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In Silico Tools for Gene Discovery

Overview of attention for book
Cover of 'In Silico Tools for Gene Discovery'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Accessing and Selecting Genetic Markers from Available Resources
  3. Altmetric Badge
    Chapter 2 In Silico Tools for Gene Discovery
  4. Altmetric Badge
    Chapter 3 Association Mapping
  5. Altmetric Badge
    Chapter 4 The ForeSee (4C) Approach for Integrative Analysis in Gene Discovery
  6. Altmetric Badge
    Chapter 5 R Statistical Tools for Gene Discovery
  7. Altmetric Badge
    Chapter 6 In silico PCR analysis.
  8. Altmetric Badge
    Chapter 7 In Silico Analysis of the Exome for Gene Discovery
  9. Altmetric Badge
    Chapter 8 In Silico Knowledge and Content Tracking
  10. Altmetric Badge
    Chapter 9 Application of Gene Ontology to Gene Identification
  11. Altmetric Badge
    Chapter 10 Phenotype mining for functional genomics and gene discovery.
  12. Altmetric Badge
    Chapter 11 Conceptual Thinking for In Silico Prioritization of Candidate Disease Genes
  13. Altmetric Badge
    Chapter 12 Web Tools for the Prioritization of Candidate Disease Genes
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    Chapter 13 Comparative View of In Silico DNA Sequencing Analysis Tools
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    Chapter 14 Mutation Surveyor: An In Silico Tool for Sequencing Analysis
  16. Altmetric Badge
    Chapter 15 In Silico Searching for Disease-Associated Functional DNA Variants
  17. Altmetric Badge
    Chapter 16 In Silico Prediction of Transcriptional Factor-Binding Sites
  18. Altmetric Badge
    Chapter 17 In silico prediction of splice-affecting nucleotide variants.
  19. Altmetric Badge
    Chapter 18 In Silico Tools for qPCR Assay Design and Data Analysis
  20. Altmetric Badge
    Chapter 19 RNA Structure Prediction
  21. Altmetric Badge
    Chapter 20 In Silico Prediction of Post-translational Modifications
  22. Altmetric Badge
    Chapter 21 In Silico Tools for Gene Discovery
Attention for Chapter 17: In silico prediction of splice-affecting nucleotide variants.
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Chapter title
In silico prediction of splice-affecting nucleotide variants.
Chapter number 17
Book title
In Silico Tools for Gene Discovery
Published in
Methods in molecular biology, January 2011
DOI 10.1007/978-1-61779-176-5_17
Pubmed ID
Book ISBNs
978-1-61779-175-8, 978-1-61779-176-5
Authors

Claude Houdayer, Houdayer, Claude

Abstract

It appears that all types of genomic nucleotide variations can be deleterious by affecting normal pre-mRNA splicing via disruption/creation of splice site consensus sequences. As it is neither pertinent nor realistic to perform functional testing for all of these variants, it is important to identify those that could lead to a splice defect in order to restrict experimental transcript analyses to the most appropriate cases. In silico tools designed to provide this type of prediction are available. In this chapter, we present in silico splice tools integrated in the Alamut (Interactive Biosoftware) application and detail their use in routine diagnostic applications. At this time, in silico predictions are useful for variants that decrease the strength of wild-type splice sites or create a cryptic splice site. Importantly, in silico predictions are not sufficient to classify variants as neutral or deleterious: they should be used as part of the decision-making process to detect potential candidates for splicing anomalies, prompting molecular geneticists to carry out transcript analyses in a limited and pertinent number of cases which could be managed in routine settings.

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The data shown below were collected from the profile of 1 X user 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 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 2 5%
United States 1 2%
Norway 1 2%
Unknown 37 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 32%
Other 5 12%
Student > Master 5 12%
Student > Ph. D. Student 4 10%
Professor > Associate Professor 3 7%
Other 7 17%
Unknown 4 10%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 14 34%
Medicine and Dentistry 11 27%
Agricultural and Biological Sciences 9 22%
Nursing and Health Professions 1 2%
Computer Science 1 2%
Other 1 2%
Unknown 4 10%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 05 December 2018.
All research outputs
#6,373,258
of 22,649,029 outputs
Outputs from Methods in molecular biology
#1,925
of 13,012 outputs
Outputs of similar age
#46,185
of 180,235 outputs
Outputs of similar age from Methods in molecular biology
#61
of 229 outputs
Altmetric has tracked 22,649,029 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 13,012 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done well, scoring higher than 84% 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 180,235 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 229 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 73% of its contemporaries.