Abstract Background Due to the difficulty in separating two (paternal and maternal) copies of a chromosome, most published human genome sequences only provide genotype information, i.e., the mixed information of the underlying two haplotypes. However, phased haplotype information is needed to completely understand complex genetic polymorphisms and to increase the power of genome-wide association studies for complex diseases. With the rapid development of DNA sequencing technologies, reconstructing a pair of haplotypes from an individual's aligned DNA fragments by computer algorithms (i.e., Single Individual Haplotyping) has become a practical haplotyping approach. Results In the paper, we combine two measures "errors corrected" and "fragments cut" and propose a new optimization model, called Balanced Optimal Partition (BOP), for single individual haplotyping. The model generalizes two existing models, Minimum Error Correction (MEC) and Maximum Fragments Cut (MFC), and could be made either model by using some extreme parameter values. To solve the model, we design a heuristic dynamic programming algorithm H-BOP. By limiting the number of intermediate solutions at each iteration to an appropriately chosen small integer k, H-BOP is able to solve the model efficiently. Conclusions Extensive experimental results on simulated and real data show that when k = 8, H-BOP is generally faster and more accurate than a recent state-of-art algorithm ReFHap in haplotype reconstruction. The running time of H-BOP is linearly dependent on some of the key parameters controlling the input size and H-BOP scales well to large input data. The code of H-BOP is available to the public for free upon request to the corresponding author.

Motivation

Some economically important plants including wheat and cotton have more than two copies of each chromosome. With the decreasing cost and increasing read length of next-generation sequencing technologies, reconstructing the multiple haplotypes of a polyploid genome from its sequence reads becomes practical. However, the computational challenge in polyploid haplotyping is much greater than that in diploid haplotyping, and there are few related methods.

Results

This article models the polyploid haplotyping problem as an optimal poly-partition problem of the reads, called the Polyploid Balanced Optimal Partition model. For the reads sequenced from a k-ploid genome, the model tries to divide the reads into k groups such that the difference between the reads of the same group is minimized while the difference between the reads of different groups is maximized. When the genotype information is available, the model is extended to the Polyploid Balanced Optimal Partition with Genotype constraint problem. These models are all NP-hard. We propose two heuristic algorithms, H-PoP and H-PoPG, based on dynamic programming and a strategy of limiting the number of intermediate solutions at each iteration, to solve the two models, respectively. Extensive experimental results on simulated and real data show that our algorithms can solve the models effectively, and are much faster and more accurate than the recent state-of-the-art polyploid haplotyping algorithms. The experiments also show that our algorithms can deal with long reads and deep read coverage effectively and accurately. Furthermore, H-PoP might be applied to help determine the ploidy of an organism.

Availability and implementation

https://github.com/MinzhuXie/H-PoPG CONTACT: xieminzhu@hotmail.comSupplementary information: Supplementary data are available at Bioinformatics online.

The identification and cloning of genes conferring mosquito refractoriness to the malaria parasite is critical for understanding malaria transmission mechanisms and holds great promise for developing novel approaches to malaria control. The mosquito midgut is the first major site of interaction between the parasite and the mosquito. Failure of the parasite to negotiate this environment can be a barrier for development and is likely the main cause of mosquito refractoriness. This paper reports a study on Aedes aegypti midgut expressed sequence tag (EST) identification and the determination of genes differentially expressed in mosquito populations susceptible and refractory to the avian malaria parasite Plasmodium gallinaceum. We sequenced a total of 1200 cDNA clones and obtained 1183 high-quality mosquito midgut ESTs that were computationally collapsed into 105 contigs and 251 singlets. All 1200 midgut cDNA clones, together with an additional 102 genetically or physically mapped Ae. aegypti clones, were spotted on single arrays with 12 replicates. Of those interrogated microarray elements, 28 (2.3%) were differentially expressed between the susceptible and refractory mosquito populations. Twenty-seven elements showed at least a two-fold increase in expression in the susceptible population level relative to the refractory population and one clone showed reduced expression. Sequence analysis of these differentially expressed genes revealed that 10 showed no significant similarity to any known genes, 6 clones had matches with unannotated genes of Anopheles gambiae, and 12 clones exhibited significant similarity to known genes. Real-time quantitative RT-PCR of selected clones confirmed the mRNA expression profiles from the microarray analysis.

In this report, we present a mesoporous carbon nanosphere that can target drugs to tumors and image tumor biomarkers. A single-strand DNA (P₀ aptamer) aptavalve was capped on the surface of doxorubicin-loaded oxide mesoporous carbon nanospheres (Dox-OMCN-P₀) through π-π stacking for real-time imaging-guided on-demand targeting drug delivery. The Dox-OMCN-P₀ could not only realize the detection of MUC1 tumor marker with a wide linear range (0.1 - 10.6 μmol/L) and a low detection limit (17.5 nmol) based on different apparatuses, but also achieve in-situ targeting imaging of cellular MUC1 concentration in vitro and in vivo via "off-on" fluorescence biosensing. Much attractively, as a real-time feedback of the diagnostic/imaging outcomes, Dox-OMCN-P₀ accomplished the on-demand targeting drug delivery in quantitative response to MUC1. Controllable chemotherapy with sustained release and pH-sensitiveness, together with the potential photothermal therapy, were also clearly demonstrated. This is a simple but advanced platform, which could well achieve the real-time switchable imaging of cellular mucin for targeting cancer therapy.

The psychological capacity to recognize that others may hold and act on false beliefs has been proposed to reflect an evolved, species-typical adaptation for social reasoning in humans; however, controversy surrounds the developmental timing and universality of this trait. Cross-cultural studies using elicited-response tasks indicate that the age at which children begin to understand false beliefs ranges from 4 to 7 years across societies, whereas studies using spontaneous-response tasks with Western children indicate that false-belief understanding emerges much earlier, consistent with the hypothesis that false-belief understanding is a psychological adaptation that is universally present in early childhood. To evaluate this hypothesis, we used three spontaneous-response tasks that have revealed early false-belief understanding in the West to test young children in three traditional, non-Western societies: Salar (China), Shuar/Colono (Ecuador) and Yasawan (Fiji). Results were comparable with those from the West, supporting the hypothesis that false-belief understanding reflects an adaptation that is universally present early in development.

BACKGROUND & AIMS: The stroma in pancreatic ductal adenocarcinoma (PDAC) contributes to its immunosuppressive nature and therapeutic resistance. Herein we sought to modify signaling and enhance immunotherapy efficacy by targeting multiple stromal components through both intracellular and extracellular mechanisms. METHODS: A murine liver metastasis syngeneic model of PDAC was treated with focal adhesion kinase inhibitor (FAKi), anti-programmed cell death protein 1 (PD-1) antibody, and stromal hyaluronan (HA) degradation by PEGylated recombinant human hyaluronidase (PEGPH20) to assess immune and stromal modulating effects of these agents and their combinations. RESULTS: The results showed that HA degradation by PEGPH20 and reduction in phosphorylated FAK expression by FAKi leads to improved survival in PDAC-bearing mice treated with anti-PD-1 antibody. HA degradation in combination with FAKi and anti-PD-1 antibody increases T-cell infiltration and alters T-cell phenotype toward effector memory T cells. FAKi alters the expression of T-cell modulating cytokines and leads to changes in T-cell metabolism and increases in effector T-cell signatures. HA degradation in combination with anti-PD-1 antibody and FAKi treatments reduces granulocytes, including granulocytic- myeloid-derived suppressor cells and decreases C-X-C chemokine receptor type 4 (CXCR4)-expressing myeloid cells, particularly the CXCR4-expressing granulocytes. Anti-CXCR4 antibody combined with FAKi and anti-PD-1 antibody significantly decreases metastatic rates in the PDAC liver metastasis model. CONCLUSIONS: This represents the first preclinical study to identify synergistic effects of targeting both intracellular and extracellular components within the PDAC stroma and supports testing anti-CXCR4 antibody in combination with FAKi as a PDAC treatment strategy.

Biomarkers lie at the heart of precision medicine. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers usually involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. Here we present SIMMS (Subnetwork Integration for Multi-Modal Signatures): an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We apply SIMMS to multiple data types across five diseases, and in each it reproducibly identifies known and novel subtypes, and makes superior predictions to the best bespoke approaches. To demonstrate its ability on a new dataset, we profile 33 genes/nodes of the PI3K pathway in 1734 FFPE breast tumors and create a four-subnetwork prediction model. This model out-performs a clinically-validated molecular test in an independent cohort of 1742 patients. SIMMS is generic and enables systematic data integration for robust biomarker discovery.

A locus on human chromosome 11q23 tagged by marker rs3802842 was associated with colorectal cancer (CRC) in a genome-wide association study; this finding has been replicated in case-control studies worldwide. In order to identify biologic factors at this locus that are related to the etiopathology of CRC, we used microarray-based target selection methods, coupled to next-generation sequencing, to study 103 kb at the 11q23 locus. We genotyped 369 putative variants from 1,030 patients with CRC (cases) and 1,061 individuals without CRC (controls) from the Ontario Familial Colorectal Cancer Registry. Two previously uncharacterized genes, COLCA1 and COLCA2, were found to be co-regulated genes that are transcribed from opposite strands. Expression levels of COLCA1 and COLCA2 transcripts correlate with rs3802842 genotypes. In colon tissues, COLCA1 co-localizes with crystalloid granules of eosinophils and granular organelles of mast cells, neutrophils, macrophages, dendritic cells and differentiated myeloid-derived cell lines. COLCA2 is present in the cytoplasm of normal epithelial, immune and other cell lineages, as well as tumor cells. Tissue microarray analysis demonstrates the association of rs3802842 with lymphocyte density in the lamina propria (p = 0.014) and levels of COLCA1 in the lamina propria (p = 0.00016) and COLCA2 (tumor cells, p = 0.0041 and lamina propria, p = 6 × 10(-5)). In conclusion, genetic, expression and immunohistochemical data implicate COLCA1 and COLCA2 in the pathogenesis of colon cancer. Histologic analyses indicate the involvement of immune pathways.

Polymorphous low-grade adenocarcinoma (PLGA) is the second most frequent type of malignant tumor of the minor salivary glands. We identified PRKD1 hotspot mutations encoding p.Glu710Asp in 72.9% of PLGAs but not in other salivary gland tumors. Functional studies demonstrated that this kinase-activating alteration likely constitutes a driver of PLGA.

Background

We introduce BPG, a framework for generating publication-quality, highly-customizable plots in the R statistical environment.

Results

This open-source package includes multiple methods of displaying high-dimensional datasets and facilitates generation of complex multi-panel figures, making it suitable for complex datasets. A web-based interactive tool allows online figure customization, from which R code can be downloaded for integration with computational pipelines.

Conclusion

BPG provides a new approach for linking interactive and scripted data visualization and is available at http://labs.oicr.on.ca/boutros-lab/software/bpg or via CRAN at https://cran.r-project.org/web/packages/BoutrosLab.plotting.general.