Biomedical subjects

Tomokazu Konishi

Publications and source records attributed to Tomokazu Konishi.

3 recordsLinked to original sources

PubMed2006

[Integrating obtained knowledge from transcriptome data by a new framework for data analysis].

Microarray analyses facilitate the investigation of quantitative information coded in the genome by measuring transcriptome, which records the decoded information from the genome. The state of a cell and differences from other states can be studied through genome information, by comparing one set of transcriptome data to other sets. Clearly, those data should be shared and compared with researchers, and the knowledge should be integrated. Unfortunately, at present data comparisons in microarray analyses are quite difficult; the accuracy as well as the reproducibility is low. The difficulties are originated from data analyses methods. Data comparison requires an intelligent framework, such as that discussed by philosopher Sir Karl R Popper. Frameworks for microarray analyses have been developed by many efforts of bioinformatitians. The frameworks currently used are being inspected and critically discussed. By checking the mathematical models that form the practical frameworks, arbitrariness such as the lack of falsifiability has been pointed out. The paradigm in this field of analyses is also criticized by disagreement with the scientific standard, and it is shown as the origin of errors in analyses. The excessive numbers of frameworks produced in an ad hoc manner has also been criticized, since the existence of so many allows researchers to select different frameworks, discussions beyond frameworks are always difficult. A new framework that uses a parametric model is introduced with an explanation of the bases of the framework and the process of testing. Additionally, differences of obtained results by these frameworks are presented using GeneChip data, in stability of log-ratio measurements and reproducibility of analyses. The possibility of artificial decoding of genome information by an extended framework is also discussed.

Gene Expression Profiling↗

PubMed2005

A thermodynamic model of transcriptome formation.

The genome supplies information on both the quality and quantity of the transcriptome. However, as it remains unknown how a cell determines transcript levels from the genome sequences, despite comprehensive knowledge of the cellular components involved, the quantity information held by the genome cannot as yet be derived from nucleotide sequences. The model presented here explains on a thermodynamic basis how the components decode the genome to form and maintain the transcriptome. The model describes the level of a transcript as a pseudo-equilibrium between velocities of synthesis and degradation, both of which are controlled by sequence-specific interactions between protein factors and nucleic acids. Each of the transcript levels can be described by a single equation expressing a function of the activity concentrations of the protein factors. Quantitative information in the genome can thus be transformed into constants determined from the nucleotide sequences. Using this model, the transcriptome can be traced back to the protein factors and the state of chromosome packaging. The total description of transcript levels allows the model to be verified through comparison of derived hypotheses with comprehensive measurements of the transcriptome. The hypotheses thus derived in the present study are well supported by experimental microarray data, confirming the appropriateness of the model.

Gene Expression Regulation↗

PubMed2004

Three-parameter lognormal distribution ubiquitously found in cDNA microarray data and its application to parametric data treatment.

BACKGROUND: To cancel experimental variations, microarray data must be normalized prior to analysis. Where an appropriate model for statistical data distribution is available, a parametric method can normalize a group of data sets that have common distributions. Although such models have been proposed for microarray data, they have not always fit the distribution of real data and thus have been inappropriate for normalization. Consequently, microarray data in most cases have been normalized with non-parametric methods that adjust data in a pair-wise manner. However, data analysis and the integration of resultant knowledge among experiments have been difficult, since such normalization concepts lack a universal standard. RESULTS: A three-parameter lognormal distribution model was tested on over 300 sets of microarray data. The model treats the hybridization background, which is difficult to identify from images of hybridization, as one of the parameters. A rigorous coincidence of the model to data sets was found, proving the model's appropriateness for microarray data. In fact, a closer fitting to Northern analysis was obtained. The model showed inconsistency only at very strong or weak data intensities. Measurement of z-scores as well as calculated ratios was reproducible only among data in the model-consistent intensity range; also, the ratios were independent of signal intensity at the corresponding range. CONCLUSION: The model could provide a universal standard for data, simplifying data analysis and knowledge integration. It was deduced that the ranges of inconsistency were caused by experimental errors or additive noise in the data; therefore, excluding the data corresponding to those marginal ranges will prevent misleading analytical conclusions.

Blotting, Northern↗