TY - JOUR AU - Sinha, Swati AU - Lynn, Andrew Michael PY - 2014 DA - 2014/07/30 TI - HMM-ModE: implementation, benchmarking and validation with HMMER3 JO - BMC Research Notes SP - 483 VL - 7 IS - 1 AB - HMM-ModE is a computational method that generates family specific profile HMMs using negative training sequences. The method optimizes the discrimination threshold using 10 fold cross validation and modifies the emission probabilities of profiles to reduce common fold based signals shared with other sub-families. The protocol depends on the program HMMER for HMM profile building and sequence database searching. The recent release of HMMER3 has improved database search speed by several orders of magnitude, allowing for the large scale deployment of the method in sequence annotation projects. We have rewritten our existing scripts both at the level of parsing the HMM profiles and modifying emission probabilities to upgrade HMM-ModE using HMMER3 that takes advantage of its probabilistic inference with high computational speed. The method is benchmarked and tested on GPCR dataset as an accurate and fast method for functional annotation. SN - 1756-0500 UR - https://doi.org/10.1186/1756-0500-7-483 DO - 10.1186/1756-0500-7-483 ID - Sinha2014 ER -