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Variant Calling

Overview of attention for book
Cover of 'Variant Calling'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Data Processing and Germline Variant Calling with the Sentieon Pipeline
  3. Altmetric Badge
    Chapter 2 MuSE: A Novel Approach to Mutation Calling with Sample-Specific Error Modeling
  4. Altmetric Badge
    Chapter 3 Octopus: Genotyping and Haplotyping in Diverse Experimental Designs
  5. Altmetric Badge
    Chapter 4 Accurate Ensemble Prediction of Somatic Mutations with SMuRF2.
  6. Altmetric Badge
    Chapter 5 Detecting Medium and Large Insertions and Deletions with transIndel
  7. Altmetric Badge
    Chapter 6 DECoN: A Detection and Visualization Tool for Exonic Copy Number Variants
  8. Altmetric Badge
    Chapter 7 FACETS: Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing
  9. Altmetric Badge
    Chapter 8 Meerkat: An Algorithm to Reliably Identify Structural Variations and Predict Their Forming Mechanisms
  10. Altmetric Badge
    Chapter 9 Structural Variant Detection from Long-Read Sequencing Data with cuteSV
  11. Altmetric Badge
    Chapter 10 Identifying Somatic Mitochondrial DNA Mutations
  12. Altmetric Badge
    Chapter 11 Identification, Quantification, and Testing of Alternative Splicing Events from RNA-Seq Data Using SplAdder
  13. Altmetric Badge
    Chapter 12 PipeIT2: Somatic Variant Calling Workflow for Ion Torrent Sequencing Data
  14. Altmetric Badge
    Chapter 13 Variant Calling from RNA-seq Data Using the GATK Joint Genotyping Workflow
  15. Altmetric Badge
    Chapter 14 UMI-Varcal: A Low-Frequency Variant Caller for UMI-Tagged Paired-End Sequencing Data
  16. Altmetric Badge
    Chapter 15 Alignment-Free Genotyping of Known Variations with MALVA
  17. Altmetric Badge
    Chapter 16 Kmer2SNP: Reference-Free Heterozygous SNP Calling Using k-mer Frequency Distributions
  18. Altmetric Badge
    Chapter 17 Somatic Single-Nucleotide Variant Calling from Single-Cell DNA Sequencing Data Using SCAN-SNV.
  19. Altmetric Badge
    Chapter 18 Copy Number Variation Detection by Single-Cell DNA Sequencing with SCOPE
  20. Altmetric Badge
    Chapter 19 Variant Annotation and Functional Prediction: SnpEff
  21. Altmetric Badge
    Chapter 20 Annotating Cancer-Related Variants at Protein–Protein Interface with Structure-PPi
  22. Altmetric Badge
    Chapter 21 Preanalytical Variables and Sample Quality Control for Clinical Variant Analysis
Attention for Chapter 9: Structural Variant Detection from Long-Read Sequencing Data with cuteSV
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)
  • High Attention Score compared to outputs of the same age and source (99th percentile)

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Chapter title
Structural Variant Detection from Long-Read Sequencing Data with cuteSV
Chapter number 9
Book title
Variant Calling
Published in
Methods in molecular biology, January 2022
DOI 10.1007/978-1-0716-2293-3_9
Pubmed ID
35751813
Book ISBNs
978-1-07-162292-6, 978-1-07-162293-3
Authors

Jiang, Tao, Liu, Shiqi, Cao, Shuqi, Wang, Yadong

Abstract

Structural Variation (SV) represents genomic rearrangements and is strongly associated with human health and disease. Recently, long-read sequencing technologies provide the opportunity to more comprehensive identification of SVs at an ever-high resolution. However, under the circumstance of high sequencing errors and the complexity of SVs, there remains lots of technical issues to be settled. Hence, we propose cuteSV, a sensitive, fast, and scalable alignment-based SV detection approach to complete comprehensive discovery of diverse SVs. The benchmarking results indicate cuteSV is suitable for large-scale genome project since its excellent SV yields and ultra-fast speed. Here, we explain the overall framework for providing a detailed outline for users to apply cuteSV correctly and comprehensively. More details are available at https://github.com/tjiangHIT/cuteSV .

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X Demographics

X Demographics

The data shown below were collected from the profiles of 78 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 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 4 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 50%
Unknown 2 50%
Readers by discipline Count As %
Computer Science 1 25%
Agricultural and Biological Sciences 1 25%
Unknown 2 50%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 38. 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 29 June 2022.
All research outputs
#1,110,100
of 25,956,379 outputs
Outputs from Methods in molecular biology
#95
of 14,486 outputs
Outputs of similar age
#27,466
of 524,675 outputs
Outputs of similar age from Methods in molecular biology
#6
of 813 outputs
Altmetric has tracked 25,956,379 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,486 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 99% 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 524,675 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 94% of its contemporaries.
We're also able to compare this research output to 813 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 99% of its contemporaries.

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