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We consider Master's degree in Chemistry, Pharmaceutical management, Mathematics, statistics, bioinformatics, biotechnology, sciences.
Please do call or email us to get more information.

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Tech Observer
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As the clock speed in computer Central Processing Units (CPUs) began to plateau, their data and task parallelism was expanded to compensate. These days (2013) it is not uncommon to find upwards of a dozen processing cores on a single CPU and each core capable of performing 8 calculations as a single operation. Graphics Processing Units were originally intended to assist CPUs by providing hardware optimised to speed up rendering highly parallel graphical data into a frame buffer. As graphical models became more complex, it became difficult to provide a single piece of hardware which implemented an optimised design for every model and every calculation the end user may desire. Instead, GPU designs evolved to be more readily programmable and exhibit greater parallelism. Top-end GPUs are now equipped with over 2,500 simple cores and have their own CUDA or OpenCL programming languages. This new found programmability allowed users the freedom to take non-graphics tasks which would otherwise have saturated a CPU for days and to run them on the highly parallel hardware of the GPU. This technique proved so effective for certain tasks that GPU manufacturers have since begun to tweak their architectures to be suitable not just for graphics processing but also for more general purpose tasks, thus beginning the evolution General Purpose Graphics Processing Unit (GPGPU).
Improvements in data capture and model generation have caused an explosion in the amount of bioinformatic data which is now available. Data which is increasing in volume faster than CPUs are increasing in either speed or parallelism. An example of this can be found here, which displays a graph of the number of proteins stored in the Protein Data Bank per year. To process this vast volume of data, many of the common tools for structure prediction, sequence analysis, molecular dynamics and so forth have now been ported to the GPGPU. The following tools are now GPGPU enabled and offer significant speed-up compared to their CPU-based counterparts:

RNA sequencing is now widely performed to study differential expression among experimental conditions. As tests are performed on a large number of genes, very stringent false discovery rate control is required at the expense of detection power. Ad hoc filtering techniques are regularly used to moderate this correction by removing genes with low signal, with little attention paid to their impact on downstream analyses.
Researchers at INRA, France propose a data-driven method based on the Jaccard similarity index to calculate a filtering threshold for replicated RNA-seq data. In comparisons with alternative data filters regularly used in practice, they demonstrate the effectiveness of the proposed method to correctly filter lowly expressed genes, leading to increased detection power for moderately to highly expressed genes. Interestingly, this data-driven threshold varies among experiments, highlighting the interest of the method proposed here.

AVAILABILITY: The proposed filtering method is implemented in the R package HTSFilter available at
Rau A, Gallopin M, Celeux G, Jaffrézic F. (2013) Data-based filtering for replicated high-throughput transcriptome sequencing experiments. Bioinformatics [Epub ahead of print]. [abstract]

An introduction to network analysis and statistical methods used in contemporary Systems Biology and Systems Pharmacology research.
Workload: 6-8 hours/week 


Avi Ma’ayan, PhDAssociate ProfessorDepartment of Pharmacology and Systems TherapeuticsIcahn School of Medicine at Mount Sina


October 2013 (7 weeks long)
Future sessions
About the Course
The course Network Analysis in Systems Biology provides an introduction to network analysis and statistical methods used in contemporary Systems Biology and Systems Pharmacology research. Students will learn how to construct, analyze and visualize different types of molecular networks, including gene regulatory networks connecting transcription factors to their target genes, protein-protein interaction networks, cell signaling pathways and networks, drug-target and drug-drug similarity networks and other functional association networks. Methods to process raw data from genome-wide RNA (microarrays and RNA-seq) and proteomics (IP-MS and phosphoproteomics) profiling will be presented. Processed data will be clustered, and gene-set enrichment analyses methods will be covered. The course will also discuss topics in network systems pharmacology including processing and using databases of drug-target interactions, drug structure, drug/adverse-events, and drug induced gene expression signatures.  
Half of the course will contain theoretical discussions of advanced topics in the field but with the assumption that the course participants are from diverse backgrounds. The course will also have practical tutorials for analyzing various high content experimental datasets going from the raw data to finished high quality figures for publication. Standard statistical methods for high content data analysis will be covered. The course is appropriate for beginning graduate students and advanced undergraduates. Lectures provide background knowledge in understanding the properties of large datasets collected from mammalian cells. In the course we will teach how these datasets can be analyzed to extract new knowledge about the system. Such analyses include clustering, data visualization techniques, network construction, and gene-set enrichment analyses. The course will be useful for students who encounter large datasets in their own research, typically genome-wide. The course will teach the students how to use existing software tools such as those developed by the Ma’ayan Laboratory at Mount Sinai, but also other freely available software tools. In addition the course requires the students to write short scripts in Python and participate in crowdsourcing microtask  projects. The ultimate aim of the course is to enable students to utilize the methods they learn here for analyzing their own data for their own projects.

Course Syllabus

Topics covered include:
  • Properties of Complex Systems: Biological vs. Technological Networks
  • Introduction to Molecular and Cell Biology for Engineers
  • Algorithms to Generate Network Topologies: Rich-Get-Richer, Small-World, Duplication-Divergence and Null Random Models
  • Network Motifs and Network Evolution
  • Types of Networks in Systems Biology
  • Drawing Networks: Ball-and-Stick Diagrams and other Network Visualizations
  • Gene Expression Data Analysis: Microarrays, RNA-seq and ChIP-seq
  • Hierarchical Clustering Plots
  • Principle Component Analysis and Multi-Dimensional Scaling
  • Gene Set Enrichment Analysis and Fuzzy Set Enrichment Analysis
  • Integrating Multiple Types of Large Datasets
  • From Gene Expression to Pathways: Expression2Kinases and Genes2Networks
  • Functional Association Networks: Sets2Networks
  • Network Pharmacology: Drug-Drug Similarity and Drug-Target Networks
  • Crowdsourcing: Microtasks and Megatasks
  • Developing Web Apps for Solving Systems Biology Research Problems

Recommended Background

Basic courses in statistics and molecular biology are useful but not required. Ability to write short scripts in languages such as Python would be useful but not necessary.  

Suggested Readings

Review articles and selected original research articles will be discussed in the lectures and can enhance understanding, but these are not required to complete the course. All materials will be from open access journals or will be provided as links to e-reprints, so there will be no cost to the student.

Course Format

The class will consist of lecture videos, which are between 8 and 15 minutes in length.  Each lecture will include a quiz and a homework assignment.
For evaluation, students will be mainly graded through their participation in the assignments and quiz completion.

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Rainbow Workflow

Rainbow is a scalable workflow for whole genome sequencing analysis. It combines:
  1. PICARD, a Java-based command-line utility that manipulates SAM/BAM files
  2. Bowtie, an ultrafast and memory efficient short read aligner
  3. SoapSNP, an accurate genotyper
  4. SNP_Merger, an in-house developed application for SNP consolidations
These tools are combined in Semi-automatic, parallel pipeline that runs in the cloud (Amazon Web Services’ EC2, S3, and Elastic MapReduce offerings in this case).


The Chromosome Walk is a virtual exhibition that explains the importance of bioinformatics in the life sciences today, and an interactive and recreational journey into the human genome.

Each one of our cells contains 23 pairs of chromosomes; one of the chromosomes comes from our mother and the other from our father. Like a recipe book, each chromosome contains a certain number of recipes, known as ‘genes’. Over 20’000 genesare recipes for proteins which are essential components of life.

Project objective: To provide a user-friendly, web-based analytical pipeline for comparative metagenomic studies. In particular, METAGENassist allows users to take bacterial census data from different environment sites or different biological hosts, and perform comprehensive multivariate statistical analyses on the data. These multivariate analyses can be done using either taxonomic or automatically generated phenotypic labels and visualized using a variety of high quality graphical tools. The bacterial census data can be derived from 16S rRNA data, NextGen shotgun sequencing or even classical microbial culturing techniques.
Data input formats: Users upload a taxonomic profile in one of several supported formats (details). A taxonomic profile file contains the names of microbial species (or other taxonomic classes) and their relative abundance in at least 2 samples; accepted formats include CSV format, or data generated by mothur, QIIME, MG-RAST, MEGAN, and STAMP. Users will also benefit by uploading an optional file containing metadata for each sample (CSV format).
Data processing: METAGENassist performs: 1) taxonomic name normalization; 2) automated taxonomic-to-phenotypic mapping using nearly 20 different phenotypic categories; 3) data integrity/quality checks and 4) data normalization via normalization by constant sum, normalization by a reference feature, sample specific normalization or auto/Pareto/range scaling.
Statistical analysis:    METAGENassist offers a wide array of commonly used statistical and machine learning methods including: univariate statistics - fold change analysis, t-tests, volcano plots, one-way ANOVA, correlation analysis;multivariate statistics - principal component analysis (PCA) and partial least squares - discriminant analysis (PLS-DA);clustering - dendrograms, heatmaps, K-means clustering, self organizing feature maps (SOM); 
supervised classification - random forests and support vector machine (SVM).
Data output: Upon completion, METAGENassist generates a variety of well-annotated tables and colorful, labeled graphs in an anti-aliased PNG format. PDF versions for some plots are also available. The processed data and images are available for download.
Please Cite:
David Arndt, Jianguo Xia, Yifeng Liu, You Zhou, An Chi Guo, Joseph A. Cruz, Igor Sinelnikov, Karen Budwill, Camilla L. Nesbø and David S. Wishart METAGENassist: A comprehensive web server for comparative metagenomics Nucleic Acids Research 2012 Jul;40(Web Server issue):W88-95. Epub 2012 May 29.


The SeqWare Pipeline sub-project is really the heart of the overall SeqWare project. This provides the core functionality of SeqWare; it is workflow developer environment and a series of tools for installing, running, and monitoring workflows.
We currently support two workflow languages (FTL markup and Java) and two workflow engines (Oozie and Pegasus). Our current recommended combination is Java workflows with the Pegasus engine.
We highly recommend you go through the UserDeveloper, and Admin tutorials since the documentation below assumes you already have.


SeqWare Pipeline has several key features that distinguish it from other open source and private workflow solutions. These include:
  • tool-agnostic
  • developer framework focused
  • focused on automated analysis
  • includes cluster abstraction
  • supports detailed provenance tracking
  • supports user-created workflows
  • implements a self-contained workflow packaging standard
  • includes fault tolerance
  • focuses on meeting workflow needs of big projects (thousands of samples)
  • is open source

Building and Installing
  • Installation
    This is our installation guide based on VMs that we recommend for most users. You will be left with a functioning SeqWare install including SeqWare Pipeline.
  • Installation From Scratch
    This guide walks you through how we built the VMs and will be of interest to anyone that needs to see the details of SeqWare setup starting with an empty Linux server. It is complicated so we highly recommend using a VM (which can be connected to a real cluster).
  • Building from Source
    These directions show you how to build the whole project, including SeqWare Pipeline, using Maven.


  • User Settings
    Information about configuring user settings files.
  • Monitor Configuration
    Setting up the SeqWare-associated tools that need to run so workflow triggering and monitoring workflows.
  • Connecting to a Real Cluster
    Once you are happy with writing, installing, and running workflows on a stand-alone VM you will want to connect to a “real” cluster. This guide walks you through the process of connecting a VM to a cluster (HPC & Hadoop, depending on your workflow engine of choice).
    Read more




Motivation: Data collection in spreadsheets is ubiquitous, but current solutions lack support for collaborative semantic annotation that would promote shared and interdisciplinary annotation practices, supporting geographically distributed players.
Results: OntoMaton is an open source solution that brings ontology lookup and tagging capabilities into a cloud-based collaborative editing environment, harnessing Google Spreadsheets and the NCBO Web services. It is a general purpose, format-agnostic tool that may serve as a component of the ISA software suite. OntoMaton can also be used to assist the ontology development process.
Availability: OntoMaton is freely available from Google widgets under the CPAL open source license; documentation and examples at:

health informatics degree for a career in medical information

A health informatics degree can lead to an exciting career which is central to the rapidly growing health care industry.  This page gives more information about the contents of the programs, lists schools where you can take them online, outlines the career prospects, and more ...
Holders of a degree in health informatics are becoming more and more important throughout the industry. Ahead of anything else, workers in informatics are charged with collecting and collating patient data. The maintenance of these records is something which is becoming ever important too.
You can study health informatics from certificate level to PhD level.  You might also like to read the pages for associated areas such as: BioinformaticsHealth Information ManagementMedical Informatics, and Nursing Informatics.

Finding the school and health informatics degree program you want

The following is a list of selected schools where you can take online programs in health informatics or closely related areas, at associate, bachelors and masters levels.  You can also use the search boxes on this page to find schools offering health informatics programs online.
    • Associate - Health Information Technology
    • Bachelor's - Biomedical Engineering Technology
    • Bachelor's - Computer Information Systems
    • Bachelor's - Computer Information Systems - Database Management
    • AAS in Health Information Technology
    • Advanced Start BSIT - Health Informatics
    • BS in Information Technology - Health Informatics
    • Nurse Informatics Certificate
    • MS in Nursing - Informatics
    • B.S. in Health Studies - Health Informatics
    • B.S. in Computer Information Systems (BS CIS) - Healthcare Informatics
    • M.S. in Health Informatics
    • Master of Science in Nursing (MSN) - BSN Track - Informatics
    • Master of Science in Nursing (MSN) - RN Track - Informatics
    • Master of Information Systems Management (M.I.S.M.) - Healthcare Informatics
    • Post-Masters Certificates in Nursing - Nursing Informatics

The information contained in this database is intended as a quick reference for retrieving the SCOGS opinion, and CFR citations on LSRO reviewed substances. It is advised that the reviewer refer to the hardcopy SCOGS report in order to make the most appropriate evaluation of the substance in question and to learn further details about potential toxicology and safety issues discussed in the LSRO review. Although reasonable effort was made to ensure that the SCOGS opinion published in this database reflects what is written in the SCOGS report, the information may not be precisely the same due to inadvertent human error. Additional information regarding the contents of this database, the History of GRAS and SCOGS reviews is available on the SCOGS main page

Records shown on this page: This page is a partial listing of all records in the SCOGS database. Additional pages/records are available for selection at the bottom of the page. To obtain all the SCOGS Opinions for the food substance, select the substance name to reveal the additional information. To search for a specific food ingredient, enter the term in the Filter box and select Show Items to display only those records that contain the selected term.

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