Genomic Resources At Carnegie Embryology
Training
Ongoing
- Nitty Gritty Workflows - progress report series
- Data Wranglers Anonymous - group study sessions
- Project Zero - learn how to organize your data!
NOTE: For hands-on sessions, please be sure to bring a laptop with the following installed:
- IGV v2.3.63 or greater … requires Java 1.7 or greater
- R v3.2.2 or greater
- RStudio v0.99.489 or greater
- PuTTY and PSCP (for Windows users)
- VPN client (for access to internal Carnegie resources)
In-Person
- Center for Computational Genomics: Practical Genomics and Short Courses
- Software Carpentry and Data Carpentry
- Welch Classes
- MARCC Training
Online
- Coursera Data Science and Genomic Data Science
- HarvardX Data Analysis for Life Sciences
- NCBI Next generation sequencing Online Workshop
- Genomics Virtual Lab Tutorials and Protocols
- Codecademy Unix and Python and Git
- Code School TryR
References
Unix and Data Science
- Unix and Perl Primer for Biologists by Bradnam & Korf
- 7 command-line tools for data science by Jeroen Janssens and Data Science at the Command Line
- 5 things every data scientist should know about Excel
Organization and Reproducibility
- Good Enough Practices for Scientific Computing Wilson 2015 draft
- A quick guide to organizing computational biology projects Noble, 2009 PLoS Comput Biol
- Ten simple rules for reproducible computational research Sandve, et al., 2013 PLoS Comput Biol
- Code and data for the social sciences: A practicioner’s guide Gentzkow and Shapiro, 2014
*Seq
- RNA-Seq: a revolutionary tool for transcriptomics Wang, Gerstein, and Snyder 2009 Nat Rev Genet
- SAMtools: Primer / Tutorial by Ethan Cerami
- RNA-seqlopedia by Cresko Lab
-
Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown Pertea, et al., 2016 Nat Protoc
- Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks Trapnell, et al., 2012 Nat Protoc
- Count-based differential expression analysis of RNA sequencing data using R and Bioconductor Anders, et al., 2013 Nat Protoc
- Practical guidelines for the comprehensive analysis of ChIP-seq data Bailey, et al., 2013 PLoS Comput Biol