000114896 001__ 114896
000114896 005__ 20181203021032.0
000114896 037__ $$aARTICLE
000114896 245__ $$aModeling sequencing errors by combining Hidden Markov models
000114896 269__ $$a2003
000114896 260__ $$c2003
000114896 336__ $$aJournal Articles
000114896 500__ $$aSwiss Institute of Bioinformatics, Switzerland. Claudio.Lottaz@molgen.mpg.de
000114896 520__ $$aAmong the largest resources for biological sequence data is the large amount of expressed sequence tags (ESTs) available in public and proprietary databases. ESTs provide information on transcripts but for technical reasons they often contain sequencing errors. Therefore, when analyzing EST sequences computationally, such errors must be taken into account. Earlier attempts to model error prone coding regions have shown good performance in detecting and predicting these while correcting sequencing errors using codon usage frequencies. In the research presented here, we improve the detection of translation start and stop sites by integrating a more complex mRNA model with codon usage bias based error correction into one hidden Markov model (HMM), thus generalizing this error correction approach to more complex HMMs. We show that our method maintains the performance in detecting coding sequences.
000114896 700__ $$aLottaz, C.
000114896 700__ $$aIseli, C.
000114896 700__ $$aJongeneel, C. V.
000114896 700__ $$g113607$$aBucher, P.$$0244404
000114896 773__ $$j19 Suppl 2$$tBioinformatics$$qii103-12
000114896 909C0 $$xU11780$$0252244$$pGR-BUCHER
000114896 909CO $$pSV$$particle$$ooai:infoscience.tind.io:114896
000114896 937__ $$aGR-BUCHER-ARTICLE-2003-004
000114896 973__ $$rREVIEWED$$sPUBLISHED$$aOTHER
000114896 980__ $$aARTICLE