ヒトゲノムチュウ ノ イデンシ ベース ノ SNP ト レンサ フヘイコウ パターン ノ バリエーション

Tatsuhiko, Tsunoda; G. Mark, Lathrop; Akihiro, Sekine; Ryo, Yamada; Atsushi, Takahashi; Yozo, Ohnishi; Toshihiro, Tanaka; Yusuke, Nakamura. Human Molecular Genetics. 2004. 13(15), p.1623-1632

Activation of an Estrogen/ Estrogen Receptor Signaling by BIG3 Through Its Inhibitory Effect on Nuclear Transport of PHB2/REA in Breast Cancer

Breast cancer is known to be a hormone-dependent disease, and estrogens through an interaction with estrogen receptor (ER) enhance the proliferative and metastatic activity of breast tumor cells. Here we show a critical role of transactivation of BIG3, brefeldin A-inhibited guanine nucleotide-exchange protein 3, in activation of the estrogen/ER signaling in breast cancer cells. Knocking-down of BIG3 expression with small-interfering RNA (siRNA) drastically suppressed the growth of breast cancer cells. Subsequent co-immunoprecipitation and immunoblotting assays revealed an interaction of BIG3 with prohibitin 2/repressor of estrogen receptor activity (PHB2/REA). When BIG3 was absent, stimulation of estradiol caused the translocation of PHB2/REA to the nucleus, enhanced the interaction of PHB2/REA and ER[alpha], and resulted in suppression of the ER[alpha]; transcriptional activity. On the other hand, when BIG3 was present, BIG3 trapped PHB2/REA in cytoplasm and inhibited its nuclear translocation, and caused enhancement of ER[alpha]; transcriptional activity. Our results imply that BIG3 overexpression is one of the important mechanisms causing the activation of the estrogen/ER[alpha]; signaling pathway in the hormone-related growth of breast cancer cells

A replication study confirmed the EDAR gene to be a major contributor to population differentiation regarding head hair thickness in Asia

Hair morphology is a highly divergent phenotype among human populations. We recently reported that a nonsynonymous SNP in the ectodysplasin A receptor (EDAR 1540T/C) is associated with head hair fiber thickness in an ethnic group in Thailand (Thai-Mai) and an Indonesian population. However, these Southeast Asian populations are genetically and geographically close, and thus the genetic contribution of EDAR to hair morphological variation in the other Asian populations has remained unclear. In this study, we examined the association of 1540T/C with hair morphology in a Japanese population (Northeast Asian). As observed in our previous study, 1540T/C showed a significant association with hair cross-sectional area (P = 2.7 × 10−6) in Japanese. When all populations (Thai-Mai, Indonesian, and Japanese) were combined, the association of 1540T/C was stronger (P = 3.8 × 10−10) than those of age, sex, and population. These results indicate that EDAR is the genetic determinant of hair thickness as well as a strong contributor to hair fiber thickness variation among Asian populations

Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data

Background: High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Methods: Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. Results: This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. Conclusion: The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers

Bigram - PGK: phosphoglycerylation prediction using the technique of bigram probabilities of position specific scoring matrix

Background: The biological process known as post-translational modification (PTM) is a condition whereby proteomes are modified that affects normal cell biology, and hence the pathogenesis. A number of PTMs have been discovered in the recent years and lysine phosphoglycerylation is one of the fairly recent developments. Even with a large number of proteins being sequenced in the post-genomic era, the identification of phosphoglycerylation remains a big challenge due to factors such as cost, time consumption and inefficiency involved in the experimental efforts. To overcome this issue, computational techniques have emerged to accurately identify phosphoglycerylated lysine residues. However, the computational techniques proposed so far hold limitations to correctly predict this covalent modification. Results: We propose a new predictor in this paper called Bigram-PGK which uses evolutionary information of amino acids to try and predict phosphoglycerylated sites. The benchmark dataset which contains experimentally labelled sites is employed for this purpose and profile bigram occurrences is calculated from position specific scoring matrices of amino acids in the protein sequences. The statistical measures of this work, such as sensitivity, specificity, precision, accuracy, Mathews correlation coefficient and area under ROC curve have been reported to be 0.9642, 0.8973, 0.8253, 0.9193, 0.8330, 0.9306, respectively. Conclusions: The proposed predictor, based on the feature of evolutionary information and support vector machine classifier, has shown great potential to effectively predict phosphoglycerylated and non-phosphoglycerylated lysine residues when compared against the existing predictors. The data and software of this work can be acquired from https://github.com/abelavit/Bigram-PGK

Overexpression of the JmjC histone demethylase KDM5B in human carcinogenesis: involvement in the proliferation of cancer cells through the E2F/RB pathway.

BACKGROUND: Although an increasing number of histone demethylases have been identified and biochemically characterized, their biological functions largely remain uncharacterized, particularly in the context of human diseases such as cancer. We investigated the role of KDM5B, a JmjC histone demethylase, in human carcinogenesis. Quantitative RT-PCR and microarray analyses were used to examine the expression profiles of histone demethylases in clinical tissue samples. We also examined the functional effects of KDM5B on the growth of cancer cell lines treated with small interfering RNAs (siRNAs). Downstream genes and signal cascades induced by KDM5B expression were identified from Affymetrix Gene Chip experiments, and validated by real-time PCR and reporter assays. Cell cycle-dependent characteristics of KDM5B were identified by immunofluorescence and FACS. RESULTS: Quantitative RT-PCR analysis confirmed that expression levels of KDM5B are significantly higher in human bladder cancer tissues than in their corresponding non-neoplastic bladder tissues (P < 0.0001). The expression profile analysis of clinical tissues also revealed up-regulation of KDM5B in various kinds of malignancies. Transfection of KDM5B-specific siRNA into various bladder and lung cancer cell lines significantly suppressed the proliferation of cancer cells and increased the number of cells in sub-G1 phase. Microarray expression analysis indicated that E2F1 and E2F2 are downstream genes in the KDM5B pathway. CONCLUSIONS: Inhibition of KDM5B may affect apoptosis and reduce growth of cancer cells. Further studies will explore the pan-cancer therapeutic potential of KDM5B inhibition.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Brain wave classification using long short - term memory based OPTICAL predictor

Brain-computer interface (BCI) systems having the ability to classify brain waves with greater accuracy are highly desirable. To this end, a number of techniques have been proposed aiming to be able to classify brain waves with high accuracy. However, the ability to classify brain waves and its implementation in real-time is still limited. In this study, we introduce a novel scheme for classifying motor imagery (MI) tasks using electroencephalography (EEG) signal that can be implemented in real-time having high classification accuracy between different MI tasks. We propose a new predictor, OPTICAL, that uses a combination of common spatial pattern (CSP) and long short-term memory (LSTM) network for obtaining improved MI EEG signal classification. A sliding window approach is proposed to obtain the time-series input from the spatially filtered data, which becomes input to the LSTM network. Moreover, instead of using LSTM directly for classification, we use regression based output of the LSTM network as one of the features for classification. On the other hand, linear discriminant analysis (LDA) is used to reduce the dimensionality of the CSP variance based features. The features in the reduced dimensional plane after performing LDA are used as input to the support vector machine (SVM) classifier together with the regression based feature obtained from the LSTM network. The regression based feature further boosts the performance of the proposed OPTICAL predictor. OPTICAL showed significant improvement in the ability to accurately classify left and right-hand MI tasks on two publically available datasets. The improvements in the average misclassification rates are 3.09% and 2.07% for BCI Competition IV Dataset I and GigaDB dataset, respectively. The Matlab code is available at https://github.com/ShiuKumar/OPTICAL

hzAnalyzer: detection, quantification, and visualization of contiguous homozygosity in high-density genotyping datasets

The analysis of contiguous homozygosity (runs of homozygous loci) in human genotyping datasets is critical in the search for causal disease variants in monogenic disorders, studies of population history and the identification of targets of natural selection. Here, we report methods for extracting homozygous segments from high-density genotyping datasets, quantifying their local genomic structure, identifying outstanding regions within the genome and visualizing results for comparative analysis between population samples

Discovering MoRFs by trisecting intrinsically disordered protein sequence into terminals and middle regions

Background Molecular Recognition Features (MoRFs) are short protein regions present in intrinsically disordered protein (IDPs) sequences. MoRFs interact with structured partner protein and upon interaction, they undergo a disorder-to-order transition to perform various biological functions. Analyses of MoRFs are important towards understanding their function. Results Performance is reported using the MoRF dataset that has been previously used to compare the other existing MoRF predictors. The performance obtained in this study is equivalent to the benchmarked OPAL predictor, i.e., OPAL achieved AUC of 0.815, whereas the model in this study achieved AUC of 0.819 using TEST set. Conclusion Achieving comparable performance, the proposed method can be used as an alternative approach for MoRF prediction