Notes

TLX, an Orphan Nuclear Receptor Using Growing Tasks in Structure along with Condition.
On a rigorous 3rd-party benchmarking simulation, it is shown to offer strong control over the false discovery rate, and at sample sizes greater than 50 per treatment group, to offer an improvement in performance over commonly used normalization factors paired with t-tests, Wilcoxon rank-sum tests and methodologies implemented by R packages. On two real datasets, it yielded valid and reproducible results that were strongly in agreement with the original findings and the existing literature, further demonstrating its robustness and future potential. Availability The data underlying this article are available online along with R code and supplementary materials at https//github.com/matthewlouisdavisBioStat/Rank-Normalization-Empowers-a-T-Test.We studied a subset of patients with autopsy-confirmed multiple system atrophy who presented a clinical picture that closely resembled either Parkinson's disease or progressive supranuclear palsy. These mimics are not captured by the current diagnostic criteria for multiple system atrophy. Among 218 autopsy-proven multiple system atrophy cases reviewed, 177 (81.2%) were clinically diagnosed and pathologically confirmed as multiple system atrophy (i.e. typical cases), while the remaining 41 (18.8%) had received an alternative clinical diagnosis, including Parkinson's disease (i.e. Parkinson's disease mimics; n = 16) and progressive supranuclear palsy (i.e. progressive supranuclear palsy mimics; n = 17). We also reviewed the clinical records of another 105 patients with pathologically confirmed Parkinson's disease or progressive supranuclear palsy, who had received a correct final clinical diagnosis (i.e. Parkinson's disease, n = 35; progressive supranuclear palsy-Richardson syndrome, n = 35; and progressive su parkinsonian disorders (Parkinson's disease mimic versus typical Parkinson's disease, OR 4.1; progressive supranuclear palsy mimic versus typical progressive supranuclear palsy, OR 8.8). The atypical multiple system atrophy cases more frequently had autonomic dysfunction within 3 years of symptom onset than the pathologically confirmed patients with Parkinson's disease or progressive supranuclear palsy (Parkinson's disease mimic versus typical Parkinson's disease, OR 4.7; progressive supranuclear palsy mimic versus typical progressive supranuclear palsy, OR 2.7). Using all included clinical features and 21 early clinical features within 3 years of symptom onset, we developed decision tree algorithms with combinations of clinical pointers to differentiate clinically atypical cases of multiple system atrophy from Parkinson's disease or progressive supranuclear palsy.
Metagenomic approaches hold the potential to characterize microbial communities and unravel the intricate link between the microbiome and biological processes. Assembly is one of the most critical steps in metagenomics experiments. It consists of transforming overlapping DNA sequencing reads into sufficiently accurate representations of the community's genomes. This process is computationally difficult and commonly results in genomes fragmented across many contigs. Computational binning methods are used to mitigate fragmentation by partitioning contigs based on their sequence composition, abundance, or chromosome organization into bins representing the community's genomes. Existing binning methods have been principally tuned for bacterial genomes and do not perform favorably on viral metagenomes.

We propose CoCoNet (Composition and Coverage Network), a new binning method for viral metagenomes that leverages the flexibility and the effectiveness of deep learning to model the co-occurrence of contigs belonging to the same viral genome and provide a rigorous framework for binning viral contigs. Our results show that CoCoNet substantially outperforms existing binning methods on viral datasets.

CoCoNet was implemented in Python and is available for download on PyPi. (https//pypi.org/). The source code is hosted on GitHub at https//github.com/Puumanamana/CoCoNet and the documentation is available at https//coconet.readthedocs.io/en/latest/index.html.

Supplementary data are available at Bioinformatics online.
Supplementary data are available at Bioinformatics online.Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. this website Among various obstacles hindering clinical translation, lacking effective methods for multimodal and multisource data integration is becoming a bottleneck. Here we proposed DeepDRK, a machine learning framework for deciphering drug response through kernel-based data integration. To transfer information among different drugs and cancer types, we trained deep neural networks on more than 20 000 pan-cancer cell line-anticancer drug pairs. These pairs were characterized by kernel-based similarity matrices integrating multisource and multi-omics data including genomics, transcriptomics, epigenomics, chemical properties of compounds and known drug-target interactions. Applied to benchmark cancer cell line datasets, our model surpassed previous approaches with higher accuracy and better robustness. Then we applied our model on newly established patient-derived cancer cell lines and achieved satisfactory performance with AUC of 0.84 and AUPRC of 0.77. Moreover, DeepDRK was used to predict clinical response of cancer patients. Notably, the prediction of DeepDRK correlated well with clinical outcome of patients and revealed multiple drug repurposing candidates. In sum, DeepDRK provided a computational method to predict drug response of cancer cells from integrating pharmacogenomic datasets, offering an alternative way to prioritize repurposing drugs in precision cancer treatment. The DeepDRK is freely available via https//github.com/wangyc82/DeepDRK.
When learning to subtype complex disease based on next-generation sequencing data, the amount of available data is often limited. Recent works have tried to leverage data from other domains to design better predictors in the target domain of interest with varying degrees of success. But they are either limited to the cases requiring the outcome label correspondence across domains or cannot leverage the label information at all. Moreover, the existing methods cannot usually benefit from other information available a priori such as gene interaction networks.

In this paper, we develop a generative optimal Bayesian supervised domain adaptation (OBSDA) model that can integrate RNA sequencing (RNA-Seq) data from different domains along with their labels for improving prediction accuracy in the target domain. Our model can be applied in cases where different domains share the same labels or have different ones. OBSDA is based on a hierarchical Bayesian negative binomial model with parameter factorization, for which the optimal predictor can be derived by marginalization of likelihood over the posterior of the parameters. We first provide an efficient Gibbs sampler for parameter inference in OBSDA. Then, we leverage the gene-gene network prior information and construct an informed and flexible variational family to infer the posterior distributions of model parameters. Comprehensive experiments on real-world RNA-Seq data demonstrate the superior performance of OBSDA, in terms of accuracy in identifying cancer subtypes by utilizing data from different domains. Moreover, we show that by taking advantage of the prior network information we can further improve the performance.

The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https//github.com/SHBLK/BSDA.
The source code for implementations of OBSDA and SI-OBSDA are available at the following link. https//github.com/SHBLK/BSDA.Next-generation sequencing studies are dependent on a high-quality reference genome for single nucleotide variant (SNV) calling. Although the two most recent builds of the human genome are widely used, position information is typically not directly comparable between them. Re-alignment gives the most accurate position information, but this procedure is often computationally expensive, and therefore, tools such as liftOver and CrossMap are used to convert data from one build to another. However, the positions of converted SNVs do not always match SNVs derived from aligned data, and in some instances, SNVs are known to change chromosome when converted. This is a significant problem when compiling sequencing resources or comparing results across studies. Here, we describe a novel algorithm to identify positions that are unstable when converting between human genome reference builds. These positions are detected independent of the conversion tools and are determined by the chain files, which provide a mapping of contiguous positions from one build to another. We also provide the list of unstable positions for converting between the two most commonly used builds GRCh37 and GRCh38. Pre-excluding SNVs at these positions, prior to conversion, results in SNVs that are stable to conversion. This simple procedure gives the same final list of stable SNVs as applying the algorithm and subsequently removing variants at unstable positions. This work highlights the care that must be taken when converting SNVs between genome builds and provides a simple method for ensuring higher confidence converted data. Unstable positions and algorithm code, available at https//github.com/cathaloruaidh/genomeBuildConversion.It is pivotal and remains challenge for cancer precision treatment to identify the survival outcome interactions between genes, cells and drugs. Here, we present siGCD, a web-based tool for analysis and visualization of the survival interaction of Genes, Cells and Drugs in human cancers. siGCD utilizes the cancer heterogeneity to simulate the manipulated gene expression, cell infiltration and drug treatment, which overcomes the data and experimental limitations. To illustrate the performance of siGCD, we identified the survival interaction partners of EGFR (gene level), T cells (cell level) and sorafenib (drug level), and our prediction was consistent with previous reports. Moreover, we validate the synergistic effect of regorafenib and glyburide, and found that glyburide could significantly improve the regorafenib response. These results demonstrate that siGCD could benefit cancer precision medicine in a wide range of advantageous application scenarios including gene regulatory network construction, immune cell regulatory gene identification, drug (especially multiple target drugs) response biomarker screening and combination therapeutic design.
Exploring the relationship between human proteins and abnormal phenotypes is of great importance in the prevention, diagnosis and treatment of diseases. The human phenotype ontology (HPO) is a standardized vocabulary that describes the phenotype abnormalities encountered in human diseases. However, the current HPO annotations of proteins are not complete. Thus, it is important to identify missing protein-phenotype associations.

We propose HPOFiller, a graph convolutional network (GCN)-based approach, for predicting missing HPO annotations. HPOFiller has two key GCN components for capturing embeddings from complex network structures 1) S-GCN for both protein-protein interaction (PPI) network and HPO semantic similarity network to utilize network weights; 2) Bi-GCN for the protein-phenotype bipartite graph to conduct message passing between proteins and phenotypes. The core idea of HPOFiller is to repeat run these two GCN modules consecutively over the three networks, to refine the embeddings. Empirical results of extremely stringent evaluation avoiding potential information leakage including cross-validation and temporal validation demonstrates that HPOFiller significantly outperforms all other state-of-the-art methods.
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