Friedberg lab-overview-grad-students

http://iddo-friedberg.net Twitter: @iddux

The Friedberg Lab

Bacterial genome evolutionProtein function prediction

MetagenomicsPhenomics


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 EA 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_HUMANMEEPQSDPSVEPPLSQETFSDLWKLLPENNVLSPLPSQAMDDLMLSPDDIEQWFTEDPGPDEAPRMPEAAPPVAPAPAAPTPAAPAPAPSWPLSSSVPSQKTYQGSYGFRLGFLHSGTAKSVTCTYSPALNKMFCQLAKTCPVQLWVDSTPPPGTRVRAMAIYKQSQHMTEVVRRCPHHERCSDSDGLAPPQHLIRVEGNLRVEYLDDRNTFRHSVVVPYEPPEVGSDCTTIHYNYMCNSSCMGGMNRRPILTIITLEDSSGNLLGRNSFEVRVCACPGRDRRTEEENLRKKGEPHHELPPGSTKRALPNNTSSSPQPKKKPLDGEYFTLQIRGRERFEMFRELNEALELKDAQAGKEPGGSRAHSSHLKSKKGQSTSRHKKLMFKTEGPDSD

Cell differentiation

Apoptosis

Biological process

?

Apoptosis

Biological process

Cell differentiation

Biological process

PREDICTED: TRUE:

MEASURING FUNCTIONAL SIMILARITY

Precision: Recall:


CAFA1 vs. CAFA2CAFA 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

http://iddo-friedberg.net Twitter: @idduxCytosol: 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 genes160,000,000 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.

1No 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

Genbank Accession #

ASF356 Clostridium bacterium 2.91 30.91 2799 31 209 143 AQFQ00000000.1 ASF360 Lactobacillus intestinalis 2.01 35.86 1930 244 19 47 AQFR00000000.1 ASF361 Lactobacillus murinus 2.17 39.96 2102 78 59.7 160 AQFS00000000.1 ASF457 Mucispirillum schaedleri 2.33 31.15 2144 39 151 142 AYGZ00000000.1 ASF492 Eubacterium plexicaudatum 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 Clostridium bacterium 6.48 47.90 6062 134 137 82 AQFU00000000.1 ASF519 Parabacteroides goldsteinii 6.87 43.45 5477 24 584 143 AQFV00000000.1

Mark Lyte's Lab: Microbial Endocrinology

Questions?