Usage via Web server

go to our website shmir.wmi.amu.edu.pl

Usage via client

Fold sequence via mfold:
./shmir_client.py mfold ACTGAUUUGAC
Create sh-miR from one siRNA strand (active) or two siRNa strands separated by space.
First strand is active, both are in 5-3 orientation

./shmir_client.py from_sirna UUUGUAUUCGCCCUAGCGC CGCUAUGGCGAAUACAAACA
Create sh-miR from transcript name.
Optional parameters
--min_gc : Minimal "GC" content in strand -- default: 40
--max_gc : Maximal "GC" content in strand -- default: 60
--max_offtarget : Maximal offtarget in strand -- default: 10
--mirna_name : The name of miRNA backbone to use -- default: "all"
--stymulators : One of ["yes", "no", "no_difference"] -- default: "no_difference"
./shmir_client.py from_transcript NM_001618.3 --mirna_name miR-30a --min_gc 22

The application was built with Python 2.7. It is divided into 3 parts:
RESTful API - the “heart” of our software - it performs all calculations. Our application is built on CentOS (Linux) operating system. To maintain better compatibility with our server we use Vagrant to create Virtual Machine and Puppet to manage infrastructure of our project. All you need to do is: type into console “vagrant up” and after a while everything is ready to work. This subproject is written with following technologies/frameworks/libraries: Flask, Flask-Cache, SqlAlchemy, PostgreSQL, Celery, Flower, Rabbitmq, Redis, BioPython, mfold (lightweight), uwsgi, sphinx, Python testing tools (mock, nose, unittest).
Console client - simple console application which communicates with API using requests library. It has 3 options: mfold (folding structure), from_sirna (create sh-miR from siRNA) and from_transcript (create sh-miR from transcript name).
Website - application written in Django framework. It enables you to use the API via graphical interface. You can also create your own account and save the history of your requests for later usage.

sh-miR designer v1.0
First version of the program presented on iGEM 2013 by our team. The user have to provide siRNA sequence (one or both strands). The miRNA database includes 5 miRNAs.
sh-miR designer v1.1
Strand discrimination functionality was added based on miRbase.
extended miRNA database
sh-miR designer v2.0
siRNA prediction algorithm was added to functionality (the input is NCBI transcript number)
user can choose GC content of siRNA molecule, maximal off-target, if he/she wants immunostimulatory sequences inside the molecule and to choose if he/she wants to include all or chosen scaffold
extended miRNA database
What we are working on:
strand discrimination based on therodynamics of siRNA ends;
preparing construct with flanking restriction enzymes
providing plasmids with parts from standard parts

In our application we are using unit tests to test alghoritms, functions and database.
We are testing inputs and outputs:
def test_validate_gc_content(self):
is_in_range = validate_gc_content('ACGT', 40, 60)
self.assertTrue(is_in_range)
Caught errors:
def multiple_input(self, data, error):
for strand in data:
with self.assertRaises(errors.InputException) as err:
validators.check_input(strand)
self.assertEqual(error, str(err.exception))
Database models:
def test_get_and_put_to_db(self):
new_obj = self.TestModel(id=153)
self.put_to_db(new_obj)
objs = self.get_all(self.TestModel)
self.assertEqual(len(objs), 1)

We are using Mock library to test our functions which are using external API’s to create fake response from server and return them into our function:
@patch.object(ncbi_api.Entrez, 'esearch')
@patch.object(ncbi_api.Entrez, 'efetch')
def test_get_data(self, mock_efetch, mock_esearch):
ids = [69]
mock_read = Mock()
mock_read.read.return_value = json.dumps({'esearchresult': {'idlist': ids}})
mock_esearch.return_value = mock_read
ncbi_api.get_data(self.GOOD_MRNA)
mock_esearch.assert_called_with(
term='NM_001128164.1', db=self.DATA_BASE, retmode='json'
)
mock_efetch.assert_called_once_with(
db=self.DATA_BASE, id=ids, rettype='fasta', retmode='text'
)

1. Boudreau RL, Monteys AM, Davidson BL. Minimizing variables among hairpin-based RNAi vectors reveals the potency of shRNAs. RNA. 2008 Sep;14(9):1834-44. Epub 2008 Aug 12.
2. Chang K, Elledge SJ, Hannon GJ. Lessons from Nature: microRNA-based shRNA libraries. Nat Methods. 2006 Sep;3(9):707-14.
3. Dow LE, Premsrirut PK, Zuber J, Fellmann C, McJunkin K, Miething C, Park Y, Dickins RA, Hannon GJ, Lowe SW. A pipeline for the generation of shRNA transgenic mice. Nat Protoc. 2012 Feb 2;7(2):374-93. doi: 10.1038/nprot.2011.446.
4. Fahim M, Larkin PJ. Designing Effective amiRNA and Multimeric amiRNA Against Plant Viruses. Methods Mol Biol. 2013;942:357-77. doi: 10.1007/978-1-62703-119-6_19.
5. Fan ZD, Zhang L, Shi Z, Gan XB, Gao XY, Zhu GQ. Artificial microRNA interference targeting AT(1a) receptors in paraventricular nucleus attenuates hypertension in rats. Gene Ther. 2012 Aug;19(8):810-7. doi: 10.1038/gt.2011.145. Epub 2011 Sep 29.
6. Fellmann C, Zuber J, McJunkin K, Chang K, Malone CD, Dickins RA, Xu Q, Hengartner MO, Elledge SJ, Hannon GJ, Lowe SW. Functional identification of optimized RNAi triggers using a massively parallel sensor assay. Mol Cell. 2011 Mar 18;41(6):733-46. Epub 2011 Feb 25.
7. Maczuga P, Lubelski J, van Logtenstein R, Borel F, Blits B, Fakkert E, Costessi A, Butler D, van Deventer S, Petry H, Koornneef A, Konstantinova P. Embedding siRNA sequences targeting Apolipoprotein B100 in shRNA and miRNA scaffolds results in differential processing and in vivo efficacy. Mol Ther. 2012 Oct 23. doi: 10.1038/mt.2012.160. [Epub ahead of print]
8. Maczuga P, Koornneef A, Borel F, Petry H, van Deventer S, Ritsema T, Konstantinova P. Optimization and comparison of knockdown efficacy between polymerase II expressed shRNA and artificial miRNA targeting luciferase and Apolipoprotein B100. BMC Biotechnol. 2012 Jul 24;12:42. doi: 10.1186/1472-6750-12-42.
9. Matveeva OV, Nazipova NN, Ogurtsov AY, Shabalina SA. Optimized models for design of efficient miR30-based shRNAs. Front Genet. 2012;3:163. Epub 2012 Aug 29.
10. McBride JL, Boudreau RL, Harper SQ, Staber PD, Monteys AM, Martins I, Gilmore BL, Burstein H, Peluso RW, Polisky B, Carter BJ, Davidson BL. Artificial miRNAs mitigate shRNA-mediated toxicity in the brain: implications for the therapeutic development of RNAi. Proc Natl Acad Sci U S A. 2008 Apr 15;105(15):5868-73. Epub 2008 Apr 8.
11. Schwab R, Ossowski S, Warthmann N, Weigel D. Directed gene silencing with artificial microRNAs. Methods Mol Biol. 2010;592:71-88.
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13. Stegmeier F, Hu G, Rickles RJ, Hannon GJ, Elledge SJ. A lentiviral microRNA-based system for single-copy polymerase II-regulated RNA interference in mammalian cells. Proc Natl Acad Sci U S A. 2005 Sep 13;102(37):13212-7. Epub 2005 Sep 1.
14. Wang X, Yang Y, Zhou J, Yu C, Cheng Y, Yan C, Chen J.Two-step method for constructing Arabidopsis artificial microRNA vectors. Biotechnol Lett. 2012 Jul;34(7):1343-9. Epub 2012 Mar 22.
15. Warthmann N, Ossowski S, Schwab R, Weigel D. Artificial MicroRNAs for Specific Gene Silencing in Rice. Methods Mol Biol. 2013;956:131-49. doi: 10.1007/978-1-62703-194-3_11.
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Bronze. The following 4 goals must be achieved:

• Register the team, have a great summer, and have fun attending the Jamboree in Boston.
  <\li>
• Create and share a description of the team's project via the iGEM wiki.
  <\li>
• Present a Poster and Talk at the Regional Jamboree and World Championship Jamboree. We will on Jamboree :)
  <\li>
• Develop and make available, via The iGEM Software Registry, an open source software that supports Synthetic Biology based on Standard Parts.<\li>
  
  

Silver: In addition to the Bronze Medal requirements, the following 4 goals must be achieved:

• Demonstrate the relevance of your development for Synthetic Biology based on standard Parts.
  
• Provide a comprehensive and well-designed User Guide. (Be creative! An instructional video may work as well.)
  
• Provide detailed API documentation, preferably, automatically built from source code documentation (use tools like doxygen, yard, rdoc, naturaldocs, etc).
  
• Demonstrate that you followed best practises in software development so that other developers can modify, use and reuse your code. Provide more than one realistic test case. Examples of best practices are automated unit testing and documentation of test coverage , bug tracking facilities, documentation of releases and changes between releases.
  
  

Gold: In addition to the Bronze and Silver Medal requirements, two additional goals must be achieved:
Provide a convincing validation, testing the performance of the development -- experimentally (can be outsourced) or by other teams and users. Note, even if the algorithm or tool turns out not to work that well, the Gold requirement is fulfilled if the test is good and the analysis convincing. Validation may include: preferably experiments, informatics analysis (complexity, run time) of an algorithm, or user studies.
And the second goal can be any one of the following:

• Make your software interact / interface with the Registry.
  
• Re-use and further develop previous iGEM software projects (or parts thereof) or use and/or improvement of existing synthetic biology tools or frameworks.
  
• Develop a well documented library or API for other developers (rather than “only” a stand-alone app for end users.)
  
• Support and use the SBOL and / or SBOLv standard.
  
• iGEM projects involve important questions beyond the bench, for example relating to (but not limited to) ethics, sustainability, social justice, safety, security, or intellectual property rights. Describe an approach that your team used to address at least one of these questions. Evaluate your approach, including whether it allowed you to answer your question(s), how it influenced the team’s scientific project, and how it might be adapted for others to use (within and beyond iGEM). We encourage thoughtful and creative approaches, and those that draw on past Policy & Practice (formerly Human Practices) activities.