genbank huge amounts of data, easily accessible

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GenBank Huge amounts of data, easily accessible

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GenBank Huge amounts of data, easily accessible. Rate of growth of phylogenetic knowledge. Number of papers with “molecular” and “phylogeny” in Web of Science. Number of studies in TreeBASE. Why have a phylogeny database?. Archive data and trees (repeat old analyses with new tools) - PowerPoint PPT Presentation

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Page 1: GenBank  Huge amounts of data, easily accessible

GenBank Huge amounts of data, easily accessible

Page 2: GenBank  Huge amounts of data, easily accessible

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1975 1980 1985 1990 1995 2000 2005

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Cumulative number

Rate of growth of phylogenetic knowledge

Number of papers with “molecular” and “phylogeny” in Web of Science

Number of studies in TreeBASE

Page 3: GenBank  Huge amounts of data, easily accessible

Why have a phylogeny database?• Archive data and trees (repeat old analyses with new

tools)• Synthesize new data sets and trees (supermatrices and

supertrees)• Big scale questions (tree shape, bias in tree building

methods, stability of trees over time)• Hypothesis testing : find all phylogenies for taxa with

members in Gondwana -- do they show similar area cladograms, amounts of sequence divergence, etc.

• Who knows…(we won’t know until we try)

Page 4: GenBank  Huge amounts of data, easily accessible

Obstacles in the way

• Ontologies (consistent names for organisms, genes, and other kinds of data)

• How to store and query trees (what kind of queries do we want?)

• Summarising information in trees (supertrees) and matrices (supermatrices)

• Visualising very big trees

Page 5: GenBank  Huge amounts of data, easily accessible

Peruvian Diving-petrel(or, what’s in a name?)

• ITIS Pelecanoides garnotii

• NCBI Pelecanoides garnoti

• TreeBASE Pelecanoides garnoti AF076073

Page 6: GenBank  Huge amounts of data, easily accessible

TreeBASE Names Projecthttp://darwin.zoology.gla.ac.uk/~rpage/TreeBASE/

• Aim is to map every name in TreeBASE onto a valid taxonomic name (i.e., a name in a database, or in the literature)

• Use exact-, substring-, and approximate string matching (+ BLAST)

• So far 26819 out of 35084 names mapped

Page 7: GenBank  Huge amounts of data, easily accessible

Hemideina maori (weta)

18 TreeBASE names = 1 real name

Page 8: GenBank  Huge amounts of data, easily accessible

3 TreeBASE names = 1 real name

catodon

catadon

macrocephalus

Physeter catodon (Sperm Whale)

Page 9: GenBank  Huge amounts of data, easily accessible

The case of the Harp sealTreeBASE and GenBank have harp seals under two different names, only ITIS knows that they are the same thing

Page 10: GenBank  Huge amounts of data, easily accessible
Page 11: GenBank  Huge amounts of data, easily accessible

• There are known knowns, things we know that we know

• There are known unknowns, things we now know we don’t know

• But there are also unknown unknowns, things we do not know we don't know

Page 12: GenBank  Huge amounts of data, easily accessible

Why taxonomy matters

(or vs. )

Page 13: GenBank  Huge amounts of data, easily accessible

Searching on “Aves” in TreeBASEfinds 4 studies with birds…

• Study #1: Gauthier, J., A.G. Kluge, and T. Rowe. 1988. Amniote phylogeny and the importance of fossils.

• Study #2: Harshman, J., C. J. Huddleston, J. P. Bollback, T. J. Parsons, and M. J. Braun. 2003 inpress. True and False Gavials: A Nuclear Gene Phylogeny of Crocodylia.

• Hedges, S. B., K. D. Moberg, and L. R. Maxson.1990. Tetrapod phylogeny inferred from 18s and 28s ribosomal RNA sequences and a review of the evidence for amniote relationships.

• van Dijk, M. A. M., E. Paradis, F. Catzeflis, and W. de Jong. 1999. The virtues of gaps: Xenarthran (Edentate) monophyly supported by a unique deletion in alphaA-crystallin.

Page 14: GenBank  Huge amounts of data, easily accessible

…but there are other birds in TreeBASE!

Page 15: GenBank  Huge amounts of data, easily accessible

Tree space in TreeBASE (overlap = 1)

Page 16: GenBank  Huge amounts of data, easily accessible

There are 24 bird studies in TreeBASE, but “tree surfing” won’t find them

Page 17: GenBank  Huge amounts of data, easily accessible

Fig. 1. The `data availability matrix' for green plant protein sequences from GenBank (release 132). A set of 130304 sequences for 14667 species sequences were clustered into 61117 groups of homologous proteins by a combination of BLAST and single-linkage clustering (using the program Blastclust from the NCBI Blast toolkit: http://www.ncbi.nlm.nih.gov/BLAST/ ). A column represents a protein or protein family; a row represents one of the species in the dataset; and a dot indicates the existence of a sequence for that species and protein. Species are sorted vertically by their number of sequences; the most-represented species ( Arabidopsis thaliana ) is at the top. Proteins are sorted horizontally by the number of taxa for which they have been sequenced; the most heavily sequenced gene ( rbcL ) is on the right. This figure shows the most heavily sampled corner of the data availability matrix; the remainder of the matrix is even more sparse.

rbcL

Arabidopsis

Page 18: GenBank  Huge amounts of data, easily accessible

Seeing the tree (best seen when printed on 1.5 m wide paper…)

Page 19: GenBank  Huge amounts of data, easily accessible

http://darwin.zoology.gla.ac.uk/~rpage/MyToL/www

Page 20: GenBank  Huge amounts of data, easily accessible

Demo 1

QuickTime™ and aBMP decompressor

are needed to see this picture.

Page 21: GenBank  Huge amounts of data, easily accessible

Demo 2

Page 22: GenBank  Huge amounts of data, easily accessible

Comparing classificationsfor Psocoptera

NCBI (GenBank)9 species

Lienhard & Smithers (2002)[courtesy of Kevin Johnson]

4363 species

Page 23: GenBank  Huge amounts of data, easily accessible
Page 24: GenBank  Huge amounts of data, easily accessible

www.biomoby.orgwww.gmod.org

Bioinformatics envy - GenBank should NOT be our role model

Page 25: GenBank  Huge amounts of data, easily accessible

From journal to database…

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Problem: not enough data and trees in journals make it into databases

Page 26: GenBank  Huge amounts of data, easily accessible

Elsevier’s journal Molecular Phylogenetics and Evolution is a

criminal waste of our efforts

Text, data, trees locked up in paper and PDF

Page 27: GenBank  Huge amounts of data, easily accessible

… the database is the journal

1. Data + trees go into database

2. Text (annotation) added

3. Automatically generate a report summarising the results

4. The report is the publication (can have a DOI)

5. Open Access data and text

“Oh, the vision thing” George Bush (snr), 1987