Difference between revisions of "BCH364C BCH394P 2017"

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== Lectures & Handouts ==
 
== Lectures & Handouts ==
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'''May 2 - May 4, 2017 - Final Project Presentations'''
'''May 2 - May 4, 2017 - Gene Presentations'''
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* Note: There are some great [http://ccbb.biosci.utexas.edu/summerschool.html short summer courses in computational biology] being offered at UT. Of particular note, introductions to [http://ccbb.biosci.utexas.edu/summerschool.html#ngs core NextGen sequencing tools], [http://ccbb.biosci.utexas.edu/summerschool.html#rna RNA-seq], and [http://ccbb.biosci.utexas.edu/summerschool.html#proteomics proteomics].
* Note: There are some great [http://ccbb.biosci.utexas.edu/summerschool.html short summer courses in computational biology] being offered at UT. Of particular note, introductions to [http://ccbb.biosci.utexas.edu/summerschool.html#corengs core NextGen sequencing tools].
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* Here's an assortment of final class projects (with permission to be posted):
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** [https://sites.google.com/view/viralpeptides Fishing in Oceanic Virus: A Search for Novel Peptides, by Austin Cole, Shaunak Kar, and Raghav Shroff]
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** [https://pailnguyen.github.io/jackal/ jackal: a cell phenotype image classification using a convolutional neural network, by Paul Nguyen and Steven Tran]
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** [https://sites.google.com/a/utexas.edu/proteinspace/home Protein chemical space, by Xun Wang, Cory D Dubois, and Viviana M June]
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** [https://sites.google.com/a/utexas.edu/pks_iii_annotation_bch364c_final Bioinformatic Analysis of KAS III, by Yu-Hsuan Lee, Geng-Min Lin, and Daan Ren]
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** [https://sites.google.com/a/utexas.edu/high-coverage-prediction-of-protein-levels/ High-coverage prediction of protein levels, by Eric A Brenner]
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** [https://github.com/dvanderwood/AltProteome/wiki Detection of Translation Products from Alternative Open Reading Frames, by Drew Vander Wood and Riddhiman K Garge]
  
 
'''April 27, 2017 - Synthetic Biology II'''
 
'''April 27, 2017 - Synthetic Biology II'''
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* '''UPDATE: Projects are now due by Saturday, midnight, April 29'''.  Turn them in as a URL to the web site you created, sent by email to the TA AND PROFESSOR. 
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* We'll be finishing the slides from Apr. 20.
  
 
'''Apr 25, 2017 - Genome Engineering'''
 
'''Apr 25, 2017 - Genome Engineering'''
* Problem Set 3 deadline has been pushed to midnight April 18, 2016.
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* Guest speaker: [https://www.researchgate.net/profile/Aashiq_Kachroo Dr. Aashiq Kachroo]. Here's a link to [https://www.sciencenews.org/article/swapping-analogous-genes-no-problem-among-species some of his recent work.]
* Guest speaker: [http://www.yeastgenome.org/cgi-bin/colleague/colleagueSearch?rm=colleague_page&id=12102 Dr. Chris Yellman]
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'''April 20, 2017 - Synthetic Biology I'''
 
'''April 20, 2017 - Synthetic Biology I'''
* '''Reminder: All gene projects are due by midnight, April 27'''.  Turn them in as a URL to the web site you created, sent by email to the TA AND PROFESSOR.   
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* '''Reminder: All projects are due by midnight, April 27'''.  Turn them in as a URL to the web site you created, sent by email to the TA AND PROFESSOR.   
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_SyntheticBio-Spring2017.pdf Today's slides]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_SyntheticBio_Spring2017.pdf Today's slides]
 
A collection of further reading, if you're so inclined:
 
A collection of further reading, if you're so inclined:
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/MinimalMycoplasma.pdf Minimal Mycoplasma]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/MinimalMycoplasma.pdf Minimal Mycoplasma]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/NewCellFromChemicalGenome.pdf A new cell from a chemically synthesized genome], [http://www.marcottelab.org/users/BCH364C_394P_2017/NewCellFromChemicalGenome.SOM.pdf SOM]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/NewCellFromChemicalGenome.pdf A new cell from a chemically synthesized genome], [http://www.marcottelab.org/users/BCH364C_394P_2017/NewCellFromChemicalGenome.SOM.pdf SOM]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/YeastSynthCsome.pdf 1/2 a synthetic yeast chromosome] and [http://syntheticyeast.org/ Build-A-Genome]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/YeastSynthCsome.pdf 1/2 a synthetic yeast chromosome] and [http://syntheticyeast.org/ Build-A-Genome]
* & the latest: [http://www.marcottelab.org/users/BCH364C_394P_2017/Science-2014-Annaluru-55-8.pdf Entire synthetic yeast chromosome]  
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/Science-2014-Annaluru-55-8.pdf Entire synthetic yeast chromosome]
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* [http://science.sciencemag.org/content/355/6329/1040.long Sc 2.0, as of 2017], with the [http://science.sciencemag.org/content/355/6329/1038 computational genome design]
 
* [http://en.wikipedia.org/wiki/Gillespie_algorithm The Gillespie algorithm]
 
* [http://en.wikipedia.org/wiki/Gillespie_algorithm The Gillespie algorithm]
 
* [https://www.igem.org/Main_Page iGEM], and an example part ([http://parts.igem.org/Featured_Parts:Light_Sensor the light sensor])
 
* [https://www.igem.org/Main_Page iGEM], and an example part ([http://parts.igem.org/Featured_Parts:Light_Sensor the light sensor])
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/BacterialPhotography.pdf Bacterial photography], and [http://www.marcottelab.org/users/BIO337_2014/UTiGEM2012.pdf UT's 2012 iGEM entry]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BacterialPhotography.pdf Bacterial photography], and [http://www.marcottelab.org/users/BIO337_2014/UTiGEM2012.pdf UT's 2012 iGEM entry]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/EdgeDetector.pdf Edge detector]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/EdgeDetector.pdf Edge detector]
* [http://www.marcottelab.org/users/BCH364C_394P_2017/nbt.2510.pdf A more recent example of digital logic]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/nbt.2510.pdf A nice example of digital logic]
 
* An example of metabolic engineering: [http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12051.html yeast making anti-malarial drugs]
 
* An example of metabolic engineering: [http://www.nature.com/nature/journal/vaop/ncurrent/full/nature12051.html yeast making anti-malarial drugs]
Food for thought:<br>
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[http://www.popsci.com/woolly-mammoth-dna-brought-life-elephant-cells Food for thought]
[http://www.nationalgeographic.com/deextinction De-extinction I], [http://science.kqed.org/quest/video/reawakening-extinct-species/ II], and [http://www.popsci.com/woolly-mammoth-dna-brought-life-elephant-cells III]
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'''April 18, 2017 - Phenologs'''
 
'''April 18, 2017 - Phenologs'''
* Remember: The final project web page is due by midnight April 27, 2016.   
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* '''Remember: The final project web page is due by midnight April 27, 2017, turned in as a URL emailed to the TA+ProfessorPlease indicate in the email if you are willing to let us post the project to the course web site.'''
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_Phenologs_Spring2017.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_Phenologs_Spring2017.pdf Today's slides]
* [http://www.marcottelab.org/paper-pdfs/PNAS_Phenologs_2010.pdf Phenologs] and the [http://www.marcottelab.org/paper-pdfs/PLoSBiology_TBZ_2012.pdf drug discovery story] we'll discuss in class
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* [http://www.marcottelab.org/paper-pdfs/PNAS_Phenologs_2010.pdf Phenologs] and the [http://www.marcottelab.org/paper-pdfs/PLoSBiology_TBZ_2012.pdf drug discovery story] we'll discuss in class. This is a fun example of the power of opportunistic data mining aka [http://researchparasite.com/ "research parasitism"] in biomedical research.
 
* Search for phenologs [http://www.phenologs.org/ here].  You can get started by rediscovering the plant model of Waardenburg syndrome.  Search among the known diseases for "Waardenburg", or enter the human genes linked to Waardenburg (Entrez gene IDs 4286, 5077, 6591, 7299) to get a feel for how this works. Also, here's [http://www.nytimes.com/2010/04/27/science/27gene.html?_r=0 Carl Zimmer's NYT article] about phenologs and the scientific process.
 
* Search for phenologs [http://www.phenologs.org/ here].  You can get started by rediscovering the plant model of Waardenburg syndrome.  Search among the known diseases for "Waardenburg", or enter the human genes linked to Waardenburg (Entrez gene IDs 4286, 5077, 6591, 7299) to get a feel for how this works. Also, here's [http://www.nytimes.com/2010/04/27/science/27gene.html?_r=0 Carl Zimmer's NYT article] about phenologs and the scientific process.
 
Tools for finding orthologs:<br>
 
Tools for finding orthologs:<br>
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'''April 13, 2017 - Networks II'''
 
'''April 13, 2017 - Networks II'''
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* We're finishing up the slides from Apr. 11.
  
 
'''April 11, 2017 - Networks'''
 
'''April 11, 2017 - Networks'''
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* Metabolic networks: [http://ca.expasy.org/cgi-bin/show_thumbnails.pl The wall chart] (it's interactive, e.g. here's [http://web.expasy.org/cgi-bin/pathways/show_image?E5&left enolase]), the current state of the [http://www.marcottelab.org/users/BCH364C_394P_2017/HumanMetabolicReactionNetwork-2013.pdf human metabolic reaction network], and older but still relevant review of [http://www.marcottelab.org/users/BCH364C_394P_2017/ChIP-chipReview.pdf transcriptional networks] (with the current record holder in this regard held by [http://www.genome.gov/10005107 ENCODE]), and an early review of [http://www.marcottelab.org/users/BCH364C_394P_2017/vonmering.pdf protein interaction extent and quality] whose lessons still hold.
 
* Metabolic networks: [http://ca.expasy.org/cgi-bin/show_thumbnails.pl The wall chart] (it's interactive, e.g. here's [http://web.expasy.org/cgi-bin/pathways/show_image?E5&left enolase]), the current state of the [http://www.marcottelab.org/users/BCH364C_394P_2017/HumanMetabolicReactionNetwork-2013.pdf human metabolic reaction network], and older but still relevant review of [http://www.marcottelab.org/users/BCH364C_394P_2017/ChIP-chipReview.pdf transcriptional networks] (with the current record holder in this regard held by [http://www.genome.gov/10005107 ENCODE]), and an early review of [http://www.marcottelab.org/users/BCH364C_394P_2017/vonmering.pdf protein interaction extent and quality] whose lessons still hold.
 
* Useful gene network resources include:
 
* Useful gene network resources include:
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** [http://www.reactome.org/ Reactome]), which we've seen before, links human genes according to reactions and pathways, and also calculated functional linkages from various high-throughput data.
 
** [http://www.functionalnet.org FunctionalNet], which links to human, worm, Arabidopsis, mouse and yeast gene networks. Not the prettiest web site, but useful, and helped my own group find genes for a wide variety of biological processes. Try searching HumanNet for the myelin regulatory factor MYRF (Entrez gene ID 745) and predicting its function, which is now known but wasn't when the network was made.  
 
** [http://www.functionalnet.org FunctionalNet], which links to human, worm, Arabidopsis, mouse and yeast gene networks. Not the prettiest web site, but useful, and helped my own group find genes for a wide variety of biological processes. Try searching HumanNet for the myelin regulatory factor MYRF (Entrez gene ID 745) and predicting its function, which is now known but wasn't when the network was made.  
 
** [http://string-db.org/ STRING] is available for many organisms, including large numbers of prokaryotes. Try searching on the <i>E. coli</i> enolase (Eno) as an example.
 
** [http://string-db.org/ STRING] is available for many organisms, including large numbers of prokaryotes. Try searching on the <i>E. coli</i> enolase (Eno) as an example.
 
** [http://www.genemania.org/ GeneMania], which aggregates many individual gene networks.
 
** [http://www.genemania.org/ GeneMania], which aggregates many individual gene networks.
 
** [http://func.mshri.on.ca/ MouseFunc], a collection of network and classifier-based predictions of gene function from [http://www.marcottelab.org/paper-pdfs/GenomeBiology_MouseFunc_2008.pdf an open contest to predict gene function in the mouse].
 
** [http://func.mshri.on.ca/ MouseFunc], a collection of network and classifier-based predictions of gene function from [http://www.marcottelab.org/paper-pdfs/GenomeBiology_MouseFunc_2008.pdf an open contest to predict gene function in the mouse].
** The best interactive tool for network visualization is [http://www.cytoscape.org/ Cytoscape]. You can download and install it locally on your computer, then visualize and annotated any gene network, such as are output by the network tools linked above.  There is also a web-based network viewer that can be incorporated into your own pages (e.g., as used in [http://www.inetbio.org/yeastnet/ YeastNet]).
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** The best interactive tool for network visualization is [http://www.cytoscape.org/ Cytoscape]. You can download and install it locally on your computer, then visualize and annotated any gene network, such as are output by the network tools linked above.  There is also a web-based network viewer that can be incorporated into your own pages (e.g., as used in [http://www.inetbio.org/yeastnet/ YeastNet]).  Here's an example file to visualize, the [http://proteincomplexes.org/static/downloads/human_protein_complex_map.cys latest version] of the [http://proteincomplexes.org/ human protein complex map].
 
Reading:<br>
 
Reading:<br>
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/YeastSGA-2016.pdf The Yeast SGA map]
 
* [http://www.marcottelab.org/paper-pdfs/ng-fraser-review.pdf Functional networks]
 
* [http://www.marcottelab.org/paper-pdfs/ng-fraser-review.pdf Functional networks]
 
* [http://www.marcottelab.org/paper-pdfs/JProteomics_GBAReview_2010.pdf Review of predicting gene function and phenotype from protein networks]
 
* [http://www.marcottelab.org/paper-pdfs/JProteomics_GBAReview_2010.pdf Review of predicting gene function and phenotype from protein networks]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_PCA_Spring2017.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_PCA_Spring2017.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/EuropeanGenesPCA.pdf European men, their genomes, and their geography]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/EuropeanGenesPCA.pdf European men, their genomes, and their geography]
* [http://projector.tensorflow.org/ Nice interactive visualization tool using PCA]
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* [http://projector.tensorflow.org/ The tSNE interactive visualization tool also performs PCA]
 
* Relevant to today's discussion for his eponymous distance measure: [http://en.wikipedia.org/wiki/Prasanta_Chandra_Mahalanobis Mahalanobis]
 
* Relevant to today's discussion for his eponymous distance measure: [http://en.wikipedia.org/wiki/Prasanta_Chandra_Mahalanobis Mahalanobis]
 
A smattering of links on PCA:<br>
 
A smattering of links on PCA:<br>
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/NBT_primer_PCA.pdf NBT Primer on PCA]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/NBT_primer_PCA.pdf NBT Primer on PCA]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/PrincipalComponentAnalysis.docx A PCA overview (.docx format)] & the [http://horicky.blogspot.com/2009/11/principal-component-analysis.html original post]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/PrincipalComponentAnalysis.docx A PCA overview (.docx format)] & the [http://horicky.blogspot.com/2009/11/principal-component-analysis.html original post]
* Science Signaling (more specifically, Neil R. Clark and Avi Ma’ayan!) had a [http://stke.sciencemag.org/cgi/content/full/sigtrans;4/190/tr3/DC1 nice introduction to PCA] that I've reposted [http://www.marcottelab.org/users/BCH364C_394P_2017/IntroToPCA.pdf here] (with [http://www.marcottelab.org/users/BCH364C_394P_2017/2001967Slides-FINAL.ppt slides])
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* Science Signaling (more specifically, Neil R. Clark and Avi Ma’ayan! '''Check out Avi's talk today, 4:00 p.m., NHB 1.720''') had a [http://stke.sciencemag.org/cgi/content/full/sigtrans;4/190/tr3/DC1 nice introduction to PCA] that I've reposted [http://www.marcottelab.org/users/BCH364C_394P_2017/IntroToPCA.pdf here] (with [http://www.marcottelab.org/users/BCH364C_394P_2017/2001967Slides-FINAL.ppt slides])
 
* Python code for [http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html performing PCA yourself]
 
* Python code for [http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html performing PCA yourself]
  
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_Classifiers_Spring2017.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_Classifiers_Spring2017.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/AMLALLclassification.pdf Classifying leukemias]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/AMLALLclassification.pdf Classifying leukemias]
* For those of you interesting in trying out classifiers on your own, here's the best open software for do-it-yourself classifiers and data mining: [http://www.cs.waikato.ac.nz/ml/weka/ Weka]
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* For those of you interesting in trying out classifiers on your own, here's the best open software for do-it-yourself classifiers and data mining: [http://www.cs.waikato.ac.nz/ml/weka/ Weka].  There is a great introduction to using Weka in this book chapter [http://link.springer.com/protocol/10.1007/978-1-4939-3578-9_17 Introducing Machine Learning Concepts with WEKA], as well as the very accessible Weka-produced book [http://www.cs.waikato.ac.nz/ml/weka/book.html Data Mining: Practical Machine Learning Tools and Techniques].
  
'''Mar 30, 2017 - Mass spectrometry proteomics'''
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'''Mar 30, 2017 - 3D Protein Structure Modeling'''
* Guest speaker: [http://www.researchgate.net/profile/Daniel_Boutz/ Dr. Daniel Boutz]
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'''Mar 28, 2017 - 3D Protein Structure Modeling'''
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* Guest speaker: [https://scholar.google.com/citations?hl=en&user=zJ8L0GcAAAAJ&view_op=list_works Dr. Kevin Drew], formerly of New York University and now at the UT Center for Systems and Synthetic Biology
 
* Guest speaker: [https://scholar.google.com/citations?hl=en&user=zJ8L0GcAAAAJ&view_op=list_works Dr. Kevin Drew], formerly of New York University and now at the UT Center for Systems and Synthetic Biology
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<!--
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/structbio_lecture_BCH364C-394P_2016.pptx Today's slides]<br>
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/structbio_lecture_BCH364C-394P_2016.pptx Today's slides]<br>
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-->
 
* The [https://www.rosettacommons.org/software Rosetta] software suite for 3D protein modeling, and [http://www.marcottelab.org/users/BCH364C_394P_2017/RosettaOverview.pdf what it can do for you]
 
* The [https://www.rosettacommons.org/software Rosetta] software suite for 3D protein modeling, and [http://www.marcottelab.org/users/BCH364C_394P_2017/RosettaOverview.pdf what it can do for you]
 
* The [http://www.rcsb.org/pdb/ Protein Data Bank], [http://toolkit.tuebingen.mpg.de/hhpred HHPRED], [https://salilab.org/modeller/ MODELLER], and [http://www.pymol.org/ Pymol]
 
* The [http://www.rcsb.org/pdb/ Protein Data Bank], [http://toolkit.tuebingen.mpg.de/hhpred HHPRED], [https://salilab.org/modeller/ MODELLER], and [http://www.pymol.org/ Pymol]
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'''Mar 28, 2017 - Mass spectrometry proteomics'''
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* Guest speaker: [http://www.researchgate.net/profile/Daniel_Boutz/ Dr. Daniel Boutz]
  
 
'''Mar 23, 2017 - Clustering II'''
 
'''Mar 23, 2017 - Clustering II'''
* We're finishing up the slides from Mar.  24.<br>
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* Fun article: [http://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2002050 All biology is computational biology]
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* We're finishing up the slides from Mar.  21.<br>
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/FuzzyK-Means.pdf Fuzzy k-means]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/FuzzyK-Means.pdf Fuzzy k-means]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/SOM-geneexpression.pdf SOM gene expression]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/SOM-geneexpression.pdf SOM gene expression]
** Links to various applications of SOMs: [http://en.wikipedia.org/wiki/Self-organizing_map 1], [http://www.bentley.edu/centers/sites/www.bentley.edu.centers/files/csbigs/hua.pdf 2], [http://vizier.u-strasbg.fr/kohonen.htx 3], [http://wn.com/Self_Organizing_Maps_Application 4]. You can run SOMs on the [http://www.math.le.ac.uk/people/ag153/homepage/PCA_SOM/PCA_SOM.html following web site]. You can also run SOM clustering with the Open Source Clustering package from problem set 3 with the '-s' option, or GUI option. See [http://bonsai.hgc.jp/~mdehoon/software/cluster/manual/SOM.html#SOM the manual] for details. (FYI, it also supports PCA). If you are not happy with Cluster's SOM function, the statistical package R also provides a package for calculating SOMs (http://cran.r-project.org/web/packages/som/index.html).  
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** Links to various applications of SOMs: [http://en.wikipedia.org/wiki/Self-organizing_map 1], [http://vizier.u-strasbg.fr/kohonen.htx 2], [http://wn.com/Self_Organizing_Maps_Application 3]. You can run SOMs on the [http://www.math.le.ac.uk/people/ag153/homepage/PCA_SOM/PCA_SOM.html following web site]. You can also run SOM clustering with the [http://bonsai.hgc.jp/~mdehoon/software/cluster Open Source Clustering package] with the '-s' option, or GUI option (here's the [http://bonsai.hgc.jp/~mdehoon/software/cluster/manual/SOM.html#SOM manual]). (FYI, it also supports PCA). If you are not happy with Cluster's SOM function, the statistical package R also provides a package for calculating SOMs (http://cran.r-project.org/web/packages/som/index.html).
 +
* [http://www.marcottelab.org/users/BCH364C_394P_2017/t-SNE.pdf t-SNE]
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** Links to various applications of t-SNE: [https://en.wikipedia.org/wiki/T-distributed_stochastic_neighbor_embedding 1], [http://lvdmaaten.github.io/tsne/ 2], [https://www.youtube.com/watch?v=RJVL80Gg3lA 3], [http://distill.pub/2016/misread-tsne/ 4]. You can run t-SNE on the [http://projector.tensorflow.org/ following web site].
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[http://www.marcottelab.org/users/BCH364C_394P_2017/ProblemSet3_2017.pdf '''Problem Set 3], due before midnight Apr. 10, 2017'''.  You will need the following software and datasets:<br>
 +
* The clustering software is available [https://software.broadinstitute.org/morpheus/ here]. There is an alternative package [http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm here] that you can download and install on your local computer if you prefer.<br>
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/yeast_aaseqs Yeast protein sequences]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/yeast_phyloprofiles2.txt Yeast protein phylogenetic profiles]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/yeast_cofractionationdata.txt Yeast protein fractionation/mass spectrometry profiles].  These additionally have common gene names (LocusID_commonname_location) which may help with the interpretation. These data come from [http://www.marcottelab.org/paper-pdfs/Nature_AnimalComplexes_2015.pdf this paper].
  
 
'''Mar 21, 2017 - Functional Genomics & Data Mining - Clustering I'''
 
'''Mar 21, 2017 - Functional Genomics & Data Mining - Clustering I'''
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/K-means-Example.ppt K-means example (.ppt)]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/K-means-Example.ppt K-means example (.ppt)]
  
[http://www.marcottelab.org/users/BCH364C_394P_2017/ProblemSet3_2016.pdf '''Problem Set 3], due before midnight Apr. 14, 2016'''.  You will need the following software and datasets:<br>
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'''Mar 14-16, 2017 - SPRING BREAK'''
* The clustering and treeview software is available [http://bonsai.hgc.jp/~mdehoon/software/cluster/software.htm here].<br>
+
* Finish HW3 and turn in the proposal for your course project.
* [http://www.marcottelab.org/users/BCH364C_394P_2017/yeast_aaseqs Yeast protein sequences]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/yeast_phyloprofiles2.txt Yeast protein phylogenetic profiles]
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* [http://www.marcottelab.org/users/BCH364C_394P_2017/yeast_microarraydata2.txt Yeast mRNA expression profiles]
+
  
'''Mar 14-16, 2017 - SPRING BREAK'''
 
* Finish HW3 and and the first mini-assignment (#1) for your gene project.
 
-->
 
 
'''Mar 9, 2017 - Motifs'''
 
'''Mar 9, 2017 - Motifs'''
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_Motifs_Spring2017.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH364C_394P_2017/BCH364C-394P_Motifs_Spring2017.pdf Today's slides]

Latest revision as of 12:28, 2 May 2017

BCH364C/BCH394P Systems Biology & Bioinformatics

Course unique #: 55120/55210
Lectures: Tues/Thurs 11 – 12:30 PM in GDC 4.302
Instructor: Edward Marcotte, marcotte @ icmb.utexas.edu

  • Office hours: Wed 11 AM – 12 noon in MBB 3.148BA

TA: Azat Akhmetov, azat @ utexas.edu

  • TA Office hours: Mon/Wed 3 PM - 4 PM in MBB 3.204 Phone: on syllabus

Lectures & Handouts

May 2 - May 4, 2017 - Final Project Presentations

April 27, 2017 - Synthetic Biology II

  • UPDATE: Projects are now due by Saturday, midnight, April 29. Turn them in as a URL to the web site you created, sent by email to the TA AND PROFESSOR.
  • We'll be finishing the slides from Apr. 20.

Apr 25, 2017 - Genome Engineering

April 20, 2017 - Synthetic Biology I

  • Reminder: All projects are due by midnight, April 27. Turn them in as a URL to the web site you created, sent by email to the TA AND PROFESSOR.
  • Today's slides

A collection of further reading, if you're so inclined:

Food for thought

April 18, 2017 - Phenologs

  • Remember: The final project web page is due by midnight April 27, 2017, turned in as a URL emailed to the TA+Professor. Please indicate in the email if you are willing to let us post the project to the course web site.
  • Today's slides
  • Phenologs and the drug discovery story we'll discuss in class. This is a fun example of the power of opportunistic data mining aka "research parasitism" in biomedical research.
  • Search for phenologs here. You can get started by rediscovering the plant model of Waardenburg syndrome. Search among the known diseases for "Waardenburg", or enter the human genes linked to Waardenburg (Entrez gene IDs 4286, 5077, 6591, 7299) to get a feel for how this works. Also, here's Carl Zimmer's NYT article about phenologs and the scientific process.

Tools for finding orthologs:

April 13, 2017 - Networks II

  • We're finishing up the slides from Apr. 11.

April 11, 2017 - Networks

  • Today's slides
  • Metabolic networks: The wall chart (it's interactive, e.g. here's enolase), the current state of the human metabolic reaction network, and older but still relevant review of transcriptional networks (with the current record holder in this regard held by ENCODE), and an early review of protein interaction extent and quality whose lessons still hold.
  • Useful gene network resources include:
    • Reactome), which we've seen before, links human genes according to reactions and pathways, and also calculated functional linkages from various high-throughput data.
    • FunctionalNet, which links to human, worm, Arabidopsis, mouse and yeast gene networks. Not the prettiest web site, but useful, and helped my own group find genes for a wide variety of biological processes. Try searching HumanNet for the myelin regulatory factor MYRF (Entrez gene ID 745) and predicting its function, which is now known but wasn't when the network was made.
    • STRING is available for many organisms, including large numbers of prokaryotes. Try searching on the E. coli enolase (Eno) as an example.
    • GeneMania, which aggregates many individual gene networks.
    • MouseFunc, a collection of network and classifier-based predictions of gene function from an open contest to predict gene function in the mouse.
    • The best interactive tool for network visualization is Cytoscape. You can download and install it locally on your computer, then visualize and annotated any gene network, such as are output by the network tools linked above. There is also a web-based network viewer that can be incorporated into your own pages (e.g., as used in YeastNet). Here's an example file to visualize, the latest version of the human protein complex map.

Reading:

Apr 6, 2017 - Principal Component Analysis (& the curious case of European genotypes)

A smattering of links on PCA:

Apr 4, 2017 - Classifiers I

Mar 30, 2017 - 3D Protein Structure Modeling

Mar 28, 2017 - Mass spectrometry proteomics

Mar 23, 2017 - Clustering II

Problem Set 3, due before midnight Apr. 10, 2017. You will need the following software and datasets:

Mar 21, 2017 - Functional Genomics & Data Mining - Clustering I

Mar 14-16, 2017 - SPRING BREAK

  • Finish HW3 and turn in the proposal for your course project.

Mar 9, 2017 - Motifs

Mar 7, 2017 - Genomes II

Mar 2, 2017 - Genome Assembly

Feb 28, 2017 - Next-generation Sequencing (NGS)

Feb 23, 2017 - Gene finding II

  • We're finishing up the slides from Feb. 21, then moving on into Genome Assembly

Feb 21, 2017 - Gene finding

Problem Set 2, due before midnight Mar. 6, 2017:

Reading:

Feb 16, 2017 - HMMs II

Feb 14, 2017 - Hidden Markov Models

  • Don't forget: Homework #2 (worth 10% of your final course grade) is due on Rosalind by 11:59PM February 20.
  • Linking out to UniProt, discussed last time
  • Today's slides

Reading:

Feb 9, 2017 - Biological databases

Feb 7, 2017 - BLAST

Feb 2, 2017 - Guest lecture: Homologs, orthologs, and evolutionary trees

  • We'll have a guest lecture by Ben Liebeskind, a postdoctoral fellow in the Center for Systems and Synthetic Biology, on decoding the evolutionary relationships among genes.
  • Today's slides

Jan 31, 2017 - Sequence Alignment II

Jan 26, 2017 - Sequence Alignment I

Problem Set I, due before midnight Feb. 6, 2017:

  • Problem Set 1
  • H. influenzae genome. Haemophilus influenza was the first free living organism to have its genome sequenced. NOTE: a few of you have pointed out that there are some additional characters in this file (from ambiguous sequence calls). For simplicity's sake, when calculating your nucleotide and dinucleotide frequencies, you can just ignore anything other than A, C, T, and G.
  • T. aquaticus genome. Thermus aquaticus helped spawn the genomic revolution as the source of heat-stable Taq polymerase for PCR.
  • 3 mystery genes (for Problem 5): MysteryGene1, MysteryGene2, MysteryGene3
  • *** HEADS UP FOR THE PROBLEM SET *** If you try to use the Python string.count function to count dinucleotides, Python counts non-overlapping instances, not overlapping instances. So, AAAA is counted as 2, not 3, dinucleotides. You want overlapping dinucleotides instead, so will have to try something else, such as the python string[counter:counter+2] command, as explained in the Rosalind homework assignment on strings.
  • For those of you who could use more tips on programming, there's a peer-led open coding hour happening on Wednesdays 4-5pm in MBB 2.232 (2nd floor lounge). It's a very informal setting where you can ask questions of more experienced programmers.

Reading:

Jan 24, 2017 - Rosalind help & programming Q/A

Jan 19, 2017 - Intro to Python

  • News of the day/Science in action: There's a huge ongoing debate raging about the development of CRISPR genome editing technology, stemming in part from an ongoing patent contest over who made key innovations in characterizing, engineering, and applying CRISPR. You can read some of the debate here, here, and here, among many other sites. There's a good chance we'll hear the major CRISPR patents decided this semester.
  • REMINDER: My email inbox is always fairly backlogged (e.g., my median time between non-spam emails yesterday was 11 minutes), so please copy the TA on any emails to me to make sure they get taken care of.
  • Today's slides
  • Python primer
  • E. coli genome
  • Python 2 vs 3?. For compatibility with Rosalind and other materials, we'll use version 2.7. The current plan is for Python 2.7 support to be halted in 2020, but there is some hope (wishful thinking?) that Python 4 will be backwards compatible, unlike Python 3.

Jan 17, 2017 - Introduction

  • Today's slides
  • Some warm-up videos to get you started on Python: Code Academy's Python coding for beginners
  • We'll be conducting homework using the online environment Rosalind. Go ahead and register on the site, and enroll specifically for BCH364C/BCH394P using this link. Homework #1 (worth 10% of your final course grade) has already been assigned on Rosalind and is due by 11:59PM January 26.
  • A useful online resource if you get bogged down: Python for Biologists. (& just a heads-up that some of their instructions for running code relate to a command line environment that's a bit different from the default one you install following the Rosalind instructions. It won't affect the programs, just the way they are run or how you specific where files are located.) However, if you've never programmed before, definitely check this out!!!
  • An oldie (by recent bioinformatics standards) but goodie: Computers are from Mars, Organisms are from Venus

Syllabus & course outline

Course syllabus

An introduction to systems biology and bioinformatics, emphasizing quantitative analysis of high-throughput biological data, and covering typical data, data analysis, and computer algorithms. Topics will include introductory probability and statistics, basics of Python programming, protein and nucleic acid sequence analysis, genome sequencing and assembly, proteomics, synthetic biology, analysis of large-scale gene expression data, data clustering, biological pattern recognition, and gene and protein networks.

Open to graduate students and upper division undergrads (with permission) in natural sciences and engineering. Prerequisites: Basic familiarity with molecular biology, statistics & computing, but realistically, it is expected that students will have extremely varied backgrounds. UGs have additional prerequisites, as listed in the catalog.

Note that this is not a course on practical sequence analysis or using web-based tools. Although we will use a number of these to help illustrate points, the focus of the course will be on learning the underlying algorithms and exploratory data analyses and their applications, esp. in high-throughput biology.

Most of the lectures will be from research articles and slides posted online, with some material from the...
Optional text (for sequence analysis): Biological sequence analysis, by R. Durbin, S. Eddy, A. Krogh, G. Mitchison (Cambridge University Press),

For biologists rusty on their stats, The Cartoon Guide to Statistics (Gonick/Smith) is very good. A reasonable online resource for beginners is Statistics Done Wrong.

Some online references:
An online bioinformatics course
Assorted bioinformatics resources on the web: Assorted links
Online probability texts: #1, #2, #3

No exams will be given. Grades will be based on online homework (counting 30% of the grade), 3 problem sets (given every 2-3 weeks and counting 15% each towards the final grade) and an independent course project (25% of final grade). The course project will consist of a research project on a bioinformatics topic chosen by the student (with approval by the instructor) containing an element of independent computational biology research (e.g. calculation, programming, database analysis, etc.). This will be turned in as a link to a web page. The final project is due by midnight, April 27, 2017. The last two classes will be spent presenting your projects to each other. (The presentation will account for 5% of the project.)

Online homework will be assigned and evaluated using the free bioinformatics web resource Rosalind.

All projects and homework will be turned in electronically and time-stamped. No makeup work will be given. Instead, all students have 5 days of free “late time” (for the entire semester, NOT per project, and counting weekends/holidays). For projects turned in late, days will be deducted from the 5 day total (or what remains of it) by the number of days late (in 1 day increments, rounding up, i.e. 10 minutes late = 1 day deducted). Once the full 5 days have been used up, assignments will be penalized 10 percent per day late (rounding up), i.e., a 50 point assignment turned in 1.5 days late would be penalized 20%, or 10 points.

Homework, problem sets, and the project total to a possible 100 points. There will be no curving of grades, nor will grades be rounded up. We’ll use the plus/minus grading system, so: A= 92 and above, A-=90 to 91.99, etc. Just for clarity's sake, here are the cutoffs for the grades: 92% = A, 90% = A- < 92%, 88% = B+ < 90%, 82% = B < 88%, 80% = B- < 82%, 78% = C+ < 80%, 72% = C < 78%, 70% = C- < 72%, 68% = D+ < 70%, 62% = D < 68%, 60% = D- < 62%, F < 60%.

Students are welcome to discuss ideas and problems with each other, but all programs, Rosalind homework, and written solutions should be performed independently. Students are expected to follow the UT honor code. Cheating, plagiarism, copying, & reuse of prior homework or programs from CourseHero, Github, or other sources are all strictly forbidden and constitute breaches of academic integrity (UT academic integrity policy) and cause for dismissal with a failing grade.

The final project web site is due by midnight April 27, 2017.