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Statistical Genomics

Overview of attention for book
Cover of 'Statistical Genomics'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 Overview of Sequence Data Formats
  3. Altmetric Badge
    Chapter 2 Integrative Exploratory Analysis of Two or More Genomic Datasets
  4. Altmetric Badge
    Chapter 3 Study Design for Sequencing Studies
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    Chapter 4 Genomic Annotation Resources in R/Bioconductor
  6. Altmetric Badge
    Chapter 5 The Gene Expression Omnibus Database
  7. Altmetric Badge
    Chapter 6 A Practical Guide to The Cancer Genome Atlas (TCGA)
  8. Altmetric Badge
    Chapter 7 Working with Oligonucleotide Arrays
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    Chapter 8 Meta-Analysis in Gene Expression Studies
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    Chapter 9 Practical Analysis of Genome Contact Interaction Experiments
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    Chapter 10 Quantitative Comparison of Large-Scale DNA Enrichment Sequencing Data
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    Chapter 11 Variant Calling From Next Generation Sequence Data
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    Chapter 12 Genome-Scale Analysis of Cell-Specific Regulatory Codes Using Nuclear Enzymes
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    Chapter 13 NGS-QC Generator: A Quality Control System for ChIP-Seq and Related Deep Sequencing-Generated Datasets
  15. Altmetric Badge
    Chapter 14 Operating on Genomic Ranges Using BEDOPS
  16. Altmetric Badge
    Chapter 15 GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality
  17. Altmetric Badge
    Chapter 16 Visualizing Genomic Data Using Gviz and Bioconductor
  18. Altmetric Badge
    Chapter 17 Introducing Machine Learning Concepts with WEKA
  19. Altmetric Badge
    Chapter 18 Experimental Design and Power Calculation for RNA-seq Experiments
  20. Altmetric Badge
    Chapter 19 It’s DE-licious: A Recipe for Differential Expression Analyses of RNA-seq Experiments Using Quasi-Likelihood Methods in edgeR
Attention for Chapter 15: GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (91st percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

1 blog
1 policy source
5 X users
1 patent


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Readers on

171 Mendeley
1 CiteULike
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Chapter title
GMAP and GSNAP for Genomic Sequence Alignment: Enhancements to Speed, Accuracy, and Functionality
Chapter number 15
Book title
Statistical Genomics
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3578-9_15
Pubmed ID
Book ISBNs
978-1-4939-3576-5, 978-1-4939-3578-9

Thomas D. Wu, Jens Reeder, Michael Lawrence, Gabe Becker, Matthew J. Brauer, Wu, Thomas D, Reeder, Jens, Lawrence, Michael, Becker, Gabe, Brauer, Matthew J, Wu, Thomas D., Brauer, Matthew J.


Ewy Mathé, Sean Davis


The programs GMAP and GSNAP, for aligning RNA-Seq and DNA-Seq datasets to genomes, have evolved along with advances in biological methodology to handle longer reads, larger volumes of data, and new types of biological assays. The genomic representation has been improved to include linear genomes that can compare sequences using single-instruction multiple-data (SIMD) instructions, compressed genomic hash tables with fast access using SIMD instructions, handling of large genomes with more than four billion bp, and enhanced suffix arrays (ESAs) with novel data structures for fast access. Improvements to the algorithms have included a greedy match-and-extend algorithm using suffix arrays, segment chaining using genomic hash tables, diagonalization using segmental hash tables, and nucleotide-level dynamic programming procedures that use SIMD instructions and eliminate the need for F-loop calculations. Enhancements to the functionality of the programs include standardization of indel positions, handling of ambiguous splicing, clipping and merging of overlapping paired-end reads, and alignments to circular chromosomes and alternate scaffolds. The programs have been adapted for use in pipelines by integrating their usage into R/Bioconductor packages such as gmapR and HTSeqGenie, and these pipelines have facilitated the discovery of numerous biological phenomena.

X Demographics

X Demographics

The data shown below were collected from the profiles of 5 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 171 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Switzerland 1 <1%
Unknown 170 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 38 22%
Researcher 35 20%
Student > Doctoral Student 18 11%
Student > Master 18 11%
Student > Bachelor 9 5%
Other 23 13%
Unknown 30 18%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 51 30%
Agricultural and Biological Sciences 50 29%
Computer Science 16 9%
Immunology and Microbiology 6 4%
Medicine and Dentistry 3 2%
Other 8 5%
Unknown 37 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. 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 23 January 2024.
All research outputs
of 25,263,619 outputs
Outputs from Methods in molecular biology
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Outputs of similar age
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Outputs of similar age from Methods in molecular biology
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Altmetric has tracked 25,263,619 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,169 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 97% 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 406,133 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 91% of its contemporaries.
We're also able to compare this research output to 1,464 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 97% of its contemporaries.