Showing posts with label bioinfo tools. Show all posts

University of Cincinnati (UC) researchers played a pivotal role in two recent cancer studies in which he used bioinformatics—specifically, public domain genomics data—to help identify a tumor suppressor gene’s role in human cancers. The approach took the work out of animal models and moved it into computer analysis.
Bioinformatics is a relatively new field of science which incorporates biology, statistics, computer science and information technology. By using large data sets and new statistical models, scientists can make discoveries or learn new insights into human disease.

Mario Medvedovic, PhD, an associate professor in the department of environmental health at the UC College of Medicine, and Jing Chen, PhD, research scientist in Medvedovic’s group, co-authored two recent National Institutes of Health-supported studies appearing in Cell and Proceedings of the National Academy of Sciences. Both studies relied on bioinformatics analysis of genomics data.

"An interesting aspect from our angle is that in both papers we used public domain genomics data to connect experimental genomics data from in vivo and in vitro models to human diseases,” says Medvedovic, whose lab has been actively gathering and processing public domain genomics data and developing bioinformatics to analyze these data. It has created web servers where anyone can go and mine these data. (http://GenomicsPortals.org and http://LincsGenomics.org)

"Re-use of public domain genomics datasets is a pretty hot topic locally and nationally.” (The U.S. Supreme Court recently heard arguments for and against patenting of human genome data. A decision is expected in June.)

Tumor Suppressors and Cancer
Much of our understanding of cancer comes from research that uncovers the molecular interactions underlying this disease. The discovery of tumor suppressor genes that control cell division and growth provides great insight into how cancer develops.

Tumor suppressors are viewed as protective genes that prevent the uncontrollable division of cells, a hallmark of cancer development. A disruption in the function of these genes can catapult cancer progression. Protein Kinase C zeta (PKC zeta) is a tumor suppressor known to play a role in different human cancers. A mutated form of PKC zeta found in people is associated with tumor development.

The exact mechanism by which PKC zeta (or lack of) affects the progression of cancer is not well understood. Recently, a team of researchers led by scientists at Sanford-Burnham Medical Research Institute identified PKC zeta’s role in prostate cancer and its mechanism of action. The same authors previously published a paper demonstrating PKC zeta to be a tumor suppressor in human and mouse intestinal cancer.

This Research: Before Bioinformatics
Prior to utilizing public domain genomics data and bioinformatics, the researchers used a mouse model deficient in tumor suppressor PTEN, which predisposes the mouse to cancer, to determine PKC zeta’s role in prostate cancer. Using this model, the researchers found that the loss of PKC zeta resulted in prostate cancer. These results directed them to establish PKC zeta’s role in human prostate cancer in the context of PTEN deficiency.

They also analyzed protein expression of PTEN and PKC zeta in human prostate cancer tissue samples and saw that there was a positive correlation between the expression of two tumor suppressors.
Using Public Domain Datasets

The researchers wanted to further assess the PTEN-dependent role of PKC zeta in normal prostate tissues, primary cancer and metastatic primary cancer tissue. With the help of Medvedovic and Chen, the team was able to establish PKC zeta’s role in human prostate cancer through bioinformatics analysis.

By mining their collection of genomics datasets, Medvedovic and Chen determined the appropriate human prostate cancer dataset to use, which provided information about gene-level changes in prostate cancer. The results of this analysis showed that reduction in expression levels of PKC zeta in metastatic cancers is dependent on a decrease in PTEN expression levels. This supported previous findings on PKC zeta’s role in colorectal cancer, and from there, the researchers were able to explore even deeper the cellular mechanism of PKC zeta in prostate cancer.

To investigate the molecular mechanisms by which PKC zeta restrains prostate cancer, researchers performed a genome-wide transcriptome analysis and identified genes that are differentially expressed between cells without PKC zeta and cells with PKC zeta. Expression levels of these genes were then used to cluster samples in the human prostate cancer datasets.

The researchers found similarities among the altered genes in cells and tumors without PKC zeta when compared with normal tissue. Bioinformatics analysis of the altered genes showed that they are involved in proliferation, growth, movement and cell death, suggesting that, when present, PKC zeta plays a role in preventing progression of prostate cancer, invasion and metastasis.

Further analysis of these differentially expressed gene signatures led to the identification of another gene
(c-Myc) as a relevant target of PKC zeta. Levels of c-Myc increase when PKC zeta is absent, and this was confirmed in the prostates of the PTEN mouse model deficient in PKC zeta. These data suggest that the loss of PKC zeta results in increased levels of c-Myc, subsequently affecting cell proliferation and growth.

Medvedovic and Chen then used a public domain ChIP-seq dataset and their own bioinformatics technique for analysis of transcription factor DNA-binding patterns to identify c-Myc targets. They found that genes upregulated in cells without PKC zeta tend to be targets of c-Myc, further underscoring the idea that PKC zeta negatively regulates c-Myc to prevent cancer. The researchers hypothesized that PKC zeta not only repressed the gene levels of c-Myc but also repressed by directly acting on c-Myc. 

The application of bioinformatics tools allowed the researchers to:

• Correlate the PTEN-dependent role of PKC zeta to human prostate cancer.
• Identify and categorize genes regulated by PKC zeta.
• Identify the clinically relevant target of PKC zeta, c-Myc.
• Provide supporting evidence that PKC zeta regulates cell division and growth by negatively regulating c-Myc, thereby preventing the development of cancer.

"Prostate cancer is the most common malignancy among men in Western countries,” the authors write. "Our observations that PKC zeta is a tumor suppressor in this type of neoplasia, and that it acts by repressing c-Myc expression and function, are likely to be highly relevant in the design of new therapeutic approaches, which are sorely needed.”

Adds Medvedovic: "The vast amount of diverse public domain genomics datasets provide a tremendous opportunity to test and postulate new hypotheses by simply re-analyzing other people’s data. Such data can also be used to better interpret results of laboratory experiments and increase their impact by making a connection with human disease.”

Medvedovic is a member of the UC Cancer Institute. Both Medvedovic and Chen work with UC’s Center for Clinical and Translational Science and Training (CCTST) to assist investigators with bioinformatics analyses. The CCTST is the academic home of UC’s institutional Clinical and Translational Science Award (CTSA) from the National Institutes of Health.

Jessica Dade is a fourth-year graduate student in molecular genetics, biochemistry and microbiology with an interest in science writing. She participated in UC’s summer research program in 2008 and began her graduate work at the university the following year. She is working in a lab at the Cincinnati Department of Veterans Affairs under the direction of George Deepe, MD, and George Smulian, MD, both of UC’s infectious diseases division.
Media Contact:     AHC Public Relations, (513) 558-4553

Law's Laws

A series of observations on Genetic Analysis algorithms and experiments

Law's First Law

"The first step in developing a new genetic analysis algorithm is to decide how to make the input data file format different from all pre-existing analysis data file formats."
A prime exemplar of this Law is the use of different codes to signify the sex of animals. For example, crimap uses '0' to represent female and '1' to represent male. The algorithm designed by Keightly et al. uses the same codes to mean the opposite sexes.The Knott & Haley QTL analysis algorithm uses codes '1' and '2'. The list goes on.

Law's Second Law

"Error messages should never be provided"
corollary...
"If error messages are provided, they should be utterly cryptic so as to convey as little information as possible to the end user"
Do you understand crimap's error messages? I thought not.

Law's Third Law

"The number of unique identifiers assigned to an individual is never less than the number of Institutions involved in the study"
... and is frequently many, many more.

Law's Fourth Law

"All scientists agree that sharing data is good and are more than happy to share everyone else's"




795638
St. Jude
Bioinformatics Associate Research Scientist (two openings), Computational
PhD in Molecular Biology, Biochemistry, Computer Science, Statistics, Mathematics, Bioinformatics, or related field required. PhD which must include research related to bioinformatics (such as analysis of sequence data, microarrays, SNPs, image data, proteomics data, or biological pathways; development of algorithms, statistical methods, or scientific software); OR If PhD with no bioinformatics research, then two (2) years of pre-or postdoctoral experience in Computational Biology or Bioinformatics research is required.
Experience with programming languages such as Perl, C, or Java required.
LICENSURE REQUIREMENTS:
None
OTHER CREDENTIAL REQUIREMENTS:
None
5/22/2013 11:41:06 AM
Memphis,TN,US

St. Jude Children's Research Hospital, founded by the late entertainer Danny Thomas, is one of the world's premier centers for the research and treatment of pediatric cancer and other catastrophic childhood diseases. St. Jude is the first and only pediatric cancer center to be designated as a Comprehensive Cancer Center by the National Cancer Institute. Children from all 50 states and from around the world have come through the doors of St. Jude for treatment, and thousands more around the world have benefited from the research conducted at St. Jude - research that is shared freely with the global medical community. St. Jude is the only pediatric cancer research center where families never pay for treatments that are not covered by insurance. No child is denied treatment because of a family's inability to pay.

Job Description:
The Computational Biology (CompBio) Department focuses on the development and application of innovative approaches for analyzing high-throughput; multi-dimensional genomic and epigenetic data generated from basic and clinical research groups studying pediatric cancer, gene therapy and infectious disease. The Department has a well-established track record in developing state-of-art computational methods for analyzing next-generation sequencing (NGS) data with high impact publications in the journals of Nature, Nature Genetics, Nature Methods, JAMA and Cancer Cell. We are looking for highly motivated and talented bioinformatics scientists who are interested in working on a large-scale clinical sequencing project with responsibilities for in-depth analysis of whole-genome, exome and RNASeq data of pediatric cancer patients. CompBio provides a highly collaborative teamwork environment, access to state-of-art computational infrastructure and deep experience in analyzing, managing, visualizing and delivering data and results generated from NGS technology.

The Bioinformatics Associate Research Scientist in the Computational Biology Department is expected to participate in data analysis, data visualization, statistical analysis, experimental design, and database development. Provides bioinformatics analysis for interdepartmental investigators and communicate and discuss with investigators on analytical process and results. Participates in the Computational Biology Department's independent research. Assists in preparing and submitting manuscripts for publication. Contributes ideas to automate or improve existing analysis methods. Assists with establishing and documenting protocols or best practices for common research tasks. Ensures that efficient and prompt help is provided to the SJCRH investigators. (MJP)




Job: NERC Bioinformatics fellowships at UCL
From the Evolution Directory (EvolDir) via Twitter.


Dear all,

UCL's Research Department of Genetics, Evolution and Environment invites expressions of interest from potential applicants to NERC's Independent Research Fellowships in Bioinformatics.

NERC has recently launched a research programme in "Mathematics and Informatics for Environmental 'Omic Data Synthesis" and funds 5-year fellowships for early-career scientists wishing to establish independent research groups. More information on the scheme and the background of the programme can be found on the NERC website (http://www.nerc.ac.uk/research/programmes/omics/events/ao-bioinformaticsfellowships.asp).

If you have the appropriate expertise and would like to apply for a fellowship hosted in our department, please get in touch with Max Reuter (m.reuter@ucl.ac.uk). We will support selected candidates through all stages of their application.

Our department fosters young talent and provides a stimulating and multi-discilinary research environment. It has strengths in evolutionary and statistical genetics, genomics, evo-devo and environmental and biodiversity research. For more information about our research, please visit the department's website (http://www.ucl.ac.uk/gee/) as well as those of departmental sub-nuits including the UCL Genetics Institute (http://www.ucl.ac.uk/ugi/) and the Centre for Biodiversity and Environment Research (http://www.ucl.ac.uk/cber/).

Best regards, Max


PS: Sorry for posting this twice, but I wanted to make sure it went to people looking at 'Job' and 'Postdoc' messages



___________________________________________________________
Max Reuter


Research Department of Genetics, Evolution and Environment
Faculty of Life Sciences
University College London
Darwin Building
Gower Street
London WC1E 6BT, UK

Phone: +44-20-76792201 (internal 32201)

Lab: http://www.homepages.ucl.ac.uk/~ucbtmre/Labsite/
Department: http://www.ucl.ac.uk/gee
Centre for Ecology and Evolution: http://www.ceevol.org.uk
___________________________________________________________
















This year’s symposium will be jointly presented by the Kansas City Area Life Sciences Institute and Frontiers: The Heartland Institute for Clinical and Translational Research. The symposium will be open to the public and feature an outstanding group of national and regional speakers. We look forward to your attendance!

View Agenda    Register Now

Date & Time

June 20, 2013Light Breakfast 7:30 – 8:00 am
Program 8:00 am – 5:30 pm

Location

Kansas City University of Medicine and Biosciences
Weaver Auditorium
1750 Independence Avenue
Kansas City, MO 64106
Directions
KCUMB
Hosted by Kansas City University of Medicine and Biosciences

 

 

 

 

 

 

PLATO, an Alternative to PLINK

Since the near beginning of genome-wide association studies, the PLINK software package (developed by Shaun Purcell’s group at the Broad Institute and MGH) has been the standard for manipulating the large-scale data produced by these studies.  Over the course of its development, numerous features and options were added to enhance its capabilities, but it is best known for the core functionality of performing quality control and standard association tests. 

Nearly 10 years ago (around the time PLINK was just getting started), the CHGR Computational Genomics Core (CGC) at Vanderbilt University started work on a similar framework for implementing genotype QC and association tests.  This project, called PLATO, has stayed active primarily to provide functionality and control that (for one reason or another) is unavailable in PLINK.  We have found it especially useful for processing ADME and DMET panel data – it supports QC and association tests of multi-allelic variants.    

PLATO runs via command line interface, but accepts a batch file that allows users to specify an order of operations for QC filtering steps.  When running multiple QC steps in a single run of PLINK, the order of application is hard-coded and not well documented.  As a result, users wanting this level of control must run a sequence of PLINK commands, generating new data files at each step leading to longer compute times and disk usage.  PLATO also has a variety of data reformatting options for other genetic analysis programs, making it easy to run EIGENSTRAT, for example.

The detail of QC output from each of the filtering steps is much greater in PLATO, allowing output per group (founders only, parents only, etc), and giving more details on why samples fail sex checks, Hardy-Weinberg checks, and Mendelian inconsistencies to facilitate deeper investigation of these errors.  And with family data, disabling samples due to poor genotype quality retains pedigree information useful for phasing and transmission tests. Full documentation and download links can be found here (https://chgr.mc.vanderbilt.edu/plato).  Special thanks to Yuki Bradford in the CGC for her thoughts on this post.  

Novus Explorer — your gateway to scientific research. This bioinformatics tool is designed to facilitate scientific exploration of related genes, diseases and pathways based on co-citations.  Type in your gene, disease or pathway of interest and press Enter to begin your exploration. Read the Novus Explorer How to Guide for more detailed instructions. If you are having problems with Flash please try clearing your browser cache.

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