====== BME 483/543: Quantitative Systems Biology
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[[proj:project|Project Deliverables]]

====== Course Material

===== Intro
  * Dropbox setup
  * Using file sharing and versioning systems: Github, SVN
  * Working with webpages in Matlab
  * File parsing strategies in Matlab. regular expression.

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* RNASeq
	* {{rnaseq:nextgen seqalign survey.2010.pdf}}
	* [[rnaseq.dbp2:index|Assignment - RefSeq Experiment in a Yeast Mutant]]


* **Genome Assembly**
  * {{course:bioinfo:genomeassembly:genomeassembly.pdf|Genome Assembly}}


* **Metagenomics, Microbiome**
  * {{3.metagenomics:metagenomics.pdf|Metagenomics}}
  * [[https://github.com/mlangill/microbiome_helper/wiki/Microbiome-Helper-Virtual-Box|Microbiome Helper Virtual Box]], [[https://github.com/mlangill/microbiome_helper/wiki/16S-tutorial-(chemerin)|16S tutorial]], [[https://github.com/mlangill/microbiome_helper/wiki/Metagenomics-Tutorial|Metagenomics tutorial]]
  * Oracle Virtual Box, ssh, passwordless login
  * Initial Assignment: Install Microbiome Helper and run through the [[https://github.com/LangilleLab/microbiome_helper/wiki/Metagenomics-Tutorial-(Humann2)|Metagenomics Tutorial]]. Compare your answers to the questions with the answers provided there.
  * {{metagenomics.tutorial:hwmetagenomics.docx}}

* **TODO**: Discuss vectorized code: e.g., remove all rows with less than 3 non-nan samples.

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  * [[http://sacan.biomed.drexel.edu/lic/reg/qsb]]
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* **Short-Read Alignment**
  * {{1.nextgen:suffixtree.pdf|Suffix Tree}}, {{1.nextgen:bwt.pdf|Burrows-Wheeler Transform}}, [[1.nextgen:exercises]]
  * [[1.nextgen.yeast:index|RefSeq Experiment in a Yeast Mutant]]
  * {{1.nexgen.yeast:bwa.compilingrunning.mp4}}


* **Functional Enrichment**
  * [[1.nextgen.yeast:index_enrich|RefSeq Experiment in a Yeast Mutant - Enrichment]]




* **Week 8**
  * Friday 4-6pm extra session at Bossone709.

* **Week 9** no class.
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====== Grading.
* Assignments: 70%:
  * Weekly programming and analysis assignments.
  * Research article review and presentation.
    * Find and discuss papers related to the topics we cover in the course. You need to contribute 5 papers (each from a different topic) (Bmes483 students: 3 papers). At least one of your papers must be found by you. You can find the others yourself or sign up for the existing papers. Prepare a power point presentation to facilitate discussion.
  * Active Participation.
* Final Projects: 30%

* Participation in all graded activities is mandatory and required for a passing grade.

* (60 pts) Your project
	* The project may not be something you work on for another course or research project.
	* (20 pts) Meet the instructor each week and discuss your progress.
	* (5 pts) Draft report
	* (15 pts) Final implementation and report
	* (20 pts) Final presentation
	* [[@/ftp/gie/proj|Sample Projects]]


* (25 pts) Paper discussion
	* Find and discuss papers related to the topics we cover in the course. You need to contribute 5 papers to 5 different topics. Fill out a {{reviewform.txt|review form}} for each of these papers and prepare a power point presentation to facilitate discussion.

* (10 pts) Seminar Presentation
	* Check out the [[@/ical/month.php?cal=dxlbio,dxlbmed,dxlcs,dxlmed,dxlsph,fcc,penabo,penaids,penbchem,penbio,pencis,penmed|Seminar Calendar]] and attend a seminar that is relevant to the course. Make sure to confirm the date & location of the seminar as our calendar may be out of date. You may also find a seminar not listed in that calendar.
	* [[https://docs.google.com/spreadsheets/d/1jPDmT9JGs9kcKlu_zVU-OIqJFugPIBDqiBhfnBGmJh4/edit?usp=sharing|Seminar sign up]]
	* Select a seminar and confirm with the instructor that the seminar is relevant to our course.
	* The seminar you select should NOT be a seminar that you have to attend as part of another course.
	* After attending the seminar, give a 15 minute presentation in class with powerpoint slides. Last week for these presentations is Week 8. Remember to upload your presentation slides before class.

* (5 pts) Attendance & active participation
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====== Topics and Resources
* Not all topics may be covered in the course.
* Project ideas
	* Method benchmarking (evaluate several methods on the same dataset(s))
	* Reproduce/improve method and results from a paper
	* Microarray Meta-analysis
	* Protein Structure mutation analysis


* Protein surface analysis {{wcached>baseid=.:wcached:surfold:}}
	* Protein surface construction
	* Dimension reduction methods
	* {{wcached>http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.63.8019&rep=rep1&type=pdf|Protein surface analysis for function annotation in high-throughput structural genomics pipeline}}
	* {{wcached>http://www.ploscompbiol.org/article/fetchObjectAttachment.action;jsessionid=B87AC269D15578CFF0AFC5FB0A04ECE5.ambra02?uri=info%3Adoi%2F10.1371%2Fjournal.pcbi.1001075&representation=PDF|A Mathematical Framework for Protein Structure Comparison}}
	* {{wcached>http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WK7-457D0X9-R-1&_cdi=6899&_user=95578&_pii=S0022283601946303&_origin=gateway&_coverDate=06%2F08%2F2001&_sk=996909996&view=c&wchp=dGLbVtb-zSkzk&md5=b603a291751e7cbf618408ba89a6d3f3&ie=/sdarticle.pdf|Surface map comparison: studying function diversity of homologous proteins}}
	* {{wcached>http://www.springerlink.com/content/yfrqrtky7845pu6j/fulltext.pdf|Protein surface similarities: a survey of methods to describe and compare protein surfaces}}

* Gene function prediction  {{wcached>baseid=.:wcached:seqfuncpred:}}
	* {{wcached>http://www.nature.com/nbt/journal/v24/n12/pdf/nbt1206-1474.pdf|The Art of Gene Function Prediction}}
	* {{wcached>http://pmb.berkeley.edu/~taylor/pmb290cg/pdfs/Eisen_theorpopbio_2002.pdf|Phylogenetic Analysis and Gene Functional Predictions}}
	* {{wcached>http://www.biomedcentral.com/content/pdf/1471-2105-8-S4-S6.pdf|Gene function prediction based on genomic context clustering and discriminative learning -- an application to bacteriophages}}
	* {{wcached>http://www.biomedcentral.com/content/pdf/1471-2105-11-2.pdf|Predicting gene function using hierarchical multi-label decision tree ensembles}}
	* {{wcached>http://www.biomedcentral.com/content/pdf/1471-2105-5-116.pdf|Applying Support Vector Machines for Gene ontology based gene function prediction}}
	* {{wcached>http://bioinformatics.oxfordjournals.org/content/21/7/1237.full.pdf|In silico gene function prediction using ontology-based pattern identification}}
	* Decision trees, ensemble learning, bagging, random forests


* Differences in microbial gene composition correlated to BMI and disease states  {{wcached>baseid=.:wcached:metagenom:}}
	* {{wcached>http://www.nature.com/nature/journal/v452/n7187/pdf/nature06810.pdf|Functional metagenomic profiling of nine biomes}}
	* {{wcached>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2972309/pdf/nihms238159.pdf|Culture-independent identification of gut bacteria correlated with
the onset of diabetes in a rat model}}
	* {{wcached>http://cmbi.bjmu.edu.cn/news/report/2010/pdf/gut_5.pdf|Gut Microbiota in Health and Disease}}
	* {{wcached>http://lacomunidad.elpais.com/blogfiles/apuntes-cientificos-desde-el-mit/humanmicrobiomeprojectnature.pdf|The Human Microbiome Project}}
	* {{wcached>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1242213/pdf/gb-2005-6-9-r78.pdf|E-Predict: a computational strategy for species identification based on observed DNA microarray hybridization patterns}}
	* {{wcached>http://www.nature.com/nature/journal/v452/n7186/pdf/nature06758.pdf|Genetics of gene expression and its effect on disease}}
	* {{wcached>http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WSN-51NFP7M-M-1B&_cdi=7051&_user=95578&_pii=S0092867410013000&_origin=browse&_coverDate=12%2F10%2F2010&_sk=998569993&view=c&wchp=dGLzVzz-zSkWb&md5=c3ce11388ca7357617116465252452d9&ie=/sdarticle.pdf|Comprehensive Polyadenylation Site Maps in Yeast and Human Reveal Pervasive Alternative Polyadenylation}}

* Secondary structure prediction {{wcached>baseid=.:wcached:strpred:}}
	* {{wcached>http://www.ntu.edu.sg/home/asjagath/papers/NguyenZuradaRajapakse2011.pdf|Toward better understanding of protein secondary structure: extracting prediction rules.}}
	* {{wcached>http://bioinformatics.oxfordjournals.org/content/21/8/1719.full.pdf|Porter - a new, accurate server for protein secondary structure prediction}}
	* {{wcached>http://cbio.ensmp.fr/~jvert/svn/bibli/local/Guo2004novel.pdf|A Novel Method for Protein Secondary Structure Prediction Using Dual-Layer SVM and Profiles}}
	* {{wcached>http://www.omicsonline.org/2153-0602/2153-0602-2-107.pdf|Protein Secondary Structure Prediction using Deterministic Sequential Sampling}}
	* {{wcached>http://www.compbio.dundee.ac.uk/ftp/pdf/Application_of_multiple_sequence_alignment_profiles_to_improve_protein_secondary_structure_prediction_2000.pdf|Application of Multiple Sequence Alignment Profiles to Improve Protein Secondary Structure Prediction}}
	* {{wcached>http://www.biomedcentral.com/content/pdf/1471-2105-12-108.pdf|TurboFold: Iterative probabilistic estimation of secondary structures for multiple RNA sequences}}
	* {{wcached>http://www.nature.com/nprot/journal/v4/n3/pdf/nprot.2009.2.pdf|Protein structure prediction on the Web: a case study using the Phyre server}}
	* {{wcached>http://www.google.com/url?sa=t&source=web&cd=1&ved=0CB8QFjAA&url=http%3A%2F%2Fwww.dbb.su.se%2F%40api%2Fdeki%2Ffiles%2F365%2F%3Dpsipred.pdf&ei=6ku4TdfiOtOjtge5tc3eBA&usg=AFQjCNGmmu0rC1Pmjy6Hom0ZPl1jc9MNEQ|protein secondary structure prediction based on position-specific scoring matrices}}

* Identification of active sites, by Rachel {{wcached>baseid=.:wcached:sitepred:}}
	* {{wcached>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1877815/pdf/1471-2105-8-119.pdf|Selective prediction of interaction sites in protein structures with THEMATICS}}
	* {{wcached>http://bioinformatics.oxfordjournals.org/content/20/suppl_1/i130.full.pdf|SCOPEC: a database of protein catalytic domains}}
	* {{wcached>http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WK7-4G1G95J-3-R&_cdi=6899&_user=95578&_pii=S0022283605004286&_origin=gateway&_coverDate=06%2F10%2F2005&_sk=996509996&view=c&wchp=dGLzVtz-zSkzV&md5=8ef4b0aebaaf2a1705298b8fdfce5877&ie=/sdarticle.pdf|Active Site Identification through Geometry-based and Sequence Profile-based Calculations: Burial of Catalytic Clefts}}
	* {{wcached>http://peds.oxfordjournals.org/content/13/2/83.full.pdf|Mapping of protein surface cavities and prediction of enzyme class by a self-organizing neural network}}
	* {{wcached>http://ibis2.tau.ac.il/wiki/nir_bental/images/b/b3/Ashkenazy_NAR_2010.pdf|ConSurf 2010: calculating evolutionary conservation in sequence and structure of proteins and nucleic acids}}
	* {{wcached>http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WK7-4CG7NH4-1-15&_cdi=6899&_user=95578&_pii=S0022283604005443&_origin=gateway&_coverDate=07%2F02%2F2004&_sk=996599997&view=c&wchp=dGLbVzz-zSkWA&md5=559a1daedbe3860871d59f6ac57c4f6f&ie=/sdarticle.pdf|Enzyme/Non-enzyme Discrimination and Prediction of Enzyme Active Site Location Using Charge-based Methods}}
	* {{wcached>http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6WK7-48YVDB9-9-1&_cdi=6899&_user=95578&_pii=S0022283603005151&_origin=gateway&_coverDate=07%2F18%2F2003&_sk=996699995&view=c&wchp=dGLzVlz-zSkzV&md5=53175b108e99aa20062f0fdf5d8c0dac&ie=/sdarticle.pdf|Using A Neural Network and Spatial Clustering to Predict the Location of Active Sites in Enzymes}}
	* {{wcached>http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1534064/pdf/1471-2105-7-312.pdf|Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties}}

* Analysis of Cell Array Data from Auto-immune Disorders to Investigate Complement Susceptibility {{wcached>baseid=.:wcached:autoimmune:}}
	* {{wcached>http://www.molgen.mpg.de/~in-vitro/15237918.pdf|Array technology and proteomics in autoimmune diseases}}
	* {{wcached>http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6VS1-472RW7N-G-3&_cdi=6249&_user=95578&_pii=S0952791502004016&_origin=gateway&_coverDate=12%2F01%2F2002&_sk=999859993&view=c&wchp=dGLzVzz-zSkWb&md5=6e48eb48d7cee8508b52b6bb18174b16&ie=/sdarticle.pdf|Genetics of autoimmune diseases in humans and in animal models}}
	* {{wcached>http://www.uth.tmc.edu/pathology/medic/immunology/Immuno/Wanstrat-Wakeland.review.pdf|The genetics of complex autoimmune diseases: non-MHC susceptibility genes}}
	* {{wcached>http://www.stanford.edu/group/robinsonlab/publications/pdfs/robinson_biotech_02.pdf|Protein and peptide array analysis of autoimmune disease}}
	* {{wcached>http://www.pnas.org/content/96/6/2907.full.pdf|Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation}}
	* {{wcached>http://www.nature.com/nm/journal/v16/n5/pdf/nm.2130.pdf|A CD8+ T cell transcription signature predicts prognosis
	in autoimmune disease}}

* Time course analysis of microarray data in new castle disease {{wcached>baseid=.:wcached:timeseriespatterns:}}
	* {{wcached>http://www.biodatamining.org.ezproxy2.library.drexel.edu/content/pdf/1756-0381-4-9.pdf|pGQL: A probabilistic graphical query language for gene expression time courses}}
	* {{wcached>http://nar.oxfordjournals.org/content/35/11/3705.full.pdf|Extracting binary signals from microarray time-course data
	* {{wcached>http://bioinformatics.oxfordjournals.org/content/23/7/866.full.pdf|Boolean dynamics of genetic regulatory networks inferred from microarray time series data}}
	* {{wcached>http://bioinformatics.oxfordjournals.org/content/21/1/71.full.pdf|A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data}}
	* {{wcached>http://www.pnas.org/content/102/36/12837.full.pdf|Significance analysis of time course microarray experiments}}
	* {{wcached>http://www.springerlink.com/content/d0t5871g2513t321/fulltext.pdf|SlopeMiner: An Improved Method for Mining Subtle Signals in Time Course Microarray Data}}

{{wcached>baseid=.:wcached:timeseriesnn:}}
* Learning expression patterns in microarray data
	* {{wcached>http://bib.oxfordjournals.org/content/8/1/32.full.pdf|Partial least squares: a versatile tool for the analysis of high- dimensional genomic data}}
	* {{wcached>http://www.tbiomed.com/content/pdf/1742-4682-2-23.pdf|Predicting transcription factor activities from combined analysis of microarray and ChIP data: a partial least squares approach}}
	* {{wcached>http://cbio.ensmp.fr/~jvert/svn/bibli/local/Segal2003Regression.pdf|Regression Approaches for Microarray Data Analysis}}
	* {{wcached>http://bioinformatics.oxfordjournals.org/content/22/3/367.full.pdf|PAGE: phase-shifted analysis of gene expression}}
	* {{wcached>http://webdav.tuebingen.mpg.de/u/karsten/Veroeffentlichungen/papers/SteDenWilZouBor09.pdf|A Robust Bayesian Two-Sample Test for Detecting Intervals of Differential Gene Expression in Microarray Time Series}}
	* {{wcached>http://www.pnas.org/content/102/24/8633.full.pdf|Identification of genes required for cellulose synthesis by regression analysis of public microarray data sets}}
	* {{wcached>http://bib.oxfordjournals.org/content/4/3/228.full.pdf|Inferring gene networks from time series microarray data using dynamic Bayesian networks}}


{{wcached>baseid=.:wcached:metagenom:}}
* Functional enrichment of microbiome sequences
	* {{wcached>http://nar.oxfordjournals.org.ezproxy2.library.drexel.edu/content/38/22/e205.full.pdf#page=1&view=FitH|Advanced computational algorithms for microbial community analysis using massive 16S rRNA sequence data}}
	* {{wcached>http://dnaresearch.oxfordjournals.org/content/16/1/1.full.pdf|The Human Intestinal Microbiome: A New Frontier of Human Biology}}


{{wcached>baseid=.:wcached:xmrvprostate:}}
* The role of XMRV in prostate cancer through analysis of microarray data
	* {{wcached>http://www.sciencedirect.com/science?_ob=MImg&_imagekey=B6VJV-4TJ5YXR-2-7&_cdi=6104&_user=95578&_pii=S1386653208003016&_origin=gateway&_coverDate=11%2F30%2F2008&_sk=999569996&view=c&wchp=dGLbVzb-zSkzk&md5=5b4be56a18e3ca3a4c6533c85f08b37c&ie=/sdarticle.pdf|Prevalence of human gammaretrovirus XMRV in sporadic prostate cancer}}
	* {{wcached>http://jvi.asm.org/cgi/reprint/82/20/9964.pdf|Integration Site Preference of Xenotropic Murine Leukemia Virus-Related Virus, a New Human Retrovirus Associated with Prostate Cancer}}


* MiRNAs
* ChipSeq
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