Welcome to Dr. Vipin's Classroom
* Facilitating Biologists Transition to Data Science *
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for live, participatory, non-mandatory assignment based learning with a personal touch
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Break the mental block !
YOU CAN CODE TOO !!!
BEGIN FROM SCRATCH !
For strong foundations in Bioinformatics and coding in R, Python & Next Generation Sequencing
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R FOR BIOLOGISTS
Biology today is fast transforming into data-science - curtsey the high-throughput technologies. Manual analysis of this data is neither feasible nor possible anymore. With the data deluge in Life Science Research, programming is fast becoming a desirable and essential skill, even for wet lab researchers, for data wrangling, analysis, and data visualization.
Coding is simple, intuitive, easy to learn and truly a universal skill today with vast applications in data analysis, data visualization, automation, scaleup and precession. Unleash the power of coding -
Make a confident start by writing your first few of the many codes you may potentially write ... with me! Rediscover yourself !
NGS Fundamentals and Data Analysis
DNASeq - Variant Calling
As Next Generation Sequencing becomes a first stop for most analysis downstream - DNASeq, RNASeq, ChIPSeq, ATACSeq, FARESeq .... etc, here is an elaborate take on the evolution of sequencing techniques - from the first to second to the third Generation, from intermediate read length (Sanger) to short reads - Illumina and Ion Torrent, to finally long read sequencing - Be my guest on this journey most remarkable as i take you through the basics of NGS Data Analysis - we extensively cover
1. The Basics of Next Generation Sequencing - Illumina, IonTorrent, Nanopore and PacBio
2. The Installations - Linux, Anaconda, Conda and tools for NGS -DNASeq analysis -Basic Linux, FASTQC, MULTIQC, SAMtools, BCFtools, IGV etc.
3. Steps involved in Genome Sequence Analysis - Quality Control, Reference Based Assembly, Variant Calling and
4.Sequencing Data Visualization with Integrated Genomics Viewer
PYTHON FOR BIOLOGISTS
Biology today is fast transforming into data-science - curtsey the high-throughput technologies. Manual analysis of this data is neither feasible nor possible anymore.
With the data deluge in Life Science Research, programming is fast becoming a desirable and essential skill, even for wet lab researchers, for data wrangling, analysis, and data visualization.
Coding is simple, intuitive, easy to learn and truly a universal skill today with vast applications in data analysis, data visualization, automation, scaleup and precession. Unleash the power of coding -
Make a confident start by writing your first few of the many codes you may potentially write ... with me! Rediscover yourself !
Break the mental block ... you can code too !
**** UPCOMING WORKSHOPS ****
BIO - PYTHON
Biopython is the most popular molecular biology package for computation. Brad Chapman and Jeff Chang developed it in 1999. It is mainly written in python but some C code is there to solve complex optimization. Biopython is a multiutility versatile package use for analysis of nucleic acid sequence, protein structure, sequence motifs, sequence alignment also machine learning.
Biopython has a lot of libraries for the help of biologists in their work as it is portable, easy, and clear.
Biopython can be used for
Biopython can be used for
Creating high quality, reusable classes for the complex bio-informatics problems
Read genetic databases like Swissport, FASTA, and many more. After reading, it parse them to python utilizable data structure.
For genomic data analysis.
Tools for protein structure and have BigSQL-storing a lot of data.
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