PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.243097v1?rss=1

Authors: Alqassem, I., Sonthalia, Y., Klitzke, E., Shim, H., Canzar, S.

Abstract:

Alternative splicing removes intronic sequences from transcripts in alternative ways to produce different forms (isoforms) of mature mRNA. The composition of expressed transcripts and their alternative forms give specific functionalities to cells in a particular condition or developmental stage. In addition, a large fraction of human disease mutations affect splicing and lead to aberrant mRNA and protein products. Current methods that interrogate the transcriptome based on RNA-seq either suffer from short read length when trying to infer full-length transcripts, or are restricted to predefined units of alternative splicing that they quantify from local read evidence. Instead of attempting to quantify individual outcomes of the splicing process such as local splicing events or full-length transcripts, we propose to quantify alternative splicing using a simplified probabilistic model of the underlying splicing process. Our model is based on the usage of individual splice sites and can generate arbitrarily complex types of splicing patterns. In our method, McSplicer, we estimate the parameters of our model using all read data at once and we demonstrate in our experiments that this yields more accurate estimates compared to competing methods. Our model is able to describe multiple effects of splicing mutations using few, easy to interpret parameters, as we illustrate in an experiment on RNA-seq data from autism spectrum disorder patients. McSplicer is implemented in Python and available as open-source at https://github.com/canzarlab/McSplicer.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.243642v1?rss=1

Authors: Peng, J., Xiang, J., Jin, X., Meng, J., Song, N., Chen, L., Tayoun, A. A., Peng, Z.

Abstract:

Purpose: The American College of Medical Genetics and Genomics, and the Association for Molecular Pathology (ACMG/AMP) have proposed a set of evidence-based guidelines to support sequence variant interpretation. The ClinGen hearing loss expert panel (HL-EP) introduced further specifications into the ACMG/AMP framework for genetic hearing loss. This study aimed to semi-automate the HL ACMG/AMP rules. Methods: VIP-HL aggregates information from external databases to automate 13 out of 24 ACMG/AMP rules specified by HL-EP, namely PVS1, PS1, PM1, PM2, PM4, PM5, PP3, BA1, BS1, BS2, BP3, BP4, and BP7. Results: We benchmarked VIP-HL using 50 variants where 83 rules were activated by the HL expert panel. VIP-HL concordantly activated 96% (80/83) rules, significantly higher than that of by InterVar (47%; 39/83). Of 4948 ClinVar star 2+ variants from 142 deafness-related genes, VIP-HL achieved an overall variant interpretation concordance in 88.0% (4353/4948). VIP-HL is available with a user-friendly web interface at http://hearing.genetics.bgi.com/. Conclusion: VIP-HL is an integrated online tool for reliable automated variant classification in hearing loss genes. It assists curators in variant interpretation and provides a platform for users to share classifications with each other.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.243675v1?rss=1

Authors: Shukla, N., Kadam, S., Padinhateeri, R., Kolthur-Seetharam, U.

Abstract:

Understanding kinetic control of biological processes is as important as identifying components that constitute pathways. Insulin-signaling (IS) is central for almost all metazoans and its perturbations are associated with various diseases and aging. While temporal phosphorylation changes and kinetic constants have provided some insights, constant or variable parameters that establish and maintain signal topology are poorly understood. Our iterative experimental and mathematical simulation-based approaches reveal novel kinetic parameters that encode concentration and nutrient dependent information. Further, we find that pulsatile fasting insulin rewires IS akin to memory and in anticipation of a fed response. Importantly, selective kinetic gating of signals and maximum connectivity, between metabolic and growth-factor arms under normo-insulinemic states, maintains network topology. In addition to unraveling kinetic constraints that determine cascade architecture, our findings will help in identifying novel therapeutic strategies that conserve coupling between metabolic and growth-factor arms, which is lost in diseases and conditions of hyperinsulinemia.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.243824v1?rss=1

Authors: Joel, I. y., ADIGUN, T. O., BANKOLE, O. O., AJIBOLA, A. O., OFENIFORO, E. B., AUTA, F. B., OZOJIOFOR, U. O., REMI-ESAN, I. A., AKANDE, A. I.

Abstract:

Histone deacetylases have been recognized as a potential target for epigenetic aberrance reversal in the various strategies for cancer therapy, with HDAC6 implicated in various forms of tumor growth and cancers. Diverse inhibitors of HDAC6 has been developed, however, there is still the challenge of iso-specificity and toxicity. In this study, we trained a Random forest model on all HDAC6 inhibitors curated in the ChEMBL database (3,742). Upon rigorous validations the model had an 85% balanced accuracy and was used to screen the SCUBIDOO database; 7785 hit compounds resulted and were docked into HDAC6 CD2 active-site. The top two compounds having a benzimidazole moiety as its zinc-binding group had a binding affinity of -78.56kcal/mol and -78.21kcal/mol respectively. The compounds were subjected to exhaustive docking protocols (Qm-polarized docking and Induced-Fit docking) in other to elucidate a binding hypothesis and accurate binding affinity. Upon optimization, the compounds showed improved binding affinity (-81.42kcal/mol), putative specificity for HDAC6, and good ADMET properties. We have therefore developed a reliable model to screen for HDAC6 inhibitors and suggested a series of benzimidazole based inhibitors showing high binding affinity and putative specificity for HDAC6.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244368v1?rss=1

Authors: Rezaei, M. R., Nazari, B., Sadri, S., yousefi, A.

Abstract:

Recent technological and experimental advances in recording from neural systems have led to a significant increase in the type and volume of data being collected in neuroscience experiments. This brings an increasing demand for development of appropriate analytical tools to analyze large scale neuroscience data. Simultaneously, advancement in deep neural networks (DNNs) and statistical modeling frameworks have provided new techniques for analysis of diverse forms of neuroscience data. DNNs like Long short-term memory (LSTM) or statistical modeling approaches like state-space point-process (SSPP) are widely used in the analysis of neural data including neural coding and inference analysis. Despite wide utilization of these techniques, there is a lack of comprehensive studies which systematically assess attributes of LSTM and SSPP approaches on a common neuroscience data analysis problem. As a result, this occasionally leads to inconsistent and divergent conclusions on the strength or weakness of either of the methodologies and also statistical significance of the analytical outcomes. In this research, we focus on providing a more systematic and multifaceted assessment of LSTM and SSPP techniques in a neural decoding problem.We examine different settings and modeling specifications to attain the optimal modeling solutions. We propose new LSTM network topologies and approximate filter solution to estimate a rat movement trajectory in a 2-D spaces using an ensemble of place cells' spiking activity. For each technique; we then study performance, computational efficiency, and generalizability of each technique in this decoding problem. By utilizing these results, we provided a succinct picture of the strength and weakness of each modeling approach and suggest who each of these techniques can be properly utilized in neural decoding problems.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244343v1?rss=1

Authors: Sienkiewicz, K., Chen, J., Chatrath, A., Lawson, J. T., Sheffield, N. C., Zhang, L., Ratan, A.

Abstract:

Joint analysis of multiple genomic data types can facilitate the discovery of complex mechanisms of biological processes and genetic diseases. We present a novel data integration framework based on non-negative matrix factorization that uses patient similarity networks. Our implementation supports continuous multi-omic datasets for molecular subtyping and handles missing data without using imputation, making it more efficient for genome-wide assays in large cohorts. Applying our approach to gene expression, microRNA expression, and methylation data from patients with lower grade gliomas, we identify a subtype with a significantly poorer prognosis. Tumors assigned to this subtype are hypomethylated genome-wide with a gain of AP-1 occupancy in the demethylated distal enhancers. These tumors' genomic profiles are similar to Grade IV gliomas: they are enriched for somatic chr7 gain, chr10 loss, and other molecular events that have yet to be used in the diagnosis of lower-grade gliomas as per the current WHO guidelines.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.09.243022v1?rss=1

Authors: Siren, K., Millard, A., Petersen, B., Gilbert, M. T. P., Clokie, M. R., Sicheritz-Ponten, T.

Abstract:

Prophages are phages that are integrated into bacterial genomes and which are key to understanding many aspects of bacterial biology. Their extreme diversity means they are challenging to detect using sequence similarity, yet this remains the paradigm and thus many phages remain unidentified. We present a novel, fast and generalizing machine learning method based on feature space to facilitate novel prophage discovery. To validate the approach, we reanalyzed publicly available marine viromes and single-cell genomes using our feature-based approaches and found consistently more phages than were detected using current state-of-the-art tools while being notably faster. This demonstrates that our approach significantly enhances bacteriophage discovery and thus provides a new starting point for exploring new biologies.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.08.242636v1?rss=1

Authors: Zhang, T., Singh, J., Litfin, T., Zhan, J., Paliwal, K., Zhou, Y.

Abstract:

Motivation: The accuracy of RNA secondary and tertiary structure prediction can be significantly improved by using structural restraints derived from evolutionary or direct coupling analysis. Currently, these coupling analyses relied on manually curated multiple sequence alignments collected in the Rfam database, which contains 3016 families. By comparison, millions of non-coding RNA sequences are known. Here, we established RNAcmap, a fully automatic method that enables evolutionary coupling analysis for any RNA sequences. The homology search was based on the covariance model built by Infernal according to two secondary structure predictors: a folding-based algorithm RNAfold and the latest deep-learning method SPOT-RNA. Results: We show that the performance of RNAcmap is less dependent on the specific evolutionary coupling tool but is more dependent on the accuracy of secondary structure predictor with the best performance given by RNAcmap (SPOT-RNA). The performance of RNAcmap (SPOT-RNA) is comparable to that based on Rfam-supplied alignment and consistent for those sequences that are not in Rfam collections. Further improvement can be made with a simple meta predictor RNAcmap (SPOT-RNA/RNAfold) depending on which secondary structure predictor can find more homologous sequences. Reliable base-pairing information generated from RNAcmap, for RNAs with high effective homologous sequences, in particular, will be useful for aiding RNA structure prediction. Availability and implementation: RNAcmap is available as a web server at https://sparks-lab.org/server/rnacmap/) and as a standalone application along with the datasets at https://github.com/sparks-lab-org/RNAcmap.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.09.242727v1?rss=1

Authors: Gloaguen, Y., Kirwan, J., Beule, D.

Abstract:

Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS which uses machine learning to replace peak curation by human experts. We show how to integrate our open source module into different LC-MS analysis workflows and quantify its performance. NeatMS is designed to be suitable for large scale studies and improves the robustness of the final peak list.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.242461v1?rss=1

Authors: Vaser, R., Sikic, M.

Abstract:

We present new methods for the improvement of long-read de novo genome assembly incorporated into a straightforward tool called Raven (https://github.com/lbcb-sci/raven). Compared with other assemblers, Raven is one of two fastest, it reconstructs the sequenced genome in the least amount of fragments, has better or comparable accuracy, and maintains similar performance for various genomes. Raven takes 500 CPU hours to assemble a 44x human genome dataset in only 259 fragments.

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