PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.385328v1?rss=1

Authors: Hong, R., Koga, Y., Bandyadka, S., Leshchyk, A., Wang, Z., Alabdullatif, S., Wang, Y., Akavoor, V., Cao, X., Sarfraz, I., Jansen, F., Johnson, W. E., Yajima, M., Campbell, J. D.

Abstract:

Performing comprehensive quality control is necessary to remove technical or biological artifacts in single-cell RNA sequencing (scRNA-seq) data. Artifacts in the scRNA-seq data, such as doublets or ambient RNA, can also hinder downstream clustering and marker selection and need to be assessed. While several algorithms have been developed to perform various quality control tasks, they are only available in different packages across various programming environments. No standardized workflow has been developed to streamline the generation and reporting of all quality control metrics from these tools. We have built an easy-to-use pipeline, named SCTK-QC, in the singleCellTK package that generates a comprehensive set of quality control metrics from a plethora of packages for quality control. We are able to import data from several preprocessing tools including CellRanger, STARSolo, BUSTools, dropEST, Optimus, and SEQC. Standard quality control metrics for each cell are calculated including the total number of UMIs, total number of genes detected, and the percentage of counts mapping to predefined gene sets such as mitochondrial genes. Doublet detection algorithms employed include scrublet, scds, doubletCells, and doubletFinder. DecontX is used to identify contamination in each individual cell. To make the data accessible in downstream analysis workflows, the results can be exported to common data structures in R and Python or to text files for use in any generic workflow. Overall, this pipeline will streamline and standardize quality control analyses for single cell RNA-seq data across different platforms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.384826v1?rss=1

Authors: Ari Yuka, S., Yilmaz, A.

Abstract:

Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in human is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this paper, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values and then calculates network-wide effects after perturbation in expression level of element(s) while utilizing miRNA interaction factors such as seed type, binding energy. We have carried out analysis of large scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer associated genes and miRNAs. Our network-based approach helps unveiling the crosstalk between node elements in miRNA:target network where abundance of targets leading to sponge effect is taken into account. The model has potential to reveal unforeseen and unpredicted regulations which are only evident when considered in network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs and available as R package ceRNAnetsim https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html .

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.373274v1?rss=1

Authors: Wang, Z., Yang, S., Koga, Y., Corbett, S. E., Johnson, W. E., Yajima, M., Campbell, J. D.

Abstract:

Complex biological systems can be understood by dividing them into hierarchies. Each level of such a hierarchy is composed of different subunits which cooperate to perform distinct biological functions. Single-cell RNA-seq (scRNA-seq) has emerged as a powerful technique to quantify gene expression in individual cells and is being used to elucidate the molecular and cellular building blocks of complex tissues. We developed a novel Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) to perform bi-clustering of co-expressed genes into modules and cells into subpopulations. This model can also quantify the relationship between different levels in a biological hierarchy by determining the contribution of each gene in each module, each module in each cell population, and each cell population in each sample. We used Celda to identify transcriptional modules and cell subpopulations in publicly-available peripheral blood mononuclear cell (PBMC) dataset. In addition to the major classes of cell types, Celda also identified a population of proliferating T-cells and a single plasma cell that was missed by other clustering methods in this dataset. Transcriptional modules captured consistency in expression patterns among genes linked to same biological functions. Furthermore, transcriptional modules provided direct insights on cell type specific marker genes, and helped understanding of subtypes of B- and T-cells. Overall, Celda presents a novel principled approach towards characterizing transcriptional programs and cellular and heterogeneity in single-cell data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.385377v1?rss=1

Authors: Coullomb, A., Pancaldi, V.

Abstract:

Motivation: Networks provide a powerful framework to analyze spatial omics experiments. However, we lack tools that integrate several methods to easily reconstruct networks for further analyses with dedicated libraries. In addition, choosing the appropriate method and parameters can be challenging. Summary: We propose tysserand, a Python library to reconstruct spatial networks from spatially resolved omics experiments. It is intended as a common tool where the bioinformatics community can add new methods to reconstruct networks, choose appropriate parameters, clean resulting networks and pipe data to other libraries. Availability: tysserand software and tutorials with a Jupyter notebook to reproduce the results are available at https://github.com/VeraPancaldiLab/tysserand

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.385427v1?rss=1

Authors: Lindgren, C. M., Adams, D. W., Kimball, B., Boekweg, H., Tayler, S., Pugh, S. L., Payne, S. H.

Abstract:

Comprehensive cancer datasets recently generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) offer great potential for advancing our understanding of how to combat cancer. These datasets include DNA, RNA, protein, and clinical characterization for tumor and normal samples from large cohorts in many different cancer types. The raw data are publicly available at various Cancer Research Data Commons. However, widespread re-use of these datasets is also facilitated by easy access to the processed quantitative data tables. We have created a Python package, cptac, which is a data API that distributes the finalized processed CPTAC datasets in a consistent, up-to-date format. This consistency makes it easy to integrate the data with common graphing, statistical, and machine learning packages for advanced analysis. Additionally, consistent formatting across all cancer types promotes the investigation of pan-cancer trends. The data API structure of directly streaming data within a programming environment enhances reproducibility. Finally, with the accompanying tutorials, this package provides a novel resource for cancer research education.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.385211v1?rss=1

Authors: Jalili, V., Clements, D., Gruning, B., Blankenberg, D., Goecks, J.

Abstract:

A growing number of biomedical methods and protocols are being disseminated as open-source software packages. When put in concert with other packages, they can execute in-depth and comprehensive computational pipelines. Therefore, their integration with other software packages plays a prominent role in their adoption in addition to their availability. Accordingly, package management systems are developed to standardize the discovery and integration of software packages. Here we study the impact of package management systems on software dissemination and their scholarly recognition. We study the citation pattern of more than 18,000 scholarly papers referenced by more than 23,000 software packages hosted by Bioconda, Bioconductor, BioTools, and ToolShed-the package management systems primarily used by the Bioinformatics community. Our results suggest that there is significant evidence that the scholarly papers' citation count increases after their respective software was published to package management systems. Additionally, our results show that the impact of different package management systems on the scholarly papers' recognition is of the same magnitude. These results may motivate scientists to distribute their software via package management systems, facilitating the composition of computational pipelines and helping reduce redundancy in package development.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.15.383943v1?rss=1

Authors: Xu, Y., Teng, Y., Dong, X., Wang, X., Zhang, C., Ren, W., Zhao, L., Luo, Y., Greening, C.

Abstract:

Background: Molecular hydrogen (H2) is a major energy source supporting bacterial growth and persistence in soil ecosystems. While recent studies have uncovered mediators of atmospheric H2 consumption, far less is understood about how soil microbial communities respond to elevated H2 levels produced through natural or anthropogenic processes. Here we performed microcosm experiments to resolve how microbial community composition, capabilities, and activities change in upland (meadow, fluvo-aquic soil) and wetland (rice paddy, anthrosols soil) soils following H2 supplementation (at mixing doses from 0.5 to 50,000 ppmv). Results: Genome-resolved metagenomic profiling revealed that these soils harbored diverse bacteria capable of using H2 as an electron donor for aerobic respiration (46 of the 196 MAGs from eight phyla) and carbon fixation (15 MAGs from three phyla). H2 stimulated the growth of several of these putative hydrogenotrophs in a dose-dependent manner, though the lineages stimulated differed between the soils; whereas actinobacterial lineages encoding group 2a [NiFe]-hydrogenases grew most in the upland soils (i.e. Mycobacteriaceae, Pseudonocardiaceae), proteobacterial lineages harboring group 1d [NiFe]-hydrogenases were most enriched in wetland soils (i.e. Burkholderiaceae). Hydrogen supplementation also influenced the abundance of various other genes associated with biogeochemical cycling and bioremediation pathways to varying extents between soils. Reflecting this, we observed an enrichment of a hydrogenotrophic Noviherbaspirillum MAG capable of biphenyl hydroxylation in the wetland soils and verified that H2 supplementation enhanced polychlorinated biphenyl (PCB) degradation in these soils, but not the upland soils. Conclusions: Our findings suggest that soils harbour different hydrogenotrophic bacteria that rapidly grow following H2 exposure. In turn, this adds to growing evidence of a large and robust soil H2 sink capable of counteracting growing anthropogenic emissions.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.16.384784v1?rss=1

Authors: Lei, Y., Li, S., Liu, Z., Wan, F., Tian, T., Li, S., Zhao, D., Zeng, J.

Abstract:

Peptide-protein interactions (PepPIs) are involved in various fundamental cellular functions and their identification is crucial for designing efficacious peptide therapeutics. To facilitate the peptide drug discovery process, a number of computational methods have been developed to predict peptide-protein interactions. However, most of the existing prediction approaches heavily depend on high-resolution structure data. Although several deep-learning-based frameworks have been proposed to predict compound-protein interactions or protein-protein interactions, few of them are particularly designed to specifically predict peptide-protein interactions. In this paper, We present a sequence-based Convolutional Attention-based neural network for Multifaceted prediction of Peptide-protein interactions, called CAMP, including predicting binary peptide-protein interactions and corresponding binding residues in the peptides. We also construct a benchmark dataset containing high-quality peptide-protein interaction pairs with the corresponding peptide binding residues for model training and evaluation. CAMP incorporates convolution neural network architectures and attention mechanism to fully exploit informative sequence-based features, including secondary structures, physicochemical properties, intrinsic disorder features and position-specific scoring matrix of the protein. Systematical evaluation of our benchmark dataset demonstrates that CAMP outperforms the state-of-the-art baseline methods on binary peptide-protein interaction prediction. In addition, CAMP can successfully identify the binding residues involved non-covalent interactions for peptides. These results indicate that CAMP can serve as a useful tool in peptide-protein interaction prediction and peptide binding site identification, which can thus greatly facilitate the peptide drug discovery process. The source code of CAMP can be found in https://github.com/twopin/CAMP.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.380105v1?rss=1

Authors: Smetanin, A., Moshkov, N., Tatarinova, T. V.

Abstract:

Summary: We developed PyLAE - a new tool for determining local ancestry along a genome using whole-genome sequencing data or high-density genotyping experiments. PyLAE can process an arbitrarily large number of ancestral populations (with or without an informative prior). Since PyLAE does not involve estimation of many parameters, it can process thousands of genomes within a day. Computational efficiency, straightforward presentation of results, and an ease of installation makes PyLAE a useful tool to study admixed populations. Availability and implementation: The source code and installation manual are available at https://github.com/smetam/pylae.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.381814v1?rss=1

Authors: Joudaki, A., Kahles, A., Ratsch, G.

Abstract:

The sharp increase in next-generation sequencing technologies' capacity has created a demand for algorithms capable of quickly searching a large corpus of biological sequences. The complexity of biological variability and the magnitude of existing data sets have impeded finding algorithms with guaranteed accuracy that efficiently run in practice. Our main contribution is the Tensor Sketch method that efficiently and accurately estimates edit distances. In our experiments, Tensor Sketch had 0.88 Spearman's rank correlation with the exact edit distance, almost doubling the 0.466 correlation of the closest competitor while running 8.8 times faster. Finally, all sketches can be updated dynamically if the input is a sequence stream, making it appealing for large-scale applications where data cannot fit into memory. Conceptually, our approach has three steps: 1) represent sequences as tensors over their sub-sequences, 2) apply tensor sketching that preserves tensor inner products, 3) implicitly compute the sketch. The sub-sequences, which are not necessarily contiguous pieces of the sequence, allow us to outperform k-mer-based methods, such as min-hash sketching over a set of k-mers. Typically, the number of sub-sequences grows exponentially with the sub-sequence length, introducing both memory and time overheads. We directly address this problem in steps 2 and 3 of our method. While the sketching of rank-1 or super-symmetric tensors is known to admit efficient sketching, the sub-sequence tensor does not satisfy either of these properties. Hence, we propose a new sketching scheme that completely avoids the need for constructing the ambient space. Our tensor-sketching technique's main advantages are three-fold: 1) Tensor Sketch has higher accuracy than any of the other assessed sketching methods used in practice. 2) All sketches can be computed in a streaming fashion, leading to significant time and memory savings when there is overlap between input sequences. 3) It is straightforward to extend tensor sketching to different settings leading to efficient methods for related sequence analysis tasks. We view tensor sketching as a framework to tackle a wide range of relevant bioinformatics problems, and we are confident that it can bring significant improvements for applications based on edit distance estimation.

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