Forth lecture in our lecture series. Introduction to different biological databases. How to use mysql or nosql database in a research setting. What data repositories are available? How to use pride, peptide pilot and co. How to formulate queries for your custom databases. Dr. Marius Codrea Dr. Sven Nahnsen

1. Dr. Sven Nahnsen/Dr. Marius Codrea,
Quantitative Biology Center (QBiC)
Data Management for Quantitative Biology
Lecture 4: Database systems

2. Database systems in modern data-driven
biomedical research
I. Typical research scenario
From samples to data
From data to databases and back
From data/databases to information
II. How to?

3. Typical research scenario
What gene(s) / protein(s) are responsible for a specific disease

4. Typical research scenarios
From samples to data

5. Millions of reads in fastq format

6. Alignment/mapping to the target genome
Which organism?
Where to get it from?
Which version?
Annotations?
Databases & Repositories

7. Digitization of biological samples
Similar for other omics technologies
Next Generation
Sequencing
f(x):->{1,0}^n

8. Shotgun Proteomics
8

9. Tousands of raw spectra

10. LC-MS/MS experiment Fragment m/z values
Theoretical fragment m/z
Compare
Q9NSC5|HOME3_HUMAN Homer protein homolog 3 - Homo sapiens (Human)
MSTAREQPIFSTRAHVFQIDPATKRNWIPAGKHALTVSYFYDATRNVYRIISIGGAKA
IINSTVTPNMTFTKTSQKFGQWDSRANTVYGLGFASEQHLTQFAEKFQEVKEAAR
LAREKSQDGGELTSPALGLASHQVPPSPLVSANGPGEEKLFRSQSADAPGPTERER
LKKMLSEGSVGEVQWEAEFFALQDSNNKLAGALREANAAAAQWRQQLEAQRAE
AERLRQRVAELEAQAASEVTPTGEKEGLGQGQSLEQLEALVQTKDQEIQTLKSQT
GGPREALEAAEREETQQKVQDLETRNAELEHQLRAMERSLEEARAERERARAEV
GRAAQLLDVSLFELSELREGLARLAEAAP
569.24
572.33
580.30
581.46
582.63
606.32
610.24
616.14
569.24
572.33
580.30
581.46
582.63
606.32
610.24
616.14
569.24
574.83
580.70
580.92
579.99
603.92
611.14
616.74
569.24
572.33
580.30
581.46
582.63
606.32
610.24
616.14
569.24
572.33
580.30
581.46
582.63
606.32
610.24
616.14
569.24
572.33
580.30
581.46
582.63
606.32
610.24
616.14
569.24
572.33
580.30
581.46
582.63
606.32
610.24
616.14
1 QRESTATDILQK 18.77
2 EIEEDSLEGLKK 14.78
Score hits
Theoretical spectra
m/z
[%]
m/z
[%]
m/z
[%]
Experimental spectra
m/z
RT
Protein Sequence
Peptide identification

11. Protein target DATABASE

12. Example Resources
● ENSEMBL + BioMart
● http://www.ensembl.org
● National Center for Biotechnology Information (NCBI) + BLAST
● http://www.ncbi.nlm.nih.gov/
● The European Bioinformatics Institute (EMBL-EBI) + Clustal
● http://www.ebi.ac.uk/services
● UniProt
● http://www.uniprot.org/
● University of California, Santa Cruz (UCSC)
● https://genome.ucsc.edu/

13. Resources

21. Example Repositories

25. https://genevestigator.com/gv/

26. Selected database systems
I. Relational databases
MySQL
II.NoSQL databases
MongoDB

27. Database systems in modern data-driven
biomedical research
I. Typical research scenarios
1.From samples to data (numbers in a file) = digitization
2.From data to databases and back
= use resources (e.g., human genome) and contribute to
repositories (data + scientific observations + metadata)
3.From data/databases to information
= data mining
II. How to?

28. Many database design & concepts
http://dataconomy.com/wp-content/uploads/2014/07/fig2large.jpg
28

29. Databases
DB = "A database is an organized collection of data" http://en.wikipedia.org/wiki/Database
DB = DB + data model for the application at hand (business logic) + implementation
DB = DB + database management system (DBMS). Software than enables:
29
CRUD
• Create entries
• Read (retrieve)
• Update / edit
• Delete
DB = DB + Administration (User privilages, monitoring)

30. Selected database systems
I. Relational databases
MySQL
II.NoSQL databases
MongoDB

31. Specific characteristics MongoDB vs MySQL
More details here: http://db-engines.com/en/system/MongoDB%3BMySQL
System Property MongoDB MySQL
Initial release 2009 1995
Current release 3.0.2, April 2015 5.6.24, April 2015
Triggers No Yes
MapReduce Yes No
Foreign keys No Yes
Transaction concepts No ACID*
*A database transaction, must be Atomic, Consistent, Isolated and Durable.

32. Relational databases
● A plausible experiment where samples are collected from different
mice before and after some treatment
● High redundancy
● Cumbersome to maintain/update

33. Mice table
Samples table
● Split the data into RELATED tables
● Low redundancy
● Easier to maintain/update (e.g., add some genotype information to mice)
1:M one-to-many relationship
Relational databases (Normalization)

34. Terminology
Fields
Record 1
Record 6
Primary
keyPrimary
key
Foreign Key
Ref
Mice.Mouse_number
● Table rows are called "records"
● Table columns are called "fields"
● The values of the primary keys uniquely identifies the rows of the table
● The foreign key uniquely links the rows of the host table to 1 record in the referencing table
Mice table
Samples table

35. Databases
DB = "A database is an organized collection of data" http://en.wikipedia.org/wiki/Database
DB = DB + data model for the application at hand (business logic) + implementation
DB = DB + database management system (DBMS). Software than enables:
35
CRUD
• Create entries
• Read (retrieve or search)
• Update / edit
• Delete
DB = DB + Administration (User privilages, monitoring)

36. Structured Query Language (SQL)
● SQL is a standard language for
creating, accessing and modifying relational databases
● MySQL implements SQL database management

37. Connect to the server and create the database
mysql ­u username ­p ­h localhost
CREATE database mouse_experiment;
USE mouse_experiment;

38. Create the tables and insert values
CREATE TABLE mice (
Mouse_number SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT,
Gender enum('Male','Female','NA') DEFAULT 'Female',
Age decimal(4,2) DEFAULT NULL,
Treatment VARCHAR(50) NOT NULL,
PRIMARY KEY (Mouse_number)
);
INSERT INTO mice (Gender, Age, Treatment)
VALUES ('Male','3','Vitamin A'),
('Male','2','Vitamin B'),
('Female','2.5','Vitamin A'),
('Female','3','Vitamin B'),
('Male','4','Vitamin A'),
('Female','2','Vitamin B');
No Mouse_number! It is the task of the DBMS to generate UNIQUE id's

39. Create the tables and insert values
CREATE TABLE samples (
Sample_ID SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT,
Mouse_number SMALLINT UNSIGNED NOT NULL,
Timepoint VARCHAR(15) NOT NULL,
PRIMARY KEY (Sample_ID),
FOREIGN KEY (Mouse_number)
REFERENCES mice(Mouse_number)
ON DELETE CASCADE
)ENGINE=InnoDB DEFAULT CHARSET=utf8;

40. Create the tables and insert values

41. Queries
SELECT field1, field2,...fieldN from table_name
[WHERE Clause]
[OFFSET M ][LIMIT N]
SELECT * from table_name
[WHERE Clause]
[OFFSET M ][LIMIT N]
Generic syntax

42. Queries
SELECT * , COUNT(*) as count_per_gender from mice
group by Gender, Treatment;
“How many males and how many females per treatment?”

43. JOIN queries
SELECT Sample_ID, Treatment, Timepoint, mice.Mouse_number
from
samples join mice
on samples.Mouse_number = mice.Mouse_number
where mice.Mouse_number=2;
“What samples do I have from mouse number 2?”

44. Delete queries
“Mouse number 3 went wrong. Let's just delete it.”
SELECT * from samples;
DELETE from mice where
Mouse_number = 3;
SELECT * from samples;
Where are these two
samples gone?

45. They were deleted!
CREATE TABLE samples (
Sample_ID SMALLINT UNSIGNED NOT NULL AUTO_INCREMENT,
Mouse_number SMALLINT UNSIGNED NOT NULL,
Timepoint VARCHAR(15) NOT NULL,
PRIMARY KEY (Sample_ID),
FOREIGN KEY (Mouse_number)
REFERENCES mice(Mouse_number)
ON DELETE CASCADE
)ENGINE=InnoDB DEFAULT CHARSET=utf8;
Does it make sense not to leave “orphan” samples in the system?

46. Extending the database
Mice table
Samples table
1:M one-to-many relationship
Protein tableM:M many-to-many relationship

47. Indexing
● Ultimately, the data is stored in files on disks
● With large amounts of data (tens of million of records),
sequential searching becomes not feasible
● MySQL uses B-trees
● “In computer science, a B-tree is a tree data structure that keeps
data sorted and allows searches, sequential access, insertions, and
deletions in logarithmic time.” http://en.wikipedia.org/wiki/B-tree

48. Indexing B-trees
Source: http://en.wikipedia.org/wiki/B-tree

49. Summary
● Database design requires domain knowledge, including
example usecases
● Normalization
● Primary and Foreign Keys
● Implement a MySQL database
● Queries & Join Queries
● Indices

53. Let us revisit ENSEMBL