Name: Inamul Hasan Madar
Type: User
Company: Korea University
Bio: OncoProteoGenomics,
NGS based Genomics,
Mass Spectrometry based Proteomics,
Precision Medicine,Computational Biology, Bioinformatics.
Location: Seoul, South Korea.
Blog: http://ionchem.korea.ac.kr
Inamul Hasan Madar's Projects
Robust Allele Specific Quantification and quality controL
Materials for the Hunter / CUNY School of Public Health R Bootcamp 2015
An interface to the clinicaltrials.gov API
Error correction for Illumina RNA-seq reads
Analysis tools for rDNA seqencing using PacBio
.NET interop library to call the R statistical language in the same process
:fire: Reactome Function Interaction Annotation version 2014-2015
A guide to reading scientific papers
Reads mass spectrometry data in mzXML format
A dotplot application for DNA/RNA sequence
A python script used to annotate genomic intervals.
REMoving Bias from Rna-seq ANalysis of Differential Transcript Stability
repack Illumina format FASTQ to a smaller binary file (.rfq), which can be further compressed by xz (.rfq.xz)
Materials for the Computational Reproducibility workshop
Resources for reproducible research.
Program directory RESPONSIFY
REVOLVER - Repeated Evolution in Cancer
Package accompanying the 'R for Proteomics' publication
Client for GREAT Analysis
A genome browser that shows long reads and complex variants better
The RMD_RUNS function will log10 transform peptide peak intensity, that is, peptide abundance data and determine if any LC-MS analyses (ie,runs) in a N x P peptide data set are statistical outliers. The statistical analysis is based on summarizing each LC-MS run as a vector of q=5 summary statistics which describe the peptide abundance distribution for a specific run; a N x q matrix is then analyzed using robust PCA to compute a robust estimate of the covariance matrix used in a the calculation of a robut Mahalanobis distance.
New user
RNAseq analysis notes from Ming Tang
Codes that I use for day-to-day work in lab.
RNA-Sequencing data differential expression analysis pipeline. Performs: genome coverage (via bedtools and HTSeq), generates Circos code and plots, differential expression (via DESeq and NOISeq), structural variant detection (e.g. fusion genes, via SVDetect) and differential exon usage (via DEXSeq).
This tutorial is created for educational purpose