2017 biological databases_part1_vupload
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This document discusses biological databases and bioinformatics. It begins by listing various related fields including biology, computer science, bioinformatics, statistics, and machine learning. It then describes different types of searches that can be performed in biological databases, including annotation searches, homology searches, pattern searches, and predictions. Finally, it mentions that databases can be used for comparisons, such as gene families and phylogenetic trees.
![Relational Database Terminology
Relational operators
• Relational
– select
rel WHERE boolean-xpr
– project
rel [ attr-specs ]
– join
rel JOIN rel
– divide by
rel DIVIDEBY rel
• Set-based
rel UNION rel
rel INTERSECT rel
rel MINUS rel
rel TIMES rel](https://image.slidesharecdn.com/2017biologicaldatabasespart1vupload-170214104905/85/2017-biological-databases_part1_vupload-40-320.jpg)
2017 biological databases_part1_vupload
- 2. FBW 14-02-2017 Biological Databases Wim Van Criekinge
- 5. 11 syllabi (2016 edition) left for 15 Euro
- 6. Math Informatics Bioinformatics, a scientific discipline ? Or the new (molecular) biology ? Theoretical Biology Computational Biology (Molecular) Biology Computer Science Bioinformatics
- 7. Lab for Bioinformatics and computational genomics
- 8. Statistics Machine Learning Text Mining Bioinformatics Discovery Informatics Informatics (Molecular) Biology
- 9. Statistics Machine Learning Text Mining Python, … Biological Databases Bioinformatics Discovery Informatics (Molecular) Biology
- 10. The most valuable programming skills to have on a resume
- 11. New kid in the coding block …
- 12. Statistics Machine Learning Text Mining Python, … Biological Databases Epigenetics Bioinformatics Discovery Informatics
- 13. Sander-Schneider • HSSP: homology derived secondary structure
- 15. Usage of the databases Annotation searches - Search for keywords, authors, features
- 16. Usage of the databases Annotation searches - Search for keywords, authors, features What is the protein sequence for human insulin? How does the 3D structure of calmodulin look like? What is the genetic location of the cystic fibrosis gene? List all intron sequences in rat.
- 17. Usage of the databases Annotation searches - Search for keywords, authors, features
- 18. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences
- 19. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Is there any known protein sequence that is similar to x? Is this gene known in any other species? Has someone already cloned this sequence?
- 20. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences
- 21. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns
- 22. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns Do my protein sequence contain any known motif (that can give me a clue about the function)? Which known sequences contain this motif? Is any part of my nucleotide sequence recognized by a transcriptional factor? List all known start, splice and stop signals in my genomic sequence.
- 23. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns
- 24. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns Predictions - Using the databases as knowledge databases
- 25. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns Predictions - Using the databases as knowledge databases What may the structure of my protein be? Secondary structure prediction. Modelling by homology. What is the gene structure of my genomic sequence? Which parts of my protein have a high antigenicity?
- 26. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns Predictions - Using the databases as knowledge databases
- 27. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns Predictions - Using the databases as knowledge databases Comparisons
- 28. Usage of the databases Annotation searches - Search for keywords, authors, features Homology (similarity) searches - Search for similar sequences Pattern searches - Search for occurrences of patterns Predictions - Using the databases as knowledge databases Comparisons Gene families Phylogenetic trees
- 29. Les 1 • Bioinformatics I Revisited in 5 slides • Why bother making databases ? • DataBases – FF • *.txt • Indexed version – Relational (RDBMS) • Access, MySQL, PostGRES, Oracle – OO (OODBMS) • AceDB, ObjectStore – Hierarchical • XML – Frame based system • Eg. DAML+OIL – Hybrid systems
- 30. GenBank Format LOCUS LISOD 756 bp DNA BCT 30-JUN-1993 DEFINITION L.ivanovii sod gene for superoxide dismutase. ACCESSION X64011.1 GI:37619753 NID g44010 KEYWORDS sod gene; superoxide dismutase. SOURCE Listeria ivanovii. ORGANISM Listeria ivanovii Eubacteria; Firmicutes; Low G+C gram-positive bacteria; Bacillaceae; Listeria. REFERENCE 1 (bases 1 to 756) AUTHORS Haas,A. and Goebel,W. TITLE Cloning of a superoxide dismutase gene from Listeria ivanovii by functional complementation in Escherichia coli and characterization of the gene product JOURNAL Mol. Gen. Genet. 231 (2), 313-322 (1992) MEDLINE 92140371 REFERENCE 2 (bases 1 to 756) AUTHORS Kreft,J. TITLE Direct Submission JOURNAL Submitted (21-APR-1992) J. Kreft, Institut f. Mikrobiologie, Universitaet Wuerzburg, Biozentrum Am Hubland, 8700 Wuerzburg, FRG
- 31. Problems with Flat files … • Wasted storage space • Wasted processing time • Data control problems • Problems caused by changes to data structures • Access to data difficult • Data out of date • Constraints are system based • Limited querying eg. all single exon GPCRs (<1000 bp)
- 32. • What is a relational database ? – Sets of tables and links (the data) – A language to query the datanase (Structured Query Language) – A program to manage the data (RDBMS) • Flat files are not relational – Data type (attribute) is part of the data – Record order mateters – Multiline records – Massive duplication • Bv Organism: Homo sapeinsm Eukaryota, … – Some records are hierarchical • Xrefs – Records contain multiple “sub-records” – Implecit “Key”
- 33. • records • fields • linear file of homogeneous records name......................... surname.................... phone........................ address...................... name......................... surname.................... phone........................ address...................... name......................... surname.................... phone........................ address...................... name......................... surname.................... phone........................ address...................... name......................... surname.................... phone........................ address...................... name......................... surname.................... phone........................ address...................... name......................... surname.................... phone........................ address...................... name......................... surname.................... phone........................ address......................
- 34. • Terms and concepts: – tuple – domain – attribute – key – integrity rules
- 35. Introduction to Database Systems • Historic Background – Hierarchical databases (IMS) - IBM 1968 • Hierarchical structures between file records – Network databases - CODASYL Group 1969 • Network structures of record types • Linked chains between 'Owner' and 'Member' records • Included in Cobol, procedural language - Manual navigation – Relational Data Model - E. F. Codd 1970 • Mathematical foundation of databases • New non-procedural language SQL - Automatic navigation – Object-relational databases – Object-oriented databases
- 36. Relational • The Relational model is not only very mature, but it has developed a strong knowledge on how to make a relational back-end fast and reliable, and how to exploit different technologies such as massive SMP, Optical jukeboxes, clustering and etc. Object databases are nowhere near to this, and I do not expect then to get there in the short or medium term. • Relational Databases have a very well-known and proven underlying mathematical theory, a simple one (the set theory) that makes possible – automatic cost-based query optimization, – schema generation from high-level models and – many other features that are now vital for mission-critical Information Systems development and operations.
- 37. The Benefits of Databases • Redundancy can be reduced • Inconsistency can be avoided • Conflicting requirements can be balanced • Standards can be enforced • Data can be shared • Data independence • Integrity can be maintained • Security restrictions can be applied
- 38. Relational Terminology ID NAME PHONE EMP_ID 201 Unisports 55-2066101 12 202 Simms Atheletics 81-20101 14 203 Delhi Sports 91-10351 14 204 Womansport 1-206-104-0103 11 Row (Tuple) Column (Attribute) CUSTOMER Table (Relation)
- 39. Relational Database Terminology • Each row of data in a table is uniquely identified by a primary key (PK) • Information in multiple tables can be logically related by foreign keys (FK) ID LAST_NAME FIRST_NAME 10 Havel Marta 11 Magee Colin 12 Giljum Henry 14 Nguyen Mai ID NAME PHONE EMP_ID 201 Unisports 55-2066101 12 202 Simms Atheletics 81-20101 14 203 Delhi Sports 91-10351 14 204 Womansport 1-206-104-0103 11 Table Name: CUSTOMER Table Name: EMP Primary Key Foreign Key Primary Key
- 40. Relational Database Terminology Relational operators • Relational – select rel WHERE boolean-xpr – project rel [ attr-specs ] – join rel JOIN rel – divide by rel DIVIDEBY rel • Set-based rel UNION rel rel INTERSECT rel rel MINUS rel rel TIMES rel
- 41. Disadvantages • size • complexity • cost • Additional hardware costs • Higher impact of failure • Recovery more difficult
- 42. • RDBM products – Free • MySQL, very fast, widely usedm easy to jump into but limited non standard SQL • PostrgreSQL – full SQLm limited OO, higher learning curve than MySQL – Commercial • MS Access – Great query builder, GUI interfaces • MS SQL Server – full SQL, NT only • Oracle, everything, including the kitchen sink • IBM DB2, Sybase
- 43. Example 3-tier model in biological database http://www.bioinformatics.be Example of different interface to the same back-end database (MySQL)
- 46. BioSQL
- 47. Conclusions • A database is a central component of any contemporary information system • The operations on the database and the mainenance of database consistency is handled by a DBMS • There exist stand alone query languages or embedded languages but both deal with definition (DDL) and manipulation (DML) aspects • The structural properties, constraints and operations permitted within a DBMS are defined by a data model - hierarchical, network, relational • Recovery and concurrency control are essential • Linking of heterogebous datasources is central theme in modern bioinformatics
- 48. What is to come ? Basic outline • Setup RDMBS • OLTP Access through CLI, dedicated client, PHP, Perl/Python • OLAP Access through Perl/Python, R .. Integration • Cytoscape Semantic Web • noSQL/Hadoop • SPARQL
- 50. 3/05/2016 Project Biological Databases 2015-2016 Biological Databases Bruno Verstraeten, Arthur Zwaenepoel, Jules Haezebrouck, Laurenz De Cock, Jonathan Walgraeve, Cedric Bogaert, Dries Schaumont
- 51. What is minecraft • Sandbox game • Designed by Markus “Notch” Persson • Mojang • Bought by Microsoft in 2014 • 70 million sold copies (june 2015)
- 53. Minecraft programming from Python
- 54. Third party mods • Extra content made by users • Adding items, magic and features to the original game • The true beauty of minecraft
- 55. And now Sciencecraft • Visualizing proteins in minecraft • Minecraft Tools python package • Data directly from PDB flat files or from the PDB server • Spigot minecraft server
- 56. The basics 1. Start a server with Minecraft Tools 2. Using python import the pdb file 3. Retrieve the coordinates from the file 4. Using the setBlock function blocks of specific colours are placed in the minecraft server to represent the protein 5. Fly around and take screenshots
- 57. Minecraft programming from Python # Connect to Minecraft from mcpi.minecraft import Minecraft mc = Minecraft.create() # Set x, y, and z variables to represent coordinates x = 10.0 y = 110.0 z = 12.0 # Change the player's position mc.player.setPos(x, y, z)
- 58. Verotoxin
- 60. Kinesine
- 61. Retrieving PDB data using SPARQL • PDB available in RDF (wwPDB) • Using python SPARQLwrapper
- 62. Using SPARQL with Python – SPARQLWrapper SPARQL endpoint
- 63. Using SPARQL with Python – SPARQLWrapper “Search engine” • Naive regex based • Returns list of all pdb entries containing a certain keyword with organism name and full description • PDB entry can be retrieved with previous query
- 65. Retrieve .xml.gz file: Actual structure information in xml file <?xml version="1.0" encoding="UTF-8" ?> <PDBx:datablock datablockName="1O9K" xmlns:PDBx="http://pdbml.pdb.org/schema/pdbx-v40.xsd" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://pdbml.pdb.org/schema/pdbx- v40.xsd pdbx-v40.xsd"> <PDBx:atom_siteCategory> <PDBx:atom_site id="1"> <PDBx:B_iso_or_equiv>62.42</PDBx:B_iso_or_equiv> <PDBx:Cartn_x>13.258</PDBx:Cartn_x> <PDBx:Cartn_y>142.706</PDBx:Cartn_y> <PDBx:Cartn_z>30.410</PDBx:Cartn_z> <PDBx:auth_asym_id>A</PDBx:auth_asym_id> <PDBx:auth_atom_id>N</PDBx:auth_atom_id> <PDBx:auth_comp_id>MET</PDBx:auth_comp_id> <PDBx:auth_seq_id>379</PDBx:auth_seq_id> <PDBx:group_PDB>ATOM</PDBx:group_PDB> <PDBx:label_alt_id xsi:nil="true" /> <PDBx:label_asym_id>A</PDBx:label_asym_id> <PDBx:label_atom_id>N</PDBx:label_atom_id> <PDBx:label_comp_id>MET</PDBx:label_comp_id> <PDBx:label_entity_id>1</PDBx:label_entity_id> <PDBx:label_seq_id>8</PDBx:label_seq_id> <PDBx:occupancy>1.00</PDBx:occupancy> …. Using SPARQL with Python – SPARQLWrapper
- 68. INTERNATIONAL GENETICALLY ENGINEERED MACHINE➤ Annual synthetic biology competition ➤ Making new organisms: biobricks ➤ Hosted by MIT: five teams in 2004, 130 teams in 2016
- 69. PAST IGEM WINNERS 2014 biosensor for olive oil quality 2015 3D printing of biofilms 2016 system for the control of co- culture stability
- 70. UGENT 2016 TEAM
- 72. FOUR WORK PACKAGES WP2: Filament WP3: Biofunction WP1: Shape WP4: Measurement
- 73. WP1: SHAPE OPTIMISATION Fogstand beetle
- 74. WP2: FILAMENT
- 77. OUR INPUT
- 78. OUR INPUT
- 80. Presenting, learning and having fun in Boston
- 81. FOLLOW UP ➤ Maker City ➤ BrainBooster session CropDesign ➤ Biodesign competition ➤ Bachelor project on 3D printing ➤ PLOS iGEM collection
- 84. 85