other resources that also list predicted interactions inferred from literature mining. Furthermore, ProteINSIDE is also capable to draw large interaction networks thanks to the use of the powerful graphical Cytoscape application. ProteINSIDE provides different options to filter large networks, making it as easy to use as the widely used resource STRING [START_REF] Franceschini | STRING v9.1: proteinprotein interaction networks, with increased coverage and integration[END_REF], and efficient to select keys proteins in a network. Moreover, to analyse locally the networks, files (e.g. .cys, xgmml, graphml) are ready to be open by a network viewer like Cytoscape (and its numerous plugins) and are downloadable from the PPi page result. To our knowledge, among the tools to mine genomics data from mammals, ProteINSIDE is the only resource that allows a very simple view and analysis of network, and prepares data for their further download and analysis by other network viewer software as Cytoscape. These features may be valuable for biologists without a strong bioinformatics background. For the less informed species, ProteINSIDE allows searching PPi in well-informed species thanks to homologous IDs. For this, ProteINSIDE automatically selects homologous IDs from its database for the wanted species. Nevertheless, user can choose to run a local Blastp to select the species with the highest sequence homology with the proteins of the input dataset, and then ProteINSIDE proceed to the selection of orthologous IDs for this species. A functional annotation of all proteins from an extended network (PPi between proteins within and outside of the dataset) is done by clicking a button on the network visualisation. Results of this annotation are available as a new analysis. In addition to biological knowledge gathering, GO annotation, and analysis of PPi, ProteINSIDE also proceeds to an in silico secretome analysis [START_REF] Caccia | Bioinformatics tools for secretome analysis[END_REF]. For this purpose, ProteINSIDE merges four strategies of analysis: signal peptide [START_REF] Petersen | SignalP 4.0: discriminating signal peptides from transmembrane regions[END_REF] and cellular location [START_REF] Emanuelsson | Predicting subcellular localization of proteins based on their N-terminal amino acid sequence[END_REF] predictions, as well as a review of GO term annotation and cellular location recorded in UniProt. This four-step analysis provides a reliable prediction of proteins secreted thanks to a signal peptide. To our knowledge, ProteINSIDE is the unique all-in-one tool that predicts secretome from a list of gene or protein IDs [START_REF] Caccia | Bioinformatics tools for secretome analysis[END_REF].

Scientists are dependent on the species of study when they choose among resources available for their genomic and proteomic data analysis. Indeed, many tools are dedicated to only one species such as BioMyn for the Human [START_REF] Ramirez | Novel search method for the discovery of functional relationships[END_REF] or DroPNet for the Drosophila [START_REF] Renaud | DroPNet: a web portal for integrated analysis of Drosophila protein-protein interaction networks[END_REF]). Moreover, many tools are dedicated to diseases studies such as NetPath [START_REF] Kandasamy | NetPath: a public resource of curated signal transduction pathways[END_REF] and ToppGene [START_REF] Chen | ToppGene Suite for gene list enrichment analysis and candidate gene prioritization[END_REF]. ProteINSIDE has been first tool designed for genomic and proteomic data analysis in ruminant species namely cattle, sheep, and goat. However, the lack of information on these species required us to add human, rat, and mouse species to do homologous analysis. Thus, IDs from these species are perfectly recognized and analysed by ProteINSIDE. To our knowledge, ProteINSIDE is the only resource that allows the user to recover biological knowledge from well-known species (human, rat or mouse) using IDs from ruminant species. This avoids losing information since many sequences or annotations remain to be stored in public databases for ruminant species and especially for goat. To our knowledge, only AgBase [START_REF] Mccarthy | AgBase: supporting functional modeling in agricultural organisms[END_REF], a manually curated gene annotation database for farm species, including cattle and sheep, is available for functional annotation. However, AgBase does not perform analysis of PPi or prediction of secreted proteins.

In this article we have presented the performances of ProteINSIDE, a new powerful workflow which gathers tools and public databases to retrieve biological information of genes or proteins lists from 6 species (Bovine, Ovine, Caprine, Human, Rat, and Murine). We have reported a tutorial to describe how to get and interpret the results of a basic and a custom analysis with ProteINSIDE. Currently, there is no tool that performs in one query the analyses proposed by ProteINSIDE. ProteINSIDE offers a friendly-user interface where user can view, work, and download the results of an analysis. ProteINSIDE gives also a single file containing all results of an analysis. Thus, ProteINSIDE offers a great support to analyse efficiently a large quantity of data from genomic and proteomic studies to gather and interpret results necessary to construct a new research hypothesis or answer to a single question. The first table lists proteins predicted as secreted by SignalP. The column "Peptide" provides the results for a positive identification of a signal peptide on a protein sequence as provided by SignalP. Identified peptides can be "noTM" (not transmembrane) or "TM" (transmembrane), only "noTM" are listed in the first table. The column "Subcellular location" provides the location of the protein declared in the UniProt database. The column "TargetP" provides the prediction of the subcellular location of the protein by TargetP software, and GO related to secretion are also listed to improve the prediction. A second table lists proteins with the "TM" prediction of SignalP, not shown in the figure since there was no result with the sample dataset. (B) A third table lists proteins potentially secreted by secretory pathways that do not involve signal peptide. In this table, GO terms, TargetP prediction, and subcellular location are also selected to improve the prediction. Firstly, user has to enter a name for the analysis, select the species of study, and directly paste the input IDs (Figure 2). User has to select settings of the analysis: the setting followed by "software" mention activates the corresponding module in the workflow, and then user can select options for chosen module(s).

Figure 1 .

 1 Figure 1. Flow chart of ProteINSIDE structure. The four workflow's modules are either all launched in the basic analysis or individually selected in the custom analysis. These modules aims to query the available biological information, annotate according to the gene ontology, predict signal peptide and visualized protein-protein interactions.

Figure 2 .

 2 Figure2. Setting up a basic analysis. First, enter a name for the analysis and select the species of study. There are two ways to submit a protein or gene list; you can use an input file or directly paste your IDs. The input file must be less than 250 kb and the file format must be specified. There is also a "Sample" button that loads parameters for an example analysis. Once everything is filled, click on the button "Run the job" to submit.

Figure 3 .

 3 Figure 3. Main page of results produced by a basic analysis. This is the first page of the results. It shows the number of proteins or genes successfully analysed by each module.

Figure 4 .

 4 Figure 4. Biological knowledge retrieval. The ID Mapping module results are listed in atable. This table provides protein IDs, gene names, summaries the protein function, chromosomal locations, data on tissue expression, and subcellular location. User can sort the table by using the dynamic table research area.

Figure 5 .

 5 Figure 5. Functional annotation according to the Gene Ontology. GO results are first extracted and classified by the number of GO terms related to Molecular Functions, Biological Processes, and Cellular Components, then visualised as diagrams or downloadable tables. (A) Main menu of GO results page, to download the results as Excel files, to view the significance of p-value range colours, or a proportion of major annotation categories as diagram. (Band C) GO terms are also sorted as two dynamic tables (a table for GO terms that annotate more than one protein on the dataset -B, and a second table for the GO terms with a single protein annotated -C). Tables can be sorted by GO term, function, protein name or ID, gene name, number of annotations, annotation frequency or annotation enrichment. (D) A third table lists all GO codes for a given protein. Users can move the cursor over a protein to be informed about the evidence code and the database source of the GO annotation (B; where IDA means "Inferred from Direct Assay").

Figure 6 .

 6 Figure 6. Prediction of secreted proteins. Proteins potentially secreted are listed as two or three downloadable dynamics tables. (A)The first table lists proteins predicted as secreted by SignalP. The column "Peptide" provides the results for a positive identification of a signal peptide on a protein sequence as provided by SignalP. Identified peptides can be "noTM" (not transmembrane) or "TM" (transmembrane), only "noTM" are listed in the first table. The column "Subcellular location" provides the location of the protein declared in the UniProt database. The column "TargetP" provides the prediction of the subcellular location of the protein by TargetP software, and GO related to secretion are also listed to improve the prediction. A second table lists proteins with the "TM" prediction of SignalP, not shown in the figure since there was no result with the sample dataset. (B) A third table lists proteins potentially secreted by secretory pathways that do not involve signal peptide. In this table, GO terms, TargetP prediction, and subcellular location are also selected to improve the prediction.

Figure 7 .

 7 Figure 7. PPi results and visualisation.Results for PPi are summarised as downloadable table and a diagram. (A) Main results are downloadable as table and network file that can be visualized using a network viewer (as Cytsocape). An online network view (made using the Cytoscape web application) is also proposed from this page result. A pie diagram indicates the number of PPi identified with the different detection methods. (B) A dynamic table lists linked proteins within the dataset, the detection method used to identify the interaction, and the database source of the interaction.

Figure 8 .

 8 Figure 8. Network visualization of the PPi results. (A) This menu provides options to filter the network by: detection method, number of interactions for a protein, type of layout, protein ID, or the values of centralities. The centralities values are useful to sort large networks and to view only a central subnetwork. The betweenness centrality quantifies how frequently a node is on the shortest path between every pair of nodes for detecting bottlenecks in a network. The closeness centrality quantifies how distant minimal paths are from a given node to all others, a large closeness indicates that a node is close to the topological center of the network. (B) The network view is a dynamic image where user can access to a protein data by clicking on a node (name, function, statistic results, and database source and link of the protein).

Figure 9 .

 9 Figure 9. Setting up a custom analysis. Firstly, user has to enter a name for the analysis, select the species of study, and directly paste the input IDs (Figure2). User has to select settings of the analysis: the setting followed by "software" mention activates the corresponding module in the workflow, and then user can select options for chosen module(s).

Figure 10 .

 10 Figure10. GOTree network visualization. Linked GO terms which annotate the dataset are linked using ancestor chart method. Each edge means that a term A is a subtype of a term B (is_a). Information about a GO is obtained by clicking on the GO or the node. Red colour is only for the GO terms relative to the Molecular Function. The degree of colour saturation is related to the number of proteins annotated by a GO (dark and clear for high and low numbers, respectively).

Figure 11 .

 11 Figure 11. Extended network of PPi with proteins outside of the dataset. This network is made of PPi retrieved by querying the BioGrid, UniProt, and IntAct databases and using PPi with human proteins outside of the dataset. Grey squares are for proteins outside the dataset; white proteins are from the dataset. We have highlighted linked proteins that are involved in pathways such as: (A) glycolysis, (B) hormone activity, (C) the growth hormone signalling, and (D) thyroid hormones signalling. (E) We have used high values of betweenness and closeness centralities (BC: 3600; CC: 0.2) to get the most central proteins of this extended network.

Table 1 . Results summary of ProteINSIDE analysis performances.

 1 The numbers are the proteins that belong to main pathways in the sample dataset, that are properly annotated by GO terms relevant to glycolysis and tricarboxylic acid (TCA) pathways, and that have been predicted as secreted by SignalP (and confirmed by GO terms, TargetP, and subcellular location) for hormones.

		Glycolysis	Hormones	TCA	Analysis duration (min)
	Dataset	33+1 duplicated	79	5	-
	Basic analysis	27	78	3	2
	Custom analysis	33	79	5	10

ACKNOWLEDGEMENTS

N. Kaspric's PhD grant is provided by the regional council of Auvergne in France, APIS-GENE and the regional information system Lifegrid with the help of Feder has provided the grant of J. Tournayre.

AUTHOR CONTRIBUTIONS

Conceive and design ProteINSIDE web service: NK, JT, MR, and MB. Perform experiments and analysed the data: NK and MB. Write the paper: NK, MB, and BP.

CONFLICT OF INTEREST DECLARATION

The authors declare no conflict of interest.

SUPPLEMENTARY DATA

High resolution files of the main figures on the paper are available for download at Genomics and Computational Biology online.

ABBREVIATIONS

BC: betweenness centrality CC: closeness centrality GO: gene ontology ID: identifier PPi: protein-protein interaction