BIOINFORMATICS

Proteomics

  1. Protein and peptide identification using mass spectrometry based proteomics and phosphoproteomics
    1. Targeted identification and relative abundance of components
    2. Identification and relative label-free quantitation of samples
    3. Identification of cross-linked peptides/proteins
    4. Identification of various post-translational modifications after phosphoproteins enrichment
  2. Bioinformatics analysis for the identification of reliable interacting proteins using multiple database search engines
    1. SEQUEST & STATQUEST
    2. MaxQuant
    3. Xtandem!
    4. MS-GF+ & Percolator
    5. Proteome Discoverer
  3. Increasing protein identification coverage through integration of search results from various search engines
    1. MSblender
  4. Generating high confidence protein interaction networks through extensive quality control, filtering, normalization using various algorithms to emphasize the significance and reliability of the interactions
    1. Purification enrichment score
    2. Hypergeometric distribution
    3. COMPASS
    4. SAINT
    5. EPIC
  5. Clustering predicted protein-protein interactions into complexes using various clustering algorithms
    1. MCL
    2. ClusterOne
    3. coreMethod
  6. Further analysis of protein interaction networks and complexes incorporating data from various public databases to propose/validate hypothesis
    1. Functional annotation and enrichment analysis
    2. Clustering analysis
    3. Pathway analysis
    4. Network Visualization

Genomics

  1. Experiment design for high-throughput genetic interaction studies
    1. Well map layouts for genetic screens
  2. 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
    1. Colony image quantification
    2. Normalization of colony sizes
    3. Generation of genetic interaction score /phenotypic fitness score
  3. Further analysis of protein interaction networks and complexes incorporating data from various public databases to propose/validate hypothesis
    1. Functional annotation and enrichment analysis
    2. Clustering analysis
    3. Pathway analysis
    4. iv. Network Visualization

Single Cell RNA-Seq

  1. Data Pre-processing and visualization
    1. Quality control
    2. Batch correction
    3. Data imputation
    4. Normalization
  2. Feature selection, dimensionality reduction and visualization
    1. PCA
    2. t-SNE
    3. UMAP
  3. Clustering and Compositional analysis
    1. Identify cell markers
    2. Detect rare cell types
  4. Pseudo time analysis and trajectory inference
  5. Differential gene expression analysis
  6. Regulatory network analysis
  7. Cell-cell interaction analysis

Whole Genome/Exome Sequencing

  1. Data pre-processing quality control
    1. FastQC
    2. Align to the genome
    3. Remove PCR products
    4. Mark duplicates
  2. Identify germline and somatic variants of interest
    1. Single nucleotide polymorphisms (SNPs)
    2. Insertions and deletions (indels)
    3. Copy number variants (CNVs)
  3. Variant annotation using
    1. ANNOVAR
    2. SnpEff