Exercise 1: Exploratory analysis of transcriptomics data (20%) Fo...

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Exercise 1: Exploratory analysis of transcriptomics data (20%)
For this exercise you worked on GEO dataset GDS5093, which contained gene expression data from four populations: (a) patients infected with dengue fever; (b) patients with dengue haemorrhagic fever; (c) patients recovering from dengue fever and (d) healthy controls. Use R to visualise the relationships between the gene expression profiles of the four populations, and identify the genes that show the most significant difference in expression between those populations. (You must include your R code for each visualisation.) What biological inferences can you draw from your results?

Exercise 2: generate a GFF3 file from the 6 peptides
The aim of this practical is to map the identified peptides to the reference human genome, add genomic location information for each peptide to the TSV file and use this to generate a GFF3 genome annotation file so that the identified peptides can be visualised in their genomic context.
1. Task 4: Create a GFF3 genome annotation file containing the peptides
Create a GFF3 file containing all the identified peptides and their E-values. To get each peptide to display correctly, you will need to include the chromosome, start and stop co-ordinates, and the exon number. Use the “color” attribute to represent the E-value of each peptide. View the GFF3 file using the Integrative Genomics Viewer (IGV).

peptide MS-GF E-value BLAST protein peptide start in protein peptide end in proteinEnsembl protein Ensembl gene Ensembl transcript chromosome peptide start on genome peptide end on genome

LYWGFFSGR 0.0084
LDALLAELR 0.000097
QLAVQSLAFNLK 0.00000356
SLQAMVAHELSNR 0.00000724
GPTLGSIETSDFTK 0.000557
GPEMTPYEGGYYHGK 0.000000304
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The aim of this study is the identification of the location of six peptide sequences on the human genome. This study would help to identify the proteins that have the same amino acid sequence and their potential role. The gff3 file annotation would help to visualize the peptide sequence in context of their respective genomic location
Individual peptides were subjected to BLASTP 2.6.0+ taking in consideration SwissProt as search database. Significant p-values were not found in the hits as the length of the peptides are very short and can generate random hits. All the peptide were from human only, so we proceed with the hits having hits on the human
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