Developers' mDay 2017. - Mladen Lazarević Seven Bridges

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This document discusses challenges and algorithms in bioinformatics. It begins by covering the birth of bioinformatics through the Human Genome Project in the 1990s-2000s. It then compares bioinformatics and data science, noting bioinformatics focuses on genomic data and scientific conclusions from typically single samples. Common bioinformatics tasks discussed include genome assembly from sequencing reads and alignment of reads to reference genomes using data structures like suffix arrays and Burrows-Wheeler transforms. The document also notes challenges in bioinformatics and applications like personalized medicine.

Technology

sevenbridges.com
Mladen Lazarević
Izazovi i algoritmi u bioinformatici

© 2016 Seven Bridges sevenbridges.com
Birth of bioinformatics -Human Genome
Project
■ Referentni genom je reprezentativni primer genoma neke vrste
■ Genom svake jedinke vrste se razlikuje od referentnog genoma na nekim
mestima
■ Referentni genom se koristi kao koordinatni sistem za genomske podatke
■ Međunarodna inicijativa da se napravi rerefenti genom za ljudsku vrstu
■ Trajala je od 1990 do 2003, i koštao preko 3B$
■ Problemi - zbog veličine genoma: kako skladištiti, pristupiti i analizirati podatke
-> Bioinformatics

© 2016 Seven Bridges sevenbridges.com
Bioinformatics vs Data science

© 2016 Seven Bridges sevenbridges.com
Bioinformatics vs Data science
● Spoj između genomskih podataka i naučnih zaključaka
● (Uglavnom) fokus na jednom semplu umesto na populaciju semplova
● Tražimo specifične motive u genomu - mutacije, eskpresije gena, strukturne varijante
○ 3,000,000,000 karaktera {A, C, G, T}
○ Veliki semplovi: 100 - 400 GB
● Problem se teško apstrahuje
○ Često se vraćamo nazad koja je biološka pozadina problema i kako su podaci generisani

© 2016 Seven Bridges sevenbridges.com
Sekvenciranje i rekonstrukcija genoma

© 2016 Seven Bridges sevenbridges.com
Bioinformatics recap (1)
Tipičan sempl (Genom):
● 200-500 miliona readova
● Svaki read ima 50-150 karaktera (A, C, G, T)
● + kvalitet očitavanja
● 30x coverage ~ 150-300GB
Različiti podaci:
● Digitalizovani DNK, RNK, proteini
● Različite tehnike (lab prep)

© 2016 Seven Bridges sevenbridges.com
Bioinformatics recap (2)
Najčešći početak:
Rekonstrukcija genoma

© 2016 Seven Bridges sevenbridges.com
CONFIDENTIAL

© 2016 Seven Bridges sevenbridges.com
CONFIDENTIAL
Genome assembly

© 2016 Seven Bridges sevenbridges.com
CONFIDENTIAL
Alignment

© 2016 Seven Bridges sevenbridges.com
Alignment

© 2016 Seven Bridges sevenbridges.com
Alignment - suffix array
Query:
O(n log m)~O(n+logm)
Space:
m~3m integers
Build (sort):
O(m2
logm)~O(m2
)~O(m logm)

© 2016 Seven Bridges sevenbridges.com
Alignment - BWT

© 2016 Seven Bridges sevenbridges.com
Alignment - BWT
● BWM podseća na suffix array
● Redosled sortiranja je isti bilo da su vrste rotacije ili sufiksi

© 2016 Seven Bridges sevenbridges.com
Alignment - BWT
Final data structure:
1. BWT
2. Checkpoints
3. Suffix array

© 2016 Seven Bridges sevenbridges.com
IZAZOVI BIOINFORMATIKE

© 2016 Seven Bridges sevenbridges.com
PRIMENA BIOINFORMATIKE

© 2016 Seven Bridges sevenbridges.com
CONFIDENTIAL
Personalizovana medicina

© 2016 Seven Bridges sevenbridges.com
CONFIDENTIAL
Primer studije personalizovane medicine

© 2016 Seven Bridges sevenbridges.com
CONFIDENTIAL
HVALA!

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Developers' mDay 2017. - Mladen Lazarević Seven Bridges

1. sevenbridges.com Mladen Lazarević Izazovi i algoritmi u bioinformatici

2. © 2016 Seven Bridges sevenbridges.com Birth of bioinformatics -Human Genome Project ■ Referentni genom je reprezentativni primer genoma neke vrste ■ Genom svake jedinke vrste se razlikuje od referentnog genoma na nekim mestima ■ Referentni genom se koristi kao koordinatni sistem za genomske podatke ■ Međunarodna inicijativa da se napravi rerefenti genom za ljudsku vrstu ■ Trajala je od 1990 do 2003, i koštao preko 3B$ ■ Problemi - zbog veličine genoma: kako skladištiti, pristupiti i analizirati podatke -> Bioinformatics

4. © 2016 Seven Bridges sevenbridges.com Bioinformatics vs Data science ● Spoj između genomskih podataka i naučnih zaključaka ● (Uglavnom) fokus na jednom semplu umesto na populaciju semplova ● Tražimo specifične motive u genomu - mutacije, eskpresije gena, strukturne varijante ○ 3,000,000,000 karaktera {A, C, G, T} ○ Veliki semplovi: 100 - 400 GB ● Problem se teško apstrahuje ○ Često se vraćamo nazad koja je biološka pozadina problema i kako su podaci generisani

6. © 2016 Seven Bridges sevenbridges.com Bioinformatics recap (1) Tipičan sempl (Genom): ● 200-500 miliona readova ● Svaki read ima 50-150 karaktera (A, C, G, T) ● + kvalitet očitavanja ● 30x coverage ~ 150-300GB Različiti podaci: ● Digitalizovani DNK, RNK, proteini ● Različite tehnike (lab prep)

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