PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.231233v1?rss=1

Authors: Olekhnovich, E. I., Ivanov, A. B., Ulyantsev, V. I., Ilina, E. N.

Abstract:

Background. Fecal microbiota transplantation (FMT) is currently used to treat recurrent clostridial colitis and other diseases. However, neither the therapeutic mechanism of the FMT nor the mechanism that allows the donor bacteria to colonize the intestine of the recipient has yet been described. Moreover, FMT is a great model for studying the ecology of host-associated microbial communities. This creates the need for experimentation with approaches to metagenomic data analysis which may be useful to the interpretation of observed biological phenomena. Methods. Here the RECAST (Recipient intestinE Colonisation AnalysiS Tool) computational approach is presented, which is based on the shotgun reads sorting process in accordance with their origin in recipient metagenome. Using the RECAST algorithm, taxonomic/functional annotation, and machine learning, the shotgun metagenomic data from three FMT studies including healthy volunteers, patients with clostridial colitis and metabolic syndrome were analyzed. Results. According to the analysis results, the colonizing and remaining microbial diversity in the post-FMT recipient metagenomic samples is clearly separated from the non-colonizers and lost. It is well explained by higher relative abundance in donor/pre-FMT recipient, Human Microbiome project metagenomes, and taxonomy. Moreover, the colonizing and remaining microbes are associated with lantibiotic and tetracyclines resistance genes. Conclusion. Based on obtained results, the previously proposed "core" human gut microbiome concept may be elaborated. The top microbes of gut microbiota form "cores", which, moreover, are mutually integrable between humans. Also, we assume that redistribution of microbial diversity in post-FMT recipients' metagenomes is due to competition of donor/recipient microbes and to host immunity. The associations of top gut microbes with lantibiotic/antibiotic resistance can be related to gut microbiota colonization resistance phenomena or anthropogenic impact.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.230631v1?rss=1

Authors: Graaf, L. v. d., Wagenaar, J. A., Zomer, A. L.

Abstract:

Antimicrobial resistance (AMR) genes in bacteria are often carried on plasmids and these plasmids can transfer AMR genes between bacteria. For molecular epidemiology purposes and risk assessment, it is important to know if the genes are located on highly transferable plasmids or in the more stable chromosomes. However, draft whole genome sequences are fragmented, making it difficult to discriminate plasmid and chromosomal contigs. Current methods that predict plasmid sequences from draft genome sequences rely on single features, like k-mer composition, circularity of the DNA molecule, copy number or sequence identity to plasmid replication genes, all of which have their drawbacks, especially when faced with large single copy plasmids, which often carry resistance genes. With our newly developed prediction tool RFPlasmid, we use a combination of multiple features, including k-mer composition and databases with plasmid and chromosomal marker proteins, to predict if the likely source of a contig is plasmid or chromosomal. The tool RFPlasmid supports models for 17 different bacterial species, including Campylobacter, E. coli, and Salmonella, and has a species agnostic model for metagenomic assemblies or unsupported organisms. RFPlasmid is available both as standalone tool and via web interface.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.02.233163v1?rss=1

Authors: Stepanov, V. G., Fox, G. E.

Abstract:

The large ribosomal RNAs of eukaryotes frequently contain expansion sequences that add to the size of the rRNAs but do not affect their overall structural layout and are compatible with major ribosomal function as an mRNA translation machine. The expansion of prokaryotic ribosomal RNAs is much less explored. In order to obtain more insight into the structural variability of these conserved molecules, we herein report the results of a comprehensive search for the expansion sequences in prokaryotic 5S rRNAs. Overall, 89 expanded 5S rRNAs of 15 structural types were identified in 15 archaeal and 36 bacterial genomes. Expansion segments ranging in length from 13 to 109 residues were found to be distributed among 17 insertion sites. The strains harboring the expanded 5S rRNAs belong to the bacterial orders Clostridiales, Halanaerobiales, Thermoanaerobacterales, and Alteromonadales as well as the archael order Halobacterales. When several copies of 5S rRNA gene are present in a genome, the expanded versions may co-exist with normal 5S rRNA genes. The insertion sequences are typically capable of forming extended helices, which do not seemingly interfere with folding of the conserved core. The expanded 5S rRNAs have largely been overlooked in 5S rRNA databases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.224592v1?rss=1

Authors: Russo, E. T., Laio, A., Punta, M.

Abstract:

As the UniProt database approaches the 200 million entries' mark, the vast majority of proteins it contains lack any experimental validation of their functions. In this context, the identification of homologous relationships between proteins remains the single most widely applicable tool for generating functional and structural hypotheses in silico. Although many databases exist that classify proteins and protein domains into homologous families, large sections of the sequence space remain unassigned. We introduce DPCfam, a new unsupervised procedure that uses sequence alignments and Density Peak Clustering to automatically classify homologous protein regions. Here, we present a proof-of-principle experiment based on the analysis of two clans from the Pfam protein family database. Our tests indicate that DPCfam automatically-generated clusters are generally evolutionary accurate corresponding to one or more Pfam families and that they cover a significant fraction of known homologs. Overall, DPCfam shows potential both for assisting manual annotation efforts (domain discovery, detection of classification inconsistencies, improvement of family coverage and boosting of clan membership) and as a stand-alone tool for unsupervised classification of sparsely annotated protein datasets such as those from environmental metagenomics studies (domain discovery, analysis of domain diversity). Algorithm implementation used in this paper is available at https://gitlab.com/ETRu/dpcfam (Requires Python 3, C++ compiler and runs on Linux systems.); data are available at https://zenodo.org/record/3934399

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.227611v1?rss=1

Authors: Barot, M., Gligorijevic, V., Cho, K., Bonneau, R.

Abstract:

Transferring knowledge between species is challenging: different species contain distinct proteomes and cellular architectures, which cause their proteins to carry out different functions via different interaction networks. Many approaches to proteome and biological network functional annotation use sequence similarity to transfer knowledge between species. These similarity-based approaches cannot produce accurate predictions for proteins without homologues of known function, as many functions require cellular or organismal context for meaningful function prediction. In order to supply this context, network-based methods use protein-protein interaction (PPI) networks as a source of information for inferring protein function and have demonstrated promising results in function prediction. However, the majority of these methods are tied to a network for a single species, and many species lack biological networks. In this work, we integrate sequence and network information across multiple species by applying an IsoRank-derived network alignment algorithm to create a meta-network profile of the proteins of multiple species. We then use this integrated multispecies meta-network as input features to train a maxout neural network with Gene Ontology terms as target labels. Our multispecies approach takes advantage of more training examples, and more diverse examples from multiple organisms, and consequently leads to significant improvements in function prediction performance. Further, we evaluate our approach in a setting in which an organism's PPI network is left out, using other organisms' network information and sequence homology in order to make predictions for the left-out organism, to simulate cases in which a newly sequenced species has no network information available.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.228601v1?rss=1

Authors: Gaziova, M., Pos, O., Krampl, W., Kubiritova, Z., Kucharik, M., Radvanszky, J., Budis, J., Szemes, T.

Abstract:

Copy number variants (CNVs) play important roles in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analysis. This led to the demand for interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors and clinical geneticists from the laborious process of annotation and classification of CNVs. Here we demonstrate that the prediction of the clinical impact of CNVs can be automated using modern machine learning methods applied to publicly available genomic annotations, requiring only basic input information about the genomic location and structural type (duplication/deletion) of the analyzed CNV. The presented approach achieved 0.95 prediction accuracy on deletions and 0.96 on duplications from the ClinVar dataset and therefore have a great potential to guide users to more precise conclusions.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.230904v1?rss=1

Authors: Yin, R., Luo, Z., Zhuang, P., Lin, Z., Kwoh, C. K.

Abstract:

Motivation: Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics. The evolution of influenza viruses remains to be the main obstacle in the effectiveness of antiviral treatments due to rapid mutations. Previous work has been investigated to reveal the determinants of virulence of the influenza A virus. To further facilitate flu surveillance, explicit detection of influenza virulence is crucial to protect public health from potential future pandemics. Results: In this paper, we propose a weighted ensemble convolutional neural network for the virulence prediction of influenza A viruses named VirPreNet that uses all 8 segments. Firstly, mouse lethal dose 50 is exerted to label the virulence of infections into two classes, namely avirulent and virulent. A numerical representation of amino acids named ProtVec is applied to the 8-segments in a distributed manner to encode the biological sequences. After splittings and embeddings of influenza strains, the ensemble convolutional neural network is constructed as the base model on the influenza dataset of each segment, which serves as the VirPreNet's main part. Followed by a linear layer, the initial predictive outcomes are integrated and assigned with different weights for the final prediction. The experimental results on the collected influenza dataset indicate that VirPreNet achieves state-of-the-art performance combining ProtVec with our proposed architecture. It outperforms baseline methods on the independent testing data. Moreover, our proposed model reveals the importance of PB2 and HA segments on the virulence prediction. We believe that our model may provide new insights into the investigation of influenza virulence.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.194266v1?rss=1

Authors: Datta, G., Hasan, N. A., Strong, M., Leach, S. M.

Abstract:

Background: The increasing incidence of drug resistance in tuberculosis and other infectious diseases poses an escalating cause for concern, emphasizing the urgent need to devise robust computational and molecular methods identify drug resistant strains. Although machine learning-based approaches using whole-genome sequence data can facilitate the inference of drug resistance, current implementations do not optimally take advantage of information in public databases and are not robust for small sample sizes and mixed attribute types. Results: In this paper we introduce the Composite MetaDistance method, an approach for feature selection and classification of high-dimensional, unbalanced datasets with mixed attribute features from various data sources. We introduce a mixed-attribute, multi-view distance function to calculate distances between samples, with optimal handling of nominal features and different feature views. We also introduce a novel feature set for drug resistance prediction in Mycobacterium tuberculosis, using data from diverse sources. We compare the performance of Composite MetaDistance to multiple machine learning algorithms for Mycobacterium tuberculosis drug resistance prediction for three drugs. Composite MetaDistance consistently outperforms existing algorithms for small sample training sets, and performs as well as other algorithms for training sets with larger sample sizes. Conclusion: The feature set formulation introduced in this paper is utilizes mutational and publicly available information for each gene, and is much richer than ever devised previously. The prediction algorithm, Composite MetaDistance, is sample size agnostic and robust especially given small sample sizes. Proper handling of nominal features improves performance even with a very small number of nominal features. We expect Composite MetaDistance to be even more robust for datasets with a higher percentage of nominal features. The algorithm is application independent and can be used for any mixed attribute dataset.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.30.229500v1?rss=1

Authors: Delomas, T. A., Willis, S. C., Schreier, A., Narum, S.

Abstract:

Variation in ploidy occurs naturally in select plant and animal species. Ploidy variation can also occur spontaneously or be induced during artificial propagation of fish and shellfish. Studying species and systems that have variable ploidy requires techniques to infer ploidy of individuals. Massively parallel sequencing of biallelic SNPs has been used to infer ploidy, but existing techniques have several drawbacks. These include being limited to only comparing a fixed number of ploidies (diploidy, triploidy, and tetraploidy) and requiring that heterozygous genotypes in an individual be identified prior to ploidy inference. We describe a method of inferring ploidy from sequencing of biallelic SNPs based on beta-binomial mixture models. This method is generalized to apply to any ploidy and does not require prior identification of heterozygous genotypes. We demonstrate efficacy of this method for comparing ancestral octoploidy, decaploidy, and dodecaploidy (tetraploidy, pentaploidy, and hexaploidy for the sequenced SNPs) in white sturgeon and diploidy and triploidy in Chinook salmon with amplicon sequencing (GT-seq) data. Results indicated that ploidy could be reliably estimated for individuals based on distinct distribution of log-likelihood ratios (LLR) for known ploidy samples of both species that were tested. Confidence in ploidy estimates increased with sequencing depth. We encourage users to explore the sequencing depths and LLR critical values that provide reliable estimates of ploidy for a given organism and set of SNPs. We expect that the R package provided will empower studies of genetic variation and inheritance in organisms that vary in ploidy naturally or as a result of artificial propagation practices.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.07.31.231472v1?rss=1

Authors: Tomaszewski, T., DeVries, R. S., Dong, M., Bhatia, G., Norsworthy, M. D., Zheng, X., Caetano-Anolles, G.

Abstract:

The massive worldwide spread of the SARS-CoV-2 virus is fueling the COVID-19 pandemic. Since the first whole-genome sequence was published in January 2020, a growing database of tens of thousands of viral genomes has been constructed. This offers opportunities to study pathways of molecular change in the expanding viral population that can help identify molecular culprits of virulence and virus spread. Here we investigate the genomic accumulation of mutations at various time points of the early pandemic to identify changes in mutationally highly active genomic regions that are occurring worldwide. We used the Wuhan NC_045512.2 sequence as a reference and sampled 15,342 indexed sequences from GISAID, translating them into proteins and grouping them by month of deposition. The per-position amino acid frequencies and Shannon entropies of the coding sequences were calculated for each month, and a map of intrinsic disorder regions and binding sites was generated. The analysis revealed dominant variants, most of which were located in loop regions and on the surface of the proteins. Mutation entropy decreased between March and April of 2020 after steady increases at several sites, including the D614G mutation site of the spike (S) protein that was previously found associated with higher case fatality rates and at sites of the NSP 12 polymerase and the NSP13 helicase proteins. Notable expanding mutations include R203K and G204R of the nucleocapsid (N) protein inter-domain linker region and G251V of the viroporin encoded by ORF3a between March and April. The regions spanning these mutations exhibited significant intrinsic disorder, which was enhanced and decreased by the N-protein and viroporin 3a protein mutations, respectively. These results predict an ongoing mutational shift from the spike and replication complex to other regions, especially to encoded molecules known to represent major beta-interferon antagonists. The study provides valuable information for therapeutics and vaccine design, as well as insight into mutation tendencies that could facilitate preventive control.

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