Microarray and RNA seq analysis using Online Tools Content: Microarray Types Microarray Vs RNA-Seq Transcriptomic Database Network Vs Enrichment Vs Pathway Connectivity Map GEO2Enrichr

1. From Expression to
Pathways & Drugs
Using Online Tools
Ali Kishk
Research Assistant, Nile University

3. Content
1. Microarray Types
2. Microarray Vs RNA-Seq
3. Transcriptomic Database
4. Network Vs Enrichment Vs Pathway
5. Connectivity Map
6. GEO2Enrichr

4. Why Transcriptomics?
1. Why Transcription?
2. Why OMICs?

5. Microarray
Image Source: https://goo.gl/images/tGexhF

6. Microarray Types
Type Application
Gene expression profiling Expression Level
SNP Array Population SNPs
Exon Array Alternative Splicing
Chromosomal microarray Copy Number Vraiation (CNV)
Fusion genes microarray Cancer

7. RNA-Seq Microarray
Cost High Low
Noise Low High
Standards Experimental Established
Data Size In GBs In MBs
New Organisms Yes No
Novel transcripts Yes No
Low Abundance
Sensitivity
Yes No
RNA-Seq Vs Expression Microarray

8. RNA-Seq in 3 words
- Sensitivity
- Reproducibility
- Discovery

9. Microarray Databases
● GEO Datasets
● GEO Profiles
RNA-Seq Databases
● GEO Datasets
● GEO Profiles
● SRA

11. Microarray Platform Record
Stable GEO accession number (GPLxxx).
Important for Pathway Analysis
Platform ID
Platform Title for DAVID

12. Network Vs Enrichment Vs Pathway
Network Analysis Enrichment Analysis Pathway Analysis
Databases - Protein Protein
Interaction (STRING)
- Co-Expression
- Pathways (GO, Reactome,
WikiPathway)
- MicroRNA (TargetScan)
- Protein Families (Pfam)
- Protein Motifs (MEME)
-Pathway
Question What are the Hub
genes / proteins in
my gene list?
What are the important MicroRNA
that affects my gene list?
What are the Pathways
MicroRNA that affects my
gene list?

13. Practical 1:
Microarray Pathway Analysis
Paper: Pezzulo, Alejandro A., et al. "HSP90 inhibitor geldanamycin reverts IL-13–
and IL-17–induced airway goblet cell metaplasia." The Journal of clinical
investigation 129.2 (2019).
Tools:
GEO2Enrichr : Microarray Analysis
DAVID : Enrichment Analysis

26. Platform Title

43. LINCS:
Library of Integrated Network-based Cellular Signatures
For ~20 K drugs on ~77 cancer cell line
Total: ~1.3 Million transcriptomic profiles.

44. Why do we need Connectivity Map
* Many drugs
* Many genes / transcripts
Assumption: ~1000 genes predicts ~20,000 expression.

45. L1000 Vs RNA-Seq
3,176 Samples from GTeX were applied to L1000
Result :
86% correlation
RNA-Seq L1000
Cost ~1k USD 2 USD for eagents
Transcripts ~ 20 k ~ 1k
Computational Analysis intensive simple

46. LINCs data types
1- Drug Perturbation
2- Down-regulation: shRNA
3- Over-expression: cDNA
4- Knockout: CRISPR

47. Disease-Drug
Image source: doi: 10.1016/j.cell.2015.05.011.

48. Target-Drug
Discovery of a selective CSNK1A1 inhibitor:
CSNK1A1 mutation in :
● myelodysplastic syndrome
● acute myeloid leukemia
No selective inhibitor
Solution:
● shRNA
● A candidate drug , strong enzymatic activity

49. Other applications
Side effect prediction
Repurposing FDA approved drugs (Polypharmacology)

50. Practical 2:
Drug Expression Similarity
Tools:
L1000CDS2

54. RNA-Seq Analysis Steps
1- Quality Control
2- Filtration & Trimming
3- Merging for Paired End
4- Alignment
5- Differential Expression Analysis
6- Network / Pathway Analysis
There other pipelines

55. RNA-Seq File Formats
FastQ files Differential Expression
TSV, CSV, TXT

56. GTeX
Genotype-Tissue Expression
Expression profiles + SNPs:
All tissues
in ~2000 post-mortem patients
Image source: doi:10.1038/nature24277

57. Practical 3:
RNA-Seq Analysis
Tools:
BioJubies

74. Conclusion
1. Pathway & Enrichment are great tools for Biological
Interpretation
1. Connectivity map is the biggest transcriptomic database
1. Connectivity map can be used in drug repurposing, side effect
prediction.
1. Connectivity map can extend its prediction on other organisms.

75. Thank You
Ph.ali.kishk@gmail.com