Sentence examples for large amounts of alignment from inspiring English sources

With the advent of NGS there has been a need to further develop Artemis to be able to visualize the large amounts of alignment data being produced.

The key component of this tool is a graphic interface with a 2D map that displays large amounts of alignments between metagenomic sequences from one or more samples and a reference genome (Fig. 1).

The presented clustering approach facilitates clasp to tackle large amounts of short match data by alignment heuristics such as segemehl or BLAST.

The comparison of the differences in alignments is not straightforward with large amounts of indels, as even the same differences can often produce different alignments with the same alignment score.

The reduced 9-taxon alignment, where parts with large amounts of missing data (due to long indels or unsequenced regions) in many sequences were excluded, contained 4045 bp.

The tool is able to cluster large amounts of data containing noise and to produce multiple sequence alignments of resulting clusters.

Automated alignment methods are clearly necessary and useful where large amounts of data are to be processed or in situations where no additional expert information is available.

A second smaller alignment, representing 30 taxa, was constructed by removing sequences with large amounts of missing data, as well as sequences demonstrated to produce phylogenetic artifacts in previous analyses.

Identification of combinations of transcriptional regulatory elements from large amounts of DNA sequences can benefit greatly from proposed bioinformatics methods as the multiple alignments and the LGM.

Due to large amounts of sequencing data available for these repeats their composition was investigated using an alignment-free approach employing oligomer frequency statistics (Macas et al. 2010).

Because the ground truth labels obtained from forced alignment of the reference transcription are not required, this lets us use large amounts of untranscribed data to increasingly better approximate the function learned by the expensive-to-evaluate NN.