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Bioinformatics Tools for Sequence Analysis

Paper Type: Free Essay Subject: Biology
Wordcount: 2312 words Published: 17th May 2018

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My aim for this project was to take an unknown nucleotide sequences and find using different bioinformatics tools available to predict species and function of my unknown sequence. Alongside this prediction I also aimed to find ancestry relationships between my unknown sequences with known sequences that may have changed overtime. And effectively communicate my results.

To achieve this objective I firstly used the tool BLAST .BLAST uses local and global libraries to identify similarities in my sequence with known sequence in the database. Once on the BLAST homepage I selected NCBI-BLAST 2 nucleotide to identify my nucleotide sequence. On the first run BLAST gave 50 results similar to my sequence of which 18 were 100% positives. At this stage further investigation was needed to obtain the correct identification of my nucleotide sequence. Going with the best result and using translation sequence BLAST was run for a second time but I chose NCBI-BLAST 2 protein. Second BLAST was run in the same as previously mentioned. Second BLAST implicated I had idenifed my nucleotide sequence as being CCAAT/enhancer-binding protein delta OS=Rattus norvegicus GN=Cebpd PE=2 SV=1.

I then went on to use CLUSTAL to produce a multiple sequence alignment of 9 different species which were related to my sequence along with my sequence. CLUSTAL Identified the difference and similarities between my species and the other 9 species and allowed me to use a option BOXSHADE to colour in the conserved residues (on attachment in red).

T-Coffee allowed me to further see which groups of my species were more closely related than others by producing a phylogram tree. The lengths of braches from the ancestry sequence or truck of tree showed how much each species had evolved overtime and from this I found that DANRE had the greatest evolutionary change whereas from CLUSTAL I had interrupted that ALIME had the greatest change in sequence overtime as it had the most change.

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Finally to complete my investigation I chose to find out about the secondary structure of my protein sequence. I used a program PSIPRED. PSIPRED used the highly sensitivity and accuracy of PSIBLAST to produce an output. From this program I found that my sequence mostly made up of helices and coils therefore indicating my structure motif are most likely to be a helices turn helices motif.

Overall I found this bioinformatics project very interesting and allowed me to diverse methods in order to achieve my objective. And general increased my knowledge


Name of software tool (program) used: BLAST

URL of program used: http://www.ebi.ac.uk/Tools/blast/


The objective is to take the assigned primary sequence and compare to the nucleotide and protein library or databases that are held in BLAST. This will help to conclude the primary sequence function and evolutionary relationship.also localise a sequence in the genome and find orthologus genes in different organisms.

Principles of the program’s operation:

Highly fast and sensitive tool picks out similarities in sequence in all reading frames of the nucleotide query and produces a comparison list from known proteins of high comparison to proteins with low comparisons. BLAST compares the sequence in the NCBI database. The similarities can be local using heuristic algorithm or global meaning that the similarities in the sequence are from certain species or family or in different species respectively. This is done by entering a FASTA sequence into the It uses seeding and words to compare with existing known sequences. Every result found is given a score, he higher the score the more similarities between the query and unknown sequence. Firstly the search is done for a word length “W” which most score at least “T”, this shows the sensitivity and speed of the search, when it is compared to a query using a given substitution matrix [www.ncbi.nlm.nih.gov/…/query_tutorial.html]. Once this initial search is complete BLAST then goes onto generate an alignment that’s exceeds “s” threshold. Each result is given an E value which indicates how significant the result and is expected by chance.

Results obtained (attach edited printout(s) if appropriate):

My sequence from BLAST suggested it was CCAAT/enhancer-binding protein delta from the organism Rattus norvegicus which is a rat. My protein is a transcription factor CELF

Identify 100%, E value=1e-155, positives 100%

My sequence recognises two motifs CCAAT homology and enhanced core homology. It is believed to be a transcriptional factor which activates the genes involved with immune response and inflammatory responses.

Conclusions drawn: BLAST was a fairly straight forward tool to use. Interrupting results from BLAST was comparatively simple as percentages were used along side E values to show how significant the results were to my sequence.



Name of software tool (program) used: T-coffee

URL of program used: http://www.ebi.ac.uk/Tools/t-coffee/index.html


To create a phylogram tree from the same species used to from BLAST 2 results to produce a multiple alignment sequence in CLUSTAL W

Principles of the program’s operation:

When an alignment is given to T-coffee it starts by comparing pair wise comparisons. This is done by taking all the pair sequences possible and comparing them producing global sequence with CLUSTAL W. a lalign comparison is also made between these same pair wise sequences. These results are then compared to the local and global library. This allows T coffee using progressive algorithm to produce a multiple alignment sequence with the most likely pair wise alignments found from the libraries. The phylogram tree allows you to see the development or evolution changes in species and differences in terminal taxa.

Results obtained (attach edited printout(s) if appropriate):

Length of the branches represents the evolutionary divergence between my selected species. My results show that all the species have an ancestry.

Conclusions drawn:

The braches lengths are proportional to the amount of evolution of the particular species for it ancestry sequence. From the common ancestry genes/sequence, visible on the attachment, Ailme had less evolutionary change with a distance of 0.00609 whereas the rat and mouse had the same initial change but then over time the mouse had a greater change than the rat with distances of 0.00750 for the rat and 0.02250 for the mouse. The greatest change accounted by the phylogram tree was of the Danre having a distance of 0.05786. Danre initially had common evolutionary change with the chick, xenla and salsa and from all the species had the grestest evolutionary change.the human,ailme,mouse and rat are more closely related as the braches from the tree are closer together. tou can also see a close relationship between salsa,danre,chick and xenla but the bovin even though it had a common ascestry sequence is not closely related to any of the other species The phylogram tree I believe gave a more quantitative analysis and from this visual analysis it was easier to interrupt which species had actually had the most evolutionary change.



Name of software tool (program) used: PSIPRED

URL of program used: http://bioinfadmin.cs.ucl.ac.uk/psipred/psiform.html

Objective: to use my protein sequence obtained from BLAST and run this amino acid sequence in PSI PRED to produce a secondary structure of my sequence indicating helices, coil and strands.

Principles of the program’s operation:

PSIPRED use analysis output from PSI-BLAST by means of two feed forward neural networks. Sequence is entered into the program by a simple single amino acid letter format or a FASTA format. The program returns results as an email. the results in the email are represented graphical and the positions of the helices, coils and strands are given above the users sequence. The results are also show the prediction line and confidence line. The user can either have used PSIPRED method or two other methods MEMSAT a transmembrane topology prediction or GenTHREADER which is based on field recognition.

Results obtained (attach edited printout(s) if appropriate):

The prediction line has the letter H,C and E donating which amino acid were involved forming a helix, coil and extended or stand respectively. from my results (on attachment) you can see that amino acids in positions 1 to 50 were involved in a coil structure alongside amino acids in positions 54-61,65-79,93-145,150-191 and 254-269. I only had one strand or extended region within my sequence which was at position 146-149 which was not long only 4 amino acids were involved. There were 6 helices in total present within my sequence they all varied in size from long and short length helices. my confidence of prediction was fairly high but at position 6 I had a confidence of 0.

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Conclusions drawn: I had met my objective to determine the secondary structure of my protein sequence that was obtained once I had run BLAST search for a second time. Although my confidence of prediction was fairly high but still with a few low number I would have used another server that did the same prediction to compare my results. Currently PSIPRED has the highest accuracy of about 70-80% for predicating secondary structure there is still room for improvement in the near further. the results from this bioinformatics program was easy to interpret and allowed me to see which amino acid sequences were involved in producing the helix, coil and strand. This was one advantage for this program and also another advantages was that it was clearly labelled and provided a key to help user to interpret there results slightly easier.


Name of software tool (program) used: CLUSTAL W

URL of program used: http://mobyle.pasteur.fr/cgi-bin/portal.py?form=clustalw-multialign


Is to perform a multiple alignment of several species obtained from BLAST to find patterns and detect or show homology between my protein and exciting families.

Principles of the program’s operation:

This program works in three approaches firstly it calculates a matrix of pairwise distances this is done from the pairwise alignments of the producing pairwise alignment. Distance is calculated by comparing non-gapped positions to mismatches in the sequences and divided by total number of non-gapped positions. From this CLUSTAL can also produce a true phylogenetic tree which show the evolutionary relationship of the different species entered into the program. The phylogenetic tree can then be used to give a progressive alignment. An alignment score is also calculated this can be done either slow which is more accurate or by Wilbur and lipman which is fast but approximate.

Results obtained (attach edited printout(s) if appropriate):

From my multiple alignment sheet (attached) there are three positions which are fully conserved between my species this is denoted by “*” these are two aspartic acid and a glutamic acid. 6 positions indicated by “:” is suggesting that these regions between my sequence and the other 9 species are different but still highly conserved. These residues are very similar and carry similar properties. Position with a dot suggested theses residues in this column are more or less similar. “-” represent evolutionary change overtime. The most observable evolutionary change was between the species of rat and species of PANDA (ailme).




uniprot|D2HVY0|D2HVY0_AILME ————————————————–






Conclusions drawn:

From my results I conclude that since there are only 3 conserved residues detected there was a vast amount of mutation and evolutionary changes between the species over time. As there are fewer conversed residue over the multiple sequences alignment it suggest that my sequence is not likely to be functionally related to the species detected by BLAST but this does not mean they are not related. As you can see above and on the attached sheet PANDA (ailme) does not have any residues similar to my sequence or the other 8 species in this region but this pattern can also been seen through most of the protein sequence against my found sequence. .


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