Hasil Pencarian  ::  Simpan CSV :: Kembali

Abstrak :
[ABSTRAK
Virus dengue terdiri atas 10 protein penyusun yang berbeda dan diklasifikasikan menjadi empat serotipe utama (DEN 1 ? DEN 4). Penelitian ini dirancang untuk melakukan pengelompokan terhadap 30 sekuens protein virus dengue yang diambil dari Virus Pathogen Database and Analysis Resource (ViPR) menggunakan metode Regularized Markov Clustering (R?MCL) dan untuk menganalisis hasilnya. Dengan menggunakan program Python 3.4, algoritma R-MCL diimplementasikan dan menghasilkan 8 kelompok dengan pusat kelompok lebih dari satu di beberapa kelompok. Banyaknya pusat kelompok menunjukkan tingkat kepadatan interaksi. Interaksi protein ? protein yang terhubung padat dalam jaringan cenderung membentuk kompleks protein yang berfungsi sebagai unit proses biologi tertentu. Hasil analisis menunjukkan hasil pengelompokan dengan R-MCL menghasilkan kelompok ? kelompok kekerabatan virus dengue berdasarkan peran yang sama dari protein penyusunnya, tanpa memperhatikan serotipenya.

---

ABSTRACT
Dengue virus consists 10 different constituent proteins and are classified into four major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and tp analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. The density of interactions protein - protein connected in a network tend to form a protein complex that serves as the unit of specific biological processes. The analyzing result shows the R-MCL clustering produces clusters of dengue virus family based on the similirity role of their constituent protein, regardless serotypes;Dengue virus consists 10 different constituent proteins and are classified into four major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and tp analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. The density of interactions protein - protein connected in a network tend to form a protein complex that serves as the unit of specific biological processes. The analyzing result shows the R-MCL clustering produces clusters of dengue virus family based on the similirity role of their constituent protein, regardless serotypes;Dengue virus consists 10 different constituent proteins and are classified into four major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and tp analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. The density of interactions protein - protein connected in a network tend to form a protein complex that serves as the unit of specific biological processes. The analyzing result shows the R-MCL clustering produces clusters of dengue virus family based on the similirity role of their constituent protein, regardless serotypes, Dengue virus consists 10 different constituent proteins and are classified into four major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and tp analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. The density of interactions protein - protein connected in a network tend to form a protein complex that serves as the unit of specific biological processes. The analyzing result shows the R-MCL clustering produces clusters of dengue virus family based on the similirity role of their constituent protein, regardless serotypes]

Abstrak :
Next generation sequencing is revolutionizing molecular biology. Owing to this new technology it is now possible to carry out a panoply of experiments at an unprecedented low cost and high speed. These go from sequencing whole genomes, transcriptomes and small non-coding RNAs to description of methylated regions, identification protein – DNA interaction sites and detection of structural variation. The generation of gigabases of sequence information for each of this huge bandwidth of applications in just a few days makes the development of bioinformatics applications for next generation sequencing data analysis as urgent as challenging.

Abstrak :
Tesis ini membahas pengelompokan virus-virus influenza A. Virus influenza A adalah virus RNA yang berbahaya, karena memiliki kemampuan mutasi yang tinggi dan menyebabkan wabah di beberapa negara. Dengan kemajuan bioinformatika, virus-virus dapat dikelompokkan dengan menganalisis sekuens-sekuens protein dari virus-virus tersebut. Markov clustering (MCL) telah diaplikasikan dengan baik pada bioinformatika, seperti; mengelompokkan jaringan-jaringan antara protein yang satu dengan yang lain, jaringan kemiripan antar protein, dan penentuan keluarga protein. Tujuan penelitian ini adalah mengelompokkan virus-virus influenza A berdasarkan protein hemaglutinin (HA) menggunakan algoritma Markov clustering (MCL) dan program menggunakan perangkat lunak Octave berbasis open source. Simulasi program menggunakan tiga buah faktor penggelembungan yang berbeda, yaitu; r = 1.5, r = 2.0, dan r = 2.5. Pengelompokan virus-virus influenza A menghasilkan dua kelompok. Kelompok pertama dengan pusat kelompoknya A/duck/Jiangsu/115/2011(H4N2) dan kelompok kedua dengan pusat kelompoknya A/duck/Victoria/0305-2/2012 (H5N3). Struktur pengelompokan virus-virus influenza A berdasarkan sekuens protein hemaglutinin (HA) yang diperoleh dengan menggunakan algoritma Markov clustering (MCL) mempunyai kemiripan struktur dengan struktur pengelompokan protein hemaglutinin (HA), dengan demikian pengelompokan virus-virus influenza A dapat mengacu pada pengelompokan keluarga protein hemaglutinin (HA). ...... The focus of this study is the clustering of influenza A viruses. Influenza A virus is an RNA virus that is dangerous, because it has a high mutation capability and caused outbreaks in several countries. With the development of bioinformatics, the viruses can be clustered by analyzing the protein sequences of these viruses. Markov clustering (MCL) has been very well applied to bioinformatics, such as to cluster protein-protein interactions (PPI) networks, determine the similarity between the protein network, and determine the protein families. The aim of this study is to cluster influenza A viruses based on hemagglutinin protein (HA) using Markov clustering (MCL) and programs using software Octave which based on open source. The simulation of program using three different inflation factors, ie; r = 1.5, r = 2.0 and r = 2.5. Clustering of influenza A viruses resulted in two clusters. The center of the first cluster is A / duck / Jiangsu / 115/2011 (H4N2) and the center of the second cluster is A / duck / Victoria / 0305-2 / 2012 (H5N3). Clustering structure of influenza A viruses using Markov clustering (MCL) have the similar structure with clustering structure of the hemaglutinin protein (HA), thus clustering of influenza A viruses can refer to the clustering of hemagglutinin proteins (HA) families.