Created by: Bettina   Created on: 2014-06-26T12:20:35.871+0200   Modified by: Gerhard   Modified on: 2016-05-20T10:36:12.641+0200
      Science lunch talk: Dissecting tumor‐immune cell interactions in solid cancers using genomic data Time: 02 June 2016, 12.00 Place: Seminar Room Ground floor, ZMF Medical University Graz, Stiftingtalstraße 24, 8010 Graz Z. Trajanoski Biocenter, Division of Bioinformatics, Medical University of Innsbruck, Innsbruck, Austria     Recent breakthroughs in cancer immunotherapy and decreasing costs of high‐throughput technologies have sparked intensive research into tumor–immune cell interactions using genomic tools. The wealth of the generated data and the added complexity pose considerable challenges and require computational tools to process analyze and visualize the data. Recently, we developed computational tools and assembled analytical pipelines to effectively mine tumor immunologic and genomic data and provide novel mechanistic insights. Results from the analyses of 19 solid cancers with >8000 patients will be...
Created by: Andrea   Created on: 2016-02-02T08:57:19.909+0100   Modified by: Andrea   Modified on: 2016-02-02T08:57:19.909+0100
http://www.donau-uni.ac.at/de/aktuell/jobs/id/19902/index.php  
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Created by: Gerhard   Created on: 2015-07-03T08:07:51.171+0200   Modified by: Gerhard   Modified on: 2015-07-03T08:15:46.000+0200
[[ Core_Facility_Computational_Bioanalytics_at_Medical_University_of_Graz ]] [[ Bioinformatics_at_AIT_Austrian_Institute_of_Technology_GmbH ]]
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Created by: Gerhard   Created on: 2015-07-03T08:05:51.818+0200   Modified by: Gerhard   Modified on: 2015-07-03T08:05:51.818+0200
Bioinformatics at AIT Austrian Institute of Technology GmbH Bioinformatics at  AIT  is an integral part of the biomarker discovery and validation efforts carried out at the molecular diagnostics group. We develop and apply advanced statistical and bioinformatical methods and provide statistical support for biomarker validation studies. Our services and methods include experiment planning, feature selection, assay standardization, and development of decision support systems. A strong focus of our research is the analysis of Next-Generation Sequencing (NGS) data. We have broad experience with the analysis of a variety of NGS methods including whole-genome, whole-exome, bisulfite, RNA, and amplicon sequencing. > >  Read more Contact: Stephan Pabinger stephan.pabinger@ait.ac.at  |  http://www.ait.ac.at   Klemens Vierlinger klemens.vierlinger@ait.ac.at  |  http://www.ait.ac.at
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Created by: Bettina   Created on: 2014-04-24T10:17:19.148+0200   Modified by: Gerhard   Modified on: 2015-07-03T08:05:38.092+0200
The  Biocomputing  and   Biomodelling   site serves as Austria’s largest platform for biostatistics and  bioinformatics experts in order to develop, share and distribute methods and findings. Questions and research problems can be discussed and solutions will be presented. In addition, the Biocomputing and Biomodelling platform also facilitates the cooperation between life science researchers and biostatisticians/bioinformaticians and can be used for document management and filesharing, for discussions as well as for scheduling of events within the community.      
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Created by: Gerhard   Created on: 2015-07-03T08:04:21.720+0200   Modified by: Gerhard   Modified on: 2015-07-03T08:04:21.720+0200
Core Facility Computational Bioanalytics at Medical University of Graz  The  Computational Bioanalytics Core Facility  (CF-CBA) , located at the Medical University Graz, is a group of statisticians and bioinformaticians who collaborate with medical research and life sciences investigators providing support and consulting, for planned and ongoing research projects, from experimental design, sample size calculation and power estimation to analysis, visualizing and interpretation of complex data. The facility currently focus in microbiome research, biomarker research and variant classification. The mission of the core is to ensure that experimental designs and data analyses take advantage of robust, efficient methods that reflect 'best practices' in biostatistics and bioinformatics.  >> Read more Contact: Andrea Groselj-Strele andrea.groselj-strele@medunigraz.at
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Created by: Andrea   Created on: 2014-11-28T12:32:26.061+0100   Modified by: Stephan   Modified on: 2015-07-01T09:02:59.499+0200
The Austrian Institute of Technology offers customized bioinformatics services ranging from experiment planning and data analysis to the development of decision support systems tailored to our customers needs. We are equipped with a state-of-the art high-performance computing infrastructure, offer statistical and data analysis guidance, and have broad experience with the analysis of experimental data. We would be happy to assist you with selecting the right methods and appropriate tools to meet your challenges. EXPERTISE AND PROVIDED SERVICES Experimental planning Sample selection, platform options, best practices, … Assay design & analysis solutions for standard biomarker validation Screenings on microarray and NGS platform, customized data analysis, … Tailored modules for biomarker discovery and diagnostic data analysis Multivariate statistics, determination of best statistical models, ... Data analysis of NGS experiments...
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Created by: Andrea   Created on: 2014-07-07T17:42:01.465+0200   Modified by: Bettina   Modified on: 2015-02-19T10:56:51.090+0100
Innovative software solutions will be available  soon in the Biocomputing and Biomodelling  app store . Platomics  is an  open software platform   designed to help researchers to store, analyze and visualize large and complex biological data, such as readouts from Next Generation Sequencers (NGS).  The software environment contains powerful features for integrating heterogeneous data and configuring multiple workflow runs, with the ability to "rewind" or access results of intermediate stages.  The user can specify the storage and computing locations depending on the nature of the data and the type of analysis being performed; for example, large non-sensitive data sets may be crunched on the public cloud, while human DNA is kept behind an institution’s firewall on local infrastructure.  The open nature of the platform means that researchers can easily configure their own workflows and encapsulate softwa...
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Created by: Katharina   Created on: 2015-02-06T14:34:07.805+0100   Modified by: Katharina   Modified on: 2015-02-06T14:36:46.701+0100
Exploring functional contexts of symbiotic sustain within lichen-associated bacteria by comparative omics.   Grube M 1 , Cernava T 2 , Soh J 3 , Fuchs S 4 , Aschenbrenner I 5 , Lassek C 4 , Wegner U 4 , Becher D 4 , Riedel K 4 , Sensen CW 3 , Berg G 2 .   Abstract Symbioses represent a frequent and successful lifestyle on earth and lichens are one of their classic examples. Recently, bacterial communities were identified as stable, specific and structurally integrated partners of the lichen symbiosis, but their role has remained largely elusive in comparison to the well-known functions of the fungal and algal partners. We have explored the metabolic potentials of the microbiome using the lung lichen Lobaria pulmonaria as the model. Metagenomic and proteomic data were comparatively assessed and visualized by Voronoi treemaps. The study was complemented with molecular, microscopic and physiological assays. We have found th...
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Created by: Katharina   Created on: 2015-02-06T14:29:02.190+0100   Modified by: Katharina   Modified on: 2015-02-06T14:29:02.190+0100
Abstract Hepatitis C virus (HCV) is a major cause of chronic liver disease affecting around 130 million people worldwide. While great progress has been made to define the principle steps of the viral life cycle, detailed knowledge how HCV interacts with its host cells is still limited. To overcome this limitation we conducted a comprehensive whole-virus RNA interference-based screen and identified 40 host dependency and 16 host restriction factors involved in HCV entry/replication or assembly/release. Of these factors, heterogeneous nuclear ribonucleoprotein K (HNRNPK) was found to suppress HCV particle production without affecting viral RNA replication. This suppression of virus production was specific to HCV, independent from assembly competence and genotype, and not found with the related Dengue virus. By using a knock-down rescue approach we identified the domains within HNRNPK required for suppression of HCV particle production. Importantly, HNRNPK was found to interact speci...
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Created by: Andrea   Created on: 2014-11-25T14:41:45.650+0100   Modified by: Andrea   Modified on: 2014-11-25T14:41:45.650+0100
This page has been moved [[Event|here]].
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Created by: Andrea   Created on: 2014-11-25T14:34:36.531+0100   Modified by: Andrea   Modified on: 2014-11-25T14:41:45.582+0100
Invitation to the Workshop Integrative Analysis Methods and Applications November 27, 2014 11:00-16:00 HS E3.1 (BCEG136) Petersgasse 12, Graz University of Technology   Organizers: Oana Tomescu and Gerhard Thallinger ACIB, ÖGBMT, TU Graz Attendance is free of charge, registration is required until Tuesday, November 25, 2014 at idaws@acib.at The workshop will focus on integrative analysis methods for OMICS data and their applications. International experts in the field will present their latest research results. „Integrative Data Analysis and Applications" Workshop Program   Date:  November 27, 2014, 11:00-16:00 Location:  Graz University of Technology, Lecture hall HS E 3.1 (BCEG136), Petersgasse 12, ground floor, 8010 Graz.   11:00 – 11:15 Gerhard Thallinger Welcome and Introduction 11:15 – 12:00   Aedin Culhane (Dana-Farber Cancer Institut...
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Created by: Andrea   Created on: 2014-11-12T18:25:16.221+0100   Modified by: Andrea   Modified on: 2014-11-12T18:25:16.221+0100
Search for Austria's biocomputing and biomodeling Courses.
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Created by: Andrea   Created on: 2014-11-12T18:19:59.775+0100   Modified by: Andrea   Modified on: 2014-11-12T18:23:38.792+0100
Search for biocomputing and biomodeling methods and their experts.
Created by: Andrea   Created on: 2014-10-13T09:55:10.062+0200   Modified by: Andrea   Modified on: 2014-11-03T15:02:45.393+0100
Gene. 2014 Oct 1;549(1):186-91. doi: 10.1016/j.gene.2014.07.066. Epub 2014 Jul 30.   Computational integration of genomic traits into 16S rDNA microbiota sequencing studies.   Keller A 1 , Horn H 2 , Förster F 2 , Schultz J 2 .     Abstract   Molecular sequencing techniques help to understand microbial biodiversity with regard to species richness, assembly structure and function. In this context, available methods are barcoding, metabarcoding, genomics and metagenomics. The first two are restricted to taxonomic assignments, whilst genomics only refers to functional capabilities of a single organism. Metagenomics by contrast yields information about organismal and functional diversity of a community. However currently it is very demanding regarding labour and costs and thus not applicable to most laboratories. Here, we show in a proof-of-concept that computational approaches are able to retain functiona...
Created by: Andrea   Created on: 2014-10-14T13:27:38.063+0200   Modified by: Andrea   Modified on: 2014-10-14T13:27:38.063+0200
Andrea Groselj-Strele andrea.groselj-strele@medunigraz.at   Katharina Eberhard katharina.eberhard@medunirgaz.at   Slave Trajanoski slave.trajanoski@medunigraz.at
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Created by: Andrea   Created on: 2014-10-10T10:42:57.061+0200   Modified by: Katharina   Modified on: 2014-10-10T13:43:46.537+0200
Core Facility Computational Bioanalytics   Team: Andrea Groselj-Strele Katharina Eberhard Slave Trajanoski   Services:       Contact: Andrea Groselj-Strele Email: andrea.groselj-strele@medunigraz.at Phone: +43 (0) 316/ 385-73012   http://www.medunigraz.at/forschen/organisation-und-services/zentrum-fuer-medizinische-forschung/
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Created by: Andrea   Created on: 2014-10-08T09:20:11.636+0200   Modified by: Andrea   Modified on: 2014-10-08T09:20:11.636+0200
Multinomial logistic regression is used to model nominal outcome variables, in which the log odds of the outcomes are modeled as a linear combination of the predictor variables. See also: http://statistics.ats.ucla.edu/stat/r/dae/mlogit.htm
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Created by: Andrea   Created on: 2014-08-26T10:58:00.092+0200   Modified by: Andrea   Modified on: 2014-08-26T11:02:55.715+0200
Bioinformatics. 2014 Aug 1;30(15):2162-70. doi: 10.1093/bioinformatics/btu189. Epub 2014 Apr 11.   A change-point model for identifying 3'UTR switching by next-generation RNA sequencing.   Wang W , Wei Z , Li H .   Author information Department of Computer Science, New Jersey Institute of Technology, Newark, NJ 07102 and Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.   Abstract   MOTIVATION: Next-generation RNA sequencing offers an opportunity to investigate transcriptome in an unprecedented scale. Recent studies have revealed widespread alternative polyadenylation (polyA) in eukaryotes, leading to various mRNA isoforms differing in their 3' untranslated regions (3'UTR), through which, the stability, localization and translation of mRNA can be regulated. However, very few, if any, methods and tools are available for di...
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Created by: Katharina   Created on: 2014-08-22T15:47:00.091+0200   Modified by: Katharina   Modified on: 2014-08-22T15:47:00.091+0200
  Welch's t-Test   In this activity we will show you how to use Welch's t -test to compare two independent samples. First, here are the assumptions for Welch's t -test:   The first sample of size n_1 is drawn from a normal population with mean \mu_1 and variance \sigma_1^2 . The second sample of size n_2 is also drawn from a normal population with a mean \mu_2 and variance \sigma_2^2 . The two samples are independent.   Unlike the independent samples in the activity Pooled t-Test , we do not assume that the variances of the two parent distributions are equal. That is, we do not assume that \sigma_1=\sigma_2 .   We learned in the activity Distribution of the Differences of the Sample Means Two Random Samples that if two samples of size n_1 and n_2 are drawn from two normal distributions, one having mean \mu_1 and variance \sigma_1^2 , the second having mean \mu_2 and variance \sigma_2^2 , the distribution of \overline{X}_1-\ov...
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Created by: Andrea   Created on: 2014-08-21T13:39:32.509+0200   Modified by: Andrea   Modified on: 2014-08-21T13:39:32.509+0200
Packages GenABEL, or *ABEL, is an umbrella name for a number of software packages aiming to facilitate statistical analyses of polymorphic genomes data. This is reach program set which now allows very flexible genome-wide association (GWA) analysis ( GenABEL , ProbABEL , MixABEL , OmicABEL ), meta-analysis ( MetABEL ), parallelization of GWA analyses ( ParallABEL ), management of very large files ( DatABEL ), and facilitates evaluation of prediction ( PredictABEL ). Most likely, you only need one of the packages for your specific task. Figure out which one you need, install, and use! If you have questions, please refer to the " Support " section of this web-site. The code for latest development versions of all packages are available from GenABEL on R-forge For stable releases, use CRAN version for R packages or links provided at this web-site GenABEL Genome-wide association analysis for quantitative, binary and time-till-event traits   MetABEL Met...
Created by: Andrea   Created on: 2014-08-20T13:06:12.807+0200   Modified by: Andrea   Modified on: 2014-08-20T13:24:09.599+0200
PLINK is a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyses in a computationally efficient manner.   The focus of PLINK is purely on analysis of genotype/phenotype data, so there is no support for steps prior to this (e.g. study design and planning, generating genotype or CNV calls from raw data). Through integration with gPLINK and Haploview , there is some support for the subsequent visualization, annotation and storage of results.   PLINK (one syllable) is being developed by Shaun Purcell at the Center for Human Genetic Research ( CHGR ), Massachusetts General Hospital ( MGH ), and the Broad Institute of Harvard & MIT, with the support of others .     http://pngu.mgh.harvard.edu/~purcell/plink/      
Created by: Katharina   Created on: 2014-08-19T14:07:48.299+0200   Modified by: Katharina   Modified on: 2014-08-19T14:07:48.299+0200
Presentation of Prof. Melton, Statistical Genetics   http://www.academia.edu/2938347/Bioinformatic_and_statistical_analysis_of_microbiome_sequence_data
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Created by: Andrea   Created on: 2014-07-07T10:24:47.249+0200   Modified by: Andrea   Modified on: 2014-07-07T10:38:55.544+0200
Description: In statistics, a generalized linear mixed model (GLMM) is a particular type of mixed model. It is an extention to the gerealized linear model in which the linear predictor contains random effects in addition to the usual fixed effects. These random effects are assumed to have a normal distribution. More details: GLMM_presentation.pdf     Tutorial: http://www.youtube.com/watch?v=LzySboviiSY   Publications:     Software:  R pagackge "lme4" manual: lme4.pdf          
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Created by: Slave   Created on: 2014-07-04T15:37:55.451+0200   Modified by: Slave   Modified on: 2014-07-04T15:48:54.110+0200
eHoPASA extension for graphical presentation of homozygous regions over LGMD2 genes Copyright 2010 by the Center for Medical Research, Medical University Graz. Written by Slave Trajanoski. For questions on EHoPASA, please contact Slave Trajanoski (slave.trajanoski@medunigraz.at).   The limb girdle muscular dystrophies (LGMD) are a clinically and genetically heterogeneous group of primary muscle disorders characterised by progressive weakness and wasting, predominating in muscles of the pelvic and shoulder girdle, with occasional involvement of the myocardium [1,2]. To date, 15 genes for autosomal recessive LGMD (LGMD2) have been identified. This short R script helps you to visualize the homozygous regions discovered with eHoPASA over these regions. The script generates a single image file in PNG format. Requirements * installed and working version of the R statistical programming language software version 2.10 or higher * the script is tested only under Windows ...
Created by: Slave   Created on: 2014-07-04T15:31:57.288+0200   Modified by: Slave   Modified on: 2014-07-04T15:31:57.288+0200
eHoPASA 0.9 Easy Homozygosity Profiling of Affymetrix SNP Arrays Copyright 2010 by the Center for Medical Research, Medical University Graz. Written by Slave Trajanoski. For questions on EHoPASA, please contact Slave Trajanoski (slave.trajanoski@medunigraz.at).   1. INTRODUCTION This program discovers homozygosity regions from an Affymetrix SNP Arrays GeneChip® Human Mapping 10K XbaI 142 2.0 and GeneChip® Human Mapping NspI 250K. Future versions will also support the latest Genome-Wide Human SNP Array 5.0 and 6.0. The software generates several result text files, which can be used for further analysis or plotting diagrams. 2. SYSTEM REQUIREMENTS At least 1GB of Memory Windows or Linux Operating System. Macintosh OS should also be supported, but it has never been tested. Java 1.5 JRE 3. INSTALLATION eHoPASA is distributed under the GNU General Public License. It is free software; you can redistribute it and/or modify it under the terms of the GNU Genera...
Created by: Andrea   Created on: 2014-07-03T17:28:20.893+0200   Modified by: Andrea   Modified on: 2014-07-03T17:30:19.405+0200
Publications: Zhao_Cai_Li_2014.pdf Zhao_Cai_Li_SuppData_2014.pdf   R code: Zhao_Cai_Li_SuppData_Code_2014.zip  
Tags: geneticsmethod