Altered assembly paths mitigate interference among paralogous complexes.

Protein complexes are fundamental to all cellular processes, so understanding their evolutionary history and assembly processes is important. Gene duplication followed by divergence is considered a primary mechanism for diversifying protein complexes. Nonetheless, to what extent assembly of present-day paralogous complexes has been constrained by their long evolutionary pathways and how cross-complex interference is avoided remain unanswered questions.

Assessing transcriptomic heterogeneity of single-cell RNASeq data by bulk-level gene expression data.

Background: Single-cell RNA sequencing (sc-RNASeq) data illuminate transcriptomic heterogeneity but also possess a high level of noise, abundant missing entries and sometimes inadequate or no cell type annotations at all. Bulk-level gene expression data lack direct information of cell population composition but are more robust and complete and often better annotated. We propose a modeling framework to integrate bulk-level and single-cell RNASeq data to address the deficiencies and leverage the mutual strengths of each type of data and enable a more comprehensive inference of their transcriptomic heterogeneity.

An integrated analysis of the cancer genome atlas data discovers a hierarchical association structure across thirty three cancer types.

Cancer cells harbor molecular alterations at all levels of information processing. Genomic/epigenomic and transcriptomic alterations are inter-related between genes, within and across cancer types and may affect clinical phenotypes. Despite the abundant prior studies of integrating cancer multi-omics data, none of them organizes these associations in a hierarchical structure and validates the discoveries in extensive external data.

Cooperative stability renders protein complex formation more robust and controllable.

Protein complexes are the fundamental units of many biological functions. Despite their many advantages, one major adverse impact of protein complexes is accumulations of unassembled subunits that may disrupt other processes or exert cytotoxic effects. Synthesis of excess subunits can be inhibited via negative feedback control or they can be degraded more efficiently than assembled subunits, with this latter being termed cooperative stability.

Characterization of gene cluster heterogeneity in single-cell transcriptomic data within and across cancer types.

Despite the remarkable progress in probing tumor transcriptomic heterogeneity by single-cell RNA sequencing (sc-RNAseq) data, several gaps exist in prior studies. Tumor heterogeneity is frequently mentioned but not quantified. Clustering analyses typically target cells rather than genes, and differential levels of transcriptomic heterogeneity of gene clusters are not characterized.

Genotypes of informative loci from 1000 Genomes data allude evolution and mixing of human populations.

Principal Component Analysis (PCA) projects high-dimensional genotype data into a few components that discern populations. Ancestry Informative Markers (AIMs) are a small subset of SNPs capable of distinguishing populations. We integrate these two approaches by proposing an algorithm to identify necessary informative loci whose removal from the data deteriorates the PCA structure.

The C-degron pathway eliminates mislocalized proteins and products of deubiquitinating enzymes.

Protein termini are determinants of protein stability. Proteins bearing degradation signals, or degrons, at their amino- or carboxyl-termini are eliminated by the N- or C-degron pathways, respectively. We aimed to elucidate the function of C-degron pathways and to unveil how normal proteomes are exempt from C-degron pathway-mediated destruction.

Population Histories of the United States Revealed through Fine-Scale Migration and Haplotype Analysis.

The population of the United States is shaped by centuries of migration, isolation, growth, and admixture between ancestors of global origins. Here, we assemble a comprehensive view of recent population history by studying the ancestry and population structure of more than 32,000 individuals in the US using genetic, ancestral birth origin, and geographic data from the National Geographic Genographic Project. We identify migration routes and barriers that reflect historical demographic events.

Correction: Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae.

[This corrects the article DOI: 10.1371/journal.pone.

Modelling bistable tumour population dynamics to design effective treatment strategies.

Despite recent advances in targeted drugs and immunotherapy, cancer remains "the emperor of all maladies" due to almost inevitable emergence of resistance. Drug resistance is thought to be driven by genetic alterations and/or dynamic plasticity that deregulate pathway activities and regulatory programs of a highly heterogeneous tumour. In this study, we propose a modelling framework to simulate population dynamics of heterogeneous tumour cells with reversible drug resistance.

MGSEA - a multivariate Gene set enrichment analysis.

Background: Gene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one platform as inputs. Numerous extensions of GSEA handling multimodal OMIC data are proposed, yet none of them explicitly captures combinatorial relations of feature scores from multiple platforms.

Explaining cancer type specific mutations with transcriptomic and epigenomic features in normal tissues.

Most cancer driver genes are involved in generic cellular processes such as DNA repair, cell proliferation and cell adhesion, yet their mutations are often confined to specific cancer types. To resolve this paradox, we explained mutation frequencies of selected genes across tumor types with four features in the corresponding normal tissues from cancer-free subjects: mRNA expression and chromatin accessibility of mutated genes, mRNA expressions of their neighbors in curated pathways and the protein-protein interaction network. Encouragingly, these transcriptomic/epigenomic features in normal tissues were closely associated with mutational/functional characteristics in tumors.

Exploring candidate biological functions by Boolean Function Networks for Saccharomyces cerevisiae.

The great amount of gene expression data has brought a big challenge for the discovery of Gene Regulatory Network (GRN). For network reconstruction and the investigation of regulatory relations, it is desirable to ensure directness of links between genes on a map, infer their directionality and explore candidate biological functions from high-throughput transcriptomic data. To address these problems, we introduce a Boolean Function Network (BFN) model based on techniques of hidden Markov model (HMM), likelihood ratio test and Boolean logic functions.

Decomposing Oncogenic Transcriptional Signatures to Generate Maps of Divergent Cellular States.

The systematic sequencing of the cancer genome has led to the identification of numerous genetic alterations in cancer. However, a deeper understanding of the functional consequences of these alterations is necessary to guide appropriate therapeutic strategies. Here, we describe Onco-GPS (OncoGenic Positioning System), a data-driven analysis framework to organize individual tumor samples with shared oncogenic alterations onto a reference map defined by their underlying cellular states.

A quantitative model for the rate-limiting process of UGA alternative assignments to stop and selenocysteine codons.

Ambiguity in genetic codes exists in cases where certain stop codons are alternatively used to encode non-canonical amino acids. In selenoprotein transcripts, the UGA codon may either represent a translation termination signal or a selenocysteine (Sec) codon. Translating UGA to Sec requires selenium and specialized Sec incorporation machinery such as the interaction between the SECIS element and SBP2 protein, but how these factors quantitatively affect alternative assignments of UGA has not been fully investigated.

Long range personalized cancer treatment strategies incorporating evolutionary dynamics.

Background: Current cancer precision medicine strategies match therapies to static consensus molecular properties of an individual's cancer, thus determining the next therapeutic maneuver. These strategies typically maintain a constant treatment while the cancer is not worsening. However, cancers feature complicated sub-clonal structure and dynamic evolution.

Analysis of spatial-temporal gene expression patterns reveals dynamics and regionalization in developing mouse brain.

Allen Brain Atlas (ABA) provides a valuable resource of spatial/temporal gene expressions in mammalian brains. Despite rich information extracted from this database, current analyses suffer from several limitations. First, most studies are either gene-centric or region-centric, thus are inadequate to capture the superposition of multiple spatial-temporal patterns.

Putative effectors for prognosis in lung adenocarcinoma are ethnic and gender specific.

Lung adenocarcinoma possesses distinct patterns of EGFR/KRAS mutations between East Asian and Western, male and female patients. However, beyond the well-known EGFR/KRAS distinction, gender and ethnic specific molecular aberrations and their effects on prognosis remain largely unexplored. Association modules capture the dependency of an effector molecular aberration and target gene expressions.

Nonstandard personalized medicine strategies for cancer may lead to improved patient outcomes.

Cancer is an evolutionary process that is driven by mutation and selection. Tumors are genetically unstable, and research has shown that this is the most efficient way for cancers to evolve. Genetic instability leads to genetic heterogeneity and dynamic change within a single individual's tumor, in turn leading to therapeutic resistance.

Suppression of the SOX2 neural effector gene by PRDM1 promotes human germ cell fate in embryonic stem cells.

The mechanisms of transcriptional regulation underlying human primordial germ cell (PGC) differentiation are largely unknown. The transcriptional repressor Prdm1/Blimp-1 is known to play a critical role in controlling germ cell specification in mice. Here, we show that PRDM1 is expressed in developing human gonads and contributes to the determination of germline versus neural fate in early development.

An integrative characterization of recurrent molecular aberrations in glioblastoma genomes.

Glioblastoma multiforme (GBM) is the most common and malignant primary brain tumor in adults. Decades of investigations and the recent effort of the Cancer Genome Atlas (TCGA) project have mapped many molecular alterations in GBM cells. Alterations on DNAs may dysregulate gene expressions and drive malignancy of tumors.

Functional characterization of motif sequences under purifying selection.

Diverse life forms are driven by the evolution of gene regulatory programs including changes in regulator proteins and cis-regulatory elements. Alterations of cis-regulatory elements are likely to dominate the evolution of the gene regulatory networks, as they are subjected to smaller selective constraints compared with proteins and hence may evolve quickly to adapt the environment. Prior studies on cis-regulatory element evolution focus primarily on sequence substitutions of known transcription factor-binding motifs.

Evolution of domain architectures and catalytic functions of enzymes in metabolic systems.

Domain architectures and catalytic functions of enzymes constitute the centerpieces of a metabolic network. These types of information are formulated as a two-layered network consisting of domains, proteins, and reactions-a domain-protein-reaction (DPR) network. We propose an algorithm to reconstruct the evolutionary history of DPR networks across multiple species and categorize the mechanisms of metabolic systems evolution in terms of network changes.

Impact of genetic dynamics and single-cell heterogeneity on development of nonstandard personalized medicine strategies for cancer.

Cancers are heterogeneous and genetically unstable. Current practice of personalized medicine tailors therapy to heterogeneity between cancers of the same organ type. However, it does not yet systematically address heterogeneity at the single-cell level within a single individual's cancer or the dynamic nature of cancer due to genetic and epigenetic change as well as transient functional changes.

Genome-wide gene expression analysis implicates the immune response and lymphangiogenesis in the pathogenesis of fetal chylothorax.

Fetal chylothorax (FC) is a rare condition characterized by lymphocyte-rich pleural effusion. Although its pathogenesis remains elusive, it may involve inflammation, since there are increased concentrations of proinflammatory mediators in pleural fluids. Only a few hereditary lymphedema-associated gene loci, e.