Author

Mesut Erzurumluoglu

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Genetics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Research Focus:differential expression· 2 Organism:human· 2 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Assay:DNA methylation· 1 Research Focus:gene regulatory network inference· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Alkes Price· 1 shared Beate St. PourcainiD· 1 shared Benjamin ElsworthiD· 1 shared Benjamin Neale· 1 shared Brendan Bulik-Sullivan· 1 shared Charles LauriniD· 1 shared David Evans· 1 shared Evie StergiakouliiD· 1 shared

Activity by Year

2014-2016 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 29 mutual co-author links

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1 shared paperBioinformatics

Alkes Price

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3 papers

Recent·Most cited

Y chromosome and mitochondrial DNA haplogroups across behavioural traits in children from the general population

Laurence J Howe, Mesut Erzurumluoglu, George Davey-Smith, Santiago Rodriguez, Evie Stergiakouli

The paper examines the association between Y chromosome and mitochondrial DNA haplogroups and behavioural traits in children, finding no significant associations.

bioRxiv · Jul 18, 2016Genetics

LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis

JIE ZHENG, Mesut Erzurumluoglu, Benjamin Elsworth, Laurence Howe, Philip Haycock, Gibran Hemani, Katherine Tansey, Charles Laurin, Beate St. Pourcain, Nicole Warrington, Hilary Finucane, Alkes Price, Brendan Bulik-Sullivan, Verneri Anttila, Lavinia Paternoster, , Benjamin Neale

LD Hub provides a centralized database and web interface for performing LD score regression on GWAS summary data to estimate SNP heritability and genetic correlations across traits.

bioRxiv · May 3, 2016Bioinformatics

Identifying highly-penetrant disease causal mutations using next generation sequencing: Guide to whole process

Mesut Erzurumluoglu

This review provides an analysis schema and key approaches to efficiently and reliably identify highly penetrant causal mutations in human disorders using next-generation sequencing.

bioRxiv · Nov 6, 2014Bioinformatics