Difference between revisions of "BIN-SEQA-Comparison"

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Sequence analysis by pattern matching.
 
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=== Objectives ===
 
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=== Outcomes ===
 
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After working through this unit you ...
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# Evaluate the output: does the algorithm (wrongly) predict TM-helices in your protein? In the shuffled sequences? Does it find all ten TM-helices in Gef1?
 
# Evaluate the output: does the algorithm (wrongly) predict TM-helices in your protein? In the shuffled sequences? Does it find all ten TM-helices in Gef1?
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Try to familiarize yourself with the offerings in the EMBOSS package. I find some of the nucleic acid tools indispensable in the lab, such as restriction-site mapping tools, and I frequently use the alignment tools <code>Needle</code> and <code>Water</code>, but by and large the utility of many of the components&ndash;while fast, efficient and straightforward to use&ndash; suffers from lack of reference and comparison and from terse output. The routines show their conceptual origin in the 1970s and 1980s. We will encounter alternatives in later assignments.
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Try to familiarize yourself with the offerings in the EMBOSS package. I find some of the nucleic acid tools indispensable in the lab, such as restriction-site mapping tools (remap), and I frequently use the alignment tools <code>Needle</code> and <code>Water</code>, but by and large the utility of many of the components&ndash;while fast, efficient and straightforward to use&ndash; suffers from lack of reference and comparison and from terse output. The routines show their conceptual origin in the 1970s and 1980s.
  
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It's interesting to consider how this collection of tools that were carefully designed some thirty years ago, as an open source replacement for a set of software tools - the GCG package - that was indispensable for molecular biology labs in the 80s and 90s, but whose cost had become prohibitive - has slowly lost relevance due to a change in computational paradigms. Everything these tools do is still correct. But fundamentally this is a building block approach, and the field has turned to programming solutions instead - as exemplified in seqinr and other R packages, and the Bioconductor project.
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=='''R''' Sequence Analysis Tools==
 
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It's interesting to see this collection of tools that were carefully designed some twenty years ago, as an open source replacement for a set of software tools - the GCG package - that was indispensable for molecular biology labs in the 80s and 90s, but whose cost had become prohibitive. Fundamentally this is a building block approach, and the field has turned to programming solutions instead.
 
  
 
As for functionality, much more sophisticated functions are available on the Web: do take a few minutes and browse the [http://bioinformatics.ca/links_directory/tag/protein-sequence-analysis curated Web services directory of bioinformatics.ca].
 
As for functionality, much more sophisticated functions are available on the Web: do take a few minutes and browse the [http://bioinformatics.ca/links_directory/tag/protein-sequence-analysis curated Web services directory of bioinformatics.ca].
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As for versatility, '''R''' certainly has the edge. Let's explore some of the functions available in the <code>seqinr</code> package that you already encountered in the introductory [[R tutorial]]. They are comparatively basic - but it is easy to prepare our own analysis.
 
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* Study the code in the <code>Sequence Analysis</code> section of the '''R''' script
 
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Revision as of 02:02, 19 November 2017

Sequence Analysis: Comparison


 

Keywords:  Sequence analysis by comparison; deterministic pattern matching; probabilistic pattern matching; HMMS; Neural Networks


 



 


 


Abstract

Sequence analysis by pattern matching.


 


This unit ...

Prerequisites

You need the following preparation before beginning this unit. If you are not familiar with this material from courses you took previously, you need to prepare yourself from other information sources:

  • Biomolecules: The molecules of life; nucleic acids and amino acids; the genetic code; protein folding; post-translational modifications and protein biochemistry; membrane proteins; biological function.

You need to complete the following units before beginning this one:


 


Objectives

This unit will ...

  • ... introduce the concepts of deterministic and probabilistic pattern matching;
  • ... demonstrate some applications in the online suite of EMBOSS tools.


 


Outcomes

After working through this unit you ...

  • ... are familar with the ideas of deterministic and probabilistic pattern matching, and how the latter relates to machine learning.


 


Deliverables

  • Time management: Before you begin, estimate how long it will take you to complete this unit. Then, record in your course journal: the number of hours you estimated, the number of hours you worked on the unit, and the amount of time that passed between start and completion of this unit.
  • Journal: Document your progress in your Course Journal. Some tasks may ask you to include specific items in your journal. Don't overlook these.
  • Insights: If you find something particularly noteworthy about this unit, make a note in your insights! page.


 


Evaluation

Evaluation: NA

This unit is not evaluated for course marks.


 


Contents

Task:



 


Analyze

Let us perform a few simple sequence analyses using the online EMBOSS tools. EMBOSS (the European Molecular Biology laboratory Open Software Suite) combines a large number of simple but fundamental sequence analysis tools. The tools can be installed locally on your own machine, or run via a public Web interface. Google for EMBOSS explorer, public access points include http://emboss.bioinformatics.nl/ .

Access an EMBOSS Explorer service and explore some of the tools:


Task:

Local composition
  1. Find pepinfo under the PROTEIN COMPOSITION heading.
  2. Retrieve the MYSPE Mbp1 related sequence from your R database, e.g. with something like
    cat(db$protein[db$protein$name == "UMAG_1122"), "sequence"]
  3. Copy and paste the sequence into the input field.
  4. Run with default parameters.
  5. Scroll to the figures all the way at the bottom.
  6. Do the same in a separate window for yeast Mbp1.
  7. Try to compare ... (kind of hard without reference, overlay and expectation, isn't it?)


Task:

Motifs
  1. Find pepcoil, an algorithm to detect coiled coil motifs.
  2. Run this with the MYSPE Mbp1 sequence and yeast Mbp1.
  3. Try to compare ... do both sequences have coiled-coil motif predictions? Are they annotated in approximately comparable regions of the respective sequence?


Task:

Transmembrane sequences
  1. Find tmap. Also find shuffleseq.
  2. Use your MYSPE sequence to annotate transmembrane helices for your protein and for a few shuffled sequences. The MYSPE is not expected to have TM helices, nor are the shuffled sequences expected to have any. If you do find some, these are most likely "false positives".
  1. Also compare the following positive control: Gef1 - a yeast chloride channel with 10 trans-membrane helices and outward localized N-terminus:
>gi|6322500|ref|NP_012574.1| Gef1p [Saccharomyces cerevisiae S288c]
MPTTYVPINQPIGDGEDVIDTNRFTNIPETQNFDQFVTIDKIAEENRPLSVDSDREFLNSKYRHYREVIW
DRAKTFITLSSTAIVIGCIAGFLQVFTETLVNWKTGHCQRNWLLNKSFCCNGVVNEVTSTSNLLLKRQEF
ECEAQGLWIAWKGHVSPFIIFMLLSVLFALISTLLVKYVAPMATGSGISEIKVWVSGFEYNKEFLGFLTL
VIKSVALPLAISSGLSVGKEGPSVHYATCCGYLLTKWLLRDTLTYSSQYEYITAASGAGVAVAFGAPIGG
VLFGLEEIASANRFNSSTLWKSYYVALVAITTLKYIDPFRNGRVILFNVTYDRDWKVQEIPIFIALGIFG
GLYGKYISKWNINFIHFRKMYLSSWPVQEVLFLATLTALISYFNEFLKLDMTESMGILFHECVKNDNTST
FSHRLCQLDENTHAFEFLKIFTSLCFATVIRALLVVVSYGARVPAGIFVPSMAVGATFGRAVSLLVERFI
SGPSVITPGAYAFLGAAATLSGITNLTLTVVVIMFELTGAFMYIIPLMIVVAITRIILSTSGISGGIADQ
MIMVNGFPYLEDEQDEEEEETLEKYTAEQLMSSKLITINETIYLSELESLLYDSASEYSVHGFPITKDED
KFEKEKRCIGYVLKRHLASKIMMQSVNSTKAQTTLVYFNKSNEELGHRENCIGFKDIMNESPISVKKAVP
VTLLFRMFKELGCKTIIVEESGILKGLVTAKDILRFKRIKYREVHGAKFTYNEALDRRCWSVIHFIIKRF
TTNRNGNVI
  1. Evaluate the output: does the algorithm (wrongly) predict TM-helices in your protein? In the shuffled sequences? Does it find all ten TM-helices in Gef1?


Try to familiarize yourself with the offerings in the EMBOSS package. I find some of the nucleic acid tools indispensable in the lab, such as restriction-site mapping tools (remap), and I frequently use the alignment tools Needle and Water, but by and large the utility of many of the components–while fast, efficient and straightforward to use– suffers from lack of reference and comparison and from terse output. The routines show their conceptual origin in the 1970s and 1980s.

It's interesting to consider how this collection of tools that were carefully designed some thirty years ago, as an open source replacement for a set of software tools - the GCG package - that was indispensable for molecular biology labs in the 80s and 90s, but whose cost had become prohibitive - has slowly lost relevance due to a change in computational paradigms. Everything these tools do is still correct. But fundamentally this is a building block approach, and the field has turned to programming solutions instead - as exemplified in seqinr and other R packages, and the Bioconductor project.

As for functionality, much more sophisticated functions are available on the Web: do take a few minutes and browse the curated Web services directory of bioinformatics.ca.


 


 


Further reading, links and resources

 


Notes


 


Self-evaluation

 



 



 

If in doubt, ask! If anything about this learning unit is not clear to you, do not proceed blindly but ask for clarification. Post your question on the course mailing list: others are likely to have similar problems. Or send an email to your instructor.


 

About ...
 
Author:

Boris Steipe <boris.steipe@utoronto.ca>

Created:

2017-08-05

Modified:

2017-08-05

Version:

1.0

Version history:

  • 1.0 First live version
  • 0.1 First stub

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