Input: GM (gene model, mostly gtf file), R (reference genome); learning-model: SVM (support vector machine), LR (logistic regression), RF (random forest), REPTree (Reduced Error Pruning Tree), BRF (balanced random forest); Features: consv (sequence conservation), SS (secondary structures), np (nucleotide patterns), MCSS ( maximum coding subsequence), MLCDS (the most-like Coding domain Sequence), Fickett (Fickett TESTCODE score), pI (isoelectric point), socf (Sequence-order correlation factors); Web: T (has web server); F (has not web server)

Software are marked with "*" can only work with limited species. Attribute of model: The key attribute to distingush models for one software. [a], the species of the training data; [b], the species of training data and the way the training data is used. Specifically, "cl" is for that both coding and noncoding sequences are real transcripts, while "sf" is for that noncoding sequences are shuffled from coding sequences; [c] which database is used; [d] the way to processing result; [e] the feature used for training. Group: Models are grouped to perform different comparisons in our research. "J" stands for J-models that were used in joint prediction and "R" stands for representative models.