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The taxonomic distribution of each read was performed with the MEGAN4 (Huson et al. 2011) software, which compares the given reads against a database of reference sequences by performing a search using the BLASTX algorithm (Altschul et al. 1997) against the NCBI-NR protein database.
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The assumption of a search for overlap between the 3' end of the given read and the 5' ends of the remaining reads was assumed when calculating ps.
Each thread maps the given read to a unique fragment of the genome.
However, we did observe that the custom protocol Agilent platform performed quite comparably to RNA-Seq, at the given read depth, in virtually all categories.
At the beginning, all primers are considered to be equally probable matches for the given read and hence assigned a probability of pprimer = 1.
For all placements of a read, it is only necessary to recalculate pairwise distances between the given read and the n reference sequences, with the pairwise distances among the reference sequences calculated once at the start of the analysis.
The intersection process also requires the positions in each position list to be adjusted at run time for its location in the given read, so they correspond to the diagonals in an alignment matrix between genome and read.
For each read we selected the set of genomic matches having maximal identity for the given read.
In this calculation, we use a matrix D of distances among all n+1 sequences (reference alignment plus the given read).
Only the perfectly matched alignments for the given read were kept and extended to 35 nt to 36 nt by adding 15 nt of upstream sequences.
Thus, in terms of the alignment matrix of a given read against the SV region we count the k-mer hits by alignment diagonal (Fig. 5).
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