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Filtering positions according to known sites of polymorphisms in the 1000 Genome and HapMap datasets, and with at least two reads being present in the Bisulfite sequencing data carrying each known allele, allowed us to capture heterozygote sites with a very low false-positive rate.
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We present computational methods for fast detection of complex variants and splicing in short reads, based on a successively constrained search process of merging and filtering position lists from a genomic index.
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Multi-individual hotspots off the BCS coast were determined through an effort-weighted kernel density analysis of 9244 filtered positions to derive an index of turtle residence probability per unit area.
Filtered positions were ascertained from mutations in 2472 EMPOP sequences.
Prior to this, repeats and low complexity regions were filtered out [ 9] and the sequences were fragmented into all possible overlapping 19-mers excluding those containing filtered positions.
At the end, the genomic coordinates of the central positions of the 19-mers containing filtered positions or matching to others were stored.
In order to neutralize biases due to poor high number of indels and low consensus values( high amino acid variability), we filtered out positions with consensus levels of less than 90%, and number of gaps of more than 10% (resulting in ~ 4%, 6%, 3 %, 3 filtered positions in serotypes 1 - 4 respectively).
Thereafter, I constructed the filtered position time series at the GEONET stations using Eq. (6) with mode 1 only.
I subsequently subtracted the estimated CMEs from the position time series to obtain the filtered position time series (Wdowinski et al., 1997).
Hence, I construct the filtered position time series at the GEONET stations using Eq. (6) with modes 1 and 2. Fig. 11.
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