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CAP3, CLC, and MIRA were comparable in terms of the number of reads used.CLC had the fewest reads with multiple matches by far, indicating that it was the least redundant of the six assemblies.

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We compared the assemblies using the following standard metrics: total number of reads used in the assembly, number of contigs 100 bases generated, N50 length of contigs (the smallest contig size in which half the assembly is represented), maximum contig length, summed contig length, and approximate time taken to perform analysis (Table ).

We include the N50 as a measure even though it is not strictly appropriate for transcriptome assemblies (where we expect the median contig length to be in the region of 1.2 kb).

We also assessed assembly integrity and completeness by comparison to four reference datasets.

The optimal assembler will use all the reads given, and will deliver assemblies with unambiguous mappings of reads to contigs, and thus putative transcripts.

CLC does not track reads at all and maps reads back to the assembly to estimate where they might belong.

Therefore, to compare the assemblies, we mapped all the reads back to each assembly using SSAHA2 [].These assemblers differ in the algorithms used (most use variations of the Overlap-Layout-Consensus (OLC) strategy, while CLC uses de Bruijn graph path finding) and how they treat individual reads (whether a read is indivisible, or can be split and ultimately be placed in different contigs).We tested two versions of Newbler because we found the frequently-used, public release version (Newbler Version 2.3, hereafter referred to as Newbler 2.3) to have several undesirable features (see below) and thus contacted the developers to discuss these.Οὐαλερίῳ [Ἀμμ]ωνι[αν]ῷ τῷ καὶ[Γεροντίῳ δι]οικοῦντ[ι](*)[ λο]γ̣ιστία̣[ν](*)[Ὀ]ξ(υρυγχίτου)παρὰ Αὐρη[λί]ου Π[ ̣]τ̣[ ̣ ̣ μη(τρὸς(?Roche 454 pyrosequencing has become a method of choice for generating transcriptome data from non-model organisms.However, most published non-model organism projects have used the Roche 454 pyrosequencing platform [A transcriptome project progresses through phases of data acquisition, assembly of the sequence reads to define putative transcripts, and then annotation and exploitation of the assembled data. Individual reads can have errors and polymorphisms that complicate recognition of overlaps, and individual transcripts (in non-normalised data) can have several orders of magnitude variation in abundance, and thus in effective coverage.

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