A computer-implemented method for generating comparative proteomic analysis reports from multiple biological samples

Abstract

The invention relates to a method for generating a computer-implemented proteomic analysis report for comparing a first biological sample (X1) that has undergone treatment and a control sample, characterized in that it comprises the following steps: (E1) receiving data relating to proteins present in each biological sample, including values ​​relating to the expression levels of the proteins in each sample; (E2) grouping the data into groups containing the data relating to the proteins present in each sample and involved in the same biological process; (E3) calculating, from the values ​​relating to the expression levels, comparative values ​​of the expression levels of the first sample to the control sample;(E4) generation of a proteomic analysis report comprising a first graph indicating a biological process and a graphic element indicating said comparative value of this biological process. Figure for the abstract: Fig. 1;

Description

Title of the invention: Computer-implemented method for generating comparative proteomic analysis reports from multiple biological samples Technical field

[0001] The present invention relates to the technical field of proteomic analysis. More specifically, it relates to automated methods for processing, comparing and visualizing proteomic data from multiple biological samples. Introduction

[0002] In the above field, it is known to use advanced techniques such as mass spectrometry to obtain detailed proteomic data. These techniques have made it possible to identify and quantify thousands of proteins present in biological samples, thus providing an overview of cellular processes and metabolic pathways. However, despite the wealth of data obtained, their interpretation and analysis remain a major challenge. Traditional methods have often relied on manual or semi-automated analyses, requiring significant expertise and being prone to human errors.

[0003] Furthermore, the comparison of proteomic profiles between different samples, such as treated and control samples, was often performed without effective visualization tools. Researchers found themselves overwhelmed by the amount of data, making it difficult to detect meaningful trends or patterns. Previous methods also did not provide effective ways to group proteins based on their biological functions or metabolic pathways, which could have facilitated interpretation.

[0004] Furthermore, although the state of the art recognized the importance of comparing protein expression levels to a control sample, it did not provide systematic methods to do so in an automated manner. Researchers often had to rely on third-party software or in-house algorithms to perform these comparisons, which could introduce inconsistencies and errors.

[0005] In summary, in the field of proteomic analysis, there is a gap regarding the automated analysis, comparison and visualization of these data in a consistent and efficient manner. This lack has hampered the ability of researchers to draw accurate conclusions and move forward quickly in their studies. Detailed description

[0006] There is therefore a need for an automated and integrated solution capable of processing, efficiently compare and visualize proteomic data from multiple samples, identify specific biological processes by grouping functionally related proteins, and present this information in a synthetic and understandable visual form, while eliminating the drawbacks and limitations associated with traditional state-of-the-art methods.

[0007] The invention is placed in this context and seeks to resolve the aforementioned drawbacks, by proposing a method for generating a computer-implemented proteomic analysis report for the comparison of two biological samples, the first biological sample having undergone treatment and the second biological sample being a control sample, the method comprising the following steps: a. a step of receiving data relating to a plurality of proteins present in each biological sample, comprising: for each protein of said plurality of proteins, an identifier of said protein and at least one value relating to the expression level of this protein in each sample; b. a step of grouping the data into data groups using at least one database containing a list of proteins, each associated with an identifier and a metabolic pathway linked to a biological process; each data group containing the data relating to the proteins present in each biological sample and involved in the same biological process; c. for each group of data, a step of calculating, from the values ​​relating to the expression rate, at least one comparative value of the expression rates of the proteins of this group in the first sample compared to the expression rates of the proteins of this group in the control sample; d. a step of generating a proteomic analysis report comprising at least one first graph indicating at least one biological process associated with a group of data and, for each biological process indicated on this graph, at least one graphic element indicating said comparative value obtained for the group of data associated with this biological process.

[0008] Advantageously, the method for generating a proteomic analysis report implemented by computer for the comparison of two biological samples allows a precise and automated analysis of the proteomic differences between at least one treated sample and a control sample, the control sample being able to be a sample of the same nature as the treated sample but having not undergone any treatment, the control sample then being called an untreated sample, or having undergone a neutral treatment, the control sample then being called a vehicle. The first step of receiving data on a plurality of proteins present in each biological sample provides a comprehensive view of the expressed proteins, facilitating detailed comparison. This is particularly useful for identifying subtle variations in protein expression that could be a consequence of treatment.

[0009] For each protein present and for each sample, said value relating to the expression level of this protein in each sample is indicated. This value is for example determined from a count of the peptides composing this protein in the sample. In the case where a protein is detected without its expression level being able to be determined, said value may be estimated from a detection limit value of the system used to generate said received data.This value may be a relative value, such as a ratio between the expression rate of this protein in the first sample, or the average of the expression rates of this protein in the first samples of the same group of samples having undergone the same treatment, and the expression rate of this protein in the second sample, or the average of the expression rates of this protein in the second samples of the same group of control samples. Alternatively or cumulatively, this value may be an absolute value, such as the expression rate of this protein in the first sample, or the average of the expression rates of this protein in the first samples of the same group of samples having undergone the same treatment.

[0010] The step of grouping data into data sets using a database containing a list of proteins associated with specific metabolic pathways allows protein expression changes to be contextualized within broader biological processes. This offers a major advantage in allowing users to understand not only which proteins are affected by a treatment, but also how these changes can influence entire metabolic pathways.

[0011] The step of calculating a comparative value of the expression levels for each group of data provides a quantitative measure of the differences between the samples. This allows an objective assessment of the effects of a treatment on protein expression, thus facilitating informed decisions on the relevance or effectiveness of a treatment.

[0012] Finally, the step of generating a proteomic analysis report with a graph indicating the biological processes and the associated comparative values ​​makes the results easily accessible and understandable. It is then easy to quickly identify the biological processes most affected by a treatment, which is essential for decision-making and planning future studies.

[0013] During this step, mass spectrometry is used to detect the peptides present in each biological sample. These detected peptides then serve as markers to identify the corresponding proteins. Each protein is identified by associating the detected peptides with a database containing a list of proteins, each associated with a unique identifier. Once the proteins have been identified, the abundance ratios are calculated. These ratios represent the quantitative relationship between the proteins present in the two biological samples being compared. If several samples with the same treatment and several control samples are present, the average of the log2 of the ratios of the average expression levels of the former to the average expression levels of the latter is calculated.This comparative value of expression levels provides a standardized measure of protein expression variation between samples.

[0014] In one embodiment of the invention, the method culminates with a step dedicated to saving and distributing the generated proteomic analysis report. In this final phase, several actions can be undertaken simultaneously or sequentially.

[0015] First, the report is saved in a digital form. This backup ensures that all valuable data and analysis are retained for later reference, review, or further analysis.

[0016] Second, the report can be transmitted to a remote digital terminal. Remote transmission ensures rapid and efficient dissemination of information, enabling real-time discussions, evaluations or decision-making.

[0017] Finally, it is also possible to print the report on paper. Although we live in the digital age, the availability of a paper version can be invaluable for presentations, meetings or simply for those who prefer a tangible reading. Printing offers additional flexibility in how information can be accessed and shared.

[0018] In the context of the present invention, "abundance ratio" means the quantitative ratio between the proteins present in two distinct biological samples. This measurement is often expressed in logarithmic terms, in particular using Log2, to facilitate the comparison and interpretation of variations in protein expression between samples.

[0019] In the context of the present invention, "mass spectrometry" means an analytical technique for identifying and quantifying molecules based on their mass and charge. It is commonly used to detect and identify peptides and proteins in biological samples, thus providing essential information for proteomic analysis.

[0020] In the context of the present invention, the term “unique identifier” means a A specific code or sequence assigned to each protein listed in a database. This identifier makes it possible to distinguish and trace each protein uniquely, thus facilitating its analysis and comparison in different samples.

[0021] In the context of the present invention, "metabolic pathway" means a series of chemical interactions occurring within a cell, generally involving several proteins and other molecules, to convert one substance into another or one signal into another. These pathways are essential for understanding biological processes and potential dysfunctions associated with certain diseases or conditions.

[0022] In a cumulative embodiment of the invention, the method for generating a proteomic analysis report comprises a prior step of implementing mass spectrometry on at least two biological samples to generate said data relating to a plurality of proteins present in each biological sample.

[0023] Advantageously, the integration of a prior step of implementing mass spectrometry on the two biological samples considerably enriches the method of generating a proteomic analysis report. Mass spectrometry is a powerful and precise technique for identifying and quantifying proteins in a biological sample. By using this method to generate data relating to a plurality of proteins present in each sample, the method guarantees reliable detection and quantification of proteins and / or peptides, even at very low concentrations.

[0024] By doing so, it is possible to obtain high-quality proteomic data, which is essential for accurate comparison between samples. In addition, mass spectrometry can detect post-translational modifications, including those induced by enzymes, and protein isoforms induced by alternative splicing, thus providing a more comprehensive and detailed view of the proteomics of the samples. As a result, the results obtained are not only quantitative but also qualitative, allowing a better understanding of the proteomic changes between treated and control samples.

[0025] Alternatively, instead of exclusively using mass spectrometry to generate data relating to a plurality of proteins present in each biological sample, other proteomic techniques may be used. For example, liquid chromatography coupled with mass spectrometry may be used so that the peptides resulting from the enzymatic hydrolysis of proteins are initially separated by reverse-phase liquid chromatography according to their hydrophobicity and thus separate and identify complex proteins, ensuring better quantification of the proteins.

[0026] If desired, in addition to mass spectrometry, fluorescence microscopy may be used to visualize the location and abundance of specific proteins in biological samples, thereby providing contextual information on the cellular localization of proteins.

[0027] Alternatively, proteomics based on protein chips could be implemented, allowing the detection of molecular interactions with other proteins, peptides, nucleic acids and other structures. This approach is particularly useful for high-throughput studies where many samples need to be analyzed quickly.

[0028] Advantageously, techniques such as electrospray ionization mass spectrometry or MALDL TOF mass spectrometry may be used, in particular for the purpose of analyzing chemical modifications of proteins.

[0029] In one embodiment of the invention, each protein listed in the database is associated with a unique identifier; in addition, the prior step of implementing mass spectrometry comprises the detection of peptides present in each biological sample, the identification of proteins present in each biological sample from the identified peptides and the association with each identified protein of one of said unique identifiers.

[0030] Advantageously, the association of each protein listed in the database with a unique identifier considerably simplifies the process of identifying and comparing proteins between different biological samples. Said unique identifier ensures that each protein is clearly recognized, thus avoiding ambiguities or confusions that could arise during data analysis. This also facilitates the integration and comparison of data between different studies or experiments, because each protein is clearly and uniformly identified.

[0031] The preliminary step of implementing mass spectrometry, which involves the detection of peptides present in each biological sample, provides increased accuracy in protein identification. By focusing first on peptides, the method allows for more sensitive detection of proteins, including those present in low quantities. In addition, the identification of proteins from the detected peptides ensures that proteins are identified on the basis of their basic constituents, thus increasing the reliability of identification.

[0032] The association of each identified protein with one of said unique identifiers further enhances the accuracy and efficiency of the process. Once a protein is identified from its peptides, it is immediately associated with its unique identifier, the unique identifier serving as a universal reference for each protein, thus ensuring clear traceability and efficient organization of the data.

[0033] In a cumulative embodiment of the invention, the data relating to a plurality of proteins present in each biological sample include, for each protein identified and regardless of the method of identification and assignment of peptides, the number of peptides detected which are associated with it.

[0034] In one embodiment of the invention, the method comprises a filtering step, prior to the grouping step, in which the data relating to the proteins present in each biological sample and comprising a single unique peptide are deleted from said data.

[0035] Advantageously, the inclusion of data relating to the number of peptides detected and composing each identified protein makes it possible to ensure a high degree of precision of the analysis, regardless of the method chosen. By knowing the number of peptides that compose a specific protein, it is possible to have a better idea of ​​the coverage and confidence of the identification of this protein. This can help to distinguish between similar proteins and to confirm the identification of less abundant proteins or proteins that might be difficult to detect.

[0036] The filtering step, prior to the clustering step, where the data associated with each protein comprising a unique peptide is removed, is particularly advantageous for improving the reliability of the analysis. Proteins identified by a single peptide can often be considered less reliable or potentially erroneous identifications. By eliminating this data, the method ensures that only proteins identified with a higher degree of confidence are considered in the final analysis. This reduces the risk of errors or false identifications, thus ensuring that the proteomic analysis report is based on robust and reliable data.

[0037] If desired, instead of simply deleting the data relating to proteins comprising a single peptide, a validation step may be integrated. Said validation step may use other techniques or methods to confirm the identification of these proteins, such as additional analyses or bioinformatics validation techniques. This would in particular ensure that certain rare or less abundant, but nevertheless relevant, proteins are not prematurely discarded.

[0038] Furthermore, in addition to filtering based on the number of peptides, other criteria could be used to improve data quality. For example, a threshold based on signal intensity or spectrum quality could be applied to eliminate protein identifications with weak or poor quality signals.

[0039] As an alternative to simply counting the number of peptides making up each protein, a more in-depth analysis of peptide motifs or specific sequences can be performed. This analysis can provide additional information on post-translational modifications or protein isoforms, in view to a more detailed analysis of the protein composition of each sample.

[0040] Advantageously, a machine learning system may be integrated to identify and eliminate false positives or to recognize recurring patterns in the data, thus improving the accuracy and reliability of the proteomic analysis.

[0041] In an alternative or cumulative embodiment of the invention, the step of grouping the data into data groups comprises a step of identifying, using at least one database containing a list of proteins each associated with an identifier and at least one interaction with another protein in this list, said interaction being involved in at least one metabolic pathway linked to a biological process, the interactions of the proteins present in each biological sample with each other, and a step of generating said data groups from said identified interactions.

[0042] Advantageously, the integration of the step of identifying the interactions of the proteins present in each biological sample with each other considerably enriches the depth of the proteomic analysis. By identifying these interactions, the method is not limited to a simple list of proteins present, but also offers an overview of the functional networks and metabolic pathways active in the sample. This allows a global understanding of the biological state of the sample by highlighting the joint functioning of the expressed proteins.

[0043] The use of at least one database containing a list of proteins, each associated with an identifier and at least one interaction with another protein, facilitates this identification. By relying on established databases, the method can quickly and efficiently identify relevant protein interactions, based on pre-existing knowledge.

[0044] By doing so, the method identifies the interactions involved in at least one metabolic pathway linked to a relevant biological process, making it possible to understand the functional implications of the proteomic data.

[0045] Finally, the step of generating data groups from the identified interactions efficiently organizes the information, making the results more accessible and understandable. By grouping the data according to interactions and metabolic pathways, the proteomic analysis report provides a structured view of the active protein networks in the sample.

[0046] In one embodiment of the invention, the generated proteomic analysis report comprises at least one other graph indicating a plurality of biological processes each associated with one of the data groups and comprising, for each biological process indicated and for each protein present in each biological sample and involved in this biological process, a graphic element indicating said value relating to the expression level of this protein, the graphic elements being linked together to form a network whose nodes are these graphic elements and the lines indicate the interactions of the proteins represented by these graphic elements with each other.

[0047] Advantageously, the presentation of the proteomic analysis report in the form of a graph indicating a plurality of biological processes provides an intuitive and understandable visualization of protein interactions and their involvement in various biological processes. This graphical representation facilitates the understanding of the complex relationships between proteins and makes it possible to quickly identify relevant metabolic pathways and biological processes.

[0048] The addition of a graphical element indicating the relative value of the expression rate of each protein for each biological process enhances the richness of the information provided. By directly visualizing the expression variations for each protein within a process, it becomes possible to identify key proteins that may be regulated or affected in a given context.

[0049] Creating a network where graphical elements are linked together to indicate protein interactions is particularly advantageous. This network provides an overview of protein relationships, making it possible to understand how proteins interact and cooperate within specific metabolic pathways. The nodes and lines of the network provide a clear visual representation of protein interactions, thus facilitating the identification of central proteins or hubs that may play a crucial role in a biological process.

[0050] By doing so, this graphical and network approach to the proteomic analysis report ensures a combination of quantitative and relational information, allowing for clear analysis and interpretation of the proteomic data.

[0051] In one embodiment of the invention, the comparative value of the expression levels of the proteins of a data group is calculated from the average of the ratios between the values ​​relating to the expression levels of the proteins of said group in the first sample and the values ​​relating to the expression levels of the proteins of said group in the second sample.

[0052] Advantageously, calculating the comparative value of the protein expression levels of a data group from the average of the ratios provides a standardized and robust method for assessing differences in protein expression between samples. By using the average of the ratios between the values ​​relating to the protein expression levels of the first sample and the second sample, the method ensures that each protein contributes equally to the overall comparative value, thus avoiding potential biases that could result from extremely abundant or rare proteins.

[0053] By doing so, it is possible to minimize the impact of extreme variations or outliers, as the average of ratios tends to be less sensitive to individual fluctuations than other measures. This makes the comparative value more representative of the entire data set, thus providing a more balanced view of protein expression differences between samples.

[0054] Furthermore, based on the average of the ratios, the method facilitates comparison between different groups of data or different studies. Allowing to quickly and easily compare the comparative values ​​of different groups or samples, because they are assured that each value has been calculated in the same standardized way. This facilitates decision-making and interpretation of results, especially in large-scale studies or meta-analyses.

[0055] In the context of this invention, proteomic analysis often requires a comparative evaluation of protein expression levels between different samples. A commonly used method to quantify these differences is the calculation of ratio abundances. This method consists of taking the average of the base 2 logarithms (Log2) of the ratios of the expression levels. This logarithmic transformation makes it possible to obtain a symmetrical distribution of the ratios, thus facilitating subsequent statistical analyses.

[0056] When multiple samples have undergone the same treatment and there are multiple control samples, a more refined approach is adopted. In this case, the average of the Log2 ratios is calculated by taking the average of the expression levels of the treated samples and dividing it by the average of the expression levels of the control samples. This method provides better accuracy because it accounts for intra-group variation and allows for a more robust comparison between treated and control samples.

[0057] One variation considered in the context of this invention is the use of pooled abundances. Rather than focusing on ratios, this method is based on the average of the expression levels of samples of the same type. This approach can be particularly useful when intra-group variations are small and the objective is to obtain an overview of general trends in expression levels.

[0058] In a cumulative embodiment of the invention, the comparative value of the expression levels of the proteins of a group of data is calculated by multiplying said average of the ratios by the ratio between the number of proteins of said group of data and the total number of proteins present in each biological sample.

[0059] Advantageously, the comparative value of the expression rates of the proteins of a group of data can be obtained by multiplying the average of the ratios by the ratio between the number of proteins of the group of data and the total number of proteins present in each biological sample introduced a dimension of weighting. This weighting makes it possible to take into account the relative representativeness of a group of proteins compared to the entire sample. Alternatively, the comparative value of the protein expression rates of a data group can be calculated by multiplying said average of the ratios by the ratio between the number of proteins in said data group and the total number of proteins that may be functionally involved in the biological process(es) associated with the data group.

[0060] By incorporating this weighting, the method ensures that larger or more representative protein groups have a proportionally greater impact on the comparative value. This is particularly advantageous to prevent smaller or less representative protein groups from exerting a disproportionate influence on the results. This ensures that the comparative value more accurately reflects the relative importance of each protein group in the overall sample.

[0061] In addition, this calculation method provides normalization that facilitates comparison between different samples or studies. By taking into account both the average of the expression ratios and the representativeness of the protein group, the method provides a standardized metric that can be used to consistently compare protein expression differences between different samples or contexts.

[0062] In summary, this weighted approach improves the robustness and relevance of the comparative value, allowing a more precise and more representative proteomic analysis of the biological samples studied.

[0063] In one embodiment of the invention, the step of calculating at least one comparative value of the expression levels comprises a sub-step of selecting the data groups from said calculated comparative values, and in that the first graph of the generated proteomic analysis report indicates only the biological processes associated with the selected data groups. Advantageously, the introduction of the sub-step of selecting the data groups from the calculated comparative values ​​allows a focus on the most relevant information. By specifically selecting the data groups based on their comparative values, the method ensures that only the most significant or relevant protein expression variations are highlighted. This avoids overloading the analysis report with information that might be less relevant or less significant for the current study.

[0064] The fact that the first graph of the generated proteomic analysis report only shows the biological processes associated with the selected data groups reinforces this focus. By presenting only the biological processes more relevant, the report becomes more readable and easier to interpret. Thus, it is possible to focus on the metabolic pathways and / or biological processes most affected or predominant between the compared samples.

[0065] This selective approach also improves reporting efficiency, by reducing the time and effort required to analyze and interpret data. By focusing on the most relevant information, the method facilitates rapid and informed decision-making, which is particularly advantageous in settings where time is of the essence, such as in clinical or agricultural diagnostics or high-throughput studies.

[0066] In a cumulative embodiment of the invention, the sub-step of selecting the data groups from said calculated comparative values ​​comprises at least one statistical analysis of said calculated comparative values.

[0067] Advantageously, the integration of a statistical analysis of the calculated comparative values ​​in the data group selection sub-step enhances the rigor and reliability of the selection process. By using statistical methods, the method ensures that the selected data groups are not only relevant, but also statistically significant. This minimizes the risk of including protein expression variations that could be due to chance or minor variations.

[0068] Statistical analysis also provides an objective method for assessing the significance of protein expression differences between samples. By relying on statistical criteria, the method avoids potential biases or subjective interpretations that could influence the selection of data groups. This ensures that the proteomic analysis report accurately reflects the true biological differences between samples.

[0069] In addition, the use of statistical analyses allows for better standardization of the process, so that data groups are selected according to uniform and reproducible criteria, thus facilitating comparison between different reports or studies. This standardization is essential to ensure the consistency and reliability of the results, particularly in large-scale studies or meta-analyses.

[0070] In summary, the integration of statistical analysis into the data group selection process improves the accuracy, reliability and reproducibility of the proteomic analysis report, thus providing a solid basis for interpretation and decision making.

[0071] Alternatively, instead of being limited to a single statistical analysis method, several statistical techniques could be employed simultaneously or sequentially to evaluate the calculated comparative values. For example, non-parametric tests could be used in addition to parametric methods to ensure that the results are not biased by non-normal distributions of the data.

[0072] Additionally, machine learning or artificial intelligence methods could be integrated to complement traditional statistical analysis. These methods could help identify complex patterns or trends in the data that might not be immediately obvious with conventional statistical techniques.

[0073] As an alternative to simple analysis of comparative values, an assessment of the variance or dispersion of the data may also be performed in order to identify groups of data that exhibit particularly high or low variability, thus providing additional insights into the stability or reliability of the protein expression measurements.

[0074] Finally, to strengthen the robustness of the selection, a cross-validation system can be implemented. By dividing the data into training and test sets, and repeating the analysis on several subsets, it can be ensured that the selected data groups are truly representative and are not the result of artifacts or biases in a particular subset of data.

[0075] In one embodiment of the invention, the sub-step of selecting the data groups from said calculated comparative values ​​comprises a variance analysis.

[0076] Advantageously, the integration of an analysis of variance (ANOVA) in the data group selection sub-step provides a robust and proven method for evaluating differences between calculated comparative values. ANOVA is specifically designed to compare the means of several groups, which makes it particularly suitable for evaluating variations in protein expression between different biological samples.

[0077] The use of ANOVA allows one to determine whether the observed differences between groups of data are statistically significant. This provides additional assurance that the identified variations are not simply the result of random variation or experimental error, but reflect true biological differences between samples.

[0078] In addition, ANOVA provides a measure of effect size, which allows for the assessment of the magnitude of differences between groups. This is particularly useful for determining whether a difference, although statistically significant, is also biologically relevant.

[0079] Furthermore, ANOVA is able to handle situations where multiple factors or variables may influence protein expression. This allows for a more nuanced and detailed analysis of the data, taking into account multiple sources of variation.

[0080] In sum, integrating analysis of variance into the data group selection process enhances the rigor and precision of the analysis, ensuring that the results are both statistically valid and biologically relevant.

[0081] Alternatively, instead of limiting oneself to a standard analysis of variance, a multivariate analysis of variance (MANOVA) could be used, allowing the effect of several independent variables on several dependent variables to be evaluated simultaneously. This would be particularly useful if one wishes to study the impact of several factors or conditions on a range of proteins or metabolic pathways.

[0082] In addition, principal component analysis (PCA) or factor analysis techniques could be integrated to reduce the dimensionality of the data and identify the main sources of variation in biological samples. These methods could help visualize the relationships between samples and identify the proteins or groups of proteins that contribute most to these variations.

[0083] As an alternative to ANOVA, non-parametric tests, such as the Kruskal-Wallis test, could be used, particularly if the data do not meet the assumptions of normality or homogeneity of variances required for ANOVA. These tests provide additional flexibility for analyzing data that do not follow a normal distribution.

[0084] Finally, to enhance the robustness of the analysis, statistical inference techniques, including bootstrap or permutation techniques, could be applied to estimate the distribution of the tested statistics and provide more accurate confidence intervals for the estimates. These methods address potential issues related to small samples or non-standard distributions, thus ensuring a more reliable and robust analysis.

[0085] In an alternative or cumulative embodiment, the sub-step of selecting the data groups from said calculated comparative values ​​comprises an analysis by statistical test, in particular a Tukey-Kramer test.

[0086] Advantageously, the integration of an analysis by statistical test, and more specifically by a Tukey-Kramer test, in the sub-step of selection of the data groups offers a rigorous method for comparing the means of several groups. The Tukey-Kramer test is particularly suitable for multiple comparisons, making it possible to identify the groups which differ significantly from each other after an analysis of variance.

[0087] Using the Tukey-Kramer test ensures that the differences identified between data groups are truly significant, while controlling the Type I error rate, which is the risk of wrongly concluding a difference. This is particularly advantageous when many groups or samples are being compared, as it minimizes the risk of false positive errors.

[0088] In addition, the Tukey-Kramer test provides confidence intervals for the differences between the means, thus providing a measure of the precision and reliability of the estimates. These intervals make it possible to determine not only whether differences exist, but also the magnitude of those differences.

[0089] In sum, the adoption of the Tukey-Kramer test in the data group selection process enhances the rigor and accuracy of the analysis. It ensures that the selected data groups reflect true biological differences between samples, while providing detailed information on the magnitude and reliability of these differences.

[0090] In one embodiment of the invention, said first graph comprises: for each biological process indicated on this graph, at least one first graduated graphic element associated with this biological process; for each treatment applied to at least one biological sample of which data has been received at the receiving stage, at least one second graphic element graduated and associated with this processing; and for each data group containing protein data present in a biological sample having undergone a treatment associated with a second graphic element and involved in the same biological process associated with a first graphic element, a third graphic element linking this first graphic element to this second graphic element, the width of the third graphic element at the level of the first and second graphic elements indicating said comparative value obtained for the group of data in relation to the graduation of said first and second graphic elements.

[0091] Advantageously, the introduction of graduated graphic elements associated with each biological process and each applied treatment provides a clear and intuitive visualization of the data. These graphical elements allow users to quickly identify and compare protein expression levels associated with different biological processes and treatments.

[0092] The third graphic element, linking the first element associated with the process biological to the second element associated with the treatment, provides a direct visual representation of the relationships between treatments and biological processes. The width of the third graphic element at the level of the first and second graphic elements, indicating the comparative value, allows for a quick interpretation of the magnitude of the effect of a treatment on a specific biological process. This visual representation facilitates the understanding of complex interactions between treatments and biological processes, without having to navigate through large data tables.

[0093] In addition, the graduation of the first and second graphical elements provides a reference scale, allowing users to quickly quantify variations in protein expression. This standardized graduation facilitates comparison between different reports or studies, ensuring consistent and uniform interpretation of the results.

[0094] In summary, the combination of these graphical elements in the first graph considerably improves the readability and usefulness of the proteomic analysis report. It allows for rapid, accurate and intuitive interpretation of the data, thus facilitating informed decision-making in research or clinical settings.

[0095] Advantageously, the structuring of the graphic elements by forming substantially a complete circle, with the biological processes arranged on a first semicircle and the treatments on a second semicircle, offers a harmonious and intuitive visualization of the interactions between the treatments and the biological processes. This circular arrangement facilitates direct comparison between the different biological samples and the associated treatments, while maximizing the use of the space available on the graph.

[0096] The third graphical elements, forming strings connecting biological processes to treatments, provide a direct visual representation of the relationships between the two. This configuration allows users to quickly identify the specific effects of each treatment on the different biological processes, without having to browse lists or tables. The width of these strings, indicating the comparative value, adds an additional dimension of information, allowing for rapid interpretation of the magnitude of a treatment's effect.

[0097] The semicircle arrangement also facilitates comparison between biological samples. For example, by placing the first biological sample that has undergone a first treatment near the control sample and the third biological sample that has undergone a second treatment, users can quickly assess the relative effects of each treatment compared to the control.

[0098] In sum, this circular configuration, combined with the visual representation of the relationships between treatments and biological processes, makes the proteomic analysis report both aesthetically pleasing and informative, facilitating the understanding and interpretation of complex data.

[0099] Advantageously, the introduction of a step of identifying claims supported by one or more biological processes enriches the informative scope of the proteomic analysis report. By associating specific claims with biological processes, users can quickly understand the importance or clinical relevance of the observed proteomic variations.

[0100] The successive arrangement of the first graphic elements associated with biological processes supporting the same claim on the first graphic facilitates the visualization and understanding of the relationships between the biological processes and their clinical or functional implications. This organization allows an intuitive reading and a direct comparison of the biological processes linked to a specific claim.

[0101] The fourth graphical element, superimposed on the set of first graphical elements associated with biological processes supporting a particular claim, provides a clear visual representation of the claim itself. This allows users to quickly identify and understand the clinical or functional implications of proteomic variations without having to refer to external explanatory texts.

[0102] Therefore, the addition of claims and their direct visual representation on the graph reinforces the clinical and functional relevance of the proteomic analysis report. Thus making it possible to easily and quickly identify the importance of the observed proteomic variations and to make informed decisions based on clearly presented and organized information.

[0103] Advantageously, the integration of another graph representing a comparative statistical distribution of the values ​​relating to the expression rate provides a detailed and in-depth perspective of the proteomic variations within a specific biological process. This representation allows users to visualize not only the average variations, but also the complete distribution of the expression rates for each protein involved.

[0104] This comparative statistical distribution provides a more nuanced understanding of the data. It allows for the identification of specific trends, anomalies, or patterns that might not be immediately evident when examining averages or comparative values ​​alone. For example, it may reveal subgroups of proteins that respond similarly or differently to a treatment, or highlight specific proteins that stand out in their behavior.

[0105] Furthermore, by providing a detailed view of the distribution of expression levels for all proteins involved in a biological process, the proteomic analysis report becomes a more robust tool for decision-making. It is then possible to use this information to identify potential targets for therapeutic interventions, or to better understand the underlying mechanisms of a disease or response to treatment.

[0106] In sum, the addition of a comparative statistical distribution graph enriches the proteomic analysis report, providing a more comprehensive and detailed perspective of proteomic variations, and thus strengthening users' ability to interpret and act on the data presented.

[0107] Of course, the various characteristics, variants and embodiments of the invention can be associated with each other in various combinations to the extent that they are not incompatible or mutually exclusive. Description of the Figures

[0108] Furthermore, various other characteristics of the invention emerge from the appended description given with reference to the drawings which illustrate non-limiting forms of embodiment of the invention and where:

[0109] [Fig.l] is a detailed flow diagram representing the different steps in the process of generating a proteomic analysis report for the comparison of two biological samples, highlighting the interactions, filtering, grouping and statistical analyses involved in the process.

[0110] [Fig.2] is a schematic representation of the samples used in the analysis proteomics. It shows three triplets for each proteomic analysis condition, CA and CB, as well as three control triplets and three vehicles. Each triplet is clearly identified by labels, allowing easy distinction between different sample types.

[0111] [Fig.3] is a schematic representation of a structured database, used to store and manage information about proteins, their unique identifiers, their interactions and associated metabolic pathways. This database is essential for the analysis and comparison of biological samples.

[0112] [Fig.4] is a graph illustrating the relationships between proteins present in the biological samples. Each vertex in the graph represents a protein, identified by a unique label. The shape of the vertices varies depending on the result of a statistical test, and their size is proportional to the comparative value of the expression levels. The edges of the graph indicate the proteins involved in the same biological process, thus providing an overview of protein interactions.

[0113] [Fig.5] is a detailed graphical representation of biological processes and associated treatments. It shows a semicircle composed of seven biological processes, each associated with a first graduated graphic element. A second semicircle completes the first, representing the treatments applied to the samples. Graphic elements connect these two semicircles, indicating the relationships between the biological processes and the treatments. These elements are superimposed by fourth graphic elements indicating the claims associated with each process.

[0114] We have represented in [Fig.l] a method for generating a proteomic analysis report implemented by computer for the comparison of two biological samples XI and X2.

[0115] The method begins with a step E0 of implementing mass spectrometry on the biological samples XI and X2. This step subsequently allows the detection of peptides present in each biological sample, the identification of proteins from the detected peptides, and the association of a unique identifier with each identified protein.

[0116] Step E0 is followed by a step E1 of receiving data. During this step, data relating to a plurality of proteins present in each biological sample are received. This data includes an identifier for each protein and a value relating to the expression level of this protein in each sample. For each identified protein, the data also includes information on the peptides detected and composing this protein, and in particular their number.

[0117] After step E1, a detection and identification step E2 is implemented. During this step, the data from mass spectrometry are used to detect the peptides present in each biological sample. These detected peptides then serve as markers to identify the corresponding proteins. Each protein is identified by associating the detected peptides with a database containing a list of proteins, each associated with a unique identifier. Once the proteins have been identified, the abundance ratios are calculated. These ratios represent the quantitative ratio between the proteins present in the two biological samples compared. If several samples with the same treatment and several control samples are present, the average of the log2 of the ratios of the averages of the expression levels of the former to the average of the expression levels of the latter is calculated.This comparative value of expression levels provides a standardized measure of protein expression variation between samples.

[0118] Step E2 is followed by a filtering step E3. Before grouping, data relating to proteins that only contain one peptide are deleted.

[0119] Then, step E3 leads to a step E4 of grouping the data into data groups. To do this, a specialized database is used. In said database, each protein is associated with a unique identifier, thus guaranteeing traceability and a clear distinction between the proteins.

[0120] In addition, said database also includes protein interactions. For each protein listed, the database provides information on the other proteins with which it interacts within the cell as well as the metabolic pathways to which the proteins belong.

[0121] In addition, the database provides information on the metabolic pathways themselves, including the biological processes they regulate or participate in. Thus, when grouping proteins, said proteins are classified according to their direct interactions and according to the metabolic pathways and biological processes with which they are associated; so as to group proteins into coherent data groups, reflecting the biological processes taking place in the sample studied.

[0122] It may be provided to use more than one database for the implementation of step E4 without departing from the scope of the present invention. It may be possible, for example, to use a database making it possible to obtain the interactions of the proteins from the identified proteins and then a second database making it possible to obtain metabolic pathways from the identified interactions. It may also be provided to use different databases, each making it possible to obtain metabolic pathways and then to consolidate the results from these different databases.

[0123] Furthermore, step E4 may be carried out automatically, the grouping being carried out by one or more data grouping algorithms, or alternatively semi-automatically, at least part of the grouping then being carried out under the supervision of a human being.

[0124] Step E4 is followed by a calculation step E5. For each data group, a comparative value of the protein expression levels is calculated based on the values ​​relating to the expression level. This comparative value is obtained by calculating the average of the log2 of the ratios between the values ​​relating to the expression levels of the proteins of the group in the first sample and the values ​​relating to the expression levels of the proteins of the group in the second sample. This average is then multiplied by the ratio between the number of proteins in the data group and the total number of proteins present in each biological sample.

[0125] Thus, the comparative value is given by the following expression:

[0126] uif i0<æu

[0127] where: X is the comparative value for a given data group, n is the number of proteins in the data group, N is the total number of proteins potentially functionally involved in the biological process(es) associated with the data group, TEC is the expression rate of protein i in the first sample and TE2; is the expression rate of protein i in the second sample.

[0128] Alternatively, the comparative value could be obtained by calculating the average of the log2 of the values ​​relating to the expression levels of the group proteins in the samples.

[0129] Step E5 leads to a step E6 of identifying claims. The claims supported by certain biological processes are identified, and the graphic elements associated with these processes are arranged successively on the graph. For each identified claim, a fourth graphic element indicating said claim is superimposed on the set of first graphic elements associated with biological processes supporting this claim.

[0130] Finally, step E6 is followed by a step E7 of selecting the data groups. This selection is based on the calculated comparative values, and statistical analyses, such as analysis of variance or the Tukey-Kramer test, can be used for this selection.

[0131] Step E7 is followed by a step E8 of generating a proteomic analysis report. A report is generated comprising at least a first graph indicating the biological processes and the associated graphic elements. For certain processes, another graph may be generated representing a comparative statistical distribution of the values ​​relating to the expression rate.

[0132] Step E9 of the method consists of saving the generated proteomic analysis report. This saving can be done in digital form on a local or remote storage medium. In parallel with the saving, the report is transmitted to a remote digital terminal via communication networks. This transmission can be done by email, file transfer or any other data communication method. In addition, the report can also be sent to a printing unit to be printed on a paper medium. Said printing can be done via a locally or remotely connected printer.

[0133] We have represented in [Fig.2] a diagram illustrating a set of samples used for proteomic analysis. The set is organized into four distinct groups, each dedicated to a specific condition or type of sample.

[0134] The samples intended to test the CA proteomic analysis condition are represented by three triplets, labeled CAI, CA2 and CA3 respectively. Each triplet is represented by an ellipse, and the three ellipses are aligned horizontally. They are clearly identified by their label.

[0135] Similar to the CA samples, the samples intended to test the CB proteomic analysis condition are labeled CB1, CB2 and CB3. These triplets are also represented by ellipses, aligned horizontally.

[0136] The control samples, which serve as a reference for the analysis and have not undergone any specific treatment, are labeled NI, N2 and N3. These triplets are represented by triangles, aligned horizontally.

[0137] Finally, the samples that underwent neutral treatment, i.e. they were exposed to a vehicle without active agent, are labeled M1, M2 and M3. These triplets are represented by rectangles, aligned horizontally. The arrangement of the samples in [Fig.2] is such that each group is clearly separated from the others, allowing rapid identification and easy visual comparison. Next to each group, a detailed legend provides information on the type of sample, the treatment undergone (if applicable) and other relevant details from the claims, such as the analytical methods used and the unique identifiers associated with each protein in the samples.

[0138] We have represented in [Fig.3] the detailed structure of a database used in the process of generating a proteomic analysis report for the comparison of two biological samples, in particular during the implementation of step E4.

[0139] The database is designed to store and manage information relating to a plurality of proteins present in biological samples. Each entry in the database corresponds to a specific protein and is associated with a unique identifier, thus ensuring the uniqueness and traceability of each protein.

[0140] In addition to the unique identifier, each entry contains data relating to the expression level of the protein in the biological samples. This data includes relative values ​​that reflect the abundance or concentration of the protein in each sample.

[0141] The database also integrates information on the metabolic pathways linked to each protein. These metabolic pathways are associated with specific biological processes. Thus, for each protein, the database provides details on the biological process(es) in which it is involved.

[0142] In addition, the database contains information on protein interactions. Each listed protein is associated with at least one other protein in the database, indicating an interaction between them. These interactions are crucial for understanding metabolic pathways and protein networks within biological samples.

[0143] In addition, the database is structured to facilitate the grouping of data into data groups. These groups are formed based on metabolic pathways and biological processes. Thus, proteins involved in the same biological process or metabolic pathway are grouped together, allowing for a coherent and structured analysis.

[0144] We have represented in [Fig.4] a graph illustrating the relationship and characteristics of the proteins present in samples XI and X2 received at step EL. This graph can be incorporated into the report generated at step E8.

[0145] Each vertex of the graph symbolizes a distinct protein from the set of samples. These vertices are clearly identified by a label, which corresponds to the unique identifier assigned to each protein during the mass spectrometry step. The shape of each vertex is determined based on the result of a Tukey-Kramer statistical test. If the comparative value comparison test is deemed significant for a protein, the vertex representing it takes the shape of a disk. Otherwise, where the test is not significant, the vertex is represented by a square.

[0146] The size of these discs and squares is proportional to the comparative value of the protein they represent. The higher this value, the larger the size of the peak, thus reflecting the relative importance of the protein in the comparison between samples XI and X2.

[0147] The opacity of the color of each vertex is adjusted to indicate the intensity of protein expression. Higher opacity means more intense protein expression, while lower opacity indicates less expression.

[0148] The edges of the graph, for their part, establish connections between the proteins that are involved in the same biological process. These edges make it possible to quickly and efficiently visualize the proteins that interact or cooperate in a given biological process.

[0149] In addition, vertex groups are formed to group proteins with a similar function or with an interaction between them. These groups are clearly delimited and allow to obtain an overview of the main functions represented by the proteins of samples XI and X2.

[0150] We have represented in [Fig.5] a graph generated during step E8, this graph illustrating the relationship between the proteins and the associated biological processes, in the context of the comparison of two biological samples XI and X2.

[0151] The graph is structured in two main semicircles. The first upper semicircle has seven distinct vertices P1, P2, P3, P4, P5, P6 and P7, each formed by an arc of a circle symbolizing a specific biological process identified in step E4 and associated with a data group, after selection in step E6. These biological processes are those in which the proteins of the samples are involved. Each vertex is marked with a label, providing a clear identification of the biological process it represents. Each vertex has a graduation.

[0152] The second lower semicircle, for its part, highlights the treatments C1 and C2 applied to the biological samples. Each treatment is also represented by a vertex, in the form of a graduated arc of a circle and each of these vertices C1 and C2 is connected to the vertices P1 to P7 of the first semicircle by ropes. These ropes symbolize the specific proteins that are present in the samples having undergone a particular treatment and that are involved in a given biological process. The width of these ropes at the vertices indicates the comparative value of the proteins for the associated treatment and biological process, as determined during step E5, thus reflecting the graduation of the graphic elements. These graduated graphic elements thus provide a visual representation of the protein expression levels associated with each biological process and treatment.

[0153] Within the first semicircle, the vertices representing the biological processes are arranged so that those supporting the same claim A1, A2, A3, identified during step E7, are positioned successively. Three distinct claims are thus highlighted, each grouping together a set of biological processes. For each identified claim, a fourth graphic element is superimposed on all the vertices associated with biological processes supporting this claim. This fourth graphic element indicates the claim in question, providing a clear overview of the claims and the biological processes associated with them.

[0154] It should be noted that in these figures the structural and / or functional elements common to the different variants may have the same references.

[0155] Of course, various other modifications may be made to the invention within the scope of the appended claims.

Claims

Claims

1. A method for generating a computer-implemented proteomic analysis report for comparing two biological samples (ECH1) and (ECH2), the first biological sample (XI) of which has undergone treatment and the second biological sample (X2) being a control sample, characterized in that it comprises the following steps: a. a step (El) of receiving data relating to a plurality of proteins present in each biological sample, comprising: for each protein of said plurality of proteins, an identifier of said protein and at least one value relating to the level of expression of this protein in each sample; b. a step (E2) of grouping the data into data groups using at least one database containing a list of proteins, each associated with an identifier and a metabolic pathway linked to a biological process; each data group containing the data relating to the proteins present in each biological sample and involved in the same biological process; c. for each group of data, a step (E3) of calculating, from the values ​​relating to the expression rate, at least one comparative value of the expression rates of the proteins of this group in the first sample compared to the expression rates of the proteins of this group in the control sample; d. a step (E4) of generating a proteomic analysis report comprising at least one first graph indicating at least one biological process associated with a group of data and, for each biological process indicated on this graph, at least one graphic element indicating said comparative value obtained for the group of data associated with this biological process.

2. A method of generating a proteomic analysis report for comparing two biological samples according to claim previous, characterized in that it comprises a prior step of implementing mass spectrometry on the two biological samples to generate said data relating to a plurality of proteins present in each biological sample.

3. Method for generating a proteomic analysis report for the comparison of two biological samples according to the preceding claim, characterized in that each protein listed in the database is associated with a unique identifier, and in that the prior step of implementing mass spectrometry comprises the detection of peptides present in each biological sample, the identification of proteins present in each biological sample from the identified peptides and the association with each identified protein of one of said unique identifiers.

4. Method for generating a proteomic analysis report for the comparison of two biological samples according to one of the preceding claims, characterized in that the step (E2) of grouping the data into data groups comprises a step of identifying, using at least one database containing a list of proteins each associated with an identifier and at least one interaction with another protein in this list, said interaction being involved in at least one metabolic pathway linked to a biological process, the interactions of the proteins present in each biological sample with each other, and a step of generating said data groups from said identified interactions.

5. Method for generating a proteomic analysis report for the comparison of two biological samples according to one of the preceding claims, characterized in that said comparative value of the expression levels of the proteins of a group of data is calculated from the average of the ratios between the values ​​relating to the expression levels of the proteins of said group in the first sample and the values ​​relating to the expression levels of the proteins of said group in the second sample.

6. Method for generating a proteomic analysis report for the comparison of two biological samples according to one of the preceding claims, characterized in that step (E3) comprises a sub-step (E3.1) of selecting the data groups from said calculated comparative values, and in that the first graph of the generated proteomic analysis report indicates only the biological processes- logical associated with the selected data groups.

7. Method for generating a proteomic analysis report for the comparison of two biological samples according to one of the preceding claims, wherein said first graph comprises: a. for each biological process indicated on this graph, at least one first graduated graphic element associated with this biological process; b. for each treatment applied to at least one biological sample from which data was received at the reception stage, at least one second graduated graphic element associated with this treatment; and c. and, for each group of data containing data relating to proteins present in a biological sample having undergone treatment associated with a second graphic element and involved in the same biological process associated with a first graphic element, a third graphic element linking this first graphic element to this second graphic element, the width of the third graphic element at the level of the first and second graphic elements indicating said comparative value obtained for the group of data in relation to the graduation of said first and second graphic elements.

8. Method for generating a proteomic analysis report for the comparison of two biological samples according to the preceding claim, the method being implemented for a plurality of biological samples including a first biological sample (XI) having undergone a first treatment, a second control biological sample (X2) and at least one third biological sample having undergone a second treatment, characterized in that the set of first graduated graphic elements associated with the biological processes involving proteins present in the first, second and third biological samples substantially form a first semicircle, in that the set of second graduated graphic elements associated with the first and second treatments substantially form a second semicircle completing the first semicircle, and in that the third graphic elements form cords connecting each a first graphic element of the first semicircle to a second graphic element of the second semicircle.

9. Method for generating a proteomic analysis report for the comparison of two biological samples according to one of claims 7 or 8, the method comprising a step of identifying at least one claim supported by one or more biological processes involving proteins present in each biological sample, in that the first graphic elements associated with biological processes supporting the same claim are arranged successively on the first graphic, and in that said first graphic comprises, for each identified claim, a fourth graphic element indicating said claim and superimposed on all of the first graphic elements associated with biological processes supporting this claim.

10. Method for generating a proteomic analysis report for the comparison of two biological samples according to one of the preceding claims, characterized in that the generated proteomic analysis report comprises, for at least one biological process associated with a group of data, at least one other graph representing a comparative statistical distribution of the values ​​relating to the expression rate of all the proteins present in each biological sample and involved in this biological process.

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