NIBB 2010INTERNSHIPPROGRAM
Prepared byWalaa A. Adly
Under supervision of:Prof. Dr. Ikuo Uchiyama
Genome informatics laboratory.

ReportI am Walaa A. Adly, I am from Egypt, a graduate student of faculty ofAgriculture, Cairo University, Biotechnology group, also I have graduatedfrom information technology institute (iti), Bioinformatics department as
fellowship, and finally I was an international intern in NIBB 2010
internship. Really it would be my pleasure to have the chance to be aninternational intern of this respectable institute in Okazaki city in Japan,
which was very different place from where I have always lived. Personally, I
have enjoyed visiting and staying in it. In addition, I had the chance to knowsuch valuable civilization, beside that the Mishima lodge, the place I stayed
in it, was very comfortable and safe, which I didn’t faced any problems ortroubles.
My internship helped me to gain a lot of experiences and skills in avariety of biological fields especially in genome informatics field as well as
being able to communicate with different culture and society. My work in
the laboratory was very interesting and enthusiastic which we able tocombine biological sciences with informational technology in addition toimplement scientific programs such as detect genes responsible for diseases
by using highly qualified software. The team was very helpful and friendly.
Really I liked all people and respected them. Also I would like to thank myprofessor for his support to me which taught me how I think about things
differently.
I am honor and proud of developing my experience and skills of thescientific research in the ideal environment for creativity. Finally I hope tostudy and complete my PhD. in this innovative and pioneer climate. Now I
will summarize my work in genome informatics laboratory.

1Introduction
Comparative genome analysis has played an important role in understanding
microbial life from a genomic prospective. Also in genomics context andgene functions are conserved between orthologs, whereas paralogs generally
acquire different functions; therefore ortholog identification is crucial foridentyfing a pair of genes that have the same function in different genomes,
especially in genome annotation. Here we will make an orthologous tablecontaining genes in a specified pathway by using RECOG system to figureout the relationship between orthologous groups involved in this pathway(1).

Background
RECOG stands for Research Environment for Comparative Genomic. It is asoftware to integrate a lot of methods of comparative genomics andincorporate the knowledge of individual genomes and gene functions into
one analysis. RECOG software has a client – server architecture which allow
users to choose a set of organisms to compare and constructs orthologous
groups among those organisms and predict the function related (1).
MBGD database stands for microbial genome database for comparativeanalysis. It provides orthology information for every gene stored in it, whichidentifies orthologous relationships among the genomes. MBGD database is
the basis of the RECOG system and we can access this database through it(1).

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The first week
I downloaded the RECOG software and started to use it to make someanalysis, through which I learned how to predict the new pathway function
using orthologous table from the software.
Functions of RECOG system:
1- Phylogenetic profile:
Phylogenetic pattern is defined as a binary vector for each orthologous
group that represents the presence or absence of genes in eachgenome, and in this method, we can predict the related function, if
two orthologous groups have similar phylogenetic patterns.
2- Domain fusion method:
We can find two genes are fused to each other in one gene. If you findsuch a pattern, you can infer that these two genes have a relatedfunction.
3- Gene neighborhood method:
Genes appeared in close position in two genomes in multiple
chromosomes then we can predict that they have a related function.
4- Neighboring gene cluster function in the program:
Identifies a set of genes that are located in both orthologous table andgenomic sequence and assign the same color to each gene in that set .
5- Zoom: to see a detailed map of a specific orthologous group for
identifying gene names.
6- Search: to search for keywords in cluster annotation and geneannotation.
7- Connect to MBGD database where we can click on compare maps andidentify conserved arrangement of the same orthologous groups
between different organisms. Choose gene list from database &3

choose ORFID to display a comparative genome region map
containing only the specified genes.
Task 1:Make list of genes with species names involved in TCA cycle:
Gene context and gene fusion are important in function prediction. If there
are two genes with the same color and closed to each other, then this is acase of gene order conservation and they may have the same function. And
if there are two genes enclosed with frame, which indicates there is a genefusion and thus they have a related function and work together.
1- I prepared a list of cog id of TCA cycle from reference (2).Then I
downloaded a file containing all COG entries from FTP site. I
prepared a file containing the correspondence between species nameof COG and that of MBGD database.
2- I wrote a Perl script which executed these files and finally produced
an output file that contains a list of genes with species names involved
in TCA cycle and we will used it in another Perl script to be used in
RECOG system.
3- This output file can be incorporated into the RECOG system byimporting the gene set file to define a gene set, which is used to filter
the orthologous table by creating new gene set combination.
Task 2:Make gene property file:
I wrote Perl script which used the downloaded COG file, the list of
COG_ID of TCA cycle. After that I can incorporate the gene property
file into RECOG system by import it. The imported properties aredisplayed in the gene information window.
Task 3:After filtering out using the TCA cycle gene set generated in task 1 on
the RECOG system, I examined the adjacency of genes on thechromosome using the neighboring gene cluster function to see to4what extent gene neighborhood relationships can be observed in thispathway and summarized the result.

A snapshot of the RECOG window that showing the adjacency of genes on thechromosome using the neighboring gene cluster function.

Task 4:Make Cluster property file:
Aim: to make correspondence between RECOG ortholog id and COG id
automatically. For this purpose, maximally assigned COGID is identified foreach orthologous group.
Task 5:Make analysis to Amino acid biosynthesis in Purine which explain therelationship between orthologous groups
Example: Gamaproteobacteria, Actinobacteria and Bacilli.

5Gammaproteobacteria

Actinobacteria

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Bacilli

Observation:
Gammaproteobacteria:
I observed that limited number of genes is clustered
Actinobacteria:
I observed that more genes are clustered than Gammaproteobacteria, butthey are separated into multiple small clusters.
Bacilli:
I observed that larger clusters of genes are existed.

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The second week
Background:
KEGG represents molecular functions using pathway map diagram which
provides a hierarchal gene function classification scheme that is much
simpler than GO which is more useful for microbial genome annotationand comparison. Also COG database is clusters of orthologous group in
microbial comparative genomics. (1)The task:
Use KEGG database and use gene property file and cluster property fileand list of KEGG database which obtained from KEGG website (FTP)and introduce them to RECOG system. Rewrite Perl scripts for using
KEGG database instead of the COG database.

A snapshot of RECOG system showing the orthologous table of genes in TCA cycle
according to KEGG database.

The result:
Related genes have the same orthologous pattern, located in the chromosome
across to each other. Also phylogenetic pattern clustering based on thepattern occurrence in each orthologous group within a genome.
8The third week
In this week I prepared & presented two presentations. The first was
titled “Bioinformatics Algorithms” from book “An introduction toBioinformatics Algorithms”(3)and I presented the second chapter“Algorithms and complexity” and defined the meaning of Algorithms andits types, the differences between biological algorithms & computer
algorithms, the difference between iterative and recursive algorithms as
Recursive algorithms can often be rewritten to use iterative loops instead,and vice versa; it is a matter of clarity that dictates which technique is easier
to use. Also I presented different algorithm design techniques likeExhaustive search or Brute force Algorithm, Branch and Bound Algorithms,
Greedy Algorithm, Dynamic programming Algorithm, Divide and conquer
Algorithm, Machine learning Algorithm and Randomized Algorithm.

The second presentation was titled “Scalar Data of Perl programinglanguage” from book “Learning Perl Book” (4). I presented the definition ofScalar and the difference between Scalar numbers & Scalar strings, Single –Quoted String Literals & Double –Quoted String Literals. Also the definitionof Scalar Variables with different operators, and finally the if Control
structure & the while control structure with scalars.

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The fourth week
Background:Some species have the same content of pathogenic element; wecompared different strains but closely related species. We compared suchstrains of some species & identified the differences among strains (wecompared species included in the same genus).
Core genome analysis is a useful approach for comparison of closely
related genomes. Core genome is the conserved region among the samespecies but each strain has the additional sequences in each variable region
so the difference between strains can be found in variable region. Genes
related to pathogenicity is often found in variable region.
Variable region often has different characteristics from core region
because prokaryotes often acquire new sequences from horizontal genetransfer. For example, the following characters may be different between
variable and core regions.
Example: Staphylococcal food poisoning, methicillin resistant
staphylococcus aureus.
Method:Identify variable region and fix region among Staphylococcus aureus
genomes. For this purpose, first we identified core region based on the orderof orthologous genes among strains using RECOG system with domclust
and core genome alignment programs. Then, we defined variable region as
genes that are not included in the core region and are not conserved
universally and listed what kind of genes are included in the variable region.
We characterized functions of genes in the variable region by using gene
ontology database that defines various gene functions in a hierarchal
manner. We used BinGO, a plugin of the CytoScape software, for knowing a
set of what kind of functions (in terms of GeneOntologyID) are enriched in
the variable region of S. aureous.
10The fifth week
Introduction:
Bacterial genome has a horizontal gene transfers, and some genes havedifferent codon usage so we can examine the codon usage to identify
horizontally transferred genes. There are many factors for determiningcodon usage such as GC content. Codon usage is generally constant within
species and often correlated with the number of tRNAs in the cell for
efficient translation. If codon preference is similar throughout one genome
so this is efficient way but if codon preference is different between genes in
one genome this is not efficient way.
Aim:
analyzing flexible region using gene ontology and observing different
aspects of flexible region by using CGAT software which stands for
Comparative Genome Analysis Tool to compare nucleotide sequences
instead of protein sequences. Also CGAT has features to calculate someproperties such as codon usage and GC content and it is client server
architecture.
We used Mackintosh machine to install the CGAT server
Results:
We compared Staphylococcus aureus COL (sac) and Staphylococcus
aureus ED98 (sad) strains using CGAT. We found a highly repetitive region
between sac (400174-581424+) and sad (338351-519601+). By examining
the paralogous relationships within this region using the RECOG system I
found that : In the species of staphylococcus aureus COL (400174581424+), all genes from SACOL0481 to SACOL0486 have similarity toeach other, which corresponds to the paralog cluster (homcluster id 4)
according to the RECOG system, and also with comparison withstaphylococcus aureus ED98 (338351-519601+), I found all genes from
SAAV_0379
to SAAV_0388 have similarity to each other which11correspond to the same paralog cluster in the RECOG system . The numberof paralogous genes are changed between strains.
Next we examined the phylogenetic relationship for each paralogous
group (homcluster id 4) in the RECOG system to check whether there is asignificant difference in the topologies of phylogenetic trees among them.
Conclusion:
After checking the tree topologies manually, we cannot find any clear
difference among tree topologies. Therefore, in this case, we cannot
conclude that recombination has a large effect on the phylogenetic tree
topologies of each orthologous group in this paralog cluster.

position: 290414 Sac – sab12

Position: 475561 in sac and 410636 in sad

Position

485542 in sac and 421735 in sad13

References(1) Prof. Dr. Ikuo Uchiyama, Book chapter, “Functional inference inmicrobial genomics based on large-scale comparative analysis”, in“Protein function prediction for omics era”, Kihara, D. ed., Springer,in press.
(2) Eugene V.Koonin and Michael Y.Galpern, Reference book “Sequence– Evolution – computation function approaches in comparativeGenomes.”(3) NEIL C.JONES AND PAVEL A.PEVZNER, Reference book
“An introduction to Bioinformatics Algorithms”(4) Randal L. Schwartz, Tom Phoenix & Briand foy. Reference book
“Learning Perl Book”

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