overview on Next generation sequencing in breast csncer
- 1. Next Generation sequencing in Breast Cancer Research By: Seham Alshehri MSc Biochemical research
- 2. OUTLINES MicroRNA (miRNA) and breast cancer. Next generation sequencing (NGS) Data analysis
- 3. Cancer overview Cancer is genetic disease Is a group of diseases characterized by uncontrolled cell division leading to a growth of abnormal cells Oncogenes=accelarators pedals TS genes=breake pedals
- 4. miRNA in Cancer Recently discovered, miRNA is small RNA molecules that play important roles in gene expression. These small genes make much smaller RNA that don’t make protein products but they react controlling the expression of other genes Some new researches showed that miRNA play roles in controlling TS & oncogenes expression!
- 5. miRNA in BC miRNAs dysregulation in BC was first described in 2005. Some studies address the potential of miRNAs as diagnostic marker for BC subtypes and as prognostic markers for patient outcomes. Most of these studies were conducted using microarrays or RT-PCR and thus limited to a subset of miRNAs. WeiWu.Hani Choudhry, Next generation sequencing in cancer research.Chrpter12,springer2013
- 6. MicroRNAs miRNAs regulate many genes critical for tumorgenesis. Studies claim expression of miRNA in BC could unfold mysteries of tumorgenesis pathways and identify potential prognostic and diagnostic markers
- 7. miRNA & NGS application
- 9. NGS main steps Template preparation Sequencing and imaging Data analysis
- 10. Choose your protocol There is a collection of next-generation sequencing (NGS) sample preparation protocols, was compiled from the scientific literature to demonstrate the wide range of scientific questions that can be addressed by Illumina’s sequencing by synthesis technology. it will inspire researchers to use these methods or to develop new ones to address new scientific challenges. These methods were developed by users, so readers should refer to the original publications for detailed descriptions and protocols.
- 11. Which protocol fits your trial!
- 13. Gene Expression Analysis Involves: High quality RNA extracted from available biological samples. large scale microarrays.
- 15. You may need to read this : http://www.transcriptome.ens.fr/sgdb/contact/download/200602_StatsP uces_INAPG.pdf
- 16. Statistical Methods for Microarray Data Analysis is going through: 1-Genetics 2- Experimental Design 3-Data Normalization 4- Gene Clustering 5-Differintial Analysis 6- Survival Classification http://www.transcriptome.ens.fr/sgdb/contact/d ownload/200602_StatsPuces_INAPG.pdf
- 17. Material & Methods Data Generation Ex: Phenotype Data, Genotype Data, Gene Expression Data… Statistical Analysis Ex: Filtering, software package( WGCNA), Gene Network mapping, functional enrichment analysis of network…
- 18. Statistical Analysis:1- Filtering FILTERING METHOD: used to isolate active genes from inactive genes by ranking them according to their expression. FILTRING CRITERIA: Mean: ranking genes based on their average level of expression in the population. Variance: ranking genes based on the variability of their expression. Sum Covariance: incorporating both diagonal and off-diagonal elements of the covariance matrix of gene expression.
- 19. WGCNA Weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for : -Finding clusters (modules) of highly correlated genes, -Summarizing such clusters using the module eigengene or an intramodular hub gene, - Relating modules to one another and to external sample traits (using eigengene network methodology), - And for calculating module membership measures http://labs.genetics.ucla.edu/horv ath/CoexpressionNetwork/Rpacka ges/WGCNA/
- 20. 2- Explore and recognize Key genes by Statistical computing: R 1) Construct a gene co-expression network - Correlation, topological overlap 2) Identify modules - Clustering, Dynamic Tree cut 3) Relate modules to external information - Gene Ontology enrichment, correlation to phenotype/linkage analyses
- 21. 2-:Identify models: Clustering Cluster Analysis Hierarchical K-means Self-organizing maps Maximum likelihood/mixture models Graphical Displays Dendrogram Heatmap Multidimensional scaling plot
- 22. Modules Dynamic Tree cut strategy Tree Dendrogram Fixed height
- 23. (System biology) Functional enrichment Interpretation of genome-scale data often includes looking for the biological functions that are enriched in lists of genes.
- 24. /http://bioinfow.dep.usal.es/coexpression 3:Relate Modules to external information: Mapping Mapping clusters to the genome at different loci: • High LOD score for cluster (s) mapped to a lucs!!! • Physical position of a QTL lucs on the genome(quantitative trait lucs) • SNPs on the QTL • Correlation analysis of the cluster with phenotype
- 25. Discussing the outcomes Results Discussion Future Plan
- 26. More Statistics There are much more steps that by the end give an initial clues, guiding the researcher to shift in system biology
- 28. Further investigations: Again to trails!!