Next-Gen Sequencing in the High School Classroom. April 11, 2017 BOSLAB Mark Hartman

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1 Next-Gen Sequencing in the High School Classroom April 11, 2017 BOSLAB Mark Hartman

2 Outline Introduce the BioSeq program Walk through one of our favorite projects as an example of what we do Technical details about the experiment How we run it in schools Show examples of some other projects

3 BioSeq program overview What is BioSeq? Educational outreach program from Tufts, Department of Chemistry, PI Prof. David Walt What do we do? Increase access to next-generation sequencing technology, promote scientific research in general, target the high school audience How are we able to do this? Funded by NIH Science Education Partnership Award (SEPA)

many contexts Sequencing and bioinformatics remain inaccessible to

4 The need for genetics outreach Genetics information is increasingly commonplace In the future we are likely to encounter sequencing in many contexts Sequencing and bioinformatics remain inaccessible to the public Disconnect between current science and the science classroom

5 Bioinformatics Inquiry through Sequencing Goal: Improve interest in and understanding of bioinformatics and sequencing, and improve attitudes toward scientific research Assessment BioSeq Educational sequencing center Support science fair projects, class projects, independent summer projects, etc. Classroom modules Run lab experiments in local high school classrooms Summer course Six-week summer class at Tufts for high school seniors to introduce sequencing and bioinformatics

6 Overview of lessons Introduction (research question) Classroom experiment (data collection) Students analyze data (interpretation of results) Sample processing and sequencing at Tufts Most activities in classroom, some activities in lab at Tufts We allow two weeks for sample processing and sequencing

Approach Inquiry-based learning Inquiry inspires student interest in science, and improves retention of science content and procedures Blanchard et al.

: A quantitative comparison of the relative effectiveness of guided inquiry and verification laboratory instruction. Science Education.

7 Approach Inquiry-based learning Inquiry inspires student interest in science, and improves retention of science content and procedures Blanchard et al. Is inquiry possible in light of accountability?: A quantitative comparison of the relative effectiveness of guided inquiry and verification laboratory instruction. Science Education. (2010) Near peer mentoring Younger students can envision themselves as future scientists Undergrads involved in both design and teaching MD Pluth, et al. Collaboration and Near-Peer Mentoring as a Platform for Sustainable Science Education Outreach. Journal of Chemical Education

8 Current and future modules Personal Microbiome Portrait Genetics of Race Mutations Investigation Food Microbiomes Microbiomes of Water Personal Genomics DNA Sequencing for Forensics

9 Current and future modules Personal Microbiome Portrait Genetics of Race Mutations Investigation Food Microbiomes Microbiomes of Water Personal Genomics DNA Sequencing for Forensics

10 Exploring Inter- and Intra-personal Variation in Microbial Communities Hartman MR, Harrington KT, Etson CM, Fierman MB, Slonim DK, Walt DR. FEMS Microbiology Letters /femsle/fnw266, (In press, 2016.)

>500,000 genes 2 1. Sender, R., Fuchs, S. & Milo, R. (2016) Revised estimates for the number of human and bacteria cells in the body.

11 Our bodies, our microbiomes Karyome 10 trillion cells 3 Gb per cell ~20,000 genes Mitochondria 100 mitochondria per cell, 5 mtdna per mitochondria = 5 quadrillion mtdna 17 Kb per mtdna 37 genes Microbiome 1 microbial cell per human cell ,000 unique species >500,000 genes 2 1. Sender, R., Fuchs, S. & Milo, R. (2016) Revised estimates for the number of human and bacteria cells in the body. PLOS Biology 14(8): e Qin et al. (2010) A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464,

Okay, so microbiomes are important but why are they a good fit for a high school NGS outreach program?

Most microbes cannot be cultured NGS has revolutionized the study of microbiomes Why are microbiomes a good

Personal microbiomes = personal relevance for students Novelty of the topic promotes openended exploration

12 Okay, so microbiomes are important but why are they a good fit for a high school NGS outreach program? Why are microbiomes a good topic for NGS experiments? Most microbes cannot be cultured NGS has revolutionized the study of microbiomes Why are microbiomes a good fit for high school outreach? Personal microbiomes = personal relevance for students Novelty of the topic promotes openended exploration Health implications are accessible Kaeberlein, Lewis, Epstein Isolating "Uncultivable" Microorganisms in Pure Culture in a Simulated Natural Environment. Science (2002). Stewart Growing unculturable bacteria. J. Bacteriol (2012).

13 Overview of lessons Introduction (research question) Classroom experiment (data collection) Students analyze data (interpretation of results) Sample processing and sequencing at Tufts Most activities in classroom, some activities in lab at Tufts We allow two weeks for sample processing and sequencing

14 Research question versus

15 Sample collection Hard Palate Swab the entire hard palate for 20 seconds Retroauricular Crease Swab back and forth along the entire crease 50 times

16 Sample collection Swab the entire hard palate for 20 seconds Use moderate force Rotate the swab so all surfaces come in contact with your palate Place the swab in a sterile tube CAREFULLY use scissors to cut the swab handle Use a marker to label your tube Hard Palate Swab the entire hard palate for 20 seconds

17 Sample processing Sampling Cell lysis PCR 1 Amplify desired regions PCR 2 Add unique barcode to each sample PCR clean up PCR clean up Quality check Library pooling Sequencing

18 Targeted vs. Total sequencing Recall our research question: We want to identify species and then compare microbial populations. From <

19 Targeted amplification Primers flank V3 and V4 hypervariable regions of 16S ribosomal RNA gene Single amplicon ~460 bp Forward: TCGTCGGCAGCGTCAGATGTGTATAAGAGACAG(N) 0-3 CCTACGGGNGGCWGCAG Reverse: GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG(N) 0-3 GACTACHVGGGTATCTAATCC Underlined region contains adapter (sequencing primer) and linker (sequence for subsequent index PCR). Kuczynski et al. Experimental and analytical tools for studying the human microbiome. Nature Reviews Genetics 13, (2012). Klindworth et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next generation sequencing based diversity studies. Nucleic Acids Res 41(1) (2013).

20 Sample processing Sampling Cell lysis PCR 1 Amplify desired regions PCR 2 Add unique barcode to each sample PCR clean up PCR clean up Quality check Library pooling Sequencing

million clusters per run Compare to the HiSeq X series which

21 Sequencing process Sequencing by synthesis technology: Sequence DNA by observing the synthesis of a complimentary strand Capacity ~10 million clusters per run Compare to the HiSeq X series which generates 6 billion reads per run Illumina MiSeq sequencer MiSeq Standard Flow Cell

22 Sequencing by synthesis K. Voelkerding, et al, Clinical Chemistry, 2009

23 Sequencing output Raw sequencing data: After base calls, demultiplexing, adapter trimming Read 1: GCCTACGGGTGGCAACAGTGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGACGGCCTTCGGGTTGTAAAGCTCTGTTAATCGGGACGAAAGGTCTTCTTGCGAATAGTTAGAAGAATTGACGGTACCGGAATAGAAAGCCACG GCTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGA Read 2: TGGCCTACAGTAGTCACTGTCTCTTATACACATCTCCGAGCCCACGAGACTGCAGCTAATCTCGTATGCCGTCTTCTGCTTGAAAAAAAAAAAAAAAAAAGAAGTAATGCAGGGGTGTGAGTGACTAAGAGGAGAGTGGTATGACATAAAACTAAGAAAACAACTAAAACA AGGGGAGGGCACAATATAACGTATCTCTGAGATGGTACTATGTGTCTGTGTAGCATCTGACATAATAACGTCCATATTCA Read 31,164: CCTACGGGTGGCTGCAGTGGGGAATCTTCCGCAATGGACGAAAGTCTGACGGAGCAACGCCGCGTGAGTGATGACGGCCTTCGGGTTGTAAAGCTCTGTTAATCGGGACGAAAGGTCCTCTTGCGAATAGTTAGAGGAATTGACGGTACCGGAATAGAAAGCCACGG CTAACTACGTGCCAGCAGCCGCGGTAATACGTAGGTGGCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGAT Desired output data: Relative abundances of microbes so we can compare populations

24 Workflow for comparing microbiome samples Raw reads QIIME tools Join paired end reads Pick operational taxonomic units Diversity analysis QIIME is an open-source bioinformatics pipeline for performing microbiome analysis (developed primarily in the Knight and Caporaso labs) Per-sample analyses Relative abundances Stratify samples Cross-sample analyses

25 Relative abundances per sample Our raw data sequences Small database of known sequences ACCTAC TCTGAT CGTCAT Overall ~1,000 taxa identified

Cross-sample comparisons Sample 1 Sample

Prevotella Anaerococcus Propionibacterium

26 Cross-sample comparisons Sample 1 Sample 2 Gemella Porphyromonas Actinobacillus Mannheimia Leptotrichia Rothia Veillonella Haemophilus Streptococcus Prevotella Anaerococcus Propionibacterium Staphylococcus Peptoniphilus Finegoldia Sample 1 Sample 2

27 Lesson #5: Results and discussion Health disclaimers Results Common bacteria Streptococcus: Typically thrives in warm and wet environments. Propionibacterium: Consumes sebum, a type of oil that be produced on your skin. Actinomyces, Clostridium, Fusobacterium, Prevotella, Veillonella: Examples of obligate anaerobes (microbes that are not tolerant of oxygen).

28 Return to the research question Revisit the students original hypotheses Survey students on their actual data-supported conclusions Why? What are the implications? versus At the completion of the experiment, students are rewarded with stickers (Is this experimental outcome supported by more rigorous analysis?)

$Calculate Unifrac score for ALL samples Phylogenetic$

29 Calculate Unifrac score for ALL samples Phylogenetic tree Dissimilarity matrix in Excel (Weighted unifrac scores)

30 Body site versus individual variation Find the distribution of dissimilarity scores for each category of sample comparisons p < 1x10-15 Higher dissimilarity metric arises from body site-to-site variation (same individual) than from person-to-person variation (same body site) (Is this really supported by more rigorous analysis?)

students in the local area Positive assessment

31 Personal Microbiome wrap-up Probably our most popular outreach activity, reaching hundreds of students in the local area Positive assessment results indicate gains in knowledge and attitudes toward science

32 Like 23andMe, but for microbiomes

33 Further Reading

34 Thanks! BioSeq team Kristin Harrington Dr. Matt Fierman Prof. Donna Slonim Prof. David Walt Walt lab Hannah DeBaets Chris Blackwood Tabitha Amondi Bridget Yang Anthony Garrity Quin Bottom-Johnson Kevin Lim Brittany Bowman Elise Gan Jack Reid Hannah Voelker Matt Cassar Partner teachers and administrators Funders

dbcamplicons pipeline Amplicons

dbcamplicons pipeline Amplicons Matthew L. Settles Genome Center Bioinformatics Core University of California, Davis settles@ucdavis.edu; bioinformatics.core@ucdavis.edu Microbial community analysis Goal: