Predicting the Pathogenic Impact of Sequence Variation in the Human Genome

Mark F Rogers; Hashem A Shihab; Michael Ferlaino; Tom R Gaunt; Colin K Campbell

doi:10.3233/978-1-61499-753-5-91

Predicting the Pathogenic Impact of Sequence Variation in the Human Genome

Mark F Rogers, Hashem A Shihab, Michael Ferlaino, Tom R Gaunt, Colin K Campbell

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Sequencing data will become widely available in clinical practice within the near future. Uptake of sequence data is currently being stimulated within the UK through the government-funded 100,000 genomes project (Genomics England), with many similar initiatives being planned and supported internationally. The analysis of the large volumes of data derived from sequencing programmes poses a major challenge for data analysis. In this paper we outline progress we have made in the development of predictors for estimating the pathogenic impact of single nucleotide variants, indels and haploinsufficiency in the human genome. The accuracy of these methods is enhanced through the development of disease-specific predictors, trained on appropriate data, and used within a specific disease context. We outline current research on the development of disease-specific predictors, specifically in the context of cancer research.

Original language	English
Title of host publication	Informatics for Health
Subtitle of host publication	Connected Citizen-Led Wellness and Population Health
Editors	Rebecca Randell, Ronald Cornet, Colin McCowan, Niels Peek, Philip J Scott
Publisher	IOS Press
Pages	91-95
Number of pages	5
ISBN (Electronic)	9781614997535
ISBN (Print)	9781614997528
DOIs	https://doi.org/10.3233/978-1-61499-753-5-91
Publication status	E-pub ahead of print - 17 May 2017
Event	Informatics for Health 2017 - Manchester, United Kingdom Duration: 24 Apr 2017 → 26 Apr 2017

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	235
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	Informatics for Health 2017
Country/Territory	United Kingdom
City	Manchester
Period	24/04/17 → 26/04/17

Keywords

Prediction
sequence data
variant
annotation
point mutation
indel

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.3233/978-1-61499-753-5-91

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Mark Rogers - Predicting the Pathogenic Impact of Sequence Variation in the Human Genome
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Rogers, M. F., Shihab, H. A., Ferlaino, M., Gaunt, T. R., & Campbell, C. K. (2017). Predicting the Pathogenic Impact of Sequence Variation in the Human Genome. In R. Randell, R. Cornet, C. McCowan, N. Peek, & P. J. Scott (Eds.), Informatics for Health: Connected Citizen-Led Wellness and Population Health (pp. 91-95). (Studies in Health Technology and Informatics; Vol. 235). IOS Press. https://doi.org/10.3233/978-1-61499-753-5-91

Rogers, Mark F ; Shihab, Hashem A ; Ferlaino, Michael et al. / Predicting the Pathogenic Impact of Sequence Variation in the Human Genome. Informatics for Health: Connected Citizen-Led Wellness and Population Health. editor / Rebecca Randell ; Ronald Cornet ; Colin McCowan ; Niels Peek ; Philip J Scott. IOS Press, 2017. pp. 91-95 (Studies in Health Technology and Informatics).

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title = "Predicting the Pathogenic Impact of Sequence Variation in the Human Genome",

abstract = "Sequencing data will become widely available in clinical practice within the near future. Uptake of sequence data is currently being stimulated within the UK through the government-funded 100,000 genomes project (Genomics England), with many similar initiatives being planned and supported internationally. The analysis of the large volumes of data derived from sequencing programmes poses a major challenge for data analysis. In this paper we outline progress we have made in the development of predictors for estimating the pathogenic impact of single nucleotide variants, indels and haploinsufficiency in the human genome. The accuracy of these methods is enhanced through the development of disease-specific predictors, trained on appropriate data, and used within a specific disease context. We outline current research on the development of disease-specific predictors, specifically in the context of cancer research.",

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Rogers, MF, Shihab, HA, Ferlaino, M, Gaunt, TR & Campbell, CK 2017, Predicting the Pathogenic Impact of Sequence Variation in the Human Genome. in R Randell, R Cornet, C McCowan, N Peek & PJ Scott (eds), Informatics for Health: Connected Citizen-Led Wellness and Population Health. Studies in Health Technology and Informatics, vol. 235, IOS Press, pp. 91-95, Informatics for Health 2017, Manchester, United Kingdom, 24/04/17. https://doi.org/10.3233/978-1-61499-753-5-91

Predicting the Pathogenic Impact of Sequence Variation in the Human Genome. / Rogers, Mark F; Shihab, Hashem A; Ferlaino, Michael et al.
Informatics for Health: Connected Citizen-Led Wellness and Population Health. ed. / Rebecca Randell; Ronald Cornet; Colin McCowan; Niels Peek; Philip J Scott. IOS Press, 2017. p. 91-95 (Studies in Health Technology and Informatics; Vol. 235).

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - Predicting the Pathogenic Impact of Sequence Variation in the Human Genome

AU - Rogers, Mark F

AU - Shihab, Hashem A

AU - Ferlaino, Michael

AU - Gaunt, Tom R

AU - Campbell, Colin K

PY - 2017/5/17

Y1 - 2017/5/17

N2 - Sequencing data will become widely available in clinical practice within the near future. Uptake of sequence data is currently being stimulated within the UK through the government-funded 100,000 genomes project (Genomics England), with many similar initiatives being planned and supported internationally. The analysis of the large volumes of data derived from sequencing programmes poses a major challenge for data analysis. In this paper we outline progress we have made in the development of predictors for estimating the pathogenic impact of single nucleotide variants, indels and haploinsufficiency in the human genome. The accuracy of these methods is enhanced through the development of disease-specific predictors, trained on appropriate data, and used within a specific disease context. We outline current research on the development of disease-specific predictors, specifically in the context of cancer research.

AB - Sequencing data will become widely available in clinical practice within the near future. Uptake of sequence data is currently being stimulated within the UK through the government-funded 100,000 genomes project (Genomics England), with many similar initiatives being planned and supported internationally. The analysis of the large volumes of data derived from sequencing programmes poses a major challenge for data analysis. In this paper we outline progress we have made in the development of predictors for estimating the pathogenic impact of single nucleotide variants, indels and haploinsufficiency in the human genome. The accuracy of these methods is enhanced through the development of disease-specific predictors, trained on appropriate data, and used within a specific disease context. We outline current research on the development of disease-specific predictors, specifically in the context of cancer research.

KW - Prediction

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KW - variant

KW - annotation

KW - point mutation

KW - indel

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T3 - Studies in Health Technology and Informatics

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EP - 95

BT - Informatics for Health

A2 - Randell, Rebecca

A2 - Cornet, Ronald

A2 - McCowan, Colin

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PB - IOS Press

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ER -

Rogers MF, Shihab HA, Ferlaino M, Gaunt TR, Campbell CK. Predicting the Pathogenic Impact of Sequence Variation in the Human Genome. In Randell R, Cornet R, McCowan C, Peek N, Scott PJ, editors, Informatics for Health: Connected Citizen-Led Wellness and Population Health. IOS Press. 2017. p. 91-95. (Studies in Health Technology and Informatics). Epub 2017 May 17. doi: 10.3233/978-1-61499-753-5-91

Predicting the Pathogenic Impact of Sequence Variation in the Human Genome

Abstract

Publication series

Conference

Keywords

UN SDGs

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