PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.21.260745v1?rss=1

Authors: Pontes, C., Ruiz-Serra, V. I., Lepore, R., Valencia, A.

Abstract:

The recent emergence of the novel SARS-CoV-2 in China and its rapid spread in the human population has led to a public health crisis worldwide. Like in SARS-CoV, horseshoe bats currently represent the most likely candidate animal source for SARS-CoV-2. Yet, the specific mechanisms of cross-species transmission and adaptation to the human host remain unknown. Here we show that the unsupervised analysis of conservation patterns across the {beta}-CoV spike protein family, using sequence information alone, can provide rich information on the molecular basis of the specificity of {beta}-CoVs to different host cell receptors. More precisely, our results indicate that host cell receptor usage is encoded in the amino acid sequences of different CoV spike proteins in the form of a set of specificity determining positions (SDPs). Furthermore, by integrating structural data, in silico mutagenesis and coevolution analysis we could elucidate the role of SDPs in mediating ACE2 binding across the Sarbecovirus lineage, either by engaging the receptor through direct intermolecular interactions or by affecting the local environment of the receptor binding motif. Finally, by the analysis of coevolving mutations across a paired MSA we were able to identify key intermolecular contacts occurring at the spike-ACE2 interface. These results show that effective mining of the evolutionary records held in the sequence of the spike protein family can help tracing the molecular mechanisms behind the evolution and host-receptors adaptation of circulating and future novel {beta}-CoVs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.19.257493v1?rss=1

Authors: Belmonte Reche, E., Serrano-Chacon, I., Gonzalez, C., Gallo, J., Banobre-Lopez, M.

Abstract:

In this paper we report the analysis of the 2019-nCoV genome and related viruses using an upgraded version of the open-source algorithm G4-iM Grinder. This version improves the functionality of the software, including an easy way to determine the potential biological features affected by the candidates found. The quadruplex definitions of the algorithm were optimized for 2019-nCoV. Using a lax quadruplex definition ruleset, which accepts amongst other parameters two residue G- and C-tracks, hundreds of potential quadruplex candidates were discovered. These sequences were evaluated by their in vitro formation probability, their position in the viral RNA, their uniqueness and their conservation rates (calculated in over three thousand different COVID-19 clinical cases and sequenced at different times and locations during the ongoing pandemic). These results were compared sequentially to other Coronaviridae members, other Group IV (+)ssRNA viruses and the entire realm. Sequences found in common with other species were further analyzed and characterized. Sequences with high scores unique to the 2019-nCoV were studied to investigate the variations amongst similar species. Quadruplex formation of the best candidates was then confirmed experimentally. Using NMR and CD spectroscopy, we found several highly stable RNA quadruplexes that may be suitable theranostic targets against the 2019-nCoV.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.19.254540v1?rss=1

Authors: Marenda, M., Lazarova, E., van de Linde, S., Gilbert, N., Michieletto, D.

Abstract:

Single-Molecule Localisation Microscopy (SMLM) allows the quantitative mapping of molecules at high resolution. However, understanding the non-random interaction of proteins requires the identification of more complex patterns than those typified by standard clustering tools. Here we introduce SuperStructure, a parameter-free algorithm to quantify structures made of inter-connected clusters, such as protein gels. SuperStructure works without a priori assumptions and is thus an ideal methodology for standardised analysis of SMLM data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.19.257501v1?rss=1

Authors: Jackson, M. A., Pearson, C., Ilott, N. E., Huus, K. E., Hegazy, A. N., Webber, J., Finlay, B. B., Macpherson, A. J., Powrie, F., Lam, L. H.

Abstract:

Background: Identifying which taxa are targeted by immunoglobulins can uncover important host-microbe interactions. Immunoglobulin binding of commensal taxa can be assayed by sorting bound bacteria from samples and using amplicon sequencing to determine their taxonomy, a technique most widely applied to study Immunoglobulin A (IgA-Seq). Previous experiments have scored taxon binding in IgA-Seq datasets by comparing abundances in the IgA bound and unbound sorted fractions. However, as these are relative abundances, such scores are influenced by the levels of the other taxa present and represent an abstract combination of these effects. Diversity in the practical approaches of prior studies also warrants benchmarking of the individual stages involved. Here, we provide a detailed description of the design strategy for an optimised IgA-Seq protocol. Combined with a novel scoring method for IgA-Seq datasets that accounts for the aforementioned effects, this platform enables accurate identification and quantification of commensal gut microbiota targeted by host immunoglobulins. Results: Using germ-free and Rag1-/- mice as negative controls, and a strain-specific IgA antibody as a positive control, we determine optimal reagents and fluorescence activated cell sorting (FACS) parameters for IgA-Seq. Using simulated IgA-Seq data, we show that existing IgA-Seq scoring methods are influenced by pre-sort relative abundances. This has consequences for the interpretation of case-control studies where there are inherent differences in microbiota composition between groups. We show that these effects can be addressed using a novel scoring approach based on posterior probabilities. Finally, we demonstrate the utility of both the IgA-Seq protocol and probability-based scores by examining both novel and published data from in vivo disease models. Conclusions: We provide a detailed IgA-Seq protocol to accurately isolate IgA-bound taxa from intestinal samples. Using simulated and experimental data, we demonstrate novel probability-based scores that adjust for the compositional nature of relative abundance data to accurately quantify taxon-level IgA binding. All scoring approaches are made available in the IgAScores R package. These methods should improve the generation and interpretation of IgA-Seq datasets and could be applied to study other immunoglobulins and sample types.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.19.258095v1?rss=1

Authors: Wang, Z., Zhang, Y., Zang, C.

Abstract:

Identification of functional transcriptional regulators (TRs) associated with chromatin interactions is an important problem in studies of 3-dimensional genome organization and gene regulation. Direct inference of TR binding has been limited by the resolution of Hi-C data. Here, we present BART3D, a computational method for inferring TRs associated with genome-wide differential chromatin interactions by comparing two Hi-C maps, leveraging public ChIP-seq data for human and mouse. We demonstrate that BART3D can detect target TRs inducing chromatin architecture changes from dynamic Hi-C profiles with TR perturbation. BART3D can be a useful tool in 3D genome data analysis and functional genomics research.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.19.256693v1?rss=1

Authors: Li, Y., Mullin, M., ZHANG, Y., Drews, F., Welch, L., Showalter, A.

Abstract:

Hydroxyproline-rich glycoproteins (HRGPs) are a superfamily of plant cell wall structural proteins that function in various aspects of plant growth and development, including pollen tube growth. We have previously characterized HRGP superfamily into three family members: the hyperglycosylated arabinogalactan-proteins, the moderately glycosylated extensins, and the lightly glycosylated proline-rich proteins. However, the mechanism of pollen-specific HRGP expression remains untouched. To this end, we developed an integrative analysis pipeline combining RNA-seq gene expression and promoter sequences that identified 15 transcriptional cis-regulatory motifs responsible for pollen-specific expression of HRGP in Arabidopsis Thaliana. Specifically, we mined the public RNA-seq datasets and identified 13 pollen-specific HRGP genes. Ensemble motif discovery with various filters identified 15 conserved promoter elements between Thaliana and Lyrata. Known motif analysis revealed pollen related transcription factors of GATA12 and brassinosteroid (BR) signaling pathway regulator BZR1. Lastly, we performed a machine learning regression analysis and demonstrated that the identified 15 motifs well captured the HRGP gene expression in pollen (R=0.61). In conclusion, we performed the integrative analysis as the first-of-its-kind study to identify cis-regulatory motifs in pollen-specific HRGP genes and shed light on its transcriptional regulation in pollen.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.19.257642v1?rss=1

Authors: Song, H., Bremer, B. J., Hinds, E. C., Raskutti, G., Romero, P. A.

Abstract:

Machine learning can infer how protein sequence maps to function without requiring a detailed understanding of the underlying physical or biological mechanisms. It's challenging to apply existing supervised learning frameworks to large-scale experimental data generated by deep mutational scanning (DMS) and related methods. DMS data often contain high dimensional and correlated sequence variables, experimental sampling error and bias, and the presence of missing data. Importantly, most DMS data do not contain examples of negative sequences, making it challenging to directly estimate how sequence affects function. Here, we develop a positive-unlabeled (PU) learning framework to infer sequence-function relationships from large-scale DMS data. Our PU learning method displays excellent predictive performance across ten large-scale sequence-function data sets, representing proteins of different folds, functions, and library types. The estimated parameters pinpoint key residues that dictate protein structure and function. Finally, we apply our statistical sequence-function model to design highly stabilized enzymes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.18.254680v1?rss=1

Authors: Bhattacharya, S., Barseghyan, H., Délot, E. C., Vilain, E.

Abstract:

Whole genome sequencing is effective at identification of small variants but, because it is based on short reads, assessment of structural variants (SVs) is limited. The advent of Optical Genome Mapping (OGM), which utilizes long fluorescently labeled DNA molecules for de novo genome assembly and SV calling, has allowed for increased sensitivity and specificity in SV detection. However, compared to small variant annotation tools, OGM-based SV annotation software has seen little development, and currently available SV annotation tools do not provide sufficient information for determination of variant pathogenicity. We developed an R-based package, nanotatoR, which provides comprehensive annotation as a tool for SV classification. nanotatoR uses both external (DGV; DECIPHER; Bionano Genomics BNDB) and internal (user-defined) databases to estimate SV frequency. Human genome reference GRCh37/38-based BED files are used to annotate SVs with overlapping, upstream, and downstream genes. Overlap percentages and distances for nearest genes are calculated and can be used for filtration. A primary gene list is extracted from public databases based on the patient's phenotype and used to filter genes overlapping SVs, providing the analyst with an easy way to prioritize variants. If available, expression of overlapping or nearby genes of interest is extracted (e.g. from an RNA-Seq dataset, allowing the user to assess the effects of SVs on the transcriptome). Most quality-control filtration parameters are customizable by the user. The output is given in an Excel file format, subdivided into multiple sheets based on SV type and inheritance pattern (INDELs, inversions, translocations, de novo, etc.). nanotatoR passed all quality and run time criteria of Bioconductor, where it was accepted in the April 2019 release. We evaluated nanotatoR's annotation capabilities using publicly available reference datasets: the singleton sample NA12878, mapped with two types of enzyme labeling, and the NA24143 trio. nanotatoR was also able to accurately filter the known pathogenic variants in a cohort of patients with Duchenne Muscular Dystrophy for which we had previously demonstrated the diagnostic ability of OGM. The extensive annotation enables users to rapidly identify potential pathogenic SVs, a critical step toward use of OGM in the clinical setting.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.20.258772v1?rss=1

Authors: Nagpal, S., Srivastava, D., Mande, S. S.

Abstract:

Topic modeling is frequently employed for discovering structures (or patterns) in a corpus of documents. Its utility in text-mining and document retrieval tasks in various fields of scientific research is rather well known. An unsupervised machine learning approach, Latent Dirichlet Allocation (LDA) has particularly been utilized for identifying latent (or hidden) topics in document collections and for deciphering the words that define one or more topics using a generative statistical model. Here we describe how SARS-CoV-2 genomic mutation profiles can be structured into a Bag of Words (BoW) to enable identification of signatures (topics) and their probabilistic distribution across various genomes using LDA. Topic models were generated using ~47000 novel corona virus genomes (considered as documents), leading to identification of 16 amino acid mutation signatures and 18 nucleotide mutation signatures (equivalent to topics) in the corpus of chosen genomes through coherence optimization. The document assumption for genomes also helped in identification of contextual nucleotide mutation signatures in the form of conventional N-grams (e.g. bi-grams and tri-grams). We validated the signatures obtained using LDA driven method against the previously reported phylogenetic clades for genomes. Additionally, we report the distribution of the identified mutation signatures on the global map of SARS-CoV-2 genomes. Use of the non-phylogenetic albeit classical approaches like topic modeling and other data centric pattern mining algorithms is therefore proposed for supplementing the efforts towards understanding the genomic diversity of the evolving SARS-CoV-2 genomes (and other pathogens/microbes).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.18.251561v1?rss=1

Authors: Zhang, Z., Park, C. Y., Theesfeld, C. L., Troyanskaya, O. G.

Abstract:

Convolutional neural networks (CNNs) have become a standard for analysis of biological sequences. Tuning of network architectures is essential for CNN's performance, yet it requires substantial knowledge of machine learning and commitment of time and effort. This process thus imposes a major barrier to broad and effective application of modern deep learning in genomics. Here, we present AMBER, a fully automated framework to efficiently design and apply CNNs for genomic sequences. AMBER designs optimal models for user-specified biological questions through the state-of-the-art Neural Architecture Search (NAS). We applied AMBER to the task of modelling genomic regulatory features and demonstrated that the predictions of the AMBER-designed model are significantly more accurate than the equivalent baseline non-NAS models and match or even exceed published expert-designed models. Interpretation of AMBER architecture search revealed its design principles of utilizing the full space of computational operations for accurately modelling genomic sequences. Furthermore, we illustrated the use of AMBER to accurately discover functional genomic variants in allele-specific binding and disease heritability enrichment. AMBER provides an efficient automated method for designing accurate deep learning models in genomics.

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