Phelim Bradley

Phelim Bradley

London, England, United Kingdom
2K followers 500+ connections

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About

Cofounder of Prolific

DPhil in Genomic Medicine and Statistics from Oxford…

Activity

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Experience

  • Pioneer Fund Graphic

    Pioneer Fund

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    London, United Kingdom

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    Cambridge, United Kingdom

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    Cork, Ireland

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    Cork, Ireland

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    Berkeley, California

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    Berkeley, California

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    Ithaca, New York Area

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    Cork, Ireland

Education

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    Y Combinator

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    Wellcome Trust Funded 4 year (1+3) Doctoral Training Centre program in Genomic Medicine and Statistics.
    On winning team at the Cambridge NHS Hackday 2013 with http://www.practiceminder.org/.
    Cofounder of the Oxford University Society for Synthetic Biology.

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    Activities and Societies: President of the University of Cambridge Dinghy Sailing Club. Fitzwilliam MCR technical officer.

    Awarded a MPhil degree with distinction (~80% average).
    Received fees studentship to the value of €7,750.
    Member of Fitzwilliam’s College.
    Masters Thesis on "Learning models for predicting the quality of genetic variant calls from DNA sequencing data".

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    Activities and Societies: Sailing club, Physics Society, Squash Club

    1st Class Honours Degree with a 82% avg.
    Studied in University College Berkeley on an exchange year 10/11
    Received Full Academic Entrance Scholarship to UCC based on leaving certificate results.
    Received the John A. Delaney Memorial Prize in Physics for the best performance in Physics

    Awarded the title of College Scholar in 2010 for undergraduate examination results

    Awarded additional scholarship for 2010 examination results.

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    Activities and Societies: Selected to compete with Christian Brothers Cork on their prizewinning Maths and Chemistry teams. Selected to compete in the National Physics and Chemistry Olympiads Completed E.C.D.L(European Computer Drivers License)

    Graduated with maximum points in top 0.2% of the country.

Publications

  • Ultrafast search of all deposited bacterial and viral genomic data

    Nature Biotechnology

    Exponentially increasing amounts of unprocessed bacterial and viral genomic sequence data are stored in the global archives. The ability to query these data for sequence search terms would facilitate both basic research and applications such as real-time genomic epidemiology and surveillance. However, this is not possible with current methods. To solve this problem, we combine knowledge of microbial population genomics with computational methods devised for web search to produce a searchable…

    Exponentially increasing amounts of unprocessed bacterial and viral genomic sequence data are stored in the global archives. The ability to query these data for sequence search terms would facilitate both basic research and applications such as real-time genomic epidemiology and surveillance. However, this is not possible with current methods. To solve this problem, we combine knowledge of microbial population genomics with computational methods devised for web search to produce a searchable data structure named BItsliced Genomic Signature Index (BIGSI). We indexed the entire global corpus of 447,833 bacterial and viral whole-genome sequence datasets using four orders of magnitude less storage than previous methods. We applied our BIGSI search function to rapidly find resistance genes MCR-1, MCR-2, and MCR-3, determine the host-range of 2,827 plasmids, and quantify antibiotic resistance in archived datasets. Our index can grow incrementally as new (unprocessed or assembled) sequence datasets are deposited and can scale to millions of datasets.

    See publication

  • Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis

    Nature Communications

    See publication

  • How Kondo-holes create intense nanoscale heavy-fermion hybridization disorder

    Proceedings of the National Academy of Sciences

    Visualization of the electronic structure at Kondo-holes created by substituting spinless thorium atoms for magnetic uranium atoms in the heavy-fermion system URu2Si2

    Other authors
    • Mohammad H. Hamidian
    • Andrew R. Schmidt
    • Inês A. Firmo
    • Milan P. Allan
    • Jim D. Garrett
    • Travis J. Williams
    • Graeme M. Luke
    • Yonatan Dubi
    • Alexander V. Balatsky
    • J. C. Davis
    See publication

Courses

  • Population Genetic Analyses of Genomic Data

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  • Functional Genomics

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  • Functional Genomics

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  • Genome Informatics

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  • Genome Informatics

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  • Introduction to Mathematical Modelling

    AM1053

  • Introduction to Mechanics

    AM1052

  • Introduction to Statistics

    ST101

  • Network Biology

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  • Network Biology

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  • Population Genetic Analyses of Genomic Data

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  • Scientific Programming

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  • Scientific Programming with R

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  • Sequence Analysis

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  • Sequence Analysis

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  • Structural Biology

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  • Structural Biology

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  • Systems Biology

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  • Systems Biology

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Projects

  • Learning models for predicting the quality of genetic variant calls from DNA sequencing data

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    In this project I used high quality pedigree data from the a 13 mem-
    ber family sequenced by Illumina to assess and improve the ltering
    of the raw variants from Illumina's iSAAC variant caller (Starling).
    First, I assess and attempt to optimising current lters, highlighting
    lters for possible improvement. I then develop a statistical learning
    model to separate true variation from artifacts arising from sequenc-
    ing technology, the underlying chemistry and alignment…

    In this project I used high quality pedigree data from the a 13 mem-
    ber family sequenced by Illumina to assess and improve the ltering
    of the raw variants from Illumina's iSAAC variant caller (Starling).
    First, I assess and attempt to optimising current lters, highlighting
    lters for possible improvement. I then develop a statistical learning
    model to separate true variation from artifacts arising from sequenc-
    ing technology, the underlying chemistry and alignment algorithms.
    Using a support vector machine classier I show that it is possible to
    dierentiate artifacts from real variants more eciently then current
    lters. Using this approach, one can see an increase both the precision
    and recall of both SNP (an increase of 0.1%,4% respectively for a
    2x250 36x build) and INDEL calling (an increase of 10% and 9%
    respectively) over current lters and approach performance achieved
    by the BWA/GATK pipeline.

    Other creators
    See project

  • Quantised Conductance in Self-Breaking Feedback-Controlled Electromigrated Nanoscale Wires

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    In this report the evolution of the quantised conductance in this self-breaking regime is investigated. A diverse and interesting range of behaviours was observed. Quantisation of conductance was observed in integer and non-integer multiples of 𝐺0. Histogram analysis and correlation analysis were used to quantify the expected variability of behaviours, identify possible preferential conductance levels and transitions and a comparison between the behaviours of gold (Au) and platinum (Pt).

    Other creators
    See project

  • Molecular Dynamics Computational Study

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    Annealing of a cluster from FCC lattice
    Adding energy to annealed cluster.
    Energy comparision of reforming cluster.
    Phase transition / specific heat.

    See project

Honors & Awards

  • Science Engineering and Food Science Graduate of the Year

    College of Science Engineering and Food Science

  • BBSRC Masters Studentship Grant

    Biotechnology and Biological Sciences Research Council

    Received to study MPhil in Computational Biology at the University of Cambridge

  • College Scholar

    University College Cork, School of Engineering, Science and Food Science

    Awarded title of College Scholar for undergraduate examination results.

  • John A. Delaney Memorial Prize in Physics

    University College Cork, Physics Dept.

    Annual award by the examiners for the best performance in Physics at the Summer First University Examination in Science.

  • Full Academic Entrance Scholarship to UCC

    University College Cork

    Received Full Academic Entrance Scholarship to UCC based on leaving certificate results (600/600).

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