Silencing a single gene in Kelly Neuroblastoma Cells with CRISPR-Cas9. Cells are not synchronized so the Cell Cycle can be a confounding effect:

• Be bold but pragmatic
• Think freely
• Meet your deadlines
• Be passionate
• Try hard and then harder
• Enjoy 🙂

3:21am, fifth coffee. Late hour inspired a nested 7-way Venn Diagram, a blob of shared miRNAs targeting E2F genes. A thing of terrible beauty, inside every human cell. Code below, a simple list rendered with the venn package.

mirnas<-list(mirnas_e2f1,mirnas_e2f2,mirnas_e2f3,mirnas_e2f6,mirnas_e2f7,mirnas_e2f8,mirnas_mycn)
 names(mirnas) <- c("E2F1","E2F2","E2F3","E2F6","E2F7","E2F8","MYCN")
 png("plots/075_mirnas_7way.png",w=1000,h=1000,p=30)
 venn(mirnas,ilab=TRUE,zcolor="style")
 dev.off()

University of Bologna

Genomics Course

Bioinformatics Lab

Teacher: Prof. Federico M. Giorgi

Teaching Assistant: Dr. Chiara Cabrelle

Duration: 60 hours (15 modules of ~4 hours + optional extras)

Exam: Oral

The course aims at giving a practical overview of all the useful tools, approaches and techniques necessary for a competitive bioinformatician in 2019.

Module 1: Introduction to and testing of the working environment

• Virtual Box
• Linux Refreshment
• Playing with a FASTA file: wc, grep, htop, regex, sed
• EMBOSS suite
• Remove/install programs using apt (htop)
• Projects and Exercise structure

Module 2: Phylogenetic Sequence Analysis

• Sequence databases: how to download sequences from NCBI
• Building a phylogenetic multifasta (MYC family)
• Multiple Sequence Alignment (Muscle, ClustalW, TCoffee)
• Building a Phylogenetyc Tree (PHYLIP)
• Phylogenetic GUI: MEGA

Module 3: Remote Homology Detection

• BLAST introduction
• Create, format and index a sequence database (BLAST formatdb)
• BLASTN/BLASTP/TBLASTX with various options
• Discover the organism of a mysterious sequence
• PSI-BLAST

Module 4: Introduction to Next Generation Sequencing

• FASTA vs FASTQ, PHRED score
• FASTQ library, single ended and paired
• FASTQC

Module 5: NGS Alignment

• Aligners: Bowtie, BWA, HiSAT
• BAM files
• Samtools: process and visualize BAM files
• Integrated Genome Viewer: visualize alignments

Module 6: Calling Mutations

• Exercise: generate BAMs
• Using Varscan
• Visualizing mutations and indels with IGV
• Larger mutations: CNVs and translocation
• GATK
• Kiss&Splice: calling mutations from RNA reads

Module 7: RNA-Seq

• Spliced aligners (TopHat, STAR, HISAT)
• Finding new transcripts (Cufflinks)
• Converting bams to counts (GFF, HTSEQ-Counts)
• Finding contaminants in human rnaseq vs. other genomes (unaligned vs H. Pylori)

Module 8: ChIP-Seq

• Exercise: align reads again
• Input reads
• Call Peaks (MACS)
• Find enriched motifs (HOMER)
• Upload Custom ENCODE tracks on Genome Browser

Module 9 (Short): Assembly

• Assembling a small bacterial genome with DNA reads with MIRA
• Classic DNA Assembly with Abyss or VELVET
• Assembling E2F3 gene with long DNA reads with Canu
• Assembling RNA-Seq transcripts with Trinity

*** end of R-free course ***

Module 10 (Long): (re)introduction to R

• Basic commands up to sapply
• RStudio
• Scatterplots, Boxplots, Violin Plots, Heatmaps
• RCircos
• Bioconductor
• Gene ID conversion
• Genomic Ranges

Module 11: Differential Expression Analysis

• Loading counts
• Normalization: RPM vs RPKM vs TPM vs Size Factors vs voom
• edgeR vs DESeq2
• Comparing two datasets
• Complex Designs
• Confounding Variables (cancer vs. normal with age difference)

Module 12: Microarrays

• Concept
• Three steps: BG correction, normalization, summarization
• RMA vs. MAS5
• Differential Expression with LIMMA
• Comparing microarrays with RNA-Seq

Module 13: Single Cell RNA-Seq

• Dropout effects and biases
• Clustering
• Seurat pipeline
• Cell Cycle bias removal
• Differential Expression and comparison with bulk RNA

Module 14: Differential Binding Analysis

• Estrogen treatment with DiffBind package
• How to assign peaks to promoters to genes (Granges)
• VULCAN package?

Module 15: Pathway Enrichment Analysis

• Databases: Gene Ontology, MSIGDB, Reactome, Biocarta, KEGG, Mapman
• Discrete enrichments: TopGO package
• Continuous enrichments: GSEA
• External resources: DAVID, Gorilla

*** Extra Modules ***

Module 16: Coexpression Analysis in R

• Correlation: Pearson, Spearman, Kendall
• Mutual Information
• Partial Correlation (A,B,C)
• Overlap with ENCODE and MSIGDB data
• ARACNe

Module 17: Alternative Transcript Counters

• Salmon
• Kallisto

Module 18: Detect gene Fusions

• RNA: Tophat fusions
• DNA: big translocation finders?

Module 19: Simple Machine Learning

• Predicting Mutations with Gene Expression
• Glmnet, lasso, gradient boost modeling, caret package

Module 20: Survival Analsyis

• Kaplan Meier Curves
• Tests
• Multiple groups
• Comparing datasets

Module 21: Building an R plot with lattice

• Canvas
• Axes
• Objects

Module 22: Clustering analysis in R

• Hierarchical clustering (hclust and pvclust)
• Treecut and dynamic treecut
• Kmeans
• Principal Component Analysis and TSNE

Module 23: DNA shape prediction in R

• The DNA shape properties (MGW, HelT, PropT, Roll, EP)
• DNAShapeR package
• Show the shape of similar promoters (H.pylori project)