Difference between revisions of "Sequence analysis"

From "A B C"
Jump to navigation Jump to search
(Created page with "<div id="BIO"> <div class="b1"> Sequence analysis </div> {{dev}} <!-- KEYWORDS FOR PAGE composition, comparison (patterns), contents (first principles, homology), conserva...")
 
Line 22: Line 22:
 
&nbsp;
 
&nbsp;
 
==Contents==
 
==Contents==
 +
 +
 +
==Examples==
 +
 +
 +
===Motifs===
 +
 +
 +
===Disorder===
 +
 +
 +
===Signal peptides===
 +
 +
 +
===Secondary Structure===
 +
{{#pmid:20221928}}
 +
 +
 +
===Transmembrane Helices===
 +
[http://www.canoz.com/benchmark/benchmark.pl BENCHMARK OF MEMBRANE HELIX PREDICTIONS FROM SEQUENCE]
 +
 +
===Location===
 +
 +
 +
==Integrated tools==
 +
{{#pmid:19389726}}
 +
http://annie.bii.a-star.edu.sg
  
  
Line 38: Line 65:
 
-->
 
-->
 
&nbsp;
 
&nbsp;
 +
 
==Further reading and resources==
 
==Further reading and resources==
 
<!-- {{#pmid:21627854}} -->
 
<!-- {{#pmid:21627854}} -->

Revision as of 21:07, 27 October 2012

Sequence analysis


This page is a placeholder, or under current development; it is here principally to establish the logical framework of the site. The material on this page is correct, but incomplete.


Summary ...



 

Contents

Examples

Motifs

Disorder

Signal peptides

Secondary Structure

Pirovano & Heringa (2010) Protein secondary structure prediction. Methods Mol Biol 609:327-48. (pmid: 20221928)

PubMed ] [ DOI ] While the prediction of a native protein structure from sequence continues to remain a challenging problem, over the past decades computational methods have become quite successful in exploiting the mechanisms behind secondary structure formation. The great effort expended in this area has resulted in the development of a vast number of secondary structure prediction methods. Especially the combination of well-optimized/sensitive machine-learning algorithms and inclusion of homologous sequence information has led to increased prediction accuracies of up to 80%. In this chapter, we will first introduce some basic notions and provide a brief history of secondary structure prediction advances. Then a comprehensive overview of state-of-the-art prediction methods will be given. Finally, we will discuss open questions and challenges in this field and provide some practical recommendations for the user.


Transmembrane Helices

BENCHMARK OF MEMBRANE HELIX PREDICTIONS FROM SEQUENCE

Location

Integrated tools

Ooi et al. (2009) ANNIE: integrated de novo protein sequence annotation. Nucleic Acids Res 37:W435-40. (pmid: 19389726)

PubMed ] [ DOI ] Function prediction of proteins with computational sequence analysis requires the use of dozens of prediction tools with a bewildering range of input and output formats. Each of these tools focuses on a narrow aspect and researchers are having difficulty obtaining an integrated picture. ANNIE is the result of years of close interaction between computational biologists and computer scientists and automates an essential part of this sequence analytic process. It brings together over 20 function prediction algorithms that have proven sufficiently reliable and indispensable in daily sequence analytic work and are meant to give scientists a quick overview of possible functional assignments of sequence segments in the query proteins. The results are displayed in an integrated manner using an innovative AJAX-based sequence viewer. ANNIE is available online at: http://annie.bii.a-star.edu.sg. This website is free and open to all users and there is no login requirement.

http://annie.bii.a-star.edu.sg


   

Further reading and resources