Gibsonthrane7688

Prediction of protein complexes from protein-protein interaction (PPI) networks is an important problem in systems biology, as they control different cellular functions. The existing solutions employ algorithms for network community detection that identify dense subgraphs in PPI networks. However, gold standards in yeast and human indicate that protein complexes can also induce sparse subgraphs, introducing further challenges in protein complex prediction.

To address this issue, we formalize protein complexes as biclique spanned subgraphs, which include both sparse and dense subgraphs. We then cast the problem of protein complex prediction as a network partitioning into biclique spanned subgraphs with removal of minimum number of edges, called coherent partition. Since finding a coherent partition is a computationally intractable problem, we devise a parameter-free greedy approximation algorithm, termed Protein Complexes from Coherent Partition (PC2P), based on key properties of biclique spanned subgraphs. Through comparison with nine contenders, we demonstrate that PC2P (1) successfully identifies modular structure in networks, as a prerequisite for protein complex prediction, (2) outperforms the existing solutions with respect to a composite score of five performance measures on 75% and 100% of the analyzed PPI networks and gold standards in yeast and human, respectively, and (3,4) does not compromise GO semantic similarity and enrichment score of the predicted protein complexes. Therefore, our study demonstrates that clustering of networks in terms of biclique spanned subgraphs is a promising framework for detection of complexes in PPI networks.

https//github.com/SaraOmranian/PC2P.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

Recent advances of long-term time-lapse microscopy have made it easy for researchers to quantify cell behavior and molecular dynamics at single-cell resolution. However, the lack of easy-to-use software tools optimized for customized research is still a major challenge for quantitatively understanding biological processes through microscopy images. Here, we present CellTracker, a highly integrated graphical user interface software, for automated cell segmentation and tracking of time-lapse microscopy images. It covers essential steps in image analysis including project management, image pre-processing, cell segmentation, cell tracking, manually correction, and statistical analysis such as the quantification of cell size and fluorescence intensity, etc. Furthermore, CellTracker provides an annotation tool and supports model training from scratch, thus proposing a flexible and scalable solution for customized dataset analysis.

CellTracker is an open-source software under the GPL-3.0 license. It is implemented in Python and provides an easy-to-use graphical user interface. The source code, instruction manual, and demos can be found at https//github.com/WangLabTHU/CellTracker.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.

The rapid progresses of high throughput sequencing technology-based omics and mass spectrometry (MS)-based proteomics such as data-independent acquisition (DIA) and its penetration to clinical studies have generated increasing number of proteomic data sets containing 100 s-1000s samples. To analyze these quantitative proteomic data sets and other -omics data sets more efficiently and conveniently, we present a web server-based software tool ProteomeExpert implemented in Docker, which offers various analysis tools for experimental design, data mining, interpretation, and visualization of quantitative proteomic data sets. ProteomeExpert can be deployed on an operating system with Docker installed or with R language environment.

The Docker image of ProteomeExpert is freely available from https//hub.docker.com/r/lifeinfo/proteomeexpert. The source code of ProteomeExpert is also openly accessible at http//www.github.com/lifeinfo/ProteomeExpert/. In addition, a demo server is provided at https//proteomic.shinyapps.io/peserver/.

SUPPLEMENTARY DATA ARE AVAILABLE AT BIOINFORMATICS ONLINE.

SUPPLEMENTARY DATA ARE AVAILABLE AT BIOINFORMATICS ONLINE.

Cancer is a highly heterogeneous disease, and virtually all types of cancer have subtypes. Understanding the association between cancers subtypes and genetic variations is fundamental to the development of targeted therapies for patients. Somatic mutation plays important roles in tumor development and has emerged as a new type of genetic variations for studying the association with cancer subtypes. However, the low prevalence of individual mutations poses a tremendous challenge to the related statistical analysis.

In this article, we propose an approach, SASOM, for the association analysis of cancer subtypes with somatic mutations. Our approach tests the association between a set of somatic mutations (from a genetic pathway) and subtypes, while incorporating functional information of the mutations into the analysis. We further propose a robust p-value combination procedure, DAPC, to synthesize statistical significance from different sources. Artenimol Simulation studies show that the proposed approach has correct type I error and tends to be more powerful than possible alternative methods. In a real data application, we examine the somatic mutations from a cutaneous melanoma dataset, and identify a genetic pathway that is associated with immune-related subtypes.

The SASOM R package is available at https//github.com/rksyouyou/SASOM-pkg. R scripts and data are available at https//github.com/rksyouyou/SASOM-analysis.

Supplementary data are available at Bioinformatics online.

Supplementary data are available at Bioinformatics online.For organic semiconductor crystals exhibiting anisotropic charge transport along different crystallographic directions, nanoconfinement is a powerful strategy to control crystal orientation by aligning the fast crystallographic growth direction(s) with the unconfined axis(es) of nanoconfining scaffolds. Here, design rules are presented to relate crystal morphology, scaffold geometry, and orientation control in solution-processed small-molecule crystals. Specifically, organic semiconductor triisopropylsilylethynyl pyranthrene needle-like crystals with a dimensionality of n = 1 and perylene platelike crystals with n = 2 were grown from solution within nanoconfining scaffolds comprising cylindrical nanopores with a dimensionality of m = 1, representing one unconfined dimension along the cylinder axis, and those comprising nanopillar arrays with a dimensionality of m = 2. For m = n systems, native crystal growth habits were preserved while the crystal orientation in n = m direction(s) was dictated by the geometry of the scaffold.