# Presentation: 074250

## Information Sources 

**Information Sources **
**for Genomics**

## Databases

- Databases contain information organized in a way that allows users/researchers to retrieve and exploit it.
- The goal is to be able to:
- Store information.
- Organize data in a way that is easier to be described and studied...
- Predict the functional role of an element.
- Understand the way things work...

## Overview

- Sequence databases
- Primary (contain “raw” data)
- Nucleotide
- Protein
- Secondary (processed information)
- Genes
- Proteins
- Classification databases
- Sequence classification
- Function classification
- Other methods
- Other specialized databases

## Nucleotide databases

- EMBL/GenBank/DDBJ (_[http://www.ncbi.nlm.nih.gov/](http://www.ncbi.nlm.nih.gov/)_,http://www.ebi.ac.uk/embl)
- Archive containing all sequences from:
- genome projects
- sequencing centers
- individual scientists
- patent offices
- The sequences are exchanged between the three centers on a daily basis
- Database is doubling every 15 months
- Sequences from >140,000 different species
- 1400 new species added every month

## Primary protein sequence databases

- Contain coding sequences derived from the translation of nucleotide sequences
- Genpept
- Coding sequences (CDS) from GenBank entries
- TrEMBL (1996)
- Automatic CDS translations from EMBL

## Errors in databanks

- There are a lot of errors in the nucleotide sequence databanks:
- In the sequences themselves:
- Sequencing errors.
- Cloning vectors inserted.
- For the annotations, the free submission of entries involves:
- Inaccuracies, omissions, and even mistakes.
- Inconsistencies between some fields.

## Redundancy

- Another major problem is redundancy.
- A lot of entries are partially or entirely duplicated:
- 20% of vertebrate sequences in GenBank.

## Overview

- Sequence databases
- Primary (contain “raw” data)
- Nucleotide
- Protein
- Secondary (processed information)
- Genes
- Proteins
- Classification databases
- Sequence classification
- Function classification
- Other methods
- Other specialized databases

## NCBI Derivative Sequence Data

**ATTGACTA**

**TTGACA**

**CGTGA**

**ATTGACTA**

**TATAGCCG**

**ACGTGC**

**ACGTGC**

**ACGTGC**

**TTGACA**

**TTGACA**

**TTGACA**

**CGTGA**

**CGTGA**

**CGTGA**

**ATTGACTA**

**ATTGACTA**

**ATTGACTA**

**ATTGACTA**

**TATAGCCG**

**TATAGCCG**

**TATAGCCG**

**TATAGCCG**

- GenBank

**TATAGCCG**

**TATAGCCG**

**TATAGCCG**

**TATAGCCG**

**AT**

**GA**

**C**

**ATT**

**GA**

**GA**

**ATT**

**ATT**

**C**

**C**

**GA**

**GA**

**ATT**

**C**

**C**

**GA**

**GA**

**ATT**

**C**

**GA**

**GA**

**ATT**

**C**

**GA**

**GA**

**ATT**

**C**

**C**

**GA**

**GA**

**ATT**

**C**

**C**

- UniGene

- RefSeq

- Genome
- Assembly

- Labs

- Curators

- Algorithms

**TATAGCCG**
**AGCTCCGATA**
**CCGATGACAA**

## RefSeq

- Curated transcripts and proteins
- reviewed by NCBI staff
- Model transcripts and proteins
- generated by computer algorithms
- Assembled Genomic Regions (contigs)
- Chromosome records

## Secondary protein databases

**SWISS-PROT (1986)**** (http://ca.expasy.org/spro)**
- Best annotated, least redundant
- Contains entries from >10,000 species
- Cross-references with >60 other databanks
**PIR** (Protein Information Resource)(http://pir.georgetown.edu)
- More automated annotation
- Collaborations with MIPS and JIPID
**Uniprot (2003)**
- UniProt (Universal Protein Resource) is a central repository of protein sequence and function created by joining the information contained in Swiss-Prot, TrEMBL, and PIR.

## Overview

- Sequence databases
- Primary (contain “raw” data)
- Nucleotide
- Protein
- Secondary (processed information)
- Genes
- Proteins
- Classification databases
- Sequence classification
- Function classification
- Other methods
- Other specialized databases

- Groups (families/clusters) of proteins based on...
- Overall sequence similarity
- Local sequence similarity
- Presence / absence of specific features
- ...
- These groups contain proteins with similar features
- Specific function, enzymatic activity
- Broad function
- ...

- Classification databases

## Overall sequence similarity

***Overall sequence similarity***

## COGs were delineated by comparing protein sequences encoded in 43 complete genomes representing 30 major phylogenetic lineages.
Each Cluster has representatives of at least 3 lineages
A function (specific or broad) has been assigned to each COG.

- Each Cluster has representatives of at least 3 lineages
- A function (specific or broad) has been assigned to each COG.

**http://www.ncbi.nlm.nih.gov/COG/**

***Clusters of orthologous groups (COGs)***

## Profiles & Pfam

- A method for classifying proteins into groups exploits region similarities, which contain valuable information
- These profiles can be used to detect distant relationships, where only few residues are conserved

## Regions similarity

***Regions similarity***

## Pfam

**http://www.sanger.ac.uk/Software/Pfam/**

## Composite pattern databases

- To simplify sequence analysis, the family databases are being integrated to create a unified annotation resource – **InterPro**
- Release 12.1 contains 12953 entries
- Central annotation resource, with pointers to its satellite dbs
- Partners are PRINTS, PROSITE, profiles & Pfam, ProDom, TIGRfam, SMART & hopefully others (*e.g*., Blocks, MetaFam)

## Slide 20

## ENZYME

**http://ca.expasy.org/enzyme/**

## ENZYME

***ENZYME***

## KEGG

- Contains information about biochemical pathways, and protein interactions
- Information is organized either based on genes or genomes

**http://www.kegg.com**

## Overview

- Sequence databases
- Primary (contain “raw” data)
- Nucleotide
- Protein
- Secondary (processed information)
- Genes
- Proteins
- Classification databases
- Sequence classification
- Function classification
- Other methods
- Other specialized databases

## Sequencing projects 

- GOLD
- find information for ongoing and finished genomic projects
- information about the phylogeny and phenotype of genomes

**http://www.genomesonline.org**

## Specialized databanks

- There is a large number of databases devoted to specific organisms.
- For some model organisms there are often concurrent systems.
- These databases are associated to sequencing or mapping projects.

## Signal transduction, regulation, protein-protein interactions
TRANSFAC (Transcription Factor database) 
BRITE (Biomolecular Relations in Information Transmission and Expression database)
DIP (Database of Interacting Proteins)
BIND (Biomolecular Interaction Network database)
BioCarta
Biochemical pathways
KLOTHO (Biochemical Compounds Declarative database)
BRENDA (enzyme information system)
LIGAND (similar to Enzyme but with more information for substrates)
Gene order and co-occurrence
STRING

- TRANSFAC (Transcription Factor database)
- BRITE (Biomolecular Relations in Information Transmission and Expression database)
- DIP (Database of Interacting Proteins)
- BIND (Biomolecular Interaction Network database)
- BioCarta
- Biochemical pathways
- KLOTHO (Biochemical Compounds Declarative database)
- BRENDA (enzyme information system)
- LIGAND (similar to Enzyme but with more information for substrates)
- Gene order and co-occurrence
- STRING

***Other specialized databases***

- 3D structures
- PDB (Protein Data Bank)
- MMDB (Molecular Modelling Data Base)
- NRL_3D (Non-Redundant Library of 3D Structures)
- SCOP (Structural Classification of Proteins)
- Polymorphism
- ALFRED (Allele Frequency Database)
- Molecular interactions
- DIP (Database of Interacting proteins)
- BIND (Biomolecular Interaction Network Database)

- Gene expression
- GXD (Mouse Gene Expression Database)
- The Stanford Microarray Database
- Mapping
- GDB (Genome Data Base)
- EMG (Encyclopedia of Mouse Genome)
- MGD (Mouse Genome Database)
- INE (Integrated Rice Genome Explorer)
- Protein quantification
- SWISS-2DPAGE
- PDD (Protein Disease Database)
- Sub2D (B. subtilis 2D Protein Index)

## Databanks interconnection

## Concluding remarks

- We have some main repositories, main databases (SwissProt), and protein classification database (COG, Pfam).
- This is the tip of the iceberg.
- They help *predict* the function, or the network of functions.
- Systems that integrate the information from several databases, visualize and allow handling of information in an intuitive way *are required*

## Profiles

- Profiles are scoring tables derived from domain alignments
- they define which residues are allowed at given positions
- which positions are conserved & which degenerate
- which positions can tolerate insertions