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
