Overview

Each student has been assigned one journal publication to present in class. The expected structure of the paper presentations is outlined in this Slideshow Template. A detailed presentation schedule is available on the internal Course Schedule. The following lists the assigned papers organized by course project topics.

Publications organized by course projects

All references in Paperpile

ChIP-Seq1 - Motif Prediction

  • Alipanahi B, Delong A, Weirauch MT, Frey BJ (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat Biotechnol 33: 831–838. PubMed
  • Tompa, M, N Li, T L Bailey, G M Church, B De Moor, E Eskin, A V Favorov, et al. 2005. “Assessing Computational Tools for the Discovery of Transcription Factor Binding Sites.” Nature Biotechnology 23 (1): 137–44. PubMed

ChiP-Seq2 - Peak Calling

  • Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nussbaum C, Myers RM, Brown M, Li W, et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol. doi: 10.1186/gb-2008-9-9-r137 PubMed
  • Wilbanks EG, Facciotti MT (2010) Evaluation of algorithm performance in ChIP-seq peak detection. PLoS One. doi: 10.1371/journal.pone.0011471 PubMed

RNA-Seq1 - RNA-Seq Aligners

  • Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol. doi: 10.1038/nbt.3519 PubMed
  • Kim D, Langmead B, Salzberg SL (2015) HISAT: a fast spliced aligner with low memory requirements. Nat Methods 12: 357–360 PubMed

RNA-Seq2 - DEG Methods

  • Love MI, Huber W, Anders S (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol 15: 550 PubMed
  • Zhou X, Lindsay H, Robinson MD (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 42: e91 PubMed

VAR-Seq1 - Functional Prediction

  • DePristo MA, Banks E, Poplin R, Garimella KV, Maguire JR, Hartl C, Philippakis AA, del Angel G, Rivas MA, Hanna M, et al (2011) A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet 43: 491–498 PubMed
  • Shihab HA, Rogers MF, Gough J, Mort M, Cooper DN, Day INM, Gaunt TR, Campbell C (2015) An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics 31: 1536–1543 PubMed