Introduction to Research Computing and OARC Systems
This fast-paced, three-hour short course offers a brief introduction to using a Linux cluster. The goal is to help users make those first few steps toward productively using a Linux cluster for research. Topics covered will include: OARC overview, Amarel, and Caliburn; OARC's support and outreach resources; high-level overview of research computing; computing in parallel; advanced networking and storage systems; accessing the cluster and basic organization (login node, compute nodes, storage spaces, network connectivity); remote access (VPN); using software modules; job scheduling with SLURM; overview of running jobs (serial, parallel, batch, interactive); writing a job script; submitting jobs; monitoring job status; running some example jobs; capturing and viewing job output/results and more.
Computational Genomics Multi-Day Workshop: RNA-Seq Part I
This workshop is intended to provide an advanced introduction to RNA-sequencing, an overview of issues to consider, and hands-on practice using current research tools available in the cloud or on OARC systems. Topics covered will include: experimental design (i.e. read length, depth, replicates); basic statistics for RNA-seq; quality control (QC) and raw read QC; FastQC reports and interpretation; quality-based trimming with Trimmomatic; read mapping and read counts; aligners (i.e. TopHat, Bowtie, BWA, STAR, HISAT2); HTSeq counting; STAR quantMode and geneCounts; post-mapping QC; visualization with IGV; all of this via lab exercises.
Computational Genomics Multi-Day Workshop: RNA-Seq Part II
This workshop is intended to extend the advanced introduction to RNA-sequencing from Part I with the following topics: data processing and filtering; introduction to R and Bioconductor; normalization; quantification and differential expression; expression quantification; relevant statistical models; multiple test correction; analysis (sample) QC; clustering; principal component analysis (PCA); heatmaps; volcano plots; functional analysis; Gene Ontology (GO) term annotation and enrichment; Gene Set Enrichment Analysis (GSEA); an overview of pathway analysis; an overview of coexpression and network analysis; feature name, term, and identifier mapping and conversion (i.e. g:Profiler, Ensembl Biomart, Synergizer); data preparation; differential expression analysis.