PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.22.349944v1?rss=1

Authors: Willforss, J., Siino, V., Levander, F.

Abstract:

Visual exploration of gene product behavior across multiple omic datasets can pinpoint technical limitations in data and reveal biological trends. The OmicLoupe software was developed to facilitate such exploration and provides more than 15 interactive cross-dataset visualizations for omic data. It expands visualizations to multiple datasets for quality control, statistical comparisons and overlap and correlation analyses, while allowing for rapid inspection and downloading of selected features. The usage of OmicLoupe is demonstrated in three diverse studies, including an analysis of SARS-CoV-2 infection across omic layers, based on previously published proteomics and transcriptomics studies. OmicLoupe is available at quantitativeproteomics.org/omicloupe .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.348607v1?rss=1

Authors: Olyaee, M. H., Khanteymoori, A.

Abstract:

Evolution of human genetics is one of the most interesting areas for researchers. Determination of Haplotypes not only makes valuable information for this purpose but also performs a major role in investigating the probable relation between diseases and genomes. Determining haplotypes by experimental methods is a time-consuming and expensive task. Recent progress in high throughput sequencing allows researchers to use computational methods for this purpose. Although, several algorithms have been proposed but they are less accurate when the error rate of input fragments increases. In this paper, first, a fuzzy conflict graph is constructed based on the similarities of all input fragments and next, the cluster centers are used as initial centers by fuzzy c-means (FCM) algorithm. The proposed method has been tested on several real datasets and compared with some current methods. The comparison with the existing approaches shows that our method can be a complementary role among the others.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.348193v1?rss=1

Authors: Grau, J., Schmidt, F., Schulz, M. H.

Abstract:

Several studies suggested that transcription factor (TF) binding to DNA may be impaired or enhanced by DNA methylation. We present MeDeMo, a toolbox for TF motif analysis that combines information about DNA methylation with models capturing intra-motif dependencies. In a large-scale study using ChIP-seq data for 335 TFs, we identify novel TFs that are affected by DNA methylation. Overall, we find that CpG methylation decreases the likelihood of binding for the majority of TFs. For a considerable subset of TFs, we show that intra-motif dependencies are pivotal for accurately modelling the impact of DNA methylation on TF binding.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.348532v1?rss=1

Authors: Hardenberg, M., Horvath, A., Ambrus, V., FUXREITER, M., Vendruscolo, M.

Abstract:

A wide range of proteins have been reported to condensate into a dense liquid phase, forming a reversible droplet state. Failure in the control of the droplet state can lead to the formation of the more stable amyloid state, which is often disease-related. These observations prompt the question of how many proteins can undergo liquid-liquid phase separation. Here, in order to address this problem, we discuss the biophysical principles underlying the droplet state of proteins by analyzing current evidence for droplet-driver and droplet-client proteins. Based on the concept that the droplet state is stabilized by the large conformational entropy associated with non-specific side-chain interactions, we develop the FuzDrop method to predict droplet-promoting regions and proteins, which can spontaneously phase separate. We use this approach to carry out a proteome-level study to rank proteins according to their propensity to form the droplet state, spontaneously or via partner interactions. Our results lead to the conclusion that the droplet state could be, at least transiently, accessible to most proteins under conditions found in the cellular environment.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.348417v1?rss=1

Authors: Novaes de Amorim, A., Saini, V., Deardon, R.

Abstract:

Accurate and reliable short-term forecasts of influenza-like illness (ILI) visit volumes at the emergency departments can improve staffing and resource allocation decisions in each hospital. In this paper, we developed a stacked ensemble model that averages the predictions from various competing methodologies in the current frontier for ILI-related forecasts. We also constructed a back-of-the-envelope prediction interval for the stacked ensemble, which provides a conservative characterization of the uncertainty in the stacked ensemble predictions. We assessed the reliability and accuracy of our models 1 to 4 weeks ahead forecasts using real-time hospital-level data on weekly ILI visit volumes during the 2012-2018 flu seasons in the Alberta Childrens Hospital, located in Calgary, Alberta, Canada. Over this time period, our models prediction deviated from the realized ILI visit volume by an average of 12% for 1 week ahead forecasts, with a 90% prediction interval having coverage rates ranging from 90.7 to 97.7%.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.21.348441v1?rss=1

Authors: Ruiz Puentes, P., Valderrama, N., Gonzalez, C., Daza, L., Munoz-Camargo, C., Cruz, J. C., Arbelaez, P.

Abstract:

The discovery and development of novel pharmaceuticals is an area of active research mainly due to the large investments required and long payback times. As of 2016, the development of a novel drug candidate required up to $ USD 2.6 billion in investment for only 10% rate of approval by the FDA. To help decreasing the costs associated with the process, a number of in silico approaches have been developed with relatively low success due to limited predicting performance. Here, we introduced a machine learning-based algorithm as an alternative for a more accurate search of new pharmacological candidates, which takes advantage of Recurrent Neural Networks (RNN) for active molecule prediction within large databases. Our approach, termed PharmaNet was implemented here to search for ligands against specific cell receptors within 102 targets of the DUD-E database, which contains 22886 active molecules. PharmaNet comprises three main phases. First, a SMILES representation of the molecule is converted into a raw molecular image. Second, a convolutional encoder processes the data to obtain a fingerprint molecular image that is finally analyzed by a Recurrent Neural Network (RNN). This approach enables precise predictions of the molecules' target on the basis of the feature extraction, the sequence analysis and the relevant information filtered out throughout the process. Molecule Target prediction is a highly unbalanced detection problem and therefore, we propose that an adequate evaluation metric of performance is the area under the Normalized Average Precision (NAP) curve. PharmaNet largely surpasses the previous state-of-the-art method with 95.8% in the Receiver Operating Characteristic curve (ROC-AUC) and 58.9% in the NAP curve. We obtained a perfect performance for human farnesyl pyrophosphate synthase (FPPS), which is a potential target for antimicrobial and anticancer treatments. We decided to test PharmaNet for activity prediction against FPPS by searching in the CHEMBL data set. We obtained [3] potential inhibitors that were further validated through both molecular docking and in silico toxicity prediction. Most importantly, one of this candidates, CHEMBL2007613, was predicted as a potential antiviral due to its involvement on the PCDH17 pathway, which has been reported to be related to viral infections.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.348052v1?rss=1

Authors: Singh, U., Wurtele, E. S.

Abstract:

Searching for ORFs in transcripts is a critical step prior to annotating coding regions in newly-sequenced genomes and to search for alternative reading frames within known genes. With the tremendous increase in RNA-Seq data, faster tools are needed to handle large input datasets. These tools should be versatile enough to fine-tune search criteria and allow efficient downstream analysis. Here we present a new python based tool, orfipy, which allows the user to flexibly search for open reading frames in fasta sequences. The search is rapid and is fully customizable, with a choice of Fasta and BED output formats. Availability and implementation: orfipy is implemented in python and is compatible with python v3.6 and higher. Source code: https://github.com/urmi-21/orfipy. Installation: from the source, or via PyPi (https://pypi.org/project/orfipy) or bioconda (https://anaconda.org/bioconda/orfipy). Contact: [email protected], [email protected] Supplementary information: Supplementary data are available at https://github.com/urmi-21/orfipy .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.344556v1?rss=1

Authors: Osmak, G., Kiselev, I., Baulina, N., Favorova, O.

Abstract:

MicroRNAs (miRNAs) are short single-stranded non-coding RNA molecules, which are involved in regulation of main biological processes, such as apoptosis, cell proliferation and differentiation, through sequence-specific interaction with target mRNAs. In this study we propose a workflow for predicting miRNAs function by analyzing the structure of the network of their target genes. This workflow was applied to study the functional role of miR-375 in the heart muscle (myocardium), since this miRNA was previously shown to be associated with heart diseases and data on its function in myocardium are mostly unclear. We identified PIK3CA, RHOA, MAPK3, PAFAH1B1, CTNNB1, MYC, PRKCA, ERBB2, and CDC42 as key genes in the miR-375 regulated network and predicted the possible function of miR-375 in the heart muscle, consisting mainly in the regulation of the Rho-GTPases-dependent signalling pathways. We implemented our algorithm for miRNA function prediction into Python module, which is available at GitHub (https://github.com/GJOsmak/miRNET).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.19.345835v1?rss=1

Authors: Bucchini, F., Del Cortona, A., Kreft, Łukasz, Botzki, A., Van Bel, M., Vandepoele, K.

Abstract:

Advances in high-throughput sequencing have resulted in a massive increase of RNA-Seq transcriptome data. However, the promise of rapid gene expression profiling in a specific tissue, condition, unicellular organism, or microbial community comes with new computational challenges. Owing to the limited availability of well-resolved reference genomes, de novo assembled (meta)transcriptomes have emerged as popular tools for investigating the gene repertoire of previously uncharacterized organisms. Yet, despite their potential, these datasets often contain fragmented or contaminant sequences, and their analysis remains difficult. To alleviate some of these challenges, we developed TRAPID 2.0, a web application for the fast and efficient processing of assembled transcriptome data. The initial processing phase performs a global characterization of the input data, providing each transcript with several layers of annotation, comprising structural, functional, and taxonomic information. The exploratory phase enables downstream analyses from the web application. Available analyses include the assessment of gene space completeness, the functional analysis and comparison of transcript subsets, and the study of transcripts in an evolutionary context. A comparison with similar tools highlights TRAPID's unique features. Finally, analyses performed within TRAPID 2.0 are complemented by interactive data visualizations, facilitating the extraction of new biological insights, as demonstrated with diatom community metatranscriptomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.20.347195v1?rss=1

Authors: Su, J., Song, Q.

Abstract:

Recent development of spatial transcriptomics (ST) is capable of associating spatial information at different spots in the tissue section with RNA abundance of cells within each spot, which is particularly important to understand tissue cytoarchitectures and functions. However, for such ST data, since a spot is usually larger than an individual cell, gene expressions measured at each spot are from a mixture of cells with heterogenous cell types. Therefore, ST data at each spot needs to be disentangled so as to reveal the cell compositions at that spatial spot. In this study, we propose a novel method, named DSTG, to accurately deconvolute the observed gene expressions at each spot and recover its cell constitutions, thus achieve high-level segmentation and reveal spatial architecture of cellular heterogeneity within tissues. DSTG not only demonstrates superior performance on synthetic spatial data generated from different protocols, but also effectively identifies spatial compositions of cells in mouse cortex layer, hippocampus slice, and pancreatic tumor tissues. In conclusion, DSTG accurately uncovers the cell states and subpopulations based on spatial localization.

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