Gene prediction and annotation Flashcards
1
Q
Identification of genes
A
- Prokaryote
- generally easy
- sequence without interruptions
- Eukaryotes
- more difficult (exons are divided)
2
Q
What is gene prediction?
A
- the identification of genetic elements in a genomic sequence
3
Q
How is gene prediction done?
A
- de-novo predictions
- training set
- evidence Based
- EST, Proteins, RNAseq
- comparatve genomics
- other genomes
- useful in the discovery of functionally relevant regions
- combiner
- training set
4
Q
GBROWSE
A
- tool for the visualization of genetic annotations
- based on a relational database
- helps in the understanding of the predictions
- de novo prediction using different predictors: many false positives
- alignment of EST and proteins: very good but not all genes are covered
- objects in the Gbrowser can be linked to the annotation platform
5
Q
Genetic annotations
A
- process of identifying the locations of genes and all of the coding regions in a genome and determining what those genes do
- is a note added as a commentary
6
Q
Functional annotation
A
- process of attaching biological information to sequences of genes or proteins
- two methods
- Automatic annotation
- relatively fast
- generally inferred from sequence similarity
- useful as starting point
- Manual annotation
- requires experts
- long process
- Automatic annotation
7
Q
Gene ontology
A
- controlled, structured and dynamic dictionary of biological processes, molecular functions and cellular components
- can be applied to all organisms
- tool for the unification of biology
- easier comparison among different species
- annotations have evidence codes
- information about the evidence from which the annotation has been made.
8
Q
Gene ontology typer of annotations?
A
- electronic annotation
- relatively quick
- large number of annotations
- sequence similarity and mapping
- useful for not well known genomes
- manual annotation
- high-quality annotation
- expert biologists needed
- time consuming
9
Q
Markov chains
A
- predicting events
- stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event
- can predict whether a certain text belongs to one language or another
- can be used to detect signatures in DNA and amino acid sequences
- discriminate between coding and non-coding regions (de novo)
- Hidden Markov Models (HMM) states are not directly visible