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Pairwise comparisons of these six assemblies suggested that assemblies produced by the same assembler were more similar to each other.
Assemblies produced by the same assembler were covered more frequently by each other.
Result also shows that more running time and RAM consuming are demanded for paired-end (PE) reads assembly than single-end (SE) reads dataset with the same assembler (Unpublished data).
Comparisons between the performance of the same assembler in different species are confounded by the different nature of the input sequence data that was provided for each species.
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These self-corrected, PacBio-only reads can be fed into the same assemblers as the hybrid reads.
To our knowledge, other transcriptome sequencing projects using the same assemblers did not report on this phenomenon [ 16- 20], although a large number of unigenes matching to one reference was observed at least once [ 16].
To eliminate erroneously assembled transcripts, we applied a stringent criteria [ 28, 29] to identify probable transcripts which should be detected by two assemblers in same sample or identified in at least two individual samples by same assembler and defined a set of 235,283 cleavage stage transcripts.
This is evident by the fact that despite using the same input, different assemblers can produce draft assemblies that differ considerably in size and error rates.
With the clear aim of reducing the number of sequencing reads from the same gene assembled into different contigs, our chosen strategy was the sequential use of two different assemblers.
Using the same de novo assemblers we applied to the simulated data set, we constructed contig sets ranging in size from 180-630 contigs for each of the input genomes.
Before de novo assembly, to generate accurate unigenes, 11,093 paired-assembly contigs were produced (Table 1) using paired-end sequences of the same clone and CAP3 assembler software [ 37].
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