Experience advanced algorithms for de novo assembly optimized for PacBio data
These algorithms offer the highest contig N50, fewest contigs, single nucleotide polymorphism and structural variant detection, and 99.999% consensus accuracy at 30X coverage. Additional tools provide extensive quality assessment for new assemblies, contigs, and visualization.
SMRT Analysis algorithms for de novo assembly
The Hierarchical Genome Assembly Process (HGAP) generates comprehensive, highly accurate de novo assemblies using a single library type. HGAP consists of pre-assembly, de novo assembly with Celera Assembler, and assembly polishing with Quiver.
BridgeMapper assesses the quality of genome assembly by generating split alignments of PacBio data, which are displayed in SMRT View.
SMRT Analysis applications for de novo assembly
This application performs high-quality de novo assembly using a single library type. It is optimized for speed, and is faster than RS_HGAP_Assembly.2. The HGAP Assembly.3 includes pre-assembly, de novo assembly with PacBio’s AssembleUnitig, assembly polishing with Quiver, and a significant speed improvement for microbial assembly. PacBio’s AssembleUnitig module replaces the most time-consuming step in Celera Assembler.
This application performs de novo assembly using a single library type and is optimized for quality. The HGAP_Assembly.2 includes pre-assembly, de novo assembly with Celera Assembler, and assembly polishing with Quiver.
BridgeMapper generates split alignments (split-read mapping) of PacBio data. Split alignments are shown in SMRT View by displaying reads with portions mapped to separate locations. If portions of a subread map to noncontiguous parts of a reference, it suggests that there are structural differences between the reference and the true genome. This can help you understand the nature and origins of those parts of the data that do not align in an ordinary mapping process. It can also be used for detecting gene fusion in resequencing applications.
This application builds a set of highly accurate long reads for use in de novo assembly. It takes each read exceeding a minimum length, aligns all reads against it, trims the edges, and then takes the consensus. It uses the Hierarchical Genome Assembly Process (HGAP).
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