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Project Study to Data Analyse

Study, Understand, Analyse and Compare



A laboratory space dedicated to project studies, experimental preparations, trial simulations,  bioinformatics and biostatistics analysis. Mathematical modeling, statistics and computer science are essential tools to design the experimentations and to understand the generated big data

As technology and testing advance, The interconnection of wet labs and dry labs have become indispensable with the advancement of technologies need more and more of computational analysis before and after the wet lab experimentations. Thanks to our partners, we propose in-silico methodological research and simulations along with experimental tests in the wet lab.

Our Dry lab services are classified into 4 categories :

Poject Studies

Project Studies

There are different steps to define before starting each project; To ensure that all these steps are considered and respected properly, and the results will be obtained with optimal conditions, GENXMAP proposes a project study prior to launch each project, These steps are done in close collaboration between the de demander (Client) and the executer (GENXMAP and partners); 


Different chapters of each project study;

  • Define the exact experimental question, and to clarify the goal of experimentation

  • Categorize the project ( based on the project complexity, time, analysis and expertises needed, cost, etc...

  • Define the project feasibility and different people/platforms involving in the project

  • Define the statistical groups needed for each project (via computational analysis, in-silico simulations, biostatisticians or bioinformaticians)

  • Define the project timeline

  • Define the yield of each step based on the starting materials

  • Risk assessment of each step

  • Define the cost of each steps 

  • Present different alternatives in each step of project ( if applicable)

  • Project study Finalisation by proposing the best strategy based on the project and optimised budget 

Data Analysis

Data analysis

Obtained tests results are often needed to be analysed by computer science to ensure the proper data distribution 

Computer models are better tools to represent a biological functions when applied to this field.

Bioinformatics and Biostatistics are interdisciplinary fields, and allows biologists to express needs for understanding complex systems, and computer scientists to develop software tools to understand biological data.

Set up the best statistical study

GENXMAP offers you to carry out the analysis of your data in bioinformatics and biostatistics. Basic analyzes are carried out in-house; for in-depth analyses, we call on our partners and collaborators. Each, expert in their area of expertise, will provide you with a precise analysis adapted to the project and up to your expectations in transparency and in complete confidentiality.

Bioinformatics and Genomics

Sequence assembly:

Sequencing techniques produce short sequences, which must then be assembled. The shotgun sequencing technique, for example, generates fragments of 35 to 900 nucleotides, which must then be assembled. The alignment of sequences for a known genome, such as the human genome, requires significant computing resources, even if advances in computing make it possible to go faster. The presence of "holes" (gaps) in the genome is frequent, and requires more targeted work in a second step.

In the case of unknown genomes (de novo sequencing), the alignment can be more complex, and it is possible that certain regions are very difficult to sequence.

Genome annotation:

Annotation is the process of marking specificities of a DNA sequence: typically introns and exons (coding sequences), regulatory sequences, methylation profiles, etc.

Evolutionary biology:

Sequence analysis can reveal links between species, which is defined by the term evolutionary biology. The phenomena studied are typically gene duplications, horizontal transfers, and large-scale genome comparisons, which make it possible to consolidate or compare the taxonomic or physiological methods used until now for the classification of species.

Bioinformatics tools will make it possible to build model populations to predict the evolution of the system over the long term.

Comparative genomics:

Sequence comparison begins with the comparison between two gene sequences from two different organisms.

The differences observed, at the level of point mutations of a nucleotide to modifications at the level of chromosomal segments such as duplications, transfers, inversions etc. understand the complexity of evolution.

Mutation analysis:

In the case of certain diseases such as cancers, the genomes of the affected cells are very largely modified: rearrangements, point mutations, etc.

Bioinformatics will allow two types of comparative analyzes based on sequencing data: between cancer cells and normal cells of an organism, and between cancer cells of an organism and cancer cells of other organisms. This type of study makes it possible to classify and list the changes in the genomes of cancer patients in order to ultimately save time in terms of diagnosis, and propose the best treatments.

Bioinformatics and Transcriptomics

Gene Expression Analysis:


Gene expression analysis gives us a snapshot of a sample’s transcriptome, providing insight into the composition and activity of cells in that sample. Most gene expression data analyses are designed to quantify changes in expression between groups of samples that differ with respect to some treatment, experimental condition or outcome.

Single-Cell Data Analysis:


Analysis of Single-cell RNA sequencing (scRNAseq) data gives the ability to study gene expression at a cellular level. This, to reveal information and variation that would otherwise be masked when considering a sample as a whole, as in the case of bulk RNAseq.

ScRNAseq data analyses are relevant to a range of applications such as: 

  • Identification of novel cell subpopulations and their gene expression profiles 

  • Identification of altered states of cell populations associated with drug response or survival 

  • Comparison of cell type composition of samples across different conditions 

  • Trajectory analysis to study development and differentiation of cell types and identification of genes associated with specific lineages or developmental states

  • Immune receptor profiling data analysis to understand the TCR, BCR, or Immunoglobulin (Ig) clonotype composition of samples

ScRNAseq data analysis of a single sample can be used to classify the cells from that sample into distinct types; this reveals information about the cell types present and how those cell types differ in terms of expression. Typically, this is done following dimension reduction and clustering using tSNE or UMAP methods. For multiple samples, scRNAseq facilitates comparisons of expression between equivalent cell types of different samples to measure the effect of a treatment, condition or outcome as well as comparisons of cell type composition of samples from multiple individuals to understand variation of cellular composition at a population scale.


We wish to contribute to the design and development of statistical methodologies used in clinical trials prior to the marketing of new drugs or cosmetic products. Epidemiological studies aimed at analyzing the factors that influence the frequency or distribution of diseases, the analysis of omic-type data (genomics, transcriptomics, proteomics, etc.), or the analysis of a new food product on its properties sensory and organoleptic… Since health issues are meaningful and this sector generates huge amounts of data, Data Science and Artificial Intelligence open up many strategic perspectives and challenges, now and in the years to come.

General Services:

  • Pre-trial planning

  • Feasibility support

  • Statistical consulting

Trial Start-Up and Planning:


We and our biostaticians partners can accompany you right from the beginning of your study, to ensure that not only the sample size, but also the hypotheses and objectives, definition of analyses sets and statistical models to be used, as well as the specified trial design, ensure the highest degree of confidence in your desired outcome right from the onset of planning.
We also know how much trials benefit from a well thought-through database setup and from providing statistical input to the risk evaluation of the trial, to ensure all risks to the trial evaluation are well understood monitored from the beginning.

Support in study protocol development:

  • Trial design

  • Sample size calculation

  • Description of hypotheses and estimands

  • Definition of analysis sets and subgroups

Support during ongoing trials:

  • Centralized Statistical Monitoring

  • Sample Size Re-estimation

  • Preparation of Statistical Analysis Plan

  • Preparation of mock Tables / Figures / Listings

Support for trial analysis:

  • Complete study analysis

  • Preparation of Statistical Report

  • Support for Clinical Study Report

  • Post-hoc Analyses

Whether you are developing a novel medicinal product, would like to expand the indication of your already available drug or aim to get a CE certification for your medical device: We are experienced and ready to support you in your endeavours. We can provide our expertise to you during different phases of your trial or throughout the whole study preparation and conduct.

Consulting & Expertise

Consulting & Expertise

In any step of your experimentation, from designing the tests to analysing the acquired data, We can put you in contact with experts in genomicstranscriptomics, bioinformatics and biostatistics domains, to provide you a turnkey solution from a very beginning of your experiment to the very end. To know more about about this service, please contact us

Technical Assistance

Our Dry lab services are classified into 4 categories:

  1. Project Studies

  2. Data Analysis

Accessible by submit your project and quotation

  3. Technical Assistance 

  4. Consulting & Expertise

Order Now

For any specific request or project,

 we will be happy to answer you

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