ALBERT

Description: Motivation: With the expansion of high-throughput technologies, understanding different kinds of genome-level data is a common task. MicroRNA (miRNA) is increasingly profiled using high-throughput technologies (microarrays or next-generation sequencing). The downstream analysis of miRNA targets can be difficult. Although there are many databases and algorithms to predict miRNA targets, there are few tools to integrate miRNA–gene interaction data into high-throughput genomic analyses. Results: We present targetHub, a CouchDB database of miRNA–gene interactions. TargetHub provides a programmer-friendly interface to access miRNA targets. The Web site provides RESTful access to miRNA–gene interactions with an assortment of gene and miRNA identifiers. It can be a useful tool to integrate miRNA target interaction data directly into high-throughput bioinformatics analyses. Availability: TargetHub is available on the web at http://app1.bioinformatics.mdanderson.org/tarhub/_design/basic/index.html . Contact: coombes.3@osu.edu

Description: Motivation: Nonlinear dose–response models are primary tools for estimating the potency [e.g. half-maximum inhibitory concentration (IC) known as IC50] of anti-cancer drugs. We present drexplorer software, which enables biologists to evaluate replicate reproducibility, detect outlier data points, fit different models, select the best model, estimate IC values at different percentiles and assess drug–drug interactions. drexplorer serves as a computation engine within the R environment and a graphical interface for users who do not have programming backgrounds. Availability and implementation: The drexplorer R package is freely available from GitHub at https://github.com/nickytong/drexplorer . A graphical user interface is shipped with the package. Contact: jingwang@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation: Identifying genes altered in cancer plays a crucial role in both understanding the mechanism of carcinogenesis and developing novel therapeutics. It is known that there are various mechanisms of regulation that can lead to gene dysfunction, including copy number change, methylation, abnormal expression, mutation and so on. Nowadays, all these types of alterations can be simultaneously interrogated by different types of assays. Although many methods have been proposed to identify altered genes from a single assay, there is no method that can deal with multiple assays accounting for different alteration types systematically. Results: In this article, we propose a novel method, integration using item response theory (integIRTy), to identify altered genes by using item response theory that allows integrated analysis of multiple high - throughput assays. When applied to a single assay, the proposed method is more robust and reliable than conventional methods such as S tudent’s t -test or the Wilcoxon rank-sum test. When used to integrate multiple assays, integIRTy can identify novel - altered genes that cannot be found by looking at individual assay separately. We applied integIRTy to three public cancer datasets (ovarian carcinoma, breast cancer, glioblastoma) for cross - assay type integration which all show encouraging results. Availability and implementation: The R package integIRTy is available at the web site http://bioinformatics.mdanderson.org/main/OOMPA:Overview . Contact: kcoombes@mdanderson.org Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Motivation : Identification of bimodally expressed genes is an important task, as genes with bimodal expression play important roles in cell differentiation, signalling and disease progression. Several useful algorithms have been developed to identify bimodal genes from microarray data. Currently, no method can deal with data from next-generation sequencing, which is emerging as a replacement technology for microarrays. Results : We present SIBER (systematic identification of bimodally expressed genes using RNAseq data) for effectively identifying bimodally expressed genes from next-generation RNAseq data. We evaluate several candidate methods for modelling RNAseq count data and compare their performance in identifying bimodal genes through both simulation and real data analysis. We show that the lognormal mixture model performs best in terms of power and robustness under various scenarios. We also compare our method with alternative approaches, including profile analysis using clustering and kurtosis (PACK) and cancer outlier profile analysis (COPA). Our method is robust, powerful, invariant to shifting and scaling, has no blind spots and has a sample-size-free interpretation. Availability : The R package SIBER is available at the website http://bioinformatics.mdanderson.org/main/OOMPA:Overview . Contact : kcoombes@mdanderson.org Supplementary information : Supplementary data are available at Bioinformatics online.

Description: Motivation: High-throughput reverse-phase protein array (RPPA) technology allows for the parallel measurement of protein expression levels in approximately 1000 samples. However, the many steps required in the complex protocol (sample lysate preparation, slide printing, hybridization, washing and amplified detection) may create substantial variability in data quality. We are not aware of any other quality control algorithm that is tuned to the special characteristics of RPPAs. Results: We have developed a novel classifier for quality control of RPPA experiments using a generalized linear model and logistic function. The outcome of the classifier, ranging from 0 to 1, is defined as the probability that a slide is of good quality. After training, we tested the classifier using two independent validation datasets. We conclude that the classifier can distinguish RPPA slides of good quality from those of poor quality sufficiently well such that normalization schemes, protein expression patterns and advanced biological analyses will not be drastically impacted by erroneous measurements or systematic variations. Availability and implementation: The classifier, implemented in the "SuperCurve" R package, can be freely downloaded at http://bioinformatics.mdanderson.org/main/OOMPA:Overview or http://r-forge.r-project.org/projects/supercurve/ . The data used to develop and validate the classifier are available at http://bioinformatics.mdanderson.org/MOAR . Contact: Kevin.Coombes@osumc.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Description: Background: Low oxygen levels are a defining characteristic of solid tumors, but the role of hypoxia in leukemogenesis remains unclear. Recent reports indicate that the endosteum at the murine bone-bone marrow (BM) interface is hypoxic, and data in a rat model demonstrate that leukemic cells infiltrating bone marrow were markedly hypoxic compared with cells in the BM of healthy rats. Hypoxia triggers a complex cellular and systemic adaptation mediated primarily through transcription by hypoxia inducible factors (HIFs) including HIF-1a. Although hypoxia is the best-characterized mechanism of HIF activation in tumors, HIF activity also can be induced in tumor cells through activation of the PI3K/Akt-signaling pathway. In this study, we assessed AKT and HIF-1a expression in newly diagnosed precursor B-cell acute lymphoblastic leukemia (pre-B ALL) and correlated the results with overall and progression-free patient survival. Methods: We analyzed expression of phosphorylated AKT (pAKT) and HIF-1a in leukemic cells by immunohistochemical methods using archival fixed, paraffin-embedded BM biopsy specimens of newly diagnosed pre-B ALL and antibodies specific for pAKT (Cell Signaling Technology, Beverly, MA) and HIF-1a (BD Biosciences, San Jose, CA). The initial observations were confirmed by a Reverse Phase Protein Array (RPPA) data set generated from protein lysates prepared from fresh blood and BM aspirate samples from patients with newly diagnosed pre-B ALL. Results: There were 26 men and 27 women with a median age of 39 years (range, 17–77). The median follow-up was 17 months (range, 1–71). The median WBC was 5.7 × 109/L (range, 0.8–369 × 109/L), the median percentage of blasts in bone marrow was 88% (range, 21–97%). Conventional cytogenetic studies detected a normal karyotype in 13 patients and abnormal karyotype in 37 patients including the Philadelphia chromosome (Ph) in 15 patients; no analyzable metaphases were recovered in 5 patients. Fluorescence in situ hybridization for BCR/ABL rearrangement was performed in all patients and was positive in all 15 patients with Ph and in 1 patient with normal conventional cytogenetics. 50 patients received HYPER-CVAD therapy, 3 patients received augmented BFM therapy. 49 (92%) patients achieved complete remission with a median time to response of 3 weeks (range, 2–8 weeks), 12 of them relapsed. 17 patients died, including 6 patients in complete remission. 3 year overall survival was 56% (CI, 46–66%). HIF-1a expression was detected in 37 (70%) patients, including 10 patients with Ph-positive ALL. HIF-1a expression was associated with expression of pAKT (R=0.4479, p