Deep Genomic-Scale Analyses of the Metazoa Reject Coelomata: Evidence from Single- and Multigene Families Analyzed Under a Supertree and Supermatrix Paradigm

Solving the phylogeny of the animals with bilateral symmetry has proven difficult. Morphological studies have suggested a variety of alternative hypotheses, of which, Hyman’s Coelomata hypothesis has become the most established. Studies based on 18S rRNA have failed to endorse Coelomata, supporting instead the rearrangement of the protostomes into two new clades: the Lophotrochozoa (including, e.g., the molluscs and the annelids) and the Ecdysozoa (including the Panarthropoda and most pseudocoelomates, such as the nematodes and priapulids). Support for this new animal phylogeny has been attained from expressed sequence tag studies, although these generally have a limited gene sampling. In contrast, deep genomic-scale analyses have often supported Coelomata. However, these studies are problematic due to their limited taxonomic sampling, which could exacerbate tree reconstruction artifacts

Data from: The shape of modern tree reconstruction methods

One characteristic of inferred phylogenetic trees is their shape. Rooted binary trees are more balanced, or symmetric, to the extent that sister groups contain similar number of leaves. Premised on the idea that macroevolutionary processes may leave a strong signature in the shape of phylogenetic trees, inferred tree shapes can be compared to expectations under probabilistic models of speciation and extinction in an attempt to make macroevolutionary inferences (e.g. Harvey and Purvis, 1991; Kirpatrick and Slatkin, 1993; Guyer and Slowinski, 1993; Mooers and Heard, 1997; Bortolussi et al., 2006)

The shape of modern tree reconstruction methods

One characteristic of inferred phylogenetic trees is their shape. Rooted binary trees are more balanced, or symmetric, to the extent that sister groups contain similar number of leaves. Premised on the idea that macroevolutionary processes may leave a strong signature in the shape of phylogenetic trees, inferred tree shapes can be compared to expectations under probabilistic models of speciation and extinction in an attempt to make macroevolutionary inferences (e.g. Harvey and Purvis, 1991; Kirpatrick and Slatkin, 1993; Guyer and Slowinski, 1993; Mooers and Heard, 1997; Bortolussi et al., 2006).SystBiol_TreeData.tarSystBiol_TreeDataFinal.tar.gzSupplementary MaterialsSupplementary materialsSupplementary_Materials.pd

CPPpred: prediction of cell penetrating peptides

Summary: Cell penetrating peptides (CPPs) are attracting much attention as a means of overcoming the inherently poor cellular uptake of various bioactive molecules. Here, we introduce CPPpred, a web server for the prediction of CPPs using a N-to-1 neural network. The server takes one or more peptide sequences, between 5 and 30 amino acids in length, as input and returns a prediction of how likely each peptide is to be cell penetrating. CPPpred was developed with redundancy reduced training and test sets, offering an advantage over the only other currently available CPP prediction method.Enterprise IrelandScience Foundation Irelan

SystBiol_TreeData.tar

SystBiol_TreeData.ta

CPPpred: prediction of cell penetrating peptides

Summary: Cell penetrating peptides (CPPs) are attracting much attention as a means of overcoming the inherently poor cellular uptake of various bioactive molecules. Here, we introduce CPPpred, a web server for the prediction of CPPs using a N-to-1 neural network. The server takes one or more peptide sequences, between 5 and 30 amino acids in length, as input and returns a prediction of how likely each peptide is to be cell penetrating. CPPpred was developed with redundancy reduced training and test sets, offering an advantage over the only other currently available CPP prediction method.Enterprise IrelandScience Foundation Irelan

Supplementary Materials

Supplementary material

PeptideLocator: prediction of bioactive peptides in protein sequences

Motivation: Peptides play important roles in signalling, regulation and immunity within an organism. Many have successfully been used as therapeutic products often mimicking naturally occurring peptides. Here we present PeptideLocator for the automated prediction of functional peptides in a protein sequence. Results: We have trained a machine learning algorithm to predict bioactive peptides within protein sequences. PeptideLocator performs well on training data achieving an area under the curve of 0.92 when tested in 5-fold cross-validation on a set of 2202 redundancy reduced peptide containing protein sequences. It has predictive power when applied to antimicrobial peptides, cytokines, growth factors, peptide hormones, toxins, venoms and other peptides. It can be applied to refine the choice of experimental investigations in functional studies of proteins.Enterprise IrelandScience Foundation Irelan

PeptideLocator: prediction of bioactive peptides in protein sequences

Motivation: Peptides play important roles in signalling, regulation and immunity within an organism. Many have successfully been used as therapeutic products often mimicking naturally occurring peptides. Here we present PeptideLocator for the automated prediction of functional peptides in a protein sequence. Results: We have trained a machine learning algorithm to predict bioactive peptides within protein sequences. PeptideLocator performs well on training data achieving an area under the curve of 0.92 when tested in 5-fold cross-validation on a set of 2202 redundancy reduced peptide containing protein sequences. It has predictive power when applied to antimicrobial peptides, cytokines, growth factors, peptide hormones, toxins, venoms and other peptides. It can be applied to refine the choice of experimental investigations in functional studies of proteins.Enterprise IrelandScience Foundation Irelan

Peptigram: a web-based application for peptidomics data visualization

Tandem mass spectrometry (MS/MS) techniques, developed for protein identification, are increasingly being applied in the field of peptidomics. Using this approach, the set of protein fragments observed in a sample of interest can be determined to gain insights into important biological processes such as signaling and other bioactivities. As the peptidomics era progresses, there is a need for robust and convenient methods to inspect and analyze MS/MS derived data. Here, we present Peptigram, a novel tool dedicated to the visualization and comparison of peptides detected by MS/MS. The principal advantage of Peptigram is that it provides visualizations at both the protein and peptide level, allowing users to simultaneously visualize the peptide distributions of one or more samples of interest, mapped to their parent proteins. In this way rapid comparisons between samples can be made in terms of their peptide coverage and abundance. Moreover, Peptigram integrates and displays key sequence features from external databases and links with peptide analysis tools to offer the user a comprehensive peptide discovery resource. Here, we illustrate the use of Peptigram on a data set of milk hydrolysates. For convenience, Peptigram is implemented as a web application, and is freely available for academic use at http://bioware.ucd.ie/peptigram.Enterprise IrelandIrish Research CouncilScience Foundation IrelandFood for Health Irelan