Motivation: Personal genomes carry inherent privacy risks and protecting privacy poses major social and technological challenges. We consider the case where a user searches for genetic information (e.g. an allele) on a server that stores a large genomic database and aims to receive allele-associated information. The user would like to keep the query and result private and the server the database.
Approach: We propose a novel approach that combines efficient string data structures such as the Burrows-Wheeler transform with cryptographic techniques based on additive homomorphic encryption. We assume that the sequence data is searchable in efficient iterative query operations over a large indexed dictionary, for instance, from large genome collections and employing the (positional) Burrows-Wheeler transform. We use a technique called oblivious transfer that is based on additive homomorphic encryption to conceal the sequence query and the genomic region of interest in positional queries.
Results: We designed and implemented an efficient algorithm for searching sequences of SNPs in large genome databases. During search, the user can only identify the longest match while the server does not learn which sequence of SNPs the user queried. In an experiment based on 2184 aligned haploid genomes from the 1000 Genomes Project, our algorithm was able to perform typical queries within approximate to 4.6 s and approximate to 10.8 s for client and server side, respectively, on laptop computers. The presented algorithm is at least one order of magnitude faster than an exhaustive baseline algorithm.

Privacy-preserving string search is a crucial task for analyzing genomics-driven big data. In this work, we propose a cryptographic protocol that uses Fully Homomorphic Encryption (FHE) to enable a client to search on a genome sequence database without leaking his/her query to the server. Though FHE supports both addition and multiplication over encrypted data, random noise inside ciphertexts grows with every arithmetic operation especially multiplication, which results in incorrect decryption when the noise amount exceeds its threshold called level. There are two approaches to avoid the incorrect decryption: one is setting the sufficient level that assures correct decryption within the limited number of operations, and the other is resetting the noise by the method called bootstrapping. It is important to find an optimal balance between overhead caused by the level and overhead caused by the bootstrapping, since using higher level deteriorates the performance of all the arithmetic operations, while the more number of bootstrappings causes more expensive overhead. In this study, we propose an efficient approach to minimize the number of bootstrappings while reducing the level as much as possible. Our experimental result shows that it runs at most 10 times faster than a naive approach.

Background: Searching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources.
Results: In order to overcome this dilemma, we developed a novel cryptographic protocol that enables database searching while keeping both the query holder's privacy and database holder's privacy. Generally, the application of cryptographic techniques to practical problems is difficult because versatile techniques are computationally expensive while computationally inexpensive techniques can perform only trivial computation tasks. In this study, our protocol is successfully built only from an additive-homomorphic cryptosystem, which allows only addition performed on encrypted values but is computationally efficient compared with versatile techniques such as general purpose multi-party computation. In an experiment searching ChEMBL, which consists of more than 1,200,000 compounds, the proposed method was 36,900 times faster in CPU time and 12,000 times as efficient in communication size compared with general purpose multi-party computation.
Conclusion: We proposed a novel privacy-preserving protocol for searching chemical compound databases. The proposed method, easily scaling for large-scale databases, may help to accelerate drug discovery research by making full use of unused but valuable data that includes sensitive information.

Intrinsically disordered proteins (IDPs) that lack stable conformations and are highly flexible have attracted the attention of biologists. Therefore, the development of a systematic method to identify polypeptide regions that are unstructured in solution is important. We have designed an indirect/reflected detection system for evaluating the physicochemical properties of IDPs using nuclear magnetic resonance (NMR). This approach employs a chimeric membrane protein-based method using the thermostable membrane protein PH0471. This protein contains two domains, a transmembrane helical region and a C-terminal OB (oligonucleotide/oligosaccharide binding)-fold domain (named NfeDC domain), connected by a flexible linker. NMR signals of the OB-fold domain of detergent-solubilized PH0471 are observed because of the flexibility of the linker region. In this study, the linker region was substituted with target IDPs. Fifty-three candidates were selected using the prediction tool POODLE and 35 expression vectors were constructed. Subsequently, we obtained N-15-labeled chimeric PH0471 proteins with 25 IDPs as linkers. The NMR spectra allowed us to classify IDPs into three categories: flexible, moderately flexible, and inflexible. The inflexible IDPs contain membrane-associating or aggregation-prone sequences. This is the first attempt to use an indirect/reflected NMR method to evaluate IDPs and can verify the predictions derived from our computational tools.

The human genome can identify an individual and determine the individual's biological characteristics, and hence has to be securely protected in order to prevent privacy issues. In this paper we point out, however, that current standard privacy-preserving cryptographic protocols may be insufficient to protect genome privacy. This is mainly due to typical characteristics of genome information; it is immutable, and an individual's genome has correlations to those of the individual's progeny. Then, as an alternative, we propose to protect genome privacy by cryptographic protocols with everlasting security, which provides an appropriate mixture of computational and information-theoretic security. We construct a concrete example of a protocol with everlasting security, and discuss its practical efficiency.

Motivation: Chromosome rearrangement events are triggered by atypical breaking and rejoining of DNA molecules, which are observed in many cancer-related diseases. The detection of rearrangement is typically done by using short reads generated by next-generation sequencing (NGS) and combining the reads with knowledge of a reference genome. Because structural variations and genomes differ from one person to another, intermediate comparison via a reference genome may lead to loss of information.
Results: In this article, we propose a reference-free method for detecting clusters of breakpoints from the chromosomal rearrangements. This is done by directly comparing a set of NGS normal reads with another set that may be rearranged. Our method SlideSort-BPR (breakpoint reads) is based on a fast algorithm for all-against-all comparisons of short reads and theoretical analyses of the number of neighboring reads. When applied to a dataset with a sequencing depth of 100x, it finds similar to 88% of the breakpoints correctly with no false-positive reads. Moreover, evaluation on a real prostate cancer dataset shows that the proposed method predicts more fusion transcripts correctly than previous approaches, and yet produces fewer false-positive reads. To our knowledge, this is the first method to detect breakpoint reads without using a reference genome.

Computational investigation of protein functions is one of the most urgent and demanding tasks in the field of structural bioinformatics. Exhaustive pairwise comparison of known and putative ligand-binding sites, across protein families and folds, is essential in elucidating the biological functions and evolutionary relationships of proteins. Given the vast amounts of data available now, existing 3D structural comparison methods are not adequate due to their computation time complexity. In this article, we propose a new bit string representation of binding sites called structural sketches, which is obtained by random projections of triplet descriptors. It allows us to use ultra-fast all-pair similarity search methods for strings with strictly controlled error rates. Exhaustive comparison of 1.2 million known and putative binding sites finished in similar to 30 h on a single core to yield 88 million similar binding site pairs. Careful investigation of 3.5 million pairs verified by TM-align revealed several notable analogous sites across distinct protein families or folds. In particular, we succeeded in finding highly plausible functions of several pockets via strong structural analogies. These results indicate that our method is a promising tool for functional annotation of binding sites derived from structural genomics projects. Proteins 2011. (c) 2012 Wiley Periodicals, Inc.

Numerous potential ligand-binding sites are available today, along with hundreds of thousands of known binding sites observed in the PDB. Exhaustive similarity search for such vastly numerous binding site pairs is useful to predict protein functions and to enable rapid screening of target proteins for drug design. Existing databases of ligand-binding sites offer databases of limited scale. For example, SitesBase covers only similar to 33 000 known binding sites. Inferring protein function and drug discovery purposes, however, demands a much more comprehensive database including known and putative-binding sites. Using a novel algorithm, we conducted a large-scale all-pairs similarity search for 1.8 million known and potential binding sites in the PDB, and discovered over 14 million similar pairs of binding sites. Here, we present the results as a relational database Pocket Similarity Search using Multiple-sketches (PoSSuM) including all the discovered pairs with annotations of various types. PoSSuM enables rapid exploration of similar binding sites among structures with different global folds as well as similar ones. Moreover, PoSSuM is useful for predicting the binding ligand for unbound structures, which provides important clues for characterizing protein structures with unclear functions. The PoSSuM database is freely available at http://possum.cbrc.jp/PoSSuM/.

Motivation: Recent progress in DNA sequencing technologies calls for fast and accurate algorithms that can evaluate sequence similarity for a huge amount of short reads. Searching similar pairs from a string pool is a fundamental process of de novo genome assembly, genome-wide alignment and other important analyses.
Results: In this study, we designed and implemented an exact algorithm SlideSort that finds all similar pairs from a string pool in terms of edit distance. Using an efficient pattern growth algorithm, SlideSort discovers chains of common k-mers to narrow down the search. Compared to existing methods based on single k-mers, our method is more effective in reducing the number of edit distance calculations. In comparison to backtracking methods such as BWA, our method is much faster in finding remote matches, scaling easily to tens of millions of sequences. Our software has an additional function of single link clustering, which is useful in summarizing short reads for further processing.

Most proteins from higher organisms are known to be multi-domain proteins and contain substantial numbers of intrinsically disordered (ID) regions. To analyse such protein sequences, those from human for instance, we developed a special protein-structure-prediction pipeline and accumulated the products in the Structure Atlas of Human Genome (SAHG) database at http://bird.cbrc.jp/sahg. With the pipeline, human proteins were examined by local alignment methods (BLAST, PSI-BLAST and Smith-Waterman profile-profile alignment), global-local alignment methods (FORTE) and prediction tools for ID regions (POODLE-S) and homology modeling (MODELLER). Conformational changes of protein models upon ligand-binding were predicted by simultaneous modeling using templates of apo and holo forms. When there were no suitable templates for holo forms and the apo models were accurate, we prepared holo models using prediction methods for ligand-binding (eF-seek) and conformational change (the elastic network model and the linear response theory). Models are displayed as animated images. As of July 2010, SAHG contains 42 581 protein-domain models in approximately 24 900 unique human protein sequences from the RefSeq database. Annotation of models with functional information and links to other databases such as EzCatDB, InterPro or HPRD are also provided to facilitate understanding the protein structure-function relationships.

Under physiological conditions, many proteins that include a region lacking well-defined three-dimensional structures have been identified, especially in eukaryotes. These regions often play an important biological cellular role, although they cannot form a stable structure. Therefore, they are biologically remarkable phenomena. From an industrial perspective, they can provide useful information for determining three-dimensional structures or designing drugs. For these reasons, disordered regions have attracted a great deal of attention in recent years. Their accurate prediction is therefore anticipated to provide annotations that are useful for wide range of applications. POODLE-I (where "I" stands for integration) is a web-based disordered region prediction system. POODLE-I integrates prediction results obtained from three kinds of disordered region predictors (POODLEs) developed from the viewpoint that the characteristics of disordered regions change according to their length. Furthermore, POODLE-I combines that information with predicted structural information by application of a workflow approach. When compared with server teams that showed best performance in CASP8, POODLE-I ranked among the top and exhibited the highest performance in predicting unfolded proteins. POODLE-I is an efficient tool for detecting disordered regions in proteins solely from the amino acid sequence. The application is freely available at http://mbs.cbrc.jp/ poodle/poodle-i.html. © 2010 - IOS Press and Bioinformation Systems e.V. and the authors. All rights reserved.

Intrinsic protein disorder is a widespread phenomenon characterised by a lack of stable three-dimensional structures and is considered to play an important role in protein-protein interactions (PPIs). This study examined the genome-wide preference of disorder in PPIs by using exhaustive disorder prediction in human PPIs. We categorised the PPIs. into three types (interaction between disordered proteins, interaction between structured proteins, and interaction between a disordered protein and a structured protein) with regard to the flexibility of molecular recognition and compared these three interaction types in an existing human PPI network with those in a randomised network. Although the structured regions were expected to become the identifiers for binding recognition, this comparative analysis revealed unexpected results. The occurrence of interactions between disordered proteins was significantly frequent, and that between a disordered protein and a structured protein was significantly infrequent. We found that this propensity was much stronger in interactions between nonhub proteins. We also analysed the interaction types from a functional standpoint by using GO, which revealed that the interaction between disordered proteins frequently occurred in cellular processes, regulation, and metabolic processes. The number of interactions, especially in metabolic processes between disordered proteins, was 1.8 times as large as that in the randomised network. Another analysis conducted by using KEGG pathways provided results where several signaling pathways and disease-related pathways included many interactions between disordered proteins. All of these analyses suggest that human PPIs preferably occur between disordered proteins and that the flexibility of the interacting protein pairs may play an important role in human PPI networks. (C) 2009 Elsevier Ltd. All rights reserved.

Protein disorder is characterized by a lack of a stable 3D structure, and is considered to be involved in a number of important protein functions such as regulatory and signalling events. We developed a web application, the POODLE-S, which predicts the disordered region from amino acid sequences by using physicochemical features and reduced amino acid set of a position-specific scoring matrix.
Availability: POODLE-S is available from http://mbs.cbrc.jp/poodle/poodle-s.htmland can be used by both academic and commercial users.
Contact: poodle@cbrc.jp.

Motivation: Recent experimental and theoretical studies have revealed several proteins containing sequence segments that are unfolded under physiological conditions. These segments are called disordered regions. They are actively investigated because of their possible involvement in various biological processes, such as cell signaling, transcriptional and translational regulation. Additionally, disordered regions can represent a major obstacle to high-throughput proteome analysis and often need to be removed from experimental targets. The accurate prediction of long disordered regions is thus expected to provide annotations that are useful for a wide range of applications.
Results: We developed Prediction Of Order and Disorder by machine LEarning (POODLE-L; L stands for long), the Support Vector Machines (SVMs) based method for predicting long disordered regions using 10 kinds of simple physico-chemical properties of amino acid. POODLE-L assembles the output of 10 two-level SVM predictors into a final prediction of disordered regions. The performance of POODLE-L for predicting long disordered regions, which exhibited a Matthew's correlation coefficient of 0.658, was the highest when compared with eight well-established publicly available disordered region predictors.
Availability: POODLE-L is freely available at http://mbs.cbrc. jp/ poodle/poodle-l.html
Contact: hirose-shuichi@aist.go.jp
Supplementary information: Supplementary data are available at Bioinformatics online.

Background: Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences.
Results: When the proposed method was evaluated on data that included 82 disordered proteins and 526 ordered proteins, its sensitivity was 0.723 and its specificity was 0.977. It resulted in a Matthews correlation coefficient 0.202 points higher than that obtained using FoldIndex, 0.221 points higher than that obtained using the method based on plotting hydrophobicity against the number of contacts and 0.07 points higher than that obtained using support vector machines (SVMs). To examine robustness against training data sparseness, we investigated the correlation between two results obtained when the method was trained on different datasets and tested on the same dataset. The correlation coefficient for the proposed method is 0.14 higher than that for the method using SVMs. When the proposed SGT-based method was compared with four per-residue predictors (VL3, GlobPlot, DISOPRED2 and IUPred ( long)), its sensitivity was 0.834 for disordered proteins, which is 0.052 - 0.523 higher than that of the per-residue predictors, and its specificity was 0.991 for ordered proteins, which is 0.036 - 0.153 higher than that of the per-residue predictors. The proposed method was also evaluated on data that included 417 partially disordered proteins. It predicted the frequency of disordered proteins to be 1.95% for the proteins with 5% - 10% disordered sequences, 1.46% for the proteins with 10% - 20% disordered sequences and 16.57% for proteins with 20% - 40% disordered sequences.
Conclusion: The proposed method, which utilizes the information of structure- unknown data, predicts disordered proteins more accurately than other methods and is less affected by training data sparseness.

In the process of making full-length cDNA, predicting protein coding regions helps both in the preliminary analysis of genes and in any succeeding process. However, unfinished cDNA contains artifacts including many sequencing errors, which hinder the correct evaluation of coding sequences. Especially, predictions of short sequences are difficult because they provide little information for evaluating coding potential. In this paper, we describe ANGLE, a new program for predicting coding sequences in low quality cDNA. To achieve error-tolerant prediction, ANGLE uses a machine-learning approach, which makes better expression of coding sequence maximizing the use of limited information from input sequences. Our method utilizes not only codon usage, but also protein structure information which is difficult to be used for stochastic model-based algorithms, and optimizes limited information from a short segment when deciding coding potential, with the result that predictive accuracy does not depend on the length of an input sequence. The performance of ANGLE is compared with ESTSCAN on four dataset each of them having a different error rate (one frame-shift error or one substitution error per 200-500 nucleotides) and on one dataset which has no error. ANGLE outperforms ESTSCAN by 9.26% in average Matthews's correlation coefficient on short sequence dataset (&lt
1000 bases). On long sequence dataset, ANGLE achieves comparable performance. © 2006 Imperial College Press.

The prediction of intrinsic disorder from amino acid sequence has been gaining increasing attention because these have come to be known as important regions for protein functions. The most common way of predicting disorder is based on binary classification with machine learning. Since amino acid composition has different propensities in the N-term, C-term, and internal regions, the accuracy of prediction increases by dividing training data into these three regions and predicting them separately. However, previous work has lacked discussion about a concrete definition of the N-term and C-term regions, and has only used the heuristic length from the terminal. Other previous work has shown that general physicochemical properties rather than specific amino acids are important factors contributing to disorder, and a reduced amino acid alphabet can maintain excellent precision in predicting disorder. In this paper, we redefine a suitable length and position for the N-term and C-term regions for predicting disorder. Moreover, we show that each region has different physicochemical properties, which are important factors contributing to disorder. We also suggest a region-specificreduced set of amino acid and modified PSSM based on that for predicting disorder. We implemented our method and (1) compare it with the conventional division method, (2) compare our feature selection with all physicochemical features, on casp6 benchmark, PDB dataset, and DisProt. The result supports that the method of new data separation is effective, and indicates each region has different physicochemical properties that are important factors for predicting protein disorders.

This paper describes a method for a WWW-based melody-retrieval system, takes a melody sung by a user as a search clue and sent over the Internet and uses it to retrieve the song's title from a music database of standard MIDI files(SMF). It was difficult to build a melody-retrieval service with a large database and with a lot of user accesses since it was quite difficult to build a system which could achieve both quick search and high matching accuracy. We propose a method of a scalable melody-retrieval system which achieves 70% matching accuracy against more than 20, 000 pieces of music and its search time is within a few seconds. © 2003 by Springer Science+Business Media New York.

This paper describes the design method of a WWW-based melody-retrieval system which takes a sung melody as a search clue and retrieves the music title from a music database of standard MIDI files(SMF) over the Internet. The most important thing in building a melody-retrieval system on the Internet is to achieve both high matching accuracy and quick search. It was., however, quite difficult to simultaneously fulfill these two conditions since it took long time, for the matching process. We propose the design of a. system which consists of parallel-ized melody-retrieval servers for building a high performance service on the Internet.

Awarded 1 / 8 demonstrations.<br />
<br />
To make our demonstration, we used a library https://github.com/aistcrypt/Lifted-ElGamal (currently, newer version of this library is in https://github.com/herumi/mcl)

Awarded 1 / 9 demonstrations.<br />
<br />
To make our demonstration, we used a library https://github.com/aistcrypt/Lifted-ElGamal (currently, newer version of this library is in https://github.com/herumi/mcl)

To make our demonstration, we used a library https://github.com/aistcrypt/Lifted-ElGamal (currently, newer version of this library is in https://github.com/herumi/mcl)

近年の研究により，ゲノム配列は非常に多様であることが示唆された．しかし，現在主流となっている情報解析の手法では，シークエンサーから出力された断片配列をまずはじめに参照ゲノムに対して貼り付けて，その結果から統計情報を得る方策がとられているため，得られる解析結果は参照ゲノムの特徴に左右されて，ゲノムの多様性を見落としてしまう問題点があった．そこで本研究では，複数のデータセットを直接的に比較して，データセット間で異なる特徴を持つゲノム配列のパターンを発見する手法の設計及び実装を行った

個人のゲノム情報や薬のたねとなる化合物情報などは，機密情報として取り扱うことが必要となる．一方，オープンサイエンスの立場からは，これらの情報を積極的に利用して他の情報と合わせてデータマイニングを行うことが重要である．本研究では，これらの生物分野の重要情報を秘匿したまま様々なデータマイニングを行う方法論の開発を行った．具体的には，化合物データベースの秘匿検索，隠れマルコフモデルを用いたゲノム情報の秘匿検索，秘匿配列アラインメントの技術を開発した

本研究では、タンパク質基質結合部位の粗視化と高速なソートアルゴリズムの適用により、大量の結合部位の比較を可能とする新たな手法を開発した。タンパク質立体構造データベース全体の規模の既知及び潜在的基質結合部位に対して開発手法を適用し、その比較結果を収載したデータベースPoSSuMを構築と公開を行った。現在PoSSuMは、550万の既知及び潜在的基質結合部位を比較して得られた4,900万の類似結合部位ペアを収載するまでに成長している。また、ドラッグリポジショニング、副作用予測などへの応用を目指し、ChEMBLのアッセイ情報へのリンクを付加した新たなデータベースPoSSuMdsの構築と公開を行った

ギガシークエンサーは,短い断片配列(リード)を大量に出力するため,高速な解析技術の開発が急務となっている.本研究では,オフセット付き鳩ノ巣原理を応用し,大量のリードから超高速に類似配列を発見するアルゴリズムSlideSortを開発した. SlideSortは従来手法と比較して,同程度のメモリで1000倍以上の速度向上を達成した.考案したアルゴリズムの応用例として,最小全域木を構築するソフトウェアの開発も行った.類似ペア検索の応用範囲は広く,上記に述べたクラスタリングの他にも,共通パターンの発見,アセンブリの効率化などに役立つと期待される