Molecular Databases and Tools

(1)

NWeHealth, The University of Manchester

Molecular

Databases

and

Tools

Afternoon Session: NCBI/EBI resources, pairwise alignment, BLAST, multiple sequence alignment and primer finding.

Dr. Georgina Moulton 21/04/2010

(2)

Exploring

bioinformatics

tools

for

pairwise

alignment,

multiple

sequence

alignment,

primer

design

and

functional

analysis.

Session

Objectives

It is the aim of this session to introduce you to the following areas: • NCBI databases and tools (mostly DNA) • Navigation between databases • Sequence databases • Data formats and conversions

• Searching sequence databases (e.g., BLAST)

• Bioinformatics tools that are available to design and choose primers. • Multiple sequence alignment programs and editors

Session

Outcomes

At the end of today’s course you will be able to: • retrieve sequences from sequence data repositories • browse the UCSC Genome Browser and navigate to other data resources • understand which databases contain which information and how to access it • know how to design primers using suitable bioinformatics tools • understand and know how to create an MSA • know which programs to use to create and visualise an MSA • able to know the advantages and disadvantages of the MSA methods/programs • know the uses of an MSA • know how to design primers using suitable bioinformatics tools (e.g., eprimer3 and primer‐ BLAST) • understand the difficulties involved in using bioinformatics tools for primer design • appreciate the difficulties when navigating various data resources

(3)

3 Pairwise

Alignment

Sequence comparisons are used to detect evolutionary relationships between organisms, proteins or gene sequences. They are also used to discover the function of a novel gene or the structure of an

unknown protein, by comparing an already characterised gene or protein, since we assume that

sequences that are very similar often have similar structure/function.

If two sequences from different organisms are evolutionary related, it means they have a common ancestor and it is said to be homologous. By comparing sequence 1 and sequence 2, or aligning

them, we may infer the evolutionary process starting from the same ancestor sequence and then

changing through mutations.

However, the snag is deciding how similar is similar. A general rule is: if your sequences are more than 100 amino acids or nucleotides long, the rule says that you can label proteins as homologous if 25% of the amino acids are identical and DNA as similar if 70% of the nucleotides are identical. Anything below this threshold is referred to as the ‘twilight zone’.

Local and Global Alignments

The two types of dynamic algorithms mentioned above are described as local and global respectively.

A local alignment identifies regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate

because of the additional challenge of identifying the regions of similarity.

A global alignment "forces" the alignment to span the entire length of all query sequences

Searching sequence databases

The growing size and diversity of the public sequence databases makes them invaluable resources

for molecular biologists. When investigating a novel DNA/protein sequence, a fast, cheap and

potentially very rewarding analysis involves scanning EMBL/GenBank, or UNIPROT/SWISSPROT for

sequences with homology to your own sequence. Database searching is one of the first and most

important steps in analysing a new sequence. If your unknown sequence has a similar copy already in the databases, a search will quickly reveal this fact and if the copy is well annotated you will have various clues to help you in further studying your sequence. Database searches usually provide the first clues of whether the sequence belongs to an already studied and well known protein family. If

(4)

there is a similarity to a sequence that is from another species, then they may be homologous (i.e.,

sequences that descended from a common ancestral sequence). Knowing the function of a

homologous sequence will often give a good indication of the identity of the unknown sequence.

Many programs for database searching already exist, but still many more are being developed. They can be spilt into two types: heuristic and dynamic algorithms.

Dynamic algorithms including Needleman and Wunsch (1970) and Smith‐Waterman (1981) can be

used, but the time taken to complete such a task is longer than desirable. To counteract this, heuristic search algorithms are used to routinely search large databases.

The most commonly employed algorithms are FASTA and BLAST (Basic Local Alignment Search Tool). The following is a brief description of some programs:

• BLAST performs fast database searching combined with rigorous statistics for judging the significance of matches.

• FASTA can be used to compare either protein or DNA sequences and hence the name, which stands for Fast‐All.

• BLITZ is an automatic electronic mail server for the MPsrch program. MPsrch allows you to perform sensitive and extremely fast comparisons of your protein sequences against Swiss‐

Prot protein sequence database using the Smith and Waterman best local similarity

algorithm. All programs identify local regions of conserved residues between sequences. This approach allows the program to identify similarities between a query sequence and sequences in the database in the shortest possible time. We’ll talk about BLAST today but you might want to look at others if time allows.

(5)

5

BLAST – the most popular and used data‐mining tool

The BLAST algorithm and family of programs rely on work on the statistics of local sequence alignments by Altschul et ali. The statistics allow us to estimate the probability of obtaining an alignment with a particular score. The BLAST algorithm permits nearly all sequence matches above a cutoff1 to be located efficiently in a database.

There are many flavours of BLAST that exist, so you can search both protein and nucleotide

sequence databases with protein or nucleotide sequences! We deal with the different flavours

today, depending on the type of query sequence and the type of biological question we hope to ask.

BLAST program Database Query

blastp Protein Protein

blastn Nucleotide Nucleotide

blastx Translated DNA Protein

tblastn Translated DNA Protein

tblastx Translated DNA Translated DNA

BLAST input parameters you can change

The default parameters that BLAST uses are quite optimal and well tested. However, here are some reasons you may wish to change the default parameters:

• The sequence your interested in contains many identical residues; it has a biased

composition (change the sequence filtering)

• BLAST doesn’t report any results (change substitution matrix or gap penalities)

• Your match has a borderline e‐value (change substitution matrix or gap penalities)

• Too many matches are reported (change database you are searching OR filter reported

entries by keyword OR increase the number of reported matches OR increase the e‐value)

BLAST

output

BLAST reports back a list of sequence matches to the query sequence ordered by score that

represents the significance of the match. In BLAST, the reported value is referred to as the p‐value, as it represents the probability of a random sequence matching a database sequence with the same

1

(6)

or better score than the query. Sometimes the e‐value is reported, which represents the number of random matches with scores greater or equal to the query sequence that would be found by chance in a database of the same size. It follows that both values, the smaller the value, the more significant the match.

What

are

you

looking

for?

Several important features are worthy of note in BLAST output:

• Look for high scores with low p‐values. This means the match is unlikely to be random.

• Look for clusters of high scores at the top of the hitlist for hint of a potential family

• Look for trends in type of sequences matched

BLASTing with DNA sequences – which program for what problem??

blastn: compares a DNA sequence with a DNA database. You can use this for mapping

oligonucleotides, cDNAs and PCR products to a genome; annotating genomic DNA; screening

repetitive elements and cross‐species sequence exploration.

blastx: use this for finding protein‐coding regions in genomic cDNA; determining if a cDNA corresponds to a known protein.

tblastx: by comparing a DNA translated into a protein with a DNA database also translated into protein allows cross‐species gene prediction at the genome or transcript level (ESTs) and searching for genes that are not yet in protein databases.

(7)

7

Dotplots – visualising a pairwise alignment

One of the earliest methods of comparing two protein or nucleotide sequences was to create a dot plot. This matrix can reveal the presence of insertions and deletions because they shift the diagonal

horizontally or vertically. There are many programs that produce dot plots; however you can do

simple dot plots by hand (DIY dot plots). A dot plot can be useful if you plot a sequence against itself as internal repeats, tandem genes, repeated domains in proteins and regions of low complexity can

be highlighted. Please note that although useful a dot plot cannot resolve similarity that is

interrupted by regions of low similarity or insertions/deletions. This is a dot plot of two similar, but not identical sequences

(8)

Sequence

Databases

and

Retrieval

There is a wealth of information that can be associated with a gene (see diagram above for a

sample). Although this data is interlinked through links, each type of information is stored

in a separate database. An example of this would be Entrez Gene (hosted at the NCBI) has a

focus

on

the

gene

information,

whereas

dbSNP, holds

database

SNP

entries.

You can

link

between the two data‐resources, so you can find out more information about the SNPs of a

particular gene.

Sequence

Retrieval

System

As these databases contain hundreds of thousands of sequences, searching through them requires

the processing power of a computer search engine. The Sequence Retrieval System (SRS) has been

designed to do just that. SRS is available at many sites over the world. However, every site allows

access to a different set of databases and, sometimes, search and analysis tools.

Of course, sequences and their information can be directly retrieved by searching primary sequence databases; for example, if you are doing more work with proteins, you might want to investigate the Expert Protein Analysis System (ExPASy) held at the Swiss Institute of Bioinformatics. This site not

only holds the SwissProt and TrEMBL databases, but also offers many tools for the user to analyse

(9)

9 Exercise

1:

Pairwise

alignments

using

EMBOSS

1) Retrieve sequences from NCBI: U14680 and NM_007294.2 and save the sequences in FASTA format in notepad. Call the filenames something sensible! (Note: files usually containing a FASTA sequence are usually given the prefix .fas or .fasta) 2) Go to the EMBOSS align website (http://www.ebi.ac.uk/Tools/emboss/align/index.html) 3) Paste one sequence into the top box and the other in the second box. Check the parameters: Molecule = DNA; and Method=EMBOSS:needle(global), and run. 4) The output file (.output) in the Needle results, click on and save the page. 5) Run the program again, but this time choose the parameter: Method=EMBOSS::smith(local). 6) Compare the results.

(10)

A

use

of

BLAST:

primer

design

Software

for

primer

design

eprimer3 (primer3) is the standard software used to design primers. Its function: picks PCR primers and

hybridization oligos (EMBOSS). eprimer3 is an interface to the 'primer3' program from the Whitehead

Institute.

Primer3 picks primers for PCR reactions, considering as criteria:

‐ oligonucleotide melting temperature, size, GC content, and primer‐dimer possibilities, ‐ PCR product size,

‐ positional constraints within the source sequence, and ‐ miscellaneous other constraints.

All of these criteria are user‐specifiable as constraints.

eprimer3 can also pick hybridisation oligos that are internal to the product.

BLAST would then check the specificity of the primers by using blastn for short exact matches. However,

more recently, a new BLAST method has become available Primer‐BLAST. This is a combination of the

primer3 software and BLAST, thus allowing you to design primers and check specificity in one search!! Primer Design Guidelines

1. primers should be 17‐28 bases in length; 2. base composition should be 50‐60% (G+C);

3. primers should end (3') in a G or C, or CG or GC: this prevents "breathing" of ends and increases efficiency of priming;

4. Tms between 55‐80oC are preferred;

5. 3'‐ends of primers should not be complementary (ie. base pair), as otherwise primer dimers will be synthesised preferentially to any other product;

6. primer self‐complementarity (ability to form 2o structures such as hairpins) should be avoided;

7. runs of three or more Cs or Gs at the 3'‐ends of primers may promote mispriming at G or C‐rich sequences (because of stability of annealing), and should be avoided.

(11)

11 Exercise

2 :

Designing

a

primer

using

Primer

‐

BLAST

Scenario

Based on your microarray results, a specific gene is upregulated under a cold stress condition. You

decided to go for a qPCR to confirm the microarray data. So, you need good primers to amplify the gene. You may decide to design the primers yourself, or you may use a program which will do it for you. Either

way, we do advise you to check the resulting primers, see where they are in the sequence, and choose

then carefully! Your experiment depends on the quality of the primers.

To perform the following exercises, you will need the nucleotide sequence of H. sapiens fau 1 gene and

the pGEM‐T vector. I have provided them at: .

The ID code for the gene sequence is P35544 (a Uniprot Identifier) and the vector sequence is at the specified place, called pGEM.fasta. 1) Go to the NCBI Entrez website and search for ‘FAU1 human’ against the Nucleotide database. At the top of the results, with a light blue background, click on the FAU link to view the entry. 2) As we want to design a set of primers to amplify this gene, we are going to use this sequence. I have already downloaded this sequence and stored in the hsfau1_dna.fasta file. Open the file in notepad.

3) Go to the Primer‐Blast website. Copy the file in the text box under the PCR template heading.

4) Keep all the parameters the same and click ‘Get primers’ 5) What do your results suggest? Would you be okay with these? Looking at the options (mentioned above), you can specify the region of gene where the program should find a good primer. There are different ways to calculate the melting temperature for the primers. Using the first formula on the whiteboard, calculate the TM for the two first primers resulting from eprimer3. This is a really simple

(12)

considered, as indicated in the second formula. Compare your result with the result obtained by eprimer3.

Exercise

3 :

primersearch

–

checking

vector

sequence

(optional)

There is another aspect that should be considered when you are chosen primers. Do the primers align to your vector sequence?

PrimerSearch

Function: Searches DNA sequences for matches with primer pairs.

Description : primersearch reads in primer pairs from an input file and searches them against sequence(s) specified by the user. Each of the primers in a pair is searched against the sequence and potential

amplimers are reported. The user can specify a maximum percent mismatch level; for example, 10%

mismatch on a primer of length 20bp means that the program will classify a primer as matching a

sequence if 18 of the 20 base pairs matches.

It will only report matches if both primers in the pair have a match in opposite orientations.

At the following website http://embossgui.sourceforge.net/demo/primersearch.html

Follow the steps:

1) Paste the fasta file from the pGEM.fasta file into the top text box and upload the primer file

PGEMxprimers for the Primer file option. Allow a 20 percentage mismatch. Click Run.

(13)

13 Multiple

Sequence

Alignment

Background

on

Multiple

Sequence

Alignments

In the construction of a multiple sequence alignment (MSA), it is assumed that all sequences are biologically or evolutionarily related. An MSA allows the identification of highly conserved regions, corresponding to important functional or structural features within families of related proteins, and hence the study of evolutionary relationships between them.

An MSA can be described as a tabular description of the relationships between proteins, where rows represent individual sequences, and the columns the residue positions. Similar residues are brought into vertical register by introducing gaps, so that the relative position of residues within the alignment is preserved. The result is an expression of the similarities and dissimilarities between the sequences.

Why?

There are many reasons why you might want to construct a multiple sequence alignment. These

include:‐

• To highlight regions of similarity, divergence and mutations.

• To provide more information than a single sequence. (e.g. for an even more sensitive search to

find other, more distant, family members.) • Creating a consensus will highlight functionally important domains or residues. • It could reveal errors in protein sequence prediction (or even in sequencing) • Secondary structure and other predictions improve with multiple alignments • Evolutionary analysis (phylogeny). • To find novel motifs (e.g. using Hidden Markov Model techniques). • To select appropriate primers for a gene family. • To be used as input to identify changes in functionality due to missense mutations (ALIGN‐GVD, SIFT)

(14)

MSA methods

MSA process can either be carried out manually in an editor (e.g., JalView, GeneDoc or CINEMA; see

table 1 below for a detailed explanation of these) or using automatic alignment programs. The

underlying process to construct an MSA is common to both manual and most automatic methods:

groups of sequences that share a high percentage identity are grouped and aligned, and then these sequence groups are alignment with each other.

When the protein family is highly conserved, both types of method are likely to produce exactly the

same alignment. However, for more diverse families, automatic alignment methods tend to be

error‐prone and result in biologically inaccurate alignments. In this case, it is better to align

sequences by hand. However, depending on the size of the protein family, this may be a time

consuming process.

Almost everyone will want to start a MSA project using one of the automatic methods and then

refining them by eye.

There are several alignment programs, separated into a number of categories, depending on the

(15)

15

Alignment Editor Description

CINEMA 5 CINEMA (Colour INteractive Editor for Multiple Alignments) is a tool for

alignment construction, modification and visualisation. In addition to its

advantage of allowing interactive alignment over the Web, CINEMA

provides links to the primary data sources, thereby giving access to up‐

to‐date sequences and alignments. The program accepts any number of sequences of any length, which may be loaded in various ways.

By default, alignments are coloured according to intuitive residue

property groups. Nevertheless, menu options allow user‐specification of

residue colours (and hence residue groups) and to swap between

different colouring alternatives. Flexible colouring facilitates the

identification of core conserved regions of alignments and especially of

key motifs that may be associated with the structure or function of the

protein.

The program offers various "pluglets": e.g., dotplots, CLUSTALW, a 3D

backbone viewer, BLAST, etc..

JalView JalView has the advantage of being available as both a downloadable

application and an applet online. The application offers a CLUSTALW

plug‐in, performs Smith Waterman pairwise alignment, and is able to

calculate and draw UPGMA and NJ trees based on percent identity

distances.

SeaView Executable binaries (and source code) are available for many platforms.

It also offers a CLUSTALW plug‐in, calculates simple dotplots, and allows motifs to be saved.

BioEdit Written for Windows 95/98/NT/2000/XP. It is an intuitive multiple

document interface with convenient features makes alignment and

manipulation of sequences relatively easy on your desktop computer.

There are additional features that allow connection to bioinformatics

(16)

MSA

and

Primer

‐

BLAST

For this example, we are going to use the Human Myglobin gene (geneid = 4151). There are three highly conserved variants of this gene: NM_203377.1, NM_005368.2 and NM_203378.1. Our aim is to design a primer that will amplify this gene.

Exercise

4:

Multiple

Alignment

of

Variants

(optional)

1) Look at the Human Myglobin gene entry by searching at the NCBI with the search term “4151[uid]”. You should be able to view all the information about the gene. 2) Retrieve sequences at NCBI by typing the following in the search box “NM_203377.1 NM_005368.2 NM_203378.1” and search against the nucleotide database. Click all tick boxes, go to top of page and change the ‘Summary’ option in the Display drop‐down menu to FASTA. Change the Send to drop‐down menu to Text and save page as “myglobin.seq”

3) Go to the EBI ClustalW website (http://www.ebi.ac.uk/Tools/clustalw2/index.html) and Upload

your myglobin.seq file in the interface. You don’t need to change any other parameters. Click Run.

4) On the results page, click ‘Start Jalview’. You will be able to see the nucleotide alignment of the 3

variants and see that there is a high level of conservation.

Exercise

4.1:

Using

the

alignment

to

choose

some

primers

(optional)

1) Using the alignment can you pick a forward and reverse primer that will be able to amplify the

myglobin gene. Remember you can use the primer design guidelines specified earlier.

I have chosen the following ones: 5’‐GATGAAGGCGTCTGAGGA‐3’ and 5’‐GATCTTGTGCTTGGTGGC‐3’. You can either use these are the ones you have chosen for the following exercise.

(17)

17 Exercise

4.2:

Using

Primer

‐

Blast

(optional)

Blast is used to compare a query sequence against a sequence database in a pairwise manner. This time we are going to use it to see check the specificity of our primers to a DNA template. Before the Primer‐Blast, you could do the same by using blastn for short exact matches!

1) Go to Primer‐Blast (http://www.ncbi.nlm.nih.gov/tools/primer‐blast/) and in under the ‘Primer

Parameters’ heading put your forward primer in the ‘Use my own forward primer (5'‐>3' on plus strand)’ box and the reverse primer in the ‘Use my own reverse primer (5'‐>3' on minus strand)’ box. Leave all other parameters the same. Click ‘Get Primers’.

Are your primers specific enough?

(18)

Exploring

Sequence

Formats,

Sequence

Databases,

Genome

Browsers

and

Multiple

Sequence

Alignments

The basis of this exercise in identifying SNPs for BRCA1 has been taken from the paper:

R. Rajasekaran, C. Sudandiradoss, C. George Priya Doss, Rao Sethumadhavan, Identification and in silico analysis of functional SNPs of the BRCA1 gene, Genomics, Volume 90, Issue 4, October 2007, Pages 447‐452.

Exercise

5:

Investigating

genes

and

SNPs

using

the

UCSC

Browser

(with

a

quick

look

at

Uniprot

for

detailed

protein

function

information)

In this exercise we want to be able to position the gene on the genome and look at its SNPs.

1) Go to the UCSC Genome Browser Gateway and go to the Human (Hs) Genome Browser Gateway

by clicking on ‘Genomes’.

2) In ‘position/search term’ text box type the accession number NP_009225

3) Look at results. What do you notice about the position of this gene on the genome?

4) Click on the 4th link for UCSC genes.

We will now manipulate which tracks we can see using the selection boxes on the web‐page.

5) Hide anything that you feel is hindering your view of the gene and its SNPs. Hint: hide spliced

ESTs, Repeat Masker, and make dense the conservation. What can you say about the SNPs in the BRCA1 gene?

6) Click on the track that is highlighted and you will a Description and Page Index. This links out to

other databases that contain important functional information.

7) Explore the UniprotKB entry. What can you tell me about the status of this entry?

8) Go back to the Description and Page Index page in UCSC browser. Now go to the Entrez Gene

entry at the NCBI. Look at the entry and the all the possible links to other NCBI data resources. What are these data resources?

(19)

19 Exercise

6:

Viewing

SNPs

from

dbSNP

on

3D

structures

using

Cn3D

(optional/demo

–

shows

how

tools

don’t

always

work

due

to

inconsistencies

in

identifiers!!)

In particular, you might want to look at the SNPs in dbSNP from the GeneView.

1) Link to the dbSNP database by clicking on the GeneView SNP Report Link under the Genotype

heading.

Can you answer the following questions from the dbSNP entry: How many gene models are there?

How many synonymous mutations and missense mutations are there? 2) To view your synonymous SNPs on a 3D structure you can use the NCBI viewer CN3D, but you will need to install this locally on your computer in order for it to work. In the table of all mutations, you will be able to detect which SNPs have been validated and are mapped to 3D structures etc.. 3) Choose a SNP that has a 3D structure (e.g., the one in exon 5). Click on the link Yes to go to the SNP3D entry. You will notice that there are several isoforms of this protein; each represented in this entry. We will concentrate on the first (isoform 1). 4) Select both the SNPs to view in Cn3D by either ticking the boxes under the heading CN3D next to the information of the SNPs and then the button ‘Selected’; or just select the button ‘All’ underneath. 5) From the structure summary page, you will be able to see how many mutations there are and where they are mapped. For the top structure, click on the pink bar to the right to see your query aligned to the structure's protein sequence, with an option to open an interactive view of the alignment and 3D structure in Cn3D. 6) View the alignment and 3D structure in Cn3D. The SNPs are marked in a gold colour. It is easier to view in the wire style (change this using the Style‐>Rendering shortcuts menu).

(20)

Exercise

7:

Retrieval

of

Sequences

from

NCBI

1) We want to download the protein sequence for this gene. Scroll down the entry until you mRNA

and Proteins section. Click on the NP_009225.1 link. This should be the first entry and should be on a line that looks something like:

NM_007294.3→NP_009225.1 breast cancer type 1 susceptibility protein isoform 1

2) Now you should be looking at the NP_009225 entry. Download the FASTA sequence for this

entry. At the top of the web‐page, underneath tabs, click on the ‘FASTA’ link. Your web‐browser should now display the FASTA sequence.

I have already downloaded this sequence for you in a file called ‘hs_brca1.fasta’. You could have

done this two ways: (1) cut and paste sequence into notepad; or (2) use download link at RHS of

page.

3) Open the ‘hs_brac1.fasta’ file in notepad to check it.

Exercise

8:

Sequence

Format

Conversion

Ultimately the sequence downloaded in the previous exercise is to be added to other BRCA1 protein sequences to create a Multiple Sequence Alignment (MSA), which then be used for further analysis in ALIGN‐GVD.

1) On your desktop, there should be a file called ‘allbrca1_unaligned.phy’. Open this file in notepad

and look at the sequence format. Do you know or recognize this format? Google it to see if it is a regular format.

This sequence format is often used with a suite of programs that concentrate on inferring

phylogenies. This file format is not popular and is not often used as an input sequence file format.

Also, remember that the sequence format is in FASTA format. YOU CANNOT MIX SEQUENCE

FORMATS IN THE SAME FILE.

In order to convert the file to the correct format, we can use Seqret.

2) Go to Seqret. Upload the file ‘allbrca1_unaligned.phy’ and leave all other options as they are.

(21)

21

3) The output can be seen in the web browser – you need to download it to the computer. Do this

by right‐clicking on the output link and choose ‘Save Target As’. Call the file

‘brca1_unaligned.fasta’.

4) Open the file in notepad and now add you human sequence from your other file to the file you

have just created (brca1_unaligned.fasta). You can do this using simple copy and paste. Save

the file, which now includes brca1 sequences AND the human sequence as

‘allbrca1_hs_unaligned.fasta’.

Exercise

9:

Creating

a

MSA

using

CLUSTALW

ClustalW is one of many MSA tools. It can be used via a web‐browser or can be installed locally on a server and used on the command line. Today we are to use the web‐browser. 1) Go to the ClustalW web‐page. Upload you file ‘allbrca1_hs_unaligned.fasta’ and run ClustalW. 2) There are four output files: you are interested in the Alignment file. Right click on the link and save to computer.

Exercise

10:

Viewing

alignment

in

BioEdit

Bioedit (and Jalview, which you may have heard of) are not only tools for visualizing MSAs, but also allow you to edit them. In this exercise we will be manipulating the alignment and the order of the

sequences so that the MSA will be able to be used in future programs. For example, ALIGN‐GVD

sets out the requirement that the MSA must be in FASTA format and the Human sequence MUST

be at the TOP of the alignment.

1) Start Bioedit, by going to the Start Menu; All Programs; Bioedit. There may also be a short cut on the desktop. 2) From the file menu, upload your alignment file produced from ClustalW. Make sure you choose the file type ‘All files’ and you are looking in the directory where your file has been saved. Have a look at the alignment using the scroll bar at the bottom. Also explore other features that are available in this editor. For example, shading the alignment according to conservation.

(22)

3) First, we are going to place the human sequence at the top of the file. The human sequence is represented by the accession number: NP_009225. To do this: highlight the sequence, left click

and hold down on the mouse whilst hovering over the selected sequence and move the

accession number (and thus the sequence) to the top of the alignment. Let go of the mouse button. Your human sequence should now be at the top of the alignment.

4) Next, using the alignment printed out from the library of ALIGN_GVD alignments, see if you can

spot any differences between your alignment and the standard. Hint: look specifically at the pufferfish and sea urchin sequences as these are more difficult to align as they are more diverse than the other sequences. Do you think you need to change anything?

There will be a demo on how to do this.

5) Once you are happy with your alignment, save it as a fasta file. To do this: go to File; Save As.

Save file as type FASTA and call it ‘allbrca_clustalw.fasta’.

Exercise

11:

Using

ALIGN

‐

GVD

to

predict

the

effect

of

missense

mutations

1) On the ALIGN‐GVD web‐site, click on the ‘Use Align GVGD’ on menu on LHS.

2) Upload your file ‘allbrca1_clustalw.fasta’ as the MSA file and the file called

‘brca1_mutations.txt’ for the substitutions list.

3) Run ALIGN‐GVD.

Are there any mutations that all likely to interfere with function? As an alternative, try using the alignment supplied ALIGN‐GVD.

Exercise

12:

Displaying

your

MSA

in

ESPript

2.2 (point

of

information/optional)

1) Go to: http://espript.ibcp.fr/ESPript/ESPript/

2) Click on: execute

3) On Main alignment file:: get the correct alignment of all the BRCA1 sequences. Then go to Output layout:

Font 7 Col 65

(23)

23

URLS used (or mentioned in this practical

Others are found in the Bioinformatics_links.txt file. Exploring function using NCBI resources

UCSC Genome Browser http://genome.cse.ucsc.edu/

NCBI http://www.ncbi.nlm.nih.gov/

Pairwise Alignment and Sequence Similarity

NCBI BLAST submission page http://www.ncbi.nlm.nih.gov/BLAST/

NCBI Sequence http://www.ncbi.nlm.nih.gov/

EXPASY translate tool http://au.expasy.org/tools/dna.html

SRS http://srs.ebi.ac.uk/

DNA Sequence Analysis (Primer searching)

Uniprot http://www.ebi.uniprot.org

eprimer3 docs http://bioweb.pasteur.fr/docs/EMBOSS/eprimer3.html

online eprimer3 http://bioweb.pasteur.fr/seqanal/interfaces/eprimer3.html

dna translator http://www.expasy.ch/tools/dna.html

Primer‐BLAST http://www.ncbi.nlm.nih.gov/tools/primer‐blast/

Multiple Protein Sequence Alignment

NCBI http://www.ncbi.nlm.nih.gov/

ClustalW http://www.ebi.ac.uk/clustalw/

Muscle http://phylogenomics.berkeley.edu/cgi‐bin/muscle/input_muscle.py

(24)

Dialign http://bibiserv.techfak.uni‐bielefeld.de/dialign/submission.html Jalview http://www.jalview.org CDD http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml. ESPript2.2 http://espript.ibcp.fr/ESPript/ESPript/ ALIGN‐GVD http://agvgd.iarc.fr/index.php iAltschul, Stephen F., Thomas L. Madden, Alejandro A. Schaffer, Jinghui Zhang, Zheng Zhang, Webb Miller, and David J. Lipman (1997) Nucleic Acids Res. 25:3389‐3402