Next-generation sequencing (NGS) technologies have led to the accumulation of high-throughput sequence data from various organisms in biology. To apply gene annotation of organellar genomes for various organisms, more optimized tools for functional gene annotation are required. Almost all gene annotation tools are mainly focused on the chloroplast genome of land plants or the mitochondrial genome of animals. We have developed a web application AGORA for the fast, user-friendly and improved annotations of organellar genomes. Annotator for Genes of Organelle from the Reference sequence Analysis (AGORA) annotates genes based on a basic local alignment search tool (BLAST)-based homology search and clustering with selected reference sequences from the NCBI database or user-defined uploaded data. AGORA can annotate the functional genes in almost all mitochondrion and plastid genomes of eukaryotes. The gene annotation of a genome with an exon–intron structure within a gene or inverted repeat region is also available. It provides information of start and end positions of each gene, BLAST results compared with the reference sequence and visualization of gene map by OGDRAW. Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/gene_project/AGORA/. The main module of the tool is implemented by the python and php, and the web page is built by the HTML and CSS to support all browsers. Supplementary data are available at Bioinformatics online.

Cryptophytes are an ecologically important group of algae comprised of phototrophic, heterotrophic and osmotrophic species. This lineage is of great interest to evolutionary biologists because their plastids are of red algal secondary endosymbiotic origin. Cryptophytes have a clear phylogenetic affinity to heterotrophic eukaryotes and possess four genomes: host-derived nuclear and mitochondrial genomes, and plastid and nucleomorph genomes of endosymbiotic origin. To gain insight into cryptophyte mitochondrial genome evolution, we sequenced the mitochondrial DNAs of five species and performed a comparative analysis of seven genomes from the following cryptophyte genera: Chroomonas, Cryptomonas, Hemiselmis, Proteomonas, Rhodomonas, Storeatula and Teleaulax. The mitochondrial genomes were similar in terms of their general architecture, gene content and presence of a large repeat region. However, gene order was poorly conserved. Characteristic features of cryptophyte mtDNAs included large syntenic clusters resembling α-proteobacterial operons that encode bacteria-like rRNAs, tRNAs, and ribosomal protein genes. The cryptophyte mitochondrial genomes retain almost all genes found in many other eukaryotes including the nad, sdh, cox, cob, and atp genes, with the exception of sdh2 and atp3. In addition, gene cluster analysis showed that cryptophytes possess a gene order closely resembling the jakobid flagellates Jakoba and Reclinomonas. Interestingly, the cox1 gene of R. salina, T. amphioxeia, and Storeatula species was found to contain group II introns encoding a reverse transcriptase protein, as did the cob gene of Storeatula species CCMP1868. These newly sequenced genomes increase the breadth of data available from algae and will aid in the identification of general trends in mitochondrial genome evolution. While most of the genomes were highly conserved, extensive gene arrangements have shuffled gene order, perhaps due to genome rearrangements associated with hairpin-containing mobile genetic elements, tRNAs with palindromic sequences, and tandem repeat sequences. The cox1 and cob gene sequences suggest that introns have recently been acquired during cryptophyte evolution. Comparison of phylogenetic trees based on plastid and mitochondrial genome data sets underscore the different evolutionary histories of the host and endosymbiont components of present-day cryptophytes.

Topology control is one of the significant research topics in traditional wireless networks. The primary purpose of topology control ensures the connectivity of wireless nodes participated in the network. Low-power Internet of Things communication networks look like wireless network environments in which the main communication devices are wireless devices with limited energy like battery. In this paper, we propose a distributed topology control algorithm by merging the combinatorial block design from a design theory with the multiples of 2. The proposed technique especially focuses on asynchronous and asymmetric neighbor discovery. The concept of block design is used to generate the neighbor discovery schedule when a target duty cycle is given. In addition, the multiples of 2 are applied to overcome the challenge of the block design and support asymmetric operation. We analyze the worst case discovery latency and energy consumption numerically by calculating the total number of slots and wake-up slots based on the given duty cycle. It shows that our proposed method has the smallest total number of slots and wake-up slots among existing representative neighbor discovery protocols. The numerical analysis represents the proposed technique find neighbors quickly with minimum battery power compared with other protocols for distributed topology control. For future research direction, we could perform a simulation study or real experiment to investigate the best parameter for choosing the multiple of a certain number.

In comparative and evolutionary genomics, a detailed comparison of common features between organisms is essential to evaluate genetic distance. However, identifying differences in matched and mismatched genes among multiple genomes is difficult using current comparative genomic approaches due to complicated methodologies or the generation of meager information from obtained results. This study describes a visualized software tool, geneCo (gene Comparison), for comparing genome structure and gene arrangements between various organisms. User data are aligned, gene information is recognized, and genome structures are compared based on user-defined GenBank files. Information regarding inversion, gain, loss, duplication and gene rearrangement among multiple organisms being compared is provided by geneCo, which uses a web-based interface that users can easily access without any need to consider the computational environment. Users can freely use the software, and the accessible URL is https://bigdata.dongguk.edu/geneCo. The main module of geneCo is implemented by Python and the web-based user interface is built by PHP, HTML and CSS to support all browsers. Supplementary data are available at Bioinformatics online.

With recent advances in next-generation sequencing (NGS) technology, large volumes of data have been produced in the form of short reads. Sequence assembly involves using initial short reads to produce progressively longer contigs, and then using scaffolds to produce the final sequence. These processes each require evaluation of the extent of homology between different sequences. However, because the NGS platforms currently being developed are diverse, and the data being produced are of different sizes and read lengths, numerous algorithms are being developed with unique methodologies to process this complex data. It is difficult for biologists to manipulate the different features involved in these algorithms. Therefore, to reduce experimental trial-and-error, different strategies are required depending on the performance and purpose of the optimal algorithm, thereby facilitating understanding of algorithm methodologies and effective use of their various features. This study is a review of the different short read alignment algorithms and NGS platforms that have been developed to date, in order to aid efficient selection of algorithms for reference sequences and mapping of DNA data.