Biocomputing and Data Analysis​

Genome wide association analysis (GWAS): Genome wide association analysis (GWAS): Genome wide association studies, identifying the relationship between variations and measured phenotypes and modelling the magnitude of the impact of these variations on this phenotype

eQTL analysis: Identifying genomic variations contributing to the change in gene expression

Es-localization analysis: Genome wide association and correlation of gene expression variations, and identifying the variations causing the phenotype in better resolution

Differential gene expression analyses

Modelling of Gene Expressions Changing Over Time

Genetic enrichment analyses in biological pathways

Analysis of a given group of genes using databases on gene pathways

Identifying SNP, small deletions and insertions using all short-read genome, exome or RNA sequencing

Identifying somatic variations

Next-generation sequencing: Identifying the number of copies and structural variations, data filtering, and quality control

Gene annotations: Identifying on which genes and on which isomorphs these variations are, and estimating the effects of these variations using computational methods

Comparative genetic analyses: Identifying the evolutionary relationships in a given group of sequences using phylogenetic trees. Modeling of the evolutionary relationships between these sequences

Chip-Seq analyses: Identifying active sites on the genome

Reassembling using short-read. This could be conducted for all genome and transcriptome reads

Modeling of complex diseases using protein-protein interactions and identifying candidate genes playing a role in these diseases

EEs-expression (co-expression) networks: Modelling of expression relationships as a network for different tissues using gene expressions and identifying new pathways inside these networks.

Microbiota analyses

Methylation analyses

Identification and annotation of small RNAs: Identifying small RNAs on the genome using short-read, and comparing these RNAs with databases such as miRbase, DASHR, and RFAM.

Hypothesis testing between patient and healthy groups

Bayesian modelling

Comparison of parametric continuous and categorical variables

Comparison of non-parametric variables

Population genetics analyses

Identifying the frequency and variance of genetic continuous or categorical characters in population or patient/control groups

Modelling of the distribution of parameters in these populations

Identifying variations under positive and negative selection

Identifying the population structure: PCA (Principal Component Analysis)

Meta-analyses of populations

Normalization of the group effect inside populations

Decreasing the number of parameters used to define populations and identifying important parameters

Identifying phylogenetic relationships in populations

Identifying gene transfers across populations

Data analysis with linear regression and logistic regression models

Survival analyses