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The decGPU algorithm output consisted of error free reads, fixed reads and discarded reads.
All tools have an amino acid sensitivity of ~95 to 96% on error free reads.
These values are higher than the corresponding gene prediction accuracy, indicating that long genes on error free reads are more likely to be predicted correctly than short genes.
On simulated Sanger sequencing reads, MetaGene and MetaGeneAnnotator show the highest gene prediction sensitivities (~94% over all species on error free reads and ~80% on reads with the highest error rate) while Orphelia has the best specificity values with ~96% on error free reads and ~92% on reads with the highest error rate (compare Table 2).
Also here, MetaGeneAnnotator has the highest gene prediction harmonic mean (94%) as depicted in Figure 2. From error free reads to reads with 0.49% errors, a drop in harmonic mean of ~9% is observed for all gene prediction methods (except for Orphelia with 12%).
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By considering multiply mapped reads on the whole genome and non-error free reads as well as accepting all mappings within the entire alignment window we receive a larger set of potential indel candidates.
Multiple error-free reads spanning the same splice junction align to the correct splice site, facilitating determination of splice boundaries.
BSMAP is the only aligner whose mapping accuracy doesn't reach 100% when mapping simulated error-free reads.
The number of error-free reads, without a single mismatch or indel, was 76.45%, 15.92% and 0% for, MiSeq, Ion Torrent and PacBio, respectively.
If every position in the genome was uniformly covered by error-free reads, and the genome had few repeats, this would result in a simple de Bruijn graph.
Having generated the set of expressed transcripts and the corresponding expression values, we simulate 75 bp paired-end error-free reads.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com