hg color hybrid graph for the error correction of long
play

HG-CoLoR: Hybrid Graph for the error Correction of Long Reads Pierre - PowerPoint PPT Presentation

Oct 09, 2023 •49 likes •1.35k views

HG-CoLoR: Hybrid Graph for the error Correction of Long Reads Pierre Morisse , Thierry Lecroq and Arnaud Lefebvre pierre.morisse2@univ-rouen.fr Laboratoire dInformatique, de Traitement de lInformation et des Syst` emes July 5, 2017

  1. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Inspiration NaS [Madoui et al., 2015] Does not locally correct erroneous regions Uses long reads as templates to generate corrected long reads from assemblies of short reads Requires the mapping of the short reads both on the long reads and against each other P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 8/30

  2. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion NaS overview NaS corrects a long read as follows: long read seeds P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 9/30

  3. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion NaS overview NaS corrects a long read as follows: seeds similar short reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 9/30

  4. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion NaS overview NaS corrects a long read as follows: P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 9/30

  5. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion NaS overview NaS corrects a long read as follows: contig P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 9/30

  6. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Main idea Generate corrected long reads from assemblies of short reads Get rid of the time consuming step of aligning the short reads against each other Focus on a seed and extend approach Rely on a hybrid structure between a de Bruijn graph and an overlap graph, built from the short reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 10/30

  7. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Main idea Generate corrected long reads from assemblies of short reads Get rid of the time consuming step of aligning the short reads against each other Focus on a seed and extend approach Rely on a hybrid structure between a de Bruijn graph and an overlap graph, built from the short reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 10/30

  8. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Main idea Generate corrected long reads from assemblies of short reads Get rid of the time consuming step of aligning the short reads against each other Focus on a seed and extend approach Rely on a hybrid structure between a de Bruijn graph and an overlap graph, built from the short reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 10/30

  9. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Introduction 1 2 Main idea Hybrid graph 3 Workflow 4 Experimental results 5 6 Conclusion P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 11/30

  10. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph Overlap graph de Bruijn graph 4 ATTGC TTGCG TGCGT ATTGCGT GCGTAAC ATAACGG 1 AACGG GCGTA ATAAC TAACG GTAAC CGTAA Idea Mix the advantages of a de Bruijn graph and of an overlap graph, and allow to compute overlaps of variable lengths between the k -mers from the reads of a given set. P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 12/30

  11. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph Overlap graph de Bruijn graph 4 ATTGC TTGCG TGCGT ATTGCGT GCGTAAC ATAACGG 1 AACGG GCGTA ATAAC TAACG GTAAC CGTAA Idea Mix the advantages of a de Bruijn graph and of an overlap graph, and allow to compute overlaps of variable lengths between the k -mers from the reads of a given set. P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 12/30

  12. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph Overlap graph de Bruijn graph 4 ATTGC TTGCG TGCGT ATTGCGT GCGTAAC ATAACGG 1 AACGG GCGTA ATAAC TAACG GTAAC CGTAA Idea Mix the advantages of a de Bruijn graph and of an overlap graph, and allow to compute overlaps of variable lengths between the k -mers from the reads of a given set. P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 12/30

  13. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph Overlap graph de Bruijn graph 4 ATTGC TTGCG TGCGT ATTGCGT GCGTAAC ATAACGG 1 AACGG GCGTA ATAAC TAACG GTAAC CGTAA Idea Mix the advantages of a de Bruijn graph and of an overlap graph, and allow to compute overlaps of variable lengths between the k -mers from the reads of a given set. P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 12/30

  14. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph Example On the set of reads S = { AAGCTTAG, CTTACGTA, GTATACTG } AAGCTT 4 5 5 3 AGCTTA GCTTAG 3 4 4 3 5 5 5 5 CTTACG TTACGT TACGTA GTATAC TATACT ATACTG 3 4 P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 13/30

  15. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph Example On the set of reads S = { AAGCTTAG, CTTACGTA, GTATACTG } AAGCTT 4 5 5 3 AGCTTA GCTTAG 3 4 4 3 5 5 5 5 CTTACG TTACGT TACGTA GTATAC TATACT ATACTG 3 4 P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 13/30

  16. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph Example On the set of reads S = { AAGCTTAG, CTTACGTA, GTATACTG } AAGCTT 4 5 5 3 AGCTTA GCTTAG 3 4 4 3 5 5 5 5 CTTACG TTACGT TACGTA GTATAC TATACT ATACTG 3 4 P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 13/30

  17. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph traversal The graph is not explicitly built Its traversal is simulated with PgSA [Kowalski et al., 2015] PgSA can index a set of reads and answer queries about strings of variable lengths One of the queries returns the positions of all the occurrences of a given string in the different reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 14/30

  18. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph traversal The graph is not explicitly built Its traversal is simulated with PgSA [Kowalski et al., 2015] PgSA can index a set of reads and answer queries about strings of variable lengths One of the queries returns the positions of all the occurrences of a given string in the different reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 14/30

  19. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph traversal The graph is not explicitly built Its traversal is simulated with PgSA [Kowalski et al., 2015] PgSA can index a set of reads and answer queries about strings of variable lengths One of the queries returns the positions of all the occurrences of a given string in the different reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 14/30

  20. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Hybrid graph traversal The graph is not explicitly built Its traversal is simulated with PgSA [Kowalski et al., 2015] PgSA can index a set of reads and answer queries about strings of variable lengths One of the queries returns the positions of all the occurrences of a given string in the different reads P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 14/30

  21. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Introduction 1 2 Main idea Hybrid graph 3 Workflow 4 Experimental results 5 6 Conclusion P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 15/30

  22. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Workflow 5 steps: Correct the short reads 1 Align the short reads on the long read, to find seeds 2 Merge the overlapping seeds 3 Link the seeds, by traversing the hybrid graph 4 Extend the obtained corrected long read, on the left (resp. right) 5 of the leftmost (resp. rightmost) seed P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 16/30

  23. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Workflow 5 steps: Correct the short reads 1 Align the short reads on the long read, to find seeds 2 Merge the overlapping seeds 3 Link the seeds, by traversing the hybrid graph 4 Extend the obtained corrected long read, on the left (resp. right) 5 of the leftmost (resp. rightmost) seed P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 16/30

  24. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Workflow 5 steps: Correct the short reads 1 Align the short reads on the long read, to find seeds 2 Merge the overlapping seeds 3 Link the seeds, by traversing the hybrid graph 4 Extend the obtained corrected long read, on the left (resp. right) 5 of the leftmost (resp. rightmost) seed P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 16/30

  25. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Workflow 5 steps: Correct the short reads 1 Align the short reads on the long read, to find seeds 2 Merge the overlapping seeds 3 Link the seeds, by traversing the hybrid graph 4 Extend the obtained corrected long read, on the left (resp. right) 5 of the leftmost (resp. rightmost) seed P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 16/30

  26. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Workflow 5 steps: Correct the short reads 1 Align the short reads on the long read, to find seeds 2 Merge the overlapping seeds 3 Link the seeds, by traversing the hybrid graph 4 Extend the obtained corrected long read, on the left (resp. right) 5 of the leftmost (resp. rightmost) seed P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 16/30

  27. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking Seeds are used as anchor points on the hybrid graph The graph is traversed to link together the seeds and assemble the k -mers P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 17/30

  28. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking Seeds are used as anchor points on the hybrid graph The graph is traversed to link together the seeds and assemble the k -mers P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 17/30

  29. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read seed 1 seed 2 seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  30. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  31. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  32. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  33. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  34. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  35. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  36. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 k − 2 . . . k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  37. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  38. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  39. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  40. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  41. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  42. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  43. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  44. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  45. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . . . . k − 1 . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  46. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . k − 1 . . . src dst dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  47. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst seed 3 k − 1 k − 2 . . . k − 1 . . . k − 2 k − 1 . . . . . . k − 1 . . . src src dst dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  48. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read linked seeds seed 3 k − 1 k − 2 . . . k − 1 . . . . . . . . . src dst k − 3 . . . k − 2 k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  49. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read src dst k − 1 k − 2 . . . k − 1 . . . . . . . . . src dst k − 3 k − 2 . . . k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  50. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 4: Seeds linking long read corrected long read k − 1 k − 2 . . . k − 1 . . . . . . . . . src dst k − 3 k − 2 . . . k − 3 . . . P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 18/30

  51. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 5: Tips extension Seeds don’t always map right at the beginning or until the end of the long read Once all the seeds have been linked, HG-CoLoR keeps on traversing the graph The traversal stops when the borders of the long read or a branching path are reached P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 19/30

  52. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 5: Tips extension Seeds don’t always map right at the beginning or until the end of the long read Once all the seeds have been linked, HG-CoLoR keeps on traversing the graph The traversal stops when the borders of the long read or a branching path are reached P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 19/30

  53. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Step 5: Tips extension Seeds don’t always map right at the beginning or until the end of the long read Once all the seeds have been linked, HG-CoLoR keeps on traversing the graph The traversal stops when the borders of the long read or a branching path are reached P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 19/30

  54. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Remark Some seeds might be impossible to link together ⇒ Production of a corrected long read fragmented in multiple parts P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 20/30

  55. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Remark Some seeds might be impossible to link together ⇒ Production of a corrected long read fragmented in multiple parts P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 20/30

  56. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Introduction 1 2 Main idea Hybrid graph 3 Workflow 4 Experimental results 5 6 Conclusion P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 21/30

  57. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Datasets HG-CoLoR was compared to NaS, and two other state-of-the-art long read hybrid correction methods: CoLoRMap [Haghshenas et al., 2016] and Jabba [Miclotte et al., 2016] The different tools were compared on the following datasets Reference genome Oxford Nanopore data Illumina data Dataset Name Strain Genome size # Reads Average length Coverage # Reads Read length Coverage E. coli E. coli K-12 substr. MG1655 4.6 Mbp 22,270 5,999 28x 775,500 300 50x Yeast S. cerevisae W303 12.4 Mbp 205,923 5,698 31x 2,500,000 250 50x P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 22/30

  58. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Alignment-based comparison Dataset Method # Reads Average length Average identity Genome coverage Runtime Original 22,270 5,999 79.46% 100% N/A CoLoRMap 22,270 6,219 89.02% 100% 8h26min E. coli Jabba 22,065 5,794 99.81% 99.41% 12min56 NaS 21,818 7,926 99.86% 100% 3 days HG-CoLoR 22,549 5,897 99.59% 100% 3h Original 205,923 5,698 55.49% 99.90% N/A CoLoRMap 205,923 5,737 39.93% 99.40% 37h36min Yeast Jabba 36,958 6,613 99.55% 93.21% 44min05 NaS 71,793 5,938 99.59% 98.70% > 16 days HG-CoLoR 71,518 6,604 99.17% 98.39% 22h P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 23/30

  59. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Alignment-based comparison Dataset Method # Reads Average length Average identity Genome coverage Runtime Original 22,270 5,999 79.46% 100% N/A CoLoRMap 22,270 6,219 89.02% 100% 8h26min E. coli Jabba 22,065 5,794 99.81% 99.41% 12min56 NaS 21,818 7,926 99.86% 100% 3 days HG-CoLoR 22,549 5,897 99.59% 100% 3h Original 205,923 5,698 55.49% 99.90% N/A CoLoRMap 205,923 5,737 39.93% 99.40% 37h36min Yeast Jabba 36,958 6,613 99.55% 93.21% 44min05 NaS 71,793 5,938 99.59% 98.70% > 16 days HG-CoLoR 71,518 6,604 99.17% 98.39% 22h P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 23/30

  60. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Alignment-based comparison Dataset Method # Reads Average length Average identity Genome coverage Runtime Original 22,270 5,999 79.46% 100% N/A CoLoRMap 22,270 6,219 89.02% 100% 8h26min E. coli Jabba 22,065 5,794 99.81% 99.41% 12min56 NaS 21,818 7,926 99.86% 100% 3 days HG-CoLoR 22,549 5,897 99.59% 100% 3h Original 205,923 5,698 55.49% 99.90% N/A CoLoRMap 205,923 5,737 39.93% 99.40% 37h36min Yeast Jabba 36,958 6,613 99.55% 93.21% 44min05 NaS 71,793 5,938 99.59% 98.70% > 16 days HG-CoLoR 71,518 6,604 99.17% 98.39% 22h P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 23/30

  61. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Alignment-based comparison Dataset Method # Reads Average length Average identity Genome coverage Runtime Original 22,270 5,999 79.46% 100% N/A CoLoRMap 22,270 6,219 89.02% 100% 8h26min E. coli Jabba 22,065 5,794 99.81% 99.41% 12min56 NaS 21,818 7,926 99.86% 100% 3 days HG-CoLoR 22,549 5,897 99.59% 100% 3h Original 205,923 5,698 55.49% 99.90% N/A CoLoRMap 205,923 5,737 39.93% 99.40% 37h36min Yeast Jabba 36,958 6,613 99.55% 93.21% 44min05 NaS 71,793 5,938 99.59% 98.70% > 16 days HG-CoLoR 71,518 6,604 99.17% 98.39% 22h P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 23/30

  62. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Alignment-based comparison Dataset Method # Reads Average length Average identity Genome coverage Runtime Original 22,270 5,999 79.46% 100% N/A CoLoRMap 22,270 6,219 89.02% 100% 8h26min E. coli Jabba 22,065 5,794 99.81% 99.41% 12min56 NaS 21,818 7,926 99.86% 100% 3 days HG-CoLoR 22,549 5,897 99.59% 100% 3h Original 205,923 5,698 55.49% 99.90% N/A CoLoRMap 205,923 5,737 39.93% 99.40% 37h36min Yeast Jabba 36,958 6,613 99.55% 93.21% 44min05 NaS 71,793 5,938 99.59% 98.70% > 16 days HG-CoLoR 71,518 6,604 99.17% 98.39% 22h P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 23/30

  63. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Assembly-based comparison Dataset Method Coverage # Expected contigs # Obtained contigs Genome coverage Identity CoLoRMap 28x 1 29 97,74% 99.81% Jabba 28x 1 41 95.76% 99.92% E. coli NaS 37x 1 1 99.90% 99.99% HG-CoLoR 29x 1 2 99.95% 99.95% CoLoRMap 14x 30 Jabba 21x 30 134 70.52% 99.83% Yeast NaS 35x 30 123 97.44% 99.77% HG-CoLoR 39x 30 108 92.19% 99.61% P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 24/30

  64. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Assembly-based comparison Dataset Method Coverage # Expected contigs # Obtained contigs Genome coverage Identity CoLoRMap 28x 1 29 97,74% 99.81% Jabba 28x 1 41 95.76% 99.92% E. coli NaS 37x 1 1 99.90% 99.99% HG-CoLoR 29x 1 2 99.95% 99.95% CoLoRMap 14x 30 Jabba 21x 30 134 70.52% 99.83% Yeast NaS 35x 30 123 97.44% 99.77% HG-CoLoR 39x 30 108 92.19% 99.61% P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 24/30

  65. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Assembly-based comparison Dataset Method Coverage # Expected contigs # Obtained contigs Genome coverage Identity CoLoRMap 28x 1 29 97,74% 99.81% Jabba 28x 1 41 95.76% 99.92% E. coli NaS 37x 1 1 99.90% 99.99% HG-CoLoR 29x 1 2 99.95% 99.95% CoLoRMap 14x 30 Jabba 21x 30 134 70.52% 99.83% Yeast NaS 35x 30 123 97.44% 99.77% HG-CoLoR 39x 30 108 92.19% 99.61% P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 24/30

  66. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Assembly-based comparison Dataset Method Coverage # Expected contigs # Obtained contigs Genome coverage Identity CoLoRMap 28x 1 29 97,74% 99.81% Jabba 28x 1 41 95.76% 99.92% E. coli NaS 37x 1 1 99.90% 99.99% HG-CoLoR 29x 1 2 99.95% 99.95% CoLoRMap 14x 30 Jabba 21x 30 134 70.52% 99.83% Yeast NaS 35x 30 123 97.44% 99.77% HG-CoLoR 39x 30 108 92.19% 99.61% P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 24/30

  67. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Assembly-based comparison Dataset Method Coverage # Expected contigs # Obtained contigs Genome coverage Identity CoLoRMap 28x 1 29 97,74% 99.81% Jabba 28x 1 41 95.76% 99.92% E. coli NaS 37x 1 1 99.90% 99.99% HG-CoLoR 29x 1 2 99.95% 99.95% CoLoRMap 14x 30 Jabba 21x 30 134 70.52% 99.83% Yeast NaS 35x 30 123 97.44% 99.77% HG-CoLoR 39x 30 108 92.19% 99.61% P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 24/30

  68. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Introduction 1 2 Main idea Hybrid graph 3 Workflow 4 Experimental results 5 6 Conclusion P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 25/30

  69. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Conclusion We introduced a new graph structure and proved its usefulness We developed a new hybrid long read error correction method We showed that this new method provides the best trade off between runtime, accuracy and genome coverage, when compared to state-of-the-art methods HG-CoLoR is available from: https://github.com/pierre-morisse/HG-CoLoR P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 26/30

  70. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Conclusion We introduced a new graph structure and proved its usefulness We developed a new hybrid long read error correction method We showed that this new method provides the best trade off between runtime, accuracy and genome coverage, when compared to state-of-the-art methods HG-CoLoR is available from: https://github.com/pierre-morisse/HG-CoLoR P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 26/30

  71. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Conclusion We introduced a new graph structure and proved its usefulness We developed a new hybrid long read error correction method We showed that this new method provides the best trade off between runtime, accuracy and genome coverage, when compared to state-of-the-art methods HG-CoLoR is available from: https://github.com/pierre-morisse/HG-CoLoR P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 26/30

  72. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Conclusion We introduced a new graph structure and proved its usefulness We developed a new hybrid long read error correction method We showed that this new method provides the best trade off between runtime, accuracy and genome coverage, when compared to state-of-the-art methods HG-CoLoR is available from: https://github.com/pierre-morisse/HG-CoLoR P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 26/30

  73. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Future work Run HG-CoLoR on larger genomes Filter out weak k -mers after the short reads correction step Build a proper assembly tool from the hybrid graph structure Adapt HG-CoLoR to self-correction P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 27/30

  74. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Future work Run HG-CoLoR on larger genomes Filter out weak k -mers after the short reads correction step Build a proper assembly tool from the hybrid graph structure Adapt HG-CoLoR to self-correction P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 27/30

  75. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Future work Run HG-CoLoR on larger genomes Filter out weak k -mers after the short reads correction step Build a proper assembly tool from the hybrid graph structure Adapt HG-CoLoR to self-correction P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 27/30

  76. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Future work Run HG-CoLoR on larger genomes Filter out weak k -mers after the short reads correction step Build a proper assembly tool from the hybrid graph structure Adapt HG-CoLoR to self-correction P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 27/30

  77. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion References I Haghshenas, E., Hach, F., Sahinalp, S. C., and Chauve, C. (2016). CoLoRMap: Correcting Long Reads by Mapping short reads. Bioinformatics , 32(17):i545–i551. Kowalski, T., Grabowski, S., and Deorowicz, S. (2015). Indexing arbitrary-length k-mers in sequencing reads. PLoS ONE , 10(7):1–14. Madoui, M.-A., Engelen, S., Cruaud, C., Belser, C., Bertrand, L., Alberti, A., Lemainque, A., Wincker, P ., and Aury, J.-M. (2015). Genome assembly using Nanopore-guided long and error-free DNA reads. BMC Genomics , 16:327. P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 28/30

  78. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion References II Miclotte, G., Heydari, M., Demeester, P ., Rombauts, S., Van de Peer, Y., Audenaert, P ., and Fostier, J. (2016). Jabba: hybrid error correction for long sequencing reads. Algorithms Mol Biol , 11:10. P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 29/30

  79. Introduction Main idea Hybrid graph Workflow Experimental results Conclusion Questions? P. Morisse, T. Lecroq, A. Lefebvre HG-CoLoR 30/30

Recommend

HG-CoLoR: enHanced de bruijn Graph for the error COrrection of LOng Reads Pierre Morisse , Thierry

HG-CoLoR: enHanced de bruijn Graph for the error COrrection of LOng Reads Pierre Morisse , Thierry

HG-CoLoR: enHanced de bruijn Graph for the error COrrection of LOng Reads Pierre Morisse , Thierry Lecroq and Arnaud Lefebvre pierre.morisse2@univ-rouen.fr Laboratoire dInformatique, de Traitement de lInformation et des Syst` emes

1.59k views • 126 slides
ERROR DETECTON & CORRECTION Error Detection EDC= Error Detection and Correction bits

ERROR DETECTON & CORRECTION Error Detection EDC= Error Detection and Correction bits

ERROR DETECTON & CORRECTION Error Detection EDC= Error Detection and Correction bits (redundancy) D = Data protected by error checking, may include header fields Error detection not 100% reliable! protocol may miss some errors,

320 views • 18 slides
. . . x 1 x 1 x 2 x 2 x 3 x 3 x n x n 31 32 3-satisfiability reduces to graph

. . . x 1 x 1 x 2 x 2 x 3 x 3 x n x n 31 32 3-satisfiability reduces to graph

Graph 3-colorability Graph 3-colorability 3-COLOR . Given a graph, is there a way to color the vertices 3-COLOR . Given a graph, is there a way to color the vertices red, green, and blue so that no adjacent vertices have the same color? red,

372 views • 5 slides
QEC11 Quantum Error Correction and Quantum Error-Correcting Codes Todd A. Brun Center for

QEC11 Quantum Error Correction and Quantum Error-Correcting Codes Todd A. Brun Center for

QEC11 Quantum Error Correction and Quantum Error-Correcting Codes Todd A. Brun Center for Quantum Information Science and Technology (CQIST) Outline 1. What is Quantum Error Correction? 2. Classical Error-Correcting Codes 3. Does Encoding Copy

811 views • 35 slides
Using Timing-Error Detection and Correction for Transient-Error Tolerance and Adaptation to PVT

Using Timing-Error Detection and Correction for Transient-Error Tolerance and Adaptation to PVT

A Power-Efficient 32b ARM ISA Processor Using Timing-Error Detection and Correction for Transient-Error Tolerance and Adaptation to PVT Variation David Bull 1 , Shidhartha Das 1 , Karthik Shivashankar 1 , Ganesh Dasika 2 , Krisztian Flautner 1 ,

778 views • 41 slides
Quantum Error Correction for Long-Distance Quantum Communication Institute of Physics,

Quantum Error Correction for Long-Distance Quantum Communication Institute of Physics,

Quantum Error Correction for Long-Distance Quantum Communication Institute of Physics, University of Mainz Peter van Loock Quantum Error Correction for Long-Distance Quantum Communication Institute of Physics, University of Mainz Peter van

581 views • 46 slides
Constructing Error- -Correction Codes Correction Codes Constructing Error from Scale- -Free

Constructing Error- -Correction Codes Correction Codes Constructing Error from Scale- -Free

International Workshop on Complex Systems and Networks 2007 Guilin, China Constructing Error- -Correction Codes Correction Codes Constructing Error from Scale- -Free Networks Free Networks from Scale Francis C.M. Lau Francis C.M. Lau

1.26k views • 114 slides
Parametric Linear System Solving with Error Correction. Cleveland Waddell S YMBOLIC -N UMERIC C

Parametric Linear System Solving with Error Correction. Cleveland Waddell S YMBOLIC -N UMERIC C

Parametric Linear System Solving with Error Correction. Cleveland Waddell S YMBOLIC -N UMERIC C OMPUTING S EMINAR September 26, 2019 2 Overview Error Correction: Parametric Linear System Solving Relating Error Correction to

1.08k views • 83 slides
MapReduce for accurate error correction of next-generation sequencing data Assoc. Prof . Liang

MapReduce for accurate error correction of next-generation sequencing data Assoc. Prof . Liang

MapReduce for accurate error correction of next-generation sequencing data Assoc. Prof . Liang Zhao School of Computing and Electronic Information Guangxi University & Taihe Hospital Oct 5th, 2016 (GXU, THH) error correction GIW2016 1

530 views • 27 slides
Quantum Error Correction by Optimal Control Basic Systems Concepts, Applications,

Quantum Error Correction by Optimal Control Basic Systems Concepts, Applications,

Quantum Error Correction by Optimal Control Basic Systems Concepts, Applications, Perspectives Theory DYNAMO Platform Applications I: Error Correction Thomas Schulte-Herbrggen Applications II: TU-Munich Fixed-Point Engineering

2.1k views • 152 slides
The Capability of Error Correction for Burst-noise Channels Using Error Estimating Code Yaoyu

The Capability of Error Correction for Burst-noise Channels Using Error Estimating Code Yaoyu

The Capability of Error Correction for Burst-noise Channels Using Error Estimating Code Yaoyu Wang Nanjing University yaoyu.wang.nju@gmail.com June 10, 2016 Yaoyu Wang (NJU) Error correction with EEC June 10, 2016 1 / 22 Overview Error

419 views • 22 slides
On Error Correction in the Exponent Chris Peikert MIT Computer Science and AI Laboratory Theory

On Error Correction in the Exponent Chris Peikert MIT Computer Science and AI Laboratory Theory

On Error Correction in the Exponent Chris Peikert MIT Computer Science and AI Laboratory Theory of Cryptography Conference 5 March 2006 Chris Peikert (MIT) On Error Correction in the Exponent TCC 2006 1 / 9 Error Correction (in the

1.04k views • 58 slides
Physical layer Error detection, correction Martin Heusse X L A TEX E Error detection

Physical layer Error detection, correction Martin Heusse X L A TEX E Error detection

Physical layer Error detection, correction Martin Heusse X L A TEX E Error detection Idea = Add redundant information in the transmitted data to check for errors or even correct them Also used for data storage, memory (Am I

229 views • 11 slides
Private quantum subsystems and error Tomas Jochym- OConnor correction Privacy & error

Private quantum subsystems and error Tomas Jochym- OConnor correction Privacy & error

Private quantum subsystems and error correction Private quantum subsystems and error Tomas Jochym- OConnor correction Privacy & error correction Restrictions of operator privacy Tomas Jochym-OConnor Generalization of subsystem

806 views • 28 slides
Long-read error correction: a survey and qualitative comparison Pierre Morisse 1 , Arnaud Lefebvre

Long-read error correction: a survey and qualitative comparison Pierre Morisse 1 , Arnaud Lefebvre

Long-read error correction: a survey and qualitative comparison Pierre Morisse 1 , Arnaud Lefebvre 2 , Thierry Lecroq 2 1 Normandie Universit e, UNIROUEN, INSA Rouen, LITIS, 76000 Rouen, France. 2 Normandie Universit e, UNIROUEN, LITIS, Rouen

623 views • 37 slides
Quantum error correction and fault tolerance John Preskill, Caltech 1 July 2004

Quantum error correction and fault tolerance John Preskill, Caltech 1 July 2004

Quantum error correction and fault tolerance John Preskill, Caltech 1 July 2004 http://www.theory.caltech.edu/~preskill/ph219/ph219_2004.html Quantum error correction Can large-scale quantum computers really be built and operated? Surely

668 views • 51 slides
RNA-seq nanopore read correction R. Chikhi, L. Lima, C. Marchet, ASTER Consortium December 2017

RNA-seq nanopore read correction R. Chikhi, L. Lima, C. Marchet, ASTER Consortium December 2017

RNA-seq nanopore read correction R. Chikhi, L. Lima, C. Marchet, ASTER Consortium December 2017 Motivation Emerging cDNA and RNA nanopore data No dedicated error-correction tool yet We evaluate existing DNA error-correction tools on

939 views • 47 slides
Quantum Error Correction and Quantum Information Theory Vinod Sharma Arun Padakandla Dept. of

Quantum Error Correction and Quantum Information Theory Vinod Sharma Arun Padakandla Dept. of

Quantum Error Correction and Quantum Information Theory Vinod Sharma Arun Padakandla Dept. of ECE Dept. of EE and CS IISc Bangalore University of Tennessee Bangalore, India Knoxville, USA July 20, 2020 1/71 Classical Error Correction

1.12k views • 71 slides
Your Color? God? He does not care He is not there Not Doing That Well I How long, O Lord?

Your Color? God? He does not care He is not there Not Doing That Well I How long, O Lord?

Your Color? God? He does not care He is not there Not Doing That Well I How long, O Lord? Will you forget me forever? How long will you hide your face from Psalm 13:16 me? How long must I bear pain in my soul, and have sorrow in my

293 views • 18 slides
Quantum Quantum Computing Sensing Algorithms Hybrid computing Compilers

Quantum Quantum Computing Sensing Algorithms Hybrid computing Compilers

QIS Workshop: Welcome Argonne PSE/CELS QIS Working Group Salman Habib CPS/HEP Divisions September 23, 2019 Quantum Quantum Computing Sensing Algorithms Hybrid computing Compilers Error correction Precision metrology

287 views • 6 slides
Quantum Error Correction Shyam Sundhar R Department of EE Mid Term Presentation, CS 682 Mid

Quantum Error Correction Shyam Sundhar R Department of EE Mid Term Presentation, CS 682 Mid

Quantum Error Correction Shyam Sundhar R Department of EE Mid Term Presentation, CS 682 Mid Term Presentation, CS 682 1 / Shyam Sundhar R (IIT K) Quantum Error Correction 32 Outline Shor Code 1 Decoherence Tackling Decoherence Error

761 views • 54 slides
Online Error Correction for the Tracking of Laparoscopic Ultrasound Diploma Thesis Tobias Reichl

Online Error Correction for the Tracking of Laparoscopic Ultrasound Diploma Thesis Tobias Reichl

Introduction Methods Results Discussion Online Error Correction for the Tracking of Laparoscopic Ultrasound Diploma Thesis Tobias Reichl (reichl@in.tum.de) 20th July 2007 Tobias Reichl Online Error Correction for the Tracking of

513 views • 33 slides
Quantum Error Correction On a quantum computer, a single electron or nucleus Peter J. Cameron in

Quantum Error Correction On a quantum computer, a single electron or nucleus Peter J. Cameron in

Classical v quantum In a classical computer, each bit of information is stored by a transistor containing trillions of electrons. Quantum Error Correction On a quantum

457 views • 3 slides
Facts and Fiction Thomas Srensen, Wiebke Langreder IWTMA April 2017 LT Long-term Correction

Facts and Fiction Thomas Srensen, Wiebke Langreder IWTMA April 2017 LT Long-term Correction

Long-term correction: Facts and Fiction Thomas Srensen, Wiebke Langreder IWTMA April 2017 LT Long-term Correction Challenges: Nature: +/- 20% energy variation possible Man-made: CREYAP 1 (blind test) indicated LT correction as biggest

275 views • 13 slides

More recommend