Open Reading Frames

In bioinformatics we look for gene-coding sequences or what we call open reading frames(ORF), entrez has a tool called ORF finder (now you know why I like entrez :-). 
http://www.ncbi.nlm.nih.gov/projects/gorf/ Let's use this tool to find out what regions will code for a gene in this sequence.

For your Classwork
ACTTTGCAGGCAGCGGCGGCCGGGGCGGAGCGGGATCGAGCCCTCGCCGCGGCCTGCCAGTCATGGGCCCGCGCCGCCGCCGCCGCCTGCCTCCCGGGCCACGCGGGCCGTGAGCGCCATGGCCGTAGCCCCCGCGGGCGGCCAGCACGCGCCAGCGCTGGAGGCCCTGCTCGGGGCGGGCGCGTTGCGGCTGCTCGACTCCTCGCAGATCGTCATCATCTCCACCGCGCCCGATGTCGGCGCCCCGCAGCTCCCCGCCGCGCCGCCCACTGGCCCTCGCGATTCTGACGTGCTGCTCTTCGCCACGCCGCAGGCGCCCCGACCCGCGCCTAGTGCACCGCGCCCGGCTCTCGGCCGCCCGCCGGTGAAACGGAGGCTGGATCTGGAGACTGACCATCAGTACCTCGCTGGTAGCAGTGGGCCATTCCGGGGCAGAGGCCGCCACCCAGGGAAAGGTGTGAAATCTCCGGGGGAGAAGTCACGCTATGAAACCTCACTAAATCTGACCACCAAACGCTTCTTGGAGCTGCTGAGCCGCTCAGCTGACGGTGTCGTTGACCTGAACTGGGCAGCTGAGGTGCTGAAGGTGCAGAAACGGCGCATCTATGACATCACCAATGTCCTGGAGGGCATCCAGCTCATTGCCAAGAAGTCCAAGAATCATATCCAGTGGCTAGGCAGCCACACCATGGTGGGGATTGGTAAGCGGCTTGAAGGCCTGACCCAGGACCTGCAGCAACTGCAGGAGAGTGAGCAGCAGCTGGATCACCTGATGCACATCTGTACCACACAGCTGCAACTGCTTTCGGAGGACTCCGACACCCAGCGCCTGGCCTATGTGACCTGCCAGGACCTTCGCAGCATTGCAGACCCTGCAGAACAGATGGTCATAGTGATCAAGGCCCCTCCTGAGACCCAACTACAAGCTGTGGATTCTTCAGAGACATTTCAGATCTCCCTTAAGAGCAAACAAGGCCCCATTGATGTTTTCCTGTGCCCGGAGGAGAGTGCAGACGGGATTAGCCCTGGGAAGACCTCATGCCAGGAGACATCCTCTGGGGAGGACCGGACTGCAGACTCTGGCCCAGCAGGGCCTCCACCATCACCTCCCTCCACATCCCCAGCCTTGGATCCCAGTCAATCCCTGTTGGGCCTGGAGCAAGAAGCAGTATTGCCACGGATGGGCCACCTGAGGGTCCCTATGGAAGAGGACCAACTGTCACCACTGGTGGCTGCTGACTCACTCCTGGAGCATGTTAAAGAAGACTTCTCTGGGCTCCTCCCTGGGGAGTTCATCAGCCTCTCCCCACCCCACGAGGCCCTTGACTATCACTTTGGTCTCGAGGAGGGTGAGGGCATTAGAGATCTCTTTGACTGTGACTTTGGGGACCTGACCCCTCTGGATTTCTGACAGAAGCCTAGGGATTCAGGGTGTCTGGAGATGCCCACCTGTCTGCAGCTTTGGAGCCTCCTGCCCTGGGCCATCCTTCCTGCCTCATTGGAATAGCACGATCCATACCCTCTGTCCCAATAGCTTCTAGCTCTGGGGTTTGGTTGCTGCCACATTGAGCAGACCAAAATGGGAAGGATGTTGTACAGTGTGTGTGCATGCACCCCACACTGCGCACTGTGTGCCTGGGGTGTGTGTCTGAGTGTGTGTGTGTGTGTGTGTGTGTGAGTGTGTGTGTGTGTGTGTGTGAGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTGTATGTATGTGTATGTGCACGTGTGCCCGGGAATGAAGGTGAACACATCTGTATGTGTGCTGCAGACACATCCTGGTGTGTCCACATGTGTGCATGGATCCATGTGTGCGCATTGGGGTGGGGGTGGGCTCTAACTGCACTTTTGGTGTCCTTGCTGCAGGGGCCCTGTGAGGCCCAGGGTGGCTGCCTGCTTTCAGAATCCTGTGTGTCAGCCAGGCCGGGTGGTACAGCTTGCCTGGCTGGGTTTGCAGGGCAGCAAGAGCACTGCTTAAAAGTTTTCCGATCGAAGCTTTAATGGAGCGTTTATTTATTTATCGAGGCCTCTGGCAAGCCTGGGGGGATAAGCAAAGGGTGGGGGGCATGGGTGATACCTTAAGTCCCTGTTCTCTGAAGCAAGGGCAGGATCCCTACCCAAGAGTTGCTGAGGCCCAAGCAGTTTATTTATTGGGAAAGGGAGAGGGAGACAGACTGACAGCCATGGATGGGCTGGAGAAACAGTCCCTTTGTACCAGTACTCCAGCCGCATGTATCCAGGGGATCTGAGATGGGGAGGGTACGTGAGGGCCTTGGCTGACTGCGGCCAGGAGGGGTGGGTATGCGTCCTTCCTATGGCTGGAGTGCTCCTCTGCTGTCCTCCCCACCCTCCAGTCTGCACTTTGATTTGTTTCCTAACAGTTCTGTTCCCTCCTGCTTTGATTTTAATAAATGTTTTGATG

1. Find the ORF regions
2. Which region is most probably the gene coding region if this sequence contains only a single gene?
3. What is the length of this most probable gene?
4. The gene will encode a protein molecule. How long will this protein molecule be?
5. How many Methionines are encoded in the gene-containing region?
6. In which position is the stop codon found?
7. What is the name of this most probable gene?
8. How did you determine the name of the best-matched gene name: i.e. what was your e-value, total score, number of gaps, %identity.

EXERCISE 1 [ ORF VERSUS GENES]

1.     In our original sequenced DNA, the following ORFs were predicted:

(a)  What is an ORF and how different is it from a gene coding region?                        (2)

(b)  Which ORF is most probably the gene-coding region? Circle it.                   (1)

(c)  How long is this open reading frame?_________________                          (1)

(d)  In which frame was this most probable ORF found?______________        (1)

(e)  Predict the length of the protein that would be coded by this ORF_________(1)

2.     The 3D structure of the resulting gene product looks like this. Describe the protein.

 (7)

Protein Sturctures Databases

The PDB archive contains information about experimentally-determined structures of proteins, nucleic acids, and complex assemblies. As a member of the wwPDB, the RCSB PDB curates and annotates PDB data according to agreed upon standards.

Use the RCSB PDB to perform simple and advanced searches based on annotations relating to sequence, structure and function, and to visualize, download, and analyze molecules.

Choose a protein of your choice according to your current research focus and view the structural components on RCSB http://www.rcsb.org/pdb/home/home.do

Have fun

For your class-work: fully describe the protein structure

Functional Analysis of proteins

Today, let's open expassy and use prosite to look at functional characterization of this protein sequence:

MVQRWLYSTNAKDIAVLYFMLAIFSGMAGTAMSLIIRLELAAPGSQYLHGNSQLFNVLVVGHAVLMIFCAPFRLIYHCIEVLIDKHISVYSINENFTVSFWFWLLVVTYMVFRYVNHMAYPVGANSTGTMACHKSAGVKQPAQGKNCPMARLTNSCKECLGFSLTPSHLGIVIHAYVLEEEVHELTKNESLALSKSWHLEGCTSSNGKLRNTGLSERGNPGDNGVFMVPKFNLNKVRYFSTLSKLNARKEDSLAYLTKINTTDFSELNKLMENNHNKTETINTRILKLMSDIRMLLIAYNKIKSKKGNMSKGSNNITLDGINISYLNKLSKDINTNMFKFSPVRRVEIPKTSGGFRPLSVGNPREKIVQESMRMMLEIIYNNSFSYYSHGFRPNLSCLTAIIQCKNYMQYCNWFIKVDLNKCFDTIPHNMLINVLNERIKDKGFMDLLYKLLRAGYVDKNNNYHNTTLGIPQGSVVSPILCNIFLDKLDKYLENKFENEFNTGNMSNRGRNPIYNSLSSKIYRCKLLSEKLKLIRLRDHYQRNMGSDKSFKRAYFVRYADDIIIGVMGSHNDCKNILNDINNFLKENLGMSINMDKSVIKHSKEGVSFLGYDVKVTPWEKRPYRMIKKGDNFIRVRHHTSLVVNAPIRSIVMKLNKHGYCSHGILGKPRGVGRLIHEEMKTILMHYLAVGRGIMNYYRLATNFTTLRGRITYILFYSCCLTLARKFKLNTVKKVILKFGKVLVDPHSKVSFSIDDFKIRHKMNMTDSNYTPDEILDRYKYMLPRSLSLFSGICQICGSKHDLEVHHVRTLNNAANKIKDDYLLGRMIKMNRKQITICKTCHFKVHQGKYNGPGL

Click on: http://www.expasy.ch/
and open PROSITE
Look at the following:
0. Domain structure of the protein
1. Clustal format(1st 3 sequences)• Retrieve the sequence LOGO from the alignment (for 15 aas)
2. Taxonomic tree view of all Swiss-Prot/TrEMBL entries matching our protein
3. Retrieve a list of all Swiss-Prot/TrEMBL entries matching our protein
4. Scan Swiss-Prot/TrEMBL entries against our protein
5. view ligand binding statistics on our protein
6. Click on sequence ID and retrieve sequence Logo from alignment

For your classwork, here is your sequence
MLDQQTINIIKATVPVLKEHGVTITTTFYKNLFAKHPEVRPLFDMGRQESLEQPKALAMT
VLAAAQNIENLPAILPAVKKIAVKHCQAGVAAAHYPIVGQELLGAIKEVLGDAATDDILD
AWGKAYGVIADVFIQVEADLYAQAVE

MULTIPLE SEQUENCE ALIGNMENT

Manually align these sequences to search for degrees of relatedness

Set your own scores for gap penalties and matches

MAKGOBA

ACTCCGTCTCAATATGTCTCA

CALCIUM

CCGACTCCGTCTCAATATGTC

SARAH

ACTCCGTTATCTCAATATGTC

SAMMY

GATGTGATGATACGTAGATTT

Now use ClustalW to alignment the following proteins given their protein IDs from UniProt.

P12882

Q9UKX2

P02565 

P11055 

P13541 

P12847 

SEQUENCE ALIGNMENT

Pairwise Sequence Alignment is used to identify regions of similarity that may indicate functional, structural and/or evolutionary relationships between two biological sequences (protein or nucleic acid).

By contrast, Multiple Sequence Alignment (MSA) is the alignment of three or more biological sequences of similar length. From the output of MSA applications, homology can be inferred and the evolutionary relationship between the sequences studied.

Global Alignment

Global alignment tools create an end-to-end alignment of the sequences to be aligned. There are separate forms for protein or nucleotide sequences.

Needle (EMBOSS)

EMBOSS Needle creates an optimal global alignment of two sequences using the Needleman-Wunsch algorithm.

Protein Nucleotide

Stretcher (EMBOSS)

EMBOSS Stretcher uses a modification of the Needleman-Wunsch algorithm that allows larger sequences to be globally aligned.

Protein Nucleotide

Local Alignment

Local alignment tools find one, or more, alignments describing the most similar region(s) within the sequences to be aligned. There are separate forms for protein or nucleotide sequences.

Water (EMBOSS)

EMBOSS Water uses the Smith-Waterman algorithm (modified for speed enhancements) to calculate the local alignment of two sequences.

Protein Nucleotide

Matcher (EMBOSS)

EMBOSS Matcher identifies local similarities between two sequences using a rigorous algorithm based on the LALIGN application.

Protein Nucleotide

Genomic Alignment

Genomic alignment tools concentrate on DNA (or to DNA) alignments while accounting for characteristics present in genomic data.

Wise2DBA 

Wise2DBA (DNA Block Aligner) aligns two sequences under the assumption that the sequences share a number of colinear blocks of conservation separated by potentially large and varied lengths of DNA in the two sequences.

Launch Wise2DBA

GeneWise 

GeneWise compares a protein sequence to a genomic DNA sequence, allowing for introns and frameshifting errors.

Launch GeneWise

PromoterWise 

PromoterWise compares two DNA sequences allowing for inversions and translocations, ideal for promoters.

CLASSWORK NO. 2

GIVEN THE 2 SEQUENCES, USE PAIRWISE ALIGNMENT TOOLS TO DETERMINE EXTENT OF IDENTITY OF THE SEQUENCES

ACTCCGTCTCAATATGTCTCAAGATGGCGGCCAATGTGGGATCGATGTTTCAATATTGGAAGCGCTTTGATTTACAGCAGCTGCAGGATTTGCGCAAGCAGGTAGCGCCGCTGCTGAAGAGTTTCCAAGGAGAGATTGATGCACTGAGTAAAAGAAGCAAGGAAGCTGAAGCAGCTTTCTTGAATGTCTACAAAAGATTGATTGACGTCCCAGATCCCGTACCAGCTTTGGATCTCGGACAGCAACTCCAGCTCAAAGTGCAGCGCCTGCACGATATTGAA

AND 

ACTCCGTCTCAATATGTCTCAAGACAATGTGGGATCGATGTTTCAATATTGGAAGCGCTTTGATTTACAGCAGCTGCAGGATTTGCGCAAGCAGGTAGCGCCGCTGCTGAAGAGTTTCCAAGGAGAGATTGATGCACTGAGTAAAAGAAGCAAGGAAGCTGAAGCATTGAATGTCTACAAAAGATTGAAAGACGTCCCAGATCCCGTACCAGCTTTGGATCTCGGACAGCAACTCCAGCTCAAAGTGCAGCGCCTGCACGATATTGAATGGCGGCGCTTTC

Bioinformatics (SCIN072) MODULE OUTLINE

MODULE OUTLINE
Module Title	BIOINFORMATICS AND SPECIAL TOPICS
Module Code	SBIN704 SCIN072	No. of Credits		20
Department	BMBT	School		SMLS
Pre-requisites Module Code	NONE	Co-requisites Module Code		NONE
Module Lecturers	Dr KLM Moganedi Dr TM Matsebatlela Dr VP Bagla
Office Address	3026 Life Science building (Moganedi) 2nd level Life Science building (Matsebatlela)
Email address	Kgabo.Moganedi@ul.ac.za Thabe.Matsebatlela@ul.ac.za victor.bagla@ul.ac.za	Telephone No.			3630 (Moganedi) 2337 (Matsebatlela) 3855 (Bagla)
Consultation Time	10h00 – 14h00 Monday to Friday	Lecture Periods			Mondays – Thursdays 08h00 – 10h00
Practical facilitator	Dr KLM Moganedi	Office address			3026 Life Science building
E-mail address	Kgabo.Moganedi@ul.ac.za	Telephone no.			3630
Important Dates	19/09 Test 1-Bioinformatics 18/09 Submission Assignment 1	Learning Hours			>3 hours per lecture >Computer exercises during own time
Quarter/Semester	4th Quarter/ 2nd Semester	Module outline evaluated by			Prof I Ncube
Module Structure	09/09/2013 – 18/10/2013 4 x 180 minutes per week, presented between 7h30 and 12h00
Assessment Method	Weighting		Description
Exercises Assignment Theory test Summative Exam	Formative assessment 60% Exercises:         25%, Test :                35% Summative assessment 40% Theory:              70%, (Bioinformatics – 30% Phytochemistry - 30% Phylogeny – 10 % Comprehension – 30%) Comprehension 30%,		Minimum Formative Assessment mark for exam admission =  40% Final mark =  60% Formative Assess Mark                                40% Summative Assess Mark Minimum Final Assessment mark to Pass = 50%
MODULE DESCRIPTION
The theory component of this module is presented in contact sessions by the lecturers. The exercises that complement theory will be performed by students on their own but guided by written instructions and descriptions from the lecturer. These will be discussed in class. The emphasis is on collection, storage and retrieval of biological data and how this information can be used to discern relatedness between organisms and to determine functions and structures of genes and proteins. The component of phytochemistry will place emphasis on phytochemical contents and medicinal value of plants in drug discovery and quality control of herbal products.
MODULE CONTENT
Collection and storage of sequences; Biological data sources; Biological Databases; Searching and retrieval of biological data from databases; Sequence Alignment; Prediction of protein-coding genes; cDNA based approach; Ab initio gene discovery; Gene identification through comparative analysis; Functional genomics: From gene sequence to function, mutagenesis as a tool for studying gene function; Transposon mutagenesis, gene knockout, knock-ins, gene silencing by antisense RNA and RNAi; Phylogenomics. Type of phytochemicals that are of relevance in ethnopharmacology, value of their prediction in extracts, approaches of prediction of actives, their extraction, fractionation, and isolation.  It will also deal with problems associated with in-vitro and in vivo assays in ethnopharmacology.
MODULE OBJECTIVES
·      To introduce the importance of bioinformatics in biological research; source and storage of biological data. ·      To acquaint students with how to search and retrieve biological data from biological databases. ·      To presents the importance of performing sequence alignments and how they are carried out. ·      To describe how protein-coding genes are predicted from nucleotide sequences and their functions are determined. ·       To learn how protein structures are predicted. ·      To present how sequences from different organisms can be compared in order to learn about their evolution in phylogenomics. ·      Provide an insight into the medicinal value of plants and how best their medical active constituents can be harnessed  either as extracts, fractions or single entities for drug discovery
LEARNING OUTCOMES
After successfully completing the module, the student should show the ability to: ·     Explain how protein and nucleotide sequences are generated, stored and retrieved from databases ·     Describe the importance of protein and nucleic acid sequences in homology searches, inference of the function of the biomolecules and evolutionary relationships between organisms ·     Demonstrate the utilization of protein and nucleic acid sequences in homology searches and inference of evolutionary relationship between organisms  ·     Discuss different approaches of determining protein-coding genes from nucleotide sequences and their levels of reliability ·     Deduce gene function from mutagenesis data ·      Have the ability to analyze and argue research information from a research paper ·      Have a knowledge of type of phytochemicals contained in plants and their biological relevance ·      Have an understanding of the approaches to identify the presence of actives in a plant ·      Know the type of solvent to use for extraction of a desired phytochemical of interest ·      Detect the presence of phytochemicals in a plant using detection methods and reagents used for TLC ·      Have the ability to detect type of compounds associated with a particular biological activity ·      Demonstrate the ability to extract, fractionate and isolate plant constituent ·      Have a knowledge of why in-vivo assays should complement in-vitro assays
ASSESSMENT CRITERIA
The students must be able to: ·     Describe protein and DNA sequencing techniques ·     Differentiate types of databases according to biological data that is stored ·     Demonstrate searching and retrieval of biological data from databases ·     Explain the biological significance of performing sequence alignments ·     Describe different approaches of determining protein-coding genes from nucleotide sequences and their levels of reliability ·     Compare and contrast different approaches of determining the function of a gene ·     Correlate sequence information and degree of relatedness between organisms ·     Explain the relevance and describe the applicability of a special topic to the research niche area in the department ·     Explain the scientific merit of a selected research paper Indicate correlation of the aim and methodology used to the findings of a research paper ·         For phytochemistry will be as expected from the learning outcomes
REFERENCE MATERIALS FOR THE MODULE
These books are available in the department (BMBT) for student loan: 1.    Lesk AM. 2002. Introduction to Bioinformatics. Oxford University press 2.    Gibson G and Muse SV. 2004. A primer of Genome Science. 2nd ed, Sinauer Associates, Inc publishers. 3.    Xiong J. 2006. Essential Bioinformatics. Cambridge University Press 4.    Felsenstein J. 2004. Inferring phylogenies. Sinauer Associates, Inc. 5.    Page RDM and Holmes EC. 1998. Molecular evolution. Blackwell Publishing company
TENTATIVE LIST OF LECTURE TOPICS
1.    Generation of biological data
2.    Collection and storage of biological data Biological Databases
3.    Searching and retrieval of biological data from databases
4.    Sequence Alignment
5.    Prediction of protein-coding genes o    Ab initio gene discovery o    Gene identification through comparative analysis
6.    Functional genomics
7.    Phylogenomics
8.    Phytochemistry and its application in drug discovery and quality control of herbal preparation
9.    Type of phytochemicals
10.  How to predict the chemical types of actives present in a plant
11.  Approaches to prediction of chemical types of actives present in a plant
12.  Extraction, fractionation and isolation of plant constituents
13.  Problems associated with in-vitro and in vivo assays in ethnopharmagology
14.  Value of predicting the chemical types of actives present in a plant
15.  Comprehension: Research paper