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    Submitter

    ABSTRACT

    Numerous lines of evidence point to a genetic basis for facial morphology in humans, yet little is known about how specific genetic variants relate to the phenotypic expression of many common facial features. We conducted genome-wide association meta-analyses of 20 quantitative facial measurements derived from the 3D surface images of 3118 healthy individuals of European ancestry belonging to two US cohorts. Analyses were performed on just under one million genotyped SNPs (Illumina OmniExpress+Exome v1.2 array) imputed to the 1000 Genomes reference panel (Phase 3). We observed genome-wide significant associations (p < 5 x 10−8) for cranial base width at 14q21.1 and 20q12, intercanthal width at 1p13.3 and Xq13.2, nasal width at 20p11.22, nasal ala length at 14q11.2, and upper facial depth at 11q22.1. Several genes in the associated regions are known to play roles in craniofacial development or in syndromes affecting the face: MAFB, PAX9, MIPOL1, ALX3, HDAC8, and PAX1. We also tested genotype-phenotype associations reported in two previous genome-wide studies and found evidence of replication for nasal ala length and SNPs in CACNA2D3 and PRDM16. These results provide further evidence that common variants in regions harboring genes of known craniofacial function contribute to normal variation in human facial features. Improved understanding of the genes associated with facial morphology in healthy individuals can provide insights into the pathways and mechanisms controlling normal and abnormal facial morphogenesis.

    ARTICLE

    Shaffer, John R., et al. 2016. "Genome-wide association study reveals multiple loci influencing normal human facial morphology." PLoS Genet 12(8):e1006149. dx.doi.org/10.1[...]06149

    BACKGROUND

    The Wistar Institute is looking for an exceptional candidate to join their Cancer Center Bioinformatics Shared Resources Team. The position is highly collaborative and interdisciplinary with opportunities to work with a variety of investigators on basic mechanisms of gene regulation, identification of disease and cancer targets and functional and diagnostic genomics. The successful candidate will play a key role in an integrative team of bioinformatics analysts and biomedical researchers. We are looking for an individual who is enthusiastic about taking a hands-on collaborative approach to understanding these problems and in making significant contributions to our efforts.

    Successful candidates will work with wet-lab researchers to translate computational models into testable experiments and should have experience communicating in a biomedical environment with broad understanding of the field. The position requires familiarity with the analysis of high throughput data from various sources including NGS sequencing, microarrays and proteomics and the capability to integrate various types of high dimensional data with functional biological information and to explore novel data representation modes with emphasis on integrating diverse result types.

    RESPONSIBILITIES

    Characteristic Duties:
    • Pre-experimental project consultations
    • Develop data analysis strategies, write algorithms as needed, and identify and deploy computational tools for the exploration of high-throughput datasets
    • Conceive, implement and test statistical models
    • Prepare project reports and discuss results, problems and solutions with users

    REQUIREMENTS

    PhD degree in Bioinformatics, Computer Science, Engineering, Statistics, Physics, or any quantitative discipline. A minimum of 2 years of post-doctoral experience in computational analysis of high dimensional biomedical data ( next generation sequencing, microarrays, proteomics) is preferred. Candidates should have outstanding academic records; a demonstrated proficiency with statistical and programmatic tools needed for the exploration of high-dimensionality datasets and be an innovative and analytical thinker with strong communication skills who can work well in an inter-disciplinary team.

    Significant experience with at least some of the following is required:
    • Experience in Matlab or R statistical environments is required
    • Experience in programming languages (Python, Perl or C/C++) is required
    • Analysis of data using machine learning approaches
    • Developing tools for data visualization
    • Implementing algorithms for data search, retrieval and annotation
    • A background in Biology or demonstrated experience working and communicating with biologists is very helpful

    COMPENSATION

    We offer an excellent benefits package.

    ABOUT US

    The Wistar Institute is located in the University City area of Philadelphia, in the heart of the University of Pennsylvania Campus. Wistar provides resources to its faculty and staff that enable them to conduct cutting edge collaborative research and provides for outstanding intellectual environments and state-of-the-art facilities. Research discoveries conducted at Wistar have led to the development of vaccines; the identification of genes associated with cancers; and the development of many other significant research technologies and tools.

    For more information about The Wistar Institute visit our website at www.wistar.org.

    HOW TO APPLY

    Curriculum Vitae and three references should be sent to: Dr. Louise Showe, The Wistar Institute, 3601 Spruce Street, Philadelphia, PA 19104 or via email to lshowe[at]wistar.org. Applicants are also encouraged to apply online by visiting www.wistar.org/the-[...]yment.

    POLICY

    It is the policy of The Wistar Institute to provide equal employment opportunities to all individuals regardless of race, color, creed, religion, national origin, ancestry, sex, age, veteran status, disability, sexual orientation, gender identity, or on the basis of genetic information, or any other characteristic protected by federal, state, or local law, with respect to all terms and conditions of employment.

    Submitter

    EXCERPT

    Now, a team of Liggins Institute researchers have shown for the first time that human mitochondrial DNA leaves the mitochondria, travels into the host cell nucleus and connects to specific genes.

    "We found evidence that mitochondria DNA and nuclear DNA 'talk to each other', and these interactions aren't random," says lead researcher Dr Justin O'Sullivan, a molecular geneticist at the University of Auckland research institute.

    The findings give weight to the idea that mitochondria do much more than supply energy and regulate a cell's metabolism – the processes that keep it alive.
    Source: medicalxpress.com/news[...].html

    ARTICLE

    Doynova, M.D., et al. 2016. "Interactions between mitochondrial and nuclear DNA in mammalian cells are non-random." Mitochondrion. dx.doi.org/10.1[...]8.003

    RESPONSIBILITIES

    Manage all aspects of multiple on-going, multi-disciplinary projects including the coordination of study design and implementation, sample collection, documentation, reporting, and communication. Opportunities for publishing scientific papers and participating in grant proposals for development of techniques and analysis related.

    Percentage of time spent on each facet of the position:
    1. Development of project-specific bioinformatics (computational and database) tools in conjunction with Faculty, Students, and Academic Staff (10%)
    2. Training and technical support for bioinformatics software including potentially teaching seminars or workshops (10%)
    3. Analysis of genomic and sequence data generated by customers in the Center (60%)
    4. Oversight of computational and informatics resources including software and hardware. Liaise with staff at the UWM computing cluster facility (10%)
    5. Preparation of manuscripts, reports, web-based public databases, and funding proposals (10%)

    REQUIREMENTS

    • Minimum of bachelor's degree in bioinformatics, molecular biology or related field
    • At least two years' experience in analyzing genomic data including de novo assemblies, multiple sequence alignment (preferably with high-throughput sequencing data), management of large datasets, and experience with gene and protein prediction, neural network analysis, and analysis of microarray data
    • Experience in programming with Perl, BioPerl or other programming related to sequence analysis, statistical packages (such as R or MATLAB)
    • Familiarity with UNIX environments and computing clusters
    • Written and verbal communication skills
    • Demonstrated evidence of teamwork collaboration

    PREFERENCES

    • Masters degree in bioinformatics, molecular biology or related field
    • Prior experience developing scientific software and informatics tools
    • Experience with analysis of next-generation sequence data for large genomes as well as microbial community analysis

    TERMS

    This position is a 12-month, 100% fixed term, renewable academic staff appointment.

    COMPENSATION

    Salary will be within Academic Staff salary range and will be commensurate with experience.

    LOCALE

    Great Lakes Genomics Center, University of Wisconsin-Milwaukee, School of Freshwater Sciences
    www4.uwm.edu/freshwater/

    HOW TO APPLY

    Applicants must apply online by visiting jobs.uwm.edu/postings/25477. All applicants must submit a cover letter outlining qualifications and interests, along with a resume that includes names of three references that can speak to the applicant's abilities, and contact information for all three references.

    DEADLINE

    Review of applications will begin on August 22, 2016 and will continue until the position is filled.
    Resources: Cello: Genetic circuit design automation
    Submitted by Prashanth Suravajhala; posted on Thursday, August 25, 2016

    Submitter

    EXCERPT

    The Cello input is a high-level logic specification written in Verilog, a hardware description language. The code is parsed to generate a truth table, and logic synthesis produces a circuit diagram with the genetically available gate types to implement the truth table. The gates in the circuit are assigned using experimentally characterized genetic gates. In assignment, a predicted circuit score guides a breadth-first search, or a Monte Carlo simulated annealing search. The assignment with the highest score is chosen, and this assignment can be physically implemented in a combinatorial number of different genetic layouts. The Eugene language is used for rule-based constrained combinatorial design of one or more final DNA sequence(s) for the designed circuit.

    AVAILABILITY

    www.cellocad.org
    github.com/CIDARLAB/cello

    ARTICLE

    Nielsen, Alec A.K., et al. 2016. "Genetic circuit design automation." Science 352(6281). dx.doi.org/10.1[...]c7341

     

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