PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.18.344739v1?rss=1

Authors: Hall, G. A., Speed, T. P., Woodruff, C. J.

Abstract:

A simple but effective method for strain-level characterisation of microbial samples using long read data is presented. The method, which relies on having a non-redundant database of reference genomes, differentiates between strains within species and determines their relative abundance. It provides markedly better strain differentiation than that reported for the latest long read tools. Good estimates of relative abundances of highly similar strains present at less than 1% are achievable with as little as 1Gb of reads. Host contamination can be removed without great loss of sample characterisation performance. The method is simple and highly flexible, allowing it to be used for various different purposes, and as an extension of other characterisation tools. A code body implementing the underlying method is freely available.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.345975v1?rss=1

Authors: Banik, A., Ahmed, S. R., Sajib, E. H., Deb, A., Sinha, S., Azim, K. F.

Abstract:

The deeper understanding of metastasis phenomenon and detection of drug targets could be a potential approach to minimize cancer mortality. In this study, attempts were taken to unmask novel therapeutics to prevent metastasis and cancer progression. Initially, we explored the physiochemical, structural and functional insights of three metastasis tumor antigens (MTAs) and evaluated some plant based bioactive compounds as potent MTA inhibitors. From 50 plant metabolites screened, isoflavone, gingerol, citronellal and asiatic acid showed maximum binding affinity with all three MTA proteins. The ADME analysis detected no undesirable toxicity that could reduce the drug likeness properties of top plant metabolites. Moreover, molecular dynamics studies revealed that the complexes were stable and showed minimum fluctuation at molecular level. We further performed ligand based virtual screening to identify similar drug molecules using a large collection of 3,76,342 compounds from DrugBank. The results suggested that several structural analogs (e.g. Tramadol, Nabumetone, DGLA, Hydrocortisone) may act as agonist to block the MTA proteins and inhibit cancer progression at early stage. The study could be useful to develop effective medications against cancer metastasis in future. Due to encouraging results, we highly recommend further in vitro and in vivo trials for the experimental validation of the findings.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.344986v1?rss=1

Authors: Morgenstern, B., Blanke, M.

Abstract:

Phylogenetic placement is the task of placing a query sequence of unknown taxonomic origin into a given phylogenetic tree of a set of reference sequences. Several approaches to phylogenetic placement have been proposed in recent years. The most accurate of them need a multiple alignment of the reference sequences as input. Most of them also need alignments of the query sequences to the multiple alignment of the reference sequences. A major field of application of phylogenetic placement is taxonomic read assignment in metagenomics. Herein, we propose App-SpaM, an efficient alignment-free algorithm for phylogenetic placement of short sequencing reads on a tree of a set of reference genomes. App-SpaM is based on the Filtered Spaced Word Matches approach that we previously developed. Unlike other methods, our approach neither requires a multiple alignment of the reference genomes, nor alignments of the queries to the reference sequences. Moreover, App-SpaM works not only on assembled reference genomes, but can also take reference taxa as input for which only unassembled read sequences are available. The quality of the results achieved with App-SpaM is comparable to the best available approaches to phylogenetic placement. However, since App-SpaM is not based on sequence alignment, it is between one and two orders of magnitude faster than those existing methods.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.345389v1?rss=1

Authors: BRIERE, G., Darbo, E., Thebault, P., Uricaru, R.

Abstract:

Background Facing the diversity of omic data and the difficulty of selecting one result over all those produced by several methods, consensus strategies have the potential to reconcile multiple inputs and to produce robust results. Results Here, we introduce ClustOmics, a generic consensus clustering tool that we use in the context of cancer subtyping. ClustOmics relies on a non-relational graph database, which allows for the simultaneous integration of both multiple omic data and results from various clustering methods. This new tool conciliates input clusterings, regardless of their origin, their number, their size or their shape. ClustOmics implements an intuitive and flexible strategy, based upon the idea of evidence accumulation clustering. It computes co-occurrences of pairs of samples in input clusters and uses this score as a similarity measure to reorganise data into consensus clusters. Conclusion We applied ClustOmics to multi-omic disease subtyping on real TCGA cancer data from ten different cancer types. We showed that ClustOmics is robust to heterogeneous qualities of input partitions, smoothing and reconciling preliminary predictions into high quality consensus clusters, both from a computational and a biological point of view. In this regard, ClustOmics is not meant to compete with other integrative tools, but rather to make profit from their various predictions when no gold-standard metric is available to assess their significance. Availability ClustOmics' source code, released under MIT licence, as well as the results obtained on TCGA cancer data are available on Github: https://github.com/galadrielbriere/ClustOmics.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.343491v1?rss=1

Authors: Janzen, T., Diaz, F.

Abstract:

Hybridization between populations or species results in a mosaic of the two parental genomes. Genome admixture has received increasing attention for its implications in speciation, human evolution, Evolve and Resequencing (E&R) and genetic mapping. However, a thorough understanding of how local ancestry changes after admixture, and how selection affects patterns of local ancestry remains elusive. The complexity of these questions limits analytical treatment, but these scenarios are specifically suitable for simulation. Currently, there is no simulation framework that models whole-genome evolution of local ancestry following admixture. Here, we present the R package GENOMEADMIXR, which uses an individual-based model to simulate genomic patterns of local ancestry following admixture forward in time. GENOMEADMIXR provides user-friendly functions to set up and analyze simulations under evolutionary scenarios with selection, linkage and migration. We show the flexible functionality of the GENOMEADMIXR workflow by demonstrating 1) how to design an E&R simulation using GENOMEADMIXR and 2) how to use GENOMEADMIXR to verify analytical expectations following from the theory of junctions. GENOMEADMIXR provides a mechanistic approach to explore expected genome responses to realistic admixture scenarios. With this package, we aim to aid researchers in testing specific hypotheses based on empirical findings involving admixing populations.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.345090v1?rss=1

Authors: Wang, Y., Hannon, E., Grant, O. A., Gorrie-Stone, T. J., Kumari, M., Mill, J., Zhai, X., McDonald-Maier, K. D., Schalkwyk, L. C.

Abstract:

Sex is an important covariate of epigenome-wide association studies due to its strong influence on DNA methylation patterns across numerous genomic positions. Nevertheless, many samples on the Gene Expression Omnibus (GEO) frequently lack a sex annotation or are incorrectly labelled. Considering the influence that sex imposes on DNA methylation patterns, it is necessary to ensure that methods for filtering poor samples and checking of sex assignment are accurate and widely applicable. In this paper, we presented a novel method to predict sex using only DNA methylation density signals, which can be readily applied to almost all DNA methylation datasets of different formats (raw IDATs or text files with only density signals) uploaded to GEO. We identified 4345 significantly (p < 0.01) sex-associated CpG sites present on both 450K and EPIC arrays, and constructed a sex classifier based on the two first components of PCAs from the two sex chromosomes. The proposed method is constructed using whole blood samples and exhibits good performance across a wide range of tissues. We further demonstrated that our method can be used to identify samples with sex chromosome aneuploidy, this function is validated by five Turner syndrome cases and one Klinefelter syndrome case. The proposed method has been integrated into the wateRmelon Bioconductor package.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.345629v1?rss=1

Authors: Reva, B., Omelchenko, T., Calinawan, A. P., Nair, S., Schadt, E., Tewari, A.

Abstract:

We propose prioritization of prostate cancer patients to PD-L1 checkpoint therapy by assessing activity of IFN-gamma/PD-L1 signaling in tumor from transcriptional profile. To this end, we introduced a new approach for inferring pathway activity and suppression (IPAS) by assessing significance of positioning pathway's genes expression levels at top (activation) or bottom (suppression) in gene expression profile of a given tumor. By ordering tumors along IFN-gamma-PD-L1 axis, we determined distinct "IFN-gamma-depleted" and "IFN-gamma-enriched" immune subtypes, genes involved in immune evasion and potential targets for combination therapy. Using IPAS scoring method, we proposed biomarker panels for accurate ranking tumors along IFN-gamma/PD-L1 axis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.16.343012v1?rss=1

Authors: Raja, K., Steill, J., Ross, I., Tsoi, L. C., Kuusisto, F., Ni, Z., Livny, M., Thomson, J., Stewart, R.

Abstract:

Literature-based discovery (LBD) uncovers undiscovered public knowledge by linking terms A to C via a B intermediate. Existing LBD systems are limited to process certain A, B, and C terms, and many are not maintained. We present SKiM (Serial KinderMiner), a generalized LBD system for processing any combination of A, Bs, and Cs. We evaluate SKiM via the rediscovery of discoveries by Don Swanson, who pioneered LBD. Using only literature from the 19th century up to a year before Swanson's discoveries, SKiM uncovers all five discoveries. We apply SKiM to repurposing drugs for 26 conditions of high prevalence. Manual analysis confirmed 65 discoveries useful for four diseases from Swanson's discoveries from one to 31 years prior to their first validation by clinical trials. SKiM predicts many new potential drug candidates representing prime targets for wet lab validation. SKiM can be applied to any biomedical inquiry sufficiently mentioned in the literature.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.16.343376v1?rss=1

Authors: Wu, S. L., Bennett, J. B., Sanchez C., H. M., Dolgert, A. J., Leon, T. M., Marshall, J. M.

Abstract:

Interest in gene drive technology has continued to grow as promising new drive systems have been developed in the lab and discussions are moving towards implementing field trials. The prospect of field trials requires models that incorporate a significant degree of ecological detail, including parameters that change over time in response to environmental data such as temperature and rainfall, leading to seasonal patterns in mosquito population density. Epidemiological outcomes are also of growing importance, as: i) the suitability of a gene drive construct for release will depend on its expected impact on disease transmission, and ii) initial field trials are expected to have a measured entomological outcome and a modeled epidemiological outcome. We present MGDrivE 2 (Mosquito Gene Drive Explorer 2): an extension of and development from the MGDrivE 1 simulation framework that investigates the population dynamics of a variety of gene drive architectures and their spread through spatially-explicit mosquito populations. Key strengths and improvements of the MGDrivE 2 framework are: i) the ability of parameters to vary with time and induce seasonal population dynamics, ii) an epidemiological module accommodating reciprocal pathogen transmission between humans and mosquitoes, and iii) an implementation framework based on stochastic Petri nets that enables efficient model formulation and flexible implementation. Example MGDrivE 2 simulations are presented to demonstrate the application of the framework to a CRISPR-based homing gene drive system intended to drive a disease-refractory gene into a population, incorporating time-varying temperature and rainfall data, and predict impact on human disease incidence and prevalence. Further documentation and use examples are provided in vignettes at the project's CRAN repository. MGDrivE 2 is an open-source R package freely available on CRAN. We intend the package to provide a flexible tool capable of modeling gene drive constructs as they move closer to field application and to infer their expected impact on disease transmission.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.16.343137v1?rss=1

Authors: Wu, J., Dong, Q., Gui, J., Zhang, J., Su, Y., Chen, K., Thompson, P. M., Caselli, R. J., Reiman, E. M., Ye, J., Wang, Y.

Abstract:

Biomarker assisted preclinical/early detection and intervention in Alzheimer's disease (AD) may be the key to therapeutic breakthroughs. One of the presymptomatic hallmarks of AD is the accumulation of beta-amyloid (A{beta}) plaques in the human brain. However, current methods to detect A{beta} pathology are either invasive (lumbar puncture) or quite costly and not widely available (amyloid PET). Our prior studies show that MRI-based hippocampal multivariate morphometry statistics (MMS) are an effective neurodegenerative biomarker for preclinical AD. Here we attempt to use MRI-MMS to make inferences regarding brain amyloid burden at the individual subject level. As MMS data has a larger dimension than the sample size, we propose a sparse coding algorithm, Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP), to generate a low-dimensional representation of hippocampal morphometry for each individual subject. Then we apply these individual representations and a binary random forest classifier to predict brain A{beta} positivity for each person. We test our method in two independent cohorts, 841 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and 260 subjects from the Open Access Series of Imaging Studies (OASIS). Experimental results suggest that our proposed PASCS-MP method and MMS can discriminate A{beta} positivity in people with mild cognitive impairment (MCI) (Accuracy (ACC)=0.89 (ADNI)) and in cognitively unimpaired (CU) individuals (ACC=0.79 (ADNI) and ACC=0.82 (OASIS)). These results compare favorably relative to measures derived from traditional algorithms, including hippocampal volume and surface area, shape measures based on spherical harmonics (SPHARM) and our prior Patch Analysis-based Surface Sparse-coding and Max-Pooling (PASS-MP) methods.

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