1. Setting up your computing environment
2. Retrieving and storing sequencing files (your own data or from public sources)
3. Checking sequence quality, trimming, general sequence manipulation
4. Mapping reads to a reference genome
5. Manipulating SAM/BAM alignment files
6. Visualizing data in a genome browser

RNA-Seq

1. De novo transcript discovery and differential analysis with Cufflinks
2. Differential expression analysis with R/Bioconductor
3. Clustering of large expression datasets (microarray or RNA-Seq)

Microarray

1. Basic analysis of Affymetrix Gene Expression Arrays using R/Bioconductor

General Tips for Data Analysis

1. Excel workarounds, adding gene annotation, X-Y plots tips, etc.

Address of the bookmark: http://homer.salk.edu/homer/basicTutorial/

More at https://gatb.inria.fr/

Address of the bookmark: https://gatb.inria.fr/

HiCdat is easy-to-use and provides solutions starting from aligned reads up to in-depth analyses. Importantly, HiCdat is focussed on the analysis of larger structural features of chromosomes, their correlation to genomic and epigenomic features, and on comparative studies. It uses simple input and output formats and can therefore easily be integrated into existing workflows or combined with alternative tools.

More at http://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-015-0678-x

Address of the bookmark: https://github.com/MWSchmid/HiCdat

KisSplice is not a full-length transcriptome assembler. This means that it will output the variable regions of the transcripts, not reconstruct them entirely.

KisSplice comes as a workflow, with several possible post-treatments meant to facilitate the analysis of the results. The choice of the post-treatment depends on the availability of a reference genome/transcriptome and on the need to perform a differential analysis, as summarised in the following table.

Address of the bookmark: http://kissplice.prabi.fr/

Keep scrolling. Using a data set about homes, we will create a machine learning model to distinguish homes in New York from homes in San Francisco.

Address of the bookmark: http://www.r2d3.us/visual-intro-to-machine-learning-part-1/

WiseScaffolder is a stand-alone semi-automatic application for genome scaffolding of pre-assembled contigs using mate-pair data. It also produces editable scaffold maps, allowing either to build gapped scaffolds or usable as a common thread for the manual improvement of scaffolds.

Description 

WiseScaffolder includes 4 subcommands: dumpconfig generates a configuration file that notably specifies the average insert size of the mate-pair library preprocess allows the detection and correction of chimerae, the estimation of contigs copy number and produces valuable outputs for the manual improvement of scaffolds scaffold constitutes the central scaffold-builder and comprises two modules:

i) the interative_scaffold_extender, which works with big, unambiguous contigs, or when they run out, single copy contigs, and

ii) the small_contig_inserter, which inserts the small contigs within scaffolds buildfasta converts the scaffold(s) map(s) into Fasta sequences.

Address of the bookmark: http://abims.sb-roscoff.fr/wisescaffolder

Address of the bookmark: http://crossmap.sourceforge.net/

Address of the bookmark: https://urgi.versailles.inra.fr/Tools/REPET/TEannot-tuto

Ryan M Layer, Colby Chiang, Aaron R Quinlan, and Ira M Hall. 2014. "LUMPY: a Probabilistic Framework for Structural Variant Discovery." Genome Biology 15 (6): R84. doi:10.1186/gb-2014-15-6-r84.

More at https://github.com/arq5x/lumpy-sv

Address of the bookmark: https://github.com/arq5x/lumpy-sv

Look for KaKs calculation:

https://github.com/fumba/kaks-calculator

http://longlab.uchicago.edu/?q=gKaKs

http://www.ncbi.nlm.nih.gov/pubmed/23314322

Address of the bookmark: http://longlab.uchicago.edu/?q=gKaKs