Difference between revisions of "BCH394P BCH364C 2019"

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== Lectures & Handouts ==
 
== Lectures & Handouts ==
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'''Apr 30 - May 7, 2019 - Final Project Presentations'''
'''May 2 - May 7, 2019 - Final Project 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] and [http://ccbb.biosci.utexas.edu/summerschool.html#rna RNA-seq], also machine learning & 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#ngs core NextGen sequencing tools] and [http://ccbb.biosci.utexas.edu/summerschool.html#rna RNA-seq].
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* We'll spend 5 minutes on the [https://utdirect.utexas.edu/ctl/ecis/ Course - Instructor Survey] Thursday morning.
* We'll spend 5 minutes on the [https://utdirect.utexas.edu/ctl/ecis/ Course - Instructor Survey] this morning.
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* Here's an assortment of final class projects (with permission to be posted):
 
* Here's an assortment of final class projects (with permission to be posted):
** [https://sites.google.com/utexas.edu/psatellite/home Analysis of ABCC11 Transcription Factors for Regulation and Expression, by Stratton Georgoulis]
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** [https://bioinformaticsnlxrps.wordpress.com/ Genomic and transcriptomic dynamics of outer membrane proteins in bacterial pathogens, by Nicholas Xerri, Rui Silva]
** [https://sites.google.com/view/clptm1/home Analysis of the CLPTM1 Gene Family, by Nathan Huang]
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** [https://sites.google.com/utexas.edu/bch364cfinalproject/home IMPACT OF CDPK PHOSPHORYLATION OF EIFISO4G1 ON PREDICTED 3D STRUCTURE, by Phillip Woolley]
** [https://sites.google.com/view/kolin-bioinformatics/home/ Big cats, by Katherine Olin]
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** [https://sites.google.com/utexas.edu/coralandcancer/home Corals, Cancer, and Colonial Fusion, by Brandon Burgman]
** [https://sites.google.com/utexas.edu/igdcom/home A Hidden Markov model for exploring function and evolution of immunoglobulin domains, by Sayer Browning, Sachit Saksena, and Lisa Strong]
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** [https://sites.google.com/utexas.edu/codonramps/home Directed evolution of codon ramps using a stochastic model, by Alexandra Lukasiewicz]
** [https://github.com/bshrestha0/BCH339N_classProject/wiki Examine evolutionary fate of missing plastid genes in Passiflora using transcriptome data, by Bikash Shrestha]
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** [https://mmcguffi.github.io/ Plasmid Annotation Tool, by Matt McGuffie]
** [https://sites.google.com/view/abcc11geneproject/ Analysis of ABCC11 Transcription Factors for Regulation and Expression, by Joshua Tran]
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** [https://sites.google.com/view/bch394pcolleenmulvihill/home Analysis of Predictive Strategies for Heterologous Expression of Human GPCRs in Saccharomyces cerevisiae, by Colleen Molvihill]
** [https://sites.google.com/view/fromthebraintoacomputer/home?authuser=1 Primate motor cortex neuronal signal serve as metrics for machine learning algorithm, by Juan Zambrano]
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** [https://sites.google.com/view/aparna-suhas-bchfinalproject/introduction A Conceptual Analysis over Alzheimer's Disease, by Aparna Kakarlapudi & Suhas Tatapudi]
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** [https://sites.google.com/a/utexas.edu/bch-339n-final-project/ Comparative Analysis of Prions Among Mammals with TSEs, by Vanessa Alexandra]
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** [https://sites.google.com/utexas.edu/yeastieboys/home Origins of Lager Yeast, by Thorin Peiser]
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** [https://sites.google.com/utexas.edu/amberless-ecoli-sequencing/home?authuser=1 Mapping mutations in amberless E. coli from nanopore sequencing, by Rachel Le and Abigail Ulloa]
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** [https://sites.google.com/site/bch339nproject/ Phylogenetic Analysis of GPCRs from Multiple Species, by Austin Arceneaux]
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** [https://github.com/Dclove123/Bioinformatics_Project Determining L- and V- regions of an Antibody Amino Acid Sequence using Hidden Markov Models and the Viterbi Algorithm, by Kevin Valdez, Kelly Sim, and Ghenica-Rose Delfin]
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** [https://sites.google.com/utexas.edu/bch339n-petbiodegradation/home PET Biodegradation, by Candice Chen, Nick Brzezniak, Max Rector]  
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'''April 30, 2019 - Synthetic Biology II'''
 
* '''Reminder: All projects are due by 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. 19.
 
  
  
'''April 25, 2019 - Synthetic Biology I'''
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'''April 23-25, 2019 - Synthetic Biology I'''
 
* '''Reminder: All projects are due by midnight, April 29'''.  Turn them in as a URL to the web site you created, sent by email to the TA AND PROFESSOR.   
 
* '''Reminder: All projects are due by midnight, April 29'''.  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/BCH394P_364C_2019/BCH394P-364C_SyntheticBio_Spring2019.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH394P_364C_2019/BCH394P-364C_SyntheticBio_Spring2019.pdf Today's slides]
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'''April 23, 2019 - Phenologs'''
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* '''Remember: The final project web page is due by midnight April 29, 2019, 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.'''  
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'''April 18, 2019 - Phenologs'''
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* [https://www.nytimes.com/2019/04/14/health/gene-editing-babies.html More on ethics, CRISPR babies] & [https://www.nytimes.com/2019/04/16/health/stanford-gene-editing-babies.html Stanford's response]
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* '''Remember: The final project web page is due by midnight April 29, 2019, 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. Also, note that ''late days can't be used for the final project'' '''  
 
* [http://www.marcottelab.org/users/BCH394P_364C_2019/BCH394P-364C_Phenologs_Spring2019.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH394P_364C_2019/BCH394P-364C_Phenologs_Spring2019.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. This is a fun example of the power of opportunistic data mining aka [http://researchparasite.com/ "research parasitism"] in biomedical research.
 
* [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.
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* 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.
 
Tools for finding orthologs:<br>
 
Tools for finding orthologs:<br>
* One good tool for discovering orthologs is [http://inparanoid.sbc.su.se/cgi-bin/index.cgi InParanoid].  Note: InParanoid annotation lags a bit, so you'll need to find the [http://www.ensembl.org/index.html Ensembl] protein id, or try a text search for the common name. Or, just link there from [http://www.uniprot.org/ Uniprot]. InParanoid tends towards higher recall, lower precision for finding orthologs. Approaches with higher precision include [http://omabrowser.org/oma/home/ OMA] (introduced in [http://www.marcottelab.org/users/BCH394P_364C_2019/OMA.pdf this paper]), [http://phylomedb.org/ PhylomeDB], and [http://eggnogdb.embl.de/#/app/home EggNOG]
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* One good tool for discovering orthologs is [http://inparanoid.sbc.su.se/cgi-bin/index.cgi InParanoid].  Note: InParanoid annotation lags a bit, so you'll need to find the [http://www.ensembl.org/index.html Ensembl] protein id, or try a text search for the common name. Or, just link there from [http://www.uniprot.org/ Uniprot]. InParanoid tends towards higher recall, lower precision for finding orthologs. Approaches with higher precision include [http://omabrowser.org/oma/home/ OMA] (introduced in [http://www.marcottelab.org/users/BCH394P_364C_2019/OMA.pdf this paper]), [http://phylomedb.org/ PhylomeDB], and [http://eggnogdb.embl.de/#/app/home EggNOG].  The various algorithms basically have different trade-offs with regard to precision vs recall, and ease of use.  For example, we use EggNOG in the lab for annotating genes in new genomes/transcriptomes because the EggNOG HMM ortholog models are easily downloadable/re-run on any set of genes you happen to be interested in.
* [http://www.marcottelab.org/users/BCH394P_364C_2019/Sonnhammer2002TiG.pdf All your ortholog definition questions answered!]
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* All (well, at least some) of [http://www.marcottelab.org/users/BCH394P_364C_2019/Sonnhammer2002TiG.pdf your ortholog definition questions answered!]
 
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'''April 18, 2019 - Networks II'''
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* We're finishing up the slides from Apr. 16.
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* An interesting re-analysis of real-world networks: [https://www.quantamagazine.org/scant-evidence-of-power-laws-found-in-real-world-networks-20180215/ Scant Evidence of Power Laws Found in Real-World Networks]
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** [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].
 
 
** 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].
 
** 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/BCH394P_364C_2019/EuropeanGenesPCA.pdf European men, their genomes, and their geography]
 
* [http://www.marcottelab.org/users/BCH394P_364C_2019/EuropeanGenesPCA.pdf European men, their genomes, and their geography]
 
* [http://projector.tensorflow.org/ The tSNE interactive visualization tool also performs PCA]
 
* [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]
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* Relevant to today's lecture 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/BCH394P_364C_2019/NBT_primer_PCA.pdf NBT Primer on PCA]
 
* [http://www.marcottelab.org/users/BCH394P_364C_2019/NBT_primer_PCA.pdf NBT Primer on PCA]
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* Python code for [http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html performing PCA yourself]. This example gives a great intro to several important numerical/statistical/data mining packages in Python, including pandas and numpy.
 
* Python code for [http://sebastianraschka.com/Articles/2015_pca_in_3_steps.html performing PCA yourself]. This example gives a great intro to several important numerical/statistical/data mining packages in Python, including pandas and numpy.
  
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'''Apr 9, 2019 - Classifiers'''
 
'''Apr 9, 2019 - Classifiers'''
 
* [http://www.marcottelab.org/users/BCH394P_364C_2019/BCH394P-364C_Classifiers_Spring2019.pdf Today's slides]
 
* [http://www.marcottelab.org/users/BCH394P_364C_2019/BCH394P-364C_Classifiers_Spring2019.pdf Today's slides]

Latest revision as of 14:11, 30 April 2019

BCH394P/BCH364C Systems Biology & Bioinformatics

Course unique #: 54044/53945
Lectures: Tues/Thurs 11 – 12:30 PM in JGB 2.202
Instructor: Edward Marcotte, marcotte @ icmb.utexas.edu

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

TA: Caitie McCaffery, clmccafferty @ utexas.edu

  • TA Office hours: Mon 11-12/Fri 2-3 in NHB 3.400B atrium (or MBB 3.128B) Phone: 512-232-3919

Lectures & Handouts

Apr 30 - May 7, 2019 - Final Project Presentations


April 23-25, 2019 - Synthetic Biology I

  • Reminder: All projects are due by midnight, April 29. 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, 2019 - Phenologs

  • More on ethics, CRISPR babies & Stanford's response
  • Remember: The final project web page is due by midnight April 29, 2019, 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. Also, note that late days can't be used for the final project
  • 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.

Tools for finding orthologs:

  • One good tool for discovering orthologs is InParanoid. Note: InParanoid annotation lags a bit, so you'll need to find the Ensembl protein id, or try a text search for the common name. Or, just link there from Uniprot. InParanoid tends towards higher recall, lower precision for finding orthologs. Approaches with higher precision include OMA (introduced in this paper), PhylomeDB, and EggNOG. The various algorithms basically have different trade-offs with regard to precision vs recall, and ease of use. For example, we use EggNOG in the lab for annotating genes in new genomes/transcriptomes because the EggNOG HMM ortholog models are easily downloadable/re-run on any set of genes you happen to be interested in.
  • All (well, at least some) of your ortholog definition questions answered!


Apr 16, 2019 - Networks

  • Today's slides
  • Metabolic networks: The wall chart (it's interactive. For example, can you find enolase?), the current state of the human metabolic reaction network, a review of mapping transcriptional networks by Chip-SEQ (with the current record holder in this regard held by ENCODE), and a recent review of protein interaction mapping in humans and how it is informing disease genetics.
  • 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.
    • 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 11, 2019 - Principal Component Analysis (& the curious case of European genotypes)

A smattering of links on PCA:


Apr 9, 2019 - Classifiers


Apr 4, 2019 - Clustering II


Apr 2, 2019 - Functional Genomics & Data Mining - Clustering I

Problem Set 3, due before midnight Apr. 11, 2019. You will need the following software and datasets:


Mar 28, 2019 - 3D Protein Structure Modeling


Mar 26, 2019 - Proteomics


Mar 19-21, 2019 - SPRING BREAK

  • Don't forget to finish HW3 and turn in the proposal for your course project by March 25th.


Mar 14, 2019 - Motifs


Mar 12, 2019 - Live Demo: Next-next-...-generation Sequencing (NGS) by nanopore


Mar 7, 2019 - Genomes II


Mar 5, 2019 - Guest Lecture: Anna Battenhouse, NGS Analysis Best Practices

  • Practical advice and best practices for NGS mapping and analysis
  • Today's slides


Feb 28, 2019 - Genome Assembly

  • Today's slides
  • A gentle reminder that Problem Set 2 is due by 11:59PM March 6
  • Also, next Tuesday will be the first of our guest lecturers, Anna Battenhouse, on practical aspects of genome sequencing/assembly
  • DeBruijn Primer and Supplement
  • If you would like a few examples of proteins annotated with their transmembrane and soluble regions (according to UniProt) to help troubleshoot your homework, here are some example yeast protein sequences.
  • From last time, some definitions of sensitivity/specificity & precision/recall. Note that the gene finding community settled early on to a different definition of specificity that corresponds to the precision or PPV in other fields. Other fields define specificity as the true negative rate.


Feb 26, 2019 - Gene finding II

  • We're finishing up the slides from Feb. 21
  • Peer-led open coding hour has been resurrected! Wed 12:30-1:30 in the MBB 2nd floor student lounge (the newly revamped eating space)


Feb 21, 2019 - Gene finding

Problem Set 2, due before midnight Mar. 6, 2019:

Reading (a couple of old classics + a review):


Feb 19, 2019 - HMMs II


Feb 14, 2019 - Hidden Markov Models

Reading:


Feb 12, 2019 - Biological databases


Feb 7, 2019 - BLAST


Feb 5, 2019 - Sequence Alignment II


Jan 31, 2019 - Sequence Alignment I

  • For those of you who might be interested, Rosalind is having a Bioinformatics Contest. Sign up runs until Feb. 2, the qualification round is Feb. 2-10, and Feb. 23 is the final round, with 24 hours to solve as many problems as you can. First prize in 2019 is to get your genome (exome) sequenced or get your own nanopore sequencer!
  • Today's slides

Problem Set I, due before midnight Feb. 11, 2019:

  • Problem Set 1
  • H. influenzae genome. Haemophilus influenza was the first free living organism to have its genome sequenced. NOTE: 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.

Reading:


Jan 29, 2019 - Intro to Python #2


Jan 24, 2019 - Intro to Python

  • REMINDER: My email inbox is always fairly backlogged (e.g., my median time between non-spam emails was 11 minutes when I measured last year), 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. Regardless, you're welcome to use whichever version you prefer, but we'll use 2.7 for all class explanations in the interests of simplicity and consistency. For beginners, the differences are quite minimal.


Jan 22, 2019 - Introduction

  • Science news of the day (& a reminder of the importance of ethics in science!): Chinese authorities say world's first gene-edited babies were illegal
  • Today's slides
  • Some warm-up videos to get you started on Python (2 not 3, unless you pay for an upgrade): Code Academy's Python coding for beginners
  • Khan Academy has archived their videos on Python here
  • We'll be conducting homework using the online environment Rosalind. Go ahead and register on the site, and enroll specifically for BCH394P-BCH364C-Spring2019 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 31.
  • 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 Python before, definitely check this out!!!

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. Undergraduates 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 29, 2019. The last 2.5 classes will be spent presenting your projects to each other. (The presentation will account for 5/25 points for 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, problem sets, and written solutions should be performed independently . Students are expected to follow the UT honor code. Cheating, plagiarism, copying, & reuse of prior homework, projects, or programs from CourseHero, Github, or any 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 29, 2019.