Sentence examples for better alignment than the from inspiring English sources

Although helixes are conserved in number, conservation of amino acid residues is extremely poor among the same helix for different regulators, with the exception of the first helix, α4, which produces a notably better alignment than the others.

If on the query sequence a short chain is fully contained in a long chain and the number of seeds in the short chain is below one-tenth of the number of seeds in the long chain, we discard all the seeds in the short chain, based on the observation that the short chain can rarely lead to a better alignment than the long chain in this case.

Different from the GaN epilayers, the InN nanorods grown on the Ti films did not show obvious better alignment than those grown on the bare glass substrates (Fig. 3a, d).

By comparing Fig. 3e, f, we can see that the nanorods on template C have better alignment than those grown on template B. XRD spectrum indicates that the nanorods are grown along the c-axis of wurtzite InN.

The nanorods grown on the HT-GaN/Ti template show much better alignment than those grown on Ti or LT-GaN/Ti templates.

From Fig. 3b, e, we can see that the InN nanorods grown on template B have much better alignment than those grown on bare glass or template A. Most of the nanorods are vertically aligned with the c-axis growth orientation, as indicated by the XRD spectrum (Fig. 4a).

Experimental results show that CNFpred generates significantly better alignments than the best profile-based and threading methods on several public (but small) benchmarks as well as our own large dataset.

Tested on public (but small) benchmarks and our large-scale in-house datasets, CNFpred generates significantly better alignments than the best profile-based method [HHpred (Hildebrand et al., 2009; Karplus et al., 1998)] and several top threading methods including BThreader (Peng and Xu, 2009), Sparks (Zhou and Zhou, 2005) and MUSTER (Wu and Zhang, 2008).

On this set of remote homologues, the more expensive method does not produce better alignments than those using a conventional substitution matrix.

The junction candidate sequence is extended on each end by adding enough bases to ensure that if the longest read in the input data set mapped to the junction across its breakpoint it would have a better alignment score than the best match to anywhere in the reference genome.

If the tRNA sequence was random, one would expect better alignments than with its expected tRNA homologue for half the tRNAs with non-cognates, hence 9.5 among 19.