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These mask heterogeneities and 'coldspots' of low coverage that may allow diseases to persist, even if overall coverage is high.
We here develop VirMAP to address the current challenges in identification and classification of low coverage and highly divergent viruses within metagenomic datasets.
In areas of low coverage, it is possible that the measured signal becomes indistinguishable from the background noise.
First, the EULER-SR assembler was used to recover sequence regions that were error prone or of low coverage.
As these individuals share parental origin, we hoped to detect additional variants above calling thresholds at regions of low coverage.
Alternatively, for clones involved in regions of low coverage, we could construct more clones targeted at those regions using known read sequence as "hooks".
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We also observed that regions of low-coverage are associated with low-complexity DNA.
The utility of low-coverage next-generation sequencing has been limited in the absence of a reference genome.
GBS of low-coverage samples is analytically challenging.
The knowledge of the intragenomic SNVs can also optimize the filtering of low-coverage cases.
Two components underlie the costs of low-coverage sequencing: sample preparation and actual sequencing.
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