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Integrated sequence analysis pipeline provides one-stop solution for identifying disease-causing mutations

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons50314

Hu,  H.
Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons129894

Wienker,  T. F.
Clinical Genetics (Thomas F. Wienker), Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50439

Musante,  L.
Familial Cognitive Disorders (Luciana Musante), Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50369

Kalscheuer,  V. M.
Chromosome Rearrangements and Disease (Vera Kalscheuer), Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50501

Ropers,  H. H.
Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society;

Fulltext (public)

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Supplementary Material (public)
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Citation

Hu, H., Wienker, T. F., Musante, L., Kalscheuer, V. M., Kahrizi, K., Najmabadi, H., et al. (2014). Integrated sequence analysis pipeline provides one-stop solution for identifying disease-causing mutations. Human Mutation, 35(12), 1427-1435. doi:10.1002/humu.22695.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0025-C2FA-E
Abstract
Next-generation sequencing has greatly accelerated the search for disease-causing defects, but even for experts the data analysis can be a major challenge. To facilitate the data processing in a clinical setting, we have developed a novel medical resequencing analysis pipeline (MERAP). MERAP assesses the quality of sequencing, and has optimized capacity for calling variants, including single-nucleotide variants, insertions and deletions, copy-number variation, and other structural variants. MERAP identifies polymorphic and known causal variants by filtering against public domain databases, and flags nonsynonymous and splice-site changes. MERAP uses a logistic model to estimate the causal likelihood of a given missense variant. MERAP considers the relevant information such as phenotype and interaction with known disease-causing genes. MERAP compares favorably with GATK, one of the widely used tools, because of its higher sensitivity for detecting indels, its easy installation, and its economical use of computational resources. Upon testing more than 1,200 individuals with mutations in known and novel disease genes, MERAP proved highly reliable, as illustrated here for five families with disease-causing variants. We believe that the clinical implementation of MERAP will expedite the diagnostic process of many disease-causing defects.