Multilocus genomic data sets can be used to infer a rich set of information about the evolutionary history of a lineage, including gene trees, species trees, and phylogenetic networks. However, user‐friendly tools to run such integrated analyses are lacking, and workflows often require tedious reformatting and handling time to shepherd data through a series of individual programs. Here, we present a tool written in Python—TREEasy—that performs automated sequence alignment (with MAFFT), gene tree inference (with IQ‐Tree), species inference from concatenated data (with IQ‐Tree and RaxML‐NG), species tree inference from gene trees (with ASTRAL, MP‐EST, and STELLS2), and phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires FASTA files and nine parameters as inputs. The tool can be run as command line or through a Graphical User Interface (GUI). As examples, we reproduced a recent analysis of staghorn coral evolution, and performed a new analysis on the evolution of the “WGD clade” of yeast. The latter revealed novel patterns that were not identified by previous analyses. TREEasy represents a reliable and simple tool to accelerate research in systematic biology (https://github.com/MaoYafei/TREEasy).

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom's predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and ExpectationMaximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom's CC subsets.

19Multilocus genomic datasets can be used to infer a rich set of information about the 20 evolutionary history of a lineage, including gene trees, species trees, and phylogenetic 21 networks. However, user-friendly tools to run such integrated analyses are lacking, 22and workflows often require tedious reformatting and handling time to shepherd data 23 through a series of individual programs. Here, we present a tool written in Python-24TREEasy-that performs automated sequence alignment (with MAFFT), gene tree 25 inference (with IQ-Tree), species inference from concatenated data (with IQ-Tree), 26 species tree inference from gene trees (with ASTRAL, MP-EST, and STELLS2), and 27 phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires 28 FASTA files and nine parameters as inputs. The Tool can be run as command line or 29 through a Graphical User Interface (GUI). As examples, we reproduced a recent 30 analysis of staghorn coral evolution, and performed a new analysis on the evolution of 31 the WGD clade of yeast. The latter revealed novel inferences that were not identified 32 by previous analyses. TREEasy represents a reliable and simple tool to accelerate 33 research in systematic biology (https://github.com/MaoYafei/TREEasy). 34 35

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