CSE 428,  Sp '19:  Approximate Schedule

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Schedule details will evolve as we go.

References:  Here is a sampling of relevant literature. If you find other interesting articles, please share them.

Most links below take you to PubMed, the NIH bibliographic database. Usually, but counterintuitively, from a PubMed abstract you click the icon of the publisher (or sometimes the icon saying "UW article online") to get to the actual article.

Journal access: Some of the journals and articles cited below are completely open access, or are freely available via PubMed Central (look for the "Free in PMC" icon).  Electronic access to other cited articles is generally free from on-campus IP addresses.  For off-campus access, follow the "[offcampus]" links or look at the UW library "proxy server" instructions.  Let me know if none of these work for you.

References -- Introduction & Overview: Some background:

1. WS Noble, "A quick guide to organizing computational biology projects." PLoS Comput. Biol., 5, #7 (2009) e1000424.
2. Wetterstrand KA. DNA Sequencing Costs: Data from the NHGRI Large-Scale Genome Sequencing Program. Available at: http://www.genome.gov/sequencingcosts/ Accessed 30 Mar 2012.
3. V Makinen, D Belazzougui, F Cunial, AI Tomescu, Genome-Scale Algorithm Design: Biological Sequence Analysis in the Era of High-Throughput Sequencing, Cambridge, 2015. (Amazon)

References -- SeqBias: Technical Biases in RNASeq

4. DC Jones, WL Ruzzo, X Peng, MG Katze, "A new approach to bias correction in RNA-Seq." Bioinformatics, 28, #7 (2012) 921-8. [offcampus]

References -- ASE: Allele-Specific Expression

5. JC Knight, "Allele-specific gene expression uncovered." Trends Genet., 20, #3 (2004) 113-6. [offcampus]
6. CG Bell, S Beck, "Advances in the identification and analysis of allele-specific expression." Genome Med, 1, #5 (2009) 56. [offcampus]
7. J Rozowsky, A Abyzov, J Wang, P Alves, D Raha, A Harmanci, J Leng, R Bjornson, Y Kong, N Kitabayashi, et 4 al., "AlleleSeq: analysis of allele-specific expression and binding in a network framework." Mol. Syst. Biol., 7, (2011) 522. [offcampus]
8. Z Liu, J Yang, H Xu, C Li, Z Wang, Y Li, X Dong, Y Li, "Comparing computational methods for identification of allele-specific expression based on next generation sequencing data." Genet. Epidemiol., 38, #7 (2014) 591-8. [offcampus]
9. DL Wood, K Nones, A Steptoe, A Christ, I Harliwong, F Newell, TJ Bruxner, D Miller, N Cloonan, SM Grimmond, "Recommendations for Accurate Resolution of Gene and Isoform Allele-Specific Expression in RNA-Seq Data." PLoS ONE, 10, #5 (2015) e0126911.
10. A Conesa, P Madrigal, S Tarazona, D Gomez-Cabrero, A Cervera, A McPherson, MW Szcześniak, DJ Gaffney, LL Elo, X Zhang, et 1 al., "A survey of best practices for RNA-seq data analysis." Genome Biol., 17, (2016) 13. [offcampus]
11. SE Castel, A Levy-Moonshine, P Mohammadi, E Banks, T Lappalainen, "Tools and best practices for data processing in allelic expression analysis." Genome Biol., 16, (2015) 195. [offcampus]

References -- Phasing: Which Variants Lie on the Same Chromosome?

12. https://en.wikipedia.org/wiki/Haplotype_estimation
13. http://www.chromosomechronicles.com/2009/09/08/phasing-determining-which-snps-are-inherited-together/ [offcampus]

References -- Single-cell RNA sequencing: Here are a few papers introducing single-cell RNA sequencing and its associated "dropout" problem. Paper 14 is about smart-seq, which is the most common scRNA-Seq protocol that sequences full transcripts (as opposed to just 5' or 3' ends). Paper 15 is a pretty good paper about dropout. There are obviously a bunch of methods trying to account for dropout, but paper 16 is a good example of a pretty straightforward zero inflation approach.

14. S Picelli, OR Faridani, AK Björklund, G Winberg, S Sagasser, R Sandberg, "Full-length RNA-seq from single cells using Smart-seq2." Nat Protoc, 9, #1 (2014) 171-81. [offcampus]
15. SC Hicks, FW Townes, M Teng, RA Irizarry, "Missing data and technical variability in single-cell RNA-sequencing experiments." Biostatistics, 19, #4 (2018) 562-578. [offcampus]
16. PV Kharchenko, L Silberstein, DT Scadden, "Bayesian approach to single-cell differential expression analysis." Nat. Methods, 11, #7 (2014) 740-2. [offcampus]

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