Version Differences for CISA

= Contig Integrator for Sequence Assembly =

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Recently, technological advances have dramatically improved throughput and quality of next-generation sequencing (NGS), and in parallel with the improvements, many algorithms have been proposed for ''de novo'' sequence assembly. Compared to the traditional Sanger sequencing technology, the NGS technologies offer several distinct features, such as large volumes of reads and short length. In order to tackle the sequence assembly problem from a collection of short sequencing reads of randomly sampled fragments, two types of algorithms–overlap-layout-consensus approach and the de Bruijn graph–are commonly utilized. Albeit the assembler are mainly based on the small number of algorithms, they differ from each in terms of dealing with errors, inconsistencies and ambiguities. Moreover, no individual assembler guarantees the best assembly of diverse species. Performing different parameter settings or different assemblers in an iterative manner to generate a draft assembly is inevitable. Nevertheless, few efforts have been made to integrate the various assemblies into a better draft which possess superior quality in both contiguity and accuracy.

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Recently, technological advances have dramatically improved throughput and quality of next-generation sequencing (NGS), and, in parallel with these improvements, numerous algorithms have been proposed for de novo sequence assembly. Compared to the traditional Sanger sequencing technology, NGS technologies offer several distinct features, such as large volumes of reads and concise length. In order to tackle the sequence assembly problem from a collection of short-sequencing reads of randomly sampled fragments, two types of algorithm, the overlap-layout-consensus approach and the de Bruijn graph, are commonly utilized (Paszkiewicz and Studholme 2010; Aerts, Narzisi et al. 2011). Although assemblers are generally based on a small number of algorithms, they differ from each other in terms of dealing with errors, inconsistencies, and ambiguities. Moreover, no individual assembler guarantees the best assembly for diverse species. Therefore, the use of various parameter settings and assemblers in an iterative manner to produce an improved draft assembly is inevitable. Nevertheless, few efforts have been made to integrate various assemblies into a draft that is marked by superior contiguity and accuracy. To the best of our knowledge, five tools, including GAA (Yao, Ye et al. 2012), GAM (Casagrande, Del Fabbro et al. 2009), MAIA (Nijkamp, Winterbach et al. 2010), minimus2 (Sommer, Delcher et al. 2007) and Reconciliator (Zimin, Smith et al. 2008), have been published for merging assemblies. However, GAM and Reconciliator require the original reads and complicated prerequisites; we therefore compared our proposed method with GAA, MAIA and minimus2.

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The qualities of genome assemblies are usually evaluated by their contiguity and the accuracy of contigs or scaffolds. The contiguity is a straightforward measurement by calculating the N50 length or the number of contigs/scaffolds, no need of the real genome. On the other hand, the accuracy of an assembly can be assessed based on alignment to a complete reference genome (Darling, et al., 2011). However, there is a transparent trade-off between contiguity and accuracy. In other words, an assembler trying to maximize the contiguity might provide a less accurate assembly, and vice versa. Since each assembler has its own features in addressing the reconstruction of a DNA sequence, can we take the advantages of all the assemblies to generate an integrated set of contigs?

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The quality of genome assemblies is usually evaluated by means of their contiguity and the accuracy of contigs or scaffolds. Contiguity is a straightforward measurement achieved by calculating the N50 length or the number of contigs/scaffolds, without mandating reference genome information to be implemented in the algorithm. On the other hand, the accuracy of an assembly can be assessed based on alignment with a complete reference genome (Darling, Tritt et al. 2011). However, there is an inevitable trade-off between contiguity and accuracy in such scenarios. Specifically, an assembler attempting to maximize contiguity might do so at the expense of a less accurate assembly, and vice versa. Since an individual assembler has its own features in addressing the reconstruction of a DNA sequence, can we take advantage of all assemblies to generate an integrated set of contigs?

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In this study, utilizing state-of-the-art assemblers to generate different sets of contigs for bacterial genomes is conducted. We have developed and built a Contig Integrator for Sequence Assembly (CISA) to integrate the sets of contigs from different assemblers and evaluated the quality of our integrated assembly. Comparing with the assembly generated by each individual assembler, the assembly sequences integrated by CISA has superior contiguity.

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In this study, state-of-the-art assemblers were used to generate different sets of contigs for bacterial genomes. We have developed and built a Contig Integrator for Sequence Assembly (CISA) to integrate the sets of contigs from different assemblers, and have evaluated the quality of our integrated assembly. Compared with the assemblies generated by each individual assembler and assembly integrator, the hybrid assembly integrated by CISA exhibits superior contiguity and accuracy.

= A schematic overview of CISA =

[[Image:Overview.jpg|left|]]

 = Contig Integrator for Sequence Assembly =
-								Recently, technological advances have dramatically improved throughput and quality of next-generation sequencing (NGS), and in parallel with the improvements, many algorithms have been proposed for ''de novo'' sequence assembly. Compared to the traditional Sanger sequencing technology, the NGS technologies offer several distinct features, such as large volumes of reads and short length. In order to tackle the sequence assembly problem from a collection of short sequencing reads of randomly sampled fragments, two types of algorithms–overlap-layout-consensus approach and the de Bruijn graph–are commonly utilized. Albeit the assembler are mainly based on the small number of algorithms, they differ from each in terms of dealing with errors, inconsistencies and ambiguities. Moreover, no individual assembler guarantees the best assembly of diverse species. Performing different parameter settings or different assemblers in an iterative manner to generate a draft assembly is inevitable. Nevertheless, few efforts have been made to integrate the various assemblies into a better draft which possess superior quality in both contiguity and accuracy.
+								Recently, technological advances have dramatically improved throughput and quality of next-generation sequencing (NGS), and, in parallel with these improvements, numerous algorithms have been proposed for de novo sequence assembly. Compared to the traditional Sanger sequencing technology, NGS technologies offer several distinct features, such as large volumes of reads and concise length. In order to tackle the sequence assembly problem from a collection of short-sequencing reads of randomly sampled fragments, two types of algorithm, the overlap-layout-consensus approach and the de Bruijn graph, are commonly utilized (Paszkiewicz and Studholme 2010; Aerts, Narzisi et al. 2011). Although assemblers are generally based on a small number of algorithms, they differ from each other in terms of dealing with errors, inconsistencies, and ambiguities. Moreover, no individual assembler guarantees the best assembly for diverse species. Therefore, the use of various parameter settings and assemblers in an iterative manner to produce an improved draft assembly is inevitable. Nevertheless, few efforts have been made to integrate various assemblies into a draft that is marked by superior contiguity and accuracy. To the best of our knowledge, five tools, including GAA (Yao, Ye et al. 2012), GAM (Casagrande, Del Fabbro et al. 2009), MAIA (Nijkamp, Winterbach et al. 2010), minimus2 (Sommer, Delcher et al. 2007) and Reconciliator (Zimin, Smith et al. 2008), have been published for merging assemblies. However, GAM and Reconciliator require the original reads and complicated prerequisites; we therefore compared our proposed method with GAA, MAIA and minimus2.
-								The qualities of genome assemblies are usually evaluated by their contiguity and the accuracy of contigs or scaffolds. The contiguity is a straightforward measurement by calculating the N50 length or the number of contigs/scaffolds, no need of the real genome. On the other hand, the accuracy of an assembly can be assessed based on alignment to a complete reference genome (Darling, et al., 2011). However, there is a transparent trade-off between contiguity and accuracy. In other words, an assembler trying to maximize the contiguity might provide a less accurate assembly, and vice versa. Since each assembler has its own features in addressing the reconstruction of a DNA sequence, can we take the advantages of all the assemblies to generate an integrated set of contigs?
+									The quality of genome assemblies is usually evaluated by means of their contiguity and the accuracy of contigs or scaffolds. Contiguity is a straightforward measurement achieved by calculating the N50 length or the number of contigs/scaffolds, without mandating reference genome information to be implemented in the algorithm. On the other hand, the accuracy of an assembly can be assessed based on alignment with a complete reference genome (Darling, Tritt et al. 2011). However, there is an inevitable trade-off between contiguity and accuracy in such scenarios. Specifically, an assembler attempting to maximize contiguity might do so at the expense of a less accurate assembly, and vice versa. Since an individual assembler has its own features in addressing the reconstruction of a DNA sequence, can we take advantage of all assemblies to generate an integrated set of contigs?
-								In this study, utilizing state-of-the-art assemblers to generate different sets of contigs for bacterial genomes is conducted. We have developed and built a Contig Integrator for Sequence Assembly (CISA) to integrate the sets of contigs from different assemblers and evaluated the quality of our integrated assembly. Comparing with the assembly generated by each individual assembler, the assembly sequences integrated by CISA has superior contiguity.
+									In this study, state-of-the-art assemblers were used to generate different sets of contigs for bacterial genomes. We have developed and built a Contig Integrator for Sequence Assembly (CISA) to integrate the sets of contigs from different assemblers, and have evaluated the quality of our integrated assembly. Compared with the assemblies generated by each individual assembler and assembly integrator, the hybrid assembly integrated by CISA exhibits superior contiguity and accuracy.
 = A schematic overview of CISA =
 [[Image:Overview.jpg|left|]]