The International Virus Bioinformatics Meeting 2022 took place online, on 23–25 March 2022, and has attracted about 380 participants from all over the world. The goal of the meeting was to provide a meaningful and interactive scientific environment to promote discussion and collaboration and to inspire and suggest new research directions and questions. The participants created a highly interactive scientific environment even without physical face-to-face interactions. This meeting is a focal point to gain an insight into the state-of-the-art of the virus bioinformatics research landscape and to interact with researchers in the forefront as well as aspiring young scientists. The meeting featured eight invited and 18 contributed talks in eight sessions on three days, as well as 52 posters, which were presented during three virtual poster sessions. The main topics were: SARS-CoV-2, viral emergence and surveillance, virus–host interactions, viral sequence analysis, virus identification and annotation, phages, and viral diversity. This report summarizes the main research findings and highlights presented at the meeting.

The mpox virus (MPXV) is mutating at an exceptional rate for a DNA virus and its global spread is concerning, making genomic surveillance a necessity. With MpoxRadar, we provide an interactive dashboard to track virus variants on mutation level worldwide. MpoxRadar allows users to select among different genomes as reference for comparison. The occurrence of mutation profiles based on the selected reference is indicated on an interactive world map that shows the respective geographic sampling site in customizable time ranges to easily follow the frequency or trend of defined mutations. Furthermore, the user can filter for specific mutations, genes, countries, genome types, and sequencing protocols and download the filtered data directly from MpoxRadar. On the server, we automatically download all MPXV genomes and metadata from the National Center for Biotechnology Information (NCBI) on a daily basis, align them to the different reference genomes, generate mutation profiles, which are stored and linked to the available metainformation in a database. This makes MpoxRadar a practical tool for the genomic survaillance of MPXV, supporting users with limited computational resources. MpoxRadar is open-source and freely accessible at https://MpoxRadar.net.

Monkeypox (Mpox) is mutating at an exceptional rate for a DNA virus and its global spread is concerning, making genomic surveillance a necessity. With MpoxRadar, we provide an interactive dashboard to track virus variants on mutation level worldwide. MpoxRadar allows users to select among different genomes as reference for comparison. The occurrence of mutation profiles based on the selected reference is indicated on an interactive world map that shows the respective geographic sampling site in customizable time ranges to easily follow the frequency or trend of defined mutations. Furthermore, the user can filter for specific mutations, genes, countries, genome types, and sequencing protocols and download the filtered data directly from MpoxRadar. On the server, we automatically download all Mpox genomes and metadata from the National Center for Biotechnology Information (NCBI) on a daily basis, align them with the different reference genomes, generate mutation profiles, which are stored and linked to the available metainformation in a database. This makes MpoxRadar a practical tool for the genomic survaillance of Mpox, supporting users with limited computational resources. MpoxRadar is open-source and freely accessible at https://MpoxRadar.net.

String matching algorithms plays an important role in many applications of computer science: in particular searching, retrieving and processing of data. Various fields that rely on computer science for computing and data processing such as science, informatics (e.g. biology, medical, and healthcare), statistics, image, video/signal processing and computational aspect of business (e.g. finance, accounting, and computer security) would benefit greatly from efficient data search algorithm, in particular string matching. Any applications involving the use of database would use string matching algorithm. Many string matching algorithms such as TBM (Turbo Boyer Moore), BMH (Boyer-Moore-Horspool), BMHS (Boyer Moore Horspool Sundays, and BMHS2 (Boyer Moore Horspool Sundays 2) were introduced based on the celebrated BM (Boyer-Moore) algorithm considered to be one of the early efficient string searching algorithms. Although these algorithm offers significant performance improvement over the BM algorithm, they were designed with the assumption of single core computer architecture which executes the algorithm in a serialized manner. Today, multiple-core-processor computers are very common, and applications are designed to process big data thanks to the advanced in computing technology of various fields. High performance computing system utilizing parallel and distributed computing has started to become popular. This work evaluates and compares the performance of the aforementioned string matching algorithms in parallel and distributed environment for high performance computing with respect to that of the serialized single-core computing platform. In this work, the variants of BM algorithms are implemented and evaluated on Apache Spark, a popular distributed computing platform, by executing a set of queries of different search pattern lengths.