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NON-CODING   RNA   PREDICTION   OF   CLINICALLY   IMPORTANT   MYCOPLASMA   BY   COMPARATIVE   GENOMIC   ANALYSIS Dissertat...
OBJECTIVES: <ul><li>To choose the best possible approach to predict the ncRNA </li></ul><ul><li>To standardize the procedu...
Past <ul><li>Sequence similarity search, Statistical analysis, Transcription signal analysis, Comparative genomic analysis...
OUTLINE INTERGENIC REGIONS OF ORGANISM OF INTEREST ↓ SEARCH FOR HOMOLOGY ACROSS RELEATED ORGANISMS ↓  PARSE THE ALIGNMENTS...
PROTEIN CODING REGION ->INTERGENIC REGION .ptt file  ↓ Co-ordinates of protein coding regions ↓ Intergenic region co-ordin...
GENOME LENGTH COMPARISION OF THE MYCOPLASMA M.gen-   Mycoplasma genetalium M.pne-   Mycoplasma pneumoniae M.pul-   Mycopla...
MYCOPLASMA  GENOME – INTERGENIC REGION BAR GRAPH SHOWING THE PERCENTAGE OF INTERGENIC REGION IN THE GENOME OF  MYCOPLASMA
PROTEIN TABLE OF THE GENOME <ul><li>Mycoplasma genitalium G37 complete genome - 0..580074 </li></ul><ul><li>480 proteins <...
Protein   Co-ordinates   Intergeinc   Co-ordinates <ul><li>735 1829 </li></ul><ul><li>1829  2761 </li></ul><ul><li>2846  4...
CURING <ul><li>Raw intergenisc coordinates </li></ul><ul><li>Starting  Ending  Length </li></ul><ul><li>1  734  734 </li><...
INTERGENIC SEQUENCES <ul><li>>L43967_2762_2845 Mycoplasma genitalium G37 intergenic sequence </li></ul><ul><li>AAAACCTTTCA...
Similarity Search - WU BLAST 2.0 <ul><li>Six genome databases were made each excluding one organism </li></ul><ul><li>Inte...
Parsing alignments - Factors <ul><li>Perl script is used to parse the blast alignments  </li></ul><ul><li>blastn2qrnadepth...
Parsing alignments – QRNA input <ul><li>Perl script generates various files </li></ul><ul><ul><li>QRNA input file :  filen...
QRNA input file <ul><li>>L43967_15317_15555-1>179-Mycoplasma </li></ul><ul><li>ACCCTCAACCTCCTGAGTGCAAATCAGGTGCTCTATCAGTTGA...
Parsing Report File <ul><li>FILE:  genblast </li></ul><ul><li>DIR:  /home/kalyankpy/coput2/blast// </li></ul><ul><li>FIRST...
No. of blastn hits selected for qrna input GRAPH SHOWING NUMBER OF ALIGNMENTS SELECTED FOR QRNA INPUT FOR EACH GENOME THRO...
QRNA  –  PARAMETERS <ul><li>Scanning window approach </li></ul><ul><ul><li>Window =150 nt; Extension = 50 nt </li></ul></u...
QRNA   OUTPUT <ul><li>#--------------------------------------------------------------------- </li></ul><ul><li>#  qrna 2.0...
<ul><li>length alignment: 150 (id=61.33) (mut=32.67) (gap=6.00)(sre_shuffled) </li></ul><ul><li>posX: 0-149 [0-145](146) -...
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 repr...
Putative Vs Annotated <ul><li>The predicted ncRNa were searched for similarity against the biochemically characterized ncR...
- In Eukaryotes <ul><li>Similarity was observed with few miRNAs that were present in the miRNA database (Rfam miRNA regist...
<ul><li>Sequences producing High-scoring Segment Pairs:  Score  P(N)  N </li></ul><ul><li>hsa-mir-190 MI0000486 Homo sapie...
<ul><li>>Hs_NTT </li></ul><ul><li>Length = 17,572 </li></ul><ul><li>Plus Strand HSPs: </li></ul><ul><li>Score = 116 (23.5 ...
CONCLUSIONS <ul><li>Comparative genomic analysis was selected for the ncRNA prediction. </li></ul><ul><li>Procedure for th...
Future   Plans <ul><li>To develop programmes for getting the intergenic region co-ordinates given the protein table file a...
ACKNOWLEDGMENTS Dr. Z. A. Rafi Dr. S. Krishnaswamy The Whole SBT family Ministry of Human Recourses Development Department...
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  1. 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. 2. OBJECTIVES: <ul><li>To choose the best possible approach to predict the ncRNA </li></ul><ul><li>To standardize the procedure required for the approach selected. </li></ul><ul><li>Identification and characterization of the ncRNAs from clinically important Mycoplasma. </li></ul><ul><li>To form the base for the automization procedure for the ncRNA prediction. </li></ul>
  3. 3. Past <ul><li>Sequence similarity search, Statistical analysis, Transcription signal analysis, Comparative genomic analysis. </li></ul><ul><li>Existing methods are biased to particular classes of ncRNAs only. </li></ul><ul><ul><li>tRNAscan-SE, Mir-Scan etc., </li></ul></ul>QRNA - A Blend <ul><li>Secondary structure alone is not statistically significant for the detection of ncRNAs. </li></ul><ul><li>Important sequences that code for proteins and performing important functions are conserved across the related organisms. </li></ul><ul><li>QRNA was developed to screen the conserved RNA secondary structures from the background of the other conserved sequences. </li></ul>
  4. 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. 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. 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. 7. MYCOPLASMA GENOME – INTERGENIC REGION BAR GRAPH SHOWING THE PERCENTAGE OF INTERGENIC REGION IN THE GENOME OF MYCOPLASMA
  8. 8. PROTEIN TABLE OF THE GENOME <ul><li>Mycoplasma genitalium G37 complete genome - 0..580074 </li></ul><ul><li>480 proteins </li></ul><ul><li>Location Strand Length PID Gene Synonym Code COG Product Product </li></ul><ul><li>735..1829 + 364 3844620 MG001 - - - (dnaN) </li></ul><ul><li>1829..2761 + 310 1045670 MG002 - - - dnaJ 2846..4798 + 650 1045671 MG003 - - - (gyrB) </li></ul><ul><li>4813..7323 + 836 1045672 MG004 - - - (gyrA) </li></ul><ul><li>7295..8548 + 417 1045673 MG005 - - - (serS) 8552..9184 + 210 1045674 MG006 - - - (tmk) </li></ul><ul><li>9157..9921 + 254 1045675 MG007 - - - hypothetical </li></ul><ul><li>9924..11252 + 442 1045676 MG008 - - - (tdhF) </li></ul><ul><li>…… …… .. … ….. ……….. ……… .. .. .. … </li></ul>
  9. 9. Protein Co-ordinates Intergeinc Co-ordinates <ul><li>735 1829 </li></ul><ul><li>1829 2761 </li></ul><ul><li>2846 4798 </li></ul><ul><li>4813 7323 </li></ul><ul><li>7295 8548 </li></ul><ul><li>8552 9184 </li></ul><ul><li>9157 9921 </li></ul><ul><li>…… . ……. </li></ul><ul><li>1 734 </li></ul><ul><li>2762 2845 </li></ul><ul><li>4799 4812 </li></ul><ul><li>7224 7294 </li></ul><ul><li>8549 8551 </li></ul><ul><li>9183 9156 </li></ul><ul><li>…… . ……. </li></ul>->
  10. 10. CURING <ul><li>Raw intergenisc coordinates </li></ul><ul><li>Starting Ending Length </li></ul><ul><li>1 734 734 </li></ul><ul><li>2762 2845 84 </li></ul><ul><li>4799 4812 14 </li></ul><ul><li>7324 7294 -29 </li></ul><ul><li>8549 8551 3 </li></ul><ul><li>9185 9156 -28 </li></ul><ul><li>9922 9923 2 </li></ul><ul><li>11253 11251 -1 </li></ul><ul><li>12041 12068 28 </li></ul><ul><li>12726 12701 -24 </li></ul><ul><li>13566 13569 4 </li></ul><ul><li>14434 14395 -38 </li></ul><ul><li>15317 15555 239 </li></ul>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
  11. 11. INTERGENIC SEQUENCES <ul><li>>L43967_2762_2845 Mycoplasma genitalium G37 intergenic sequence </li></ul><ul><li>AAAACCTTTCATTTTTAATGTGTTATAATTATTTGTTATGCCATAAATTTAGTTTGTGGC </li></ul><ul><li>AAAAGCTTCTGTACTGTTTATTTA </li></ul><ul><li>>L43967_15317_15555 Mycoplasma genitalium G37 intergenic sequence </li></ul><ul><li>ACCCTCAACCTCCTGAGTGCAAATCAGGTGCTCTATCAGTTGAGCTACATCCCCATTATT </li></ul><ul><li>GGTGGAAGTAAATGGACTTGAACCATCGACCTCACCCTTATCAGGGGTGTGCTCTAACCA </li></ul><ul><li>ACTGAGCTATACTTCCAAGCATAATCCTAAGGGTATTTAACTAATTATTATAACAATTTT </li></ul><ul><li>AATTTAACCAAAATACCCCTCGAATTTTAACAGTTTTTATAATCAAAACAGCTAATTTT </li></ul><ul><li>>L43967_19760_19824 Mycoplasma genitalium G37 intergenic sequence </li></ul><ul><li>ATAAATTTAATAGTGTTGAAAGACAAACATTATTAATTTTTGATCAGCTAAATAAAACAA </li></ul><ul><li>AGCAA </li></ul><ul><li>>L43967_20356_20543 Mycoplasma genitalium G37 intergenic sequence </li></ul><ul><li>CTCAAAAAACTAATACATCAAACTTCAACCGTTTACTTTTTTATGAACAAGCACTACAAA </li></ul><ul><li>GGTTTTATGAAGAATTATTTCAAATAGATTATTTAAGAAGATTTGAAAACATTCCCATTA </li></ul><ul><li>AAGATAAGAATCAAATTGCGCTTTTTAAAACTGTTTTTGATGATTACAAAACCATTGATT </li></ul><ul><li>TAGCAGAA </li></ul><ul><li>………………………………………………………………………………………………………… .. </li></ul>Intergenic sequences extracted in Fasta format
  12. 12. Similarity Search - WU BLAST 2.0 <ul><li>Six genome databases were made each excluding one organism </li></ul><ul><li>Intergenic sequences of each organism were searched for similarity (blastn) against the database which doesn’t consist the organisms genome </li></ul>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
  13. 13. Parsing alignments - Factors <ul><li>Perl script is used to parse the blast alignments </li></ul><ul><li>blastn2qrnadepth.pl is used to parse the alignments. </li></ul><ul><li>Factors considered in parsing </li></ul><ul><ul><li>I trimming </li></ul></ul><ul><ul><ul><li>Evalue </li></ul></ul></ul><ul><ul><ul><li>Minimum and Maximum Identity of alignments </li></ul></ul></ul><ul><ul><ul><li>Length of the alignment </li></ul></ul></ul><ul><ul><li>II trimming </li></ul></ul><ul><ul><ul><li>Score </li></ul></ul></ul><ul><ul><ul><li>Depth of alignments </li></ul></ul></ul><ul><ul><ul><li>Shift </li></ul></ul></ul>
  14. 14. Parsing alignments – QRNA input <ul><li>Perl script generates various files </li></ul><ul><ul><li>QRNA input file : filename.q file </li></ul></ul><ul><ul><ul><li>It is a collection of sequences in fasta format, where two sequences are the two component of an alignmnet with gaps left in place. </li></ul></ul></ul><ul><ul><li>Parsing report file : filename.q.rep </li></ul></ul><ul><ul><ul><li>It is a report of the blastn alignment that have been pruned in the process of creating the QRNA input file. </li></ul></ul></ul>
  15. 15. QRNA input file <ul><li>>L43967_15317_15555-1>179-Mycoplasma </li></ul><ul><li>ACCCTCAACCTCCTGAGTGCAAATCAGGTGCTCTATCAGTTGAGCTACATCCCCATTATT </li></ul><ul><li>GGTGGAAGTAAATGGACTTGAACCATCGACCTCACCCTTATCAGGGGTGTGCTCTAACCA </li></ul><ul><li>ACTGAGCTATACTTCCAAGCATAATCCTAAGGGTAT-TTAACTA-ATTATTATAACAATT </li></ul><ul><li>T </li></ul><ul><li>>gb-U00089--19096>19275-Mycoplasma </li></ul><ul><li>ACCCTCAACCTCCTGAGTGCAAATCAGGTGCTCTATCAGTTGAGCTACATCCCCATTATT </li></ul><ul><li>GGTGGAAGTAAATGGACTTGAACCATCGACCTCACCCTTATCAGGGGTGTGCTCTAACCA </li></ul><ul><li>ACTGAGCTATACTTCCAGGCAAAATCTTC-GTACAGGTTCGCTTCATAATTATATTAATT </li></ul><ul><li>T </li></ul><ul><li>>L43967_19760_19824-5<65-Mycoplasma </li></ul><ul><li>TTGCTTTGTTTTATTTAGCTGATCAA-AAATTAATAATGTTTGTCTTTCAACACTATTAA </li></ul><ul><li>AT </li></ul><ul><li>>emb-BX293980.1--57200>57261-Mycoplasma </li></ul><ul><li>TTGTTTTGTTTTATTTAATTGATCAATAAATTGATTTAGTTTATCTTTATTTATTAATAA </li></ul><ul><li>AT </li></ul>
  16. 16. Parsing Report File <ul><li>FILE: genblast </li></ul><ul><li>DIR: /home/kalyankpy/coput2/blast// </li></ul><ul><li>FIRST TRIMMING </li></ul><ul><li>Minimum length = 1 </li></ul><ul><li>Maximum Evalue = 0.01 </li></ul><ul><li>Minimum %id = 0 </li></ul><ul><li>Maximum %id = 100 </li></ul><ul><li>SECOND TRIMMING </li></ul><ul><li>Alignments culled by = SC </li></ul><ul><li>Depth of alignments = 1 </li></ul><ul><li>shift = 1 </li></ul><ul><li>113-QUERY: L43967_546708_546877 Mycoplasma genitalium G37 intergenic sequence </li></ul><ul><li>Total # alignments: 1121 After First trimming: 88 After Second trimming: 2 </li></ul><ul><li>57-QUERY: L43967_325878_326027 Mycoplasma genitalium G37 intergenic sequence </li></ul><ul><li>Total # alignments: 152 After First trimming: 3 After Second trimming: 3 </li></ul><ul><li>……………………………………………………………………………………………… . </li></ul><ul><li>……………………………………………………………………………………………… . </li></ul><ul><li>Total #Queries 122 </li></ul><ul><li>Total #Alignments 53927 ave_len = 309.5 </li></ul><ul><li>After first trimming 18851 ave_len = 552.6 </li></ul><ul><li>After second trimming 386 ave_len = 404.2 </li></ul>
  17. 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. 18. QRNA – PARAMETERS <ul><li>Scanning window approach </li></ul><ul><ul><li>Window =150 nt; Extension = 50 nt </li></ul></ul><ul><li>Maximum length 9999999 </li></ul><ul><li>Local viterbi algorithm </li></ul><ul><li>RIBOPROB matrix </li></ul><ul><li>Shuffling the sequence maintaining the composition </li></ul>
  19. 19. QRNA OUTPUT <ul><li>#--------------------------------------------------------------------- </li></ul><ul><li># qrna 2.0.1 (Tue Aug 19 11:30:55 CDT 2003) using squid 1.5m (Sept 1997) </li></ul><ul><li>#--------------------------------------------------------------------- </li></ul><ul><li># PAM model = BLOSUM62 </li></ul><ul><li>#--------------------------------------------------------------------- </li></ul><ul><li># RNA model = /mix_tied_linux.cfg </li></ul><ul><li># RIBOPROB matrix = /RIBOPROB85-60.mat </li></ul><ul><li>#--------------------------------------------------------------------- </li></ul><ul><li># seq file = /home/kalyankpy/perlscriptresult/genblast.q </li></ul><ul><li># #seqs: 772 (max_len = 3420) </li></ul><ul><li>#--------------------------------------------------------------------- </li></ul><ul><li># window version: window = 150 slide = 50 -- length range = [0,9999999] </li></ul><ul><li>#--------------------------------------------------------------------- </li></ul><ul><li># 1 [both strands] (sre_shuffled) </li></ul><ul><li>>L43967_1_734-90>722-Mycoplasma (664) </li></ul><ul><li>>gb-U00089--130>767-Mycoplasma (664) </li></ul><ul><li>length of whole alignment after removing common gaps: 664 </li></ul><ul><li>Divergence time (variable): 0.401 </li></ul><ul><li>[alignment ID = 61.75 MUT = 29.67 GAP = 8.58 </li></ul><ul><li>………………………………………………………… ……………… .. ( CONTD..) </li></ul>
  20. 20. <ul><li>length alignment: 150 (id=61.33) (mut=32.67) (gap=6.00)(sre_shuffled) </li></ul><ul><li>posX: 0-149 [0-145](146) -- (0.42 0.08 0.06 0.43) </li></ul><ul><li>posY: 0-149 [0-144](145) -- (0.37 0.11 0.06 0.46) </li></ul><ul><li>L43967_1_734-90 TTAATTTTATTAAAACTATAACTTATTTTTTATAAACATTCTATGTTTTT </li></ul><ul><li>gb-U00089--130> TTTATTTTATTAAAATTATAATGTATTTTTGTTAAATTTT.TAATTCTTT </li></ul><ul><li>……………………………………………………………………………………………………………………………………………………………………………… </li></ul><ul><li>LOCAL_DIAG_VITERBI -- [Inside SCFG] </li></ul><ul><li>OTH ends *(+) = (0..[150]..149) </li></ul><ul><li>OTH ends (-) = (0..[150]..149) </li></ul><ul><li>COD ends *(+) = (120..[27]..146) </li></ul><ul><li>COD ends (-) = (41..[12]..52) </li></ul><ul><li>RNA ends *(+) = (0..[21]..20) </li></ul><ul><li>RNA ends (-) = (0..[150]..149) </li></ul><ul><li>winner = OTH </li></ul><ul><li>OTH = 184.281 COD = 166.408 RNA = 179.710 </li></ul><ul><li>logoddspostOTH = 0.000 logoddspostCOD = -17.873 logoddspostRNA = -4.571 </li></ul><ul><li>sigmoidalOTH = 4.571 sigmoidalCOD = -17.932 sigmoidalRNA = -4.571 </li></ul>QRNA OUTPUT
  21. 21. Number of ncRNA predicted for each organism No. of ncRNAs predicted
  22. 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. 23. Putative Vs Annotated <ul><li>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) </li></ul><ul><ul><li>Found similar to the Mc_MCS4 ncRNA of Mycoplasma capricolum. </li></ul></ul><ul><ul><ul><li>Mc_MCS4 was already characterized to be having extensive homology with the eukaryotic U6 snRNA . </li></ul></ul></ul><ul><ul><li>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. </li></ul></ul><ul><ul><ul><li>MicF was characterised to be regulating the expression of OmpF protein in these organisms . </li></ul></ul></ul><ul><ul><li>Similarity was also found with OxyS ncRNA of E.coli. </li></ul></ul><ul><ul><ul><li>OxyS was found to modulate the expression of various genes in response to Hydrogen peroxide. </li></ul></ul></ul>
  24. 24. - In Eukaryotes <ul><li>Similarity was observed with few miRNAs that were present in the miRNA database (Rfam miRNA registry) </li></ul><ul><ul><ul><li>Same stretch of sequence was present in Human, Rat and Mouse miRNA. </li></ul></ul></ul><ul><li>Small stretches of similarity was observed with various ncRNAs playing role in regulation of development also. </li></ul>
  25. 25. <ul><li>Sequences producing High-scoring Segment Pairs: Score P(N) N </li></ul><ul><li>hsa-mir-190 MI0000486 Homo sapiens miR-190 stem-loop 91 0.26 1 </li></ul><ul><li>rno-mir-190 MI0000933 Rattus norvegicus miR-190 stem-loop 91 0.26 1 </li></ul><ul><li>mmu-mir-190 MI0000232 Mus musculus miR-190 stem-loop 86 0.48 1 </li></ul><ul><li>>hsa-mir-190 MI0000486 Homo sapiens miR-190 stem-loop </li></ul><ul><li>Length = 85 </li></ul><ul><li>Minus Strand HSPs: </li></ul><ul><li>Score = 91 (19.7 bits), Expect = 0.31, P = 0.26 </li></ul><ul><li>Identities = 45/68 (66%), Positives = 45/68 (66%), Strand = Minus / Plus </li></ul><ul><li>Query: 77 AGGTTTAGGTGTTCT-TATTT-ATTTATTAGGTTGTTTAGTT--TC-AATTATTTTTGGA 23 </li></ul><ul><li>||| | |||| | | ||| || |||||||||||| | || || || ||| | | | </li></ul><ul><li>Sbjct: 4 AGGCCTCTGTGTGATATGTTTGATATATTAGGTTGTT-ATTTAATCCAACTATATATCAA 62 </li></ul><ul><li>Query: 22 ATACTAGT 15 </li></ul><ul><li>| | || | </li></ul><ul><li>Sbjct: 63 ACA-TATT 69 </li></ul>
  26. 26. <ul><li>>Hs_NTT </li></ul><ul><li>Length = 17,572 </li></ul><ul><li>Plus Strand HSPs: </li></ul><ul><li>Score = 116 (23.5 bits), Expect = 0.025, P = 0.024 </li></ul><ul><li>Identities = 60/94 (63%), Positives = 60/94 (63%), Strand = Plus / Plus </li></ul><ul><li>Query: 11 TATTTAATATTTATAATTGCTATTTAGCATCTTAAAA-AAGA-CG-TCTTT-AAA-TATA 65 </li></ul><ul><li>|| |||| | || ||| | | || | |||| | ||| | |||| ||| |||| </li></ul><ul><li>Sbjct: 5336 TACATAAT-TAGATCATTTATTCTAAGTAAATTAAGAGAAGCTCTATCTTCCAAAATATA 5394 </li></ul><ul><li>Query: 66 GATAGTTATACTAATTAGAAAATAGTTAAT-AAG 98 </li></ul><ul><li>|||| | || ||| |||| | ||||| ||| </li></ul><ul><li>Sbjct: 5395 GATATCTCTAGCAAT-AGAAGAGTTTTAATTAAG 5427 </li></ul>
  27. 27. CONCLUSIONS <ul><li>Comparative genomic analysis was selected for the ncRNA prediction. </li></ul><ul><li>Procedure for the prediction was standardized. </li></ul><ul><li>One of the putative ncRNA was found to be similar to the already characterized ncRNA from the same genus. </li></ul><ul><li>Conserved region of MicF was found to be present in the putative ncRNA also. </li></ul><ul><li>Identification of the eukaryotic miRNA counterpart in Mycoplasma . </li></ul>
  28. 28. Future Plans <ul><li>To develop programmes for getting the intergenic region co-ordinates given the protein table file as input. </li></ul><ul><li>To verify the genuinity of the predictions beyond the homologous regions found in bacteria. </li></ul><ul><li>To extend the prediction procedure for Eukaryotes. </li></ul><ul><li>To develop the procedure required for classification of the predicted ncRNAs into subclasses. </li></ul><ul><li>To identify the functions of the putative ncRNAs by searching their effector targets. </li></ul><ul><li>To automize the whole procedure. </li></ul>
  29. 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
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