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Transcriptome assemblies are smaller than genome assemblies and thus should be more computationally tractable, but are often harder because individual contigs can have highly variable read coverage.
Comparing single assemblers, Newbler 2.5 performed best on our trial data set, but other assemblers were closely comparable.
Combining differently optimal assemblies from different programs however gave a more credible final product, and this strategy is recommended.
Transcriptome sequencing projects for non-model organisms are popular because they cost less and are more computationally tractable than full genome sequencing projects, but still yield sufficient information to meet the requirements of many research programs.
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.
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Seq Man assemblies performed best on the criterion of recapitulating known transcripts, and had more novel sequence than the other assemblers, but generated an excess of small, redundant contigs.Reads were trimmed for adapters leaving a total of 741,387 reads with 205,065,666 bases used in all assembly experiments (trimmed read length histograms in Additional file For each assembler, we used the default parameters recommended for transcriptome assembly (details are given in Methods).After assembly, contigs less than 100 bases in length and singletons were discarded for subsequent analyses.They provided a pre-release version (Newbler Version 2.5 p1, hereafter referred to as Newbler 2.5) that addresses some of the issues identified.
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We did not include TGICL  are popular assemblers for second-generation sequence data and can use Roche 454 pyrosequencing reads, they are primarily assemblers for genome sequence data from short-read platforms that rely on the high and even coverage depths afforded by these massively parallel technologies.
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 ).
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