Determination of metallothionein-3 by a competitive enzyme-linked immunosorbent assay in experimental animals.

An easy and specific enzyme-linked immunoassay (ELISA) for the determination of metallothinein-3 (MT-3) in experimental animals for the research of heavy metal and chemical toxicity has not been reported yet. Therefore, we have developed a competitive ELISA, using a specific monoclonal antibody raised against human recombinant MT-3 (rMT-3). The epitope mapping of the antibody was conducted using mouse, rat, and human MT-3s and peptide fragments of human MT-3.

Comparison and enumeration of chemical graphs.

Chemical compounds are usually represented as graph structured data in computers. In this review article, we overview several graph classes relevant to chemical compounds and the computational complexities of several fundamental problems for these graph classes. In particular, we consider the following problems: determining whether two chemical graphs are identical, determining whether one input chemical graph is a part of the other input chemical graph, finding a maximum common part of two input graphs, finding a reaction atom mapping, enumerating possible chemical graphs, and enumerating stereoisomers.

Network completion using dynamic programming and least-squares fitting.

We consider the problem of network completion, which is to make the minimum amount of modifications to a given network so that the resulting network is most consistent with the observed data. We employ here a certain type of differential equations as gene regulation rules in a genetic network, gene expression time series data as observed data, and deletions and additions of edges as basic modification operations. In addition, we assume that the numbers of deleted and added edges are specified.

PROSPER: an integrated feature-based tool for predicting protease substrate cleavage sites.

The ability to catalytically cleave protein substrates after synthesis is fundamental for all forms of life. Accordingly, site-specific proteolysis is one of the most important post-translational modifications. The key to understanding the physiological role of a protease is to identify its natural substrate(s).

An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins.

Zinc-binding proteins are the most abundant metalloproteins in the Protein Data Bank where the zinc ions usually have catalytic, regulatory or structural roles critical for the function of the protein. Accurate prediction of zinc-binding sites is not only useful for the inference of protein function but also important for the prediction of 3D structure. Here, we present a new integrative framework that combines multiple sequence and structural properties and graph-theoretic network features, followed by an efficient feature selection to improve prediction of zinc-binding sites.

DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition.

Motivation: It is well known that the accuracy of RNA secondary structure prediction from a single sequence is limited, and thus a comparative approach that predicts a common secondary structure from aligned sequences is a better choice if homologous sequences with reliable alignments are available. However, correct secondary structure information is needed to produce reliable alignments of RNA sequences. To tackle this dilemma, we require a fast and accurate aligner that takes structural information into consideration to yield reliable structural alignments, which are suitable for common secondary structure prediction.

A clique-based method using dynamic programming for computing edit distance between unordered trees.

Many kinds of tree-structured data, such as RNA secondary structures, have become available due to the progress of techniques in the field of molecular biology. To analyze the tree-structured data, various measures for computing the similarity between them have been developed and applied. Among them, tree edit distance is one of the most widely used measures.

Protein complex prediction via improved verification methods using constrained domain-domain matching.

Identification of protein complexes within protein-protein interaction networks is one of the important objectives in functional genomics. Ozawa et al. proposed a verification method of protein complexes by introducing a structural constraint.

FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model.

Single amino acid variants (SAVs) are the most abundant form of known genetic variations associated with human disease. Successful prediction of the functional impact of SAVs from sequences can thus lead to an improved understanding of the underlying mechanisms of why a SAV may be associated with certain disease. In this work, we constructed a high-quality structural dataset that contained 679 high-quality protein structures with 2,048 SAVs by collecting the human genetic variant data from multiple resources and dividing them into two categories, i.

Finding a periodic attractor of a Boolean network.

In this paper, we study the problem of finding a periodic attractor of a Boolean network (BN), which arises in computational systems biology and is known to be NP-hard. Since a general case is quite hard to solve, we consider special but biologically important subclasses of BNs. For finding an attractor of period 2 of a BN consisting of n OR functions of positive literals, we present a polynomial time algorithm.

Rtips: fast and accurate tools for RNA 2D structure prediction using integer programming.

We present a web-based tool set Rtips for fast and accurate prediction of RNA 2D complex structures. Rtips comprises two computational tools based on integer programming, IPknot for predicting RNA secondary structures with pseudoknots and RactIP for predicting RNA-RNA interactions with kissing hairpins. Both servers can run much faster than existing services with the same purpose on large data sets as well as being at least comparable in prediction accuracy.

Specificity, sensitivity, and operability of RSID™-urine for forensic identification of urine: comparison with ELISA for Tamm-Horsfall protein.

In this study, the specificity, sensitivity, and operability of RSID™-Urine, a new immunochromatographic test for urine identification, was evaluated and compared with ELISA detection of Tamm-Horsfall protein (THP). Urine was successfully identified among other body fluids using RSID™-Urine and ELISA detection of THP. The detection limit of RSID™-Urine equated to 0.

Identification of nasal blood by real-time RT-PCR.

A new approach for the identification of body fluid stains by comparing specific mRNA expression levels has been extensively studied in recent years. Here, we examine whether nasal blood, which is regarded as one of the most difficult types of blood to identify, can be identified by comparing mRNA expression levels of target genes specific to saliva, nasal secretion, and blood. The saliva-specific statherin gene (STATH) was found to be expressed at high levels in not only saliva (dCt value: 1.

Enumerating tree-like chemical graphs with given upper and lower bounds on path frequencies.

Background: Enumeration of chemical graphs satisfying given constraints is one of the fundamental problems in chemoinformatics and bioinformatics since it leads to a variety of useful applications including structure determination of novel chemical compounds and drug design.

Results: In this paper, we consider the problem of enumerating all tree-like chemical graphs from a given set of feature vectors, which is specified by a pair of upper and lower feature vectors, where a feature vector represents the frequency of prescribed paths in a chemical compound to be constructed. This problem can be solved by applying the algorithm proposed by Ishida et al.

TANGLE: two-level support vector regression approach for protein backbone torsion angle prediction from primary sequences.

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the C(α)-N bond (Phi) and the C(α)-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures.

Detection of bacterial 16S ribosomal RNA genes for forensic identification of vaginal fluid.

To preliminarily evaluate the applicability of bacterial DNA as a marker for the forensic identification of vaginal fluid, we developed and performed PCR-based detection of 16S ribosomal RNA genes of Lactobacillus spp. dominating the vagina and of bacterial vaginosis-related bacteria from DNA extracted from body fluids and stains. As a result, 16S ribosomal RNA genes of Lactobacillus crispatus, Lactobacillus jensenii and Atopobium vaginae were specifically detected in vaginal fluid and female urine samples.

Evaluation of latex agglutination tests for fibrin-fibrinogen degradation products in the forensic identification of menstrual blood.

The identification of menstrual blood is important when discriminating menstruation from vaginal trauma in sexual assault cases. The aim of this study was to evaluate two fibrin-fibrinogen degradation product (FDP)-latex agglutination test kits, FDPL® Test (FDP-L) and FDP Plasma "RD" (FDP-P), for their ability to forensically identify menstrual blood. Sensitivity and specificity of the two kits were compared for menstrual blood and various body fluids, and the sensitivity of the FDP-latex agglutination test kit was also compared with that of an immunochromatographic test for human hemoglobin.

Using binding profiles to predict binding sites of target RNAs.

Prediction of RNA-RNA interaction is a key to elucidating possible functions of small non-coding RNAs, and a number of computational methods have been proposed to analyze interacting RNA secondary structures. In this article, we focus on predicting binding sites of target RNAs that are expected to interact with regulatory antisense RNAs in a general form of interaction. For this purpose, we propose bistaRNA, a novel method for predicting multiple binding sites of target RNAs.

Prediction using step-wise L1, L2 regularization and feature selection for small data sets with large number of features.

Background: Machine learning methods are nowadays used for many biological prediction problems involving drugs, ligands or polypeptide segments of a protein. In order to build a prediction model a so called training data set of molecules with measured target properties is needed. For many such problems the size of the training data set is limited as measurements have to be performed in a wet lab.

Expression of statherin mRNA and protein in nasal and vaginal secretions.

Nasal secretion has been regarded as one of the most difficult body fluids to identify and is especially difficult to discriminate from vaginal secretions and saliva. At present, few specific markers are known for nasal secretions. The aim of this study is to find a new approach for the identification of nasal secretions.

Efficient enumeration of stereoisomers of outerplanar chemical graphs using dynamic programming.

Exhaustive and nonredundant generation of stereoisomers of a chemical compound with a specified constitution is an important tool for molecular structure elucidation and molecular design. It is known that many chemical compounds have outerplanar graph structures. In this paper we deal with chemical compounds composed of carbon, hydrogen, oxygen, and nitrogen atoms whose graphical structures are outerplanar and consider stereoisomers caused only by asymmetry around carbon atoms.

Conditional random field approach to prediction of protein-protein interactions using domain information.

Background: For understanding cellular systems and biological networks, it is important to analyze functions and interactions of proteins and domains. Many methods for predicting protein-protein interactions have been developed. It is known that mutual information between residues at interacting sites can be higher than that at non-interacting sites.

IPknot: fast and accurate prediction of RNA secondary structures with pseudoknots using integer programming.

Motivation: Pseudoknots found in secondary structures of a number of functional RNAs play various roles in biological processes. Recent methods for predicting RNA secondary structures cover certain classes of pseudoknotted structures, but only a few of them achieve satisfying predictions in terms of both speed and accuracy.

Results: We propose IPknot, a novel computational method for predicting RNA secondary structures with pseudoknots based on maximizing expected accuracy of a predicted structure.

Trials of the detection of semen and vaginal fluid RNA using the genome profiling method.

The identification of sperm at the scene of a sexual crime is important evidence that can be used to prove that a crime took place. We used the new genome profiling (GP) method in this study to identify sperm and vaginal fluid from RNA extracted from bodily fluids. We randomly amplified genes via a PCR approach from these semen and vaginal fluid samples and performed temperature gradient gel electrophoresis between 15-65°C.