During mammalian preimplantation development, the cells of the blastocyst's inner cell mass differentiate into the epiblast and primitive endoderm lineages, which give rise to the fetus and extra-embryonic tissues, respectively. Extra-embryonic endoderm (XEN) differentiation can be modeled in vitro by induced expression of GATA transcription factors in mouse embryonic stem cells. Here, we use this GATA-inducible system to quantitatively monitor the dynamics of global proteomic changes during the early stages of this differentiation event and also investigate the fully differentiated phenotype, as represented by embryo-derived XEN cells. Using mass spectrometry-based quantitative proteomic profiling with multivariate data analysis tools, we reproducibly quantified 2,336 proteins across three biological replicates and have identified clusters of proteins characterized by distinct, dynamic temporal abundance profiles. We first used this approach to highlight novel marker candidates of the pluripotent state and XEN differentiation. Through functional annotation enrichment analysis, we have shown that the downregulation of chromatin-modifying enzymes, the reorganization of membrane trafficking machinery, and the breakdown of cell-cell adhesion are successive steps of the extra-embryonic differentiation process. Thus, applying a range of sophisticated clustering approaches to a time-resolved proteomic dataset has allowed the elucidation of complex biological processes which characterize stem cell differentiation and could establish a general paradigm for the investigation of these processes. STEM CELLS 2015;33:2712-2725 SIGNIFICANCE STATEMENTAs cells specialize to carry out specific tasks during the development of embryos, the collection of proteins that they express (the proteome) changes in a defined and coordinated manner. In this work we map these changes during the targeted differentiation of embryonic stem cells into one of the first specialized cell types that appears in mammalian development. This roadmap of proteome change will be an important reference to better understand how changes in the building blocks of cells drive the change of a cell's function, and will be useful to validate future efforts in which immature stem cells are being differentiated into cell types for regenerative approaches.

Motivation: Simple bioinformatic tools are frequently used to analyse time-series datasets regardless of their ability to deal with transient phenomena, limiting the meaningful information that may be extracted from them. This situation requires the development and exploitation of tailor-made, easy-to-use and flexible tools designed specifically for the analysis of time-series datasets.Results: We present a novel statistical application called CLUSTERnGO, which uses a model-based clustering algorithm that fulfils this need. This algorithm involves two components of operation. Component 1 constructs a Bayesian non-parametric model (Infinite Mixture of Piecewise Linear Sequences) and Component 2, which applies a novel clustering methodology (Two-Stage Clustering). The software can also assign biological meaning to the identified clusters using an appropriate ontology. It applies multiple hypothesis testing to report the significance of these enrichments. The algorithm has a four-phase pipeline. The application can be executed using either command-line tools or a user-friendly Graphical User Interface. The latter has been developed to address the needs of both specialist and non-specialist users. We use three diverse test cases to demonstrate the flexibility of the proposed strategy. In all cases, CLUSTERnGO not only outperformed existing algorithms in assigning unique GO term enrichments to the identified clusters, but also revealed novel insights regarding the biological systems examined, which were not uncovered in the original publications.Availability and implementation: The C++ and QT source codes, the GUI applications for Windows, OS X and Linux operating systems and user manual are freely available for download under the GNU GPL v3 license at http://www.cmpe.boun.edu.tr/content/CnG.Contact: sgo24@cam.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Tracking a human head in a complicated scene with changing object pose, illumination conditions, and many occluding objects, is the subject of this paper. We present a general tracking algorithm, which uses a combination of object color statistics and object texture features with motion estimation. The object is defined by an ellipse window that is initially selected by the user. Color statistics are obtained by calculating object color histogram in the YCrCb space, with more resolution reserved for chroma components. In addition to the conventional discrete color histogram, a novel method, Uniform Fuzzy Color Histogram (UFCH) is proposed. The object texture is represented by lower frequency components of the object's discrete cosine transform (DCT), and local binary patterns (LBP). By using the tracker, performances of different features and their combinations are tested. The tracking procedure is based on constant velocity motion estimation by condensation particle filter, in which the sample set is obtained by the translation of the object window. Histogram comparison is based on Bhattacharyya coefficient, and DCT comparison is calculated by sum of squared differences (SSD). Similarity measures are joined by combining their independent likelihoods. As the combined tracker follows different features of the same object, it is an improvement over a tracker that makes use of only color statistics or texture information. The algorithm is tested and optimized on the specific application of embedding interactive object information to movies.

ÖZETÇEBu çalışmada, kısa zaman serilerini bölüntülemek için bir sonsuz karışım modeli öneriyoruz. Bileşenleri parçalı dogrusal diziler olan bu modeli Çin lokantası süreci ile inşa ediyoruz ve gözlem atamaları üzerindeki sonsal dagılımı daraltılmış Gibbs örneklemesi ile hesaplıyoruz. Parçalı bir dogrusal dizi, gözlemlerden daha az parametre ile ifade edilmektedir. Dolayısıyla, olabilirligin ortalama parametresi, bileşen parametreleri üzerinde bir matris dönüşümü ile elde edilmektedir. Bu matris, parçalı dogrusal diziyi tanımlayan kurallara göre oluşturulmaktadır. ABSTRACTIn this paper, we present an infinite mixture model to partition short time series data. Components of this mixture model are piecewise linear sequences. The model is constructed using Chinese restaurant process and the posterior distribution over the sample assignments are calculated using collapsed Gibbs sampling. A piecewise linear sequence is represented by fewer parameters than its observations. Thus, the mean parameter of the likelihood is obtained by applying a matrix transformation on the component parameters. This matrix is constructed by a special method according to the rules that define our piecewise linear sequences.

No abstract