Best practices for RNA-seq
Conesa A et al (2016). A survey of best practices for RNA-seq data analysis. Genome Biology 17:13.
Owen N, Moosajee M (2019). RNA-sequencing in ophthalmology research: considerations for experimental design and analysis. Therapeutic Advances in Ophthalmology 11:2515841419835460.
Sims D et al (2014). Sequencing depth and coverage: key considerations in genomic analyses. Nature Reviews Genetics 15:121-132.
Liu Y, Zhou J, White KP (2014). RNA-seq differential expression studies: more sequence or more replication? Bioinformatics 30:301-304.
Best practices for single-cell RNA-seq
Bacher R, Kendziorski C (2016). Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biology 17:63.
Luecken MD, Theis FJ (2019). Current best practices in single-cell RNA-seq analysis. Mol Syst Biol. 15:e8746.
Haque A et al (2017). A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications. Genome Medicine 9:75.
Lafzi et al (2018). Guidelines for the experimental design of single-cell RNA sequencing studies. Nature Protocols 13:2742-2757.
Best practices for Ribo-seq
Ingolia NT (2016). Ribosome Footprint Profiling of Translation throughout the Genome. Cell 165:22-33.
Calviello L, Ohler U (2017). Beyond Read-Counts: Ribo-seq Data Analysis to Understand the Functions of the Transcriptome. Trends Genetics 33:728-744.
Best practices for ChIP-seq
Bailey T et al (2013). Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Computational Biology 9:e1003326.
Landt SG et al (2012). ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia. Genome Research 22:1813-1831.
Jordán-Pla A, Visa N (2018). Considerations on Experimental Design and Data Analysis of Chromatin Immunoprecipitation Experiments. Methods Mol Biol. 1689:9-28.
Best Practices for Genome-seq, Exome-seq, Targeted re-sequencing
Koboldt DC (2020). Best practices for variant calling in clinical sequencing. Genome Medicine 12:91.
