Farhan's blogs: An Optimistic View of a Bioinformatician

GENE EXPRESSION ANALYSIS USING BRB ARRAY TOOL In    this tutorial I've discussed about all the basic requirements to carry out the Gene Expression Analysis using BRB-Array Tool.Watch the videos to make it understandable and helps you fully to use these tools with your data science related research projects. Part 1 Part 2 Basic Requirements for Performing Transcriptomic Analysis using BRB Array Tool As in my previous videos I have talked about the installation of BRB Array Analysis Tool.The next step is to find out the basic things which are required as a input for Big Data Analysis: Raw Data Sample Description File Array Design For instance,if we want to find out that which gene signature is associated with Tumor Recurrence we can use this BRB Array Tool. Thank you Will be waiting for your valuable response.

Installing and Understanding BRB - Array Tool As in our scientific community, its sad that we have very rare opportunities for a  data scientist to carry out his research. Specially in less fortune countries we have very limited resources for data science projects. In this tutorial, I've introduced you guys with one of the tool that will help the biologists and students as well to analyze the Big Data. BRB-ARRAY ANALYSIS TOOLS BRB-ArrayTools is an integrated package for the visualization and statistical analysis of  Microarray gene expression Copy number Methylation   RNA-Seq data Characteristics It was developed by professional statisticians experienced in the analysis of microarray data. The analytic and visualization tools are integrated into Excel as an add-in .  The analytic and visualization tools themselves are developed in the powerful R statistical system, in C and in Java applications.  The system incorporates a variety of powerful a

DOWNLOADING AND USING GEO ARRAY DATASETS  In this video I have discussed about publicly available data that can be used for the bioinformatics data analysis using BRB Array Analysis tool. GEO DATABASE  GEO stands for "Gene Expression Omnibus".It  is a public repository that archives and freely distributes high throughput gene expression data submitted by the scientific community.  GEO currently stores approximately a billion individual gene expression measurements, derived from over 100 organisms. It also addresses data science related issues. AVAILABILITY OF DATA The data in GEO can be queried using two NCBI Entrez databases: Entrez GEO-DataSets  It provides an experiment-centric view of the data in GEO. Experiments of interest may be located by searching for attributes such as free text keywords, organism, and experimental variable information.  When a relevant DataSet is identified, that experiment can be further explored for gene

Demonstration of SmithWaterman Algorithm In this tutorial I've demonstrated the implementation of SmithWaterman Algorithm. The Smith Waterman algorithm is a dynamic programming algorithm that builds a real or implicit array where each cell of the array represents a sub problem in the alignment problem discovered by Smith and Waterman in 1981. Part 1 Part 2 Part 3 Smith Waterman Algorithm This Algorithm takes alignments of any length, at any location, in any sequence, and determines whether an optimal alignment can be found. Based on these calculations, scores or weights are assigned to each character to character comparison. Positive for exact matches/substitutions. Negative for insertions/deletions. In weight matrices, scores are added together and the highest scoring alignment is reported. Goal of Smith Waterman Algorithm It finds solutions to smaller pieces of the problem and then puts them all together to form a complete and optimal final

Demonstration of Needleman Wunsch Algorithm In this tutorial I've explained how the sequence alignment works using Needleman Wunsch Algorithm.This algorithm was developed by Saul B.Needleman and Christian D Wunsch and  it was published in 1970. Part 1 Part 2 Part 3 Part 4 NEEDLEMAN WUNSCH ALGORITHM A smart way to reduce the massive number of possibilities that need to be considered, yet still guarantees that the best solution will be found. The basic idea is to build up the best alignment by using optimal alignments of smaller subsequences. The Needleman-Wunsch algorithm is an example of dynamic programming, a discipline invented by Richard  Bellma . GOALS of Needleman Wunsch Algorithm The main goal is to find out the similarity index among the closely related species.The purpose of this algorithm is to align the protein or nucleotide sequences.Similarities in the sequence are scored in the  matrix.This will also allows the detection of

Needleman Wunsch VS Smith Waterman Algorithm Alignment of the sequences is important to discover the functional,structural and evolutionary relationship between the two genes or proteins.In this video I've compared Needleman Wunsch and Smith Waterman Algorithm. GLOBAL ALIGNMENT Needlman Wunch Algorithm, generally based on dynamic Programming. Attempts to align the maximum portion of the sequence. Suitable for aligning closely related or equal in length sequences. Tools for Global Alignment : EMBOSS NEEDLE NEEDLEMAN WUNSCH GLOBAL ALIGN NUCLEOTIDE SEQUENCES (Specialized BLAST) LOCAL ALIGNMENT Smith Waterman Algorithm are more suitable for dissimilar sequences. Stretches of sequences with high density of matches are aligned in local alignment. Suitable for different length and conserved sequences. Tools for LOCAL Alignment: BLAST EMBOSS LALIGN For more details please watch the videos Thank you

Bioinformatics Analysis:Big Data in Disease Informatics(Part 3) As in my previous lecture I've discussed about TCGA Database for the data Analysis. Now in this tutorial I'm going to talk about cbioPortal that is another genomic database having different data types information. cBioPortal for Cancer Genomics cBioPortal gives us access to download datasets of different data types: Cell lines         Adrenal glands Ampulla of Vater Biliary Tract Kidney,lungs This is one of the easiest way to access the data for our research.We can get all the information about different types of cancer.For instance, we can get the expression information of liver cancer dataset as shown in figure below. For more details watch the videos Your feedback would be much appreciated Thank you

Disease Informatics: An Application of Bioinformatics to help the Biologists In this video I've talked about the publicly available databases having alot of information/data for the biological Research. All we need to do is to be well aware of how to use them? NGS BASED METHODS to detect Genetic Variations In this tutorial, I've explained about different platforms that can be useful for the generation of data. Whole genome PCR amplicon Transcriptome RNA Exon capture Transcriptome   THE CANCER GENOME ATLAS TCGA Affiliated by National Cancer Institute US. Information about 30,000 genomes of cancer patients is available here. Thank you

Disease Informatics: An Application of Bioinformatics to help the Biologists In this video I've explained some of the solutions that are provided by the bioinformaticians to help the biologists. And how Big Data is important for the students of biology? HOW TO DEAL WITH BIG DATA ? Mutations Any certain change that occurs in our DNA sequence, either due to mistakes when the DNA is copied or as the result of environmental factors such as UV light. What kind of Mutations are possible? Missense Mutation Nonsense Mutation Insertion Deletion Duplication Frameshift Mutation Repeat Expansion How Mutations can be a cause of any change in disease activity pathways? An example to explain how data is integrated and we can get more information from it. Activation of ONCOGENE. Inhibition of TUMOR SUPPRESSOR gene. POINT MUTATIONS A change in single nucleotide base are called as single base substitution. DNA Copy Number

HOW TO ANALYZE THE TRANSCRIPTOME DATA OF ANY CANCER In this video I've tried to help the biologist by explaining them how to use Big Data for their Research analysis while using user friendly techniques.   cBioPortal for Cancer Genomics The cBioPortal for Cancer Genomics is an open-access, open-source resource for interactive exploration of multidimensional cancer genomics data sets.  Goals of cBioPortal: To significantly lower the barriers between complex genomic data and cancer researchers. By providing rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects. To empower researchers to translate these rich data sets into biologic insights and clinical applications. The cBioPortal for Cancer Genomics provides visualization, analysis and download of large-scale cancer genomics data sets. To learn more about how to use this user friendly portal watch this tutorial. Wil