Development of Bioinformatics Infrastructure for Genomics Research in H3Africa

Background: Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet’s role has evolved in response to changing needs from the consortium and the African bioinformatics community. Objectives: H3ABioNet set out to develop core bioinformatics infrastructure and capacity for genomics research in various aspects of data collection, transfer, storage, and analysis. Methods and Results: Various resources have been developed to address genomic data management and analysis needs of H3Africa researchers and other scientific communities on the continent. NetMap was developed and used to build an accurate picture of network performance within Africa and between Africa and the rest of the world, and Globus Online has been rolled out to facilitate data transfer. A participant recruitment database was developed to monitor participant enrollment, and data is being harmonized through the use of ontologies and controlled vocabularies. The standardized metadata will be integrated to provide a search facility for H3Africa data and biospecimens. Because H3Africa projects are generating large-scale genomic data, facilities for analysis and interpretation are critical. H3ABioNet is implementing several data analysis platforms that provide a large range of bioinformatics tools or workflows, such as Galaxy, the Job Management System, and eBiokits. A set of reproducible, portable, and cloud-scalable pipelines to support the multiple H3Africa data types are also being developed and dockerized to enable execution on multiple computing infrastructures. In addition, new tools have been developed for analysis of the uniquely divergent African data and for downstream interpretation of prioritized variants. To provide support for these and other bioinformatics queries, an online bioinformatics helpdesk backed by broad consortium expertise has been established. Further support is provided by means of various modes of bioinformatics training. Conclusions: For the past 4 years, the development of infrastructure support and human capacity through H3ABioNet, have significantly contributed to the establishment of African scientific networks, data analysis facilities, and training programs. Here, we describe the infrastructure and how it has affected genomics and bioinformatics research in Africa

Development of Bioinformatics Infrastructure for Genomics Research:

Although pockets of bioinformatics excellence have developed in Africa, generally, large-scale genomic data analysis has been limited by the availability of expertise and infrastructure. H3ABioNet, a pan-African bioinformatics network, was established to build capacity specifically to enable H3Africa (Human Heredity and Health in Africa) researchers to analyze their data in Africa. Since the inception of the H3Africa initiative, H3ABioNet's role has evolved in response to changing needs from the consortium and the African bioinformatics community

Aplikasi pencarian rute tercepat kendaraan bermotor di Kota Malang menggunakan algoritma dijkstra dan multiple regression

INDONESIA: Kota Malang termasuk salah satu kota yang memiliki tingkat kepadatan penduduk yang sangat tinggi. Kemacetan lalu lintas sering menjadi masalah besar bagi masyarakat. Berdasarkan data hasil riset INRIX pada tahun 2017, lembaga survei asal Amerika Serikat yang berfokus pada riset lalu lintas jalan menunjukkan bahwa kemacetan di Malang Raya mengalahkan Kota Surabaya yang berada di posisi kesembilan. Dibutuhkan sebuah petunjuk arah agar masyarakat dapat terhindar dari kemacetan dengan estimasi waktu yang cepat. Berdasarkan beberapa pertimbangan tersebut, maka perlu adanya suatu penelitian untuk menentukan jalur tercepat menuju stasiun Kotabaru di Kota Malang. Sebuah aplikasi pencarian rute tercepat kendaraan bermotor di kota Malang dibangun dengan menggunakan algoritma dijkstra dan multiple regression. Penelitian ini bertujuan untuk mengetahui akurasi rute tercepat yang dihasilkan dari aplikasi yang dibangun menggunakan algoritma dijkstra dan multiple regression. Data yang digunakan dalam penelitian ini adalah data jumlah kendaraan pada setiap jalan dari rute yang telah ditentukan dan data kecepatan yang diperoleh dari kecepatan rata-rata kendaraan bermotor yang diambil secara langsung. Kemudian diproses menggunakan algoritma multiple regression agar menghasilkan sebuah bobot. Bobot tersebut digunakan untuk data masukan pada penentuan rute tercepat menggunakan algoritma dijkstra. Kemudian dihitung persentase error-nya. Sehingga akurasi aplikasi dapat dihitung berdasarkan persentase error yang dihasilkan. Proses pengujian dilakukan dengan membandingkan data waktu tempuh yang dihasilkan aplikasi dengan data testing rute secara langsung. Dari 10 kali percobaan yang telah dilakukan, persentase error yang terkecil yang dihasilkan adalah sebesar 3,67% dan persentase error terbesar yang dihasilkan adalah sebesar 20,98%. Nilai rata-rata persentase error adalah sebesar 11,21% dan nilai akurasi yang dihasilkan adalah 88,79%. Sebagai perbandingan, dilakukan perhitungan persentase error terhadap aplikasi yang sudah ada yaitu Google maps. Nilai persentase error yang dihasilkan oleh aplikasi lebih kecil jika dibandingkan dengan nilai persentase error yang dihasilkan oleh Google maps, yaitu sebesar 38,30%. ENGLISH: Malang City is one of the cities that has a very high population density. Congestions are often a big problem for the community. Based on INRIX's research data in 2017, a survey institute from the United States that focuses on road traffic research shows that congestion in Malang City beats Surabaya City which is in ninth position. A direction is needed so that people can avoid congestion with a fast estimate of time. Based on these considerations, it is necessary to have a research to determine the fastest route to Kotabaru station in Malang City. A search application for the fastest route of vehicles in Malang City was built using the dijkstra algorithm and multiple regression. This research aims to determine the accuracy of the fastest route generated from the application built using the dijkstra algorithm and multiple regression. The data used in this research is the data on the number of vehicles on each road from a predetermined route and speed data obtained from the average speed of vehicles taken directly. Then it is processed using a multiple regression algorithm to produce a weight. This weight is used for input data in determining the fastest route using the dijkstra algorithm. Then the error percentage is calculated. So that the accuracy of the application can be calculated based on the percentage of errors generated. The testing process is done by comparing the travel time data generated by the application with route testing data directly. Of the 10 experiments that have been carried out, the smallest percentage error produced is 3.67% and the largest percentage of errors generated is 20.98%. The average value of error percentage is 11.21% and the resulting accuracy value is 88.79%. For comparison, a percentage error is calculated on an existing application, namely Google maps. The percentage value of errors generated by the application is smaller when compared to the percentage value of errors generated by Google maps, which is equal to 38.30%

Pan-genomic analysis of Corynebacterium amycolatum gives insights into molecular mechanisms underpinning the transition to a pathogenic phenotype

Corynebacterium amycolatum is a nonlipophilic coryneform which is increasingly being recognized as a relevant human and animal pathogen showing multidrug resistance to commonly used antibiotics. However, little is known about the molecular mechanisms involved in transition from colonization to the MDR invasive phenotype in clinical isolates. In this study, we performed a comprehensive pan-genomic analysis of C. amycolatum, including 26 isolates from different countries. We obtained the novel genome sequences of 8 of them, which are multidrug resistant clinical isolates from Spain and Tunisia. They were analyzed together with other 18 complete or draft C. amycolatum genomes retrieved from GenBank. The species C. amycolatum presented an open pan-genome (alpha = 0.854905), with 3,280 gene families, being 1,690 (51.52%) in the core genome, 1,121 related to accessory genes (34.17%), and 469 related to unique genes (14.29%). Although some classic corynebacterial virulence factors are absent in the species C. amycolatum, we did identify genes associated with immune evasion, toxin, and antiphagocytosis among the predicted putative virulence factors. Additionally, we found genomic evidence for extensive acquisition of antimicrobial resistance genes through genomic islands

Data_Sheet_1_Pan-genomic analysis of Corynebacterium amycolatum gives insights into molecular mechanisms underpinning the transition to a pathogenic phenotype.docx

Corynebacterium amycolatum is a nonlipophilic coryneform which is increasingly being recognized as a relevant human and animal pathogen showing multidrug resistance to commonly used antibiotics. However, little is known about the molecular mechanisms involved in transition from colonization to the MDR invasive phenotype in clinical isolates. In this study, we performed a comprehensive pan-genomic analysis of C. amycolatum, including 26 isolates from different countries. We obtained the novel genome sequences of 8 of them, which are multidrug resistant clinical isolates from Spain and Tunisia. They were analyzed together with other 18 complete or draft C. amycolatum genomes retrieved from GenBank. The species C. amycolatum presented an open pan-genome (α = 0.854905), with 3,280 gene families, being 1,690 (51.52%) in the core genome, 1,121 related to accessory genes (34.17%), and 469 related to unique genes (14.29%). Although some classic corynebacterial virulence factors are absent in the species C. amycolatum, we did identify genes associated with immune evasion, toxin, and antiphagocytosis among the predicted putative virulence factors. Additionally, we found genomic evidence for extensive acquisition of antimicrobial resistance genes through genomic islands.</p