PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.360198v1?rss=1

Authors: Junet, V., Farres, J., Mas, J. M., Daura, X.

Abstract:

Cross-(multi)platform normalization of gene-expression microarray data remains an unresolved issue. Despite the existence of several algorithms, they are either constrained by the need to normalize all samples of all platforms together, compromising scalability and reuse, by adherence to the platforms of a specific provider, or simply by poor performance. In addition, many of the methods presented in the literature have not been specifically tested against multi-platform data and/or other methods applicable in this context. Thus, we set out to develop a normalization algorithm appropriate for gene-expression studies based on multiple, potentially large microarray sets collected along multiple platforms and at different times, applicable in systematic studies aimed at extracting knowledge from the wealth of microarray data available in public repositories; for example, for the extraction of Real-World Data to complement data from Randomized Controlled Trials. Our main focus or criterion for performance was on the capacity of the algorithm to properly separate samples from different biological groups.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.359075v1?rss=1

Authors: Ivanov, M. V., Bubis, J. A., Gorshkov, V., Abdrakhimov, D. A., Kjeldsen, F., Gorshkov, M. V.

Abstract:

Proteome-wide analyses most often rely on tandem mass spectrometry imposing considerable instrumental time consumption that is one of the main obstacles in a broader acceptance of proteomics in biomedical and clinical research. Recently, we presented a fast proteomic method termed DirectMS1 based on MS1-only mass spectra acquisition and data processing. The method allowed significant squeezing of the proteome-wide analysis to a few minute time frame at the depth of quantitative proteome coverage of 1000 proteins at 1% FDR. In this work, to further increase the capabilities of the DirectMS1 method, we explored the opportunities presented by the recent progress in the machine learning area and applied the LightGBM tree-based learning algorithm into the scoring of peptide-feature matches when processing MS1 spectra. Further, we integrated the peptide feature identification algorithm of DirectMS1 with the recently introduced peptide retention time prediction utility, DeepLC. Additional approaches to improve performance of the DirectMS1 method are discussed and demonstrated, such as FAIMS coupled to the Orbitrap mass analyzer. As a result of all improvements to DirectMS1, we succeeded in identifying more than 2000 proteins at 1% FDR from the HeLa cell line in a 5 minute LC-MS1 analysis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.28.355156v1?rss=1

Authors: Bulusheva, I., Belova, V., Nikashin, B., Korostin, D.

Abstract:

Here we present the devised BC-store - a program for analyzing and selecting sets of barcodes for sequencing on platforms manufactured by MGI Tech (China). The app is available as an open source in Python3 and as a desktop version. The application allows analyzing the compatibility of barcodes on a single lane of a flow cell in a set in the case of equal and arbitrary fractions. In addition, with the help of this tool barcodes can be added to an existing set with custom share options. In this paper we describe how BC-store works for different tasks and consider the effectiveness of using BC-store in sequence lab routine tasks.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.28.359919v1?rss=1

Authors: Wang, Y., Tong, Y., Zhang, Z., Zheng, R., Huang, D., Yang, J., Zong, H., Tan, F., Zhang, X.

Abstract:

Molecular mechanisms of virus-related diseases involve multiple factors, including viral mutation accumulation and integration of a viral genome into the host DNA. With increasing attention being paid to virus-mediated pathogenesis and the development of many useful technologies to identify virus mutations (VMs) and viral integration sites (VISs), abundant literatures on these topics are available in PubMed. However, knowledge of VMs and VISs is widely scattered in numerous published papers, and the association of VMs with VISs in the viral genome or the functional annotation of VISs still lacks integration and curation. To address these challenges, we built a database of human disease-related Virus Mutations, Integration sites and Cis-effects (ViMIC), which specialize in three features: virus mutation sites, viral integration sites and target genes. In total, the ViMIC provides information on 6,461 VMs, 79,089 VISs, and 15,056 viral target genes of 8 viruses in 65 human diseases obtained from literatures. Furthermore, in ViMIC, users are allowed to explore the cis-effects of virus-host interactions by surveying 78 histone modifications, binding of 1,358 transcription regulators, and chromatin accessibility on these VISs. We believe ViMIC will become a valuable resource for the virus research community. The database is available at http://bmtongji.cn/ViMIC/index.php.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.28.359869v1?rss=1

Authors: Lagler, T. M., Yang, Y., Abnousi, A., Hu, M., Li, Y.

Abstract:

Genome-wide chromatin conformation capture technologies such as Hi-C are commonly employed to study chromatin spatial organization. In particular, to identify statistically significant long-range chromatin interactions from Hi-C data, most existing methods such as Fit-Hi-C/FitHiC2 and HiCCUPS assume that all chromatin interactions are statistically independent. Such an independence assumption is reasonable at low resolution (e.g., 40Kb bin), but is invalid at high resolution (e.g., 5 or 10Kb bins) since spatial dependency of neighboring chromatin interactions is non-negligible at high resolution. Our previous hidden Markov random field based methods accommodate spatial dependency but are computationally intensive. It is urgent to develop approaches that can model spatial dependence, in a computationally efficient and scalable manner. Here, we develop HiC-ACT, an aggregated Cauchy test (ACT) based approach, to improve the detection of chromatin interactions by post-processing results from methods assuming independence. To benchmark the performance of HiC-ACT, we re-analyzed deeply sequenced Hi-C data from a human lymphoblastoid cell line GM12878 and mouse embryonic stem cell line (mESC). Our results demonstrate advantages of HiC-ACT in improving sensitivity with controlled type-I error. By leveraging information from neighboring chromatin interactions, HiC-ACT enhances the power to detect interactions with lower signal to noise ratio and similar (if not stronger) epigenetic signatures that suggest regulatory roles. We further demonstrate that HiC-ACT peaks show higher overlap with known enhancers than Fit-Hi-C/FitHiC2 peaks, in both GM12878 and mESC. HiC-ACT, effectively a summary statistic based approach, is computationally efficient (~6 minutes and ~2GB memory to process 25,000 pairwise interactions).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.28.358507v1?rss=1

Authors: Steinke, K., Mohie, O. S., Weber, T., Kovacs, A. T.

Abstract:

Microbes produce a plethora of secondary metabolites that although not essential for primary metabolism benefit them to survive in the environment, communicate, and influence differentiation. Biosynthetic gene clusters (BGCs) responsible for the production of these secondary metabolites are readily identifiable on the genome sequence of bacteria. Understanding the phylogeny and distribution of BGCs helps us to predict natural product synthesis ability of new isolates. Here, we examined the inter- and intraspecies patterns of absence/presence for all BGCs identified with antiSMASH 5.0 in 310 genomes from the B. subtilis group and assigned them to defined gene cluster families (GCFs). This allowed us to establish patterns in distribution for both known and unknown products. Further, we analyzed variations in the BGC structure of particular families encoding for natural products such as plipastatin, fengycin, iturin, mycosubtilin and bacillomycin. Our detailed analysis revealed multiple GCFs that are species or clade specific and few others that are scattered within or between species, which will guide exploration of the chemodiversity within the B. subtilis group. Uniquely, we discovered that partial deletion of BGCs and frameshift mutations in selected biosynthetic genes are conserved within phylogenetically related isolates, although isolated from around the globe. Our results highlight the importance of detailed analysis of BGCs and the remarkable phylogenetically conserved errodation of secondary metabolite biosynthetic potential in the B. subtilis group.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.358408v1?rss=1

Authors: Hoffman, C., Siddiqui, N. Y., Fields, I., Gregory, W. T., SImon, H., Mooney, M. A., Wolfe, A. J., Karstens, L.

Abstract:

The human bladder contains bacteria in the absence of infection. Interest in studying these bacteria and their association with bladder conditions is increasing, but the chosen experimental method can limit the resolution of the taxonomy that can be assigned to the bacteria found in the bladder. 16S rRNA gene sequencing is commonly used to identify bacteria, but is typically restricted to genus-level identification. Our primary aim was to determine if accurate species-level identification of bladder bacteria is possible using 16S rRNA gene sequencing. We evaluated the ability of different classification schemes, each consisting of combinations of a 16S rRNA gene variable region, a reference database, and a taxonomic classification algorithm to correctly classify bladder bacteria. We show that species-level identification is possible, and that the reference database chosen is the most important component, followed by the 16S variable region sequenced.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.358283v1?rss=1

Authors: Wang, Y., Le Cao, K.-A.

Abstract:

Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to, and obscure any factors of interest. Existing batch correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce a new multivariate and non-parametric batch correction method based on Partial Least Squares Discriminant Analysis. PLSDA-batch first estimates treatment and batch variation with latent components to then subtract batch variation from the data. The resulting batch effect corrected data can then be input in any downstream statistical analysis. Two variants are also proposed to handle unbalanced batch x treatment designs and to include variable selection during component estimation. We compare our approaches with existing batch correction methods removeBatchEffect and ComBat on simulated and three case studies. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, and especially when batch effects have high variability. Reproducible code and vignettes are available on GitHub.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.312116v1?rss=1

Authors: Gu, Z., Huebschmann, D.

Abstract:

Motivation: Functional enrichment analysis or gene set enrichment analysis is a basic bioinformatics method that evaluates biological importance of a list of genes of interest. However, it may produce a long list of significant terms with highly redundant information that is difficult to summarize. Current tools to simplify enrichment results by clustering them into groups either still produce redundancy between clusters or do not retain consistent term similarities within clusters. Results: we proposed a new method named binary cut for clustering similarity matrices of functional terms. Through comprehensive benchmarks on both simulated and real-world datasets, we demonstrated that binary cut can efficiently cluster functional terms into groups where terms showed more consistent similarities within groups and were more mutually exclusive between groups. We compared binary cut clustering on the similarity matrices from different similarity measurements and we found the semantic similarity worked well with binary cut while the similarity matrices based on gene overlap showed less consistent patterns and they were not recommended to work with binary cut. We implemented the binary cut algorithm into an R package simplifyEnrichment which additionally provides functionalities for visualizing, summarizing and comparing the clusterings. Availability and implementation: The simplifyEnrichment package and the documentations are available at https://bioconductor.org/packages/simplifyEnrichment/. The reports for the analysis of all datasets benchmarked in the paper are available at https://simplifyenrichment.github.io/. The scripts that performed the analysis are available at https://github.com/jokergoo/simplifyEnrichment_manuscript.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.27.358226v1?rss=1

Authors: Warrell, J., Mohsen, H., Gerstein, M.

Abstract:

Deep learning methods have achieved state-of-the-art performance in many domains of artificial intelligence, but are typically hard to interpret. Network interpretation is important for multiple reasons, including knowledge discovery, hypothesis generation, fairness and establishing trust. Model transformations provide a general approach to interpreting a trained network post-hoc: the network is approximated by a model, which is typically compressed, whose structure can be more easily interpreted in some way (we call such approaches interpretability schemes). However, the relationship between compression and interpretation has not been fully explored: How much should a network be compressed for optimal extraction of interpretable information? Should compression be combined with other criteria when selecting model transformations? We investigate these issues using two different compression-based schemes, which aim to extract orthogonal kinds of information, pertaining to feature and data instance-based groupings respectively. The first (rank projection trees) uses a structured sparsification method such that nested groups of features can be extracted having potential joint interactions. The second (cascaded network decomposition) splits a network into a cascade of simpler networks, allowing groups of training instances with similar characteristics to be extracted at each stage of the cascade. We use predictive tasks in cancer and psychiatric genomics to assess the ability of these approaches to extract informative feature and data-point groupings from trained networks. We show that the generalization error of a network provides an indicator of the quality of the information extracted; further we derive PAC-Bayes generalization bounds for both schemes, which we show can be used as proxy indicators, and can thus provide a criterion for selecting the optimal compression. Finally, we show that the PAC-Bayes framework can be naturally modified to incorporate additional criteria alongside compression, such as prior knowledge based on previous models, which can enhance interpretable model selection.

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