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Clinical Bioinformatics

Overview of attention for book
Cover of 'Clinical Bioinformatics'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 From the Phenotype to the Genotype via Bioinformatics
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    Chapter 2 Production and Analytic Bioinformatics for Next-Generation DNA Sequencing
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    Chapter 3 Analyzing the Metabolome
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    Chapter 4 Statistical Perspectives for Genome-Wide Association Studies (GWAS)
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    Chapter 5 Bioinformatics Challenges in Genome-Wide Association Studies (GWAS).
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    Chapter 6 Studying cancer genomics through next-generation DNA sequencing and bioinformatics.
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    Chapter 7 Using Bioinformatics Tools to Study the Role of microRNA in Cancer
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    Chapter 8 Chromosome Microarrays in Diagnostic Testing: Interpreting the Genomic Data
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    Chapter 9 Bioinformatics Approach to Understanding Interacting Pathways in Neuropsychiatric Disorders
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    Chapter 10 Pathogen Genome Bioinformatics
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    Chapter 11 Setting up next-generation sequencing in the medical laboratory.
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    Chapter 12 Managing incidental findings in exome sequencing for research.
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    Chapter 13 Approaches for Classifying DNA Variants Found by Sanger Sequencing in a Medical Genetics Laboratory
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    Chapter 14 Designing algorithms for determining significance of DNA missense changes.
  16. Altmetric Badge
    Chapter 15 Clinical Bioinformatics
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    Chapter 16 Natural language processing in biomedicine: a unified system architecture overview.
  18. Altmetric Badge
    Chapter 17 Candidate gene discovery and prioritization in rare diseases.
  19. Altmetric Badge
    Chapter 18 Computer-Aided Drug Designing
Attention for Chapter 12: Managing incidental findings in exome sequencing for research.
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Chapter title
Managing incidental findings in exome sequencing for research.
Chapter number 12
Book title
Clinical Bioinformatics
Published in
Methods in molecular biology, January 2014
DOI 10.1007/978-1-4939-0847-9_12
Pubmed ID
Book ISBNs
978-1-4939-0846-2, 978-1-4939-0847-9
Authors

Marcus J Hinchcliffe, Marcus J. Hinchcliffe

Abstract

Exome sequencing for research has become available for broadly based genomic studies as well as smaller targeted investigations. New exome research projects being considered will intentionally process a large amount of common and rare DNA variation for the purpose of finding specific links between genotype and phenotype. However, the risks of uncovering a clinically relevant incidental finding are not uniform across projects but are highly dependent on the question being asked and exactly how it is intended to be answered.Factors that influence the possibility of revealing a clinically relevant incidental DNA variation include the following: The overall design of the study and the number of participants involved, the mode of inheritance of the phenotype including whether the phenotype is likely to have a monogenic or a complex inheritance, whether the study is assessing a known list of genes or not, and whether the causative DNA variation is likely to be rare or common. Importantly, differing bioinformatics DNA variant filtering strategies strongly influence the odds of discovering an incidental finding. This chapter provides a framework for understanding and assessing the likelihood of discovering clinically relevant, incidental DNA variations that are not directly related to the question being addressed in a particular exome research project. It also outlines DNA variant filtering and functional informatics approaches that can investigate specific genomic questions while minimizing the risks of uncovering an incidental finding.

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X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 20%
Lecturer 1 20%
Student > Doctoral Student 1 20%
Unknown 2 40%
Readers by discipline Count As %
Agricultural and Biological Sciences 2 40%
Medicine and Dentistry 1 20%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 30 May 2014.
All research outputs
#14,781,203
of 22,756,196 outputs
Outputs from Methods in molecular biology
#4,672
of 13,089 outputs
Outputs of similar age
#183,089
of 305,260 outputs
Outputs of similar age from Methods in molecular biology
#182
of 597 outputs
Altmetric has tracked 22,756,196 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,089 research outputs from this source. They receive a mean Attention Score of 3.3. This one has gotten more attention than average, scoring higher than 59% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 305,260 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 37th percentile – i.e., 37% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 597 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.