1. Advanced Bioinformatics
24/06/2018
Medicago
Basic project:
Study gene expression under a single condition

2. Team members
Jente
Lifei
Yuebang
Nick

3. Our chosen eukaryotic organism:
24/06/2018
Yeast

4. Input data Fastq files as sequence data
Genome.fa file as a reference genome
Genes.gtf

5. Tophat, Cufflinks and Cuffmerge
Genes.gtf, genome.* and the fastq files are used to generate .bam files
The accepted_hits.bam is used by Cufflinks to generate a file called transcripts.gtf
Because the experiment was in triplo, we get 3 transcripts.gtf files. These are merged together with Cuffmerge.

6. gtf_to_fasta
With the program gtf_to_fasta we create a fasta file which contains all the transcripts with sequences.
So now we have a fasta and a gtf file to extract data from with the help of programs and scripts.

7. The Big Hash Table From the FASTA we use/determine:
Gene_id
Sequence length
GC content
Codon usage
From the GTF we use/determine:
Expression level
Inter-transcript size
Intron length

8. Reading gtf file: Sort top 100 expressed genes
From the GTF we use/determine:
Gene_id
Expression level
Inter-transcript size
Intron length

9. Key point: First order, then get top 100 genes.
Build hash table: gene_id(keys) to FPKM, intron length, inter-transcript(values).

10. Using array:Gene_ID and FPKM in seq[8]
Inter-transcript: use defined($seq2[1])
Intron length: divid into different conditons (subroutines)
After reading next transcript line, calculate last intron
length .

11. Important: hash table –matching!

12. Why we need to analyse FPKM, intron length,inter-transcript(correlation)?

13. FPKM: gene expression level
Intron length: positive to gene expression level
Inter-transcript: gene density

14. Reading the fasta file The important information is the sequence.
From this GC content, codon usage etc. can be determined.
To couple this info to the gtf output, we analyse the ID as well.

15. Reading the fasta file
The analysis was performed by reading the file line by line, just like the exercises.
Then the ID was extracted from the first line and saved in a heshtable.
Normally heshtables have only a key and one value but we managed to put arrays in these values.

16. Reading the fasta file
>xxxxx 1: gene_id etcetcetcetcetcetc. AGCTGCTAGGCTGCGCATCGTGAGCTGCCTTG %hesh ID; seqLength, GC_content, codonUsage

17. Combine the best of both!
The array values from the %gtf hesh table are pushed into the %fasta hesh table.
For example:
my $newval = $gtf {$i} [0];
my $newval2 = $gtf {$i} [1];
$fasta{$ID} }, "$newval\t", "$newval2\t”;

18. # Heshtable # In this way we obtained a table that contained:
ID; length, CUP, GC, TSp, TEp, ITL, Intron size(s)
We give options to show a variable number of genes and to sort on specific parameters.
Now Jente will unleash his package…

19. Package: Jente My Package Codon Usage Bias R: correlations Jente
24/06/2018
My Package
Codon Usage Bias
R: correlations
Jente

20. Codon Usage Bias Relative Synonymous Codon Usage (RSCU)
Effective Numbers of Codons (NC)

21. Codon Usage Bias
RSCU
Not in pipeline
Optional subroutine

22. Codon Usage Bias
NC = 𝐹2 + 1 𝐹3 + 5 𝐹4 + 3 𝐹6 Only possible for sequences that use all amino acids Codon Usage Proportion (CUP) CUP = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑈𝑠𝑒𝑑 𝐶𝑜𝑑𝑜𝑛𝑠 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑃𝑜𝑠𝑠𝑖𝑏𝑙𝑒 𝐶𝑜𝑑𝑜𝑛𝑠

23. R: Correlations
R =
FPKM GC

24. Highly expressed genes have a more extreme codon bias
R: Correlations
R =
Highly expressed genes have a more extreme codon bias
tRNAs?

25. Highly expressed genes are smaller
R: Correlations
R = -0,1282
Highly expressed genes are smaller
More efficient?

26. Longer genes use more codons...
R: Correlations
R = 0,9588
Longer genes use more codons...

27. Visualize highly expressed genes in the interaction network
What are Networks?
A map of interactions or relationships
A collection of nodes and links (edges)
Why Network?
predict protein function through identification of partners
Protein’s relative position in a network
Mechanistic understanding of the gene-function & phenotype association

28. Visualize highly expressed genes in the interaction network
24/06/2018

29. Interaction network (1)
Download Yeast Interactome:

30. Interaction network (2)
Runing Cytoscape and import yeast Interactome

31. Interaction network (3)
Visualize analysis of the interaction network

32. Interaction network (4)
Visualize the highly expressed genes in interaction network

33. Interaction network (5)

34. Interaction network (6)
Top 100 genes interactome data

35. Interaction network (7)

36. Interaction network (8)

37. Interaction network (9)

38. Interaction network (10)
Visualize the highly expressed genes in interaction network

39. Interaction network (11)
Interaction network of top 100 intractome data

40. Interaction network (12)

41. GO graph (1)
Intall BiNGO

42. GO graph (2)
Import the top 100 expression genes, and start BiNGO

43. GO graph (3)

44. Conclusion
In the CCSB-Y|1 file, 8 genes of top 100 highly expressed genes are found, and no directly interaction among them in the interaction network
It is confirmed highly expressed genes are related to production of protein by GO term.

45. Thank you forever 