Modeling sequencing errors by combining Hidden Markov models

URN to cite this document:: urn:nbn:de:bvb:355-epub-329497
DOI to cite this document:: 10.5283/epub.32949

Lottaz, Claudio ; Iseli, Christian ; Jongeneel, C. Victor ; Bucher, Philipp

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Date of publication of this fulltext: 02 Dec 2015 10:12

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Abstract

Among the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone ...