Iowa State Bioinformatics BCB Symposium 2018 - There and Back Again
1 like224 views
This document summarizes Adina Howe's presentation on the rapid advances in DNA sequencing technologies and their applications to microbial ecology. Some key points include: - Next generation sequencing (NGS) has led to drastically reduced costs of DNA sequencing, enabling large-scale metagenomic projects. - Computational challenges remain for assembling metagenomic datasets due to their diversity, variability in abundance between organisms, and sequencing errors. Methods like digital normalization help address these challenges. - NGS is revolutionizing fields like microbial ecology by allowing sequencing of entire communities directly from environmental samples without requiring isolation or culture of individual organisms.
Iowa State Bioinformatics BCB Symposium 2018 - There and Back Again
- 1. THERE AND BACK AGAIN 4th Annual BCB Symposium, March 30, 2018 Adina Howe Assistant Professors Department of Agricultural and Biosystems Engineering Iowa State University www.germslab.org NGS SEQUENCING
- 2. HOW DID WE GET HERE
- 3. Understanding community dynamics ¨ Who is there? ¨ What are they doing? ¨ How are they doing it? Kim Lewis, 2010
- 4. Gene / Genome Sequencing ¨ Collect samples ¨ Extract DNA ¨ Sequence DNA ¨ “Analyze” DNA to identify its content and origin Taxonomy (e.g., pathogenic E. Coli) Function (e.g., degrades cellulose)
- 5. Cost of Sequencing Stein, Genome Biology, 2010 E. Coli genome 4,500,000 bp ($4.5M, 1992) 1990 1992 1994 1996 1998 2000 2003 2004 2006 2008 2010 2012 Year 0.1 1 10 100 1,000 10,000 100,000 1,000,000 DNASequencing,Mbpper$ 10,000,000 100,000,000
- 6. Rapidly decreasing costs with NGS Sequencing Stein, Genome Biology, 2010 Next Generation Sequencing 4,500,000 bp (E. Coli, $100, presently) 1990 1992 1994 1996 1998 2000 2003 2004 2006 2008 2010 2012 Year 0.1 1 10 100 1,000 10,000 100,000 1,000,000 DNASequencing,Mbpper$ 10,000,000 100,000,000
- 7. Effects of low cost sequencing… First free-living bacterium sequenced for billions of dollars and years of analysis Personal genome can be mapped in a few days and hundreds of dollars
- 8. The experimental continuum Single Isolate Pure Culture Enrichment Mixed Cultures Natural systems
- 9. The era of big data in biology Stein, Genome Biology, 2010 Computational Hardware (doubling time 14 months) Sanger Sequencing (doubling time 19 months) NGS (Shotgun) Sequencing (doubling time 5 months) 1990 1992 1994 1996 1998 2000 2003 2004 2006 2008 2010 2012 Year 0 1 10 100 1,000 10,000 100,000 1,000,000 DiskStorage,Mb/$ 0.1 1 10 100 1,000 10,000 100,000 1,000,000 DNASequencing,Mbpper$ 10,000,000 100,000,000 0.1 1 10 100 1,000 10,000 100,000 1,000,000 10,000,000 100,000,000
- 10. Postdoc experience with data 2003-2008 Cumulative sequencing in PhD = 2000 bp 2008-2009 Postdoc Year 1 = 50 Gbp 2009-2010 Postdoc Year 2 = 450 Gbp 2014 = 50 Tbp today = 0.5 to 1 Tbp budgeted per project (PhD student)
- 11. Tackling Soil Biodiversity Source: Chuck Haney C. Titus Brown, James Tiedje, Qingpeng Zhang, Jason Pell (MSU) Janet Jansson, Susannah Tringe (JGI)
- 13. THE DIRT ON SOIL Biodiversity in the dark, Wall et al., Nature Geoscience, 2010 Jeremy Burgress MAGNIFICENT BIODIVERSITY
- 14. THE DIRT ON SOIL SPATIAL HETEROGENEITY http://www.fao.org/ www.cnr.uidaho.edu
- 15. THE DIRT ON SOIL DYNAMIC
- 16. THE DIRT ON SOIL INTERACTIONS: BIOTIC, ABIOTIC, ABOVE, BELOW, SCALES Philippot, 2013, Nature Reviews Microbiology
- 18. de novo assembly vCompresses dataset size significantly vImproved data quality (longer sequences, gene order) vReference not necessary (novelty) Raw sequencing data (“reads”) Computational algorithms Informative genes / genomes
- 20. Shotgun sequencing and de novo assembly It was the Gest of times, it was the wor , it was the worst of timZs, it was the isdom, it was the age of foolisXness , it was the worVt of times, it was the mes, it was Ahe age of wisdom, it was th It was the best of times, it Gas the wor mes, it was the age of witdom, it was th isdom, it was tIe age of foolishness It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness
- 21. Practical Challenges – Intensive computing Howe et al, 2014, PNAS Months of “computer crunching” on a super computer
- 22. Practical Challenges – Intensive computing Howe et al, 2014, PNAS Months of “computer crunching” on a super computer Assembly of 300 Gbp (70,000 genomes worth) can be done with any assembly program in less than 14 GB RAM and less than 24 hours. 50 Gbp = 10,000 genomes
- 23. Four main challenges for de novo sequencing of metagenomes. ¨ Repeats. ¨ Low coverage. ¨ Errors - these introduce breaks in the construction of contigs. ¨ Variation in coverage – transcriptomes and metagenomes (PCR amplification biases) Challenge: assembler must distinguish between erroneous connections and real connections.
- 24. Natural community characteristics u Diverse è Many organisms (genomes)
- 25. Natural community characteristics u Diverse è Many organisms (genomes) u Variable abundance è Most abundant organisms, sampled more often è Assembly requires a minimum amount of sampling è More sequencing, more errors Sample 1x
- 26. Natural community characteristics u Diverse è Many organisms (genomes) u Variable abundance è Most abundant organisms, sampled more often è Assembly requires a minimum amount of sampling è More sequencing, more errors Sample 1x Sample 10x
- 27. Natural community characteristics u Diverse è Many organisms (genomes) u Variable abundance è Most abundant organisms, sampled more often è Assembly requires a minimum amount of sampling è More sequencing, more errors Sample 1x Sample 10x Overkill
- 28. Digital normalization True sequence (unknown) Reads (randomly sequenced) Brown et al., 2012, arXiv Howe et al., 2014, PNAS Zhang et al., 2014, PLOS One Titus Brown , Jason Pell, Qingpeng Zhang
- 29. Digital normalization True sequence (unknown) Reads (randomly sequenced) X Brown et al., 2012, arXiv Howe et al., 2014, PNAS Zhang et al., 2014, PLOS One Titus Brown , Jason Pell, Qingpeng Zhang
- 30. Digital normalization True sequence (unknown) Reads (randomly sequenced) X X X X X X X X X X X Brown et al., 2012, arXiv Howe et al., 2014, PNAS Zhang et al., 2014, PLOS One X = Sequencing errors L Titus Brown , Jason Pell, Qingpeng Zhang
- 31. Digital normalization True sequence (unknown) Reads (randomly sequenced) X X X X X X X X X X X Brown et al., 2012, arXiv Howe et al., 2014, PNAS Zhang et al., 2014, PLOS One Titus Brown , Jason Pell, Qingpeng Zhang
- 32. Digital normalization True sequence (unknown) Reads (randomly sequenced) X X X X X X X X X If next read is from a high coverage region - discard X X Brown et al., 2012, arXiv Howe et al., 2014, PNAS Zhang et al., 2014, PLOS One Titus Brown , Jason Pell, Qingpeng Zhang
- 33. Digital normalization True sequence (unknown) Reads (randomly sequenced) X X X X X X X X X X X X X X X X X X X X X X X X Redundant reads (not needed for assembly) Brown et al., 2012, arXiv Howe et al., 2014, PNAS Pell et al, 2012, PNAS Zhang et al., 2014, PLOS One v Scales datasets for assembly up to 95% - same assembly outputs. v Genomes, mRNA-seq, metagenomes (soils, gut, water)
- 34. Tackling Soil Biodiversity Source: Chuck Haney C. Titus Brown, James Tiedje, Qingpeng Zhang, Jason Pell (MSU) Janet Jansson, Susannah Tringe (JGI)
- 35. The reality?
- 36. More like… Source: Chuck HaneyHowe et. al, 2014, PNAS
- 37. The Future ¨ More data, more samples, better references ¨ Expense will be in sampling – not sequencing or even data analysis ¨ All biologists will need to know how to use a pipette and write computer programs ¨ Large-scale, collaborative projects rather than single PI efforts
- 38. Soils @ ISU ¨ CABBI DOE Bioenergy Research Center / ISU: Can we predict where to grow the best bioenergy crops? How come some bioenergy crops are so good at finding nitrogen and some not? ¨ USDA ARS / ISU : What happens during composting? Does the compost source matter? What is the timing of nutrients? Can we manage it? ¨ USDA ARS / ISU : Soil as an environmental pathway: What happens in the soil in traditionally managed agriculture? Manure-amended soils?
- 39. PART II. Two blog posts on this: adina.github.io “Job Hunt Wrap Up” “My job application to ISU”
- 40. Background Purdue University, BSME, Mechanical Engineering Purdue University, MS, Environmental Engineering (Sustainability)
- 41. Background Purdue University, BSME, Mechanical Engineering Purdue University, MS, Environmental Engineering (Sustainability) University of Iowa, PhD, Environmental Engineering (Microbiology/Bioremediation)
- 42. Background Purdue University, BSME, Mechanical Engineering Purdue University, MS, Environmental Engineering (Sustainability) University of Iowa, PhD, Environmental Engineering (Microbiology/Bioremediation) Michigan State University NSF Postdoc Math and Biology Fellow (cross-training) Microbial Ecology (Jim Tiedje) Bioinformatics (Titus Brown)
- 43. Background Purdue University, BSME, Mechanical Engineering Purdue University, MS, Environmental Engineering (Sustainability) University of Iowa, PhD, Environmental Engineering (Microbiology/Bioremediation) Michigan State University NSF Postdoc Math and Biology Fellow (cross-training) Microbial Ecology (Jim Tiedje) Bioinformatics (Titus Brown) Computational Biologist Microbiology / Microbial Ecology
- 44. Business vs. govt vs. academics
- 47. What I turned to…
- 48. Three main questions ¨ When do I start looking for jobs? ¨ Where do I look for jobs? ¨ What should I say about myself?
- 49. Job Hunt Statistics ¨ 2 Postdocs ¨ 2 first author papers ¨ 3 non-first author papers ¨ $3 million grant funded (co-PI) ¨ $325K grants historically (PI) ¨ Backup plan – full staff scientist at Argonne National Laboratory
- 50. Job hunt statistics (cont.) ¨ Environmental engineering, agricultural engineering, biology, microbiology, ecology and evolution ¨ 30 applications ¤ Prioritized < 10 applications, 3 versions ¤ Research statement, teaching statement, CV, references ¨ 5 on-site interviews (see blog for questions I was asked) ¨ 1 phone interview ¨ 8 rejection communications (dream offer tangent…) ¨ 2 offers
- 51. Evaluate yourself. Pros ¨ Skill set unique (for now) ¨ Diverse systems ¨ Engr / Biology / Ecology ¨ Comfortable ¨ Great reference writers ¤ Note to talk about reference writer packet Cons ¨ Defining “who” I am ¨ Publication productivity ¨ Husband required
- 52. Want it. ¨ Personal Impact ¤ Research ¤ Mentorship ¨ Enjoy what you’re selling ¤ Grant writing ¤ Publishing ¤ Presenting ¨ No man is an island
- 53. Believe it. ¨ I am very good at what I do. ¨ My work deserves to be funded. ¨ No imposter syndrom-ing during application writing
- 54. Prioritize the effort. ¨ Job hunting requires: ¤ Mood ¤ Time ¤ Emotions ¤ Focus ¨ You have another job ¨ Wrote applications on weekend (half day)
- 55. Only apply if you can imagine yourself accepting the offer.
- 56. Stay organized ¨ Addressees ¨ Specifics ¨ Deadlines ¨ References ¨ Save postings & application
- 57. Be positive. ¨ It is about fit. ¨ Many factors you cannot control. ¨ This is not an evaluation of your worth. ¨ Resilience is key.
- 58. Let it be “Out the door”
- 59. Manage your own expectations. ¨ Know myself. ¨ Let myself win & lose. ¨ Get jazzed during the interview. ¨ Find nice, hardy laughing people. ¨ Find really smart people that would help mentor me.. ¨ Work hard but find balance. ¨ Dream it.
- 60. The ISU job posting
- 62. Thank you! BCB: Shane Dooley Schuyler Smith Paul Villenueva www.germslab.org