Hideo Bannai

Identification of novel adrenomedullin in mammals: a potent cardiovascular and renal regulator

We have identified cDNA encoding a new member of the adrenomedullin (AM) family, AM2, for the first time in mammals (mouse, rat and human). The predicted precursor carried mature AM2 in the C‐terminus, which had an intramolecular ring formed by an S–S bond and a possibly amidated C‐terminus. Phylogenetic analyses clustered AM2 and AM into two distinct but closely related groups. Similarity of exon–intron structure and synteny of neighboring genes showed that mammalian AM2 is an ortholog of pufferfish AM2 and a paralog of mammalian AM. AM2 mRNA was expressed in submaxillary gland, kidney, stomach, ovary, lymphoid tissues and pancreas of mice, but not in adrenal and testis. Intravenous injection of synthetic mature AM2 decreased arterial pressure more potently than AM, and induced antidiuresis and antinatriuresis in mice. These results show that at least two peptides, AM and AM2, comprise an adrenomedullin family in mammals, and that AM2 may play pivotal roles in cardiovascular and body fluid regulation.

Inferring Strings from Graphs and Arrays

This paper introduces a new problem of inferring strings from graphs, and inferring strings from arrays. Given a graph G or an array A, we infer a string that suits the graph, or the array, under some condition. Firstly, we solve the problem of finding a string w such that the directed acyclic subsequence graph (DASG )o fw is isomorphic to a given graph G. Secondly, we consider directed acyclic word graphs (DAWGs) in terms of string inference. Finally, we consider the problem of finding a string w of a minimal size alphabet, such that the suffix array (SA )o f w is identical to a given permutation p = p1 ,...,p n of integers 1 ,...,n . Each of our three algorithms solving the above problems runs in linear time with respect to the input size.

The "runs" theorem

We give a new characterization of maximal repetitions (or runs) in strings based on Lyndon words. The characterization leads to a proof of what was known as the “runs” conjecture [R. M. Kolpakov an...

Utilizing evolutionary information and gene expression data for estimating gene networks with bayesian network models.

Since microarray gene expression data do not contain sufficient information for estimating accurate gene networks, other biological information has been considered to improve the estimated networks. Recent studies have revealed that highly conserved proteins that exhibit similar expression patterns in different organisms, have almost the same function in each organism. Such conserved proteins are also known to play similar roles in terms of the regulation of genes. Therefore, this evolutionary information can be used to refine regulatory relationships among genes, which are estimated from gene expression data. We propose a statistical method for estimating gene networks from gene expression data by utilizing evolutionarily conserved relationships between genes. Our method simultaneously estimates two gene networks of two distinct organisms, with a Bayesian network model utilizing the evolutionary information so that gene expression data of one organism helps to estimate the gene network of the other. We show the effectiveness of the method through the analysis on Saccharomyces cerevisiae and Homo sapiens cell cycle gene expression data. Our method was successful in estimating gene networks that capture many known relationships as well as several unknown relationships which are likely to be novel. Supplementary information is available at http://bonsai.ims.u-tokyo.ac.jp/~tamada/bayesnet/.

Counting Parameterized Border Arrays for a Binary Alphabet

The parameterized pattern matching problem is a kind of pattern matching problem, where a pattern is considered to occur in a text when there exists a renaming bijection on the alphabet with which the pattern can be transformed into a substring of the text. A parameterized border array (p-border array ) is an analogue of a border array of a standard string, which is also known as the failure function of the Morris-Pratt pattern matching algorithm. In this paper we present a linear time algorithm to verify if a given integer array is a valid p-border array for a binary alphabet. We also show a linear time algorithm to compute all binary parameterized strings sharing a given p-border array. In addition, we give an algorithm which computes all p-border arrays of length at most n , where n is a a given threshold. This algorithm runs in time linear in the number of output p-border arrays.

Fast q-gram mining on SLP compressed strings

We present simple and efficient algorithms for calculating q-gram frequencies on strings represented in compressed form, namely, as a straight line program (SLP). Given an SLP of size n that represents string T, we present an O(qn) time and space algorithm that computes the occurrence frequencies of all q-grams in T. Computational experiments show that our algorithm and its variation are practical for small q, actually running faster on various real string data, compared to algorithms that work on the uncompressed text. We also discuss applications in data mining and classification of string data, for which our algorithms can be useful.

EFFICIENTLY FINDING REGULATORY ELEMENTS USING CORRELATION WITH GENE EXPRESSION

We present an efficient algorithm for detecting putative regulatory elements in the upstream DNA sequences of genes, using gene expression information obtained from microarray experiments. Based on a generalized suffix tree, our algorithm looks for motif patterns whose appearance in the upstream region is most correlated with the expression levels of the genes. We are able to find the optimal pattern, in time linear in the total length of the upstream sequences. We implement and apply our algorithm to publicly available microarray gene expression data, and show that our method is able to discover biologically significant motifs, including various motifs which have been reported previously using the same data set. We further discuss applications for which the efficiency of the method is essential, as well as possible extensions to our algorithm.

Unsupervised spam detection based on string alienness measures

We propose an unsupervised method for detecting spam documents from a given set of documents, based on equivalence relations on strings. We give three measures for quantifying the alienness (i.e. how different they are from others) of substrings within the documents. A document is then classified as spam if it contains a substring that is in an equivalence class with a high degree of alienness. The proposed method is unsupervised, language independent, and scalable. Computational experiments conducted on data collected from Japanese web forums show that the method successfully discovers spams.

Simpler and Faster Lempel Ziv Factorization

We present a new, simple, and efficient approach for computing the Lempel-Ziv (LZ77) factorization of a string in linear time, based on suffix arrays. Computational experiments on various data sets show that our approach constantly outperforms the fastest previous algorithm LZ OG (Ohlebusch and Gog 2011), and can be up to 2 to 3 times faster in the processing after obtaining the suffix array, while requiring the same or a little more space.

Shortest Unique Substrings Queries in Optimal Time

We present an optimal, linear time algorithm for the shortest unique substring problem, thus improving the algorithm by Pei et al. (ICDE 2013). Our implementation is simple and based on suffix arrays. Computational experiments show that our algorithm is much more efficient in practice, compared to that of Pei et al.