Bioinformatics Presentation - Training of MiRON

1. Welcome to BIOINFORMATICS
-MiRON

2. Outline
 Workshops chronology on hands out
 Brief background information
 Applications & role
 Bioinformatics tools
 Practical classes
 Problem solving exercises
 What’s expected of you ?
 Questions/comments are welcome at all
points

3. Aims
 To introduce the concepts and language of
bioinformatics.
 To provide an understanding of how nucleic acid
and protein sequence data is obtained and
analysed.
 To develop skills in utilising online databases and
interpreting data.
 To develop an understanding of how bioinformatics
can be applied to solve specific problems in
biomedical science.
 To develop transferable IT and communications
skills.

4. In this workshop…..
 You will learn about how data is
generated and analysed
 As well as what the generated data can
tell us about the molecular biology of
organisms
 And various practical applications of
this knowledge

5. What is bioinformatics?

6. Why bioinformatics?
 Over the past decade massive amounts
of sequence data have been generated
 This has more recently been joined by
gene expression data obtained from
microarrays and proteomic technologies
 This vast amount of data can only be
analysed using various specialised
computer algorithms

7. Main Topics (Review............)
 Genome organisation and analysis
 Functional genomics
 Advanced techniques in molecular biology
 Archives, information retrieval and alignments:
 Nucleic acid sequence databases; genome
databases; protein sequence databases; database
searching
 Dot plots (SIMILARITY MATRX) and sequence
alignments (PSI BLAST);
 Genome expression: Microarray analysis,
proteomics, eukaryotic genome expression

8. What bioinformatcian think
they are

9. What they do

10. Examples of Bioinformatics
 Database interfaces
 Genbank/EMBL/DDBJ, Medline, SwissProt, PDB,
…
 Sequence alignment
 BLAST, FASTA
 Multiple sequence alignment
 Clustal W, MultAlin, DiAlign
 Gene finding
 Genscan, GenomeScan, GeneMark, GRAIL
 Protein Domain analysis and identification
 pfam, BLOCKS, ProDom,
 Pattern Identification/Characterization
 Gibbs Sampler, AlignACE, MEME
 Protein Folding prediction
 PredictProtein, SwissModeler

11. Five W that all biologists
should know
 NCBI (The National Center for Biotechnology Information;
 http://www.ncbi.nlm.nih.gov/
 EBI (The European Bioinformatics Institute)
 http://www.ebi.ac.uk/
 The Canadian Bioinformatics Resource
 http://www.cbr.nrc.ca/
 SwissProt/ExPASy (Swiss Bioinformatics Resource)
 http://expasy.cbr.nrc.ca/sprot/
 PDB (The Protein Databank)
 http://www.rcsb.org/PDB/

12. Remember while using web
server-based tools
 You are using someone else’s
computer
 You are (probably) getting a reduced
set of options or capacity
 Servers are great for sporadic or proof-
of-principle work, but for intensive work,
the software should be obtained and
run locally

13. Human Gene Index Database
 HGI is a database of expressed DNA
sequences, mostly made of ESTs, which are
a type of partial cDNA
 EST stands for Expressed Sequence Tag
 These short sequences were created using
essentially the same method used to make
cDNAs
 As such they represent the expressed part of
a genome and are made from mRNA which is
ultimately expressed from GENES

15. Gene Structure

16. Similarity Searching
 There are a variety of computer
programs that are used for making
comparisons between DNA sequences.
 The most popular is known as BLAST
(Basic Local Alignment Search Tool)
 BLAST is free at the NCBI website

17. BLAST is Complex
 Similarity searching relies on the concepts of
alignment and distance between pairs of
sequences.
 Distances can only be measured between
aligned sequences (match vs. mismatch at
each position).
 A similarity search is a process of testing the
best alignment of a query sequence with
every sequence in a database.

18. Workshop -1 (database search & inference of possible
homology)
Please refer to getting started with bioinformatics
INTRO TO BLAST
 Basic Local Alignment Search Tool
 It is used to compare a query sequence with those contained in
nucleotide databases by aligning the query sequence with
previously characterised genes, therefore helping in identifying
genes.
 The emphasis of this tool is to find regions of sequence
similarity between two different genes.
 These sequence alignments can yield clues about the structure
and function of a novel sequence, and about its evolutionary
history and homology with other sequences in the database.

19. BLAST has Automatic
Translation
 BLASTX makes automatic translation (in all
6 reading frames) of your DNA query
sequence to compare with protein
databanks
 TBLASTN makes automatic translation of
an entire DNA database to compare with
your protein query sequence
 Only make a DNA-DNA search if you are
working with a sequence that does not code
for protein.

20. A typical sequence ready for
submission to BLAST
>THC2465887
GGCTGCGGAGGACCGACCGTCCCCACGCCTGCCGCCCCGCGACCCCGACCGCCAGCATGATCGCCGCGCAGCTCCTGGCC
TATTACTTCACGGAGCTGAAGGATGACCAGGTCAAAAAGATTGACAAGTATCTCTATGCCATGCGGCTCTCCGATGAAAC
TCTCATAGATATCATGACTCGCTTCAGGAAGGAGATGAAGAATGGCCTCTCCCGGGATTTTAATCCAACAGCCACAGTCA
AGATGTTGCCAACATTCGTAAGGTCCATTCCTGATGGCTCTGAAAAGGGAGATTTCATTGCCCTGGATCTTGGTGGGTCT
TCCTTTCGAATTCTGCGGGTGCAAGTGAATCATGAGAAAAACCAGAATGTTCACATGGAGTCCGAGGTTTATGACACCCC
AGAGAACATCGTGCACGGCAGTGGAAGCCAGCTTTTTGATCATGTTGCTGAGTGCCTGGGAGATTTCATGGAGAAAAGGA
AGATCAAGGACAAGAAGTTACCTGTGGGATTCACGTTTTCTTTTCCTTGCCAACAATCCAAAATAGATGAGGCCATCCTG
ATCACCTGGACAAAGCGATTTAAAGCGAGCGGAGTGGAAGGAGCAGATGTGGTCAAACTGCTTAACAAAGCCATCAAAAA
GCGAGGGGACTATGATGCCAACATCGTAGCTGTGGTGAA

21. BLAST OUTPUT

22. BLAST line-up of human v canine partial cDNAs for
hexokinase 1
Query: 3034 TGCATGGTTTGATTTTGACCTGGTC---C---CCC-ACGTGTGAAGTGTAGTGGCATCCA 3086
|||||| | |||||| |||||||| | ||| ||||||||||| |||||||| |||
Sbjct: 75 TGCATGATCTGATTTCAACCTGGTCGTACGCTCCCCACGTGTGAAGTTTAGTGGCACCCA 134
Query: 3087 TTTCTAATGTATGCATTCATCCAACAGAGTTATTTATTGGCTGGAGATGGAAAATCACAC 3146
|||| | | | ||||||| || |||||||||||||||||| ||||| ||| |||| |
Sbjct: 135 TTTCCAGTCTCTGCATTCGTCTGACAGAGTTATTTATTGGCCCAAGATGAAAAGTCACGC 194
Query: 3147 CACCTGACAGGCCTTCTGGG-CCTCCAAAGCCCATCCTTGGGGTTCCCCCTCCCTGTGTG 3205
|| | | |||||||| |||| |||| ||||| ||||||||| | | |||||||||
Sbjct: 195 CATCCGCCAGGCCTTATGGGGCCTCTGCAGCCCGTCCTTGGGGACACATC-CCCTGTGTG 253
Query: 3206 AAATGTATTATCACCAGCAGACACTGCCGGGCCTCC-C-TCCCGGGGGCACTGCCTGAAG 3263
||||||||||||||||||||||||||||||| |||| | |||| |||||| | | |
Sbjct: 254 AAATGTATTATCACCAGCAGACACTGCCGGGACTCCTCCTCCCAGGGGCA-T-CTTAGCT 311
Query: 3264 GCGAG-TGTGGGCATAGCATTAGCTGCTTCCTCCCCTCCTG-GCA-CCCACTGTGGCC-T 3319
|| | | | |||| ||||| || | ||| | | | |||| | || | |
Sbjct: 312 GCTTCCTCCCGTCCCAGCACCCACTGCTGTCTGGCGTCCCGAGGATCCCA-TCAGGACGT 370
Query: 3320 GGC-ATCGCATCGTGGTGTGTCAATGCCACAAAATCGTGTGTCCGTGGAACCAGTCCTAG 3378
| | || || | | |||| | || || | || ||| | | || || |
Sbjct: 371 GTCCATGCCACTGAGTCGTGTG--T-CCGTGGAA-C-TG-GTCAGAGCCACT--TCGTGA 422
Query: 3379 CCGCGTGTGACAGTCTTGCATTCTGTTTGTCTCGTGGGGGGAGGTGGACAG-TCCTGCGG 3437
| | | || || ||| | ||| | | | | || || ||||| ||
Sbjct: 423 CAGTCT-TG-CATTCTGTCTGTCT--TGGGGTGGNNGGNAAGNNNNNCCANNTCCTGTGG 478
Query: 3438 -AAAT--GTGTCTTGTCTCCATTTGGA-TAAAA-GGAA-CCAA--CCAACAAACAATGCC 3489
||| | | |||| |||||||||| ||||| |||| |||| ||||||| || ||||
Sbjct: 479 GAAAAAGGGGCCTTGGCTCCATTTGGGGTAAAAAGGAAACCAAACCCAACAA-CAGTGCC 537
Query: 3490 A-TCACTGG-AATTTCCC-ACCG-CTTT--GTGAGCCGTG-TCGTATGA-CCTAGTAAAC 3541
||| ||| |||| ||| | | |||| ||||||| || | |||||| ||||| ||
Sbjct: 538 CCTCATTGGGAATTCCCCCATTGGCTTTTTGTGAGCCATGGTTGTATGAACCTAGGTAAA 597
Query: 3542 TTTGT 3546
|| |
Sbjct: 598 CTTNT 602

23. Understand the
Statistics!
 BLAST produces an E-value for every match
 This is the same as the P value in a statistical test
 A match is generally considered significant if the
E-value < 0.05 (smaller numbers are more significant)
 Very low E-values (e-100) are homologs or
identical genes
 Moderate E-values are related genes
 Long regions of moderate similarity are more
important than short regions of high identity.

24. BLAST is Approximate
 BLAST makes similarity searches very
quickly because it takes shortcuts.
 looks for short, nearly identical “words” (11 bases)
 It also makes errors
 misses some important similarities
 makes many incorrect matches
 easily fooled by repeats or skewed composition

25. Bad Genome
Annotation
 Gene finding is at best only 90%
accurate.
 New sequences are automatically
annotated with BLAST scores.
 Bad annotations propagate
 Its going to take us 10-20 years or more
to sort this mess out!

26. Conclusions
 We have only touched small parts of
the elephant
 Trial and error (intelligently) is often
your best tool
 Keep up with the main five sites, and
you’ll have a pretty good idea of what is
happening and available