Project PRACE 1IP, WP7.4

Transcription

1 Project PRACE 1IP, WP7.4 Plamenka Borovska, Veska Gancheva Computer Systems Department Technical University of Sofia

2 The Team is consists of 5 members: 2 Professors; 1 Assist. Professor; 2 Researchers; Coordinator: Prof. PhD Plamenka Borovska

3 Task1: Parallel computer simulation for investigating the interaction of influenza virus type A genome with the host genome. Task2: Parallel performance evaluation, scaling and profiling of software packages for genomic sequence processing.

4 Biological sequence processing is a key of information technology for molecular biology. As the number of DNA and protein sequences in databases increases, it is important to be able to create sequence alignments and searching for very large number of sequences. Bioinformatics algorithms used in processing wide range data require large computational resources. Comparison of the existing sequences: pairwise alignment; multiple alignment; phylogenetic reconstruction; recombination analysis.

5 Major goal: Find the similarity between two (or more) DNA-sequences by finding a good alignment between them. DNA-sequence-1 tcctctgcctctgccatcat---caaccccaaagt tcctgtgcatctgcaatcatgggcaaccccaaagt DNA-sequence-2 Alignment

6 The Basic Local Alignment Search Tool is by far the most widely used to look at sequence alignments and similarities. BLAST searches to find sequences in the database that are similar to the query sequence. BLAST efficiently calculates local pairwise alignments between sequences. BLAST utilize heuristics approach for increasing the performance of the alignment searching.

7 Multiple alignment is an extension of pairwise alignment to incorporate more than two sequences into an alignment. Multiple alignment methods try to align all of the sequences in a specified set. The most popular multiple alignment tool is CLUSTAL. Multiple sequence alignment is computationally difficult and is classified as an NP-Hard problem.

8 ClustalW proceeds in three stages: pairwise alignment (PA), guide tree (GT) and multiple alignment (MA). Pairwise alignment computes the optimal alignment cost for each sequences pairs and a distance matrix is built up. An evolutionary tree is computed from the distance matrix and is used as guide tree. The algorithm aligns the sequences progressively according to the branching order in the guide tree.

9 Thousands of influenza viral genome sequences originating from different viral isolates are accumulated in GeneBank. The huge amount of biological sequences requires efficient parallel tools and powerful computers for structural genomic and functional analysis. The great challenges are: to optimize parallel computational models; to optimize parallel program implementations; to evaluate the scalability of parallel software.

10 To achieve the aim the following specific subtasks have been solved: Local database of all influenza A viral genomes sequences Local database of common influenza A virus hosts Consensus motifs and variable domains Homology between viral RNAs and host genome Identification of recombination hot-spots in the influenza virus

11 The following methods have been applied. DNA and RNA databases have been screened by local searching algorithm based on mpiblast. Consensus and variable domains in influenza A viral genomes have been searched by comparative analysis of influenza RNA using multiple sequence alignment method ClustalW; Recombination sites have been identified and phylogenetic tree have been studied by the RAT (Recombination Analysis Tool) and Phylip respectively.

12 To achieve the aim the following specific subtasks have been solved: Local database of 35 types nucleotide mosaic virus sequences Local database of 35 types protein nucleotide mosaic virus sequences local alignment and homology searching of all mosaic virus isolates Parallel performance evaluation and scaling of software mpiblast and ScalaBlast in various modes: blastn, blastp, blastx, tblastn, tblastx, searching of consensus domains in protein sequences, limited multiple alignment Parallel performance evaluation and scaling of multiple sequence alignment method ClustalW

13 The main goal of this research is to investigate the scalability of sequence alignment software on supercomputer BlueGene/P. mpiblast for sequence alignment and searching Clistalw for multiple sequence alignment The case study is comparison of all available influenza virus A aimed to finding the consensus and variable domains and searching the similarity of consensus motifs of influenza virus A in the human genome.

14 Parallel computational models; Parallel program implementations; Experimental datasets; Supercomputer BlueGene/P. Parallel computational models Experimental datasets Parallel program implementations BlueGene/P

15 Bulgarian IBM Blue Gene/P supercomputer two racks, 2048 PowerPC 450 computing nodes, 8192 cores; 4 TB random access memory; maximum LINPACK performance Rmax= Tflops; MPICH2 distribution of the MPI standard; Operation System - SuSE Linux Enterprise Server 10.

16 In order to achieve the aims, next software is installed, tested, verified and experimentally studied: mpiblast for local alignment and homology searching; Scalablast for local alignment and homology searching; ClustalW for multiple sequence alignment; Phylip for phylogenetic studies; RAT (Recombination Analysis Tool) for investigation and visualization of the "hot" spots of recombination; Scalasca for performance analysis; Cube 3.1 for performance parameters visualization.

17 A mirror local database in working format of the existing database installed on the supercomputer BlueGene/P, allows online updating of data. Nucleotide and protein sequences obtained from different isolates of 35 types mosaic virus (tomato, potato, tobacco, celery, etc.); Nucleotide and protein sequences obtained from different isolates of influenza virus - all the available isolates of the 8 segments of the influenza virus A (all subtypes); Complete human genome extracted from GenBank.

18 All existing sequences of influenza virus obtained from various isolates have been segmented based on specified criteria such as subtype, segment, host, and region. Influenza Virus Type Host Segment Subtype Type A All Hosts PB2 All Subtypes Type B Human PB1 H1N1 Type C Avian PA H3N8 Swine HA H7N7 Horse NP NA MP NS

19 Different data sets of influenza virus nucleotide sequences have been combined based on specified criteria such as subtype, segment, host, region, into a batched virtual query in order to search for similarities with the human genome. This allows: comparing a large set of sequences against a sequence database simultaneously by sending batched virtual query; reducing the execution time, respectively improving the performance.

20 Dividing up the database (human genome) into multiple fragments 64 small segments of approximately equal size stored in the shared memory. The searching of a fragment is independent of any other fragment.

21 The master process: distributes the virtual query and part of the database to the worker processes; receives the results of the comparison; sends new parts of the database for the next comparison to the worker processes; integrates the results obtained from them. Each worker process: compares the virtual query with a segment of the database; sends the results of the comparison to the master process.

22 The model is based on: data parallelism using the message passing programming model Master process Termination Termination Send results Task distribution Send results Termination Task distribution Send results Task distribution Slave process Slave process Slave process

23 The model is based on: phase based parallel algorithmic paradigm PROCESS PROCESS PROCESS PROCESS PAIRWISE ALIGNMENT INTERACTION PHASE PROCESS PROCESS PROCESS PROCESS GUIDE TREE INTERACTION PHASE PROCESS PROCESS PROCESS PROCESS MULTIPLE ALIGNMENT INTERACTION PHASE

24 Local alignment and homology searching of all nucleotide sequences of mosaic virus using mpiblast

25 Local alignment and homology searching of protein sequences of mosaic virus using mpiblast

26 Local alignment and homology searching of protein sequences of mosaic virus using scalablast

27 Local alignment and homology searching of nucleotide sequences of mosaic virus using scalablast

28 Database of nucleotide sequences: human_genomic Query of protein sequence: alpha_1_interferon_[homo_sapiens] Query of nucleotide sequence: alcoholgene Using formula is: Speedup = T (1)/T (p) Number of cores Scalability of the parallel system in respect of the speedup for various sizes of queries and numbers of cores

29 Execution time in the case of human and avian influenza virus A/H1N1 nucleotide segment 4 (HA) used for searching in the human genome in respect to various virtual batched queries size and numbers of cores (4, 128, 256 and 512). Time [min] Time [min] Batch queries Batch queries

30 Database of nucleotide sequences: human_genomic 3.4 billion bases Query of nucleotide sequences: human and avian influenza virus A/H1N1 segment 4 (HA) query sequence length bases batch query size varies from 60 to 6729 sequences Scalability of the parallel system in respect to the machine size and batch query size

31 The results show that the batch size impacts the execution time, because more sequences need to be searched. The parallel system shows good scalability in respect to both the number of cores size and the workload size.

32 128 experiments comprise nucleotide sequences of all the available isolates of the 8 segments of viruses A (avian, human, swine, horse) Scalability of the parallel system in respect to the machine size and number of sequences

33 Increasing the number of cores results in accelerating computations is 26.84% for the case of 1024 cores and 34.9% for the case of 2048 cores respectively Parallel execution time decrease for the cases of 1024 and 2048 cores vs. 512 cores

34 process rank 0 sends messages (function MPI_send())

35 slave processes receive massages function MPI_Recv()).

36 well balanced except for the process rank 0 that performs synchronization, communication and data sending to all other processors

37 computational imbalance of the parallel system is 0 i.e. the workload is evenly distributed among the cores

38 The recombination sites of all existing nucleotide sequences of influenza virus, after multiple alignment by ClustalW algorithm, have been investigated and visualized using the software package Recombination Analysis Tool. Recombination in the case of all Hosts Influenza Virus A/H1N1 segment 2

39 The consensus motifs and variable domains of Influenza virus A have been determined and output by utilizing the biological sequence alignment editor UGENE UniPro.

40 Representation of circular phylogenetic trees by PHYLIP software package

41 The WP7.4 team results can be summarized as follows: Parallel computational model for biological sequence processing have been suggested; Parallel software for in-silico molecular biology experiments have been installed, configured and verified experimentally. Mirror local database is developed and installed on the supercomputer BlueGene/P: all the available isolates of the 8 segments of the influenza virus A (all subtypes); all the available isolates of 35 types of mosaic virus; complete human genome;

42 Performance parameters such as execution time, speedup, and scalability of sequence alignment have been estimated experimentally; The performance estimation and analyses show that the parallel system has good scalability and is well balanced both in respect to the workload and machine size; The case study is investigating the influenza virus and mosaic virus variability, including: finding out consensus motifs and variable domains in the different segments of influenza virus A and mosaic virus; searching for homology with human genome; identification of recombination hot-spots; phylogenetic studies of the influenza virus genome.

43 The WP7.4 results on Task 1 and Task 2 are published in 6 papers in Proceedings of International Conferences: IEEE - International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications WSEAS World Scientific and Engineering Academy and Society; ACM - International Conference CompSysTech; IADIS - International Conference Applied Computing; Computer Science - organized by Computer System Department, Technical University Sofia.

44 P. Borovska, O.Nakov, V. Gancheva, I. Georgiev, Parallel Genome Sequence Searching on Supercomputer BlueGene/P, European Conference of Computer Science (ECCS'10), 30 November 2 December 2010, Puerto De La Cruz, Tenerife. P. Borovska, V. Gancheva, St. Markov, Parallel Performance Evaluation of Sequence Nucleotide Alignment on the Supercomputer BlueGene/P, 5th EUROPEAN COMPUTING CONFERENCE (ECC '11), April 2011, Paris, France. P. Borovska, V. Gancheva, G. Dimitrov, K. Chintov, S. Gurov, Parallel Performance Evaluation of Multithreaded Local Sequence Alignment, International Conference CompSysTech, June 2011, Vienna, Austria. P. Borovska, I. Georgiev, Influenza Virus Investigation Using Visualization Methods and Tools, International Conference Computer Science, 1-3 September 2011, Ohrid, Macedonia. P. Borovska, V. Gancheva, St. Markov, I. Georgiev, E. Assenov, Parallel Performance and Profiling of Multiple Sequence Nucleotide Alignment on the Supercomputer Blue Gene/P, The 6 th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, September 2011, Prague, Czech Republic. P. Borovska, O.Nakov, V. Gancheva, I. Georgiev, St. Markov, In Silico Biological Experiments for Investigating the Pandemic Influenza Virus A Variability on the Supercomputer BlueGene/P, IADIS International Conference Applied Computing 2011, 6-8 November 2011, Rio de Janeiro, Brazil.

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