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
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
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
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
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
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
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
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
foosball ➤ Writes code faster than is humanly possible* ➤ Shares my sense of humour ➤ Has a unique perspective on the importance of calendars ➤ Gifted communicator
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
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!
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
TEmporal-LOgic sPEcifications ➤ PIGEONS: Photographically InteGrated En-suite for the OligoNucleotide Screening ➤ MOUSE: Mitochondrial and Other Useful SEquences
➤ 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
➤ 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
➤ 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
➤ 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
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!
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
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
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:
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%
— 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.
— 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.
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
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
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!
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!