Optimizing Hybrid de Novo Transcriptome Assembly and Extending Genomic Resources for Giant Freshwater Prawns (Macrobrachium rosenbergii): The Identification of Genes and Markers Associated with Reproduction.

The giant freshwater prawn, Macrobrachium rosenbergii, a sexually dimorphic decapod crustacean is currently the world's most economically important cultured freshwater crustacean species. Despite its economic importance, there is currently a lack of genomic resources available for this species, and this has limited exploration of the molecular mechanisms that control the M. rosenbergii sex-differentiation system more widely in freshwater prawns. Here, we present the first hybrid transcriptome from M. rosenbergii applying RNA-Seq technologies directed at identifying genes that have potential functional roles in reproductive-related traits. A total of 13,733,210 combined raw reads (1720 Mbp) were obtained from Ion-Torrent PGM and 454 FLX. Bioinformatic analyses based on three state-of-the-art assemblers, the CLC Genomic Workbench, Trans-ABySS, and Trinity, that use single and multiple k-mer methods respectively, were used to analyse the data. The influence of multiple k-mers on assembly performance was assessed to gain insight into transcriptome assembly from short reads. After optimisation, de novo assembly resulted in 44,407 contigs with a mean length of 437 bp, and the assembled transcripts were further functionally annotated to detect single nucleotide polymorphisms and simple sequence repeat motifs. Gene expression analysis was also used to compare expression patterns from ovary and testis tissue libraries to identify genes with potential roles in reproduction and sex differentiation. The large transcript set assembled here represents the most comprehensive set of transcriptomic resources ever developed for reproduction traits in M. rosenbergii, and the large number of genetic markers predicted should constitute an invaluable resource for future genetic research studies on M. rosenbergii and can be applied more widely on other freshwater prawn species in the genus Macrobrachium.

KoNA: Korean Nucleotide Archive as A New Data Repository for Nucleotide Sequence Data.

During the last decade, the generation and accumulation of petabase-scale high-throughput sequencing data have resulted in great challenges, including access to human data, as well as transfer, storage, and sharing of enormous amounts of data. To promote data-driven biological research, the Korean government announced that all biological data generated from government-funded research projects should be deposited at the Korea BioData Station (K-BDS), which consists of multiple databases for individual data types. Here, we introduce the Korean Nucleotide Archive (KoNA), a repository of nucleotide sequence data. As of July 2022, the Korean Read Archive in KoNA has collected over 477 TB of raw next-generation sequencing data from national genome projects. To ensure data quality and prepare for international alignment, a standard operating procedure was adopted, which is similar to that of the International Nucleotide Sequence Database Collaboration. The standard operating procedure includes quality control processes for submitted data and metadata using an automated pipeline, followed by manual examination. To ensure fast and stable data transfer, a high-speed transmission system called GBox is used in KoNA. Furthermore, the data uploaded to or downloaded from KoNA through GBox can be readily processed using a cloud computing service called Bio-Express. This seamless coupling of KoNA, GBox, and Bio-Express enhances the data experience, including submission, access, and analysis of raw nucleotide sequences. KoNA not only satisfies the unmet needs for a national sequence repository in Korea but also provides datasets to researchers globally and contributes to advances in genomics. The KoNA is available at https://www.kobic.re.kr/kona/.

CDRgator: An Integrative Navigator of Cancer Drug Resistance Gene Signatures.

Understanding the mechanisms of cancer drug resistance is a critical challenge in cancer therapy. For many cancer drugs, various resistance mechanisms have been identified such as target alteration, alternative signaling pathways, epithelial-mesenchymal transition, and epigenetic modulation. Resistance may arise via multiple mechanisms even for a single drug, making it necessary to investigate multiple independent models for comprehensive understanding and therapeutic application. In particular, we hypothesize that different resistance processes result in distinct gene expression changes. Here, we present a web-based database, CDRgator (Cancer Drug Resistance navigator) for comparative analysis of gene expression signatures of cancer drug resistance. Resistance signatures were extracted from two different types of datasets. First, resistance signatures were extracted from transcriptomic profiles of cancer cells or patient samples and their resistance-induced counterparts for >30 cancer drugs. Second, drug resistance group signatures were also extracted from two large-scale drug sensitivity datasets representing ~1,000 cancer cell lines. All the datasets are available for download, and are conveniently accessible based on drug class and cancer type, along with analytic features such as clustering analysis, multidimensional scaling, and pathway analysis. CDRgator allows meta-analysis of independent resistance models for more comprehensive understanding of drug-resistance mechanisms that is difficult to accomplish with individual datasets alone (database URL: http://cdrgator.ewha.ac.kr).

A ChIP-Seq Data Analysis Pipeline Based on Bioconductor Packages.

Nowadays, huge volumes of chromatin immunoprecipitation-sequencing (ChIP-Seq) data are generated to increase the knowledge on DNA-protein interactions in the cell, and accordingly, many tools have been developed for ChIP-Seq analysis. Here, we provide an example of a streamlined workflow for ChIP-Seq data analysis composed of only four packages in Bioconductor: dada2, QuasR, mosaics, and ChIPseeker. 'dada2' performs trimming of the high-throughput sequencing data. 'QuasR' and 'mosaics' perform quality control and mapping of the input reads to the reference genome and peak calling, respectively. Finally, 'ChIPseeker' performs annotation and visualization of the called peaks. This workflow runs well independently of operating systems (e.g., Windows, Mac, or Linux) and processes the input fastq files into various results in one run. R code is available at github: https://github.com/ddhb/Workflow_of_Chipseq.git.