The phrase "alignment algorithm is" is correct and usable in written English.
It can be used when discussing a specific algorithm related to alignment in various contexts, such as computer science, data analysis, or machine learning.
Example: "The alignment algorithm is crucial for ensuring that the data sets are properly synchronized before analysis."
Alternatives: "alignment procedure is" or "alignment method is".
Besides this, alignment algorithm is able to align very long reads, but its performance significantly degrades on long reads (whereas it is quite efficient on short reads).
The well known miRanda program employs a two-step algorithm: in the first step, an alignment algorithm is used to align the seed region of the miRNA to the 3′UTRs of the target mRNAs and in the second step, cross-species conservation and target accessibility are used for confirming the target, in addition to the several other aforementioned factors [22,23].
In the proposed algorithm, the input of the sequence alignment algorithm is a pair (text line; fixation line) we want to match.
A segmental variant of the dynamic alignment algorithm is used to find in the phonetic output of an ASR transcript the substring of phonemes that best matches the proper name's phonetization.
The input of the sequence alignment algorithm is the whole sequence S and the whole sequence T. The algorithm tries then to find the best alignment between S and T by using the similarity measure.
However, as a reference, it can be said that when the point clouds have an overlap of more than 70%, the time spent by the alignment algorithm is, in most cases, less than 5 s, while for very low overlap percentages, that value can be exceeded.
The MEGA 5 ClustalW multiple alignment algorithm was used to align all 102 sequences [ 36].
The default parameters for TrakEM2's non-linear elastic alignment algorithm were sufficient to align the images, producing some deformations that are likely to affect our measurements only slightly and similarly across preparations.
In this study, automatic alignment algorithm was developed for wafer dicing.
A time alignment algorithm was used to compensate for differences in utterance lengths and misalignments between phonemes.
In order to evaluate the applicability of our approach, the proposed alignment algorithm was used in the development of a new gene prediction tool called Exon_Finder2.
