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

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

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

An integrative machine learning approach for prediction of toxicity - related drug safety

Recent trends in drug development have been marked by diminishing returns caused by the escalating costs and falling rates of new drug approval. Unacceptable drug toxicity is a substantial cause of drug failure during clinical trials and the leading cause of drug withdraws after release to the market. Computational methods capable of predicting these failures can reduce the waste of resources and time devoted to the investigation of compounds that ultimately fail. We propose an original machine learning method that leverages identity of drug targets and off-targets, functional impact score computed from Gene Ontology annotations, and biological network data to predict drug toxicity. We demonstrate that our method (TargeTox) can distinguish potentially idiosyncratically toxic drugs from safe drugs and is also suitable for speculative evaluation of different target sets to support the design of optimal low-toxicity combinations

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

Linkage disequilibrium of evolutionarily conserved regions in the human genome

BACKGROUND: The strong linkage disequilibrium (LD) recently found in genic or exonic regions of the human genome demonstrated that LD can be increased by evolutionary mechanisms that select for functionally important loci. This suggests that LD might be stronger in regions conserved among species than in non-conserved regions, since regions exposed to natural selection tend to be conserved. To assess this hypothesis, we used genome-wide polymorphism data from the HapMap project and investigated LD within DNA sequences conserved between the human and mouse genomes. RESULTS: Unexpectedly, we observed that LD was significantly weaker in conserved regions than in non-conserved regions. To investigate why, we examined sequence features that may distort the relationship between LD and conserved regions. We found that interspersed repeats, and not other sequence features, were associated with the weak LD tendency in conserved regions. To appropriately understand the relationship between LD and conserved regions, we removed the effect of repetitive elements and found that the high degree of sequence conservation was strongly associated with strong LD in coding regions but not with that in non-coding regions. CONCLUSION: Our work demonstrates that the degree of sequence conservation does not simply increase LD as predicted by the hypothesis. Rather, it implies that purifying selection changes the polymorphic patterns of coding sequences but has little influence on the patterns of functional units such as regulatory elements present in non-coding regions, since the former are generally restricted by the constraint of maintaining a functional protein product across multiple exons while the latter may exist more as individually isolated units

Opal+: length - specific MoRF prediction in intrinsically disordered protein sequences

Intrinsically disordered proteins (IDPs) contain long unstructured regions, which play an important role in their function. These intrinsically disordered regions (IDRs) participate in binding events through regions called molecular recognition features (MoRFs). Computational prediction of MoRFs helps identify the potentially functional regions in IDRs. In this study, OPAL+, a novel MoRF predictor, is presented. OPAL+ uses separate models to predict MoRFs of varying lengths along with incorporating the hidden Markov model (HMM) profiles and physicochemical properties of MoRFs and their flanking regions. Together, these features help OPAL+ achieve a marginal performance improvement of 0.4-0.7% over its predecessor for diverse MoRF test sets. This performance improvement comes at the expense of increased run time as a result of the requirement of HMM profiles. OPAL+ is available for download at https://github.com/roneshsharma/OPAL-plus/wiki/OPAL-plus-Download