Meta-analysis of haplotype-association studies: comparison of methods and empirical evaluation of the literature

<p>Abstract</p> <p>Background</p> <p>Meta-analysis is a popular methodology in several fields of medical research, including genetic association studies. However, the methods used for meta-analysis of association studies that report haplotypes have not been studied in detail. In this work, methods for performing meta-analysis of haplotype association studies are summarized, compared and presented in a unified framework along with an empirical evaluation of the literature.</p> <p>Results</p> <p>We present multivariate methods that use summary-based data as well as methods that use binary and count data in a generalized linear mixed model framework (logistic regression, multinomial regression and Poisson regression). The methods presented here avoid the inflation of the type I error rate that could be the result of the traditional approach of comparing a haplotype against the remaining ones, whereas, they can be fitted using standard software. Moreover, formal global tests are presented for assessing the statistical significance of the overall association. Although the methods presented here assume that the haplotypes are directly observed, they can be easily extended to allow for such an uncertainty by weighting the haplotypes by their probability.</p> <p>Conclusions</p> <p>An empirical evaluation of the published literature and a comparison against the meta-analyses that use single nucleotide polymorphisms, suggests that the studies reporting meta-analysis of haplotypes contain approximately half of the included studies and produce significant results twice more often. We show that this excess of statistically significant results, stems from the sub-optimal method of analysis used and, in approximately half of the cases, the statistical significance is refuted if the data are properly re-analyzed. Illustrative examples of code are given in Stata and it is anticipated that the methods developed in this work will be widely applied in the meta-analysis of haplotype association studies.</p

Implementasi Sumber Daya Penyimpanan Dinamis Pada Cloud

Seiring dengan perkembangan jaman kebutuhan dan ketergantungan manusia terhadap komputer semakin besar seiring pekembangan dunia teknologi modern. Penggunaan komputer sendiri dari berbagai bidang untuk menyelesaikan masalah komputasi dari skala kecil sampai besar. Tidak dapat dipungkiri dengan berkembangnya aplikasi dalam dunia komputer membutuhkan sumber daya yang lebih besar, sehingga kurang efisien apabila hanya dikerjakan dengan sebuah mesin fisik saja. Berbagai cara dilakukan untuk mengatasi masalah ini salah satunya dengan virtualisasi. Dalam penelitian ini dilakukan implementasi manajemen sumber daya penyimpanan dinamis pada Cloud. Virtulasisasi yang digunakan dalam penelitian ini dengan menggunakan virtual box. Penyimpanan dinamis yang digunakan dalam penelitian ini dengan mengimplementasi fitur Logical Volume Management. Hasil dari penelitian ini didapatkan sebuah sistem yang dapat melakukan penambahan kapasitas storage secara dinamis dan otomatis tanpa adanya dOwntime dan rebooting pada sistem

Evaluation of methods for predicting the topology of β-barrel outer membrane proteins and a consensus prediction method

BACKGROUND: Prediction of the transmembrane strands and topology of β-barrel outer membrane proteins is of interest in current bioinformatics research. Several methods have been applied so far for this task, utilizing different algorithmic techniques and a number of freely available predictors exist. The methods can be grossly divided to those based on Hidden Markov Models (HMMs), on Neural Networks (NNs) and on Support Vector Machines (SVMs). In this work, we compare the different available methods for topology prediction of β-barrel outer membrane proteins. We evaluate their performance on a non-redundant dataset of 20 β-barrel outer membrane proteins of gram-negative bacteria, with structures known at atomic resolution. Also, we describe, for the first time, an effective way to combine the individual predictors, at will, to a single consensus prediction method. RESULTS: We assess the statistical significance of the performance of each prediction scheme and conclude that Hidden Markov Model based methods, HMM-B2TMR, ProfTMB and PRED-TMBB, are currently the best predictors, according to either the per-residue accuracy, the segments overlap measure (SOV) or the total number of proteins with correctly predicted topologies in the test set. Furthermore, we show that the available predictors perform better when only transmembrane β-barrel domains are used for prediction, rather than the precursor full-length sequences, even though the HMM-based predictors are not influenced significantly. The consensus prediction method performs significantly better than each individual available predictor, since it increases the accuracy up to 4% regarding SOV and up to 15% in correctly predicted topologies. CONCLUSIONS: The consensus prediction method described in this work, optimizes the predicted topology with a dynamic programming algorithm and is implemented in a web-based application freely available to non-commercial users at

OMPdb: a database of β-barrel outer membrane proteins from Gram-negative bacteria

We describe here OMPdb, which is currently the most complete and comprehensive collection of integral β-barrel outer membrane proteins from Gram-negative bacteria. The database currently contains 69 354 proteins, which are classified into 85 families, based mainly on structural and functional criteria. Although OMPdb follows the annotation scheme of Pfam, many of the families included in the database were not previously described or annotated in other publicly available databases. There are also cross-references to other databases, references to the literature and annotation for sequence features, like transmembrane segments and signal peptides. Furthermore, via the web interface, the user can not only browse the available data, but submit advanced text searches and run BLAST queries against the database protein sequences or domain searches against the collection of profile Hidden Markov Models that represent each family’s domain organization as well. The database is freely accessible for academic users at http://bioinformatics.biol.uoa.gr/OMPdb and we expect it to be useful for genome-wide analyses, comparative genomics as well as for providing training and test sets for predictive algorithms regarding transmembrane β-barrels

Algorithms for incorporating prior topological information in HMMs: application to transmembrane proteins

BACKGROUND: Hidden Markov Models (HMMs) have been extensively used in computational molecular biology, for modelling protein and nucleic acid sequences. In many applications, such as transmembrane protein topology prediction, the incorporation of limited amount of information regarding the topology, arising from biochemical experiments, has been proved a very useful strategy that increased remarkably the performance of even the top-scoring methods. However, no clear and formal explanation of the algorithms that retains the probabilistic interpretation of the models has been presented so far in the literature. RESULTS: We present here, a simple method that allows incorporation of prior topological information concerning the sequences at hand, while at the same time the HMMs retain their full probabilistic interpretation in terms of conditional probabilities. We present modifications to the standard Forward and Backward algorithms of HMMs and we also show explicitly, how reliable predictions may arise by these modifications, using all the algorithms currently available for decoding HMMs. A similar procedure may be used in the training procedure, aiming at optimizing the labels of the HMM's classes, especially in cases such as transmembrane proteins where the labels of the membrane-spanning segments are inherently misplaced. We present an application of this approach developing a method to predict the transmembrane regions of alpha-helical membrane proteins, trained on crystallographically solved data. We show that this method compares well against already established algorithms presented in the literature, and it is extremely useful in practical applications. CONCLUSION: The algorithms presented here, are easily implemented in any kind of a Hidden Markov Model, whereas the prediction method (HMM-TM) is freely available for academic users at , offering the most advanced decoding options currently available

ANALISIS PERBANDINGAN PENGARUH POLITICAL CONNECTIONS PADA JUMLAH DEPOSITO BANK UMUM KONVENSIONAL YANG DIMODERASI OLEH INDEX KORUPSI ANTARA NEGARA INDONESIA DAN SINGAPURA

Penelitian ini bertujuan untuk mengetahui pengaruh political connection pada bank umum konvensional yang ada di Negara Indonesia dan Singapura terhadap jumlah deposit. Political connection pada penelitian ini diukur dari jajaran eksekutif ataupun pemilik saham mayoritas dimana ada atau tidaknya hubungan atau koneksi dengan pemerintahan pada negara tersebut dari jajaran eksekutif ataupun pemilik saham mayoritas. Sedangkan tingkat deposit diukur dengan jumlah dari deposito yang ada pada suatu bank dan dimoderasi oleh index korupsi masing-masing negara tersebut. Data yang digunakan dalam penelitian ini adalah data sekunder yang bersumber dari laporan keuangan tahunan bank umum konvensional. Populasi obyek penelitian ini terdiri dari 101 bank yang ada di Indonesia dan 88 bank yang ada di Singapura. Penelitian ini menggunakan program Stata versi 13.0 for windows sebagai alat untuk uji pengaruh atau uji regresi. Hasil temuan penelitian ini menunjukkan beberapa hal, yakni 1) Pengaruh Political connection, index korupsi, dan firm size; interest rate; NPL; ROA; Inflation; Credit risk terhadap tingkat deposito 2) terdapat masalah multikolonieritas dan heterokedastisitas data. Pada negara Indonesia political connection tidak berpengaruh positif signifikan, akan tetapi ukuran perusahaan; tingkat suku bunga deposito; dan tingkat inflasi berpengaruh signifikan terhadap jumlah deposito. Sedangkan, negara Singapura political tidak berpengaruh negative signifikan. Akan tetapi ukuran perusahaan, resiko kredit, dan ROA berpengaruh signifikan terhadap jumlah deposito. Hasil dari penelitian ini dapat menjadi bahan pertimbangan jajaran eksekutif bank dalam mengambil keputusan untuk meningkatkan jumlah deposito. Kata kunci: koneksi politik, jumlah deposito, index korups

PENGARUH LDR, IPR, APB, NPL, PPAP, IRR, PDN, BOPO, FBIR DAN FACR TERHADAP RETURN ON ASSET (ROA) PADA BANK UMUM SWASTA NASIONAL GO PUBLIC

This research aims to analyze whether LDR, IPR, APB, NPL, PPAP, IRR, PDN, BOPO, FBIR and FACR simultaneously and partially have influence significant toward ROA on Go Public Private National Banks. Samples in research are ICB Bumiputera Bank, Nusantara Parahyangan Bank and Windu kentjana Bank. Data and data collecting method in this reserch uses secondary data. The data are taken from published financial report of Go Public Private National Banks begun from first quarter at year 2009 until second quarter at year 2012. The technique of data analysis uses multiple regression analysis. The result of the research showed that LDR, IPR, APB, NPL, PPAP, IRR, PDN, BOPO, FBIR and FACR simultaneously have influence significant toward ROA on Go Public Private National Banks. FBIR partially have influence positive significant toward ROA on Go Public Private National Banks. BOPO partially have influence negative significant toward ROA on Go Public Private National Banks. And the other hand, LDR, IPR and FACR partially have influence positive unsignificant toward ROA on Go Public Private National Banks. APB, NPL and PPAP partially have influence negative unsignificant toward ROA on Go Public Private National Banks. IRR and PDN partially have influence unsignificant toward ROA on Go Public Private National Banks. And among the ten variable most dominant variable was the BOPO. Key word : Return on Asset, Go Public Private National Banks LDR, IPR, APB, NPL, PPAP, IRR, PDN, BOPO, FBIR and FAC

A database for G proteins and their interaction with GPCRs

BACKGROUND: G protein-coupled receptors (GPCRs) transduce signals from extracellular space into the cell, through their interaction with G proteins, which act as switches forming hetero-trimers composed of different subunits (α,β,γ). The α subunit of the G protein is responsible for the recognition of a given GPCR. Whereas specialised resources for GPCRs, and other groups of receptors, are already available, currently, there is no publicly available database focusing on G Proteins and containing information about their coupling specificity with their respective receptors. DESCRIPTION: gpDB is a publicly accessible G proteins/GPCRs relational database. Including species homologs, the database contains detailed information for 418 G protein monomers (272 Gα, 87 Gβ and 59 Gγ) and 2782 GPCRs sequences belonging to families with known coupling to G proteins. The GPCRs and the G proteins are classified according to a hierarchy of different classes, families and sub-families, based on extensive literature searchs. The main innovation besides the classification of both G proteins and GPCRs is the relational model of the database, describing the known coupling specificity of the GPCRs to their respective α subunit of G proteins, a unique feature not available in any other database. There is full sequence information with cross-references to publicly available databases, references to the literature concerning the coupling specificity and the dimerization of GPCRs and the user may submit advanced queries for text search. Furthermore, we provide a pattern search tool, an interface for running BLAST against the database and interconnectivity with PRED-TMR, PRED-GPCR and TMRPres2D. CONCLUSIONS: The database will be very useful, for both experimentalists and bioinformaticians, for the study of G protein/GPCR interactions and for future development of predictive algorithms. It is available for academics, via a web browser at the URL

A Hidden Markov Model method, capable of predicting and discriminating β-barrel outer membrane proteins

BACKGROUND: Integral membrane proteins constitute about 20–30% of all proteins in the fully sequenced genomes. They come in two structural classes, the α-helical and the β-barrel membrane proteins, demonstrating different physicochemical characteristics, structure and localization. While transmembrane segment prediction for the α-helical integral membrane proteins appears to be an easy task nowadays, the same is much more difficult for the β-barrel membrane proteins. We developed a method, based on a Hidden Markov Model, capable of predicting the transmembrane β-strands of the outer membrane proteins of gram-negative bacteria, and discriminating those from water-soluble proteins in large datasets. The model is trained in a discriminative manner, aiming at maximizing the probability of correct predictions rather than the likelihood of the sequences. RESULTS: The training has been performed on a non-redundant database of 14 outer membrane proteins with structures known at atomic resolution; it has been tested with a jacknife procedure, yielding a per residue accuracy of 84.2% and a correlation coefficient of 0.72, whereas for the self-consistency test the per residue accuracy was 88.1% and the correlation coefficient 0.824. The total number of correctly predicted topologies is 10 out of 14 in the self-consistency test, and 9 out of 14 in the jacknife. Furthermore, the model is capable of discriminating outer membrane from water-soluble proteins in large-scale applications, with a success rate of 88.8% and 89.2% for the correct classification of outer membrane and water-soluble proteins respectively, the highest rates obtained in the literature. That test has been performed independently on a set of known outer membrane proteins with low sequence identity with each other and also with the proteins of the training set. CONCLUSION: Based on the above, we developed a strategy, that enabled us to screen the entire proteome of E. coli for outer membrane proteins. The results were satisfactory, thus the method presented here appears to be suitable for screening entire proteomes for the discovery of novel outer membrane proteins. A web interface available for non-commercial users is located at: , and it is the only freely available HMM-based predictor for β-barrel outer membrane protein topology

PERUBAHAN STRUKTUR EKONOMI DALAM PEMBANGUNAN DI INDONESIA DENGAN KERANGKA ANALISIS CHENERY DAN SYRQUIN PERIODE TAHUN 1978-1998

Pembangunan ekonomi yang dilakukan oleh suatu negara tidak hanya akan berdampak pada semakin tingginya tingkat kesejahteraan masyarakat yang ditandai dengan semakin tingginya Gross National Product (GNP) dari tahun ke tahun. Disamping adanya pertumbuhan ekonomi, pembangunan yang dilakukan juga berdampak pada perubahan struktur ekonomi seiring dengan peningkatan pendapatan per kapita yang terjadi. Perubahan struktur yang terjadi berupa terjadinya pergeseran peran tiap-tiap sektor ekonomi baik ditinjau dari struktur permintaan domestik, struktur produksi dan struktur perdagangan internasional. Pada dasarnya perubahan struktur ekonomi yang terjadi selama proses pembangunan, telah lama menjadi bahan kajian beberapa ekonom. Salah satu studi yang cukup terkenal adalah penelitian yang dilakukan oleh Chenery dan Syrquin, berdasarkan data ekonomi negara-negara berkembang pada tahun 1950 -1970. Dalam penelitian ini kerangka analisis yang digunakan oleh Chenery dan Syrquin tersebut digunakan sebagai dasar penelitian untuk meninjau perekonomian Indonesia dalam dua dekade terakhir yaitu mulai tahun 1978 hingga 1998. Tujuan utama dari penelitian ini adalah untuk mengetahui apakah hipotesis yang diajukan oleh Chenery dan Syrquin mengenai perubahan struktur permintaan domestik, struktur produksi dan perdagangan internasional berlaku dalam kasus perekonomian Indonesia. Hasil penelitian menunjukkan bahwa secara umum terdapat kesesuaian antara analisis Chenery dan Syrquin dengan kasus Indonesia. Dalam hal ini faktor pendapatan per kapita memegang peranan penting dalam menjelaskan perubahan-perubahan struktur yang terjadi. Adanya variasi antara model Chenery dan Syrquin dengan realita ekonomi Indonesia disebabkan oleh adanya faktor eksternal yang tidak terduga sebelumnya, seperti kasus ekonomi Indonesia mulai pertengahan 1997 yang telah mengakibatkan penurunan cukup besar dalam pendapatan per-kapita masyarakat Indonesia. Di samping itu adanya gejolak harga minyak dunia sedikit banyak berpengaruh terhadap struktur perdagangan Indonesia dan pola konsumsi pemerintah, mengingat selama ini minyak dan gas (migas) merupakan salah satu sumber penerimaan negara yang penting peranannya bagi pembangunan nasional