Student Paper Presentations

4 minute read

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. Students can use any slide show presentation software they wish, but should follow the presentation structure of the template. A detailed presentation schedule is available in the internal Presentation Schedule sheet. The grading of both the paper and project presentations includes anonymous feedback from all students as well as the instructor, where understanding of the material, clarity of the oral presentations and critical thinking are the main grading criteria. The grading forms will be provided in the Presentation Schedule (internal google sheet) shortly before the presentations start on May 13th and May 15th. The following lists the assigned papers organized by course project topics.

Publications organized by course projects

All references in Paperpile

RNA-Seq Aligners

  1. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley R, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol 14: R36. PubMed
  2. Bray NL, Pimentel H, Melsted P, Pachter L (2016) Near-optimal probabilistic RNA-seq quantification. Nat Biotechnol 34: 525–527. PubMed

DEG Methods

  1. 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
  2. Guo Y, Li C-I, Ye F, Shyr Y (2013) Evaluation of read count based RNAseq analysis methods. BMC Genomics 14 Suppl 8: S2. PubMed
  3. Zhou X, Lindsay H, Robinson MD (2014) Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Res 42: e91. PubMed

Functional Enrichment Methods

  1. Hänzelmann S, Castelo R, Guinney J (2013) GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics 14: 7. PubMed
  2. Koopmans F (2024) GOAT: efficient and robust identification of gene set enrichment. Commun Biol 7: 744. PubMed
  3. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102: 15545–15550. PubMed

Cluster Analysis

  1. Abu-Jamous B, Kelly S (2018) Clust: automatic extraction of optimal co-expressed gene clusters from gene expression data. Genome Biol 19: 172. PubMed
  2. Rodriguez MZ, Comin CH, Casanova D, Bruno OM, Amancio DR, Costa L da F, Rodrigues FA (2019) Clustering algorithms: A comparative approach. PLoS One 14: e0210236. PubMed
  3. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9: 559–559. PubMed

Differentially Expressed Transcript Variants

  1. Anders S, Reyes A, Huber W (2012) Detecting differential usage of exons from RNA-seq data. Genome Res 22: 2008–2017. PubMed
  2. Shen S, Park JW, Lu Z-X, Lin L, Henry MD, Wu YN, Zhou Q, Xing Y (2014) rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc Natl Acad Sci U S A 111: E5593-601. PubMed
  3. Pimentel H, Bray NL, Puente S, Melsted P, Pachter L (2017) Differential analysis of RNA-seq incorporating quantification uncertainty. Nat Methods 14: 687–690. PubMed

VAR-Seq Analysis

  1. Poplin R, Chang P-C, Alexander D, Schwartz S, Colthurst T, Ku A, Newburger D, Dijamco J, Nguyen N, Afshar PT, et al (2018) A universal SNP and small-indel variant caller using deep neural networks. Nat Biotechnol 36: 983–987. PubMed
  2. Cooke DP, Wedge DC, Lunter G (2021) A unified haplotype-based method for accurate and comprehensive variant calling. Nat Biotechnol 39: 885–892. PubMed
  3. Li H (2011) A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27: 2987–2993. PubMed

Drug-Target Analysis

  1. Abramson J, Adler J, Dunger J, Evans R, Green T, Pritzel A, Ronneberger O, Willmore L, Ballard AJ, Bambrick J, et al (2024) Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature 630: 493–500. PubMed
  2. Rifaioglu AS, Nalbat E, Atalay V, Martin MJ, Cetin-Atalay R, Doğan T (2020) DEEPScreen: high performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chem Sci 11: 2531–2557. PubMed
  3. Ye Q, Hsieh C-Y, Yang Z, Kang Y, Chen J, Cao D, He S, Hou T (2021) A unified drug-target interaction prediction framework based on knowledge graph and recommendation system. Nat Commun 12: 6775. PubMed
Last modified 2025-05-26: some edits (1747f3b)