iGEM 2020 — Team SJTU-software

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:SJTU-software/Notebook

# Notebook

Document the dates you worked on your project. This should be a detailed account of the work done each day for your project.

### What should this page have?

* Chronological notes of what your team is doing.
* Brief descriptions of daily important events.
* Pictures of your progress.
* Mention who participated in what task.

### Inspiration

You can see what others teams have done to organize their notes:

* [2018 Munich](https://2018.igem.org/Team%3AMunich/Notebook)
* [2014 ATOMS-Turkiye](https://2014.igem.org/Team%3AATOMS-Turkiye/Notebook)
* [2014 Tec Monterrey](https://2014.igem.org/Team%3ATec-Monterrey/ITESM14_project.html#tab_notebook)
* [2014 Kyoto](https://2014.igem.org/Team%3AKyoto/Notebook/Magnetosome_Formation#title)
* [2014 Cornell](https://2014.igem.org/Team%3ACornell/notebook)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:SJTU-software/Engineering

* [Home](/Team%3ASJTU-software)
* [Project](/Team%3ASJTU-software/Description)
  + [Description](/Team%3ASJTU-software/Description)
  + [Engineering](/Team%3ASJTU-software/Engineering)
  + [Implementation](/Team%3ASJTU-software/Implementation)
  + [Contribution](/Team%3ASJTU-software/Contribution)
  + [Agriculture](/Team%3ASJTU-software/Agriculture)
  + [Software](/Team%3ASJTU-software/Software)
* [Human practices](/Team%3ASJTU-software/Human_Practices)
  + [Overview](/Team%3ASJTU-software/Human_Practices)
  + [Integrated\_HP](/Team%3ASJTU-software/Integrated_HP)
  + [Collaborations](/Team%3ASJTU-software/Collaborations)
  + [Partnership](/Team%3ASJTU-software/Partnership)
  + [Education](/Team%3ASJTU-software/Education)
* [Team](/Team%3ASJTU-software/Members)
  + [Members](/Team%3ASJTU-software/Members)
  + [Attributions](/Team%3ASJTU-software/Attributions)
* [Awards](/Team%3ASJTU-software/Awards)
  + [Medals](https://2020.igem.org/Team%3ASJTU-software/Medals)
  + [Awards](/Team%3ASJTU-software/Awards)
* [Safety](/Team%3ASJTU-software/Safety)

[![](https://static.igem.org/mediawiki/2020/e/ec/T--SJTU-software--logo2.png)
![](https://static.igem.org/mediawiki/2020/e/ec/T--SJTU-software--logo2.png)](https://2020.igem.org/Team%3ASJTU-software)

![](https://static.igem.org/mediawiki/2020/2/29/T--SJTU-software--bg.jpg)

![](https://static.igem.org/mediawiki/2020/f/f0/T--SJTU-software--head.png)

![](https://static.igem.org/mediawiki/2020/a/a8/T--SJTU-software--EN.png)

Overview

In order to analyze rice pan-genome data, we tried various kinds of algorithms, including a series of statistical approaches like logistic regression and Markov chain Monte Carlo (MCMC), and machine learning methods like SVM, \*etc\*. Most of them don't perform well, either generate false-positive results or takes too much time.

Besides the analysis which lent the tools like Random Forest and Cluster, we adopted and modified a novel global search algorithm that has never been used in Genome-Wide Association Studies. Rather than detecting the relations one by one, our algorithm can draw a directed acyclic graph (DAG) at a time with a low false-positive rate. We adopt a two-stage Bayesian network method to implement a global search of DAG to identify genome-wide interactions with multiple outcomes. This method integrates the advantages of score-based methods and constraint-based methods to learn the structure of the phenotype-related Bayesian network. We used the Bayesian information criterion (BIC) because its maximization led to consistent model selection in the classical large-sample limit. The algorithm makes the maximization of the BIC computationally feasible for much larger graphs. It maximizes the BIC in a greedy way but still guarantees consistency in the large-sample limit. It still has exponential-time complexity in the worst case, but only polynomial complexity in the average case where the size of the largest clique in a graph grows only logarithmically with the number of nodes.

All in all, our method improved accuracy and efficiency compared to several common methods with no need to predetermine the outcome variables. We attached the result to our database so that users could use our database in a broadened way. As long as the user entered a certain gene name, some information like gene classifications and annotations, sample information and categories, and protein sequences, also related genes could be shown. It offers new choices for the experimenter to investigate the functions and enlarges the vision to do genetic reprogramming. If they are willing to add more phenotype data to our database, we can also do a similar analysis for them.

Gene-gene Interaction Analysis

![](https://static.igem.org/mediawiki/2020/4/41/T--SJTU-software--engnri.png)

Database

Our database is mainly divided into two parts, classified by original data and result data.

I. Original data

Whole\_Gene\_Annotion: This table mainly contains gene annotions as well as it contains details of all DNA sequences (including CDS, 5UTR,3UTR). The format is the standard format of gff File.

Part\_Of\_Gene\_Annotion: This table is similar to the Whole\_Gene\_Annotion. The only difference of them is that Part\_Of\_Gene\_Annotion only contains rough information of all DNA sequences (NOT including CDS, 5UTR,3UTR, ONLY gene).

Protein\_Seq: This table mainly contains the protein sequences encoded by the gene, and also records the frequency of the gene in all samples and its importance (Core, Soft core, Distributed and Rare).

Gene\_Phe: This table mainly contains some phenotypic characteristics of samples (flag\_leaf\_angle, leaf\_width, grain\_length, height, leaf\_angle, leaf\_length).

![](https://static.igem.org/mediawiki/2020/e/ec/T--SJTU-software--ER5.png)

II. Result data

Gene\_Relation: This table mainly contains related genes. We used GES algorithm to analyze the evolutionary relationship between different genes, and the analysis results were imported into the database.

![](https://static.igem.org/mediawiki/2020/6/64/T--SJTU-software--ER6.png)

Sample\_Relation: This table mainly contains related samples. We used GES algorithm to analyze the evolutionary relationship between different genes, and the analysis results are imported into the database.

Phe\_By\_Gene: This table mainly contains genes which decide the phenotypic characteristics. We used GES and RF algorithm to get the genes that affect the phenotypic characteristics.

![](https://static.igem.org/mediawiki/2020/c/c8/T--SJTU-software--ER7.png)

References

D. Chickering. Optimal structure identification with greedy search. Journal of Machine Learning Research, 3(3):507–554, 2002.
D. Chickering, D. Heckerman, and C. Meek. Largesample learning of Bayesian networks is NP-hard.
J. Mach. Learn. Res., 5:1287–1330, December 2004. T. Claassen and T. Heskes. A logical characterization of constraint-based causal discovery. In Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), pages 135–144, 2011.
T. Claassen, J. Mooij, and T. Heskes. Proof supplement to Learning sparse causal models is not NP-hard. Technical report, Faculty of Science, Radboud University Nijmegen, 2013. http://www.cs.ru.nl/~tomc/docs/NPHardSup.pdf.
D. Colombo, M. Maathuis, M. Kalisch, and T. Richardson. Learning high-dimensional DAGs with latent and selection variables. The Annals of Statistics, 40(1):294–321, 2012.
G. Cooper. The computational complexity of probabilistic inference using Bayesian belief networks. Artificial Intelligence, (42):393–405, 1990.
J. Cussens. Bayesian network learning with cutting planes. In Proc. of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), pages 153–160. AUAI Press, 2011.
R. Evans and T. Richardson. Maximum likelihood fitting of acyclic directed mixed graphs to binary data. In Proc. of the 26th Conference on Uncertainty in Artificial Intelligence, pages 177–184, 2010.
M. Garey and D. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.
H. Freeman and Co., 1979. O. Goldreich. Computational Complexity: A Conceptual Perspective. Cambridge University Press, 2008.
D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. The MIT Press, 2009.
D. Margaritis and S. Thrun. Bayesian network induction via local neighborhoods. In Advances in Neural Information Processing Systems 12, pages 505–511, 1999.
J. Pearl. Causality: models, reasoning and inference. Cambridge University Press, 2000.
J. Pearl and T. Verma. A theory of inferred causation. In Knowledge Representation and Reasoning: Proc. of the Second Int. Conf., pages 441–452, 1991.
T. Richardson and P. Spirtes. Ancestral graph Markov models. Ann. Stat., 30(4):962–1030, 2002.
P. Spirtes, C. Meek, and T. Richardson. An algorithm for causal inference in the presence of latent variables and selection bias. In Computation, Causation, and Discovery, pages 211–252. AAAI Press, Menlo Park, CA, 1999.
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search. The MIT Press, Cambridge, Massachusetts, 2nd edition, 2000.
J. Tian, A. Paz, and J. Pearl. Finding minimal dseparators. Technical Report R-254, UCLA Cognitive Systems Laboratory, 1998.
C. Yuan and B. Malone. An improved admissible heuristic for learning optimal Bayesian networks. In Proc. of the 28th Conference on Uncertainty in Artificial Intelligence (UAI), pages 924–933, Corvallis, Oregon, 2012. AUAI Press.
J. Zhang. On the completeness of orientation rules for causal discovery in the presence of latent confounders and selection bias. Artificial Intelligence, 172(16-17):1873–1896, 2008.
Schatz, M. C.,Maron, L. G.,Stein, J. C., etc., Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica[J]. Genome Biol, 2014, 15 (11),p 506.
Li, Z.,Pinson, S.,Stansel, J., etc., Identification of quantitative trait loci (QTLs) for heading date and plant height in cultivated rice (Oryza sativa L.)[J]. Theoretical and Applied Genetics, 1995, 91 (2),pp 374-381.
Li, J.-Y.,Wang, J.,Zeigler, R. S., The 3,000 rice genomes project: new opportunities and challenges for future rice research[J]. GigaScience, 2014, 3 (1),pp 1-3.
Yao, W.,Li, G.,Zhao, H., etc., Exploring the rice dispensable genome using a metagenome-like assembly strategy[J]. Genome Biol, 2015, 16 p 187.
Schatz, M. C.,Maron, L. G.,Stein, J. C., etc., Whole genome de novo assemblies of three divergent strains of rice, Oryza sativa, document novel gene space of aus and indica[J]. Genome Biol, 2014, 15 (11),p 506.
Sasaki, T.,Burr, B., International Rice Genome Sequencing Project: the effort to completely sequence the rice genome[J]. Current opinion in plant biology, 2000, 3 (2),pp 138-142.
Li, Z.,Pinson, S.,Stansel, J., etc., Identification of quantitative trait loci (QTLs) for heading date and plant height in cultivated rice (Oryza sativa L.)[J]. Theoretical and Applied Genetics, 1995, 91 (2),pp 374-381.
Cao, J.,Schneeberger, K.,Ossowski, S., etc., Whole-genome sequencing of multiple Arabidopsis thaliana populations[J]. Nature Genetics, Oct, 2011, 43 (10),pp 956-U60.
Aflitos, S.,Schijlen, E.,de Jong, H., etc., Exploring genetic variation in the tomato (Solanum section Lycopersicon) clade by whole-genome sequencing[J]. Plant Journal, Oct, 2014, 80 (1),pp 136-148.
Zhang, Z.,Mao, L.,Chen, H., etc., Genome-wide mapping of structural variations reveals a copy number variant that determines reproductive morphology in cucumber[J]. The Plant Cell, 2015, 27 (6),pp 1595-1604.

* overview
* Gene-gene Interaction Analysis
* Database
* References

![](https://static.igem.org/mediawiki/2020/b/b6/T--SJTU-software--footer.png)

* **Address**
* NO. 800 Dongchuan Road, Minhang District, Shanghai, China

* **Contact Us**
* lvruiqi@sjtu.edu.cn

* **Software**
* [Welcome to use our Riss!](http://49.234.95.158:8000/Riss)

* **Related Links**
* [Shanghai Jiao Tong University](http://en.sjtu.edu.cn/)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:SJTU-software/Experiments

# Experiments

Describe the research, experiments, and protocols you used in your iGEM project. These should be detailed enough for another team to repeat your experiments.

If you made Parts this year, please remember to put all information, characterization, and measurement data on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).

### What should this page contain?

* Protocols
* Experiments

### Inspiration

* [2014 Colombia](https://2014.igem.org/Team%3AColombia/Protocols)
* [2014 Imperial](https://2014.igem.org/Team%3AImperial/Protocols)
* [2014 Caltech](https://2014.igem.org/Team%3ACaltech/Project/Experiments)

[![](https://static.igem.org/mediawiki/2020/8/8b/HQ_page_logo.jpg)](https://2020.igem.org)

# Team:SJTU-software/Results

# Results

You can describe the results of your project and your future plans here.

### What should this page contain?

* Clearly and objectively describe the results of your work.
* Future plans for the project.
* Considerations for replicating the experiments.

### Describe what your results mean

* Interpretation of the results obtained during your project. Don't just show a plot/figure/graph/other, tell us what you think the data means. This is an important part of your project that the judges will look for.
* Show data, but remember **all measurement and characterization data must also be on the Part's Main Page on the [Registry](http://parts.igem.org/Main_Page).** Otherwise these data will not be in consideration for any medals or part awards!
* Consider including an analysis summary section to discuss what your results mean. Judges like to read what you think your data means, beyond all the data you have acquired during your project.

### Project Achievements

You can also include a list of bullet points (and links) of the successes and failures you have had over your summer. It is a quick reference page for the judges to see what you achieved during your summer.

* A list of linked bullet points of the successful results during your project
* A list of linked bullet points of the unsuccessful results during your project. This is about being scientifically honest. If you worked on an area for a long time with no success, tell us so we know where you put your effort.

### Inspiration

See how other teams presented their results.

* [2014 TU Darmstadt](https://2014.igem.org/Team%3ATU_Darmstadt/Results/Pathway)
* [2014 Imperial](https://2014.igem.org/Team%3AImperial/Results)
* [2014 Paris Bettencourt](https://2014.igem.org/Team%3AParis_Bettencourt/Results)