Bioinformatics using R training in Delhi ncr

Bioinformatics using R

Starting Bioinformatics with R
• Introduction
• Getting started and installing libraries
• Reading and writing data
• Filtering and subsetting data
• Basic statistical operations on data
• Generating probability distributions
• Performing statistical tests on data
• Visualizing data
• Working with PubMed in R
• Retrieving data from BioMart
Introduction to Bioconductor
• Introduction
• Installing packages from Bioconductor
• Handling annotation databases in R
• Performing ID conversions
• The KEGG annotation of genes
• The GO annotation of genes
• The GO enrichment of genes
• The KEGG enrichment of genes
• Bioconductor in the cloud
Sequence Analysis with R
• Introduction
• Retrieving a sequence
• Reading and writing the FASTA file
• Getting the detail of a sequence composition
• Pairwise sequence alignment
• Multiple sequence alignment
• Phylogenetic analysis and tree plotting
• Handling BLAST results
• Pattern finding in a sequence
Protein Structure Analysis with R
• Introduction
• Retrieving a sequence from UniProt
• Protein sequence analysis
• Computing the features of a protein sequence
• Handling the PDB file
• Working with the InterPro domain annotation
• Understanding the Ramachandran plot
• Searching for similar proteins
• Working with the secondary structure features of proteins
• Visualizing the protein structures
Analyzing Microarray Data with R
• Introduction
• Reading CEL files
• Building the ExpressionSet object
• Handling the AffyBatch object
• Checking the quality of data
• Generating artificial expression data
• Data normalization
• Overcoming batch effects in expression data
• An exploratory analysis of data with PCA
• Finding the differentially expressed genes
• Working with the data of multiple classes
• Handling time series data
• Fold changes in microarray data
• The functional enrichment of data
• Clustering microarray data
• Getting a co-expression network from microarray data
• More visualizations for gene expression data
Analyzing GWAS Data
• Introduction
• The SNP association analysis
• Running association scans for SNPs
• The whole genome SNP association analysis
• Importing PLINK GWAS data
• Data handling with the GWASTools package
• Manipulating other GWAS data formats
• The SNP annotation and enrichment
• Testing data for the Hardy-Weinberg equilibrium
• Association tests with CNV data
• Visualizations in GWAS studies
Analyzing Mass Spectrometry Data
• Introduction
• Reading the MS data of the mzXML/mzML format
• Reading the MS data of the Bruker format
• Converting the MS data in the mzXML format to MALDIquant
• Extracting data elements from the MS data object
• Preprocessing MS data
• Peak detection in MS data
• Peak alignment with MS data
• Peptide identification in MS data
• Performing protein quantification analysis
• Performing multiple groups’ analysis in MS data
• Useful visualizations for MS data analysis
Analyzing NGS Data
• Introduction
• Querying the SRA database
• Downloading data from the SRA database
• Reading FASTQ files in R
• Reading alignment data
• Preprocessing the raw NGS data
• Analyzing RNAseq data with the edgeR package
• The differential analysis of NGS data using limma
• Enriching RNAseq data with GO terms
• The KEGG enrichment of sequence data
• Analyzing methylation data
• Analyzing ChipSeq data
• Visualizations for NGS data
Machine Learning in Bioinformatics
• Introduction
• Data clustering in R using k-means and hierarchical clustering
• Visualizing clusters
• Supervised learning for classification
• Probabilistic learning in R with Naïve Bayes
• Bootstrapping in machine learning
• Cross-validation for classifiers
• Measuring the performance of classifiers
• Visualizing an ROC curve in R
• Biomarker identification using array data

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