Download workshop-1

Survey
yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project

Document related concepts

Cre-Lox recombination wikipedia , lookup

Gene regulatory network wikipedia , lookup

Transcriptional regulation wikipedia , lookup

Gene desert wikipedia , lookup

Exome sequencing wikipedia , lookup

Gene expression profiling wikipedia , lookup

Gene expression wikipedia , lookup

Promoter (genetics) wikipedia , lookup

Whole genome sequencing wikipedia , lookup

Ancestral sequence reconstruction wikipedia , lookup

Molecular ecology wikipedia , lookup

Community fingerprinting wikipedia , lookup

Silencer (genetics) wikipedia , lookup

Point mutation wikipedia , lookup

Genomic library wikipedia , lookup

Non-coding DNA wikipedia , lookup

Endogenous retrovirus wikipedia , lookup

Molecular evolution wikipedia , lookup

Genome evolution wikipedia , lookup

Artificial gene synthesis wikipedia , lookup

RNA-Seq wikipedia , lookup

Transcript
Bioinformatics Workshops 1 & 2
1. use of public database/search sites
- range of data and access methods
- interpretation of search results
- understanding the meaning & effect of
search (e.g. BLAST) parameters
2. functional analysis of single sequences
- i.e. how to work out what your unknown
protein might be doing
- complex searches for (e.g.) patterns of
motifs & secondary structure elements
Workshop 1.
overall survey of
data
Main data axes
Biological origin
of sequences
Main Portals
Genes vs.loci
Mutation
between species
-> orthologs
Mutation
between
duplications ->
domains
Database
searches vs.
genome
browsers
Finding similar
sequences
BLAST, et al
E-values!
Search methods
– 2D vs. 3D
Random
sequences
Search methods
– similarity vs.
models vs.
comparative
Using Public Data Resources
• There is (are!) data out there
• There are methods out there
• Quite often they are combined
– BLAST searches of sequence databases
Notes…
• Sequence databases
– Entrez queries…
•
•
•
•
Genome browsers/databases
Regulatory Elements
SNPs
Functional Sequence Models (PFam domains,
etc.)
• Expression Data
– Array data
– in situ data
Notes II
• Blast parameters
– Low complexity: frameshifted cDNA
– miRNAs vs genome
– morpholinos for other genes
– -q-2 for EST vs EST alignments
– Entrez queries
What have we got…
~ gene
gene model
locus
genome
primary transcript
mRNA
protein
Derivative Sequences
mRNA
clone into cDNA
library
5’ EST
Single pass sequence from
each end of the clone
3’ EST
cDNA sequence
Multiple pass sequencing
over whole length of the
clone
Initial Growth of Databases
• Lots of ESTs were generated
• Some clones were selected for full-insert
sequencing -> cDNAs
• cDNAs were translated to yield presumed
protein sequences
Then Came Genomes
• With increasing larger fragments of
genomic sequence came the ability to
align cDNAs to create gene models
• And then to apply our understanding of
exon/intron structure to predict theoretical
genes…
Introns and Exons
mRNA
CTACCATCCATGCTAACCATTCTACCATTTTATACTCATGCAACGGACCGTAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
gene model
genome
exon
intron
exon
intron
exon
splice sites
CTACCATCCATGCTAACCATTCTAC
CATTTTATACTCATGCAACGGACCGT
AGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
CTACCATCCATGCTAACCATTCTACGTAAGTCATCTATATCAATATTATTTCAGCATTTTATACTCATGCAACGGACCGTGTCAGTATTACAGAGCGTAGTCGCTTAGCATCCTTTATAACTGGCTA
GTAAG. .TTTCAG
donor
acceptor
Gene Predictions
Given:
- coding sequence must run from ATG – STOP codon in-frame
- introns GT. . . . . . AG can be spliced out
Also take a statistical approach:
- coding and non-coding sequence are slightly different in composition
- some ‘possible’ splice sites are more likely than others
scan genomic sequence …
. . .CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA. .
. . .CGTCGTATGGCTTCGATTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA.
.CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA.
. .
.
.CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA.
.
. . .CGTCGTATGGCTTCGATGTAGTACATCGGATCGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA.
. .
most likely gene model
. . .CGTCGTATGGCTTCGATGTAGTACATCGGATCGGTATGGAATCATTTCAGTCGCTAGCTAGCCTAACGTATATAGCTAGGTAAGACTA. .
Supporting Evidence!
exons:
1
2
3
4
gene model
genome
EST evidence
We note that even though there is good evidence for the existence of all four
exons, there is no evidence that all the exons would appear on a real transcript. An
alternative transcript, skipping exon 3, would be plausible, if a little unlikely.
This gets less ambiguous as more ESTs are available, and clones are sequenced
at both ends (which helps put distant exons into the same transcripts), and
eventually full-length transcript sequences are available.
So What’s in the Databases Now?
• At NCBI
– 15,000,000 EST sequences
– 3,329,110 non-redundant DNA sequences (excluding
ESTs, etc.)
– 2,693,904 non-redundant translated coding
sequences
– 954,378 Protein Reference Sequences sequences
(RefSeq)
• But the majority of RefSeq may be translations
of theoretical transcripts…
Main Data Axes
• Europe: EBI/EMBL
– Swiss-Prot/Trembl/Ensembl/UniProt
• US: NIH/NCBI
– GenBank/UniGene/RefSeq/Entrez
• Japan: DNA Data Bank of Japan
– National Institute of Genetics
Synchronisation…
You submit a sequence
ATCGATCGATCATAGTATGCTAGCTGCTA
GenBank
EMBL
BC009638.1
ATCGATCGATCATAGTATGCTAGCTGCTA
DDBJ
Sequences, Accession Numbers
and Genes
NM_001015922.1
gi=62860271
GATCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
NM_001015922.2
gi=62860589
GACCGTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAAA
BC009638.1
gi=16307106
GTTCGATTAGCTAGGGACACCACCGATCGATATGACCACAAAA
Main Data Portals
•
•
•
•
•
NCBI Entrez Databases
ExPASy Proteomics Server
DNA Data Bank of Japan DDBJ
EBI Ensembl Genome Browser
Santa Cruz Genome Browser