Sentence examples for alignment reading from inspiring English sources

Grouping regions into chunks allows us to avoid frequent index loading which is very expensive in comparison to alignment reading.

The initial alignment reads and their re-mapped tails composed "piece-alignments", which were then clustered if similarly mapped tails were shared based on location and orientation.

As common aligners can allow a small amount of mismatches during alignment, reads containing SNPs may still be mapped to the reference.

Specifically, for each sample, we mapped the filtered BWA alignment reads to a chromosome and exact base pair locations for input into the CNV-seq software.

Prior to alignment, reads were trimmed using a sliding window of 8nt.

Before alignment, reads with a low quality and adapters were detected using FastQC and removed.

The RNA2MAP mapping tool generated two types of alignment: reads uniquely mapped to miRNAs and reads generating multiple hits.

Based on the mapping results, the alignment reads and unique hit reads in each sample were classified by SOAP2.

Reads that corresponded to flag 1796 in the bam alignment file (flag 1796: read unmapped, not primary alignment, read fail quality check, read is PCR or optical duplicate) were filtered out.

In each alignment reads were manually ordered into groups at each distinguishing SNP position in a graphical sequence editor window (GDE, [ 31]).

Based on the quality of the alignment, reads with a sequence identity of less than 90% and less than 100% sodium bisulfite conversion were filtered out.