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Matthew Wakefield

Researcher at Walter and Eliza Hall Institute of Medical Research

Publications -  157
Citations -  13189

Matthew Wakefield is an academic researcher from Walter and Eliza Hall Institute of Medical Research. The author has contributed to research in topics: Genome & Pension. The author has an hindex of 42, co-authored 151 publications receiving 11034 citations. Previous affiliations of Matthew Wakefield include Children's Hospital Oakland Research Institute & La Trobe University.

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Gene ontology analysis for RNA-seq: accounting for selection bias

TL;DR: Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.
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Genome of the marsupial Monodelphis domestica reveals innovation in non-coding sequences

Tarjei S. Mikkelsen, +238 more
- 10 May 2007 - 
TL;DR: A high-quality draft of the genome sequence of the grey, short-tailed opossum is reported, indicating a strong influence of biased gene conversion on nucleotide sequence composition, and a relationship between chromosomal characteristics and X chromosome inactivation.
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Pyevolve: a toolkit for statistical modelling of molecular evolution

TL;DR: PyEvolve provides flexible functionality that can be used either for statistical modelling of molecular evolution, or the development of new methods in the field, and implements numerous optimisations that make highly parameter rich likelihood functions solvable within hours on multi-cpu hardware.
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Genome analysis of the platypus reveals unique signatures of evolution

Wesley C. Warren, +104 more
- 08 May 2008 - 
TL;DR: It is found that reptile and platypus venom proteins have been co-opted independently from the same gene families; milk protein genes are conserved despite platypuses laying eggs; and immune gene family expansions are directly related to platypUS biology.
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Transcript length bias in RNA-seq data confounds systems biology

TL;DR: Transcript length bias for calling differentially expressed genes is a general feature of current protocols for RNA-seq technology and has implications for the ranking of differentially expression genes, and in particular may introduce bias in gene set testing for pathway analysis and other multi-gene systems biology analyses.