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Parasite Genomics Protocols

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Cover of 'Parasite Genomics Protocols'

Table of Contents

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    Book Overview
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    Chapter 1 The eukaryotic pathogen databases: a functional genomic resource integrating data from human and veterinary parasites.
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    Chapter 2 From sequence mapping to genome assemblies.
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    Chapter 3 Sequencing and annotation of mitochondrial genomes from individual parasitic helminths.
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    Chapter 4 A Beginners Guide to Estimating the Non-synonymous to Synonymous Rate Ratio of all Protein-Coding Genes in a Genome.
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    Chapter 5 Exploiting Genetic Variation to Discover Genes Involved in Important Disease Phenotypes
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    Chapter 6 Identification and analysis of ingi-related retroposons in the trypanosomatid genomes.
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    Chapter 7 Approaches for Studying mRNA Decay Mediated by SIDER2 Retroposons in Leishmania
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    Chapter 8 Gene Suppression in Schistosomes Using RNAi.
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    Chapter 9 Construction of Trypanosoma brucei Illumina RNA-Seq Libraries Enriched for Transcript Ends.
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    Chapter 10 Techniques to Study Epigenetic Control and the Epigenome in Parasites
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    Chapter 11 The Genome-Wide Identification of Promoter Regions in Toxoplasma gondii.
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    Chapter 12 RNA-Seq Approaches for Determining mRNA Abundance in Leishmania.
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    Chapter 13 Protein microarrays for parasite antigen discovery.
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    Chapter 14 A transposon-based tool for transformation and mutagenesis in trypanosomatid protozoa.
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    Chapter 15 Separation of Basic Proteins from Leishmania Using a Combination of Free Flow Electrophoresis (FFE) and 2D Electrophoresis (2-DE) Under Basic Conditions
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    Chapter 16 Proteomic Analysis of Posttranslational Modifications Using iTRAQ in Leishmania.
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    Chapter 17 Large-Scale Differential Proteome Analysis in Plasmodium falciparum Under Drug Treatment.
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    Chapter 18 Parasite Genomics Protocols
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    Chapter 19 Molecular Genotyping of Trypanosoma cruzi for Lineage Assignment and Population Genetics.
  21. Altmetric Badge
    Chapter 20 Screening Leishmania donovani Complex-Specific Genes Required for Visceral Disease.
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    Chapter 21 ERRATUM: From Sequence Mapping to Genome Assemblies
Attention for Chapter 4: A Beginners Guide to Estimating the Non-synonymous to Synonymous Rate Ratio of all Protein-Coding Genes in a Genome.
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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 (84th percentile)
  • High Attention Score compared to outputs of the same age and source (93rd percentile)

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Chapter title
A Beginners Guide to Estimating the Non-synonymous to Synonymous Rate Ratio of all Protein-Coding Genes in a Genome.
Chapter number 4
Book title
Parasite Genomics Protocols
Published in
Methods in molecular biology, January 2015
DOI 10.1007/978-1-4939-1438-8_4
Pubmed ID
Book ISBNs
978-1-4939-1437-1, 978-1-4939-1438-8
Authors

Daniel C Jeffares, Bartłomiej Tomiczek, Victor Sojo, Mario Dos Reis, Daniel C. Jeffares, Mario dos Reis, Jeffares, Daniel C., Tomiczek, Bartłomiej, Sojo, Victor, Reis, Mario dos, Reis, Mario, dos Reis, Mario

Abstract

The ratio of non-synonymous to synonymous substitutions (dN/dS) is a useful measure of the strength and mode of natural selection acting on protein-coding genes. It is widely used to study patterns of selection on protein genes on a genomic scale-from the small genomes of viruses, bacteria, and parasitic eukaryotes to the largest eukaryotic genomes. In this chapter we describe all the steps necessary to calculate the dN/dS of all the genes using at least two genomes. We include a brief discussion on assigning orthologs, and of codon-aware alignment of orthologs. We then describe how to use the CODEML program of the PAML package for phylogenetic analysis to calculate the dN/dS and how to perform some statistical tests for positive selection. We then outline some methods for interpreting output and describe how one may use this data to make discoveries about the biology of your species. Finally, as a worked example we show all the steps we used to calculate dN/dS for 3,261 orthologs from six Plasmodium species, including tests for adaptive evolution (see worked_example.pdf).

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 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 308 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Poland 1 <1%
Uruguay 1 <1%
Switzerland 1 <1%
Unknown 304 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 84 27%
Researcher 44 14%
Student > Master 44 14%
Student > Bachelor 29 9%
Student > Doctoral Student 17 6%
Other 31 10%
Unknown 59 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 105 34%
Biochemistry, Genetics and Molecular Biology 93 30%
Immunology and Microbiology 13 4%
Computer Science 5 2%
Environmental Science 3 <1%
Other 21 7%
Unknown 68 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 28 November 2022.
All research outputs
#4,209,983
of 25,205,864 outputs
Outputs from Methods in molecular biology
#1,043
of 14,135 outputs
Outputs of similar age
#55,315
of 365,472 outputs
Outputs of similar age from Methods in molecular biology
#67
of 996 outputs
Altmetric has tracked 25,205,864 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,135 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done particularly well, scoring higher than 92% 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 365,472 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 84% of its contemporaries.
We're also able to compare this research output to 996 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 93% of its contemporaries.