PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.16.342626v1?rss=1

Authors: Wang, Q., Boenigk, S., Boehm, V., Gehring, N. H., Altmueller, J., Dieterich, C.

Abstract:

The current ecosystem of single cell RNA-seq platforms is rapidly expanding, but robust solutions for single cell and single molecule full- length RNA sequencing are virtually absent. A high-throughput solution that covers all aspects is necessary to study the complex life of mRNA on the single cell level. The Nanopore platform offers long read sequencing and can be integrated with the popular single cell sequencing method on the 10x Chromium platform. However, the high error-rate of Nanopore reads poses a challenge in downstream processing (e.g. for cell barcode assignment). We propose a solution to this particular problem by using a hybrid sequencing approach on Nanopore and Illumina platforms. Our software ScNapBar enables cell barcode assignment with high accuracy, especially if sequencing saturation is low. ScNapBar either uses unique molecular identifier (UMI) or Naive Bayesian approaches to assign cell barcodes, depending on the available Illumina sequencing depth. We have benchmarked the two approaches on simulated and real Nanopore datasets. We further applied ScNapBar to pools of cells with an active or a silenced non-sense mediated RNA decay pathway. Our Nanopore read assignment distinguishes the respective cell populations and reveals characteristic nonsense-mediated mRNA decay events depending on cell status.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.16.342287v1?rss=1

Authors: Mogro, E. G., Ambrosis, N. M., Lozano, M. J.

Abstract:

Motivation: Bacterial genomes are composed by a core and an accessory genome. The first composed of housekeeping and essential genes, while the second is composed, in its majority, of mobile genetic elements, including transposable elements (TEs). Insertion sequences (ISs), the smallest TEs, have an important role in genome evolution, and contribute to bacterial genome plasticity and adaptability. ISs can spread in a genome, presenting different locations in nearly related strains, and producing phenotypic variations. Few tools are available which can identify differentially located ISs (DLIS) on assembled genomes. Results: We developed ISCompare to profile IS mobilization events in related bacterial strains using complete or draft genome assemblies. ISCompare was validated using artificial genomes with simulated random IS insertions and real sequences, achieving the same or better results than other available tools, with the advantage that ISCompare can analyse multiple ISs at the same time and outputs a list of candidate DLIS. We think that ISCompare provides an easy and straightforward approach to look for differentially located ISs on bacterial genomes. Availability and implementation: ISCompare was implemented in python3 and its source code is freely available for download at https://github.com/maurijlozano/ISCompare. Supplementary information: Supplementary data are available at Bioinformatics online.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.16.342915v1?rss=1

Authors: Chao, J. T., Roskelley, C. D., Loewen, C. J. R.

Abstract:

Genetic testing is widely used in evaluating a patient's predisposition for developing a malignancy. In the case of cancer, when a functionally impactful inherited mutation (i.e. genetic variant) is identified in a disease-relevant gene, the patient is at elevated risk of developing a lesion in their lifetime. Unfortunately, as the rate and coverage of genetic testing has accelerated, our ability to make informed assessments regarding the functional status of the variants has lagged. Currently, there are two main strategies for assessing variant functions: in silico predictions or in vitro testing. The first approach is to build generalist computational prediction software using theoretical parameters such as amino acid conservation as feature inputs. These types of software can classify any variant of any gene. Although versatile, their non-specific design and theoretical assumptions result in different models frequently producing conflicting classifications. The second approach is to develop gene-specific assays. Although each assay is tailored to the physiological function of the gene, this approach requires significant investments. Therefore, there is an urgent need for more practical, streamlined and cost-effective methods. To directly address these issues, we designed a new approach of using alterations in protein subcellular localization as a key indicator of loss of function. Thus, new variants can be rapidly functionalized by using high-content microscopy. To facilitate the analysis of large amounts of image data, we developed a new software, named MAPS for machine-assisted phenotype scoring, that utilizes deep learning (DL) techniques to extract and classify cell-level phenotypes. This new Python-based toolkit helps users leverage commercial cloud-based DL services that are easy to train and deploy to fit varying experimental conditions. Model training is entirely code-free and can be done with limited number of images. Users simply input the trained endpoints into MAPS to accomplish cell detection, phenotype discovery and phenotype classification. Thus, MAPS allows cell biologists to easily apply DL to accelerate their image analysis workflow.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.340315v1?rss=1

Authors: Yuen, Z. W.-S., Srivastava, A., McNevin, D., Jack, C., Eyras, E.

Abstract:

DNA methylation plays a fundamental role in the control of gene expression and genome integrity. Although there are multiple tools that enable its detection from Nanopore sequencing, their accuracy remains largely unknown. Here, we present a systematic benchmarking of tools for the detection of CpG methylation from Nanopore sequencing using individual reads, control mixtures of methylated and unmethylated reads, and bisulfite sequencing. We found that tools showed a tradeoff between false positives and false negatives, and presented a high dispersion with respect to the expected methylation frequency values. We described various strategies to improve the accuracy of these tools and proposed a new method, METEORE (https://github.com/comprna/METEORE), based on the combination of the predictions from two or more tools that has improved accuracy over individual tools. Snakemake pipelines are provided for reproducibility and to enable the systematic application of our analyses to other datasets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.15.340752v1?rss=1

Authors: Schwarz, D., Georges, G., Kelm, S., Shi, J., Vangone, A., Deane, C. M.

Abstract:

Co-evolution analysis can be used to accurately predict residue-residue contacts from multiple sequence alignments. The introduction of machine-learning techniques has enabled substantial improvements in precision and a shift from predicting binary contacts to predicting distances between pairs of residues. These developments have significantly improved the accuracy of de novo prediction of static protein structures. Here we examine the potential of these residue-residue distance predictions to predict protein flexibility rather than static structure. We used DMPfold to predict distance distributions for every residue pair in a set of proteins that showed both rigid and flexible behaviour. Residue pairs that were in contact in at least one reference structure were considered and classified as rigid, flexible or neither. The predicted distance distribution of each residue pair was analysed for local maxima of probability indicating the most likely distance or distances between a pair of residues. The average number of local maxima per residue pair was found to be different between the sets of rigid and flexible residue pairs. Flexible residue pairs more often had multiple local maxima in their predicted distance distribution than rigid residue pairs suggesting that the shape of predicted distance distributions is predictive of rigidity or flexibility of residue pairs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.340299v1?rss=1

Authors: Zhang, J., Liu, L., Xu, T., Zhang, W., Zhao, C., Li, S., Li, J., Rao, N., Le, T. D.

Abstract:

Background: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. Results: In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to use single-cell miRNA-mRNA co-sequencing data to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks to understand miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells to help understand cell-cell crosstalk. Conclusions: To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.338921v1?rss=1

Authors: Lu, M., An, S., Wang, R., Wang, J., Yu, C.

Abstract:

With the precision of mass spectrometer going higher and the emergence of data independence acquisition (DIA), the file size is increasing rapidly. Beyond the widely-used open format mzML (Deutsch 2008), near-lossless or lossless compression algorithms and formats have emerged. The data precision is often related to the instrument and subsequent processing algorithms. Unlike storage-oriented formats, which focusing more on lossless compression and compression rate, computation-oriented formats focus as much on decoding speed and disk read strategy as compression rate. Here we describe "Aird", an opensource and computation-oriented format with controllable precision, flexible indexing strategies and high compression rate. Aird uses JavaScript Object Notation (JSON) for metadata storage, multiple indexing, and reordered storage strategies for higher speed of data randomly reading. Aird also provides a novel compressor called Zlib-Diff-PforDelta (ZDPD) for m/z data compression. Compared with Zlib only, m/z data size is about 65% lower in Aird, and merely takes 33% decoding time.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.338004v1?rss=1

Authors: Feng, F., Yao, Y., Wang, X. Q. D., Zhang, X., Liu, J.

Abstract:

The resolution of chromatin conformation capture technologies keeps increasing, and the recent nucleosome resolution chromatin contact maps allow us to explore how fine-scale 3D chromatin organization is related to epigenomic states in human cells. Using publicly available Micro-C datasets, we have developed a deep learning model, CAESAR, to learn a mapping function from epigenomic features to 3D chromatin organization. The model accurately predicts fine-scale structures, such as short-range chromatin loops and stripes, that Hi-C fails to detect. With existing epigenomic datasets from ENCODE and Roadmap Epigenomics Project, we successfully imputed high-resolution 3D chromatin contact maps for 91 human tissues and cell lines. In the imputed high-resolution contact maps, we identified the spatial interactions between genes and their experimentally validated regulatory elements, demonstrating CAESAR's potential in coupling transcriptional regulation with 3D chromatin organization at high resolution.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.14.336982v1?rss=1

Authors: Liu, Z., Ding, H., She, J., Chen, C., Zhang, W.-G., Yang, E.

Abstract:

Circular RNAs (circRNAs) are involved in various biological processes and in disease pathogenesis. However, only a small number of functional circRNAs have been identified among hundreds of thousands of circRNA species, partly because most current methods are based on circular junction counts and overlook the fact that circRNA is formed from the host gene by back-splicing (BS). To distinguish between expression originating from BS and that from the host gene, we present DEBKS, a software program to streamline the discovery of differential BS between two rRNA-depleted RNA sequencing (RNA-seq) sample groups. By applying real and simulated data and employing RT-qPCR for validation, we demonstrate that DEBKS is efficient and accurate in detecting circRNAs with differential BS events between paired and unpaired sample groups. DEBKS is available at https://github.com/yangence/DEBKS as open-source software.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.13.337097v1?rss=1

Authors: Baral, A., Gorkhali, R., Basnet, A., Koirala, S., Bhattarai, H. K.

Abstract:

L-Asparaginase II (asnB), a periplasmic protein, commercially extracted from E. coli and Erwinia, is often used to treat Acute Lymphoblastic Leukemia. L-Asparaginase is an enzyme that converts L-asparagine to aspartic acid and ammonia. Cancer cells are dependent on asparagine from other sources for growth and when these cells are deprived of asparagine by the action of the enzyme the cancer cells selectively die. Questions remain as to whether asnB from E. coli and Erwinia is the best asparaginase as they have many side-effects. asnB with the lowest Michaelis constant (Km) (most potent), and with the lowest immunogenicity is considered the most optimal enzyme. In this paper asnB sequence of E. coli was used to search for homologous proteins in different bacterial and archaeal phyla and a maximum likelihood phylogenetic tree was constructed. The sequences that are most distant from E. coli and Erwinia were considered best candidates in terms of immunogenicity and were chosen for further processing. The structures of these proteins were built by homology modeling and asparagine was docked with these proteins to calculate the binding energy. asnBs from Streptomyces griseus, Streptomyces venezuelae and Streptomyces collinus were found to have the highest binding energy i.e. -5.3 kcal/mol, -5.2 kcal/mol, and -5.3 kcal/mol respectively (Higher than the E.coli and Erwinia asnBs) and were predicted to have the lowest Kms as we found that there is an inverse relationship between binding energy and Km. Besides predicting the most optimal asparaginase, this technique can also be used to predict the most optimal enzymes where the substrate is known and the structure of one of the homologs is solved.

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