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We used the Sailfish algorithm, which uses a global approach to optimize read placements on genes, to avoid false-positive expression signals of silent genes.
We obtained 97 million uniquely mapped singleton reads and 11 million paired-end reads for the MeDIP samples and 26 million singleton reads for the input samples with high quality read placements against the human genome (Maq quality > 10).
For the PE read to reference genome alignment, it is important to choose a highly variant tolerant short read aligner that is able to reliably align most of the short reads to their true source locations in the reference genome while minimizing the number of false-positive read placements.
The use of PE read alignments in this step is also important because the additional sequence information, provided by the second read in a PE, increases the specificity of the alignment process compared with single-end reads, and thus reduces the number of false read placements.
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Additional small scaffolds were identified and incorporated using paired read placement.
This speed comes at a significant cost however, as CLC does not track read placement in the assembly.
Our curation method verified unique paired read placement throughout the reconstructed genomes, a requirement consistent with standard methods of isolate genomics.
All the assemblers returned a fasta file with contigs, and, except CLC, they all returned an ACE file with read placement information for each contig.
Usually, longer fragments (several hundred base pairs) are sequenced from both ends (paired-end sequencing) to provide additional information on correct read placement in the assembly.
On the other hand, existing read placement methods based on phylogeny-aware alignment (Berger et al., 2011) handle the query sequences separately and delete sites inferred as insertions, limiting their use to phylogenetic placement only.
Tracking of read placement in an assembly is a very valuable feature, as it allows inspection of the data underpinning suspect assemblies, and thus CLC sacrifices speed for verifiability.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com