associating biomedical terms: case study for acetylation aaron buechlein indiana university school...

Associating Biomedical Terms:Case Study for Acetylation

Aaron BuechleinIndiana University School of InformaticsAdvisor: Dr. Predrag Radivojac

Overview• Background

• Previous Work

• Methods

• Results

Central Dogma

Background

Previous Work

Methods

Results

http://www.accessexcellence.org/RC/VL/GG/images/central.gif

Post-Translational Modifications (PTMs)

Background

Previous Work

Methods

Results

Acetylation

• Acetylation involves the substitution of an acetyl group (-COCH3) for hydrogen

• Typically occurs on N-terminal tails and lysine residues (Lys or K)

Background

Previous Work

Methods

Results

Previous Predictors

• Several PTM predictors have been created prior to this work

• There are also acetylation predictors prior

• NetAcet is a predictor for only N-terminal sites• AutoMotif Server is a predictor for various PTMs and

includes an acetylation portion• PAIL is a lysine acetylation predictor

Background

Previous Work

Methods

Results

Methods

• Create Dataset

• Download articles relevant to acetylation and extract sites

• Rank articles in order to elucidate sites quickly• SwissProt and Human Protein Reference Database

(HPRD)

• Create Predictors

• Leave – one – protein – out validation• Matlab

Background

Previous Work

Methods

Results

Article Retrieval

• Searched individual journal sites for articles relevant to acetylation

• Saved resultant html pages for each journal

• These pages were then used as the input for a web crawler to download articles

• Due to varying journal site construction each journal required a unique regular expression to extract links for articles

Background

Previous Work

Methods

Results

Rank Articles

• First locate occurrences of first phrase: “phrase 1”

• A = {a1, a2, …, a|A |}

• Next locate occurrences of second phrase: “phrase 2”

• R = {r1, r2…, r|R|}

•

• c and d are constants• x is the distance in characters between r and the nearest

word a

Background

Previous Work

Methods

Results

An example: acetylation

Background

Previous Work

Methods

Results 1. word “acetylat”

A = {a1, a2, …, am}

2. regular expression

(k lys lysine)(space)*(digit)+

R = {r1, r2, …, rn}

An example: acetylation

Background

Previous Work

Methods

Results

n

i i ArscoreS1

),(

Score for article S:

and

where

An example: acetylation

n

i i ArscoreS1

),(

|))()((|),( kii apositionrpositionfArscore

|)()(|minarg ...1 jimj apositionrpositionk

Score for article S:

where:

and

Papers with S > 100 are rich in sites; if S < 30 “twilight” zone

Background

Previous Work

Methods

Results

0 100 200 300 400 500 600 700 800 900 10000

1

2

3

4

5

6

7

8

9

10

Distance in characters

f(x)

xexf 005.010)(

Elucidate Sites

• Sites were manually extracted from articles beginning with the highest rank

• The original experimental paper for these sites was verified for traceable evidence

• Sites were extracted from SwissProt

• Sites were extracted from HPRD

Background

Previous Work

Methods

Results

Predictors

• Support Vector Machine

• Artificial Neural Network

• Decision Tree

Background

Previous Work

Methods

Results

Predictor Input

• Positives taken as all lysines found to be acetylated

• Negatives taken as all lysines not found to be acetylated

• Features created based on characteristics surrounding lysines

• Amino acid content, hydrophobicity, charge, disorder, etc.

Background

Previous Work

Methods

Results

Predictor Input

Background

Previous Work

Methods

Results

Protein Features Acetylated

1 8 1 0.48609 0.001767 0.48979 0.51508 1

1 7 1 0.92146 0.03019 0.96423 0.79416 1

1 0 0 0.50622 0.015251 0.52335 0.51855 0

2 10 2 0.2008 0.038708 0.25441 0.36071 1

2 1 0 0.62016 0.009772 0.62846 0.67525 0

2 0 0 0.27783 0.028957 0.32162 0.34207 0

3 11 1 0.89239 0.018354 0.91884 0.88125 1

3 12 2 0.87354 0.022307 0.90349 0.87446 1

3 8 1 0.81549 0.025339 0.85289 0.85702 1

3 2 0 0.84588 0.024766 0.88219 0.86599 0

Article and Ranking Results

• 4888 articles from 10 sites were searched• Nature provided 2147 articles• Science Direct provided1519 articles

• The highest ranking article was obtained from the Journal of Biological Chemistry• Score of 151.87 • Contained 10 acetylation sites

• The highest ranking article was obtained from Nature when histones are excluded• Previously ranked at #5• score of 116.36• Contained 9 unique acetylation sites

Background

Previous Work

Methods

Results

Top 25

Rank Score Sites Article Source1) 151.8667 10 Journal of Biological Chemistry2) 123.2314 12 Cell / Science Direct3) 121.9031 6 Nature4) 117.7988 9 Journal of Proteome Research5) 116.3582 9 Nature6) 111.1745 14 Biochemistry7) 104.4652 6 Cell / Science Direct8) 104.0166 7 Nature9) 102.0683 13 Molecular Cell / Science Direct

10) 98.80812 6 Journal of Biological Chemistry11) 97.64634 6 Biochemistry12) 96.76536 6 Journal of Biological Chemistry13) 96.0845 9 International Journal of Mass Spectrometry / Science Direct14) 88.12967 9 Biochemistry15) 86.17157 6 Journal of Biological Chemistry16) 81.78705 5 Nucleic Acids Research17) 81.30967 6 Biochemistry18) 81.06128 6 Molecular Cell / Science Direct19) 80.74899 9 Journal of Biological Chemistry20) 80.16261 9 Nature21) 79.65658 6 Molecular Cell / Science Direct22) 77.9022 4 Cell / Science Direct23) 77.88304 5 Nucleic Acids Research24) 77.60087 8 Gene / Science Direct25) 77.44198 6 Journal of the American Society for Mass Spectrometry

Background

Previous Work

Methods

Results

Ranking Results

• Articles with scores greater than 30 had potential for providing at least one site

• As scores approached 30, articles became less fruitful

Background

Previous Work

Methods

Results

Dataset Results

• Dataset included 1442 total sites and 1085 non-redundant sites

• HPRD contributed 90 total sites• Swiss-Prot contributed 825• Our Study contributed 527

Background

Previous Work

Methods

Results

Background

Previous Work

Methods

Results

Dataset Results

Sensitivity, Specificity, and Precision

• Sensitivity(sn) -

• Specificity(sp) -

• Precision(pr) -

Background

Previous Work

Methods

Results

Accuracy and AUC

• Accuracy(acc) -

• Area Under Curve(AUC)• Refers to the area under the Receiver Operating Curve

(ROC)• ROC is the graphical plot of sensitivity vs. 1-specificity

Background

Previous Work

Methods

Results

SVM Predictor

DegreePolynomial kernel

sn sp pr acc AUC

p = 1 52.3 71.0 24.6 61.6 65.2

p = 2 46.1 69.8 20.3 57.9 62.8

p = 3 31.6 80.8 23.5 56.2 60.3

DegreeGaussian kernel

sn sp pr acc AUC

σ = 10-2 43.8 75.8 24.9 59.8 64.3

σ = 10-3 54.1 72.1 25.9 63.1 68.1

σ = 10-6 52.8 70.7 24.6 61.8 65.3

Background

Previous Work

Methods

Results

Artificial Neural Network

Hidden Neurons

Artificial Neural Network

sn sp pr acc AUC

1 68.0 47.7 20.7 57.8 61.9

3 65.2 47.7 19.4 56.4 58.9

5 65.0 47.2 19.1 56.1 57.5

Background

Previous Work

Methods

Results

Decision Tree

AlgorithmDecision Tree

sn sp pr acc AUC

Decision Tree 61.7 45.9 18.3 53.8 42.1

Background

Previous Work

Methods

Results

Algorithm Comparison

Algorithm sn sp pr acc AUC

SVM 54.1 72.1 25.9 63.1 68.1

Neural Network 68.0 47.7 20.7 57.8 61.9

Decision Tree 61.7 45.9 18.3 53.8 42.1

Background

Previous Work

Methods

Results

I would like to acknowledge those who have helped me throughout the duration of this project, Dr. Predrag Radivojac, Dr. Haixu Tang, and Wyatt Clark

I welcome your questions and/or comments

An example: acetylation

1. word “acetylat”

A = {a1, a2, …, am}

2. regular expression

(k lys lysine)(space)*(digit)+

R = {r1, r2, …, rn}

Background

Previous Work

Methods

Results

An example: acetylation

Background

Previous Work

Methods

Results

n

i i ArscoreS1

),(

Score for article S:

and

where