BioMed-R-2020

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BIOL47120 Biomedical Genomics II
Spring 2020, Saturdays 9-12 noon, Hunter North Building 1001G
Instructor: Weigang Qiu, Ph.D., Professor, Department of Biological Sciences, Hunter College, CUNY; Email: weigang@genectr.hunter.cuny.edu
T.A.: Christopher Panlasigui; Hunter College; Email: christopher.panlasigui47@myhunter.cuny.edu
Office: B402 Belfer Research Building, 413 East 69th Street, New York, NY 10021, USA; Office hour: Wed 3-5pm
MA plot Volcano plot Heat map
fold change (y-axis) vs. total expression levels (x-axis)
p-value (y-axis) vs. fold change (x-axis)
genes significantly down or up-regulated (at p<1e-4)

Course Overview

Welcome to Introductory BioMedical Genomics, a seminar course for advanced undergraduates and graduate students. A genome is the total genetic content of an organism. Driven by breakthroughs such as the decoding of the first human genome and rapid DNA and RNA-sequencing technologies, biomedical sciences are undergoing a rapid & irreversible transformation into a highly data-intensive field, that requires familiarity with concepts in both biology, computational, and data sciences.

Genome information is revolutionizing virtually all aspects of life sciences including basic research, medicine, and agriculture. Meanwhile, use of genomic data requires life scientists to be familiar with concepts and skills in biology, computer science, as well as statistics.

This workshop is designed to introduce computational analysis of genomic data through hands-on computational exercises. Students are expected to be able to replicate key results of data analysis from published studies.

The pre-requisites of the course are college-level courses in molecular biology, cell biology, and genetics. Introductory courses in computer programming and statistics are preferred but not strictly required.

Learning goals

By the end of this course successful students will be able to:

  • Describe next-generation sequencing (NGS) technologies & contrast it with traditional Sanger sequencing
  • Explain applications of NGS technology including pathogen genomics, cancer genomics, human genomic variation, transcriptomics, meta-genomics, epi-genomics, and microbiome.
  • Visualize and explore genomics data using R & RStudio
  • Replicate key results using a raw data set produced by a primary research paper

Web Links

Quizzes and Exams

Student performance will be evaluated by attendance, weekly assignments, quizzes, and a final report:

  • Attendance & In-class participation: 50 pts
  • Assignments: 5 x 10 = 50 pts
  • Quizzes: 2 x 25 pts = 50 pts
  • Mid-term: 50 pts
  • Final presentation & report: 100 pts

Total: 300 pts

Tips for Success

To maximize the your experience we strongly recommend the following strategies:

  • Follow the directions for efficiently, finding high-impact papers, reading science research papers and preparing presentations.
  • Read the papers, watch required videos and do the exercises regularly, long before you attend class.
  • Attend all classes, as required. Late arrival results in loss of points.
  • Keep up with online exercises. Don’t wait until the due date to start tasks.
  • Take notes or annotate slides while attending the lectures.
  • Listen actively and participate in class and in online discussions.
  • Review and summarize material within 24 hrs after class.
  • Observe the deadlines for submitting your work. Late submissions incur penalties.
  • Put away cell phones, do not TM, email or play computer games in class.

Hunter/CUNY Policies

  • Policy on Academic Integrity

Hunter College regards acts of academic dishonesty (e.g., plagiarism, cheating on homework, online exercises or examinations, obtaining unfair advantage, and falsification of records and official documents) as serious offenses against the values of intellectual honesty. The College is committed to enforcing the CUNY Policy on Academic Integrity, and we will pursue cases of academic dishonesty according to the Hunter College Academic Integrity Procedures. Students will be asked to read this statement before exams.

  • ADA Policy

In compliance with the American Disability Act of 1990 (ADA) and with Section 504 of the Rehabilitation Act of 1973, Hunter College is committed to ensuring educational parity and accommodations for all students with documented disabilities and/or medical conditions. It is recommended that all students with documented disabilities (Emotional, Medical, Physical, and/or Learning) consult the Office of AccessABILITY, located in Room E1214B, to secure necessary academic accommodations. For further information and assistance, please call: (212) 772- 4857 or (212) 650-3230.

  • Syllabus Policy

Except for changes that substantially affect implementation of the evaluation (grading) statement, this syllabus is a guide for the course and is subject to change with advance notice, announced in class or posted on Blackboard.

Course Schedule

Feb 1, 2020

  • Introduction
  • R Tutorial 1: Use interface, basic operations, load data. Slides:
    File:R-part-1.pdf
Assignment 1 (10 pts; Due next class 2/8, in hard copy)
  • (3 pts) Print a copy of your first R script, with proper annotations
  • (3 pts) Transform the following "untidy/wide" table into a "tidy/tall" table (print a hard copy)
PropertyName,Density_250m,Density_500m,Density_1000m
HighbridgePark,0.006561319,0.009462031,0.010578611
BronxRiverParkway,0.001318749,0.001978858,0.002652118
CrotonaPark,0.009412087,0.01164712,0.01202321
ClaremontPark,0.016391948,0.019972485,0.020350481
VanCortlandtPark,0.000550151,0.000979312,0.001372675
  • (4 pts) Make a single slide of a primary research paper using next-generation sequencing (NGS) technologies, show the following
    • proper citation (authors, title, year, journal, URL)
    • NGS method (Illumina, PacBio, or NanoPore)
    • NGS application (genomics, cancer, transcriptome, microbiome, proteome, metagenomics, human variation, etc)
    • a key figure, with a caption explaining x-axis, y-axis, samples, experiments
    • raw data table (show first few columns and first few rows)
    • for example, a student has worked on tissue regeneration, the search in PubMed with key words "regeneration zebra fish transcriptome" found the following primary paper as the best because of the high quality of journal and the availability of raw data: https://www.ncbi.nlm.nih.gov/pubmed/28096348

Feb 8, 2019

Assignment 2 (10 pts; Due next class 2/15, in hard copy)
  • (3 pts) Print a copy of your 2nd R script, with proper annotations
  • (4 pts) Show following commands with the chaining operator ("%>%") for the "iris" data set (4 individual commands; not a single one)
    • Select columns "Sepal.Length" & "Species"
    • Filter rows 2 through 10
    • Add a column "logSepalLength" by taking the logarithm of the said column
    • Calculate mean and standard deviation of Petal.Length in each species
  • (3 pts) Transform the "iris" data table into a "tidy/tall" table (manually, show first 10 rows, print a hard copy)

Feb 15, 2019

  • NGS presentations (Group II)
  • R Tutorial. Part 3. Data visualization with ggplot2. Slides:
    File:R-tutorials-3.pdf
  • No assignment (go over slides and 3 tutorial scripts to prepare for Quiz next week)

Feb 22, 2019

  • Quiz 1 (Open Book)
  • R Tutorial: Part 4. BioStat (chi-square & t-test) Lecture slides:
    File:R-tutorial-4.pdf
Assignment 3 (10 pts). In-class workshop. Evaluation of papers according to the following rubrics (submit by email)
  • Citation & PubMed Link
  • Main research question
  • Samples, sample sizes, & controls
  • Omics technologies (e.g., genomics, metagenomics, microbiome, transcriptome, proteome, mythylome, RNA-seq, 16S amplicon sequencing)
  • Sequencing platform (e.g., illumina, PacBio, nanopore)
  • Main computational tools (e.g., R, RStudio, QIMME)
  • Main graphics (e.g., scatterplot, boxplot, heatmap, vocano plot)
  • Main statistical analysis (e.g., t-test, chi-square, regression analysis)
  • Data set: a short description & links

Feb 29, 2019

  • Student submissions
Student & project type Citation & PubMed link Research question Study Design: samples, sample size & controls Omics tech & NGS platform Computational tools Data visualization Statistical tests Data description & links
Tahir - cancer microbiome Kostic, A. D., et al. (2012). Genomic analysis identifies association of Fusobacterium with colorectal carcinoma. Genome research, 22(2), 292–298. PubMed How does the composition of tumorous colorectal carcinoma tissue microbiome differ from non-tumorous adjacent tissue? Colorectal carcinoma (Tumor) tumor tissue and non-tumorous adjacent nonneoplastic (Normal) tissue); 95 tumor/normal paired samples (190 total samples); Non tumorous adjacent nonneoplastic tissue as controls 16S rDNA amplicon sequencing; 454 GS FLX Sequencing Mothur Bar plots, Boxplots, Scatterplots, Cladogram Linear Discriminate Analysis (LDA) and Wilcox Rank Sum Test (non-parametric t-test) NCBI Sequence Read Archive accession no. SRP000383. Pre-processed dataset can also be retrieved from R package, phyloseq: filepath = system.file("extdata", "study_1457_split_library_seqs_and_mapping.zip", package = "phyloseq"); kostic = microbio_me_qiime(filepath). The Kostic dataset is a phyloseq object (S4) consisting of sam_table, otu_table, table, phy_tree, and tax_table. Sample table includes metadata of samples collected including: Diagnosis, Race, Gender, etc.
Junho - yeast transcriptome Gierlin ́ski M, Cole C, Schofield P, Schurch NJ, Sherstnev A, Singh V, Wrobel N, Gharbi K, Simpson G, Owen- Hughes T, Blaxter M, and Barton GJ. Statistical models for RNA-seq data derived from a two-condition 48-replicate experiment. Bioinformatics, 31(22):1–15, 2015. These estimates are typically based on statistical models such as the negative binomial distribution, which is employed by the tools, edgeR, DESeq and cuffdiff. Until now, the validity of these models has usually been tested on either low-replicate RNA-seq data or simulations. RNA-seq dataset to date that contains mRNA from 48 replicates of two S. cerevisiae populations: wildtype vs snf2 knock-out mutants Illumina HiSeq 2000 RStudio scatterplot, boxplot, heatmap t-test, Wald test (2 factors); LRT for multiple factor ftp.sra.ebi.ac.uk/vol1/fastq/ERR458/ERR458493/ERR458493.fastq.gz; ERR458493.fastq.gz; WT_1_Aligned.sortedByCoord.out; WT_2_Aligned.sortedByCoord.out; SNF2_1_Aligned.sortedByCoord.out.bam.bai; SNF2_2_Aligned.sortedByCoord.out.bam.bai
Brian - mouse platelet transcriptome Rowley, Jesse W et al. “Genome-wide RNA-seq analysis of human and mouse platelet transcriptomes.” Blood vol. 118,14 (2011) How can we use RNA-seq analysis to identify key genetic expression differences in human/mouse platelet cells? 8 - 16 mouse samples (male & female); 2 human samples (male & female) Illumina GAIIx Aligned via Novoalignment / downstream analysis done in Perl / RPKM calculation scatter plots / pie charts / RefSeq gene annotations with RPKM expression levels / histograms Spearman rank correlation analysis Mouse & Human BAM files: https://bioserver.hci.utah.edu/gnomex/analysis/(analysisPanel:2430) https://bioserver.hci.utah.edu/gnomex/analysis/(analysisPanel:2431) (LINKS don't work)
Qinfan - wildlife microbiome Comparing Microbiome Sampling Methods in a Wild Mammal: Fecal and Intestinal Samples Record Different Signals of Host Ecology, Evolution If there any differences between microbial communities from fecal and intestinal mucosa? Fecal and intestinal tissue samples from 37 bats in Lamanai, Belize. 55 DNA samples , 29 intestinal and 24 guano Illumina 16S rRNA Rstudio boxplot (Alpha diversity – Shannon and Faith’s Index) , Barplot(Abundance; Heatmap (test bacterial family abundance) , Scatterplot ( beta diversity) t-test, Wilconxon sign rank test and permutational multivariate analysis of variance This is raw, demultiplexed 16S sequence data. Data is available on NCBI Sequence Read Archive under BioProject # PRJNA428973, QIIME2 mapping file and annotated feature table are available on Figshare.
Brittany - human genome variation Belsare, S. et al. Evaluating the quality of the 1000 genomes project data. BMC Genomics 20, 620 (2019). Use data from the 1000 Genomes Project to determine whether there are significant differences in variants of pain receptor genes between ethnic groups. 2,504 individuals from 26 different ethnic groups from Africa, Asia, Europe, America

Genetic variants: rs4633, rs4680, rs4818, rs6269, rs740603, rs1051660, rs1799971, rs7958311, rs40434, rs2066713

whole genome sequencing, deep exome sequencing, dense microarray genotypiny; Illumina, 10X Genomics RStudio bar plot, pie chart, heatmap, Manhattan plot chi-square, ANOVA To search by variant and download table of allele frequencies and genotype frequencies; To download whole genomes
Hao - microRMA biomarker Vila-Navarro, Elena et al. “MicroRNAs for Detection of Pancreatic Neoplasia: Biomarker Discovery by Next-generation Sequencing and Validation in 2 Independent Cohorts.” Annals of surgery vol. 265,6 (2017): 1226-1234. How to find the new microRNA-based biomarkers for early detection of pancreatic neoplasia by anakyzing the miRNome of PDAC and the IMPN. A. Pancreatic tissues (n = 165); B. Biomarker discovery in a set of 18 surgical samples (11 PDAC, 4 IPMN, 3 C). C. MiRNA validation in 2 different set of samples. Set 1—52 surgical samples (24 PDAC, 7 IPMN, 6 chronic pancreatitis, 15 C), and set 2—95 endoscopic ultrasound-guided fine-needle aspirations (60 PDAC, 9 IPMN, 26 C). Illumina Genome Analyzer IIx Illumina GA pipeline software; R/Bioconductor Volcano plot, Heatmap, pie chart, ROC curve t-test Discriminatory power of immature granulocyte count (IG), cell-free DNA (cfDNA), mitochondrial DNA (mtDNA), nuclear DNA (ncDNA), phagocytic index (PI) and revised BAUX (rBAUX) for sepsis at different time
Marvin - microbiome Marangoni, A. et al. (2020). Pharyngeal microbiome alterations during Neisseria gonorrhoeae infection. PLoS One, 15(1). Identify the bacterial community profiles of the pharyngeal microbiome associated with Neisseria gonorrhoeae infection. Does gonorrhea infection change the microbiome community in oropharynx? : Pharyngeal swap taken from men who have sex with men and reported having unsafe orogenital intercourse. 70 samples were taken with 45 no infection (n= 45, no infection), and 25 diagnosed with Neisseria gonorrhoeae infection (n= 25, infected). Pharyngeal swap taken from men who have sex with men and reported having unsafe orogenital intercourse. 70 samples were taken with 45 no infection (n= 45, no infection), and 25 diagnosed with Neisseria gonorrhoeae infection (n= 25, infected). R package vegan, PICRUST software, Prism, MATLAB Alpha-diversity box plot of Chao1 and Shannon index, principal coordinate analysis plot (scatterplot), pie charts, data tables Non-parametric Monte Carlo-based test, Whitney t-test. (Requesting), NCBI Short Read Archive accession number PRJNA556341
Ann- disease transcriptome Mastrokolias A, Ariyurek Y, Goeman JJ, et al. Huntington's disease biomarker progression profile identified by transcriptome sequencing in peripheral blood. Eur J Hum Genet. 2015;23(10):1349–1356. Can the researchers identify accessible biomarkers for Huntington’s Disease to monitor disease progression and therapy response? RNA blood samples - 91 mutation carriers (27 presymptomatic and, 64 symptomatic) and 33 controls transcriptome sequencing; Illumina GA Pipeline R boxplots; scatterplot Student’s T-tests, linear modeling function in R, linear regression model Gene Expression Omnibus (GEO) under accession number GSE51799.
Vhy-Shelta - single cell transcriptome E. A. Stadtmauer et al., Science 10.1126/science.aba7365 (2020) First in human phase I clinical trial to test the safety and feasibility of multiplex CRISPR-Cas9 editing to engineer T cells in three patients with refractory cancer. Can CRISPR-Cas9 can be used as a synthetic biology cancer immunotherapy application in refractory cancer? Experimental group: n=3, 2 patients with refractory advanced myeloma and one patient with refractory metastatic sarcoma. Types of controls: Patient Untransduced T cells and cells transduced with NY-ESO TCR without CRISPR, positive reference sample containing 1x10^3 copies of synthetic template plasmid, healthy donor (ex vivo CD4+and CD8+ T cells from patients or healthy donor controls) Illumina seq- HiSeq400, MiSeq iGuide, Cellranger v3.0.2, Seurat v3.1.0, Python Box plots, bar graphs, scatter plots, Venn diagrams, heat maps, cytometry plots, UMAP plots, Swimmer’s plot, piecewise linear model, computed tomography scan Paired student t-tests, unpaired student’s t-tests, GraphPad, prism, parameter estimates, random estimates

Table 4S (excel file) presents the information for interpreting scRNAseq Metadata tables (4a-c) See supplementary materials Table 5S s Differentially expressed genes in NY-ESO-1 TCR+ T cells during various days pre and post infusion. Data Link (See Table 4S and Supplementary materials Table 5S)

Andrew - wildlife transcriptome [[ https://doi.org/10.1073/pnas.1813775116%7CYoung, Ferkin, et al. (2019)]] Conserved transcriptomic profiles underpin monogamy across vertebrates Is there a universal transcriptomic code underlying monogamy in vertebrates? Sequenced and compared neural transcriptomic profiles from reproductive males of closely related monogamous and nonmonogamous species from four major classes of vertebrates (n = 3 pooled individuals per species): Mammalia (Microtus ochrogaster versus Microtus pennsylvanicus and Peromyscus californicus versus Peromyscus maniculatus); Reptilia–Aves (A. spinoletta versus P. modularis); Amphibia (Ranitomeya imitator versus Oophaga pumilio); and Actinopterygii (Xenotilapia spilotera versus Xenotilapia ornatipinnis) ? Bioconductor DESeq2 Volcano Plot, (RRHO) Rank Rank Hypergeometric Overlap, HeatMap Spearman's Rank Correlation Coefficient, Linear Regression, Principle Component ANalysis Data Appendix, Dataset_S01, Dataset_S02
Adam - Single cell transcriptome Hong et al. (2019). Single-cell transcriptomics reveals multi-step adaptations to endocrine therapy. Nature Communications , 10 (1), 3840. How do clonal genetic diversity and transcriptional plasticity play a role in the early and late endocrine therapy for luminal breast cancers? MCF7, long-term oestrogen-deprived cells (LTED) , Primary-metastatic breast cancer cells were derived from pleuraleffusions of patients with metastatic breast cancers, and cells from 20 patients with luminal tumors treated by aromatase inhibitors (10 responders and 10 non-responders). Illumina HiSeq 4000 R Box plot, volcano plot, heatmap, gene network, venn diagrams, bar graphs, line plot Statistical Analysis Kruskal-Wallis test, two-tailed paired t-test, Wilcoxon signed-rank test, hypergeometric test, permutation test, regression GSE122743. Supplementary Data is also provided. Description of supplementary data
  • Paper evaluation & selection
  • R Tutorial: Part 4. BioStat (regression & ANOVA)

March 7, 2019

  • Self study & prepare for mid-term (no class)

March 14, 2019

  • Mid-term exam (50 pts). Open Boook

March 22, 2019

  • R tutorial: Section 5.3. t-test
  • Group presentations (Data visualization)

March 28, 2019

  • (Self study; No live class)
  • Abstract (200 words; individualized; due 3/30)
  • Review contingency test & two-sample t-test
  • Generate preliminary graphs

March 30, 2019

  • 20 pts Quiz on contingency test & two-sample t-test
  • Group presentations (Show preliminary graphs)
  • Material & Methods (due 4/6)

April 4, 2019

  • 20 pts Quiz
  • R tutorial: Section 5.4. Regression analysis
  • Results (due 4/13)
    • Tables to show the dataset you work on (not all, but a sample)
    • Figures with legend (R methods, x & y-axis, conclusion)
    • 1-paragraph summary of your results

April 18, 2019

  • 20 pts Quiz. Regression analysis
  • Background & Introduction (due 5/4)

April 25, 2019

  • Final presentation I. Graded on:
    • Objective (original & your own)
    • Material & methods (original & your own)
    • Results (your own)
    • Conclusion (your own)
    • Conclusion (due 5/11)

May 2, 2019

  • Self study: Prepare your 10-slide presentation
  • No class (instructor travels)

May 16, 2019, 9-1pm

  • Final presentation
  • May 22, 2018 (Wed, 5pm) Final Report Due (hard copy; n my office or in mailbox)
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