# Presentation: 285228

## Infrastructure for High-Throughput Automated Analysis of Genomes.

**Natalia Maltsev, Elizabeth Marland, Gong Xin Yu, **
**Alex Rodrigez, Dinanath Sulakhe, Praveen Chandromohan, Rich Lusk, Luke Ulrich.**

***MCS ***
***Argonne National Laboratory.***

## Biology in a Nutshell(for people with little knowledge but infinite intelligence)

-  Genome consists of genes
-  **Gene**  **Protein**: Object description Object instantiation
-  **Protein ********Functions**
-  **Enzymes**: proteins that catalyze biochemical reactions
**Pathway**: sequence of reactions
-  **Network** (directed graph): set of pathways with metabolites as vertices and enzymes as edges

-  **Genome** (ROM): assembly code on how to build proteins
-  Instructions: A, C, T, G
-  3 variables  amino acid

**Notes:**
As I said at the beginning, biology is a very fascinating area. It will constantly challenge you with its complexity and will encourage you with its simplicity once you discover it. One of the complexities that I found almost frustrating is a terminology. That’s why I decided to give this very brief background information, or “Biology on a Palm” from the computer scientist perspective. I assume that you have very little knowledge but infinite intelligence. As you know, now we have so-called “Post-Genome Era”. So you can see how much we are behind. Some people had been working in this area for the whole “Genome Era” but we decided to join them only now. But it is NOT late. Because all the advances made during Genome Period have actually made this area even more complex. Now it challenges us with lots of computational problems that even Terflop Machines are not capable of managing.

## Why Biotechnological Revolution?

**98**** published genomes**
**652**** on-going genomes**

-  **High-throughput technologies provide huge amounts of biological data:**
-  Sequence data
- Data describing functional Networks (Metabolism, Regulation, Gene Expression)
- Dynamic data
-  **Progress of Computer Science and Computer Technologies and Bioinformatics allows to analyze this data**

## General Project Architecture

**Sequence Analysis module**
**In G-Wiz**

**Stages of analysis:**
-  Determine components of the system (assign functions to the genes)
-  Establish relationships between components (static model)
- Develop dynamic model of the system

## Goals of the Project

- Development of Integrated Computational infrastructure GWiz, tools and algorithms for
- High-throughput genetic sequence analysis (assignments of functions to the genes in sequenced genomes)
- Metabolic Reconstructions from sequence data (static models)
- Set a stage for the development of dynamic models

## Genome Analysis and Database Update (GADU)
GADU (Alex Rodriguez) is an automated system which performs following important tasks:
Data Acquisition.
Data Analysis.
Data Storage and Presentation.

**GADU **(Alex Rodriguez)** is an automated system which performs following important tasks:**
**Data Acquisition.**
**Data Analysis.**
**Data Storage and Presentation.**

- .........		Lets Get Started......

## GADU SystemData Acquisition

- Data Analysis

- Data Storage

## Data: Classes

- Sequence data
- DNA sequences, Protein Sequences – NCBI, GenBank, SwissProt, TIGR, sequencing projects
- Data describing Networks
- Metabolic Networks (EMP database, KEGG, etc)
- Regulatory Networks (Sentra, TransFac, etc)
- Gene Expression data (Experimental)
- Other experimental data
- Dynamic Data (experimental and literature)
- Organisms data
- Phenotypic data
- Physiological data
- Data models and the results of analyses

## Biological Data: Problems

**Size:**
**The last release of Genbank is : Uncompressed, the Release 130.0 flat files require roughly 70.15 GB (sequence files only) or 78.75 GB (including the 'short directory' and 'index'files).**
**Gwiz currently 83 complete genomes + ~30 genomes in the next month**
**Complexity ****(variety of data classes with complex relationships between them)**
**Inconsistency of Formats**

## Data Acquisition Module

**Data Analysis Module**

** ****This process is scheduled to**
**  ****run once every week.**
-  **An e-mail is sent to the group**
**  ****whenever a new genome or an**
**  ****update is downloaded.**
** ****Once a decision is made, the**
**  ****Genomes are run through the**
**  ****different tools for analysis.**

## GADU SystemData Acquisition

- Data Analysis

- Data Storage

## Data Analysis Module..

**   **
**Goal:**      **Assignment of functions to the genes in newly sequenced genomes**
**Method:**** ****Comparative analysis of Sequences with unknown functions with the sequences with known function**

**The various tools that we are presently using are:**
- 	 **-Blast (dynamic programming)**
**	**** ****-Blocks (HMM-based)**
**	**** ****-Pfam (HMM-based)**
**	**** ****-ClustalW**
**	**** ****-InterPro (HMM-based)**
**	**** ****-SVM-based algorithms**
**Other...**

**Phosphofructokinase from E. coli**
**PFK E.coli****	****GSGITKGLGAGANPEVGRN****A****A****D****EDRDALRAAL****E****GSDMVFIAAGMGGGTGTGAAPVVAE**
**? Y. pestis****	****GSGITKGLGAGANPEVGRN****G****A****E****EDRDALRAAL****D****GSDMVFIAAGMGGGTGTGAAPVVAE**
-                       **********************************   *   ******************    **********************************************

## Data Analysis

**Now that we have Data...**
**After the appropriate data formatting, genomes are submitted to a number of bioinformatics tools (e.g. Blast, Blocks, Pfam) and algorithms developed at MCS (e.g. clustering algorithms, SVM-based algorithms) for analysis and high-throughput assignment of functions to the genes.**
**Data: Embarrassingly parallel:**** ****one sequence at a time is submitted to the bioinformatics tools (Blast, Pfam, etc)**
**Size:**
**The last release of Genbank is : Uncompressed, the Release 130.0 flat files require roughly 70.15 GB (sequence files only) or 78.75 GB (including the 'short directory' and 'index‘ files).**
**NR database: Nucleotide: 1,70 GB (compressed), Protein: 550 MB**
**Gwiz**** currently 83 complete genomes + ~30 genomes in the next month**
**The Jobs are submitted to the Chiba Cluster using GADU**

## Computational Requirements

**A. Conventional tools. We will run each of them on approx 900,000 sequences**
**Blast **
**4000 sequences (one average bacterial genome) x 18 nodes (36 cpu) -> 4 hrs (550 MHz) on one cpu for 1000 sequences ~144 hrs**
**Pfam ****-- 100 sec/sequence**
**Blocks --**** 55 sec/seq **
**B. This set of tools we will run on the order of 30,000 families. And also it will be put into an interactive workbench**
**1. ClustalW (40 seq) **
-         **CPU Speed: 2.2 Ghz**
**        ****Time for MSA => 12.5 seconds**
**        ****Total cycles needed: 12.5 s * (2.2e9) = ~27e9 cycles**
**        ****Time for Bootstrapping => 9 s**
**        ****Total cycles needed: 9 s * (2.2e9) = ~18.2e9 cycles**
**2. T_coffee (40 seq, aligning via clustalw and lalign)**
-         **CPU Speed: 2.2 Ghz**
**        ****Time for MSA => 11 min 22 s = 682 s**
**        ****Total cycles needed: 682 s * (2.2e9) = 1.5e12 cycles**
**3. MAST (searching against database with PSSM)**
-         **CPU Speed: 2.2 Ghz**
**        ****Time for search: 26 min = 1560 s**
**        ****Total cycles needed: 1560 s * (2.2e9) = 3.432e12 cycles **

## Data Analysis...(Contnd...)

**STATISTICS**

-  **Size of Training Set / Test Set : 1.5 GB each. (Huge Data Set as a Whole)**
** ****Each node in Chiba City : 500 MHz CPU 512 MB  RAM**
** ****Approx Run Time for 1 Functional Group in a node:**
**Learning Phase :  4 hours**
**Classification Phase : 2 hours**
**Total runtime for 2500 groups in 50 Nodes of Chiba (dual processors) approximates to 6 days!**

- SVM for Protein Identification

## GADU SystemData Acquisition

- Data Analysis

- Data Storage

## Data Storage

**Data acquired from public databases (flat files) **
**Genomes** (in a flat file) takes about 5mins for parsing and entering into oracle. Presently 83 Genomes have been entered into Oracle.
**Non Redundant Database (flat files) , is updated every week **
-     (Deena reduced the loading time to about **10mins** including parsing, by first writing the data onto multiple files and then loading them into Oracle using SQL-Loader from initial **10hours**)I
**Various genome annotations**
**Metabolic data**
**Phenotypic data**
**The results of analysis of sequences and genomes from the tools run in parallel.**

**Relational Database (Oracle) has been developed locally (D. Sulakhe, G.X. Yu, A. Rodrigez, Liz Marland). It contains: **

## E-R Model (just a fragment..)

## Conclusions:

- Automated High-Throughput analysis of Biological data is unavoidable
- Integrated Computational Environments for analysis of the genomes are essential
- Computational power and storage are becoming bottlenecks
- Data fidelity, connectivity, complexity are becoming an increasing problem in biology
- Solutions?
- 				TeraGrid, Condor, Chimera!!!