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Assemblathon to Zykovich: An A–Z that reflects a decade at the UC Davis Genome Center

Keith Bradnam
November 20, 2015

Assemblathon to Zykovich: An A–Z that reflects a decade at the UC Davis Genome Center

My exit seminar that looks back to many of the varied projects that I have been involved with over the last decade.

Keith Bradnam

November 20, 2015
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  1. ASSEMBLATHON TO ZYKOVICH: AN A-Z THAT REFLECTS A DECADE AT

    THE UC DAVIS GENOME CENTER Keith Bradnam
  2. A

  3. $1,000 buys you raw DNA sequence data It does not

    buy you the people, technology, or expertise to put it into a form that is useful to scientists
  4. GENOME ASSEMBLY APPROXIMATOR: FUN FOR ALL THE FAMILY ➤ Take

    100 identical jigsaw sets ➤ Mix all pieces together ➤ Throw away 10% of the pieces ➤ Randomly mix in the contents of an unrelated puzzle ➤ Throw away the cover of the box ➤ Make one jigsaw from the pieces
  5. WHY 'ASSEMBLATHON'? ➤ CASP - Critical Assessment of protein Structure

    Prediction ➤ GASP - Genome Annotation aSsessment Project ➤ EGASP - the human ENCODE Genome Annotation aSsessment Project ➤ nGASP - nematode Genome Annotation aSsessment Project ➤ RGASP - RNA-seq Genome Annotation aSsessment Project ➤ dnGASP - de novo Genome Assembly aSsessment Project
  6. A

  7. B

  8. 180L. Genomics Laboratory (5) Lecture—2 hours; laboratory—6 hours; discussion—1 hour.

    Molecular and Cellular Biology 182. Computational approaches to model and analyze biological information about genomes, transcriptomes, and proteomes. Topics include genome assembly and annotation, mRNA and small RNA profiling, proteomics, protein-DNA and protein-protein interactions, network analysis, and comparative genomics. Computer programming experience not required. Brady, Dawson, Dinesh-Kumar, Harada, Korf, Maloof
  9. 180L. Genomics Laboratory (5) Lecture—2 hours; laboratory—6 hours; discussion—1 hour.

    Molecular and Cellular Biology 182. Computational approaches to model and analyze biological information about genomes, transcriptomes, and proteomes. Topics include genome assembly and annotation, mRNA and small RNA profiling, proteomics, protein-DNA and protein-protein interactions, network analysis, and comparative genomics. Computer programming experience not required. Brady, Dawson, Dinesh-Kumar, Harada, Korf, Maloof
  10. 180L. Genomics Laboratory (5) Lecture—2 hours; laboratory—6 hours; discussion—1 hour.

    Molecular and Cellular Biology 182. Computational approaches to model and analyze biological information about genomes, transcriptomes, and proteomes. Topics include genome assembly and annotation, mRNA and small RNA profiling, proteomics, protein-DNA and protein-protein interactions, network analysis, and comparative genomics. Computer programming experience not required. Brady, Dawson, Dinesh-Kumar, Harada, Korf, Maloof, Comai
  11. 180L. Genomics Laboratory (5) Lecture—2 hours; laboratory—6 hours; discussion—1 hour.

    Molecular and Cellular Biology 182. Computational approaches to model and analyze biological information about genomes, transcriptomes, and proteomes. Topics include genome assembly and annotation, mRNA and small RNA profiling, proteomics, protein-DNA and protein-protein interactions, network analysis, and comparative genomics. Computer programming experience not required. Brady, Dawson, Dinesh-Kumar, Harada, Korf, Maloof, Comai Course first started April 1, 2013
  12. 180L. Genomics Laboratory (5) Lecture—2 hours; laboratory—6 hours; discussion—1 hour.

    Molecular and Cellular Biology 182. Computational approaches to model and analyze biological information about genomes, transcriptomes, and proteomes. Topics include genome assembly and annotation, mRNA and small RNA profiling, proteomics, protein-DNA and protein-protein interactions, network analysis, and comparative genomics. Computer programming experience not required. Brady, Dawson, Dinesh-Kumar, Harada, Korf, Maloof, Comai Course first started April 1, 2013 Planned during 2012–2013
  13. B

  14. C

  15. ➤ Academic Federation Communications Committee ➤ Academic Federation representative to

    the Academic Senate Committee on Information Technology ➤ Academic Federation representative to the Campus Council for Information Technology (CCFIT)
  16. ➤ Academic Federation Communications Committee ➤ Academic Federation representative to

    the Academic Senate Committee on Information Technology ➤ Academic Federation representative to the Campus Council for Information Technology (CCFIT) Turned down an offer to join the Committee on Committees
  17. C

  18. Dawei Lin, Vince Buffalo, Joseph Fass, Monica Britton, Blythe Durbin-Johnson,

    Nikil Joshi, Richard Feltstykket, (Mike Lewis), Adam Schaal, and Matt Settles
  19. C

  20. WHY DON'T I LIKE CEGMA? ➤ Generates lots of support

    emails ➤ Hard to install ➤ Requires four other tools to all be pre-installed ➤ Originally used WU-BLAST which changed its license from free to paid ➤ Code documentation is not great ➤ It relies on a dataset of conserved protein groups that was published in 2003
  21. D

  22. D

  23. D

  24. E

  25. F

  26. F

  27. ?

  28. F

  29. G

  30. H

  31. I

  32. SOME FACTS ABOUT IAN ➤ Plays a mean game of

    foosball ➤ Writes code faster than is humanly possible*
  33. SOME FACTS ABOUT IAN ➤ Plays a mean game of

    foosball ➤ Writes code faster than is humanly possible* ➤ Shares my sense of humour
  34. SOME FACTS ABOUT IAN ➤ Plays a mean game of

    foosball ➤ Writes code faster than is humanly possible* ➤ Shares my sense of humour ➤ Has a unique perspective on the importance of calendars
  35. SOME FACTS ABOUT IAN ➤ Plays a mean game of

    foosball ➤ Writes code faster than is humanly possible* ➤ Shares my sense of humour ➤ Has a unique perspective on the importance of calendars ➤ Gifted communicator
  36. SOME FACTS ABOUT IAN ➤ Plays a mean game of

    foosball ➤ Writes code faster than is humanly possible* ➤ Shares my sense of humour ➤ Has a unique perspective on the importance of calendars ➤ Gifted communicator ➤ Enthusiastic, kind, and generous supporter to all in his lab
  37. SOME FACTS ABOUT IAN ➤ Plays a mean game of

    foosball ➤ Writes code faster than is humanly possible* ➤ Shares my sense of humour ➤ Has a unique perspective on the importance of calendars ➤ Gifted communicator ➤ Enthusiastic, kind, and generous supporter to all in his lab * does not include comments!
  38. "Wasn't particularly interested in the subject when I took the

    class (bioinformatics can be as dry as it sounds) but Ian did an amazing job. One of the few teachers who realizes that making clear analogies that everyone can relate to is a great way to convey abstract and complex concepts. These analogies also tended to be funny and thus more memorable." From RateMyProfessors.com
  39. THINGS IAN LIKES ➤ Driving cars ➤ World War II

    planes ➤ Archery ➤ The Centathlon
  40. THINGS IAN LIKES ➤ Driving cars ➤ World War II

    planes ➤ Archery ➤ The Centathlon ➤ Tree damage to houses*
  41. THINGS IAN LIKES ➤ Driving cars ➤ World War II

    planes ➤ Archery ➤ The Centathlon ➤ Tree damage to houses* * just kidding!
  42. J

  43. SOME JABBA AWARD WINNERS ➤ SPINGO: Species-level IdentificatioN of metaGenOmic

    amplicons ➤ SEABED: Small molEcule activity scanner weB servicE baseD ➤ TOGGLE: TOolbox for Generic nGs anaLysEs
  44. SOME JABBA AWARD WINNERS ➤ ANTELOPE: Analysis of Networks through

    TEmporal-LOgic sPEcifications ➤ PIGEONS: Photographically InteGrated En-suite for the OligoNucleotide Screening ➤ MOUSE: Mitochondrial and Other Useful SEquences
  45. K

  46. L

  47. M

  48. Minor Groove Mark I Charlie Nicolet, Ian Korf, Mike Lewis,

    Matt Holland, Kate Caldwell, and Keith Bradnam
  49. N

  50. LIFE AFTER SANGER ➤ Second generation ➤ Third generation ➤

    Fourth generation ➤ Next-generation ➤ Next-next generation ➤ Next-next-next generation
  51. WHAT IS HELICOS? ➤ Second generation ➤ Third generation ➤

    "lies in between the transition of next-generation sequencing to third generation"
  52. O

  53. OPEN SCIENCE IS GOOD SCIENCE ➤ Publish in open-access journals

    ➤ Use pre-print servers, e.g. arxiv.org, bioarxiv.org ➤ Share your slides and posters ➤ Share data, e.g. at figshare.com ➤ Share code, e.g. github.com ➤ Share ideas ➤ Let people tweet about your talks
  54. OPEN SCIENCE IS GOOD SCIENCE ➤ Publish in open-access journals

    ➤ Use pre-print servers, e.g. arxiv.org, bioarxiv.org ➤ Share your slides and posters ➤ Share data, e.g. at figshare.com ➤ Share code, e.g. github.com ➤ Share ideas ➤ Let people tweet about your talks Not all science happens in peer-reviewed publications
  55. O

  56. ORCID ➤ ORCID iDs are unique, persistent identifiers for researchers

    ➤ Used globally by publishing companies, funding agencies, academic software providers ➤ But can also connect other scientific outputs, e.g. datasets, peer reviews, code repositories ➤ Managed by non-profit organisation ➤ Only takes 30 seconds to create one ➤ Funding agencies are starting to mandate their use
  57. ORCID ➤ ORCID iDs are unique, persistent identifiers for researchers

    ➤ Used globally by publishing companies, funding agencies, academic software providers ➤ But can also connect other scientific outputs, e.g. datasets, peer reviews, code repositories ➤ Managed by non-profit organisation ➤ Only takes 30 seconds to create one ➤ Funding agencies are starting to mandate their use orcid.org/0000-0002-3881-294X
  58. Auto-Update Single sign-on Social login (facebook & Google) Contributorship badges

    Publons integration Federated login arXiv integration GitHub integration
  59. P

  60. Q

  61. THOUGHTS ON QUALIFYING EXAMS ➤ Students love that I never

    ask any questions and rarely give feedback.
  62. THOUGHTS ON QUALIFYING EXAMS ➤ Students love that I never

    ask any questions and rarely give feedback.
  63. THOUGHTS ON QUALIFYING EXAMS ➤ Students love that I never

    ask any questions and rarely give feedback. ➤ I am lying.
  64. THOUGHTS ON QUALIFYING EXAMS ➤ Students love that I never

    ask any questions and rarely give feedback. ➤ I am lying.
  65. THOUGHTS ON QUALIFYING EXAMS ➤ Students love that I never

    ask any questions and rarely give feedback. ➤ I am lying. ➤
  66. THOUGHTS ON QUALIFYING EXAMS ➤ Students love that I never

    ask any questions and rarely give feedback. ➤ I am lying. ➤
  67. R

  68. 0 7.5 15 22.5 30 1 2 3 4 5

    6 7 8 Comments per page Kristen Beck
  69. 0 7.5 15 22.5 30 1 2 3 4 5

    6 7 8 Comments per page Kristen Beck
  70. S

  71. T

  72. A picture is worth a thousand words … so consider

    this a very long abstract takehomemesage.com
  73. T

  74. WHY USE TWITTER ➤ Engage your peers ➤ Learn about

    science discoveries ➤ Job opportunities ➤ Funding opportunities ➤ Changes to funding policy ➤ Follow along with conferences ➤ #icanhazpdf
  75. U

  76. V

  77. W

  78. THE NEW GENOME CENTER WEBSITE ➤ Modern, responsive design ➤

    RSS feed for news ➤ Events calendar ➤ >100 news/event updates in last year ➤ Twitter account (@genomecenter) with ~375 followers
  79. THE NEW GENOME CENTER WEBSITE ➤ Modern, responsive design ➤

    RSS feed for news ➤ Events calendar ➤ >100 news/event updates in last year ➤ Twitter account (@genomecenter) with ~375 followers Many thanks to Adam Schaal for all his help!
  80. STEP 2: GENDER BIAS AT 40 GENOMICS INSTITUTES/CENTERS ➤ Logged

    gender of 1,039 people ➤ Research centres in N. America, Europe, Asia, and Australia ➤ Focused on senior positions: Faculty, Team leaders etc. ➤ Published data to Figshare ➤ http://dx.doi.org/10.6084/m9.figshare.1466790
  81. STEP 2: GENDER BIAS AT 40 GENOMICS INSTITUTES/CENTERS ➤ Logged

    gender of 1,039 people ➤ Research centres in N. America, Europe, Asia, and Australia ➤ Focused on senior positions: Faculty, Team leaders etc. ➤ Published data to Figshare ➤ http://dx.doi.org/10.6084/m9.figshare.1466790
  82. STEP 2: GENDER BIAS AT 40 GENOMICS INSTITUTES/CENTERS ➤ Only

    3/40 had worse gender bias compared to CSHL meeting
  83. STEP 2: GENDER BIAS AT 40 GENOMICS INSTITUTES/CENTERS ➤ Only

    3/40 had worse gender bias compared to CSHL meeting ➤ Only 3 institutes had >40% women in senior roles
  84. STEP 2: GENDER BIAS AT 40 GENOMICS INSTITUTES/CENTERS ➤ Only

    3/40 had worse gender bias compared to CSHL meeting ➤ Only 3 institutes had >40% women in senior roles ➤ None had >50% women
  85. STEP 2: GENDER BIAS AT 40 GENOMICS INSTITUTES/CENTERS ➤ Only

    3/40 had worse gender bias compared to CSHL meeting ➤ Only 3 institutes had >40% women in senior roles ➤ None had >50% women ➤ Average proportion of women in senior research roles:
  86. STEP 2: GENDER BIAS AT 40 GENOMICS INSTITUTES/CENTERS ➤ Only

    3/40 had worse gender bias compared to CSHL meeting ➤ Only 3 institutes had >40% women in senior roles ➤ None had >50% women ➤ Average proportion of women in senior research roles: 23.6%
  87. MY PLEDGE For these reasons I feel that conference organizers

    — in the fields of genomics and bioinformatics — should be aiming for at least a third of all speakers to be women. Ideally, we want to be doing better than this which is why I suggest this as an absolute minimum target.
  88. MY PLEDGE For these reasons I feel that conference organizers

    — in the fields of genomics and bioinformatics — should be aiming for at least a third of all speakers to be women. Ideally, we want to be doing better than this which is why I suggest this as an absolute minimum target. I don't attend many conferences, but from now on I won't be attending any if at least 33% of the talks are not by women.
  89. THEN THIS HAPPENED ➤ I was invited to speak. ➤

    I accepted but waited for final speaker lineup to appear…
  90. THEN THIS HAPPENED ➤ I was invited to speak. ➤

    I accepted but waited for final speaker lineup to appear… ➤ 28.2% women speakers
  91. THEN THIS HAPPENED ➤ I cancelled my talk ➤ Asked

    them to give slot to a woman instead
  92. THEN THIS HAPPENED ➤ I cancelled my talk ➤ Asked

    them to give slot to a woman instead ➤ Men can be part of the solution
  93. Z

  94. KORF LAB 2005–2015 Genís Parra Tali Elfersi Yi Zhang Parawee

    Lekprasert Kalyn Records Alicia Winquist Vince Ramey Rajiv Pandey Reza Garajehdaghi Raymond Yu Matt Wong Artem Zykovich Kim Blahnik Shahram Emami Ken Yu Ian Korf Yen Duong Paul Lott Daniël Melters Matthew Porter Ravi Dandekar Tiffany Ho Abby Yu Roy Chu Alex Godbout Priyanka Kulkarni Zhanghang Yan Maxine Umeh Sam Westreich Hannah Lyman Keith Dunaway Kristen Beck Stella Hartono Danielle Lemay Claire Shu Ben Edwards Ian Haydon Derrick Hicks Kelly Ostrom Michael Adler Jillian Ng Gina Turco Natalie Tellis Anna Marie Tuazon Alan Raetz Allen Kovach Cristel Thomas John Smolka
  95. THESE GO TO 11 1. Find out what I'm up

    to at keithbradnam.com 2. And maybe at acgt.me
  96. THESE GO TO 11 1. Find out what I'm up

    to at keithbradnam.com 2. And maybe at acgt.me 3. And of course on twitter at @kbradnam
  97. THESE GO TO 11 1. Find out what I'm up

    to at keithbradnam.com 2. And maybe at acgt.me 3. And of course on twitter at @kbradnam 4. Reminder, I am still around for a little while longer!
  98. THESE GO TO 11 1. Find out what I'm up

    to at keithbradnam.com 2. And maybe at acgt.me 3. And of course on twitter at @kbradnam 4. Reminder, I am still around for a little while longer! 5. Sign up for an ORCID iD at orcid.org
  99. THESE GO TO 11 1. Find out what I'm up

    to at keithbradnam.com 2. And maybe at acgt.me 3. And of course on twitter at @kbradnam 4. Reminder, I am still around for a little while longer! 5. Sign up for an ORCID iD at orcid.org 6. Please stop using the old Genome Center logo!
  100. THESE GO TO 11 7. Software names do not have

    to be acronyms! 8. Especially when they're bogus acronyms.
  101. THESE GO TO 11 7. Software names do not have

    to be acronyms! 8. Especially when they're bogus acronyms. 9. Consider the gender bias of conferences you attend
  102. THESE GO TO 11 7. Software names do not have

    to be acronyms! 8. Especially when they're bogus acronyms. 9. Consider the gender bias of conferences you attend 10. Thank you for putting up with me
  103. THESE GO TO 11 7. Software names do not have

    to be acronyms! 8. Especially when they're bogus acronyms. 9. Consider the gender bias of conferences you attend 10. Thank you for putting up with me 11. Goodbye!