↓ Skip to main content

Selenoproteins

Overview of attention for book
Cover of 'Selenoproteins'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 SECISearch3 and Seblastian: In-Silico Tools to Predict SECIS Elements and Selenoproteins
  3. Altmetric Badge
    Chapter 2 Selenoprofiles: A Computational Pipeline for Annotation of Selenoproteins
  4. Altmetric Badge
    Chapter 3 SelGenAmic: An Algorithm for Selenoprotein Gene Assembly
  5. Altmetric Badge
    Chapter 4 Selenocysteine tRNA[Ser]Sec, the Central Component of Selenoprotein Biosynthesis: Isolation, Identification, Modification, and Sequencing
  6. Altmetric Badge
    Chapter 5 Identification and Characterization of Proteins that Bind to Selenoprotein 3′ UTRs
  7. Altmetric Badge
    Chapter 6 Specific Chemical Approaches for Studying Mammalian Ribosomes Complexed with Ligands Involved in Selenoprotein Synthesis
  8. Altmetric Badge
    Chapter 7 In Vitro Translation Assays for Selenocysteine Insertion
  9. Altmetric Badge
    Chapter 8 Studying Selenoprotein mRNA Translation Using RNA-Seq and Ribosome Profiling
  10. Altmetric Badge
    Chapter 9 Modification of Selenoprotein mRNAs by Cap Tri-methylation
  11. Altmetric Badge
    Chapter 10 Total Selenium Quantification in Biological Samples by Inductively Coupled Plasma Mass Spectrometry (ICP-MS)
  12. Altmetric Badge
    Chapter 11 Quantification of SeMet and SeCys in Biological Fluids and Tissues by Liquid Chromatography Coupled to Inductively Coupled Plasma Mass Spectrometry (HPLC-ICP MS)
  13. Altmetric Badge
    Chapter 12 Simultaneous Speciation of Selenoproteins and Selenometabolites in Plasma and Serum
  14. Altmetric Badge
    Chapter 13 Radioactive 75 Se Labeling and Detection of Selenoproteins
  15. Altmetric Badge
    Chapter 14 Nonradioactive Isotopic Labeling and Tracing of Selenoproteins in Cultured Cell Lines
  16. Altmetric Badge
    Chapter 15 Detection of Selenoproteins by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP MS) in Immobilized pH Gradient (IPG) Strips
  17. Altmetric Badge
    Chapter 16 Imaging of Selenium by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS) in 2-D Electrophoresis Gels and Biological Tissues
  18. Altmetric Badge
    Chapter 17 Overexpression of Recombinant Selenoproteins in E. coli
  19. Altmetric Badge
    Chapter 18 Preparation of Selenocysteine-Containing Forms of Human SELENOK and SELENOS
  20. Altmetric Badge
    Chapter 19 Selenocysteine-Mediated Expressed Protein Ligation of SELENOM
  21. Altmetric Badge
    Chapter 20 Monitoring of Methionine Sulfoxide Content and Methionine Sulfoxide Reductase Activity
  22. Altmetric Badge
    Chapter 21 Selective Evaluation of Thioredoxin Reductase Enzymatic Activities
  23. Altmetric Badge
    Chapter 22 Association of Single Nucleotide Polymorphisms in Selenoprotein Genes with Cancer Risk
  24. Altmetric Badge
    Chapter 23 Identification of Genetic Disorders Causing Disruption of Selenoprotein Biosynthesis
Attention for Chapter 1: SECISearch3 and Seblastian: In-Silico Tools to Predict SECIS Elements and Selenoproteins
Altmetric Badge

Mentioned by

twitter
1 tweeter

Readers on

mendeley
8 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Chapter title
SECISearch3 and Seblastian: In-Silico Tools to Predict SECIS Elements and Selenoproteins
Chapter number 1
Book title
Selenoproteins
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7258-6_1
Pubmed ID
Book ISBNs
978-1-4939-7257-9, 978-1-4939-7258-6
Authors

Marco Mariotti

Abstract

The computational identification of selenoprotein genes is complicated by the dual meaning of the UGA codon as stop and selenocysteine. SECIS elements are RNA structures essential for selenocysteine incorporation, which have been used as markers for selenoprotein genes in many bioinformatics studies. The most widely used tool for eukaryotic SECIS finding has been recently improved to its third generation, SECISearch3. This program is also a component of Seblastian, a pipeline for the identification of selenoprotein genes that employs SECIS finding as the first step. This chapter constitutes a practical guide to use SECISearch3 and Seblastian, which can be run via webservers at http://seblastian.crg.eu / or http://gladyshevlab.org/SelenoproteinPredictionServer/ .

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 25%
Student > Postgraduate 2 25%
Student > Doctoral Student 2 25%
Researcher 1 13%
Unknown 1 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 3 38%
Agricultural and Biological Sciences 2 25%
Mathematics 1 13%
Immunology and Microbiology 1 13%
Medicine and Dentistry 1 13%
Other 0 0%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 17 September 2017.
All research outputs
#10,959,353
of 13,796,475 outputs
Outputs from Methods in molecular biology
#4,380
of 8,600 outputs
Outputs of similar age
#198,553
of 269,598 outputs
Outputs of similar age from Methods in molecular biology
#1
of 1 outputs
Altmetric has tracked 13,796,475 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,600 research outputs from this source. They receive a mean Attention Score of 2.2. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 269,598 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them