Computational Biology
A.Y. 2019/2020
Learning objectives
The course will introduce computational approaches recently developed for studying biological systems with a focus on biotechnological applications: the identification of essential genes (Tn-Seq and network analysis), or genes that are involved in interesting processes (Tn-Seq) together with methods to study gene regulation (ChIP-Seq, small RNAs). On these premises we will then discuss how to engineer eco-systems (community engineering) and how metabolic optimization can be achieved (model-guided metabolic engineering).
An introduction to computational methods for the characterization of protein structures, with biochemical basis.
An introduction to computational methods for the characterization of protein structures, with biochemical basis.
Expected learning outcomes
The course will introduce students to the computational techniques that are at the basis of the identification of important genes in Tn-seq datasets and to the structural analysis of networks with the aim of identifying genes that can be manipulated for specific objectives. Techniques to study gene regulation will also be discussed (ChIP-Seq, sRNA). As a conclusion, we will discuss modeling techniques at the basis of the engineering of metabolic systems to optimize specific processes for industrial purposes.
In a next part the student will face the problem of protein structure reconstruction and prediction.
In a next part the student will face the problem of protein structure reconstruction and prediction.
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
1. Exploring function and regulation:
a. Tn-mutagenesis for the discovery of essential genes or genes involved in specific processes [Burby et al., 2017, van Opijnen et al., 2015, Van Opijnen and Camilli, 2013, Peng et al., 2017, Kwon et al., 2016, DeJesus et al., 2017];
b. ChIP-Seq [Wu and Ji, 2013, Angelini and Costa, 2014, Allhoff et al., 2014, Zhang and Wang, 2015, Ji et al., 2013, A et al., 2008, Jiang and Mortazavi, 2018]:
i. Histone markers in Eukaryotes;
ii. Transcription factors in Bacteria and the identification of binding sites ;
c. sRNA-sequencing and the quest for targets aided by PARE (degradome sequencing) in Plants [Addo-Quaye et al., 2009, German et al., 2009];
d. Metagenomic, metatranscriptomic and the engineering of microbial communities e.g. [Ronda et al., 2019, Yu et al., 2019, Oyserman et al., 2018, Lam et al., 2019];
2. Network concepts
a. Definitions and concepts e.g. [Klein et al., 2012, Albert et al., 2000, Albert, 2006, Barabasi, 2002, Barabasi and Oltvai, 2004];
b. Topological analysis for the identification of essential/important genes [Yu et al., 2007, McDermott et al., 2009, Ekman et al., 2006, Agarwal et al., 2010, Kahali et al., 2009, Palumbo et al., 2014];
c. Network motifs [Mangan et al., 2003, Mangan and Alon, 2003, Shen-Orr et al., 2002]
d. Communities [Fortunato and Barthelemy, 2007];
e. Effect of structure on function: the case of epidemic spreading [Pastor-Satorras and Vespignani, 2001]
3. Metabolic engineering:
a. Basics of metabolic networks and modelling e.g. [Fell, 2005] ;
b. Pareto optimality and the optimization of metabolism e.g [Vijayaku- mar et al., 2018, Angione et al., 2013, Costanza et al., 2012];
c. Examples from publications e.g. from [Yu et al., 2019, Kim et al., 2018, Park et al., 2018, Donia, 2015]
4. Genome Wide Association Studies (4h) e.g. [Zhang et al., 2018, Gondro, 2013, Chen and Shapiro, 2015]
a. Quantitative Trait Loci in Plants e.g. [Veeresha et al., 2015]
b. Bacteria and the prediction of antibiotic resistance genes e.g. [Mason et al., 2018, Bradley et al., 2015]
5. Drug repurposing e.g. [Peyvandipour et al., 2018]
6. Protein Structure and its analysis
a. The main chemical and geometrical properties of protein structures will be shown: secondary (alpha helix, beta sheets and coil) and tertiary structures. TIM barrel will be used as an example of protein fold ductility.
b. Covalent and non-covalent bonds are fundamental for protein folding: peptide bond, salt bridges, van der Waals interactions and hydrogen bonds. The role of water in protein folding.
c. Computer analysis of protein structures to verify several of the protein properties discussed during course.
d. The evolution of the structure of globular proteins, of membrane proteins and of intrinsically disordered proteins will be accompanied by test of protein structure predictions.
e. Structure prediction by homology modelling
f. Molecular dynamics simulations
g. Structure prediction and refinement by molecular dynamics
a. Tn-mutagenesis for the discovery of essential genes or genes involved in specific processes [Burby et al., 2017, van Opijnen et al., 2015, Van Opijnen and Camilli, 2013, Peng et al., 2017, Kwon et al., 2016, DeJesus et al., 2017];
b. ChIP-Seq [Wu and Ji, 2013, Angelini and Costa, 2014, Allhoff et al., 2014, Zhang and Wang, 2015, Ji et al., 2013, A et al., 2008, Jiang and Mortazavi, 2018]:
i. Histone markers in Eukaryotes;
ii. Transcription factors in Bacteria and the identification of binding sites ;
c. sRNA-sequencing and the quest for targets aided by PARE (degradome sequencing) in Plants [Addo-Quaye et al., 2009, German et al., 2009];
d. Metagenomic, metatranscriptomic and the engineering of microbial communities e.g. [Ronda et al., 2019, Yu et al., 2019, Oyserman et al., 2018, Lam et al., 2019];
2. Network concepts
a. Definitions and concepts e.g. [Klein et al., 2012, Albert et al., 2000, Albert, 2006, Barabasi, 2002, Barabasi and Oltvai, 2004];
b. Topological analysis for the identification of essential/important genes [Yu et al., 2007, McDermott et al., 2009, Ekman et al., 2006, Agarwal et al., 2010, Kahali et al., 2009, Palumbo et al., 2014];
c. Network motifs [Mangan et al., 2003, Mangan and Alon, 2003, Shen-Orr et al., 2002]
d. Communities [Fortunato and Barthelemy, 2007];
e. Effect of structure on function: the case of epidemic spreading [Pastor-Satorras and Vespignani, 2001]
3. Metabolic engineering:
a. Basics of metabolic networks and modelling e.g. [Fell, 2005] ;
b. Pareto optimality and the optimization of metabolism e.g [Vijayaku- mar et al., 2018, Angione et al., 2013, Costanza et al., 2012];
c. Examples from publications e.g. from [Yu et al., 2019, Kim et al., 2018, Park et al., 2018, Donia, 2015]
4. Genome Wide Association Studies (4h) e.g. [Zhang et al., 2018, Gondro, 2013, Chen and Shapiro, 2015]
a. Quantitative Trait Loci in Plants e.g. [Veeresha et al., 2015]
b. Bacteria and the prediction of antibiotic resistance genes e.g. [Mason et al., 2018, Bradley et al., 2015]
5. Drug repurposing e.g. [Peyvandipour et al., 2018]
6. Protein Structure and its analysis
a. The main chemical and geometrical properties of protein structures will be shown: secondary (alpha helix, beta sheets and coil) and tertiary structures. TIM barrel will be used as an example of protein fold ductility.
b. Covalent and non-covalent bonds are fundamental for protein folding: peptide bond, salt bridges, van der Waals interactions and hydrogen bonds. The role of water in protein folding.
c. Computer analysis of protein structures to verify several of the protein properties discussed during course.
d. The evolution of the structure of globular proteins, of membrane proteins and of intrinsically disordered proteins will be accompanied by test of protein structure predictions.
e. Structure prediction by homology modelling
f. Molecular dynamics simulations
g. Structure prediction and refinement by molecular dynamics
Prerequisites for admission
None mandatory, suggested Bioinformatics
Teaching methods
Lessons supported by projected material plus interactive lessons at the computer. Students will be stimulated to participate actively to the lesson/discussion to improve their skills by analysing the cited literature. We strongly suggest to attend all the lessons.
Teaching Resources
For the exam we will mainly refer to the slides that will be available for download after each lesson on the ariel website.
The following is a list of articles/books that can be used by students to explore more in detail some of the issues discussed in the lessons.
Alouev A, Johnson DS, Sidow Sundquist A, Medina C, Anton E, Batzoglou S, Myers RM, Anton Valouev, David S Ds David S Johnson, Andreas Sundquist, Catherine Medina, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, Arend Sidow, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, and Arend Sidow. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nature Methods, 5(9):829-834, 2008. ISSN 1548-7091. doi: 10.1038/nmeth.1246.Genome-Wide. URL http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.1246.html.
Charles Addo-Quaye, Webb Miller, and Michael J Axtell. CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics, 25(1):130-131, jan 2009. ISSN 1367-4803. doi: 10.1093/bioinformatics/btn604. URL https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btn604.
Sumeet Agarwal, Charlotte M Deane, Mason a. Porter, and Nick S Jones. Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks. PLoS Computational Biology, 6(6):e1000817, jun 2010. ISSN 1553-7358. doi: 10.1371/journal.pcbi.1000817. URL http://dx.plos.org/10.1371/journal.pcbi.1000817.
R 'eka Albert. General Network Theory R 'eka Albert. Technical report, 2006.
R 'eka Albert, H Jeong, and Albert-Laszlo Barabasi. Error and attack tolerance of complex networks. Nature, 406(6794): 378-382, jul 2000. ISSN 1476-4687. doi: 10.1038/35019019. URL http://www.ncbi.nlm.nih.gov/pubmed/10935628.
Manuel Allhoff, Kristin Ser 'e, Heike Chauvistr 'e, Qiong Lin, Martin Zenke, Ivan G. Costa, H. Chauvistre, Ivan G. Costa, Qiong Lin, Manuel Allhoff, and K. Sere. Detecting differential peaks in ChIP-seq signals with ODIN. Bioinformatics, 30(24):3467-3475, 2014. ISSN 14602059. doi: 10.1093/bioinformatics/btu722.
Claudia Angelini and Valerio Costa. Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA- seq data: statistical solutions to biological problems. Frontiers in Cell and Developmental Biology, 2(September):51, 2014. ISSN 2296-634X. doi: 10.3389/fcell.2014.00051. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid= 4207007{\&}tool=pmcentrez{\&}rendertype=abstract.
Claudio Angione, Giovanni Carapezza, Jole Costanza, Pietro Lio, and Giuseppe Nicosia. Pareto optimality in organelle energy metabolism analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(4):1032-1044, 2013. ISSN 15455963. doi: 10.1109/TCBB.2013.95.
Albert-Laszlo Barabasi. Linked: The New Science of Everything, 2002. URL http://www.amazon.com/ Linked- Everything- Connected- Else- Means/dp/0452284392{\%}5Cnhttp://scholar.google.com/scholar?hl=en{\&}btnG=Search{\& }q=intitle:Linked:+How+Everything+Is+Connected+to+Everything+Else+and+What+It+Means{\#}3{\%}5Cnhttp://link.aip.org/ link/?AJPIAS/71/4.
Albert-Laszlo Barabasi and Zolt 'an N Oltvai. Network biology: understanding the cell's functional organization. Nature Reviews Genetics, 5(2):101-113, feb 2004. ISSN 1471-0056. doi: 10.1038/nrg1272. URL http://www.ncbi.nlm.nih.gov/ pubmed/14735121.
Phelim Bradley, N Claire Gordon, Timothy M Walker, Laura Dunn, Simon Heys, Bill Huang, Sarah Earle, Louise J Pankhurst, Luke Anson, Mariateresa de Cesare, Paolo Piazza, Antonina A Votintseva, Tanya Golubchik, Daniel J Wilson, David H Wyllie, Roland Diel, Stefan Niemann, Silke Feuerriegel, Thomas A Kohl, Nazir Ismail, Shaheed V Omar, E Grace Smith, David Buck, Gil McVean, A Sarah Walker, Tim E A Peto, Derrick W Crook, and Zamin Iqbal. Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nature Communications, 6(1):10063, 2015. ISSN 2041-1723. doi: 10.1038/ncomms10063. URL http: //www.nature.com/doifinder/10.1038/ncomms10063.
Peter E Burby, Taylor M Nye, Jeremy W Schroeder, and Lyle A Simmons. Implementation and Data Analysis of Tn- seq, Whole-Genome Resequencing, and Single-Molecule Real-Time Sequencing for Bacterial Genetics. Journal of Bacteriology, 199(1):1-11, jan 2017. ISSN 0021-9193. doi: 10.1128/JB.00560-16. URL http://jb.asm.org/lookup/doi/10. 1128/JB.00560- 16.
Peter E. Chen and B. Jesse Shapiro. The advent of genome-wide association studies for bacteria. Current Opinion in Microbiology, 25:17-24, 2015. ISSN 18790364. doi: 10.1016/j.mib.2015.03.002. URL http://dx.doi.org/10.1016/j.mib. 2015.03.002.
Jole Costanza, Giovanni Carapezza, Claudio Angione, Pietro Li 'o, Giuseppe Nicosia, Pietro Lio, and Giuseppe Nicosia. Robust Design of Microbial Strains. Bioinformatics (Oxford, England), 28(23):3097-3104, oct 2012. ISSN 1367-4811. doi: 10.1093/bioinformatics/bts590. URL http://www.ncbi.nlm.nih.gov/pubmed/23044547.
Michael A. DeJesus, Subhalaxmi Nambi, Clare M. Smith, Richard E. Baker, Christopher M. Sassetti, and Thomas R. Ioerger. Statistical analysis of genetic interactions in Tn-Seq data. Nucleic Acids Research, 45(11):1-11, 2017. ISSN 13624962. doi: 10.1093/nar/gkx128.
Mohamed S. Donia. A Toolbox for Microbiome Engineering. Cell Systems, 1(1):21-23, 2015. ISSN 24054720. doi: 10.1016/j.cels.2015.07.003. URL http://dx.doi.org/10.1016/j.cels.2015.07.003.
Diana Ekman, Sara Light, Asa K Bj ̈orklund, and Arne Elofsson. What properties characterize the hub proteins of the protein-protein interaction network of Saccharomyces cerevisiae? Genome Biology, 7(6):R45, jan 2006. ISSN 1465-6914. doi: 10.1186/gb-2006-7-6-r45. URL http://www.ncbi.nlm.nih.gov/pubmed/16780599.
David A Fell. Metabolic control analysis. In Lilia Alberghina and Hans V Westerhoff, editors, Topics in Current Genetics, Vol. 13: Systems Biology, volume 13, pages 69-80. Springer-Verlag Berlin Heidelberg, 2005. doi: 10.1007/b137745. URL http://www.springerlink.com/index/yr8xfyk0uakek9qu.pdf.
Santo Fortunato and Marc Barthelemy. Resolution limit in community detection. Proceedings of the Na- tional Academy of Sciences, 104(1):36-41, jan 2007. ISSN 0027-8424. doi: 10.1073/pnas.0605965104. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1765466{\&}tool=pmcentrez{\&}rendertype=abstracthttp:// www.pnas.org/cgi/doi/10.1073/pnas.0605965104.
Marcelo a German, Shujun Luo, Gary Schroth, Blake C Meyers, and Pamela J Green. Construction of Parallel Analysis of RNA Ends (PARE) libraries for the study of cleaved miRNA targets and the RNA degradome. Nature protocols, 4 (3):356-362, jan 2009. ISSN 1750-2799. doi: 10.1038/nprot.2009.8. URL http://www.ncbi.nlm.nih.gov/pubmed/19247285.
Cedric Gondro. Genome-Wide Association Studies and Genomic Prediction, volume 1019. 2013. ISBN 978-1-62703-446-3. doi: 10.1007/978-1-62703-447-0. URL http://link.springer.com/10.1007/978-1-62703-447-0.
H Ji, X Li, Q.-f. Wang, and Y Ning. Differential principal component analysis of ChIP-seq. Proceedings of the national Academy of Sciences, apr 2013. ISSN 0027-8424. doi: 10.1073/pnas.1204398110. URL http://www.pnas.org/cgi/doi/10. 1073/pnas.1204398110.
Shan Jiang and Ali Mortazavi. Integrating ChIP-seq with other functional genomics data. Briefings in Functional Genomics, 17(2):104-115, 2018. ISSN 20412657. doi: 10.1093/bfgp/ely002.
Bratati Kahali, Shandar Ahmad, and Tapash Chandra Ghosh. Exploring the evolutionary rate differences of party hub and date hub proteins in Saccharomyces cerevisiae protein-protein interaction network. Gene, 429(1-2):18-22, jan 2009. ISSN 1879-0038. doi: 10.1016/j.gene.2008.09.032. URL http://dx.doi.org/10.1016/j.gene.2008.09.032http://www. ncbi.nlm.nih.gov/pubmed/18973798.
Byoungjin Kim, Robert Binkley, Hyun Uk Kim, and Sang Yup Lee. Metabolic engineering of Escherichia coli for the enhanced production of l-tyrosine. Biotechnology and Bioengineering, 115(10):2554-2564, 2018. ISSN 10970290. doi: 10.1002/bit.26797.
C. Klein, A. Marino, M.-F. Sagot, P.V. Milreu, and M. Brilli. Structural and dynamical analysis of biological networks. Briefings in Functional Genomics, 11(6), 2012. ISSN 20412649. doi: 10.1093/bfgp/els030.
Young Min Kwon, Steven C. Ricke, and Rabindra K. Mandal. Transposon sequencing: methods and expanding applica- tions. Applied Microbiology and Biotechnology, 100(1):31-43, 2016. ISSN 14320614. doi: 10.1007/s00253-015-7037-8.
Kathy N. Lam, Margaret Alexander, and Peter J. Turnbaugh. Precision Medicine Goes Microscopic: Engineering the Microbiome to Improve Drug Outcomes. Cell Host & Microbe, 26(1):22-34, 2019. ISSN 19313128. doi: 10.1016/j.chom. 2019.06.011. URL https://linkinghub.elsevier.com/retrieve/pii/S1931312819303002.
Shmoolik Mangan and Uri Alon. Structure and function of the feed-forward loop network motif. Proceedings of the national Academy of Sciences, 100(21):11980-11985, oct 2003. ISSN 0027-8424. doi: 10.1073/pnas.2133841100. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=218699{\&}tool=pmcentrez{\&}rendertype=abstract.
Shmoolik Mangan, A Zaslaver, and Uri Alon. The Coherent Feedforward Loop Serves as a Sign- sensitive Delay Element in Transcription Networks. Journal of molecular biology, 334:197-204, 2003. doi: 10.1016/j.jmb.2003.09.049. URL http://www.ncbi.nlm.nih.gov/pubmed/14607112.
Amy Mason, Dona Foster, Phelim Bradley, Tanya Golubchik, Michel Doumith, N Claire Gordon, Bruno Pichon, Zamin Iqbal, Peter Staves, Derrick Crook, A Sarah Walker, Angela Kearns, and Tim Peto. Accuracy of different bioinformatics methods in detecting antibiotic resistance and virulence factors from Staphylococcus aureuswhole genome sequences. Journal of Clinical Microbiology, 56(9):JCM.01815—-17, 2018. ISSN 0095-1137. doi: 10.1128/JCM.01815-17. URL http://jcm.asm.org/lookup/doi/10.1128/JCM.01815-17.
Jason E McDermott, Ronald C Taylor, Hyunjin Yoon, and Fred Heffron. Bottlenecks and hubs in inferred networks are important for virulence in Salmonella typhimurium. Journal of Computational Biology, 16(2):169-180, feb 2009. ISSN 1557-8666. doi: 10.1089/cmb.2008.04TT. URL http://www.ncbi.nlm.nih.gov/pubmed/19178137http://www.liebertpub.com/ doi/10.1089/cmb.2008.04TT.
Ben O. Oyserman, Marnix H. Medema, and Jos M. Raaijmakers. Road MAPs to engineer host microbiomes. Current Opinion in Microbiology, 43:46-54, 2018. ISSN 18790364. doi: 10.1016/j.mib.2017.11.023. URL https://doi.org/10. 1016/j.mib.2017.11.023.
Maria Concetta Palumbo, Sara Zenoni, Marianna Fasoli, M 'elanie Massonnet, Lorenzo Farina, Filippo Castiglione, Mario Pezzotti, and Paola Paci. Integrated network analysis identifies fight-club nodes as a class of hubs encompassing key putative switch genes that induce ma jor transcriptome reprogramming during grapevine development. The Plant Cell, 26(12):4617-4635, 2014. ISSN 1532-298X. doi: 10.1105/tpc.114.133710. URL http://www.plantcell.org/lookup/doi/10.1105/tpc.114.133710{\%}5Cnhttp://www.pubmedcentral.nih.gov/articlerender. fcgi?artid=4311215{\&}tool=pmcentrez{\&}rendertype=abstract.
Seon Young Park, Robert M. Binkley, Won Jun Kim, Mun Hee Lee, and Sang Yup Lee. Metabolic engineering of Escherichia coli for high-level astaxanthin production with high productivity. Metabolic Engineering, 49(August):105-115, 2018. ISSN 10967184. doi: 10.1016/j.ymben.2018.08.002. URL https://doi.org/10.1016/j.ymben.2018.08.002.
Romualdo Pastor-Satorras and Alessandro Vespignani. Epidemic Spreading in Scale-Free Networks. Physical Review Letters, 86(14):3200-3203, apr 2001. ISSN 0031-9007. doi: 10.1103/PhysRevLett.86.3200. URL http://link.aps.org/ doi/10.1103/PhysRevLett.86.3200.
Chong Peng, Yan Lin, Hao Luo, and Feng Gao. A comprehensive overview of online resources to identify and predict bacterial essential genes. Frontiers in Microbiology, 8(NOV):1-13, 2017. ISSN 1664302X. doi: 10.3389/fmicb.2017. 02331.
Azam Peyvandipour, Nafiseh Saberian, Adib Shafi, Michele Donato, and Sorin Draghici. Systems biology: A novel computational approach for drug repurposing using systems biology. Bioinformatics, 34(16):2817-2825, 2018. ISSN 14602059. doi: 10.1093/bioinformatics/bty133.
Carlotta Ronda, Sway P. Chen, Vitor Cabral, Stephanie J. Yaung, and Harris H. Wang. Metagenomic engineer- ing of the mammalian gut microbiome in situ. Nature Methods, 16(2):167-170, 2019. ISSN 15487105. doi: 10.1038/s41592-018-0301-y. URL http://dx.doi.org/10.1038/s41592-018-0301-y.
Shai S Shen-Orr, Ron Milo, Shmoolik Mangan, and Uri Alon. Network motifs in the transcriptional regulation network of Escherichia coli. Nature Genetics, 31(1):64-68, may 2002. ISSN 1061-4036. doi: 10.1038/ng881. URL http: //www.ncbi.nlm.nih.gov/pubmed/11967538.
Tim Van Opijnen and Andrew Camilli. Transposon insertion sequencing: A new tool for systems-level analysis of mi- croorganisms. Nature Reviews Microbiology, 11(7):435-442, 2013. ISSN 17401526. doi: 10.1038/nrmicro3033. URL http://dx.doi.org/10.1038/nrmicro3033.
Tim van Opijnen, David W. Lazinski, and Andrew Camilli. Genome-wide fitness and genetic interactions determined by Tn-seq, a high-throughput massively parallel sequencing method for microorganisms. Current Protocols in Microbiology, 2015(April):1E.3.1-1E.3.24, 2015. ISSN 19348533. doi: 10.1002/9780471729259.mc01e03s36.
B A Veeresha, Rudra Naik, M B Chetti, S A Desai, and Suma S Biradar. Qtl Mapping in Crop Plants: Principles and Applications. International Journal of Development Research, 5(1):2961-2965, 2015. URL http://www.journalijdr.com.
Supreeta Vijayakumar, Pietro Lio, and Claudio Angione. Optimisation of multi-omic genome-scale models: methodologies, hands-on tutorial and perspectives. In Methods in Molecular Biology, volume 1716, pages 0-37. 2018. ISBN 978-1-4939- 7527-3. doi: 10.1007/978-1-4939-7528-0. URL http://link.springer.com/10.1007/978-1-4939-7528-0.
George Wu and Hongkai Ji. ChIPXpress : using publicly available gene expression data to improve ChIP-seq and ChIP- chip target gene ranking. BMC Bioinformatics, 14(1):1, 2013. ISSN BMC Bioinformatics. doi: 10.1186/1471-2105-14-188. URL BMCBioinformatics.
Haiyuan Yu, Philip M Kim, Emmett Sprecher, Valery Trifonov, and Mark B Gerstein. The importance of bottlenecks in protein networks: correlation with gene essentiality and expression dynamics. PLoS Computational Biology, 3(4):e59, apr 2007. ISSN 1553-7358. doi: 10.1371/journal.pcbi.0030059. URL http://www.ncbi.nlm.nih.gov/pubmed/17447836.
Tao Yu, Yasaman Dabirian, Quanli Liu, Verena Siewers, and Jens Nielsen. Strategies and challenges for metabolic rewiring. Current Opinion in Systems Biology, 15:30-38, 2019. ISSN 24523100. doi: 10.1016/j.coisb.2019.03.004. URL https://doi.org/10.1016/j.coisb.2019.03.004.
Shengkui Zhang, Xin Chen, Cheng Lu, Jianqiu Ye, Meiling Zou, Kundian Lu, Subin Feng, Jinli Pei, Chen Liu, Xincheng Zhou, Ping'an Ma, Zhaogui Li, Cuijuan Liu, Qi Liao, Zhiqiang Xia, and Wenquan Wang. Genome-Wide Association Studies of 11 Agronomic Traits in Cassava (Manihot esculenta Crantz). Frontiers in Plant Science, 9(April):1-15, 2018. doi: 10.3389/fpls.2018.00503.
Yipu Zhang and Ping Wang. A Fast Cluster Motif Finding Algorithm for ChIP-Seq Data Sets. BioMed Research Interna- tional, 2015, 2015. ISSN 23146141. doi: 10.1155/2015/218068.
The following is a list of articles/books that can be used by students to explore more in detail some of the issues discussed in the lessons.
Alouev A, Johnson DS, Sidow Sundquist A, Medina C, Anton E, Batzoglou S, Myers RM, Anton Valouev, David S Ds David S Johnson, Andreas Sundquist, Catherine Medina, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, Arend Sidow, Elizabeth Anton, Serafim Batzoglou, Richard M Myers, and Arend Sidow. Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nature Methods, 5(9):829-834, 2008. ISSN 1548-7091. doi: 10.1038/nmeth.1246.Genome-Wide. URL http://www.nature.com/nmeth/journal/vaop/ncurrent/full/nmeth.1246.html.
Charles Addo-Quaye, Webb Miller, and Michael J Axtell. CleaveLand: a pipeline for using degradome data to find cleaved small RNA targets. Bioinformatics, 25(1):130-131, jan 2009. ISSN 1367-4803. doi: 10.1093/bioinformatics/btn604. URL https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/btn604.
Sumeet Agarwal, Charlotte M Deane, Mason a. Porter, and Nick S Jones. Revisiting Date and Party Hubs: Novel Approaches to Role Assignment in Protein Interaction Networks. PLoS Computational Biology, 6(6):e1000817, jun 2010. ISSN 1553-7358. doi: 10.1371/journal.pcbi.1000817. URL http://dx.plos.org/10.1371/journal.pcbi.1000817.
R 'eka Albert. General Network Theory R 'eka Albert. Technical report, 2006.
R 'eka Albert, H Jeong, and Albert-Laszlo Barabasi. Error and attack tolerance of complex networks. Nature, 406(6794): 378-382, jul 2000. ISSN 1476-4687. doi: 10.1038/35019019. URL http://www.ncbi.nlm.nih.gov/pubmed/10935628.
Manuel Allhoff, Kristin Ser 'e, Heike Chauvistr 'e, Qiong Lin, Martin Zenke, Ivan G. Costa, H. Chauvistre, Ivan G. Costa, Qiong Lin, Manuel Allhoff, and K. Sere. Detecting differential peaks in ChIP-seq signals with ODIN. Bioinformatics, 30(24):3467-3475, 2014. ISSN 14602059. doi: 10.1093/bioinformatics/btu722.
Claudia Angelini and Valerio Costa. Understanding gene regulatory mechanisms by integrating ChIP-seq and RNA- seq data: statistical solutions to biological problems. Frontiers in Cell and Developmental Biology, 2(September):51, 2014. ISSN 2296-634X. doi: 10.3389/fcell.2014.00051. URL http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid= 4207007{\&}tool=pmcentrez{\&}rendertype=abstract.
Claudio Angione, Giovanni Carapezza, Jole Costanza, Pietro Lio, and Giuseppe Nicosia. Pareto optimality in organelle energy metabolism analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10(4):1032-1044, 2013. ISSN 15455963. doi: 10.1109/TCBB.2013.95.
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Assessment methods and Criteria
Written exam
BIO/10 - BIOCHEMISTRY
BIO/11 - MOLECULAR BIOLOGY
INF/01 - INFORMATICS
BIO/11 - MOLECULAR BIOLOGY
INF/01 - INFORMATICS
Single bench laboratory practical: 16 hours
Lessons: 40 hours
Lessons: 40 hours
Shifts:
Professor(s)