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
More robust R programming, testing and debugging
Material for R/Bioconductor Proteomics Workshop at Stellenbosch University, October 2016
RNAseq analysis in R workshop in Hobart
Material of the course "Machine Learning in Bioinformatics" (2017 Summer semester)
Workshop materials for shotgun metagenomics
Data Carpentry in R course held at School of Clinical Medicine, Cambridge
Classifies genes as an oncogene, tumor suppressor gene, or as a non-driver gene by using Random Forests
361 Division - Scientific Training, Education and Learning
GUI
Meta-analysis of A549 cell line RNA-seq
An R package of functions I use day-to-day in analysing various next-generation sequencing data, in particular whole genome bisulfite sequencing.
:microscope: Assemble large genomes using short reads
ClinGen Actionability Curation Interface
Webserver for ADAGE models.
Advanced R programming: a book
A tool for genotyping Variable Number Tandem Repeats (VNTR) from sequence data
Advanced R 1-day course taught at the University of Cambridge
Affymetrix microarrays of different technology versions are very often used in transcriptomics analysis. Quality control and normalization approaches do exist, especially as packages in Bioconductor/R. However: -Procedures are often different between teams. -They are not always easy to access as they run through command lines. -It is often not clear what the meaning of the specific settings and results are -They are not always usable for the newer technology types of arrays. To tackle this, we proposed an automated well-documented and user-friendly pipeline for Affymetrix microarray quality control and normalization.
Automatic Filtering, Trimming, Error Removing and Quality Control for fastq data