Involved with important cellular or gene functions and implicated with many kinds of cancers, piRNAs, or piwi-interacting RNAs, are of small non-coding RNA with around 19–33 nt in length. Given a small non-coding RNA molecule, can we predict whether it is of piRNA according to its sequence information alone? Furthermore, there are two types of piRNA: one has the function of instructing target mRNA deadenylation, and the other does not. Can we discriminate one from the other? With the avalanche of RNA sequences emerging in the postgenomic age, it is urgent to address the two problems for both basic research and drug development. Unfortunately, to the best of our knowledge, so far no computational methods whatsoever could be used to deal with the second problem, let alone deal with the two problems together. Here, by incorporating the physicochemical properties of nucleotides into the pseudo K-tuple nucleotide composition (PseKNC), we proposed a powerful predictor called 2L-piRNA. It is a two-layer ensemble classifier, in which the first layer is for identifying whether a query RNA molecule is piRNA or non-piRNA, and the second layer for identifying whether a piRNA is with or without the function of instructing target mRNA deadenylation. Rigorous cross-validations have indicated that the success rates achieved by the proposed predictor are quite high. For the convenience of most biologists and drug development scientists, the web server for 2L-piRNA has been established at http://bioinformatics.hitsz.edu.cn/2L-piRNA/, by which users can easily get their desired results without the need to go through the mathematical details.