PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.242347v1?rss=1

Authors: Eguchi, R. R., Anand, N., Choe, C. A., Huang, P.-S.

Abstract:

While deep learning models have seen increasing applications in protein science, few have been implemented for protein backbone generation -- an important task in structure-based problems such as active site and interface design. We present a new approach to building class-specific backbones, using a variational auto-encoder to directly generate the 3D coordinates of immunoglobulins. Our model is torsion- and distance-aware, learns a high-resolution embedding of the dataset, and generates novel, high-quality structures compatible with existing design tools. We show that the Ig-VAE can be used to create a computational model of a SARS-CoV2-RBD binder via latent space sampling. We further demonstrate that the model's generative prior is a powerful tool for guiding computational protein design, motivating a new paradigm under which backbone design is solved as constrained optimization problem in the latent space of a generative model.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244020v1?rss=1

Authors: Yan, R., Liu, X., Dutcher, J., Tumminia, M., Villalba, D., Cohen, S., Creswell, D., Creswell, K., Mankoff, J., Dey, A., Doryab, A.

Abstract:

This paper presents CoRhythMo, the first computational framework for modeling biobehavioral rhythms - the repeating cycles of physiological, psychological, social, and environmental events - from mobile and wearable data streams. The framework incorporates four main components: mobile data processing, rhythm discovery, rhythm modeling, and machine learning. We use a dataset of smartphone and Fitbit data collected from 138 college students over a semester to evaluate the frameworks ability to 1) model biobehavioral rhythms of students, 2) measure the stability of their rhythms over the course of the semester, 3) model differences between rhythms of students with different health status, and 4) predict the mental health status in students using the model of their biobehavioral rhythms. Our evaluation provides evidence for the feasibility of using CoRhythMo for modeling and discovering human rhythms and using them to assess and predict different life and health outcomes.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244848v1?rss=1

Authors: Nicholls, S. M., Aubrey, W., de Grave, K., Schietgat, L., Creevey, C. J. M., Clare, A.

Abstract:

Motivation: Population-level genetic variation enables competitiveness and niche specialization in microbial communities. Despite the difficulty in culturing many microbes from an environment, we can still study these communities by isolating and sequencing DNA directly from an environment (metagenomics). Recovering the genomic sequences of all isoforms of a given gene across all organisms in a metagenomic sample would aid evolutionary and ecological insights into microbial ecosystems with potential benefits for medicine and biotechnology. A significant obstacle to this goal arises from the lack of a computationally tractable solution that can recover these sequences from sequenced read fragments. This poses a problem analogous to reconstructing the two sequences that make up the genome of a diploid organism (i.e. haplotypes), but for an unknown number of individuals. Results: The problem of single individual haplotyping (SIH) was first formalised by Lancia et al in 2001. Now, nearly two decades later, we discuss the complexity of "haplotyping" metagenomic samples, with a new formalisation of Lancia et al's data structure that allows us to effectively extend the single individual haplotype problem to microbial communities. This work describes and formalizes the problem of recovering genes (and other genomic subsequences) from all individuals within a complex community sample: which we term the metagenomic individual haplotyping (MIH) problem. We also provide software implementations of our proposed pairwise single nucleotide variant (SNV) co-occurrence matrix and greedy graph traversal algorithm. Availability and implementation: Our reference implementation of the described pairwise SNV matrix (Hansel) and greedy haplotype path traversal algorithm (Gretel) are open source, MIT licensed and freely available online at github.com/samstudio8/hansel and github.com/samstudio8/gretel, respectively.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.244632v1?rss=1

Authors: Aktas, E.

Abstract:

There are some mutations are known related to SARS-CoV-2. Together with these mutations known, we tried to show other newly mutations regionally. According to our results which 4326 whole sequences are used, we found that some mutations occur only in a certain region, while some other mutations are found in each regions. Especially in Asia, more than one mutation (three different mutations are found in QLA46612 isolated from South Korea) was seen in the same sequence. Although we detected a huge number of mutations (we found more than seventy in Asia) by regions, some of them were predicted that damage spike's protein structure by using bioinformatic tools. The predicted results are G75V (isolated from North America), T95I (isolated from South Korea), G143V (isolated from North America), M177I (isolated Asia), L293M (isolated from Asia), P295H (isolated from Asia), T393P (isolated from Europe), P507S (isolated from Asia), D614G (isolated from all regions) respectively. Also, in this study, we tried to show how possible binding sites of ligands change if the spike protein structure is damaged and whether more than one mutation affects ligand binding was estimated using bioinformatics tools. Interestingly, mutations that predicted to damage the structure do not affect ligand binding sites, whereas ligands' binding sites were affected in those with multiple mutations. Focusing on mutations may opens up the window to exploit new future therapeutic targets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.241729v1?rss=1

Authors: Edgar, R. C., Taylor, J., Altman, T., Barbera, P., Meleshko, D., Lin, V., Lohr, D., Novakovsky, G., Al-Shayeb, B., Banfield, J., Korobeynikov, A., Chikhi, R., Babaian, A.

Abstract:

Public sequence data represents a major opportunity for viral discovery, but its exploration has been inhibited by a lack of efficient methods for searching this corpus, which is currently at the petabase scale and growing exponentially. To address the ongoing pandemic caused by Severe Acute Respiratory Syndrome Coronavirus 2 and expand the known sequence diversity of viruses, we aligned pangenomes for coronaviruses (CoV) and other viral families to 5.6 petabases of public sequencing data from 3.8 million biologically diverse samples. To implement this strategy, we developed a cloud computing architecture, `Serratus`, tailored for ultra-high throughput sequence alignment at the petabase scale. From this search, we identified and assembled thousands of CoV and CoV-like genomes and genome fragments ranging from known strains to putatively novel genera. We generalise this strategy to other viral families, identifying several novel deltaviruses and huge bacteriophages. To catalyse a new era of viral discovery we made millions of viral alignments and family identifications freely available to the research community (https://serratus.io). Expanding the known diversity and zoonotic reservoirs of CoV and other emerging pathogens can accelerate vaccine and therapeutic developments for the current pandemic, and help us anticipate and mitigate future ones.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.10.231720v1?rss=1

Authors: Newman, R., Pietras, C. M., Qu, F., Sapashnik, D., Kofman, L., Butze, S., Ocitti, F., Fried, I., Slonim, D.

Abstract:

Motivation: Drug re-positioning allows expedited discovery of new applications for existing compounds, but re-screening vast compound libraries is often prohibitively expensive. "Connectivity mapping" is a process that links drugs to diseases by identifying drugs whose impact on expression in a collection of cells most closely reverses the disease's impact on expression in disease-relevant tissues. The high throughput LINCS project has expanded the universe of compounds, cellular perturbations, and cell types for which data are available, but even with this effort, many potentially clinically useful combinations are missing. To evaluate the possibility of finding disease-relevant drug connectivity despite missing data, we compared methods using cross-validation on a complete subset of the LINCS data. Results: Modified recommender systems with either neighborhood-based or SVD imputation methods were compared to autoencoders and two naive methods. All were evaluated for accuracy in prediction of both expression signatures and connectivity query responses. We demonstrate that cellular context is important, and that it is possible to predict cell-specific drug responses with improved accuracy over naive approaches. Neighborhood-based collaborative filtering was the most successful, improving prediction accuracy in all tested cells. We conclude that even for cells in which drug responses have not been fully characterized, it is possible to identify drugs that reverse the expression signatures observed in disease.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.242305v1?rss=1

Authors: Roe, D., Vierra-Green, C., Pyo, C.-W., Geraghty, D. E., Spellman, S. R., Maiers, M., Kuang, R.

Abstract:

Human chromosome 19q13.4 contains genes encoding killer-cell immunoglobulin-like receptors (KIR). Reported haplotype lengths range from 67 to 269 kilobases and contain 4 to 18 genes. The region has certain properties such as single nucleotide variation, structural variation, homology, and repetitive elements that make it hard to align accurately beyond single gene alleles. To the best of our knowledge, a multiple sequence alignment of KIR haplotypes has never been published or presented. Such an alignment would be useful to precisely define KIR haplotypes and loci, provide context for assigning alleles (especially fusion alleles) to genes, infer evolutionary history, impute alleles, interpret and predict co-expression, and generate markers. In order to extend the framework of KIR haplotype sequences in the human genome reference, 27 new sequences were generated including 24 haplotypes from 12 individuals of African American ancestry that were selected for genotypic diversity and novelty to the reference, to bring the total to 68 full length genomic KIR haplotype sequences. We leveraged these data and tools from our long-read KIR haplotype assembly algorithm to define and align KIR haplotypes at <5 kb resolution on average. We then used a standard alignment algorithm to refine that alignment down to single base resolution. This processing demonstrated that the high-level alignment recapitulates human-curated annotation of the human haplotypes as well as a chimpanzee haplotype. Further, assignments and alignments of gene alleles were consistent with their human curation in haplotype and allele databases. These results define KIR haplotypes as 14 loci containing 9 genes. The multiple sequence alignments have been applied in two software packages as probes to capture and annotate KIR haplotypes and as markers to genotype KIR from WGS.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.06.240655v1?rss=1

Authors: Wu, D., Jiao, N., Zhu, R., Zhang, Y., Gao, W., Fang, S., Li, Y., Cheng, S., Tian, C., Lan, P., Loomba, R., Zhu, L.

Abstract:

Objective: Keystone species are required for the integrity and stability of an ecological community, and therefore, are potential intervention targets for microbiome related diseases. Design: Here we describe an algorithm for the identification of keystone species from cross-sectional microbiome data of non-alcoholic fatty liver disease (NAFLD) based on causal inference theories and dynamic intervention modeling (DIM). Results: Eight keystone species in the gut of NAFLD, represented by P. loveana, A. indistinctus and D. pneumosintes, were identified by our algorithm, which could efficiently restore the microbial composition of the NAFLD toward a normal gut microbiome with 92.3% recovery. These keystone species regulate intestinal amino acids metabolism and acid-base environment to promote the growth of the butyrate-producing Lachnospiraceae and Ruminococcaceae species. Conclusion: Our method may benefit microbiome studies in the broad fields of medicine, environmental science and microbiology.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.241901v1?rss=1

Authors: Kim, Y.-S., Jonhson, G. D., Seo, J., Barrera, A., Majoros, W. H., Ochoa, A., Allen, A. S., Reddy, T. E.

Abstract:

High-throughput reporter assays such as self-transcribing active regulatory region sequencing (STARR-seq) have made it possible to measure genome-wide regulatory element activity across the human genome. The assays, however, also present substantial analytical challenges. Here, we identify technical biases that explain most of the variance in STARR-seq signals. We then develop a statistical model to correct those biases and to improve detection of regulatory elements. This approach substantially improves precision and recall over current methods, improves detection of both activating and repressive regulatory elements, and controls for false discoveries despite strong local signal correlations.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.08.07.241331v1?rss=1

Authors: Badea, L., Stanescu, E.

Abstract:

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert's subjective interpretation, such as a histopathologist's description of tissue slide images in terms of complex visual features (e.g. 'acinar structures'). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

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