Add algorithm details to improve tutorials

[Delphine-L]

Provide trainings with details about the algorithms of the main analysis to help user really understand what is happening .

[Delphine-L]

Assembly

• [ ] De Bruijn Graph Assembly
• [ ] Introduction to Genome Assembly
• [ ] De novo transcriptome reconstruction with RNA-Seq

Alignment

• [ ] Making sense of a newly assembled genome
• [ ] 16S Microbial Analysis with mothur (extended)
• [ ] 16S Microbial Analysis with mothur (short)
• [ ] Analyses of metagenomics data - The global picture
• [ ] Differential abundance testing of small RNAs

Lineage tree

• [ ] Downstream Single-cell RNA analysis with RaceID

Clustering (see machine learning material)

• [ ] 16S Microbial Analysis with mothur (extended)
• [ ] 16S Microbial Analysis with mothur (short)
• [ ] Analyses of metagenomics data - The global picture
• [ ] Mass spectrometry imaging: Examining the spatial distribution of analytes
• [ ] Downstream Single-cell RNA analysis with RaceID
• [ ] Small Non-coding RNA Clustering using BlockClust

Blast type

• [ ] Genome Annotation
• [ ] Making sense of a newly assembled genome
• [ ] Genome annotation with Maker
• [ ] Genome annotation with Prokka

Mapping

• [ ] Formation of the Super-Structures on the Inactive X
• [ ] Essential genes detection with Transposon insertion sequencing
• [ ] DNA Methylation data analysis
• [ ] Hi-C analysis of Drosophila melanogaster cells using HiCExplorer
• [ ] Peptide and Protein Quantification via Stable Isotope Labelling (SIL)
• [ ] Mapping
• [ ] RAD-Seq Reference-based data analysis
• [ ] CLIP-Seq data analysis from pre-processing to motif detection
• [ ] De novo transcriptome reconstruction with RNA-Seq
• [ ] Pre-processing of Single-Cell RNA Data
• [ ] RNA-Seq reads to counts
• [ ] Reference-based RNA-Seq data analysis
• [ ] Exome sequencing data analysis for diagnosing a genetic disease
• [ ] Identification of somatic and germline variants from tumor and normal sample pairs
• [ ] Mapping and molecular identification of phenotype-causing mutations

Linkage analysis

• [ ] Mapping and molecular identification of phenotype-causing mutations

Correlation

• [ ] Formation of the Super-Structures on the Inactive X
• [ ] Hi-C analysis of Drosophila melanogaster cells using HiCExplorer
• [ ] CLIP-Seq data analysis from pre-processing to motif detection

Normalization

• [ ] Formation of the Super-Structures on the Inactive X
• [ ] Downstream Single-cell RNA analysis with RaceID

Regression Analyses (see machine learning material)

• [ ] Analysis of molecular dynamics simulations

SNP detection (see variant calling)

• [ ] RAD-Seq Reference-based data analysis
• [ ] RAD-Seq de-novo data analysis
• [ ] Exome sequencing data analysis for diagnosing a genetic disease
• [ ] Identification of somatic and germline variants from tumor and normal sample pairs

PCA

• [ ] Analysis of molecular dynamics simulations
• [ ] Mass spectrometry imaging: Examining the spatial distribution of analytes

Supervised classification (see machine learning material)

• [ ] Mass spectrometry imaging: Examining the spatial distribution of analytes

Phenology

• [ ] Regional GAM

These look like they would need algorithm description but I couldn't identify them

• [ ] Running molecular dynamics simulations using GROMACS
• [ ] Setting up molecular systems
• [ ] Genome annotation with Prokka (explain prokka suite of tools)
• [ ] Hi-C analysis of Drosophila melanogaster cells using HiCExplorer
• [ ] Metaproteomics tutorial \

[Delphine-L]

The algorithms are found in several tutorials, I think we should write them snippets like. We do an extensive review and comparison of the several mapping algorithms for example, then link it in every training that involves mapping.

[teresa-m]

I will work on the 'Alignment' section.

[Delphine-L]

I will work on the PCA section

[teresa-m]

I will start working on the Clustering algorithms