SlideShare a Scribd company logo
1 of 29
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: ,[object Object],[object Object],[object Object],[object Object]
Past ,[object Object],[object Object],[object Object],QRNA -  A Blend ,[object Object],[object Object],[object Object]
OUTLINE INTERGENIC REGIONS OF ORGANISM OF INTEREST ↓ SEARCH FOR HOMOLOGY ACROSS RELEATED ORGANISMS ↓  PARSE THE ALIGNMENTS WITH CERTAIN CUTOFFS ↓ THE ALIGNMENTS WERE GIVEN AS INPUT FOR THE QRNA ↓ PUTATIVE ncRNA  blastn Perl scripts
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 extractseq –regions @rangefile -separate
GENOME LENGTH COMPARISION OF THE MYCOPLASMA M.gen-   Mycoplasma genetalium M.pne-   Mycoplasma pneumoniae M.pul-   Mycoplasma pulmonis M.gal-    Mycoplasma gallisepticum M.myc- Mycoplasma mycoides M.pen- Mycoplasma penetrans 9,63,879 M.pulmonis 8,16,394 M.pneumoniae 13,58,633 M.penetrans 12,11,703 M.mycoides 580,074 M.genitalium 9,96,422 M.gallisepticum Genome size Organism
MYCOPLASMA  GENOME – INTERGENIC REGION BAR GRAPH SHOWING THE PERCENTAGE OF INTERGENIC REGION IN THE GENOME OF  MYCOPLASMA
PROTEIN TABLE OF THE GENOME ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Protein   Co-ordinates   Intergeinc   Co-ordinates ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],->
CURING ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Starting Ending Length 1 734 734 2762 2845 84 15317 15555 2390 Intergenic region coordiantes which are  more than 50 nucleotides in length GRAPH SHOWING THE CULLING OF THE INTERGENIC SEQUENCES BY THE  C PROGRAMME  THAT SELECTS THE REGIONS WHOSE LENGTH IS GREATER THAN OR EQUAL TO 50 NUCLEOTIDES ONLY
INTERGENIC SEQUENCES ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Intergenic sequences extracted in Fasta format
Similarity Search - WU BLAST 2.0 ,[object Object],[object Object],Table showing the list of databases made and the organisms M.gallisepticum M.genitalium M.mycoides M.penetrans M.pneumoniae M.pulmonis ggpppdb M.mycoides M.gallisepticum M.mycoides M.penetrans M.pneumoniae M.pulmonis gampppdb M.genitalium M.genitalium M.mycoides M.penetrans M.pneumoniae M.pulmonis gempppdb M.gallisepticum Organisms in Database Database Created Organism M.gallisepticum M.genitalium M.mycoides M.penetrans M.pneumoniae ggmpepndb M.pulmonis M.gallisepticum M.genitalium M.mycoides M.penetrans M.pulmonis ggmpepudb M.pneumoniae M.gallisepticum M.genitalium M.mycoides M.pneumoniae M.pulmonis ggmpnpudb M.penetrans Organisms in Database Database Created Organism
Parsing alignments - Factors ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Parsing alignments – QRNA input ,[object Object],[object Object],[object Object],[object Object],[object Object]
QRNA input file ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Parsing Report File ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
No. of blastn hits selected for qrna input GRAPH SHOWING NUMBER OF ALIGNMENTS SELECTED FOR QRNA INPUT FOR EACH GENOME THROUGH THE PERLSCRIPT 560263 430551 154026 360830 44433 53927 No. of alignments
QRNA  –  PARAMETERS ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
QRNA   OUTPUT ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],QRNA   OUTPUT
Number of ncRNA predicted for each organism No. of ncRNAs predicted
PICTURE SHOWING THE LENGTH RANGE OF NON-CODING RNAs. (Vertical bars represent the spread of scores and horizontal bar represent the average) Length Range of Non-coding RNA predicted
Putative Vs Annotated ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
- In Eukaryotes ,[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
CONCLUSIONS ,[object Object],[object Object],[object Object],[object Object],[object Object]
Future   Plans ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ACKNOWLEDGMENTS Dr. Z. A. Rafi Dr. S. Krishnaswamy The Whole SBT family Ministry of Human Recourses Development Department of Education Department of Science and Technology Department of Biotechnology All my classmates

More Related Content

What's hot

Recent advances in CRISPR-CAS9 technology: an alternative to transgenic breeding
Recent advances in CRISPR-CAS9 technology: an alternative to transgenic breedingRecent advances in CRISPR-CAS9 technology: an alternative to transgenic breeding
Recent advances in CRISPR-CAS9 technology: an alternative to transgenic breedingJyoti Prakash Sahoo
 
Getting started with CRISPR: a review of gene knockout and homology-directed ...
Getting started with CRISPR: a review of gene knockout and homology-directed ...Getting started with CRISPR: a review of gene knockout and homology-directed ...
Getting started with CRISPR: a review of gene knockout and homology-directed ...Integrated DNA Technologies
 
Genome editing comes of age
Genome editing comes of ageGenome editing comes of age
Genome editing comes of ageJan Hryca
 
CRISPR: Gene editing for everyone
CRISPR: Gene editing for everyoneCRISPR: Gene editing for everyone
CRISPR: Gene editing for everyoneCandy Smellie
 
F Giordano ScanPAV Analysis Pipeline
F Giordano ScanPAV Analysis PipelineF Giordano ScanPAV Analysis Pipeline
F Giordano ScanPAV Analysis PipelineFrancesca Giordano
 
GENASSIST™ CRISPR & rAAV Genome Editing Tools
GENASSIST™ CRISPR & rAAV Genome Editing ToolsGENASSIST™ CRISPR & rAAV Genome Editing Tools
GENASSIST™ CRISPR & rAAV Genome Editing ToolsCandy Smellie
 
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...Bioo Scientific
 
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...Candy Smellie
 
Rewriting the Genome Using CRISPR and Synthetic Biology
Rewriting the Genome Using CRISPR and Synthetic Biology Rewriting the Genome Using CRISPR and Synthetic Biology
Rewriting the Genome Using CRISPR and Synthetic Biology Integrated DNA Technologies
 
Vector delivery
Vector deliveryVector delivery
Vector deliveryzwiegers
 
Molecular characterization of Pst isolates from Western Canada
Molecular characterization of Pst isolates from Western CanadaMolecular characterization of Pst isolates from Western Canada
Molecular characterization of Pst isolates from Western CanadaBorlaug Global Rust Initiative
 
Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...
Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...
Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...Afnan Zuiter
 
Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...
Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...
Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...eventi-ITBbari
 
Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...
Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...
Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...Thermo Fisher Scientific
 
DNA Methylation: An Essential Element in Epigenetics Facts and Technologies
DNA Methylation: An Essential Element in Epigenetics Facts and TechnologiesDNA Methylation: An Essential Element in Epigenetics Facts and Technologies
DNA Methylation: An Essential Element in Epigenetics Facts and TechnologiesQIAGEN
 
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...Joseph Hughes
 
Genome editing & targeting tools
Genome editing & targeting toolsGenome editing & targeting tools
Genome editing & targeting toolsS Rasouli
 

What's hot (20)

Recent advances in CRISPR-CAS9 technology: an alternative to transgenic breeding
Recent advances in CRISPR-CAS9 technology: an alternative to transgenic breedingRecent advances in CRISPR-CAS9 technology: an alternative to transgenic breeding
Recent advances in CRISPR-CAS9 technology: an alternative to transgenic breeding
 
Getting started with CRISPR: a review of gene knockout and homology-directed ...
Getting started with CRISPR: a review of gene knockout and homology-directed ...Getting started with CRISPR: a review of gene knockout and homology-directed ...
Getting started with CRISPR: a review of gene knockout and homology-directed ...
 
Genome editing comes of age
Genome editing comes of ageGenome editing comes of age
Genome editing comes of age
 
Crispr
CrisprCrispr
Crispr
 
CRISPR: Gene editing for everyone
CRISPR: Gene editing for everyoneCRISPR: Gene editing for everyone
CRISPR: Gene editing for everyone
 
F Giordano ScanPAV Analysis Pipeline
F Giordano ScanPAV Analysis PipelineF Giordano ScanPAV Analysis Pipeline
F Giordano ScanPAV Analysis Pipeline
 
Congtig39
Congtig39Congtig39
Congtig39
 
GENASSIST™ CRISPR & rAAV Genome Editing Tools
GENASSIST™ CRISPR & rAAV Genome Editing ToolsGENASSIST™ CRISPR & rAAV Genome Editing Tools
GENASSIST™ CRISPR & rAAV Genome Editing Tools
 
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...
Bioo Scientific - Reduced Bias Small RNA Library Prep with Gel-Free or Low-In...
 
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...
Genome Editing Comes of Age; CRISPR, rAAV and the new landscape of molecular ...
 
Rewriting the Genome Using CRISPR and Synthetic Biology
Rewriting the Genome Using CRISPR and Synthetic Biology Rewriting the Genome Using CRISPR and Synthetic Biology
Rewriting the Genome Using CRISPR and Synthetic Biology
 
Vector delivery
Vector deliveryVector delivery
Vector delivery
 
Molecular characterization of Pst isolates from Western Canada
Molecular characterization of Pst isolates from Western CanadaMolecular characterization of Pst isolates from Western Canada
Molecular characterization of Pst isolates from Western Canada
 
Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...
Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...
Bioinformatic analysis of the Proanthocyanidin Biosynthetic Pathway in Hawtho...
 
ChIP-seq
ChIP-seqChIP-seq
ChIP-seq
 
Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...
Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...
Ernesto Picardi – Bioinformatica e genomica comparata: nuove strategie sperim...
 
Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...
Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...
Speeding up sequencing: Sequencing in an hour enables sample to answer in a w...
 
DNA Methylation: An Essential Element in Epigenetics Facts and Technologies
DNA Methylation: An Essential Element in Epigenetics Facts and TechnologiesDNA Methylation: An Essential Element in Epigenetics Facts and Technologies
DNA Methylation: An Essential Element in Epigenetics Facts and Technologies
 
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
How to Standardise and Assemble Raw Data into Sequences: What Does it Mean fo...
 
Genome editing & targeting tools
Genome editing & targeting toolsGenome editing & targeting tools
Genome editing & targeting tools
 

Viewers also liked

Bio 127 lec 5 Microbiology: Physiological Requirements of bacteria
Bio 127 lec 5 Microbiology: Physiological Requirements of bacteriaBio 127 lec 5 Microbiology: Physiological Requirements of bacteria
Bio 127 lec 5 Microbiology: Physiological Requirements of bacteriaShaina Mavreen Villaroza
 
Bacterial Physiology and genetics
Bacterial Physiology and geneticsBacterial Physiology and genetics
Bacterial Physiology and geneticsIbo Barznji
 
sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...
sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...
sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...JYOTI DEVENDRA
 
Whole Transcriptome Amplfication from Single Cell
Whole Transcriptome Amplfication from Single CellWhole Transcriptome Amplfication from Single Cell
Whole Transcriptome Amplfication from Single CellQIAGEN
 
Finalized MycoAlert Presentation
Finalized MycoAlert PresentationFinalized MycoAlert Presentation
Finalized MycoAlert PresentationErnie Desmarais
 

Viewers also liked (10)

Pcr protocol
Pcr protocolPcr protocol
Pcr protocol
 
Bacteria
BacteriaBacteria
Bacteria
 
Chloroplast Bibo
Chloroplast BiboChloroplast Bibo
Chloroplast Bibo
 
Bio 127 lec 5 Microbiology: Physiological Requirements of bacteria
Bio 127 lec 5 Microbiology: Physiological Requirements of bacteriaBio 127 lec 5 Microbiology: Physiological Requirements of bacteria
Bio 127 lec 5 Microbiology: Physiological Requirements of bacteria
 
Bacterial Physiology and genetics
Bacterial Physiology and geneticsBacterial Physiology and genetics
Bacterial Physiology and genetics
 
sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...
sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...
sem4-cdna sythesis,pcr,designing primers for pcr, synthesis of genes, shotgun...
 
Mirna 2017
Mirna 2017Mirna 2017
Mirna 2017
 
Whole Transcriptome Amplfication from Single Cell
Whole Transcriptome Amplfication from Single CellWhole Transcriptome Amplfication from Single Cell
Whole Transcriptome Amplfication from Single Cell
 
Micro rna
Micro rnaMicro rna
Micro rna
 
Finalized MycoAlert Presentation
Finalized MycoAlert PresentationFinalized MycoAlert Presentation
Finalized MycoAlert Presentation
 

Similar to Non-Coding RNA Prediction of Mycoplasma by Comparative Genomics

Project report-on-bio-informatics
Project report-on-bio-informaticsProject report-on-bio-informatics
Project report-on-bio-informaticsDaniela Rotariu
 
SAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene ExpressionSAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene ExpressionAashish Patel
 
genomeannotation2013-140127002622-phpapp02.ppt
genomeannotation2013-140127002622-phpapp02.pptgenomeannotation2013-140127002622-phpapp02.ppt
genomeannotation2013-140127002622-phpapp02.pptMohamedHasan816582
 
Bioinformatics.Practical Notebook
Bioinformatics.Practical NotebookBioinformatics.Practical Notebook
Bioinformatics.Practical NotebookNaima Tahsin
 
Microarray biotechnologg ppy dna microarrays
Microarray biotechnologg ppy dna microarraysMicroarray biotechnologg ppy dna microarrays
Microarray biotechnologg ppy dna microarraysayeshasattarsandhu
 
SAGE (Serial analysis of Gene Expression)
SAGE (Serial analysis of Gene Expression)SAGE (Serial analysis of Gene Expression)
SAGE (Serial analysis of Gene Expression)talhakhat
 
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Elia Brodsky
 
International Journal of Engineering Research and Development
International Journal of Engineering Research and DevelopmentInternational Journal of Engineering Research and Development
International Journal of Engineering Research and DevelopmentIJERD Editor
 
RNA Sequencing Research
RNA Sequencing ResearchRNA Sequencing Research
RNA Sequencing ResearchTanmay Ghai
 
Bioinformatics MiRON
Bioinformatics MiRONBioinformatics MiRON
Bioinformatics MiRONPrabin Shakya
 

Similar to Non-Coding RNA Prediction of Mycoplasma by Comparative Genomics (20)

Project report-on-bio-informatics
Project report-on-bio-informaticsProject report-on-bio-informatics
Project report-on-bio-informatics
 
SAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene ExpressionSAGE- Serial Analysis of Gene Expression
SAGE- Serial Analysis of Gene Expression
 
genomeannotation2013-140127002622-phpapp02.ppt
genomeannotation2013-140127002622-phpapp02.pptgenomeannotation2013-140127002622-phpapp02.ppt
genomeannotation2013-140127002622-phpapp02.ppt
 
Bioinformatics.Practical Notebook
Bioinformatics.Practical NotebookBioinformatics.Practical Notebook
Bioinformatics.Practical Notebook
 
bioinformatic.pptx
bioinformatic.pptxbioinformatic.pptx
bioinformatic.pptx
 
Microarray biotechnologg ppy dna microarrays
Microarray biotechnologg ppy dna microarraysMicroarray biotechnologg ppy dna microarrays
Microarray biotechnologg ppy dna microarrays
 
SAGE (Serial analysis of Gene Expression)
SAGE (Serial analysis of Gene Expression)SAGE (Serial analysis of Gene Expression)
SAGE (Serial analysis of Gene Expression)
 
Bioinformatics
BioinformaticsBioinformatics
Bioinformatics
 
Project_702
Project_702Project_702
Project_702
 
31931 31941
31931 3194131931 31941
31931 31941
 
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...
Mastering RNA-Seq (NGS Data Analysis) - A Critical Approach To Transcriptomic...
 
proteome.pptx
proteome.pptxproteome.pptx
proteome.pptx
 
Mirnapcrarray
MirnapcrarrayMirnapcrarray
Mirnapcrarray
 
Genome annotation 2013
Genome annotation 2013Genome annotation 2013
Genome annotation 2013
 
International Journal of Engineering Research and Development
International Journal of Engineering Research and DevelopmentInternational Journal of Engineering Research and Development
International Journal of Engineering Research and Development
 
RNA Sequencing Research
RNA Sequencing ResearchRNA Sequencing Research
RNA Sequencing Research
 
Bioinformatics MiRON
Bioinformatics MiRONBioinformatics MiRON
Bioinformatics MiRON
 
Genome comparision
Genome comparisionGenome comparision
Genome comparision
 
Qi liu 08.08.2014
Qi liu 08.08.2014Qi liu 08.08.2014
Qi liu 08.08.2014
 
Mi rna assays 2012
Mi rna assays 2012Mi rna assays 2012
Mi rna assays 2012
 

Non-Coding RNA Prediction of Mycoplasma by Comparative Genomics

  • 1. 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
  • 2.
  • 3.
  • 4. OUTLINE INTERGENIC REGIONS OF ORGANISM OF INTEREST ↓ SEARCH FOR HOMOLOGY ACROSS RELEATED ORGANISMS ↓ PARSE THE ALIGNMENTS WITH CERTAIN CUTOFFS ↓ THE ALIGNMENTS WERE GIVEN AS INPUT FOR THE QRNA ↓ PUTATIVE ncRNA blastn Perl scripts
  • 5. 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 extractseq –regions @rangefile -separate
  • 6. GENOME LENGTH COMPARISION OF THE MYCOPLASMA M.gen- Mycoplasma genetalium M.pne- Mycoplasma pneumoniae M.pul- Mycoplasma pulmonis M.gal- Mycoplasma gallisepticum M.myc- Mycoplasma mycoides M.pen- Mycoplasma penetrans 9,63,879 M.pulmonis 8,16,394 M.pneumoniae 13,58,633 M.penetrans 12,11,703 M.mycoides 580,074 M.genitalium 9,96,422 M.gallisepticum Genome size Organism
  • 7. MYCOPLASMA GENOME – INTERGENIC REGION BAR GRAPH SHOWING THE PERCENTAGE OF INTERGENIC REGION IN THE GENOME OF MYCOPLASMA
  • 8.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17. No. of blastn hits selected for qrna input GRAPH SHOWING NUMBER OF ALIGNMENTS SELECTED FOR QRNA INPUT FOR EACH GENOME THROUGH THE PERLSCRIPT 560263 430551 154026 360830 44433 53927 No. of alignments
  • 18.
  • 19.
  • 20.
  • 21. Number of ncRNA predicted for each organism No. of ncRNAs predicted
  • 22. PICTURE SHOWING THE LENGTH RANGE OF NON-CODING RNAs. (Vertical bars represent the spread of scores and horizontal bar represent the average) Length Range of Non-coding RNA predicted
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29. ACKNOWLEDGMENTS Dr. Z. A. Rafi Dr. S. Krishnaswamy The Whole SBT family Ministry of Human Recourses Development Department of Education Department of Science and Technology Department of Biotechnology All my classmates