Sentence examples for merging assemblies from from inspiring English sources

It is reasonable that merging assemblies from multiple assemblers might yield a combined assembly with higher accuracy.

For the multiple k-mer assembler Trans-ABySS, merging assemblies from individual samples had the following shortcomings: shorter N50 (Table  3), fewer number of mappable reads, reduced proportions of bases covered from full-length cDNA and public ESTs, and slightly increased redundancy (Table  4).

Nonetheless, it also appears that merging assemblies from a diversity of algorithms could be beneficial.

Alternatively, merging Trinity assemblies slightly decreased the base coverage of full-length transcripts (Table  5) and N50 (Table  3), suggesting that merging assemblies from individual samples decreased continuity.

After merging assemblies from individual samples, the overall contig numbers from these six assemblies were still very high (ranging from 227,879 to 720,131), even when considering the various transcript isoforms (Table  3, Additional file 2: Table S1).

Based on these results, it seems that an alignment-based approach to merge assemblies from different algorithms can indeed yield an improvement compared with single high-quality assemblies.

The Oases-M pipeline was used to merge assemblies from single k-mer Oases assemblies either with all transcripts from k = 19 to 71 (Oases-M-wide-range, or MW), or with a narrower range of k = 21, 31, 41, 51 and 61 (Oases-M-narrow-range, or MN).

For the single k-mer assembler Trinity, merging assemblies of individual samples increased the number of mappable reads (Table  4) and the proportion of bases covered from full-length cDNA and public ESTs (Tables  6, 7), suggesting that the merging strategies broadened the coverage of the assemblies produced by Trinity.

Whereas previously the six assemblies had generated from 12,000 to 36,000 contigs and spanned 14.5 Mb to 21.4 Mb (Table 4), we found that merging assemblies resulted in a much narrower range of contig numbers and total span (Table 9).

For multiple RNA-Seq samples, Cufflinks first constructs a set of isoforms from multiple samples, followed by Cuffmerge merging assembly results from each individual sample.

However, merging assembly results from different runs is not a straightforward task.