# Presentation: 525340

## Ronald Taylor  and Mudita Singhal
Computational Biology & Bioinformatics Group
Computational Sciences & Mathematics Division
Pacific Northwest National Laboratory (PNNL)
Richland, Washington

Email: ronald.taylor.@pnl.gov,  mudita.singhal@pnl.gov 

- Computational Biology & Bioinformatics Group
- Computational Sciences & Mathematics Division
- Pacific Northwest National Laboratory (PNNL)
- Richland, Washington
- Email: _[ronald.taylor.@pnl.gov](mailto:ronald.taylor.@pnl.gov)_,  _[mudita.singhal@pnl.gov](mailto:mudita.singhal@pnl.gov)_

**Network inference and analysis using   SEBINI - CABIN**
**NCI caBIG ICR meeting, 9/17/07**

## Topics

- Background on network inference problem
- Description of the Software Environment for Biological Network Inference (SEBINI) platform
- Description of analysis tool Collective Analysis of Biological Interaction Networks (CABIN)
- Description of use of SEBINI-CABIN in inference & refinement of protein-protein interaction networks for DOE GTL project

## Network = set of nodes + a set of directed or undirected edges between the nodes

## Types of networks (I)

- transcriptional regulatory network
- genes are the nodes, edges are directed
- input: set of microarray runs measuring mRNA expression
- protein-protein interaction network
- proteins are the nodes, edges are undirected
- input: set of mass spec bait-prey experiments

## Types of networks (II)

- signal transduction network
- proteins are the nodes, edges are directed
- input: set of experiments measuring protein activation (e.g., phosphorylation) /inactivation
- interspecies or intraspecies communications network in microbial communities
- nodes are cells of excreted organic compounds, edges are directed
- input: set of experiments measuring levels of excreted molecules

## Methods used to provide data useful in the inference of regulatory networks 

** **Prediction of transcription factor (TF) binding sites – e.g., Dr. Lee Ann McCue’s work at PNNL, also MotifMogul software at ISB.
-  Public and commercial databases (for example: TRANSFAC, SCPD), gradually collecting wet lab experiments that identify TFs and their binding sites, one by one.
-  Tiling arrays - CHiP-chip, yielding binding sites for TFs
-  Other new technologies to find binding sites, their binding TFs, and interactions between binding sites (eg, ChIA- PET)

## More methods for finding networks 

-  Projects to find protein-protein interactions – e.g., PNNL/ORNL GTL mass spec bait-prey project.
** **Computational algorithms that infer regulatory edges based (primarily) on correlations in state – the principal algorithms used in SEBINI. Require a large amount of array or protein expression data. More powerful algorithms/models are needed to infer specific gene-to-gene connections than the typical statistical techniques used for clustering.

## Methods for inference of regulatory networks  - problems (I)

- TFBS prediction based on sequence and phylogenetic comparison is very hard. Tiling arrays are expensive – we test one putative source TF at a time.
- Fundamental problems to both, and other latest technologies that identify binding sites:
-  Target prediction is dependent on proximity (nearness) of TFBS to gene to infer target. In eukaryotes, ~70% of TFs bind far from their targets (A. Aderem).
-   Some proteins directly regulate but do not bind to DNA. They bind or activate a protein(s) that then binds to DNA. Thus - only can seen through statistical inference.

## Methods used for inferring  regulatory networks  - problems (II)

-  Neither TFBS prediction nor tiling yields regulation type (activator / inhibitor), just that there is binding.
-  It is common in bacteria for a TF to lie between genes transcribed in different directions. May regulate one or both - which choice is unknown from tiling and TFBS prediction.
-  Computational methods based on correlations in state require large data sets - many array runs. But array runs (or their new tech equivalent) will get cheaper.

## Conclusion

- Bonus for framework for use of such algorithms such as SEBINI: building a repository of the high-throughput data used by such algorithms to infer regulatory edges provides the organized data sets for investigation of edge motifs and state functions governing the state of target genes – topics for future research.

## Possible sources of inference algorithms

** **Probabilistic graphical models (structure learning Bayesian networks, among others)
-  Information theory (mutual information based, and conditional mutual info)
-  Classical statistics, analysis of correlation - e.g., Pearson correlation
-  Machine learning – decision trees (C4.5, ID3), supervised and unsupervised
-  Data mining – association rule mining
-  Pattern classification
-  Deductive reasoning / inductive logic programming
-  Neural networks
-  Fuzzy logic?

## We use correlation to infer causation (regulation)

- Such patterns of partial correlations, possibly combined with information on the genes in the proposed network from other data sources (functional info, membership in regulon, etc) are the basis upon which the algorithms in SEBINI’s toolkit infer regulatory networks.

## Sampling of algorithms publicly available

-  From information theory - based on use of mutual information (MI)
-  CLR - Context Likelihood of Relatedness - Tim Gardner’s lab at Boston U. - uses locally-based scoring relative to all MI scores for each node in a proposed regulatory edge. http://gardnerlab.bu.edu.
-  ARACNE - Andrea Califano’s group at Columbia U. - uses Data Processing Inequality (DPI) for postprocessing, and bootstrapping. http://amdec-bioinfo.cu-genome.org/html/caWorkbench/upload/
- From probabilistic graphical models - structure learning Bayesian networks
- BANJO - Bayesian Network Inference with Java Objects - Alex Hartemink’s lab at Duke U. http://www.cs.duke.edu/~amink/software/banjo

## Why Mutual Information?

- Why not  use Euclidean distance or Pearson for pair-wise calculations, as is done in gene clustering?
- Answer: unlike Euclidean and Pearson, MI does not assume that the relationship between the genes is linear. A major advantage of information theory is its nonparametric nature. Entropy does not require any assumptions about the distribution of variables. MI provides a general measurement for dependencies in the data, in particular positive, negative, and nonlinear correlations.
- “MI is arguably the best measure of statistical correlation in a non-linear setting” - “Reverse engineering cellular networks” (ARACNE paper), A. Margolin et al., Nature Protocols, 1:2, 2006.
- Also see: I Priness et al, “Evaluation of gene-expression clustering via mutual information distance measure”, BMC Bioinformatics, March 2007.

## What MI and structure learning BN do not yield

- We are not doing dynamic modeling. We are finding a network structure - a static “wiring diagram, which is a necessary starting point for dynamic modeling.
- We are not determining the state function that governs the state of a target. That is, we are not determining how inputs are combined to yield the new target state. We might later attempt this using the state values used here for finding structure, fitting the states to possible Boolean logic gate equations on the inferred structure, or fit to ODEs, but the inference algorithms discussed here are not designed to do this in and of themselves.

## The short introduction: SEBINI provides an Open Source platform that aids in more accurate reconstruction of the topology of regulatory and interaction  networks, with much less effort, in less time.

- Software Environment for BIological Network Inference
- (SEBINI)

- SEBINI has been created to provide an interactive environment for the evaluation and deployment  of algorithms used in the reconstruction of the structure of  biological regulatory and interaction networks.
- It is dual-purpose. SEBINI compares and trains network inference methods on artificial networks and simulated node level (e.g., gene expression) perturbation data. The inference algorithms are also used on experimental data uploaded into SEBINI.

## Slide 18

## Slide 19

## SEBINI

- A suite of programs operates on a central relational database. The user interface is web-based, operated by Java servlets.
- A collection of inference algorithms is provided (mutual information based, Bayesian network structure learning, classical Pearson correlation, etc) and an API has been created to allow addition of other inference methods in a (fairly) well-defined manner.

## Software Environment for BIological Network Inference (SEBINI)

- Central relational
- database (PostgreSQL)

- Input Module
- High-throughput
- experimental data

- PNNL’s Bioinformatics
- Resource Manager (BRM)

- Builder Module
- Simulated high-throughput
- expression data for
- artificial networks

- User interface – web site
- operated by Java servlets

- Topological statistics, network
- annotation, post-inference processing;
- scoring & error analysis (on artificial
- data sets)

- Visualization of inferred networks
- via Cytoscape w/ CABIN analysis

- Collection of network inference algorithms. User selects algorithm and data set, runs alg to infer a network (a set of edges).
- Mutual information-based and Bayesian network structure learning algorithms provided for  learning regulatory networks.
- Also: PNNL/ORNL algorithm for learning protein-protein interaction networks from PNNL/ORNL bait-prey
-  experiment mass spec data sets. Inferred networks permanently stored back into database.

**Software Environment for BIological Network Inference (SEBINI)**

- Text files
- (flat files)

- PNNL’s PRISM
- database system

- Human-readable reports on
- inferred networks

- Machine-readable network structure files
- for dynamic modeling programs

## Slide 22

## SEBINI architecture & implementation (I)

-   ~130 Java programs (classes) and growing rapidly.
-  All inter-servlet communication is routed through a CentralControl class. This environment is not a spider web – there is a control chokepoint. where all servlet communication can be monitored.
- Algorithm handlers are called directly from the Java servlets for the corresponding web pages.
- ~40 PostgreSQL database tables. Slowly growing; stable at present.
-  Data security – project based. Upon login, the user is assigned a 32 digit hex digit JSessionID, which is checked before display of every web page.

## SEBINI architecture & implementation (II)

-   While the SEBINI was originally designed to infer directed (regulatory) networks, the code now allows undirected networks, so algorithms that infer interaction networks can be used and such networks (e.g., protein-protein interaction networks) permanently stored and analyzed.
-  Design issues: interface for user navigation among huge data sets, database design to map inferred networks and inferred edges back to original network and expression data – IDs must be carried forward.
- expression data  one-to-many via binning alg choice  binned exp data  one-to-many via inference alg choice  inferred_network

## SEBINI architecture & implementation (III)

-  Design issues (continued): multi-threaded;  job monitoring; web pages to view all data points, working towards ***transparency*** of all data in all tables (security access permitting) in the database via display through the web site.
-  Permanent storage of binned / processed data sets. Novel. Important for efficiency, transparency, speed of response, analysis of results.
-  Job times recorded to millisec. Algorithms can be compared on efficiency vs relative power.
-  A Java handler class is created for each new algorithm, to “wrap it”, ie, to handle communication with the database and web site.

## SEBINI architecture & implementation (IV)

-   SEBINI was initially implemented on a Dell desktop running Red Hat Linux, using Java ver. 1.4, PostgreSQL ver. 7.4, and Tomcat 4.1. Cytoscape is used for network visualization, invoked through Java Web Start. CABIN is then launched from Cytoscape.
-  SEBINI  has also been installed on a Windows web server that is accessible from outside the PNNL, using Java ver. 1.5, Tomcat ver. 5, and PostgreSQL 8.1. Jakarta Commons Java libraries are used for data file uploads [Jakarta]. Also, installed on Mac laptop.
-  Machine-specific parameters are stored in an easily changed properties text file.
-  public demo site: https://www.emsl.pnl.gov/SEBINI/

## SEBINI flow of control, for experimental data

** **Log into a project, or create a new project
-  Create a network set (container) for the one experimental network
-  Upload the experimental expression data file
-  Select a processing or binning algorithm and run on the data
-  Select an inference algorithm, select the alg parameters, and infer a network.
-  Visualize the inferred network, now in the database, using Cytoscape
-  View topological statistics
-  View node and edge annotations
-  Generate a human-readable report
-  Export the topology in a format suitable for input into dynamic simulations

## Support for algorithm development = artificial data sets

- Examples:
-  Static gene expression data sets - from Pedro Mendes’ lab at VBI
- Dynamic generation of Boolean value data sets within SEBINI
- SynTReN - from Kathleen Marchal’s group in Belgium. Interaction kinetics modeled on Michaelis-Menten and Hill kinetics. Being added into SEBINI. Flexible in topology, etc, but does not do time series. (Tim Van den Bulcke et al., BMC Bioinformatics, Jan 2006.)

- Artificial topologies and corresponding simulated gene expression data sets allow an
- Inference algorithm to be precisely scored against a known network - precision, recall, F-measure.

## SEBINI flow of control, for synthetic data

** **Log into a project, or create a new project
-  Select a topology build algorithm, enter param values, and create a synthetic network set
-  Select an expression set build algorithm, enter param values, and create synthetic expression sets
-  Select a processing or binning algorithm and run the data
-  Select an inference algorithm, enter param values, and infer network
-  Visualize and compare the real and inferred network(s),  using Cytoscape. Conduct further analysis using CABIN.
-  View  topological statistics
-  View precision, recall, F-measure statistics for precise measure of how well the inference alg performed against the “gold standard”, the known synthetic network.

## Another project using SEBINI-CABIN

## SEBINI-CABIN based mass spec bait-prey data analysis pipeline

- identify the PNNL PRISM bait-prey peptide/protein data set desired, using LIMS db, or identify ORNL text file desired
- upload the selected data set into SEBINI
- process the data set, doing a peptide-to-protein collapse within SEBINI
- invoke the BEPro algorithm to return those prey believed to truly associate with baits
- store bait-prey edges into SEBINI as a protein-protein interaction network
- visualize in Cytoscape or as node-and-edge table
- use CABIN for network comparison and analysis against other bioinformatics sources.

## Bayesian Estimator of Protein-Protein Association Probabilities (BEPro) algorithm

- Bait protein selects for binding partners that have high affinity (“prey”) for the bait.
- In our pull-down assays, the cell lysate is treated with a peptidase such as trypsin and the resulting peptides are identified by liquid chromatography (LC) followed by tandem mass spectrometry (MS/MS).
- Protein associations are then inferred from the list of proteins (more specifically, from the peptide evidence assigned to each protein by SEQUEST).
- Several sources of error can lead to false prey identification.
- BEPro is designed to look at the evidence across the entire set of assays to find the true bait-prey interactions.

## BEPro algorithm (II)

- SEBINI performs peptide-to-protein collapse (simplest way: use # of peptides reported as peptide evidence as single numeric value for protein)
- After collapse, we have prey-by-bait frequency matrix summarizing the freq each prey is observed with each bait across experiment set.
-  BEPro alg is invoked, with the matrix passed in by the SEBINI handler.

## Slide 40

## BEPro algorithm (III)

- BEPro evaluates the uniformity of the freq distribution for each prey across the set of pulldowns and prey specificity is assigned using a likelihood ratio test (LRT).
- BEPro uses Monte Carlo simulation to assign conservative parameter estimates for LRT significance tests and to simulate sampling distributions to estimate LRT p-values.
- BEPro then estimates the level of association between each bait-prey pair in the freq matrix by calculating its posterior Bayes Odds of association from it observed freq and prior prob estimates that depend upon the prey’s specificity.
- Finally, BEPro classifies each bait-prey pair as associated (interacting) or not, by comparing the pair’s Bayes Odds score with a user-chosen cutoff value.

## BEPro algorithm (IV)

- BEPro is available separately as Matlab code with a Java wrapper graphical interface from
-          _[http://www.pnl.gov/statistics/BEPro3/index.htm](http://www.pnl.gov/statistics/BEPro3/index.htm)_
- Contact: Don Daly at PNNL (statistician; developed the Matlab code)
- Paper submitted to J. of Proteome Research: J. L. Sharp, K. K. Anderson, D. S. Daly, D. L. Auberry, W. R. Cannon, A. M. White, and V. Kery. “Inferring protein-protein associations with pull-down LC-MS assay experiments”.
- Paper accepted: Dec 2007 Workshop on Machine Learning in Biomedicine and Bioinformatics of the Sixth International Conference on Machine Learning and Applications (ICMLA ‘07): R. C. Taylor, M. Singhal, et al. “SEBINI-CABIN: an analysis pipeline for biological network inference, with a case study in protein-protein interaction network reconstruction”.

## BEPro algorithm (V)

- Parameter settings for sample run on Rhodopseudomonas palustris (R. palustris)
- Estimate of the max number of prey proteins that can be observed with the given analysis method (1,700)
- Estimate of the total number of proteins in the R. palustris proteome (4,000)
- The protein association score threshold - set to 0, so that any positive value indicates “present”
- The number of Monte Carlo simulations to perform (50,000)
- The max false positive rate, used to control the False Discovery Rate (0.05)
- The posterior probability of protein-protein association threshold, i.e., the cutoff applied to the final association value to determine whether there is an interaction (a network edge to store in SEBINI) - set to 0.05.
- RESULTS: using these param values, the BEPro specific LRT-Bayes algorithm returned **3,370 edges** to store as the interaction network from a set of 854 SEQUEST runs from the ORNL/PNNL project.

## Slide 44

## Slide 45

## Slide 46

## Slide 47

## Slide 48

## Slide 49

## Slide 50

## Slide 51

## Slide 52

## Slide 53

## Slide 54

## Slide 55

## Talking points on SEBINI (I)

- concept of a “network set”  storage of networks at  multiple levels of the cell  start of analysis framework for connecting levels
- one project = one db  and “pg_dump <database>”
- – extremely important for (a) import/export/backup, (b) easy to “freeze” and create and update “warehouse” for analysis
- “one-off” alg implementation replaced in SEBINI by permanent knowledge on how to productively employ the algorithms (parameters used stored, runs recorded  in job table)

## Talking points on SEBINI (II)

- full system – registration, project creation and control , deletion/removal, security, encryption, 60,000+ lines of code, massive data sets (navigation via primary keys for speed)
- algorithms can be anything that operate on nodes (e.g., node annotation via retrieval from public databases or via literature search)
- permanent storage allows post-processing and annotation. E.g., topology-based edge additions to existing core subnet using less stringent cutoffs

## Some goals for SEBINI

-  Make network inference a starting point, not an end point (currently an end point that is usually not even reached) Simple deployment of state-of-the-art algs not previously available to a biology lab, available over the web or via local SEBINI install.
-  Advance the field by improving the algorithms. Possibility of combining alg output (as done in GRAIL), now that alg results are stored in same database. Develop expertise on how much data is needed, appropriate cutoffs, species-specific post-processing, the weaknesses of a given method, what background information on a genome is most useful to supplement the primary expression data.

## SEBINI Acknowledgements

- For work on SEBINI: Anuj Shah for work on Cytoscape, MI alg translation,
- statistics; Meridith Blevins (SULI); Charles Treatman (SULI)
- Funding: SEBINI was initially developed under the LDRD (Lab Directed
- Research and Development) Program at the Pacific Northwest National
- Laboratory, a multiprogram national laboratory operated by Battelle for the U.S.
- Department of Energy under Contract DE-AC06-76RL01830. Current funding
- comes from the joint PNNL/ORNL protein-protein interaction network mapping
- GTL project (Genomics:GTL Center for Molecular and Cellular Systems), and the
- EMSL Membrane Grand Challenge at PNNL

- PUBLIC DEMO WEB SITE:
- https://www.emsl.pnl.gov/SEBINI/

## 

CABIN: Collective Analysis of Biological Interaction Networks

Mudita Singhal
caBIG f2f meeting
September 17, 2007

**CABIN: Collective Analysis of Biological Interaction Networks**
**Mudita Singhal**
**caBIG f2f meeting**
**September 17, 2007**

## Motivation

- Validation of experimentally observed interactions
- Current method: checking interactions in public datasources like Prolinks, STRING, DIP, BIND, Literature
- Traditional spreadsheet technique to make sense of confidence values from multiple sources of evidence
- No tool to assign weights to “sources of evidence”/features based on how much we trust the predictions
- Difficulty in deciding cutoff of confidence for filtering a dense network
- Difficulty of obtaining evidence from heterogeneous databases and typical identifier mapping problems (redundancy, missing attributes etc)
- Proliferation of public data sources and computational methods providing functional evidence of interactions

- Once we get an inferred
- interaction network
- (experimentally or computationally).
- Then what?
- How to validate it?
- How does it connect to known pathways?

## CABIN (not caBIN) IntroductionCollective Analysis of Biological Interaction Networks

- Exploratory visual analytic tool for enabling the process of multiple evidence fusion for interaction networks
- Plug-in for an open-source tool Cytoscape
- Effective use of p-values, confidence scores rather than a voting scheme for combining evidences
- Multiple views highlighting different aspects of the data
- Familiar visualizations like networks, scatter plots, histograms and sliders
- Advanced functionalities to map node attributes across networks and creating sub-networks
- Written in Java (cross-platform) and freely available for download

## Data integration, Evaluation and Prediction

## Installing and Launching CABIN

- Download jar file from: _[http://www.sysbio.org/dataresources/cabin.stm](http://www.sysbio.org/dataresources/cabin.stm)_
- Copy/Move jar file to “plug-ins” directory of Cytoscape
- CABIN shows up in the plug-ins management menu of Cytoscape

## Use case scenarios

- Validating experimental observations
- Comparing network obtained from an experimental technique with networks obtained from bioinformatics sources (such as Prolinks, DIP, BIND etc) and inference algorithms (such as CLR, ARACNE etc)
- Network annotation or extension
- Using interaction edges obtained from bioinformatics data sources for a set of detected proteins/genes to extend or annotate a core network
- Designing new experiments
- Integrating interaction evidence from bioinformatics data sources to construct a template/skeleton of a network and using that to determine set of interesting proteins to be experimentally verified.
- Comparing inference algorithms
- Exploration of dense networks

- Validating experimental observations scenario is
- discussed in the following slides

## Organism: Rhodopseudomonas palustris (Rpal)

Experimental pull-down interactions (using BEPro algorithm – Daly et al.)

Other predictive/inference methods to predict functional linkages between proteins

Rpal_pulldown_specific
Rpal_phylogenetic
Rpal_genecluster
Rpal_rosettastone
Rpal_geneneighbor
Rpal_interolog
Rpal_regulog
Rpal_location

- Experimental pull-down interactions (using BEPro algorithm – Daly et al.)
- Other predictive/inference methods to predict functional linkages between proteins
- Rpal_pulldown_specific
- Rpal_phylogenetic
- Rpal_genecluster
- Rpal_rosettastone
- Rpal_geneneighbor
- Rpal_interolog
- Rpal_regulog
- Rpal_location

**Details of sample Evidence-Networks**

- CABIN is generic, facilitating analysis
-  of networks derived using any
-  inference/prediction
- methods of choice

## Network inference from pull-down data using BEPro algorithm

- Obtained from joint Oak Ridge National Laboratory (ORNL) and Pacific Northwest National Laboratory’s (PNNL) Microbial Protein-Protein Interactions (MiPPI) project funded by the US Department of Energy’s Genomes-to-Life (GtL) program
- Tandem affinity purification method is used to study protein-protein interactions
- BEPro : Bayesian Estimator of Protein-Protein Association Probabilities algorithm
- The BEPro program uses two algorithms designed to overcome errors pf pull-down experiments and produces a set of reliable protein-protein interactions, i.e., a network topology, by examining an annotated matrix across a set of assays.
- <rpal_pulldown_specific.sif>
- The pulldown specific network was created using the Rpal854 dataset which includes 854 samples for 291 bait proteins, processed using the BEPRO3 algorithm in SEBINI. Interactions were included if BEPRO3 indicated that they were specific binding interactions based on homogenity of the prey protein hits.

## Evidence networks from Prolinks database

- <rpal_phylogenetic.sif>
- The Phylogenetic Profile network is created by using the phylogenetic profile method from the Prolinks database which uses the presence and absence of proteins across multiple genomes to detect functional linkages. [http://128.97.39.94/cgi_files/functionator/about.html]
- <rpal_genecluster.sif>
- The Gene Cluster method from the Prolinks database is used to create the gene cluster network which uses genome proximity to predict functional linkage. [http://128.97.39.94/cgi_files/functionator/about.html]
- <rpal_rosettastone.sif>
- The Rosetta Stone network is created by using the Rosetta stone scores from the Prolinks database which uses a gene fusion event in a second organism to infer functional relatedness. [http://128.97.39.94/cgi_files/functionator/about.html]
- <rpal_geneneighbor.sif>
- The Gene Neighbor method in the Prolinks database uses both gene proximity and phylogenetic distribution to infer linkage. [http://128.97.39.94/cgi_files/functionator/about.html]

## Evidence networks from Interolog and Regulog based information

- <rpal_interolog.sif>
- The interolog method predicts an interaction between two proteins if they are both homologs of two proteins known to interact. Known protein interactions are gathered from databases of experimentally-determined protein-protein interactions (e.g. BIND, DIP) and PSI-BLAST is used to determine similarity between this set and all proteins in a target organism. [http://bioverse.compbio.washington.edu/]
- <rpal_regulog.sif>
- Regulogs are regulatory interactions inferred by homology. A regulog is predicted by determining similarity to a known transcription factor (TF) and the TF's target protein. Finally the nucleotide similarity in the upstream transcriptional promoter regions is determined and used to filter the regulog predictions: if there are similar promoter sequences then a regulog is predicted. [http://bioverse.compbio.washington.edu/]

## Evidence networks from sub-cellular localization information

- <rpal_location.sif>
- The location network is created by looking at the sub-cellular compartment information of the two proteins in all possible interactions amongst the proteins in the pull-down experiments. The edge was assigned a value of 1 if both the proteins were localized in the same compartment and a value of 0 otherwise. If the compartment information for atleast one of the proteins was unavailable or the protein was localized in multiple compartments, then a value of 0.5 was given to the interactions involving those proteins.

## Importing networks in CABIN       -cont-

**Loading sample networks**
- 	Choose the sample networks, default weight
- 	and missing value for all networks.
- 	Click on the “Data Details” button to view the details on the sample data

## Understanding the viewers in CABIN

- CABIN provides multiple coordinated views representing different aspects of the data
- Imported Evidence-Network Panel
- Scatter plot matrix Viewer
- Table/Matrix Viewer
- Network Viewer
- Weighted Combined Viewer
- Filter Panel

## Understanding the viewers in CABIN            -cont-

**   ****Scatter plot matrix Viewer**
- Each point in the plots can be a collection of interactions
- Points color coded based on density from yellow to red; red signifying multiple points with those values in the two networks
- Shows the relation amongst the evidence-values for interactions in one network versus the other network
- Click on a plot to open it in individual window

## Understanding the viewers in CABIN            -cont-

**  ****	****Table/Matrix Viewer**
- Each row in the table represents an interaction
- Each column represents an evidence-source/imported network
- Cell values show the corresponding values of an interaction in an evidence-source
- Color coded based on values
- Right click in a column to sort column values in ascending or descending order
- Slider controls on top and left to zoom in and out
- Ctrl-click to select multiple rows/interactions that get selected in other views as well
- Right click options to save selected or all interactions in a tab-delimited file on local machine
- Right click option to create a sub-network from selected interactions

## Understanding the viewers in CABIN            -cont-

**Network Viewer**
- Cytoscape like view of network represented as nodes and edges
- With a lot of interactions it becomes a hair ball structure so not much useful
- Useful to view filtered interactions after applying filters (discussed later)
- From the drop down menu     select network you want to view in this Viewer
- Right click and drag to zoom in and out
- Left Click and drag to select interactions
- Selected interactions shown in blue
- Right click options to save selected or all interactions in tab delimited files locally
- Right click option to create sub-network from selected points

## Understanding the viewers in CABIN            -cont-

**Weighted Combined Viewer**
- X axis represents interactions, with each point representing an interaction
- Y axis plots the weighted combined score for each interaction calculated by taking the average of the weighted sum of values for each evidence-source
- Zooming in and out buttons in toolbar
- Left click and drag to select points in a range or use filters
- Right click options to save selected or all interactions in a tab-delimited file on local machine
- Right click option to create a sub-network from selected interactions

## Understanding the viewers in CABIN            -cont-

**Filter Panel**
- Right click on Filters(Root) to add a filter
- Select the network on which you want to add a filter
- Use the slider to select the range of values you want to keep. For example Step1 shows that we have chosen a cutoff of 0.4 for the pulldown_specific network
- Add multiple filters and choose “AND” or relation amongst them

**Step1**

**Step2**

## Searching for proteins of interest

- Search for interactions involving a particular protein by specifying protein1 and selecting protein2 as “ALL”
- Search for multiple proteins by separating them with commas
- Regular expression based searches by constructing character ranges ([A-z]) or number ranges ([0-100]) in the expression
- Interactions involving “searched proteins” get highlighted in blue
- Can save selected interactions to a file or create a sub-network

## Creating sub-networks

- Right click on the Weighted-Combined Evidence Viewer, Network Viewer or the Matrix/Table Viewer to create a sub-network from selected or all interactions
- New sub-network created is automatically imported within CABIN
- Enter a name and a weight/confidence for the sub-network in step2 interface
- Select the network-value you want to assign to the interactions
- Select the levels/depth for the sub-network
- None - *only selected interactions*
- Level 1 – *selected interactions and neighboring interactions one-hop away*
- Level 2* – selected interactions and neighboring interactions up to 2 hops away*

**Step2**

**Step1**

## Exporting/Saving interactions

- All viewers provide functionality to save selected or all interactions to a file locally
- Tab-delimited files are saved with interaction values from all evidence-sources along with the weighted-combined value

## Interesting observations in sample data

- How many experimentally observed interactions can we validate by using atleast one other evidence source?
- Scatter plot matrix viewer shows that rpal_pulldown_specific network has a good agreement with the Prolinks predictions.
- Evidence-source not in agreement in other evidence sources?
- There is very low overlap between the regulog interaction network with the other networks (points along the axis). This can be attributed to the obvious fact that protein-protein interactions (and/or interologs) are very different from regulatory interactions (and/or regulogs). Regulatory interactions act through an intermediate (the promoter region) so the transcription factor (TF) and TF target do not need to physically make contact. Only in (probably rare) cases where the protein produced from the TF gene target binds to its TF generally to inhibit its activity (an auto-regulatory loop) would you see both a protein-protein and regulatory interaction between the same pair of proteins.
- Any interesting interactions?
- We see two such interactions in this case; easily finding such interactions that may warrant further investigation is one advantage of CABIN.

## Interesting observations in sample data               							

- How to filter this dense network?
- The interactions can be filtered based on a cutoff. Example: filter added on the location network with a cutoff of 0.4. Applying such a filter eliminated the interactions whose proteins are located in separate cellular compartments.

## Partial list of evidence-sources for network inference

- Bioinformatics Data Sources for protein-protein interactions
- Prolinks: _http://mysql5.mbi.ucla.edu/cgi-bin/functionator/pronav_
- String: _http://string.embl.de/_
- Intact: _http://www.ebi.ac.uk/intact/site/index.jsf_
- DIP: _http://dip.doe-mbi.ucla.edu/_
- BIND: _http://bond.unleashedinformatics.com/Action?_
- HPRD: _[http://www.hprd.org/](http://www.hprd.org/)_
- Agilent literature Search: Plug-in of Cytoscape
- Inference algorithms from microarray data (available in SEBINI)
- CLR: _[http://](http://gardnerlab.bu.edu/)__[gardnerlab.bu.edu](http://gardnerlab.bu.edu/)_
- BANJO: _[http://](http://www.cs.duke.edu/~amink/software/banjo/)__[www.cs.duke.edu/~amink/software/banjo](http://www.cs.duke.edu/~amink/software/banjo/)__[/](http://www.cs.duke.edu/~amink/software/banjo/)_
- ARACNE: Margolin, A.A., et al., Reverse engineering cellular networks. Nature Protocols, 2006. 1(2): p. 663-672

## CABIN Summary

- CABIN is an exploratory analysis tool for filtering and analyzing dense networks that look like hair-ball structures in a node-edge representation
- Integrating evidence from bioinformatic and experimental techniques can help eliminate false positives
- Choosing the right data sources and their weight assignment is crucial to conduct an un-biased analysis
- CABIN does not give a final answer, it just helps see interesting aspects in the data. Experimentally verifying observations in CABIN is critical

## Detailed Resources on CABIN

- Website: http://www.sysbio.org/capabilities/compbio/cabin.stm
- User Manual:
- 	http://gnode2.pnl.gov/cabin/cabin_usermanual.pdf
- Publications:
- 	Singhal, M., Domico, K., "CABIN: Collective Analysis of Biological Interaction Networks", Computational Biology and Chemistry 2007, 31(3): 222-225
- Contact email id: cabin@pnl.gov
- Contact person: Mudita Singhal (Project Lead)
- Team: Kelly Domico, Jason McDermott, Ron Taylor, Don Daly, Bill Cannon, Mudita Singhal

## CABIN Acknowledgements

- 	CABIN was initially developed under the LDRD (Lab Directed Research and Development) Program at the Pacific Northwest National Laboratory, a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy under Contract DE-AC06-76RL01830. CABIN is currently funded by the Genomes to Life project jointly lead by PNNL and ORNL.
- 	We acknowledge contributions from Elie Naulleau [semiosys@semiophore.net] in obtaining the updated version of the jMatrixView open source library and making it available for public use.
- 	The sample protein-protein interaction data were obtained from the Center for Molecular and Cellular Systems (mippi.ornl.gov) which is sponsored at Oak Ridge National Laboratory by the U.S. Department of Energy Office of Biological and Environmental Research.