# Presentation: 125896

## Having it all

- Complete: every occurrence is found
- Precise: every occurrence is accurate
- Comprehensive: all types of features
- Richly described: biological functional and cross-species data
- What is needed to make this really happen?
- Can’t be swept under the rug.

**Notes:**
What we would ideally like to have for every sequenced genome is this. This is just my way of saying what has already been said. My concern and interest is the practical problem of implementing this from an informatics and organizational standpoint. 

## Managing annotations changes

- More information:
- Assembly changes, transcriptome data, comparative data, Cis regulatory element data, Mass spec data, repeat studies
- Will lead to:
- Additions, merges, splits, splerges, deletions, new feature types
- Compounded by:
- Conflict resolution and innate complexity
- alternate transcripts, dicistronic genes, overlaps and intersections

**Notes:**
Even if we only consider protein coding transcripts for the moment there are still many issues that make completeness and precision difficult to achieve. Multiple groups are producing data. These data may either: confirm (trans-genic results), conflict (for example different gene prediction results) or inform (geographic spatial variation)

## The Essentials

- Full-length cDNA sequences and other ‘hard’ biological evidence
- High-quality assemblies
- Manual editors for curation
- Combiners
- Annotation standards and verification
- Tracking and versioning
- Open source software components and standards are critical to long term success

**Notes:**
Those who are responsible for correlating the data (the curators) would like all of the following.

## Integration

- Agree on Standards
- GFF3 file format
- Sequence Ontology
- Gene Ontology
- Phenotype...
- Agree on Process
- Exchange (DAS2)
- Convergence
- Versioning
- Feedback

**Notes:**
SO enables description of the kind splice context that Steve Mount described

## GFF3

- Need a common exchange format
- To share and distribute data
- Easy to transfer between databases
- Then can be visualized and seen by everyone!
- And can be edited/commented I.e. Apollo

**Notes:**
Uses SO, easy, light-weight

## SO enables rigorous description and querying of the data

- How often are exons unique to a transcript?
- How often does an exon appear in all of the transcripts?
- For exons that occur in all the transcripts, How often are they coding exons?
- For exons that occur in only one of the transcripts, how often are they noncoding?
- Do unique exons contain the stop codon more often than exons in all the transcripts?

**Notes:**
Steve Mount

## Annotation integration

- DAS2
- IGB (Gregg Helt @ Affy)
- Apollo (Suzanna Lewis @ Berkeley)
- Allow exchange of annotations between researchers at different sites.

**Notes:**
Go to the originator of the data for the most reliable and recent set of peptides

## Design and Plan for Integration

- It is essential to tightly define standards at the outset
- It is essential to have on-going assessment and evaluation of accumulated pooled data

**Notes:**
Formalize a contract for the exchange of data at the outset. It is _NOT_ enough just to put your data out there. It is not enough just to collect it. Your partners & collaborators must be able to integrate and use your data.
Don’t defer validation and concordance issues, make it an integral part of the process. Decide at the how comments will be reflected back. The system should not be open-loop, but must be closed-loop. This morning Elise said that the biggest challenge facing the human encode project was integration. Therefore, the practical aspects of how we will integrate all of these data should fully incorporated.