Bioinformatics MSc - Genome bioinformatics

1. Genome Bioinformatics
y.wurm@qmul.ac.uk

2. Genomics?

3. Genomics - Wikipedia
Genomics is a discipline in genetics that applies recombinant DNA, DNA
sequencing methods, and bioinformatics to sequence, assemble, and
analyze the function and structure of genomes (the complete set of DNA
within a single cell of an organism).[1][2] Advances in genomics have
triggered a revolution in discovery-based research to understand even
the most complex biological systems such as brain.[3] The field includes
efforts to determine the entire DNA sequence of organisms and fine-scale
genetic mapping. The field also includes studies of intragenomic
phenomena such as heterosis, epistasis, pleiotropy and other
interactions between loci and alleles within the genome.[4] !
!
In contrast, the investigation of the roles and functions of single genes is
a primary focus of molecular biology or genetics and is a common topic
of modern medical and biological research. Research of single genes
does not fall into the definition of genomics unless the aim of this genetic,
pathway, and functional information analysis is to elucidate its effect on,
place in, and response to the entire genome's networks.[5][6]

4. Estevezj - CC3 Wikimedia
http://upload.wikimedia.org/wikipedia/commons/7/73/Number_of_prokaryotic_genomes_and_sequencing_costs.svg Ⓐ
Ⓑ Ⓒ

5. • Genomics
• Biodiversity assessments
• Stool microbiome sequencing
• Personalized medicine
• Cancer genomics

6. Challenges
1. Getting up and running with Unix
2. Algorithms in Bioinformatics: strengths & weaknesses
3. Bioinformatics databases
4. DIY: genome assembly & identifying variants.

7. Getting up and running with Unix
& High Performance Computing
(HPC)
ITS Research Team (Lukasz Zalewski):
1. Install virtualbox & biolinux.
2. Introduction to Unix
3. Using Apocrita HPC = “the cluster”
!

8. Algorithms for sequence alignment.
- dotplots- the concept of distance: Euclidean, hamming,
Levenshtein
- dynamic programming and the Smith Waterman algorithm
- local, global, semiglobal alignments
- gap penalty models
- basics of approximate methods (Blast)
- scoring matrices (PAM, Blosum)
- Profiles and PSI-Blast

9. Algorithms for sequence alignment.
Take home message?
•Algorithms are approximate
•Results aren’t perfect
•Computers can get it wrong

10. BLAST is unable to detect any similarity between
these 2 sequences:
Gp-9 1 ATGAAGACGTTCGTATTGCATATTTTTATTTTTGCTCTCGTGGCTTTCGCTTCTGCATCT 60
||||||||||| |||||||||| ||||||||| |||||||| |||||||||| |||||
K2000 1 ATGAAGACGTTGGTATTGCATAATTTTATTTT---TCTCGTGGATTTCGCTTCTCCATCT 57
!
Gp-9 61 CGTGATAGCGCGAGGAAGATAGGATCCCAATATGACAATTACGCGACTTGCTTAGCCGAA 120
||||| ||||||| || ||| ||||||||| |||||| |||||| ||||||||| |||||
K2000 58 CGTGAGAGCGCGAAGACGATGGGATCCCAACATGACATTTACGCCACTTGCTTACCCGAA 117
!
Gp-9 121 CATAGTCTAACAGAGGATGACATCTTCTCGATTGGTGAAGTATCAAGTGGCCAGCACAAA 180
|||| ||||| || |||| || | ||||||||| ||||||||| |||||||||| |||||
K2000 118 CATAATCTAAGAGGGGATAACGTTTTCTCGATTCGTGAAGTATAAAGTGGCCAGGACAAA 177
!
Gp-9 181 ACCAATCATGAAGATACCGAACTACACAAAAATGGTTGCGTCATGCAATGTTTGTTAGAA 240
|||| ||||||||| |||||||| ||||||||| || ||||||| |||||||| ||||||
K2000 178 ACCAGTCATGAAGAAACCGAACTCCACAAAAATCGTCGCGTCATACAATGTTTATTAGAA 237
!
Gp-9 241 AAAGATGGACTGATGTCTGGAGCTGATTATGATGAAGAGAAAATGCGTGAGGACTATATC 300
|||||||| |||||| ||| ||| ||||||||| ||| |||||||||| |||||||||
K2000 238 TAAGATGGAATGATGTGTGGGGCTAATTATGATGGAGAAAAAATGCGTGCTGACTATATC 297
!
Gp-9 301 AAGGAA------ACAGGTGCTCAACCAGGAGATCAAAGGATAGAAGCTCTGAATGCCTGC 354
| |||| || |||| |||||||||| |||| |||| |||| |||||||||| | |
K2000 298 AGGGAATCAGGTACCGGTGGTCAACCAGGACATCAGAGGAGAGAACCTCTGAATGCGTAC 357
!
Gp-9 355 ATGCAAGAAACAAAAGACATGGAGGATAAATGTGACAAAAGCTTGCTCCTTGTAGCATGT 414
||||||||| ||||||| ||| ||| |||||| ||||||||| | || ||| |||||
K2000 358 ATGCAAGAATCAAAAGATATGCAGGTTAAATGGCACAAAAGCT---TTCTAGTAACATGT 414
!
Gp-9 415 GTCTTAGCAGCTGAAGCTGTGCTCGCCGATTCTAACGAAGGAGCATAA 462
| |||||||| | |||||| ||||| |||||| ||||||||| ||||
K2000 415 ATTTTAGCAGCGGGAGCTGTTCTCGCGGATTCTCACGAAGGAGAATAA 462

11. Algorithms for sequence alignment.
Take home message?
• Algorithms are approximate
• Results depend on:
• underlying biology
• approximations made by algorithms
• search and database size

12. Databases for Bioinformatics
• Biological databases & access to the annotated genomes
• NCBI
• Ensembl
• UCSC
• Entrez & Biomart
• Genbank/Uniprot
!
• Cancer resources and data portals
• TCGA, ICGC and Cosmic

13. Databases for Bioinformatics
Take home message?

14. Genome Assembly & variant calling
• Processing raw data
• Genome assembly algorithms
• Read mapping
• Quality Assurance processes
• Calling & visualising variants
• Automated gene prediction
• Doing things in the command-line

15. Bruno
Vieira
Rodrigo
Pracana

16. Old & modern assembly
algorithms
• Overlap-layout consensus
!
• De bruijn-based.