Modeling sequencing errors by combining Hidden Markov models

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Modeling sequencing errors by combining Hidden Markov models

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 ...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2003

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btg1067