Sentence examples for spurious calls from inspiring English sources

The classification model trained by RandomForests can detect near 90% spurious calls owing to misalignments.

We also describe the post-processing methods utilized in VARiD to filter some types of spurious calls.

This allowed for significant refinement of the list of CNVs in the population, eliminating all singleton CNV occurrences in the population – usually the most enriched for spurious calls.

To avoid these spurious calls caused by erroneous end parts of intraspecies alignments, we skip these tests when the maximum descent covers almost the entire length of the paralogs (over 80%).

This held true despite high specificity thresholds implemented to reduce spurious calls, suggesting that relying on only one of these platforms risks eliminating a substantial amount of the underlying variation.

Our preliminary manual inspection of randomly selected gene clusters revealed that the majority of the predicted AS isoforms corresponded to spurious calls including RNA degradation products, sequence gaps denoted by Ns that were introduced in the scaffolding step and clustering of unrelated sense-antisense transcripts among others (Additional files 5 and 6).

However, at regular intervals, we have to rehearse the arguments about why accused men should not be given the cover of anonymity in some spurious call for equality.

Family data are very useful for CNV analysis because the accuracy of calls is increased, as a spurious call in both a parent and child is very unlikely.

In order to avoid spurious calling of SNPs due to mapping of reads from paralogous genomic regions, we downloaded UCSC tracks of Crg (36mer) and Duke (35mer) Alignability scores, as well as DAC blacklisted regions; SNPs and indels that fell into non-unique regions of the genome (with Alignability scores <1.0) were excluded from further analyses, as were variants within in UCSC blacklisted regions.

The approach used to filter out spurious SNP calls due to base calling errors involves clipping the end of reads, so that only the portion of reads with high-confidence bases is used for alignment and variant calling.

Also, aligning to a reference that differs too much from the reads may result in reference bias in variant calling or spurious variant calling due to poor alignments [ 4].