CSE 428,  Sp '21:  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.

NOTE: I have stopped maintaining the reference list here, if favor of a shared Zotero group that we can all update. I will periodically dump a "Zotero Report" here.

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.

References -- Idea #1: Ribosomal leader ncRNAs autoregulation. Here are a few papers that seem like a good start on methods. Paper 2 is the source of the L19 example I presented. Its methodology was more broadly aimed than just ribosomal leaders, but streamlining it to focus on leaders shouldn't be too difficult. Paper 3 is a recent paper focused exclusively on ribosomal leaders, but in prokaryotes. Paper 4 (posted online just a few days ago, and I think led by an undergrad...) looks at several yeast species (also single celled eukaryotes). Again, its aims were broader but it did turn up one or two examples in ribosomal genes--not experimentally verified, but suggestive. Paper 5 describes the guts of the CMfinder algorithm; almost certainly more detail than you need, but listed in case you are curious. CMfinder was used in both 2 and 3. The yeast paper used a different, newer suite of tools; whether newer means better I don't know; time and interest permitting, I'd be happy to see both approaches applied to our data.

2. Z Yao, J Barrick, Z Weinberg, S Neph, R Breaker, M Tompa, WL Ruzzo, "A computational pipeline for high- throughput discovery of cis-regulatory noncoding RNA in prokaryotes." PLoS Comput Biol, 3, #7 (2007) e126.
3. I Eckert, Z Weinberg, "Discovery of 20 novel ribosomal leader candidates in bacteria and archaea." BMC Microbiol, 20, #1 (2020) 130.
4. William Gao, Thomas A. Jones, and Elena Rivas. "Discovery of 17 conserved structural RNAs in fungi." (25 March 2021) bioRxiv preprint https://doi.org/10.1101/2021.02.01.429235 [offcampus]
5. Z Yao, Z Weinberg, WL Ruzzo, "CMfinder--a covariance model based RNA motif finding algorithm." Bioinformatics, 22, #4 (2006) 445-52. [offcampus]
6. Valerio Vitali, Rebecca Rothering, Francesco Catania. "Fifty generations of amitosis: tracing asymmetric allele segregation in polyploid cells with single-cell DNA sequencing." (30 March 2021) bioRxiv preprint doi: https://doi.org/10.1101/2021.03.29.437473 [offcampus]

References -- Idea #2: Deep Learning for ncRNA discovery/classification. Papers 7, 8 are the two papers Alyssa referenced.

7. TMR Noviello, F Ceccarelli, M Ceccarelli, L Cerulo, "Deep learning predicts short non-coding RNA functions from only raw sequence data." PLoS Comput Biol, 16, #11 (2020) e1008415.
8. T Chantsalnyam, DY Lim, H Tayara, KT Chong, "ncRDeep: Non-coding RNA classification with convolutional neural network." Comput Biol Chem, 88, (2020) 107364. [offcampus]
9. N Navarin, F Costa, "An efficient graph kernel method for non-coding RNA functional prediction." Bioinformatics, 33, #17 (2017) 2642-2650. [offcampus] This is an earlier paper, cited by and benchmarked against #7; by their benchmarks, #7 is better, but may still have interest.

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