1. General management
We held two workshops to report on research progress and promote close cooperation. We participated in theInternational Cancer Genome Consortium (ICGC) 6th Scientific Workshop held in France on March 21–22, andgathered information regarding large-scale data analysis methods and the status of research on the use ofnext-generation sequencers in cancer genome analysis. We also delivered a talk on large-scale life data analysis at
the Biophysical Society of Japan’s “High Performance Computational Approaches to Biological Functions”
symposium held on September 16, 2011.
In addition to the above, we also held periodical meetings among research program participants to coordinate
research activities.
2. Development of data processing system for next-generation sequencer data analysis
We developed GHOSTX, a sequence homology search tool, and parallelized GHOSTX with an MPI/Open MP
hybrid to create GHOST-MP. Using the K computer, we optimized the GHOST-MP code and database to improve
execution speed and parallel performance within and between nodes (Figure 1).

Figure 1. Execution times when applying GHOST-MP to metagenomic analysis
The horizontal axis shows the GHOST-MP version, and vertical axis, execution times. “I/O”,“Alignment” and “Search” represent execution times respectively for file I/O, homologous sequencealignment, and homologous sequence search. Execution speed was improved with Ver.0.2.5 being 1.7
times the speed of Ver.0.2.1.

For this research project, we used a suffix array for both query and database sequences to enable GHOSTX toperform sensitive, high-speed searches. We compared GHOSTX with the BLAST and BLAT sequence homology
analysis tools using actual data (fragments of 60–75 bases) from soil microorganism metagenomes and simulated
data for fragments of about 500 and 1000 bases. To assess sensitivity, we compared agreement of results withSSEARCH results, assuming the latter to be correct since the SSEARCH program uses the Smith-Waterman
algorithm to make very precise calculations of optimum alignments. When we ran GHOSTX using parameters
prioritized for speed, it proved to be faster than both BLAST and BLAT, completing searches at about 100 times1

the speed of BLAST. Sensitivity was lower than BLAST, but higher than BLAT. When using parameters
prioritized for sensitivity, search speed was slower than BLAT, but about 20 times faster than BLAST, while
delivering similar sensitivity (Figure 2).
In short, our GHOSTX tool enables homology analysis at the speed and sensitivity required for metagenomic
analysis.

Figure 2. Comparison of the sensitivity of GHOSTX, BLAST and BLAT
The degree of agreement with the results of SSEARCH is shown for each tool.
The vertical axis shows agreement rate with SSEARCH results, and horizontal axis, E-values.
The agreement rates are calculated for each E-values.

23. Development of methods for predicting RNA-RNA interaction and comprehensive analysis of RNA data
Since any analysis of RNA sequence data needs to consider secondary structure in order to produce accurate
results, comprehensive analysis of RNA data also requires consideration of secondary structure. We accordingly
developed a method that takes secondary structure into account, and carried out molecular simulation to analyze
the tertiary structure of RNA molecules.
To develop methods for analyzing RNA secondary structure, we developed Raccess, a software package thatcomputes the accessibility of all the segments of a fixed length for a given RNA sequence, and IPknot, a software
package for predicting RNA secondary structures with pseudoknots based on maximizing the expected accuracyof a predicted structure. We have published papers in international journals on both tools.
For molecular simulation, we also looked into developing a method that packages coarse-grained modeling ofRNA molecules with parameters for predicting base-pairing probability and other aspects of RNA secondarystructure.
For tertiary structure analysis, we developed RASSIE, a fragment assembly software package for accurately
predicting RNA tertiary structures by using known secondary structure information. We subsequently publisheddetails of RASSIE.
We also developed a long string NGS simulation data assembly tool for RNA-RNA interaction evaluation toconsider the effectiveness of applying RNA-RNA interaction prediction techniques to comprehensive analysis ofRNA data.

34. Development of large-scale biomolecular network analysis techniques
We devised a seed network method as a large-scale biomolecular network inference method that incorporates
biological knowledge, and developed a software prototype for inferring intermolecular control networks. We thenextended this prototype on the K computer to enable large-scale parallel execution. We named this tool BENIGN(Biologically Extensible Network Inference Software for Gene Expression Analysis).
Table 1 shows BENIGN’s parallel execution performance on the K computer. For data, we used changes ingene expression profile during the differentiation of mouse mesenchymal stem cells into adipocytes. The gene set
that we focused on was 113 genes known to be involved in adipocyte differentiation, and genes encoding 833transcription factors that showed changes in expression during differentiation, making for a total of 946 genes.
Table 1. BENIGN’s parallel execution performance
Calculated nodes

Execution time (seconds)
6,114

2683

Speed improvement
1.00

12,2281386

1.94

Parallel efficiency
0.97

In Table 1, execution time for 6,114 nodes is used as a base for measuring speed improvement and parallel
efficiency by strong scaling. As the table shows, excellent parallel efficiency of 97% was achieved with theexecution of 12,228 nodes.
We were also able to confirm the validity of the results of biomolecular network analysis for the process ofadipocyte differentiation using BENIGN.
Going forward, we plan to enhance BENIGN’s dynamic load distribution function and other components, andmake the most of K’s immense processing power by conducting network inference on a whole gene set that
includes other genes in addition to transcription factor genes. We have also been supplied with an assortment ofRNA-Seq gene expression data for mouse adipocyte tissue by research colleague Professor Teruo Kawada ofKyoto University’s Graduate School of Agriculture, and we plan to use it to attempt the inference of large-scalebiomolecular networks for total RNA, including miRNA and other non-coding RNA.

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5. Metagenomic analysis and comparative genome analysis research
Phylogenetic tree inference involves theTable 1. Comparison of maximum likelihood programs forphylogenetic tree inference

multiple alignment processing of basesequences obtained from genomes to infer
phylogenetic trees. In conjunction withadvances in computational performance inrecent years, the phylogenetic tree inference
method that we used for this research
project—the maximum likelihood
method—is being used increasingly in lifescience research fields, and it has won broad
acceptance. The maximum likelihood
method runs comparisons among multiple
candidate phylogenetic trees to select theML stands for maximum likelihood, MP for maximum parsimony,

phylogenetic tree shape that best matches

and BI for Bayesian inference.

the given sequence data. Inferring the besttree shape requires the comprehensive

searching of all candidate phylogenetic trees, but since the number of candidate tree shapes grows exponentiallyto the order of O(2NN!) where N is the number of sequence fragments, the comprehensive searching of wholecandidate tree shapes for more than a certain number of sequences is theoretically impossible. As such, search
space needs to be narrowed down when implementing the program in order to enable maximum likelihood
inference. Since search methods differ according to program, implementing programs on the K computer requires
different parallel optimization for each program. We accordingly prepared for this research project by firstinvestigating programs for their compatibility with the K computer’s parallel architecture (Table 1). Ourinvestigation found that RAxML, a program developed by A. Stamatakis and others at Germany’s
Ludwig-Maximilians University, is the most suitable program, so we used the RAxML code as a base fordeveloping our software.
Since it uses MPI, the RAxML program in principle meets the parallel programming requirements of the Kcomputer. However, we found that RAxML could not be used as it is on the K computer because it also
incorporates other parallelization methods such as p-Thread in parts. We accordingly implemented the programwith parallelization methods that could be used on the K computer. We compared the parallel efficiency of K’sarchitecture with a PC cluster, and found that the RAxML program is in principle compatible with the K
computer’s architecture.

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