Sars-cov-2, Leishmaniasis_RNAseq_publicdata

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Contents

1. Genome Alignment

2. DEG Analysis

3. EM algo. Motif study

4. GO Analysis & GSEA

• based on RNAseq data (include Ribo-seq, CLIP-seq)

Lesson 1: Genome Alignment

• 2023-04-10

Alignment of Sars-Cov2 genome: egypt/nrc-01 and Variant calling

In this session, participants will delve into the intricacies of genome alignment with a focus on the Sars-Cov2 genome (Egypt/NRC-01) and variant calling. Utilizing raw data in FASTA format obtained from the NCBI SRA database, participants will gain proficiency in samtools application and alignment based on indexing.

>CODE 001 : raw_001_Align_Sars_Cov2.py

Lesson 2: Differential Gene Expression (DEG) Analysis

• 2023-05-08

Differential gene expression of leishmaniasis

This lesson revolves around conducting differential gene expression analyses for leishmaniasis using RNA-seq data. Participants will engage in transcriptomic profiling, exploring various forms of DEG analysis.

>CODE 002 : 002_DEG_analysis.ipynb

Lesson 3: Expectation Maximization (EM) Algorithm for Motif Study

• 2023-05-15

Finding motif by commonly used EM algorithm

Participants will delve into the EM algorithm, a widely employed tool in motif finding for sequencing data analysis. Understanding the composition of the EM algorithm in the context of various sequencing algorithms, participants will interpret the significance of each process and validate the results.

>CODE 003 : 003_Motif_EM.ipynb

Lesson 4: Gene Ontology (GO) Analysis and Gene Set Enrichment Analysis (GSEA)

• 2023-05-22

Gene function in term and it's abundance on leishmaniasis

Diverging from conventional analyses, this session emphasizes functional expression in gene ontology (term) networks and pathway analysis for leishmaniasis. Participants will engage in quantitative analysis with a focus on gene function and its abundance.

>CODE 004 : 004_GO_GSEA.ipynb