Genome-wide functional analysis using the barcode sequence alignment and statistical analysis (Barcas) tool

Pipelines of pooled library screen analysis. Table S1. Public library sets of shRNA, sgRNA and deletion mutant strains. Figure S2. A list of wrong barcodes from 1,230 shRNAs of TRC library. Table S2. Sequences of 25 barcodes with abnormally increased mapping counts by imperfect matching. Table S3. A list of the used options for each tool. Table S4. Comparison of mapping results and speed by three tools. (PDF 7691 kb

Bioinformatics services for analyzing massive genomic datasets

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and ensuing computational problems. In Korea, the amount of genomic data has been increasing rapidly in the recent years. Leveraging these big data requires researchers to use large-scale computational resources and analysis pipelines. A promising solution for addressing this computational challenge is cloud computing, where CPUs, memory, storage, and programs are accessible in the form of virtual machines. Here, we present a cloud computing-based system, Bio-Express, that provides user-friendly, cost-effective analysis of massive genomic datasets. Bio-Express is loaded with predefined multi-omics data analysis pipelines, which are divided into genome, transcriptome, epigenome, and metagenome pipelines. Users can employ predefined pipelines or create a new pipeline for analyzing their own omics data. We also developed several web-based services for facilitating down-stream analysis of genome data. Bio-Express web service is freely available at https://www. bioexpress.re.kr/. ?? 2020, Korea Genome Organization

Synaptotagmin 11 scaffolds MKK7-JNK signaling process to promote stem-like molecular subtype gastric cancer oncogenesis

Background: Identifying biomarkers related to the diagnosis and treatment of gastric cancer (GC) has not made significant progress due to the heterogeneity of tumors. Genes involved in histological classification and genetic correlation studies are essential to develop an appropriate treatment for GC. Methods: In vitro and in vivo lentiviral shRNA library screening was performed. The expression of Synaptotagmin (SYT11) in the tumor tissues of patients with GC was confirmed by performing Immunohistochemistry, and the correlation between the expression level and the patient's survival rate was analyzed. Phospho-kinase array was performed to detect Jun N-terminal kinase (JNK) phosphorylation. SYT11, JNK, and MKK7 complex formation was confirmed by western blot and immunoprecipitation assays. We studied the effects of SYT11 on GC proliferation and metastasis, real-time cell image analysis, adhesion assay, invasion assay, spheroid formation, mouse xenograft assay, and liver metastasis. Results: SYT11 is highly expressed in the stem-like molecular subtype of GC in transcriptome analysis of 527 patients with GC. Moreover, SYT11 is a potential prognostic biomarker for histologically classified diffuse-type GC. SYT11 functions as a scaffold protein, binding both MKK7 and JNK1 signaling molecules that play a role in JNK1 phosphorylation. In turn, JNK activation leads to a signaling cascade resulting in cJun activation and expression of downstream genes angiopoietin-like 2 (ANGPTL2), thrombospondin 4 (THBS4), Vimentin, and junctional adhesion molecule 3 (JAM3), which play a role in epithelial-mesenchymal transition (EMT). SNU484 cells infected with SYT11 shRNA (shSYT11) exhibited reduced spheroid formation, mouse tumor formation, and liver metastasis, suggesting a pro-oncogenic role of SYT11. Furthermore, SYT11-antisense oligonucleotide (ASO) displayed antitumor activity in our mouse xenograft model and was conferred an anti-proliferative effect in SNU484 and MKN1 cells. Conclusion: SYT11 could be a potential therapeutic target as well as a prognostic biomarker in patients with diffuse-type GC, and SYT11-ASO could be used in therapeutic agent development for stem-like molecular subtype diffuse GC.ope

High-Throughput Chronological Lifespan Screening of the Fission Yeast Deletion Library Using Barcode Sequencing

Ageing is associated with the development of several chronic illnesses, including cardiovascular diseases, diabetes and cancer. To understand the genetic components driving cellular ageing in higher organisms, like ourselves, we study simple eukaryotic model systems which are more accessible and easier to manipulate than higher eukaryotes. This is possible due to the remarkably conserved ageing mechanisms that occurs between species. Here, we employ fission yeast one of the simplest eukaryotic model organisms to study cellular ageing. In this work, we de- coded the fission yeast deletion collection using our in-house developed pipeline, developed an improved version of Bar-seq along with a custom-developed analysis pipeline, determined a method for high-quality RNA extraction and RNA-seq from long-term quiescent yeast cells, and finally, performed a high-throughput Bar-seq screen to profile the chronological lifespan of our decoded strains. We describe bar- code decoding of 94% of the gene deletions; validation of our Bar-seq developed method; identification of ncRNAs as elements important for the cellular quiescence maintenance; Bar-seq screening of the competitively grown decoded strains which identified several long-lived and short-lived mutants following glucose-starvation and cellular culture re-growth; and also, validation of the top hits using isogenic cell cultures revealing eight novel gene deletions important for the early life maintenance, as well as ten novel gene deletion mutants with pro-ageing effects. Overall, in addition to providing rich datasets, we describe several high-throughput methods that can be used for future genome-wide studies, whereby the complementarity of genomics and transcriptomics can be coupled together to further advance our understanding of the genetic factors underpinning cellular ageing in humans