A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework
<p>Abstract</p> <p>Background</p> <p>State-of-the-art high-throughput sequencers, e.g., the Illumina HiSeq series, generate sequencing reads that are longer than 150 bp up to a total of 600 Gbp of data per run. The high-throughput sequencers generate lengthier reads wit...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2012-12-01
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| Series: | BMC Genomics |
| _version_ | 1818068038930399232 |
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| author | Chang Yu-Jung Chen Chien-Chih Chen Chuen-Liang Ho Jan-Ming |
| author_facet | Chang Yu-Jung Chen Chien-Chih Chen Chuen-Liang Ho Jan-Ming |
| author_sort | Chang Yu-Jung |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>State-of-the-art high-throughput sequencers, e.g., the Illumina HiSeq series, generate sequencing reads that are longer than 150 bp up to a total of 600 Gbp of data per run. The high-throughput sequencers generate lengthier reads with greater sequencing depth than those generated by previous technologies. Two major challenges exist in using the high-throughput technology for <it>de novo </it>assembly of genomes. First, the amount of physical memory may be insufficient to store the data structure of the assembly algorithm, even for high-end multicore processors. Moreover, the graph-theoretical model used to capture intersection relationships of the reads may contain structural defects that are not well managed by existing assembly algorithms.</p> <p>Results</p> <p>We developed a distributed genome assembler based on string graphs and MapReduce framework, known as the CloudBrush. The assembler includes a novel edge-adjustment algorithm to detect structural defects by examining the neighboring reads of a specific read for sequencing errors and adjusting the edges of the string graph, if necessary. CloudBrush is evaluated against GAGE benchmarks to compare its assembly quality with the other assemblers. The results show that our assemblies have a moderate N50, a low misassembly rate of misjoins, and indels of > 5 bp. In addition, we have introduced two measures, known as precision and recall, to address the issues of faithfully aligned contigs to target genomes. Compared with the assembly tools used in the GAGE benchmarks, CloudBrush is shown to produce contigs with high precision and recall. We also verified the effectiveness of the edge-adjustment algorithm using simulated datasets and ran CloudBrush on a nematode dataset using a commercial cloud. CloudBrush assembler is available at <url>https://github.com/ice91/CloudBrush</url>.</p> |
| first_indexed | 2024-12-10T15:33:14Z |
| format | Article |
| id | doaj.art-cf66067bf5e5455680c2351aeeec7a90 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-12-10T15:33:14Z |
| publishDate | 2012-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-cf66067bf5e5455680c2351aeeec7a902022-12-22T01:43:19ZengBMCBMC Genomics1471-21642012-12-0113Suppl 7S2810.1186/1471-2164-13-S7-S28A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing frameworkChang Yu-JungChen Chien-ChihChen Chuen-LiangHo Jan-Ming<p>Abstract</p> <p>Background</p> <p>State-of-the-art high-throughput sequencers, e.g., the Illumina HiSeq series, generate sequencing reads that are longer than 150 bp up to a total of 600 Gbp of data per run. The high-throughput sequencers generate lengthier reads with greater sequencing depth than those generated by previous technologies. Two major challenges exist in using the high-throughput technology for <it>de novo </it>assembly of genomes. First, the amount of physical memory may be insufficient to store the data structure of the assembly algorithm, even for high-end multicore processors. Moreover, the graph-theoretical model used to capture intersection relationships of the reads may contain structural defects that are not well managed by existing assembly algorithms.</p> <p>Results</p> <p>We developed a distributed genome assembler based on string graphs and MapReduce framework, known as the CloudBrush. The assembler includes a novel edge-adjustment algorithm to detect structural defects by examining the neighboring reads of a specific read for sequencing errors and adjusting the edges of the string graph, if necessary. CloudBrush is evaluated against GAGE benchmarks to compare its assembly quality with the other assemblers. The results show that our assemblies have a moderate N50, a low misassembly rate of misjoins, and indels of > 5 bp. In addition, we have introduced two measures, known as precision and recall, to address the issues of faithfully aligned contigs to target genomes. Compared with the assembly tools used in the GAGE benchmarks, CloudBrush is shown to produce contigs with high precision and recall. We also verified the effectiveness of the edge-adjustment algorithm using simulated datasets and ran CloudBrush on a nematode dataset using a commercial cloud. CloudBrush assembler is available at <url>https://github.com/ice91/CloudBrush</url>.</p> |
| spellingShingle | Chang Yu-Jung Chen Chien-Chih Chen Chuen-Liang Ho Jan-Ming A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework BMC Genomics |
| title | A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework |
| title_full | A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework |
| title_fullStr | A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework |
| title_full_unstemmed | A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework |
| title_short | A de novo next generation genomic sequence assembler based on string graph and MapReduce cloud computing framework |
| title_sort | de novo next generation genomic sequence assembler based on string graph and mapreduce cloud computing framework |
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