Friedberg lab-overview-grad-students-2019-nr

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

About me
●
2003: PhD Hebrew University, Jerusalem
●
2003-2007: Postdoc, Burnham Institute, CA
●
2007-2009: Researcher, UC San Diego
●
2009-2015: Assistant Professor, Miami
University, Ohio
●
2015- Associate Professor, Iowa State
University

Friedberg Lab Members
Naihui Zhou
Nafiz Hamid
Huy Nguyen
Parnal Joshi

Lab philosophy
●
Ask biological questions that have computational answers
●
You may answer something else. That's great.
●
There's treasure everywhere.

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

Reconstructing the Ancestry of
Orthobolocks

A conserved orthoblock

An unconserved orthoblock:
phenylacetate degradation
Martin and McInerney BMC Evolutionary Biology volume 9:36
(2009)

An unconserved orthoblock
Phenylacetate degradation:
use it or lose it

The 3D Chromosome and Gene Expression

Motivation
The genome is organized into different organizational structures.
These structures play important role in gene regulation, manifested as expression values

Motivation

HiC looks at proximal pairs of DNA loci
using chrosslinking and sequencing

Initial pipeline

Hierarchical Markov Random Field
Model

Antibiotic resistance
WHO (2014): “A problem so serious that it threatens the achievements
of modern medicine... A post-antibiotic era, in which common infections and
minor injuries can kill, far from being an apocalyptic fantasy,
is instead a very real possibility for the 21st century.”

Example - Image classification
27
Deep
learning
algorithm
learning to
classify
images
Imagenet (huge dataset)
Separate but relevant task
Smaller different dataset -
of hotdogs and not-hotdogs

Natural Language Processing
28
English wikipedia
LSTM
language
model
Sentiment classification
task
Smaller different dataset
(Sentiment classification task)

Antibiotic resistance classification
29
UniRef50 bacteria
protein sequences
(734948 sequences)
LSTM
protein
sequence
language
model
Classify protein
sequences into 14
classes of antibiotic
resistance
Classification on COALA 0.4 cdhit dataset

Neural Network Architecture
30

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
?

CAFA Timeline
p re pa rati on
p redi cti on
annotati ong rowt h assessment
Participants
Organizers
t i me
Sep. 2013
J an.2014
Sep.2014 M ar .2015
Launch
C A FA 2
Cl ose
submi ssi on
Rel ease
results
t0 t1

Assessment
DNA binding
True function
Predicted function

Nucleic Acid binding
DNA binding
Binding
Assessment
True function
Predicted function

Nucleic Acid binding
DNA binding
Binding
rRNA binding
RNA binding
Assessment
True Positives : 2
False Positives: 2
False Negatives: 1
True function
Predicted function

Precision Recall
00 1
1
Recall
Precision
2/4
2/3

AUTHOR ZZZ
MODEL 1
KEYWORDS sequence alignment.
T96060020120 GO:0008270 0.80
T96060020120 GO:0003700 0.80
T96060020120 GO:0006351 0.80
T96060020119 GO:0005730 0.01
T96060020119 GO:0003676 0.07
T96060020119 GO:0005622 0.07
T96060020119 GO:0046872 0.07
T96060020118 GO:0008270 0.75
T96060020118 GO:0006351 0.68
T96060020118 GO:0003677 0.67
T96060020118 GO:0005634 0.67
T96060020118 GO:0006355 0.55
T96060020118 GO:0003700 0.34
Protein ID GO term Confidence

Precision Recall Curve
00 1
1
Recall
Precision
2/4
2/3
●
Results are calculated for each threshold of
prediction confidence

Molecular Function
Benchmark: all species Type: no knowledge Mode: full Metric: F-max

“Is we learning?”
Molecular Function

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

Friedberg lab-overview-grad-students-2019-nr

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