PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.17.301614v1?rss=1

Authors: Rahman, S. A., Singh, J., Singh, H., Hasnain, S. E.

Abstract:

We are in the midst of the third severe coronavirus outbreak caused by SARS-CoV-2 with unprecedented health and socio-economic consequences due to the COVID-19. Globally, the major thrust of scientific efforts has shifted to the design of potent vaccine and anti-viral candidates. Earlier genome analyses have shown global dominance of some mutations purportedly indicative of similar infectivity and transmissibility of SARS-CoV-2 worldwide. Using high-quality large dataset of 25k whole-genome sequences, we show emergence of new cluster of mutations as result of geographic evolution of SARS-CoV-2 in local population ({greater than or equal to}10%) of different nations. Using statistical analysis, we observe that these mutations have either significantly co-occurred in globally dominant strains or have shown mutual exclusivity in other cases. These mutations potentially modulate structural stability of proteins, some of which forms part of SARS-CoV-2-human interactome. The high confidence druggable host proteins are also up-regulated during SARS-CoV-2 infection. Mutations occurring in potential hot-spot regions within likely T-cell and B-cell epitopes or in proteins as part of host-viral interactome, could hamper vaccine or drug efficacy in local population. Overall, our study provides comprehensive view of emerging geo-clonal mutations which would aid researchers to understand and develop effective countermeasures in the current crisis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.16.300186v1?rss=1

Authors: Sabary, O., Yucovich, A., Shapira, G., Yaakobi, E.

Abstract:

In the trace reconstruction problem a length-n string x yields a collection of noisy copies, called traces, y1, ..., yt, where each yi is independently obtained from x by passing through a deletion channel, which deletes every symbol with some fixed probability. The main goal under this paradigm is to determine the required minimum number of i.i.d traces in order to reconstruct x with high probability. The trace reconstruction problem can be extended to the model where each trace is a result of x passing through a deletion-insertion-substitution channel, which introduces also insertions and substitutions. Motivated by the storage channel of DNA, this work is focused on another variation of the trace reconstruction problem, which is referred by the DNA reconstruction problem. A DNA reconstruction algorithm is a mapping R: ({Sigma}q*)t [->] {Sigma}q* which receives t traces y1, ..., yt as an input and produces x^, an estimation of x. The goal in the DNA reconstruction problem is to minimize the edit distance de>(x,x^) between the original string and the algorithm's estimation. For the deletion channel case, the problem is referred by the deletion DNA reconstruction problem and the goal is to minimize the Levenshtein distance dL(x,x^). In this work, we present several new algorithms for these reconstruction problems. Our algorithms look globally on the entire sequence of the traces and use dynamic programming algorithms, which are used for the shortest common supersequence and the longest common subsequence problems, in order to decode the original sequence. Our algorithms do not require any limitations on the input and the number of traces, and more than that, they perform well even for error probabilities as high as 0.27. The algorithms have been tested on simulated data as well as on data from previous DNA experiments and are shown to outperform all previous algorithms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.16.299594v1?rss=1

Authors: Hekel, R., Budis, J., Kucharik, M., Radvanszky, J., Szemes, T.

Abstract:

Introduction: Current and future applications of genomic data may raise ethical and privacy concerns. Processing and storing genomic data introduces a risk of abuse by a potential adversary since the human genome contains information about sensitive personal traits. For this reason, we developed a privacy preserving method, called Varlock, for secure storage and dissemination of sequenced genomic data. Materials and methods: The Varlock uses a set of population allele frequencies to mask personal alleles detected in genomic reads. Each detected allele is replaced by a randomly selected population allele concerning its frequency. Masked alleles are preserved in an encrypted confidential file that can be shared, in whole or in part, using public-key cryptography. Results: Our method masked personal variants and introduced new variants called on an individual's genome, while alternative alleles with lower population frequency were masked and introduced more often. We performed joint PCA analysis of personal and masked VCFs, showing that the VCFs between the two groups can not be trivially mapped. Moreover, the method is reversible; therefore, personal alleles can be unmasked in specific genomic regions on demand. Conclusion: Our method masks personal alleles within mapped reads while preserving valuable non-sensitive properties of sequenced DNA fragments for further research. Accordingly, masked reads can be stored publicly, since they are deprived of sensitive personal information. Personal alleles may be restored in arbitrary genomic regions for interested parties: patients, medical units, and researchers. Keywords: genome, privacy, personal data

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.297747v1?rss=1

Authors: Milchevskaya, V., Nikitin, A. M., Lukshin, S. A., Filatov, I. V., Kravatsky, Y. V., Tumanyan, V. G., Esipova, N. G., Milchevskiy, Y. V.

Abstract:

Motivation: Local protein structure is usually described via classifying each peptide to a unique element from a set of pre-defined structures. These so-called structural alphabets may differ in the number of structures or the length of peptides. Most methods that predict the local structure of a protein from its sequence rely on this kind of classification. However, since all peptides assigned to the same class are indistinguishable, such an approach may not be sufficient to model protein folding with high accuracy. Results: We developed a method that predicts the structural representation of a peptide from its sequence. For 5-mer peptides, we achieved the Q16 classification accuracy of 67.9%, which is higher than what is currently reported in the literature. Importantly, our prediction method does not utilize information about protein homologues but only physicochemical properties of the amino acids and the statistics of the structures but relies on a comprehensive feature-generation procedure based only on the protein sequence and the statistics of resolved structures. We also show that the 3D coordinates of a peptide can be uniquely recovered from its structural coordinates, and show the required conditions for that under various geometric constraints. Availability: The online implementation of the method is provided freely at http://pbpred.eimb.ru Contact: [email protected] or [email protected] Supplementary information: Supplementary data are available online at http://pbpred.eimb.ru/S/index.html

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.298356v1?rss=1

Authors: Vundavilli, H., Datta, A., Tripathi, L., Mizuguchi, K.

Abstract:

Systems biology aims to understand how holistic systems theory can be used to explain the observable living system characteristics, and mathematical modeling tools have been successful in understanding the intricate relationships underlying cellular functions. Lately, researchers have been interested in understanding molecular mechanisms underlying obesity, which is a major health concern worldwide and has been linked to several diseases. Various mechanisms such as peroxisome proliferator-activated receptors (PPARs) are known to modulate obesity-induced inflammation and its consequences. In this study, we have modeled the PPAR pathway using a Bayesian model and inferred the sub-pathways that are potentially responsible for the activation of the output processes that are associated with high fat diet (HFD)-induced obesity. We examined a previously published dataset from a study that compared gene expression profiles of 40 mice maintained on HFD against 40 mice fed with chow diet (CD). Our simulations have highlighted that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice and are therefore favorable targets for anti-obesity strategies. We further cross-validated our observations with experimental results from the literature. We believe that mathematical models such as those presented in the present study can help in inferring other pathways and deducing significant biological relationships.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.297820v1?rss=1

Authors: Lu, X., Meng, J., Zhou, Y., Jiang, L., Yan, F.

Abstract:

Stratification of cancer patients into distinct molecular subgroups based on multi-omics data is an important issue in the context of precision medicine. Here we present MOVICS, an R package for multi-omics integration and visualization in cancer subtyping. MOVICS provides a unified interface for 10 state-of-the-art multi-omics integrative clustering algorithms, and incorporates the most commonly used downstream analyses in cancer subtyping researches, including characterization and comparison of identified subtypes from multiple perspectives, and verification of subtypes in external cohort using a model-free approach for multiclass prediction. MOVICS also creates feature rich customizable visualizations with minimal effort. Availability and implementation: MOVICS package and online tutorial are freely available at https://github.com/xlucpu/MOVICS.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.14.291484v1?rss=1

Authors: Tumescheit, C., Firth, A. E., Brown, K.

Abstract:

Background: Throughout biology, multiple sequence alignments (MSAs) form the basis of much investigation into biological features and relationships. These alignments are at the heart of many bioinformatics analyses. However, sequences in MSAs are often incomplete or very divergent, which leads to poorly aligned regions or large gaps in alignments. This slows down computation and can impact conclusions without being biologically relevant. Therefore, cleaning the alignment by removing these regions can substantially improve analyses. Results: We present a comprehensive, user-friendly MSA trimming tool with multiple visualisation options. Our highly customisable command line tool aims to give intervention power to the user by offering various options, and outputs graphical representations of the alignment before and after processing to give the user a clear overview of what has been removed. The main functionalities of the tool include removing regions of low coverage due to insertions, removing gaps, cropping poorly aligned sequence ends and removing sequences that are too divergent or too short. The thresholds for each function can be specified by the user and parameters can be adjusted to each individual MSA. CIAlign is complementary to existing alignment trimming tools, with an emphasis on solving specific and common alignment problems and on providing transparency to the user. Conclusion: CIAlign effectively removes poorly aligned regions and sequences from MSAs and provides novel visualisation options. This tool can be used to improve the alignment quality for further analysis and processing. The tool is aimed at anyone who wishes to automatically clean up parts of an MSA and those requiring a new, accessible way for visualising large MSAs.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.298117v1?rss=1

Authors: Burskaia, V., Artyushin, I., Potapova, N., Konovalov, K., Bazykin, G. A.

Abstract:

In a wide range of taxa, proteins encoded by mitochondrial genomes are involved in adaptation to lifestyle that requires oxygen starvation or elevation of metabolism rate. It remains poorly understood to what extent adaptation to similar conditions is associated with parallel changes in these proteins. We search for genetic signal of parallel or convergent evolution in recurrent molecular adaptation to high altitude, migration, diving, wintering, unusual flight abilities, or loss of flight in mitochondrial genomes of birds. Developing on previous work, we design an approach for detection of recurrent coincident changes in genotype and phenotype, indicative of an association between the two. We describe a number of candidate sites involved in recurrent adaptation in ND genes. However, we find that the majority of convergence events can be explained by random coincidences without invoking adaptation.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.14.297424v1?rss=1

Authors: M. A. Basher, A. R., Hallam, S.

Abstract:

Machine learning methods show great promise in predicting metabolic pathways at different levels of biological organization. However, several complications remain that can degrade prediction performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of enzymes and pathways within a dataset. This class imbalance problem is commonly encountered by the machine learning community when the proportion of instances over class labels within a dataset are uneven, resulting in a poor predictive performance for underrepresented classes. Here, we present leADS, multi-label learning based on active dataset subsampling, that leverages the idea of subsampling points from a pool of data to reduce the negative impact of training loss due to class imbalance. Specifically, leADS performs an iterative process to: (i)- construct an acquisition model in an ensemble framework; (ii) select informative points using an appropriate acquisition function; and (iii) train on selected samples. Multiple base learners are implemented in parallel where each is assigned a portion of labeled training data to learn pathways. We benchmark leADS using a corpora of 10 experimental datasets manifesting diverse multi-label properties used in previous pathway prediction studies, including manually curated organismal genomes, synthetic microbial communities, and low complexity microbial communities. Resulting performance metrics equaled or exceeded previously reported machine learning methods for both organismal and multi-organismal genomes while establishing an extensible framework for navigating class imbalances across diverse real-world datasets.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.09.15.298505v1?rss=1

Authors: Bukszar, J., van den Oord, E. J.

Abstract:

The large number of existing databases provides a freely available independent source of information with a considerable potential to increase the likelihood of identifying genes for complex diseases. We developed a flexible framework for integrating such heterogeneous databases into novel large scale genetic studies and implemented the methods in a freely-available, user-friendly R package called MIND. For each marker, MIND computes the posterior probability that the marker has effect in the novel data collection based on the information in all available data. MIND 1) relies on a very general model, 2) is based on the mathematical formulas that provide us with the exact value of the posterior probability, and 3) has good estimation properties because of its very efficient parameterization. For an existing data set, only the ranks of the markers are needed, where ties among the ranks are allowed. Through simulations, cross-validation analyses involving 18 GWAS, and an independent replication study of 6,544 SNPs in 6,298 samples we show that MIND 1) is accurate, 2) outperforms marker selection for follow up studies based on p-values, and 3) identifies effects that would otherwise require replication of over 20 times as many markers.

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