DockerBIO: web application for efficient use of bioinformatics Docker images

Background and Objective Docker is a light containerization program that shows almost the same performance as a local environment. Recently, many bioinformatics tools have been distributed as Docker images that include complex settings such as libraries, configurations, and data if needed, as well as the actual tools. Users can simply download and run them without making the effort to compile and configure them, and can obtain reproducible results. In spite of these advantages, several problems remain. First, there is a lack of clear standards for distribution of Docker images, and the Docker Hub often provides multiple images with the same objective but different uses. For these reasons, it can be difficult for users to learn how to select and use them. Second, Docker images are often not suitable as a component of a pipeline, because many of them include big data. Moreover, a group of users can have difficulties when sharing a pipeline composed of Docker images. Users of a group may modify scripts or use different versions of the data, which causes inconsistent results. Methods and Results To handle the problems described above, we developed a Java web application, DockerBIO, which provides reliable, verified, light-weight Docker images for various bioinformatics tools and for various kinds of reference data. With DockerBIO, users can easily build a pipeline with tools and data registered at DockerBIO, and if necessary, users can easily register new tools or data. Built pipelines are registered in DockerBIO, which provides an efficient running environment for the pipelines registered at DockerBIO. This enables user groups to run their pipelines without expending much effort to copy and modify them

Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells

AbstractPiwi-interacting RNAs (piRNAs) are 26–31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWI and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies

An integrative analysis of cellular contexts, miRNAs and mRNAs reveals network clusters associated with antiestrogen-resistant breast cancer cells

<p>Abstract</p> <p>Background</p> <p>A major goal of the field of systems biology is to translate genome-wide profiling data (e.g., mRNAs, miRNAs) into interpretable functional networks. However, employing a systems biology approach to better understand the complexities underlying drug resistance phenotypes in cancer continues to represent a significant challenge to the field. Previously, we derived two drug-resistant breast cancer sublines (tamoxifen- and fulvestrant-resistant cell lines) from the MCF7 breast cancer cell line and performed genome-wide mRNA and microRNA profiling to identify differential molecular pathways underlying acquired resistance to these important antiestrogens. In the current study, to further define molecular characteristics of acquired antiestrogen resistance we constructed an “integrative network”. We combined joint miRNA-mRNA expression profiles, cancer contexts, miRNA-target mRNA relationships, and miRNA upstream regulators. In particular, to reduce the probability of false positive connections in the network, experimentally validated, rather than prediction-oriented, databases were utilized to obtain connectivity. Also, to improve biological interpretation, cancer contexts were incorporated into the network connectivity.</p> <p>Results</p> <p>Based on the integrative network, we extracted “substructures” (network clusters) representing the drug resistant states (tamoxifen- or fulvestrant-resistance cells) compared to drug sensitive state (parental MCF7 cells). We identified un-described network clusters that contribute to antiestrogen resistance consisting of miR-146a, -27a, -145, -21, -155, -15a, -125b, and let-7s, in addition to the previously described miR-221/222.</p> <p>Conclusions</p> <p>By integrating miRNA-related network, gene/miRNA expression and text-mining, the current study provides a computational-based systems biology approach for further investigating the molecular mechanism underlying antiestrogen resistance in breast cancer cells. In addition, new miRNA clusters that contribute to antiestrogen resistance were identified, and they warrant further investigation.</p

Cancer association study of aminoacyl-tRNA synthetase signaling network in glioblastoma.

Aminoacyl-tRNA synthetases (ARSs) and ARS-interacting multifunctional proteins (AIMPs) exhibit remarkable functional versatility beyond their catalytic activities in protein synthesis. Their non-canonical functions have been pathologically linked to cancers. Here we described our integrative genome-wide analysis of ARSs to show cancer-associated activities in glioblastoma multiforme (GBM), the most aggressive malignant primary brain tumor. We first selected 23 ARS/AIMPs (together referred to as ARSN), 124 cancer-associated druggable target genes (DTGs) and 404 protein-protein interactors (PPIs) of ARSs using NCI's cancer gene index. 254 GBM affymetrix microarray data in The Cancer Genome Atlas (TCGA) were used to identify the probe sets whose expression were most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). The analysis identified 122 probe sets as survival signatures, including 5 of ARSN (VARS, QARS, CARS, NARS, FARS), and 115 of DTGs and PPIs (PARD3, RXRB, ATP5C1, HSP90AA1, CD44, THRA, TRAF2, KRT10, MED12, etc). Of note, 61 survival-related probes were differentially expressed in three different prognosis subgroups in GBM patients and showed correlation with established prognosis markers such as age and phenotypic molecular signatures. CARS and FARS also showed significantly higher association with different molecular networks in GBM patients. Taken together, our findings demonstrate evidence for an ARSN biology-dominant contribution in the biology of GBM

ARSN biology-dominant groups in patients with GBM.

<p>(<b>a</b>) We identified probe sets whose expression most strongly correlated with survival (Kaplan-Meier plots versus survival times, log-rank t-test <0.05). This analysis identified that 122 resulting probe sets of ARSN, DTGs, and PPIs that were correlated with survival in patients with GBM. Then, we performed a supervised clustering with the probesets and GBM subtypes such as proneural (PN), proliferative (Prolif) and mesenchymal (Mes). This analysis showed that 61 probeset as signature genes were differentially expressed in the three discrete subgroups. The 61 probe sets are presented in matrix format, where rows represent individual genes and columns represent each tissue. Each cell in the matrix represents the expression level of a gene in an individual tissue. Red and green cells reflect high and low expression levels, respectively. (<b>b</b>) Tumor subgroups are distinguished by CARS and FARS. Horizontal bars denote mean values. CARS is enriched in Mes and Prolif subgroups, while FARS in PN subgroup. Each Kaplan-Meier plot of overall survival in 130 GBM patients grouped on the basis of expression of CARS and FARS. The difference between two groups was significant when the P value was less than 0.05. (<b>c</b>) Hierarchical clustering of the GSE4290 dataset of 81 GBM samples from patients with GBM and 23 non-tumor tissues based on the 61 probe sets. Each gene with an expression status were shown in Supplementary <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040960#pone.0040960.s021" target="_blank">Figure S21</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0040960#pone.0040960.s023" target="_blank">S23</a>. Nine probes were significantly overexpressed in the non-tumor samples, with 2 probes not showing in this analysis.</p

Correlation patterns of 23 ARSs and AIMPs to three different genesets.

<p>(<b>a</b>) We identified 846 resulting probe sets including 168 DTGs and 678 PPIs that can directly interact with ARSN using 254 GBM affymetrix U133plus2 microarray dataset in TCGA. For the comparison, we also selected 978 probe sets among 1874 nonCAGs. To understand ARSN-DTGs/PPIs/nonCAGs interactions and visualize the relationship between genesets, a correlation map was made on the basis of their correlation levels with each set. The probe sets are presented in matrix format, where rows represent individual genes of DTGs, PPIs, and nonCAGs, respectively, and columns represent each gene of ARSN. Each cell in the matrix represents the correlation level of a gene in an ARSN. Red color indicates that the gene tends to be up or down-regulated together; Blue color indicates the opposite tendency (The darker, the stronger the association between two genes). (<b>b</b>) Hierarchical clustering analysis showed that ARSN were shared by three groups with 31 DTGs (FDR <0.005). 31 DTGs were generated on a supervised hierarchical clustering analysis. (<b>c</b>) Hierarchical clustering of ARSN based on the 16 DTGs based on nonlinear association between two gene expression sets. 16 DTGs were correlated with three subgroups of ARSN.</p

Cancer-associated interactions between 23 ARSs and AIMPs, and three genesets.

<p>3501 genes were selected by manual curation, clinical examination and causal relationship to cancer. Using 11 public database showing the curated interactions of human proteins (HPRD, BioGRID, KEGG, Reactome, BIND, MINT, IntAct, InnateDB, DIP, STRING, and PharmDB), we further selected 124 DTGs and 404 genes as PPIs of ARSs. Using a cancer-associated interactions analysis, a cancer-association map was established to display how much ARSs and AIMPs could be differently interacted to ten different cancers. Each brown node indicates each gene of respective cancer and each node size indicates the degree of cancer-dependent co-association of a gene. Line indicates the co-association between ten cancers and seven ARSN. The cancer node size indicates the number of interactions with the brown node gene. Seven components of ARSN (green nodes) show relatively higher cancer-associated network.</p

Outline of analysis procedures with each geneset showing the general steps required to identify genes that modulate a specific phenotype: selection of genes with the desired phenotype, and identification of phenotype-inducing ARSN and corresponding cancer-associated druggable target genes.

<p>Outline of analysis procedures with each geneset showing the general steps required to identify genes that modulate a specific phenotype: selection of genes with the desired phenotype, and identification of phenotype-inducing ARSN and corresponding cancer-associated druggable target genes.</p

In-Plane Thermal Conductivity of Polycrystalline Chemical Vapor Deposition Graphene with Controlled Grain Sizes

Manipulation of the chemical vapor deposition graphene synthesis conditions, such as operating <i>P</i>, <i>T</i>, heating/cooling time intervals, and precursor gas concentration ratios (CH₄/H₂), allowed for synthesis of polycrystalline single-layered graphene with controlled grain sizes. The graphene samples were then suspended on 8 μm diameter patterned holes on a silicon-nitride (Si₃N₄) substrate, and the in-plane thermal conductivities <i>k</i>(<i>T</i>) for 320 K < <i>T</i> < 510 K were measured to be 2660–1230, 1890–1020, and 680–340 W/m·K for average grain sizes of 4.1, 2.2, and 0.5 μm, respectively, using an opto-thermal Raman technique. Fitting of these data by a simple linear chain model of polycrystalline thermal transport determined <i>k</i> = 5500–1980 W/m·K for single-crystal graphene for the same temperature range above; thus, significant reduction of <i>k</i> was achieved when the grain size was decreased from infinite down to 0.5 μm. Furthermore, detailed elaborations were performed to assess the measurement reliability of <i>k</i> by addressing the hole-edge boundary condition, and the air-convection/radiation losses from the graphene surface