Next generation sequencing

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

8. Next Generation Sequencing
(NGS)

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!

12. NGS methodology cascade
www.nature.com/jid/journalhttp://
a.html2013248/full/jid8/n133/v

13. Gene Expression Analysis Involves:
 High quality RNA extracted from available
biological samples.
 large scale microarrays.

14. Reading Arrays:
STATISTICAL Genetics
Analysis

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!!