NON-CODING RNA PREDICTION OF CLINICALLY IMPORTANT MYCOPLASMA BY COMPARATIVE GENOMIC ANALYSIS Dissertation submitted to the Madurai Kamaraj University in partial fulfillment for the requirement of Masters of Science in Biotechnology Regn. No:A242009 School of Biotechnology Madurai Kamaraj University Madurai
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NON-CODING RNA PREDICTION OF CLINICALLY
IMPORTANT MYCOPLASMA BY COMPARATIVE
GENOMIC ANALYSIS
Dissertation submitted to the Madurai Kamaraj University in partial fulfillment
for the requirement of Masters of Science in Biotechnology
Regn. No:A242009
School of Biotechnology
Madurai Kamaraj University
Madurai
OBJECTIVES:
• To choose the best possible approach to predict the
ncRNA
• To standardize the procedure required for the
approach selected.approach selected.
• Identification and characterization of the ncRNAs
from clinically important Mycoplasma.
• To form the base for the automization procedure for
the ncRNA prediction.
Past
• Sequence similarity search, Statistical analysis, Transcription signal analysis,
Comparative genomic analysis.
• Existing methods are biased to particular classes of ncRNAs only.
•tRNAscan-SE, Mir-Scan etc.,
QRNA - A BlendQRNA - A Blend
• Secondary structure alone is not statistically significant for the detection of
ncRNAs.
• Important sequences that code for proteins and performing important functions
are conserved across the related organisms.
QRNA was developed to screen the conserved RNA secondary structures from the
background of the other conserved sequences.
OUTLINE
INTERGENIC REGIONS OF ORGANISM OF INTEREST
↓
SEARCH FOR HOMOLOGY ACROSS RELEATED ORGANISMS
↓
PARSE THE ALIGNMENTS WITH CERTAIN CUTOFFS
blastn
Perl scripts
PARSE THE ALIGNMENTS WITH CERTAIN CUTOFFS
↓
THE ALIGNMENTS WERE GIVEN AS INPUT FOR THE QRNA
↓
PUTATIVE ncRNA
PROTEIN CODING REGION→INTERGENIC REGION
.ptt file
↓
Co-ordinates of protein coding regions
↓
Intergenic region co-ordinates
↓↓
Intergenic region co-ordinates
difference > 50 nucleotides
↓
Range file
↓
Intergenic sequence extraction by EMBOSS application
•The predicted ncRNa were searched for similarity against the biochemically characterized ncRNA of Bacteria ( Non-coding RNA database at http://biobases.ibch.poznan.pl/nc, updated 2002)
•Found similar to the Mc_MCS4 ncRNA of Mycoplasma capricolum.
•Mc_MCS4 was already characterized to be having extensive homology with
the eukaryotic U6 snRNA.
•Another motif in one of the putative ncRNA was found to be conserved •Another motif in one of the putative ncRNA was found to be conserved across E.coli, S.typhi, K.pneumoniae as a part of MicF ncRNA in these organsims.
•MicF was characterised to be regulating the expression of OmpF protein in
these organisms.
•Similarity was also found with OxyS ncRNA of E.coli.
•OxyS was found to modulate the expression of various genes in response to
Hydrogen peroxide.
- In Eukaryotes
• Similarity was observed with few miRNAs that were present in the miRNA database (Rfam miRNA registry)
• Same stretch of sequence was present in Human, • Same stretch of sequence was present in Human,
Rat and Mouse miRNA.
• Small stretches of similarity was observed with various ncRNAs playing role in regulation of development also.
Sequences producing High-scoring Segment Pairs: Score P(N) N
hsa-mir-190 MI0000486 Homo sapiens miR-190 stem-loop 91 0.26 1
• Comparative genomic analysis was selected for the ncRNA prediction.
• Procedure for the prediction was standardized.
• One of the putative ncRNA was found to be similar to the already characterized ncRNA from similar to the already characterized ncRNA from the same genus.
• Conserved region of MicF was found to be present in the putative ncRNA also.
• Identification of the eukaryotic miRNA counterpart in Mycoplasma.
Future Plans• To develop programmes for getting the intergenic
region co-ordinates given the protein table file as
input.
• To verify the genuinity of the predictions beyond
the homologous regions found in bacteria.
• To extend the prediction procedure for Eukaryotes.• To extend the prediction procedure for Eukaryotes.
• To develop the procedure required for classification
of the predicted ncRNAs into subclasses.
• To identify the functions of the putative ncRNAs by
searching their effector targets.
• To automize the whole procedure.
ACKNOWLEDGMENTSDr. Z. A. Rafi
Dr. S. Krishnaswamy
The Whole SBT family
Ministry of Human Recourses Development
Department of Education
Department of Science and TechnologyDepartment of Science and Technology