PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.383075v1?rss=1

Authors: Habib, P.

Abstract:

The term chimeric virus was not popular in the last decades. Recently, according to current sequencing efforts in discovering COVID-19 Secrets, the generated information assumed the presence of 6 Coronavirus main strains, but coronavirus diverges into hundreds of sub-strains. the bottleneck is the mutation rate. With two mutation/month, humanity will meet a new sub-strain every month. Tracking new sequenced viruses is urgently needed because of the pathogenic effect of the new sub-strains. here we introduce COVATOR, A user-friendly and python-based software that identifies viral chimerism. COVATOR aligns input genome and protein that has no known source, against genomes and protein with known source, then gives the user a graphical summary.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.380733v1?rss=1

Authors: Lozupone, C. A., Shaffer, M., Thurimella, K.

Abstract:

Background: Microbiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations. Additionally, modules provide biological insight as groups of microbes can have relationships among themselves. Results: To address these challenges we developed SCNIC: Sparse Cooccurrence Network Investigation for Compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. We applied SCNIC to a published dataset comparing microbiome composition in men who have sex with men (MSM) who were at a high risk of contracting HIV to non-MSM. By applying SCNIC we achieved increased statistical power and identified microbes that not only differed with MSM-status, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. Conclusions: SCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. Using SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.363689v1?rss=1

Authors: Kerstens, H., Hehir-Kwa, J., van de Geer-de Jong, E., van Run, C., Verwiel, E., van der Leest, D., Tops, B., Kemmeren, P.

Abstract:

Motivation: The increase in speed, reliability and cost-effectiveness of high-throughput sequencing has led to the widespread clinical application of genome (WGS), exome (WXS) and transcriptome analysis. WXS and RNA sequencing is now being implemented as standard of care for patients and for patients included in clinical studies. To keep track of sample relationships and analyses, a platform is needed that can unify metadata for diverse sequencing strategies with sample metadata whilst supporting automated and reproducible analyses. In essence ensuring that analysis is conducted consistently, and data is Findable, Accessible, Interoperable and Reusable (FAIR). Results: We present 'Trecode', a framework that records both clinical and research sample (meta) data and manages computational genome analysis workflows executed for both settings. Thereby achieving tight integration between analyses results and sample metadata. With complete, consistent and FAIR (meta) data management in a single platform, stacked bioinformatic analyses are performed automatically and tracked by the database ensuring data provenance, reproducibility and reusability which is key in worldwide collaborative translational research. Availability and implementation: The Trecode data model, codebooks, NGS workflows and client programs are currently being cleared from local compute infrastructure dependencies and will become publicly available in spring 2021. Contact: [email protected]

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.382739v1?rss=1

Authors: Ali Hassan, A., Ibrahim, M. E.

Abstract:

Chemokines are small transmembrane proteins with immune surveillance and immune cell recruitment functions. the expression of CCR5 gene affects virus production and viral load(1). The CCR5 gene contains two introns, three exons, and two promoters, and it is necessary as a co-receptor for the entry of the macrophage-tropic HIV strains. Mutations in the coding region of CCR5 affect the protein structure, which will affect production, chemokine binding, transport, signaling and expression of the CCR5 receptor. SNPs within CCR5 gene were retrieved from ensemble database. Coding SNPs were analyzed using SNPnexus. Coding non-synonymous SNPs in CCR5 binding domains with Viral gp120 were analyzed using SIFT, PolyPhen and I-mutant tools. Project HOPE then used to modelled the 3D structure of the protein resulting from these SNPs. Non-coding SNPs that affects miRNAs in 3' rejoin were analyzing using PolymiRTS. SNPs that affect transcription factor binding were analyzed using regulomeDB. (178) non-synonyms missense SNPs were found to have deleterious and damaging effect on the structure and function of the protein. In CCR5 binding domains with Viral gp120: 3 SNPs rs145061115, rs199824195 and rs201797884 were found to affect both structure and function and stability of chemokine protein. The 2 SNPs rs185691679 and rs199722070 has a role in disruption and creation of the target sites in miRNA seeds due to their high conservation score. Mutations in CCR5 gene may explain and represent the molecular basis of the resistance to HIV infection.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.382994v1?rss=1

Authors: Sun, Z., Huang, S., Zhang, M., Zhu, Q., Haiminen, N., Carrieri, A.-P., Vazquez-Baeza, Y., Parida, L., Kim, H.-C., Knight, R., Liu, Y.-Y.

Abstract:

Accurate microbial identification and abundance estimation are crucial for metagenomics analysis. Various methods for classifying metagenomic data and estimating taxonomic profiles, broadly referred to as metagenomic profilers, have been developed. Yet, benchmarking metagenomic profilers remains challenging because some tools are designed to report relative sequence abundance while others report relative taxonomic abundance. Here, we show how misleading conclusions can be drawn by neglecting this distinction between relative abundance types when benchmarking metagenomic profilers. Moreover, we show compelling evidence that interchanging sequence abundance and taxonomic abundance will influence both per-sample summary statistics and cross-sample comparisons. We suggest that the microbiome research community should pay attention to potentially misleading biological conclusions arising from this issue when benchmarking metagenomic profilers, by carefully considering the type of abundance data that was analyzed and interpreted, and clearly stating the strategy used for metagenomic profiling.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.382119v1?rss=1

Authors: Johnson, D., Cochrane, K., Davey, R. P., Etuk, A., Gonzalez-Beltran, A., Haug, K., Izzo, M., Larralde, M., Lawson, T. N., Minotto, A., Moreno, P., Nainala, V. C., O'Donovan, C., Pireddu, L., Roger, P., Shaw, F., Steinbeck, C., Weber, R. J., Sansone, S.-A., Rocca-Serra, P.

Abstract:

Background: The Investigation/Study/Assay (ISA) Metadata Framework is an established and widely used set of open-source community specifications and software tools for enabling discovery, exchange and publication of metadata from experiments in the life sciences. The original ISA software suite provided a set of user-facing Java tools for creating and manipulating the information structured in ISA-Tab - a now widely used tabular format. To make the ISA framework more accessible to machines and enable programmatic manipulation of experiment metadata, a JSON serialization ISA-JSON was developed. Results: In this work, we present the ISA API, a Python library for the creation, editing, parsing, and validating of ISA-Tab and ISA-JSON formats by using a common data model engineered as Python class objects. We describe the ISA API feature set, early adopters and its growing user community. Conclusions: The ISA API provides users with rich programmatic metadata handling functionality to support automation, a common interface and an interoperable medium between the two ISA formats, as well as with other life science data formats required for depositing data in public databases.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.13.381624v1?rss=1

Authors: Xi, S., Gibilisco, L., Kummer, M., Biber, K., Wachter, A., Woodbury, M.

Abstract:

Single-nucleus RNA sequencing (sNuc-RNAseq) is an emerging powerful genomics technology that combines droplet microfluidics with next-generation sequencing to interrogate transcriptome changes at single nucleus resolution. Here we developed Abacus, a flexible UMI counter software for sNuc-RNAseq analysis. Abacus draws extra information from sequencing reads mapped to introns of pre-mRNAs (~60% of total data) that are ignored by many single-cell RNAseq analysis pipelines. When applied to our pilot human brain sNuc-RNAseq data, ABACUS nearly doubled the number of nuclei identified by the CellRanger workflow, recovering a large number of nuclei from non-neuronal cells. By incorporating intronic reads into gene expression quantification, we showed that they encoded additional and valid transcription features of individual cells and could be used to improve cluster resolution of different cell types. By separately counting UMIs derived from forward and reverse intronic reads and from exonic reads, Abacus gives users flexibility in representing genes expressed at different abundance levels. In summary, Abacus represents a flexible, improved workflow for sNuc-RNAseq data processing and analysis.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.382713v1?rss=1

Authors: Shibuya, Y., Kucherov, G.

Abstract:

Motivation: In many bioinformatics pipelines, k-mer counting is often a required step, with existing methods focusing on optimizing time or memory usage. These methods usually produce very large count tables explicitly representing k-mers themselves. Solutions avoiding explicit representation of k-mers include Minimal Perfect Hash Functions (MPHFs) or Count-Min sketches. The former is only applicable to static maps not subject to updates, while the latter suffers from potentially very large point-query errors, making it unsuitable when counters are required to be highly accurate. Results: We introduce Set-Min sketch, a sketching technique inspired by Count-Min sketch, for representing associative maps, more specifically, k-mer count tables. We show that Set-Min sketch provides a very low error rate, both in terms of the probability and the size of errors, much lower than a Count-Min sketch of similar dimensions. On the other hand, Set-Min sketches are shown to take up to an order of magnitude less space than MPHF-based solutions, especially for large values of k. Space-efficiency of Set-min takes advantage of the power-law distribution of k-mer counts in genomic datasets.

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.382580v1?rss=1

Authors: Koutrouli, M., Theodosiou, T., Iliopoulos, I., Pavlopoulos, G.

Abstract:

In this article we present the Network Analysis Profiler (NAP v2.0), a web tool to directly compare the topological features of multiple networks simultaneously. NAP is written in R and Shiny and currently offers both 2D and 3D network visualization as well as simultaneous visual comparisons of node- and edge-based topological features both as bar charts or as a scatterplot matrix. NAP is fully interactive and users can easily export and visualize the intersection between any pair of networks using Venn diagrams or a 2D and a 3D multi-layer graph-based visualization. NAP supports weighted, unweighted, directed, undirected and bipartite graphs and is available at: http://bib.fleming.gr:3838/NAP/. Its code can be found at: https://github.com/PavlopoulosLab/NAP

Copy rights belong to original authors. Visit the link for more info

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.14.382655v1?rss=1

Authors: Coskun, M., Koyuturk, M.

Abstract:

Motivation: Link prediction is an important and well-studied problem in computational biology, with a broad range of applications including disease gene prioritization, drug disease associations, and drug response in cancer. The general principle in link prediction is to use the topological characteristics and the attributes--if available-- of the nodes in the network to predict new links that are likely to emerge/disappear. Recently, graph representation learning methods, which aim to learn a low-dimensional representation of topological characteristics and the attributes of the nodes, have drawn increasing attention to solve the link prediction problem via learnt low-dimensional features. Most prominently, Graph Convolution Network (GCN)-based network embedding methods have demonstrated great promise in link prediction due to their ability of capturing non-linear information of the network. To date, GCN-based network embedding algorithms utilize a Laplacian matrix in their convolution layers as the convolution matrix and the effect of the convolution matrix on algorithm performance has not been comprehensively characterized in the context of link prediction in biomedical networks. On the other hand, for a variety of biomedical link prediction tasks, traditional node similarity measures such as Common Neighbor, Ademic-Adar, and other have shown promising results, and hence there is a need to systematically evaluate the node similarity measures as convolution matrices in terms of their usability and potential to further the state-of-the-art. Results: We select 8 representative node similarity measures as convolution matrices within the single-layered GCN graph embedding method and conduct a systematic comparison on 3 important biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) prediction, protein-protein interaction (PPI) prediction. Our experimental results demonstrate that the node similarity-based convolution matrices significantly improves GCN-based embedding algorithms and deserve more attention in the future biomedical link prediction Availability: Our method is implemented as a python library and is available at githublink

Copy rights belong to original authors. Visit the link for more info