BIOINFORMATICS
Proteomics
- Protein and peptide identification using mass spectrometry based proteomics and phosphoproteomics
- Targeted identification and relative abundance of components
- Identification and relative label-free quantitation of samples
- Identification of cross-linked peptides/proteins
- Identification of various post-translational modifications after phosphoproteins enrichment
- Bioinformatics analysis for the identification of reliable interacting proteins using multiple database search engines
- SEQUEST & STATQUEST
- MaxQuant
- Xtandem!
- MS-GF+ & Percolator
- Proteome Discoverer
- Increasing protein identification coverage through integration of search results from various search engines
- MSblender
- Generating high confidence protein interaction networks through extensive quality control, filtering, normalization using various algorithms to emphasize the significance and reliability of the interactions
- Purification enrichment score
- Hypergeometric distribution
- COMPASS
- SAINT
- EPIC
- Clustering predicted protein-protein interactions into complexes using various clustering algorithms
- MCL
- ClusterOne
- coreMethod
- Further analysis of protein interaction networks and complexes incorporating data from various public databases to propose/validate hypothesis
- Functional annotation and enrichment analysis
- Clustering analysis
- Pathway analysis
- Network Visualization
Genomics
- Experiment design for high-throughput genetic interaction studies
- Well map layouts for genetic screens
- Genetic and Chemogenomics processing and statistical analysis to generate gene-gene or drug-gene interaction networks through extensive quality control, filtering, normalization using gitter and SGA Tools algorithm to emphasize the significance and reliability of the interactions
- Colony image quantification
- Normalization of colony sizes
- Generation of genetic interaction score /phenotypic fitness score
- Further analysis of protein interaction networks and complexes incorporating data from various public databases to propose/validate hypothesis
- Functional annotation and enrichment analysis
- Clustering analysis
- Pathway analysis
- iv. Network Visualization
Single Cell RNA-Seq
- Data Pre-processing and visualization
- Quality control
- Batch correction
- Data imputation
- Normalization
- Feature selection, dimensionality reduction and visualization
- PCA
- t-SNE
- UMAP
- Clustering and Compositional analysis
- Identify cell markers
- Detect rare cell types
- Pseudo time analysis and trajectory inference
- Differential gene expression analysis
- Regulatory network analysis
- Cell-cell interaction analysis
Whole Genome/Exome Sequencing
- Data pre-processing quality control
- FastQC
- Align to the genome
- Remove PCR products
- Mark duplicates
- Identify germline and somatic variants of interest
- Single nucleotide polymorphisms (SNPs)
- Insertions and deletions (indels)
- Copy number variants (CNVs)
- Variant annotation using
- ANNOVAR
- SnpEff