PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.20.305474v1?rss=1

Authors: Chen, X., Yang, Z., Chen, W., Zhao, Y., Farmer, A., Tran, B., Furtak, V., Moos, M., Xiao, W., Wang, C.

Abstract:

Single-cell RNA sequencing (scRNA-seq) is developing rapidly, and investigators seeking to use this technology are left with a variety of options for both experimental platform and bioinformatics methods. There is an urgent need for scRNA-seq reference datasets for benchmarking of different scRNA-seq platforms and bioinformatics methods. To be broadly applicable, these should be generated from renewable, well characterized reference samples and processed in multiple centers across different platforms. Here we present a benchmarking scRNA-seq dataset that includes 20 scRNA-seq datasets acquired either as a mixtures or as individual samples from two biologically distinct cell lines for which a large amount of multi-platform whole genome sequencing data are also available. These scRNA-seq datasets were generated from multiple popular platforms across four sequencing centers. Our benchmark datasets provide a resource that we believe will have great value for the single-cell community by serving as a reference dataset for evaluating various bioinformatics methods for scRNA-seq analyses, including but not limited to data preprocessing, imputation, normalization, clustering, batch correction, and differential analysis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.17.302307v1?rss=1

Authors: Apostolides, M., Jiang, Y., Husic, M., Siddaway, R., Hawkins, C., Turinsky, A., Brudno, M., Ramani, A.

Abstract:

Motivation: Gene fusions are often associated with cancer, yet current fusion detection tools vary in their calling approaches, making selecting the right tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. Results: MetaFusion is a flexible meta-calling tool that amalgamates the outputs from any number of fusion callers. Results from individual callers are converted into Common Fusion Format, a new file type that standardizes outputs from callers. Calls are then annotated, merged using graph clustering, filtered and ranked to provide a final output of high confidence candidates. MetaFusion consistently outperformed individual callers with respect to recall and precision on real and simulated datasets, achieving up to 100% precision. Thus, an ensemble calling approach is imperative for high confidence results. MetaFusion also labels fusions found in databases using the FusionAnnotator package, and is provided with a benchmarking toolkit to calibrate new callers. Availability: MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.18.303156v1?rss=1

Authors: Rautiainen, M., Marschall, T.

Abstract:

Motivation: De Bruijn graphs can be constructed from short reads efficiently and have been used for many purposes. Traditionally long read sequencing technologies have had too high error rates for de Bruijn graph-based methods. Recently, HiFi reads have provided a combination of long read length and low error rate, which enables de Bruijn graphs to be used with HiFi reads. Results: We have implemented MBG, a tool for building sparse de Bruijn graphs from HiFi reads. MBG outperforms existing tools for building dense de Bruijn graphs, and can build a graph of 50x coverage whole human genome HiFi reads in four hours on a single core. MBG also assembles the bacterial E. coli genome into a single contig in 8 seconds. Availability: Package manager: https://anaconda.org/bioconda/mbg and source code: https://github.com/maickrau/MBG

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.19.304741v1?rss=1

Authors: Raaisa, R., Lathwal, S., Chubukov, V., Kibbey, R. G., Jha, A. K.

Abstract:

Background: Stable isotope-based approaches are used in the field of metabolomics for quantification and identification of metabolites, discovery of new pathways and measurement of intracellular fluxes. In these experiments, often performed with mass spectrometry (MS), data must be corrected for natural abundance of isotopes. Various stand-alone tools with their own separate data formats and learning curves exist for correction of data collected at different resolutions, for tandem MS, and for different number of tracer elements. Results: We present a Python package, Corna, that combines natural abundance correction workflows for several experimental conditions and can be used as a one-stop-shop for stable isotope labeled experiments. We validate the algorithms in Corna with published tools, where available, and include new features, such as correction of two tracer elements, that are not yet implemented in any existing software application as per our knowledge. We also present the integration of Corna with an existing open source peak integration software. The integrated workflow can reduce processing times for a typical stable isotope based workflow from days to hours for a familiar user. Conclusions: Algorithmic advancements have been keeping up with the developments in mass spectrometry technologies and have been the focus of most existing tools for natural abundance correction. However, in this high throughput era, it is also important to recognize user experience, and integrated and reproducible workflows. Corna has been written in Python and is designed for users who have access to large amounts of data from different kinds of experiments and want to integrate a natural abundance correction tool seamlessly in their pipelines. The latest version of Corna can be accessed at https://github.com/raaisakuk/NA_Correction.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.18.304337v1?rss=1

Authors: Gisonno, R. A., Masson, T., Ramella, N. A., Barrera, E. E., Romanowski, V., Tricerri, M. A.

Abstract:

Apolipoprotein A-I (apoA-I) has a key function in the reverse cholesterol transport mediated by the high-density lipoprotein (HDL) particles. However, aggregation of apoA-I single point mutants can lead to hereditary amyloid pathology. Although several studies have tackled the biophysical and structural impacts introduced by these mutations, there is little information addressing the relationship between the evolutionary and structural features that contribute to the amyloid behavior of apoA-I. We combined evolutionary studies, in silico saturation mutagenesis and molecular dynamics (MD) simulations to provide a comprehensive analysis of the conservation and pathogenic role of the aggregation-prone regions (APRs) present in apoA-I. Sequence analysis demonstrated the pervasive conservation of an APR, designated here APR1, within the N-terminal [a]-helix bundle. Moreover, stability analysis carried out with the FoldX engine showed that this motif contributes to the marginal stability of apoA-I. Structural properties of the full-length apoA-I model suggest that aggregation is avoided by placing APRs into highly packed and rigid portions of its structure. Compared to HDL-deficiency or natural silent variants extracted from the gnomAD database, the thermodynamic and pathogenic impact of apoA-I point mutations associated with amyloid pathologies were found to show a higher destabilizing effect. MD simulations of the amyloid variant G26R evidenced the partial unfolding of the [a]-helix bundle and the occurrence of {beta}-strand secondary elements at the C-terminus of apoA-I. Our findings highlight APR1 as a relevant component for apoA-I structural integrity and emphasize a destabilizing effect of amyloid variants that leads to the exposure of APRs. This information contributes to our understanding of how apoA-I, with its high degree of structural flexibility, maintains a delicate equilibrium between its native structure and intrinsic tendency to form amyloid aggregates. In addition, our stability measurements could be used as a proxy to interpret the structural impact of new mutations affecting apoA-I.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.18.304089v1?rss=1

Authors: Bayat, F., Davoodi Monfared, M.

Abstract:

Identifying genetic markers for cancer is one of the main challenges in the recent researches. Between different cohorts of genetic markers such as genes or a group of genes like pathways or subnetwork, identifying functional modules like subnetwork markers has been more challenging. Network-based classification methods have been successfully used for finding effective cancer subnetwork markers. Combination of metabolic networks and molecular profiles of tumor samples has led researchers to a more accurate prediction of subnetwork markers. However, topological features of the network have not been considered in the activity of the subnetwork. Here, we apply a novel protein-protein interaction network-based classification method that considers topological features of the network in addition to the expression profiles of the samples. We have considered the problem of identifying cancer subnetwork markers as a multi-objective problem which in this approach, each subnetwork's activity level is measured according to both objectives of the problem; Differential expression level of the genes and topological features of the nodes in the network. We found that the subnetwork markers identified by this method achieve higher performance in the classification of cancer outcome in comparison to the other subnetwork markers.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.19.304584v1?rss=1

Authors: Li, T., Shi, J., Wu, Y., Zhou, P.

Abstract:

The RNA velocity provides a new avenue to study the stemness and lineage of cells in the development in scRNA-seq data analysis. Some promising extensions of it are proposed and the community is experiencing a fast developing period. However, in this stage, it is of prime importance to revisit the whole process of RNA velocity analysis from the mathematical point of view, which will help to understand the rationale and drawbacks of different proposals. The current paper is devoted to this purpose. We present a thorough mathematical study on the RNA velocity model from dynamics to downstream data analysis. We derived the analytical solution of the RNA velocity model from both deterministic and stochastic point of view. We presented the parameter inference framework based on the maximum likelihood estimate. We also derived the continuum limit of different downstream analysis methods, which provides insights on the construction of transition probability matrix, root and ending-cells identification, and the development routes finding. The overall analysis aims at providing a mathematical basis for more advanced design and development of RNA velocity type methods in the future.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.19.304949v1?rss=1

Authors: Huang, Y.-T., Liu, P.-Y., Shih, P.-W.

Abstract:

Nanopore sequencing has been widely used for reconstruction of a variety of microbial genomes. Owing to the higher error rate, the assembled genome requires further error correction. Existing methods erase many of these errors via deep neural network trained from Nanopore reads. However, quite a few systematic errors are still left on the genome. This paper proposed a new model trained from homologous sequences extracted from closely-related genomes, which provides valuable features missed in Nanopore reads. The developed program (called Homopolish) outperforms the state-of-the-art Racon/Medaka and MarginPolish/HELEN pipelines in metagenomic and isolates of bacteria, viruses and fungi. When Homopolish is combined with Medaka or with HELEN, the genomes quality can exceed Q50 on R9.4 flowcells. The genome quality can be also improved on R10.3 flowcells (Q50-Q90). We proved that Nanopore-only sequencing can now produce high-quality genomes without the need of Illumina hybrid sequencing.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.19.304782v1?rss=1

Authors: Bebris, K., Polaka, I.

Abstract:

Advances in sequencing technology have led to an ever increasing amount of available short read sequencing data. This has, consequently, exacerbated the need for efficient and precise classification tools that can be used in the analysis of this data. As it stands, recent years have shown that massive leaps in performance can be achieved when it comes to approaches that are based in heuristics, and alongside these improvements there has been an ever increasing interest in applying deep learning techniques to revolutionize this classification task. We attempt to gather up these approaches and to evaluate their performance in a reproducible fashion to get a better perspective on the current state of deep learning based methods when it comes to the classification of short read sequencing data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.18.301721v1?rss=1

Authors: Freshour, S., Kiwala, S., Cotto, K. C., Coffman, A. C., McMichael, J. F., Song, J., Griffith, M., Griffith, O. L., Wagner, A. H.

Abstract:

The Drug-Gene Interaction Database (DGIdb, www.dgidb.org) is a web resource that provides information on drug-gene interactions and druggable genes from various sources including publications, databases, and other web-based sources in one resource. These drug, gene, and interaction claims are normalized and grouped to identify aliases, merge concepts, and reduce redundancy. The information contained in this resource is available to users through a straightforward search interface, an application programming interface (API), and TSV data downloads. DGIdb 4.0 is the latest major update of this database. Seven new sources have been added, bringing the total number of sources included to 41. Of the previously aggregated sources, 15 have been updated. DGIdb 4.0 also includes improvements to the process of drug normalization and grouping of imported sources. Other notable updates include further development of automatic jobs for routine data updates, more sophisticated query scores for interaction search results, extensive manual curation of interaction source link outs, and the inclusion of interaction directionality. A major focus of this update was integration with crowd-sourced efforts, including leveraging the curation activities of Drug Target Commons, using Wikidata to facilitate term normalization, and integrating into NDEx for producing network representations.

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