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The remaining 5 AJ markers under SSPI selection gave unreadable sequence data.

Our results showed that GRR produced either similar or even lower prediction accuracies as compared to RR-BLUP, which indicates that marker selection by giving different degree of penalization through the application of different shrinkage effects is inadequate for the tested traits.

Forward selection gave the same result.

For a given upper bound B on the cardinality of the marker set, the marker selection process first selects the top scoring pairs and then sequentially adds additional markers into the active set until the total number of markers in the active set reaches the upper bound B. This is done following the algorithm below: Denote the set of remaining markers as ẞ.

Detailed information on sample collection, marker selection, PCR amplification, and phylogenetic inferences are given in the Text S2, S3, S4, S5 and S6 and Fig. S1.

For a given edge and a given edge orienting score (e.g. LEO.NB.OCA), NEO implements a robustness analysis with respect to automatic marker selection (see Figures 5, 6, and 7).

Second, pairwise comparative marker selection analysis revealed that only a small number of miRNAs are strongly induced or repressed at any given stage of development (Fig. 4c g and Supplementary Data).

However, marker selection is still a fundamental challenge.

The MSSC-MSF uses multispectral-spatial classification for marker selection, and MSF is built on the obtained markers.

The random selection of the markers in the proposed methods is at a higher speed compared to the marker selection done by connected component analysis [22].

The following describes our marker selection criteria.