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Sequence substitutions, insertions and deletions (indels) are represented in the generated consensus where the majority of sequence information—even that contained within unaligned sequence regions—supports their presence. At regions where reads deviate sufficiently from the reference sequence, unaligned sequence context is used to locally reassemble a consensus. In this way, Kindel generates a data-specific reference sequence that maximises overall read-reference similarity. An elegant streaming consensus approach to consensus inference was implemented in OCOCO [@ococo], but like other approaches it fails to reconcile indels. +Kindel is a collection of tools for inferring consensus sequence from an alignment of nucleotide sequences in Sequence Alignment/Map (SAM) format [@samtools] in the presence of substitutions, insertions and deletions (indels). At regions where reads deviate sufficiently from the reference sequence, partially unaligned sequence context is used to perform local reassembly. In this way, Kindel generates a data-specific reference sequence that maximises overall read-reference similarity. While an elegant streaming approach to consensus inference was implemented in OCOCO [@ococo], like other approaches it fails to reconcile indels. Kindel was developed for inferring consensus of highly diverse populations of RNA viruses such as hepatitis C and HIV, and is tested with deep sequenced hepatitis C alignments generated by BWA-MEM [@bwa] and Segemehl [@segemehl]. Furthermore, Kindel may be used to quantify and visualise subconsensus variation in allele frequencies across a reference sequence, facilitating comparison of intrapatient population state among multiple individuals and/or timepoints. Kindel is implemented as a Python 3 package with a command line interface. - -### Core functionality - -![clip-dominant region](kindelflow.png) - - - - - -### Reassembly of clip-dominant regions (CDRs) with `--realign` - -![clip-dominant region](cdrs.png) - +![Usage overview.](kindelflow.png) +![Local reassembly of clip-dominant regions (CDRs). Leveraging partially aligned reads, consensus can be accurately inferred across unrepresentative and poorly covered regions of reference sequence.](cdrs.png) # References