No Promoter Left Behind

NPLB finds promoter architectures (PAs) and their corresponding promoter elements (PEs) from a given set of promoter sequences. It is available both as a web based application and as a downloadable software.


No Promoter Left Behind (NPLB) finds the optimal number of promoter architectures (PAs), each with their own set of promoter elements (PEs), from a fasta file of promoter sequences.

NPLB has two commands: promoterLearn to learn new models and promoterClassify to identify new PAs using an existing model.

The following files are saved upon execution:

NPLB can be run with several options in order to optimize the execution time and the results. Note that promoterLearn learns models by varying number of PAs in a method similar to binary search since the cross-validation likelihood for a model typically increases with the number of PAs till the optimal is reached and decreases afterward. In other words, to save time, models are not learned by linearly varying the count of PAs.

Publication: Mitra S. and Narlikar L., No Promoter Left Behind (NPLB): learn de novo promoter architectures from genome-wide transcription start sites, Bioinformatics, 32(5):779-781, 2016. [ Full Text ]

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Image matrix of raw data
Image matrix of PAs found by NPLB