Entropy of complex relevant components of Boolean networks

Boolean network models of strongly connected modules are capable of capturing the high regulatory complexity of many biological gene regulatory circuits. We study numerically the previously introduced basin entropy, a parameter for the dynamical uncertainty or information storage capacity of a network as well as the average transient time in random relevant components as a function of their connectivity. We also demonstrate that basin entropy can be estimated from time-series data and is therefore also applicable to non-deterministic networks models.Comment: 8 pages, 6 figure

Integrative analysis of key candidate genes and signaling pathways in autoimmune thyroid dysfunction related to anti-CTLA-4 therapy by bioinformatics

Cytotoxic T lymphocyte-associated antigen-4 (CTLA-4), the first immune checkpoint to be targeted clinically, has provided an effective treatment option for various malignancies. However, the clinical advantages associated with CTLA-4 inhibitors can be offset by the potentially severe immune-related adverse events (IRAEs), including autoimmune thyroid dysfunction. To investigate the candidate genes and signaling pathways involving in autoimmune thyroid dysfunction related to anti-CTLA-4 therapy, integrated differentially expressed genes (DEGs) were extracted from the intersection of genes from Gene Expression Omnibus (GEO) datasets and text mining. The functional enrichment was performed by gene ontology (GO) annotation and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis. Protein-protein interaction (PPI) network, module enrichment, and hub gene identification were constructed and visualized by the online Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. A total of 22 and 17 integrated human DEGs in hypothyroidism and hyperthyroidism group related to anti-CTLA-4 therapy were identified, respectively. Functional enrichment analysis revealed 24 GO terms and 1 KEGG pathways in the hypothyroid group and 21 GO terms and 2 KEGG pathways in the hyperthyroid group. After PPI network construction, the top five hub genes associated with hypothyroidism were extracted, including ALB, MAPK1, SPP1, PPARG, and MIF, whereas those associated with hyperthyroidism were ALB, FCGR2B, CD44, LCN2, and CD74. The identification of the candidate key genes and enriched signaling pathways provides potential biomarkers for autoimmune thyroid dysfunction related to anti-CTLA-4 therapy and might contribute to the future diagnosis and management of IRAEs for cancer patients

The allele distribution in next-generation sequencing data sets is accurately described as the result of a stochastic branching process

With the availability of next-generation sequencing (NGS) technology, it is expected that sequence variants may be called on a genomic scale. Here, we demonstrate that a deeper understanding of the distribution of the variant call frequencies at heterozygous loci in NGS data sets is a prerequisite for sensitive variant detection. We model the crucial steps in an NGS protocol as a stochastic branching process and derive a mathematical framework for the expected distribution of alleles at heterozygous loci before measurement that is sequencing. We confirm our theoretical results by analyzing technical replicates of human exome data and demonstrate that the variance of allele frequencies at heterozygous loci is higher than expected by a simple binomial distribution. Due to this high variance, mutation callers relying on binomial distributed priors are less sensitive for heterozygous variants that deviate strongly from the expected mean frequency. Our results also indicate that error rates can be reduced to a greater degree by technical replicates than by increasing sequencing depth

Identity-by-descent filtering of exome sequence data for disease–gene identification in autosomal recessive disorders

Motivation: Next-generation sequencing and exome-capture technologies are currently revolutionizing the way geneticists screen for disease-causing mutations in rare Mendelian disorders. However, the identification of causal mutations is challenging due to the sheer number of variants that are identified in individual exomes. Although databases such as dbSNP or HapMap can be used to reduce the plethora of candidate genes by filtering out common variants, the remaining set of genes still remains on the order of dozens

Differences in use of electronic nicotine delivery systems by smoking status and demographic characteristics among Australian young adults

Issue addressed: Electronic nicotine delivery systems (ENDS) continue to grow in popularity, particularly among young adults. Understanding by whom various ENDS are being used and why within this population segment can assist the development of appropriate interventions targeting those most vulnerable to use and inform policy decisions in this area. Accordingly, this study assessed the demographic characteristics associated with ENDS use among Australian young adults, and reasons for current e-cigarette use. Methods: An online survey was administered to 1116 Australians aged 18-25 years (59% female). Results: Smokers were more likely than non-smokers to report (a) ever use of ENDS (67% vs 28%), (b) ever use of an e-cigarette (64% vs 24%), e-cigar (17% vs 8%) or e-hookah (15% vs 8%) and (c) current use of e-cigarettes (19% vs 6%). Male smokers were more likely than female smokers to be current e-cigarette users. The most common reason for current e-cigarette use was enjoyment. Conclusions: Young male adults appear to be particularly vulnerable to becoming regular e-cigarette users. This demographic group may need to be a primary focus of prevention and intervention efforts. Contrary to popular belief, smoking cessation was not a primary reason for e-cigarette use. So what?: Greater efforts are needed to educate young adults on the harms associated with ENDS use, especially if use is being driven by the perception that they are a harmless means of amusement. Maintaining existing regulations limiting the accessibility of these products is crucial to ensuring widespread use is minimised

Somatic neurofibromatosis type 1 (NF1) inactivation events in cutaneous neurofibromas of a single NF1 patient

Neurofibromatosis type 1 (NF1) (MIM#162200) is a relatively frequent genetic condition that predisposes to tumor formation. The main types of tumors occurring in NF1 patients are cutaneous and subcutaneous neurofibromas, plexiform neurofibromas, optic pathway gliomas, and malignant peripheral nerve sheath tumors. To search for somatic mutations in cutaneous (dermal) neurofibromas, whole-exome sequencing (WES) was performed on seven spatially separated tumors and two reference tissues (blood and unaffected skin) from a single NF1 patient. Validation of WES findings was done using routine Sanger sequencing or Sequenom IPlex SNP genotyping. Exome sequencing confirmed the existence of a known familial splice-site mutation NM_000267.3:c.3113+1G>A in exon 23 of NF1 gene (HGMD ID CS951480) in blood, unaffected skin, and all tumor samples. In five out of seven analyzed tumors, we additionally detected second-hit mutations in the NF1 gene. Four of them were novel and one was previously observed. Each mutation was distinct, demonstrating the independent origin of each tumor. Only in two of seven tumors we detected an additional somatic mutation that was not associated with NF1. Our study demonstrated that somatic mutations of NF1 are likely the main drivers of cutaneous tumor formation. The study provides evidence for the rareness of single base pair level alterations in the exomes of benign NF1 cutaneous tumors.European Journal of Human Genetics advance online publication, 8 October 2014; doi:10.1038/ejhg.2014.210

Combining callers improves the detection of copy number variants from whole-genome sequencing

Copy Number Variants (CNVs) are deletions, duplications or insertions larger than 50 base pairs. They account for a large percentage of the normal genome variation and play major roles in human pathology. While array-based approaches have long been used to detect them in clinical practice, whole-genome sequencing (WGS) bears the promise to allow concomitant exploration of CNVs and smaller variants. However, accurately calling CNVs from WGS remains a difficult computational task, for which a consensus is still lacking. In this paper, we explore practical calling options to reach the best compromise between sensitivity and sensibility. We show that callers based on different signal (paired-end reads, split reads, coverage depth) yield complementary results. We suggest approaches combining four selected callers (Manta, Delly, ERDS, CNVnator) and a regenotyping tool (SV2), and show that this is applicable in everyday practice in terms of computation time and further interpretation. We demonstrate the superiority of these approaches over array-based Comparative Genomic Hybridization (aCGH), specifically regarding the lack of resolution in breakpoint definition and the detection of potentially relevant CNVs. Finally, we confirm our results on the NA12878 benchmark genome, as well as one clinically validated sample. In conclusion, we suggest that WGS constitutes a timely and economically valid alternative to the combination of aCGH and whole-exome sequencing