Friedberg lab-overview-grad-students

http://iddo-friedberg.net Twitter: @iddux
The Friedberg Lab
Bacterial genome evolution
Protein function prediction
Metagenomics
Phenomics

About me
● 2003: PhD Hebrew University, Jerusalem
● 2003-2006: Postdoc, Burnham Institute, CA
● 2006-2009: Researcher, UC San Diego
● 2009-2015: Assistant Professor, Miami
University, Ohio
● 2015- Associate Professor, Iowa State
University

Friedberg Lab Members
Naihui Zhou
Nafiz Hamid
Xiao Hu
Ataur Katebi
Huy Nguyen

Lab philosophy
● Ask biological questions that have computational answers
● You may answer something else. That's great.
● There's treasure everywhere. But no data dredging

Starting question:
how do complex
structures evolve?

What is an operon?
● Operons are (almost) unique to Prokaryotes.
Transcription
polycistronic
mRNA
Translation
Gene 1 Gene 2 Gene 3Regulation

Gene Blocks
Transcription
mRNA
transcripts
Translation
Gene 1 Gene 2 Gene 3
● Gene blocks are any suspected syntenic group of open
reading frames (ORFs) which have a maximum allowed
spacing. For my research this maximum is 500 nt.

Background
● Operons are an important feature in
prokaryotic genetics.
– Often contain full metabolic pathways.
● a set of chemical processes transforming
one compound into another.
– Regulate groups of genes.
– Allow for the frequent transfer of gene blocks
between organisms.
● Therefore, studying operon evolution helps us
to understand metabolic pathway formation.

How we model changes in gene
blocks
● We borrow ideas from
sequence evolution, but
genes are the atom of
change.
– Changes are called
events.
– There are more possible
events modeling gene
block evolution than in
biological sequence
evolution.
5' ATCCGA 3'
ATCCGT ATC-GA

Events investigated
●Deletions
●Duplications
● Splits
Gaps exceeding
100 kbp are common

Results: Normalized interspecies event rate

Results: Normalized interspecies event rate
We asked: how do complex structures evolve?
We answered: the relationship between conservation and function.

Ancestry of Orthobolocks
1
2
3
4
5
A B C D E
A B C D E
A B C D E
A B C D E
A B C D ED
A B C D EA B C D E

Species tree shows ancestry
1
2
3
4
5
A B C D E
A B C D E
A B C D E
A B C D E
A B C D ED
A B C D EA B C D E

Ancestral resolution
1
2
3
4
5
A B C D E
A B C D E
A B C D E
A B C D E
A B C D E
A B C D E
A B C D ED
A B C D EA B C D E
A B C D ED
A B C D ED

Science Olympics:
Timed Challenges

Why CAFA?
“On the one hand, we have enormous “protein” databases that are replete with
errors, wishful thinking, phantoms, and uncertainties. On the other, we have a
tiny fraction of real proteins that have been studied in any depth.”
–- Dan Graur
Biggest problem in molecular
biology: < $1,000 genome,
BUT:
$20,000- >$10,000,000
annotation.

CAFA
● The Critical Assessment of Function Annotation
● Hundreds of scientists trying to predict protein
function from sequence
● A friendly competition between scientific teams

The Protein function prediction problem
>sp|P04637|P53_HUMAN
MEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTED
PGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFL
HSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTE
VVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSD
CTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTE
EENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFR
ELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD
Cell differentiation
Apoptosis
Biological process
?

Apoptosis
Biological process
Cell differentiation
Biological process
PREDICTED: TRUE:
MEASURING FUNCTIONAL SIMILARITY
Precision: Recall:

CAFA1 vs. CAFA2
CAFA 1 (2011) CAFA2 (2014)
Methods 54 129
Groups 29 57
Targets 50,000 108,000
Benchmarks ~840 3,681
Target types No knowledge No knowledge, partial knowledge
Ontologies MFO, BPO MFO,BPO,CCO,HPO
Target set choice Full mode Full mode, partial (>5000) mode
Assessment Fmax Fmax, Smin

Molecular Function, no-knowledge, full set

Biological Process, no-knowledge, full set

Cellular Component, no-knowledge, full set

Cytosol: the “cellular default”

Human Phenotype Ontology
Many terms (>80) per protein. Some easily predicted,
Others not predicted at all.

“Is we learning?”
Molecular Function

“Is we learning?”
Biological Process

CAFA Team
Predrag Radivojac
Casey Greene
Sean Mooney
Maria Martin
Claire O'Donovan

Metagenomics

Bacteria naturally live...

...everywhere but...

...as communities

...metagenomic revolution
6,000,000 genes
>6,000,000,000 BP
~900 ribotypes
3,000,000 genes
160,000,000 BP
>1,100 ribotypes

What can we learn and how?

Babies are a Good Model for
Humans (With Texas A&M)

Microbial community
(metagenome)
Gut epithelial cells
(transcriptome)
454 sequencing
Who? (Phylogenetic analysis)
What? (functional analysis)
Clean & RT
Codelink chip

Microbial community
(metagenome)
Gut epithelial cells
(transcriptome)

Altered Schaedler Flora
• Bacteria originate from mice
– Unique niche
• Stable GI community
• All cultivable in anaerobic chamber
• Advantages
– Representative community
• Animals are healthy/normal growth
– Specific host immune responses
• Antibodies
• T-cell recall
– Evaluate entire GI microbiota
• Quantitative changes
• Spatial redistribution
• Community gene expression
• Mutation identification

ASF
The Altered Schaedler Flora (ASF)
Greg Phillips & Michael Wannenmueller
Wannemuehler, et al. 2014. Genome Announc. 2.
1
No Proteobacteria in ASF *Specific Pathogen Free
ASF
*1
Table 1: Genome sequencing results of the ASF community
ASF
#
Taxonomy Genome
Size(Mb)
%GC Gene
count
Contig
count
N50
(Kb)
Fold
Coverage
GenbankAccession#
ASF356 Clostridiumbacterium 2.91 30.91 2799 31 209 143 AQFQ00000000.1
ASF360 Lactobacillusintestinalis 2.01 35.86 1930 244 19 47 AQFR00000000.1
ASF361 Lactobacillusmurinus 2.17 39.96 2102 78 59.7 160 AQFS00000000.1
ASF457 Mucispirillumschaedleri 2.33 31.15 2144 39 151 142 AYGZ00000000.1
ASF492 Eubacteriumplexicaudatum 6.51 42.86 6217 248 74.4 119 AQFT00000000.1
ASF500 Pseudoflavonifractor sp. 3.70 58.77 3563 42 300 137 AYJP00000000.1
ASF502 Clostridiumbacterium 6.48 47.90 6062 134 137 82 AQFU00000000.1
ASF519 Parabacteroidesgoldsteinii 6.87 43.45 5477 24 584 143 AQFV00000000.1

Mark Lyte's Lab: Microbial Endocrinology

Friedberg lab-overview-grad-students

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