Episoder
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.21.392621v1?rss=1
Authors: Chibani, C. M., Mahnert, A., Borrel, G., Almeida, A., Werner, A., Brugere, J.-F., Gribaldo, S., Finn, R. D., Schmitz, R. A., Moissl-Eichinger, C.
Abstract:
The human gut microbiome plays an important role in health and disease, but the archaeal diversity therein remains largely unexplored. Here we report the pioneering analysis of 1,167 non-redundant archaeal genomes recovered from human gastrointestinal tract microbiomes across countries and populations. We identified three novel genera and 15 novel species including 52 previously unknown archaeal strains. Based on distinct genomic features, we warrant the split of the Methanobrevibacter smithii clade into two separate species, with one represented by the novel Candidatus M. intestini. Patterns derived from 1.8 million proteins and 28,851 protein clusters coded in these genomes showed a substantial correlation with socio-demographic characteristics such as age and lifestyle. We infer that archaea are actively replicating in the human gastrointestinal tract and are characterized by specific genomic and functional adaptations to the host. We further demonstrate that the human gut archaeome carries a complex virome, with some viral species showing unexpected host flexibility. Our work furthers our current understanding of the human archaeome, and provides a large genome catalogue for future analyses to decipher its role and impact on human physiology.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.21.392613v1?rss=1
Authors: Moussa, M. M. R., Mandoiu, I. I.
Abstract:
The variation in gene expression profiles of cells captured in different phases of the cell cycle can interfere with cell type identification and functional analysis of single cell RNA-Seq (scRNA-Seq) data. In this paper, we introduce SC1CC (SC1 - Cell Cycle analysis tool), a computational approach for clustering and ordering single cell transcriptional profiles according to their progression along cell cycle phases. We also introduce a new robust metric, GSS (Gene Smoothness Score) for assessing the cell cycle based order of the cells. SC1CC is available as part of the SC1 web-based scRNA-Seq analysis pipeline, publicly accessible at https://sc1.engr.uconn.edu/.
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Mangler du episoder?
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.21.392761v1?rss=1
Authors: Barthelson, K., Pederson, S. M., Newman, M., Jiang, H., Lardelli, M.
Abstract:
Background: Mutations in PRESENILIN 2 (PSEN2) cause early disease onset familial Alzheimer's disease (EOfAD) but their mode of action remains elusive. One consistent observation for all PRESENILIN gene mutations causing EOfAD is that a transcript is produced with a reading frame terminated by the normal stop codon : the 'reading frame preservation rule'. Mutations that do not obey this rule do not cause the disease. The reasons for this are debated. Methods: A frameshift mutation (psen2N140fs) and a reading frame-preserving mutation (psen2T141_L142delinsMISLISV) were previously isolated during genome editing directed at the N140 codon of zebrafish psen2 (equivalent to N141 of human PSEN2). We mated a pair of fish heterozygous for each mutation to generate a family of siblings including wild type and heterozygous mutant genotypes. Transcriptomes from young adult (6 months) brains of these genotypes were analysed. Bioinformatics techniques were used to predict cellular functions affected by heterozygosity for each mutation. Results: The reading frame preserving mutation uniquely caused subtle, but statistically significant, changes to expression of genes involved in oxidative phosphorylation, long term potentiation and the cell cycle. The frameshift mutation uniquely affected genes involved in Notch and MAPK signalling, extracellular matrix receptor interactions and focal adhesion. Both mutations affected ribosomal protein gene expression but in opposite directions. Conclusion: A frameshift and frame-preserving mutation at the same position in zebrafish psen2 cause discrete effects. Changes in oxidative phosphorylation, long
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.21.392878v1?rss=1
Authors: Wojtowicz, D., Hoinka, J., Amgalan, B., Kim, Y.-A., Przytycka, T. M.
Abstract:
Many mutagenic processes leave characteristic imprints on cancer genomes known as mutational signatures. These signatures have been of recent interest regarding their applicability in studying processes shaping the mutational landscape of cancer. In particular, pinpointing the presence of altered DNA repair pathways can have important therapeutic implications. However, mutational signatures of DNA repair deficiencies are often hard to infer. This challenge emerges as a result of deficient DNA repair processes acting by modifying the outcome of other mutagens. Thus, they exhibit non-additive effects that are not depicted by the current paradigm for modeling mutational processes as independent signatures. To close this gap, we present RepairSig, a method that accounts for interactions between DNA damage and repair and is able to uncover unbiased signatures of deficient DNA repair processes. In particular, RepairSig was able to replace three MMR deficiency signatures previously proposed to be active in breast cancer, with just one signature strikingly similar to the experimentally derived signature. As the first method to model interactions between mutagenic processes, RepairSig is an important step towards biologically more realistic modeling of mutational processes in cancer. The source code for RepairSig is publicly available at https://github.com/ncbi/RepairSig.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.22.393108v1?rss=1
Authors: Loewenthal, G., Rapoport, D., Avram, O., Moshe, A., Itzkovitch, A., Israeli, O., Azouri, D., Cartwright, R. A., Mayrose, I., Pupko, T.
Abstract:
Insertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here we introduce several improvements to indel modeling: (1) while previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here, we propose a richer model that explicitly distinguishes between the two; (2) We introduce numerous summary statistics that allow Approximate Bayesian Computation (ABC) based parameter estimation; (3) We develop a neural-network model-selection scheme to test whether the richer model better fits biological data compared to the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed indel model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate. Finally, we demonstrate that indel rates are negatively correlated to the effective population size across various phylogenomic clades.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.22.392217v1?rss=1
Authors: Armingol, E., Joshi, C. J., Baghdassarian, H., Shamie, I., Ghaddar, A., Chan, J., Her, H.-L., O'Rourke, E. J., Lewis, N. E.
Abstract:
Cell-cell interactions are crucial for multicellular organisms as they shape cellular function and ultimately organismal phenotype. However, the spatial code embedded in the molecular interactions that drive and sustain spatial organization, and in the organization that in turns drives intercellular interactions across a living animal remains to be elucidated. Here we use the expression of ligand-receptor pairs obtained from a whole-body single-cell transcriptome of Caenorhabditis elegans larvae to compute the potential for intercellular interactions through a Bray-Curtis-like metric. Leveraging a 3D atlas of C. elegans' cells, we implement a genetic algorithm to select the ligand-receptor pairs most informative of the spatial organization of cells. Validating the strategy, the selected ligand-receptor pairs are involved in known cell-migration and morphogenesis processes and we confirm a negative correlation between cell-cell distances and interactions. Thus, our computational framework helps identify cell-cell interactions and their relationship with intercellular distances, and decipher molecular bases encoding spatial information in a whole animal. Furthermore, it can also be used to elucidate associations with any other intercellular phenotype and applied to other multicellular organisms.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.22.393074v1?rss=1
Authors: Roy, N., Kabir, A. H., Zahan, N., Mouna, S. T., Chakravarty, S., Rahman, A. H., Bayzid, M. S.
Abstract:
Rice genetic diversity is regulated by multiple genes and is largely dependent on various environmental factors. Uncovering the genetic variations associated with the diversity in rice populations is the key to breed stable and high yielding rice varieties. We performed Genome Wide Association Studies (GWAS) on 7 rice yielding traits (grain length, grain width, grain weight, panicle length, leaf length, leaf width, and leaf angle) based on 39,40,165 single nucleotide polymorphisms (SNPs) in a population of 183 rice landraces of Bangladesh. Our studies reveal various chromosomal regions that are significantly associated with different traits in Bangladeshi rice varieties. We also identified various candidate genes, which are associated with these traits. This study reveals multiple candidate genes within short intervals. We also identified SNP loci, which are significantly associated with multiple yield-related traits. The results of these association studies support previous findings as well as provide additional insights into the genetic diversity of rice. This is the first known GWAS study on various yield-related traits in the varieties of Oryza sativa available in Bangladesh, the fourth largest rice producing country. We believe this study will accelerate rice genetics research and breeding stable high-yielding rice in Bangladesh.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.22.393165v1?rss=1
Authors: Yan, H., Song, Q., Lee, J., Schiefelbein, J., Li, S.
Abstract:
An essential step of single-cell RNA sequencing analysis is to classify specific cell types with marker genes in order to dissect the biological functions of each individual cell. In this study, we integrated five published scRNA-seq datasets from the Arabidopsis root containing over 25,000 cells and 17 cell clusters. We have compared the performance of seven machine learning methods in classifying these cell types, and determined that the random forest and support vector machine methods performed best. Using feature selection with these two methods and a correlation method, we have identified 600 new marker genes for 10 root cell types, and more than 70% of these machine learning-derived marker genes were not identified before. We found that these new markers not only can assign cell types consistently as the previously known cell markers, but also performed better than existing markers in several evaluation metrics including accuracy and sensitivity. Markers derived by the random forest method, in particular, were expressed in 89-98% of cells in endodermis, trichoblast, and cortex clusters, which is a 29-67% improvement over known markers. Finally, we have found 111 new orthologous marker genes for the trichoblast in five plant species, which expands the number of marker genes by 58-170% in non-Arabidopsis plants. Our results represent a new approach to identify cell-type marker genes from scRNA-seq data and pave the way for cross-species mapping of scRNA-seq data in plants.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.19.390773v1?rss=1
Authors: Ge, X., Chen, Y. E., Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, L. D., Li, W., Li, J. J.
Abstract:
High-throughput biological data analysis commonly involves the identification of "interesting" features (e.g., genes, genomic regions, and proteins), whose values differ between two conditions, from numerous features measured simultaneously. To ensure the reliability of such analysis, the most widely-used criterion is the false discovery rate (FDR), the expected proportion of uninteresting features among the identified ones. Existing bioinformatics tools primarily control the FDR based on p-values. However, obtaining valid p-values relies on either reasonable assumptions of data distribution or large numbers of replicates under both conditions, two requirements that are often unmet in biological studies. To address this issue, we propose Clipper, a general statistical framework for FDR control without relying on p-values or specific data distributions. Clipper is applicable to identifying both enriched and differential features from high-throughput biological data of diverse types. In comprehensive simulation and real-data benchmarking, Clipper outperforms existing generic FDR control methods and specific bioinformatics tools designed for various tasks, including peak calling from ChIP-seq data, differentially expressed gene identification from RNA-seq data, differentially interacting chromatin region identification from Hi-C data, and peptide identification from mass spectrometry data. Notably, our benchmarking results for peptide identification are based on the first mass spectrometry data standard that has a realistic dynamic range. Our results demonstrate Clipper's flexibility and reliability for FDR control, as well as its broad applications in high-throughput data analysis.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.20.391912v1?rss=1
Authors: Choudhary, K. S., Fahy, E., Coakley, K., Sud, M., Maurya, M. R., Subramaniam, S.
Abstract:
With the advent of high throughput mass spectrometric methods, metabolomics has emerged as an essential area of research in biomedicine with the potential to provide deep biological insights into normal and diseased functions in physiology. However, to achieve the potential offered by metabolomics measures, there is a need for biologist-friendly integrative analysis tools that can transform data into mechanisms that relate to phenotypes. Here, we describe MetENP, an R package, and a user-friendly web application deployed at the Metabolomics Workbench site extending the metabolomics enrichment analysis to include species-specific pathway analysis, pathway enrichment scores, gene-enzyme information, and enzymatic activities of the significantly altered metabolites. MetENP provides a highly customizable workflow through various user-specified options and includes support for all metabolite species with available KEGG pathways. MetENPweb is a web application for calculating metabolite and pathway enrichment analysis.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.19.390542v1?rss=1
Authors: Burstein, D., Fullard, J., Roussos, P.
Abstract:
Prior to identifying clusters in single cell gene expression experiments, selecting the top principal components is a critical step for filtering out noise in the data set. Identifying these top principal components typically focuses on the total variance explained, and principal components that explain small clusters from rare populations will not necessarily capture a large percentage of variance in the data. We present a computationally efficient alternative for identifying the optimal principal components based on the tails of the distribution of variance explained for each observation. We then evaluate the efficacy of our approach in three different single cell RNA-sequencing data sets and find that our method matches, or outperforms, other selection criteria that are typically employed in the literature. Availability and implementation: pcqc is written in Python and available at github.com/RoussosLab/pcqc
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.17.367490v1?rss=1
Authors: Rizwan, S., Pike, D., Poudel, S., Nanda, V.
Abstract:
Cofactor binding sites in proteins often are composed of favorable interactions of specific cofactors with the sidechains and/or backbone protein fold motifs. In many cases these motifs contain left-handed conformations which enable tight turns of the backbone that present backbone amide protons in direct interactions with cofactors termed 'cationic nests'. Here, we defined alternating handedness of secondary structure as a search constraint within the PDB to systematically identify these cofactor binding nests. We identify unique alternating handedness structural motifs which are specific to the cofactors they bind. These motifs can guide the design of engineered folds that utilize specific cofactors and also enable us to gain a deeper insight into the evolution of the structure of cofactor binding sites.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.17.386813v1?rss=1
Authors: Dragan, I., Sparso, T., Kuznetsov, D., Slieker, R., Ibberson, M.
Abstract:
Summary: dsSwissKnife is an R package that enables several powerful analyses to be performed on federated datasets. The package works alongside DataSHIELD and extends its functionality. We have developed and implemented dsSwissKnife in a large IMI project on type 2 diabetes, RHAPSODY, where data from 10 observational cohorts have been harmonised and federated in CDISC SDTM format and made available for biomarker discovery. Availability and implementation: dsSwissKnife is freely available online at https://github.com/sib-swiss/dsSwissKnife. The package is distributed under the GNU General Public License version 3 and is accompanied by example files and data.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.20.391045v1?rss=1
Authors: Planell, N., Lagani, V., Sebastian-Leon, P., van der Kloet, F., Ewing, E., Karathanasis, N., Urdangarin, A., Arozarena, I., Jagodic, M., Tsamardinos, I., Tarazona, S., Conesa, A., tegner, j., Gomez-Cabrero, D.
Abstract:
Technologies for profiling samples using different omics platforms have been at the forefront since the human genome project. Large-scale multi-omics data hold the promise of deciphering different regulatory layers. Yet, while there is a myriad of bioinformatics tools, each multi-omics analysis appears to start from scratch with an arbitrary decision over which tools to use and how to combine them. It is therefore an unmet need to conceptualize how to integrate such data and to implement and validate pipelines in different cases. We have designed a conceptual framework (STATegra), aiming it to be as generic as possible for multi-omics analysis, combining machine learning component analysis, non-parametric data combination and a multi-omics exploratory analysis in a step-wise manner. While in several studies we have previously combined those integrative tools, here we provide a systematic description of the STATegra framework and its validation using two TCGA case studies. For both, the Glioblastoma and the Skin Cutaneous Melanoma cases, we demonstrate an enhanced capacity to identify features in comparison to single-omics analysis. Such an integrative multi-omics analysis framework for the identification of features and components facilitates the discovery of new biology. Finally, we provide several options for applying the STATegra framework when parametric assumptions are fulfilled, and for the case when not all the samples are profiled for all omics. The STATegra framework is built using several tools, which are being integrated step-by-step as OpenSource in the STATegRa Bioconductor package https://bioconductor.org/packages/release/bioc/html/STATegra.html.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.20.390930v1?rss=1
Authors: Das Roy, R., Hallikas, O., Christensen, M. M., Renvoise, E., Jernvall, J.
Abstract:
Exploration of genetically modified organisms, developmental processes, diseases or responses to various treatments require accurate measurement of changes in gene expression. This can be done for thousands of genes using high throughput technologies such as microarray and RNAseq. However, identification of differentially expressed (DE) genes poses technical challenges due to limited sample size, few replicates, or simply very small changes in expression levels. Consequently, several methods have been developed to determine DE genes, such as Limma, RankProd, SAM, and DeSeq2. These methods identify DE genes based on the expression levels alone. As genomic co-localization of genes is generally not linked to co-expression, we deduced that DE genes could be detected with the help of genes from chromosomal neighbourhood. Here, we present a new method, DELocal, which identifies DE genes by comparing their expression changes to changes in adjacent genes in their chromosomal regions. Our results show that DELocal provides distinct benefits in the identification of DE genes. Furthermore, our comparative analysis of the dispersal of genes with related functions suggests that DELocal is applicable to a wide range of developmental systems. With increasing availability of genomic data, gene neighbourhood can become a powerful tool to detect differential expression.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.20.391011v1?rss=1
Authors: Laws, R. L., Paul, P., Mosites, E., Scobie, H., Clarke, K. E. N., Slayton, R. B.
Abstract:
Background : Congregate settings are at risk for coronavirus disease 2019 (COVID-19) outbreaks. Diagnostic testing can be used as a tool in these settings to identify outbreaks and to control transmission. Methods : We used transmission modeling to estimate the minimum number of persons to test and the optimal frequency to detect small outbreaks of COVID-19 in a congregate facility. We also estimated the frequency of testing needed to interrupt transmission within a facility. Results : The number of people to test and frequency of testing needed depended on turnaround time, facility size, and test characteristics. Parameters are calculated for a variety of scenarios. In a facility of 100 people, 26 randomly selected individuals would need to be tested at least every 6 days to identify a true underlying prevalence of at least 5%, with test sensitivity of 85%, and greater than 95% outbreak detection sensitivity. Disease transmission could be interrupted with universal, facility-wide testing with rapid turnaround every three days. Conclusions : Testing a subset of individuals in congregate settings can improve early detection of small outbreaks of COVID-19. Frequent universal diagnostic testing can be used to interrupt transmission within a facility, but its efficacy is reliant on rapid turnaround of results for isolation of infected individuals.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.20.391029v1?rss=1
Authors: Covell, D. G.
Abstract:
A joint analysis of NCI60 small molecule screening data, their genetically defective genes and mechanisms of action (MOA) of FDA approved cancer drugs screened in the NCI60 is proposed for identifying links between chemosensitivity, genomic defects and MOA. Self-organizing-maps (SOMs) are used to organize the chemosensitivity data. Students t-tests are used to identify SOM clusters with chemosensitivity for tumor cells harboring genetically defective genes. Fishers exact tests are used to reveal instances where defective gene to chemosensitivity associations have enriched MOAs. The results of this analysis find a relatively small set of defective genes, inclusive of ABL1, AXL, BRAF, CDC25A, CDKN2A, IGF1R, KRAS, MECOM, MMP1, MYC, NOTCH1, NRAS, PIK3CG, PTK2, RPTOR, SPTBN1, STAT2, TNKS and ZHX2, as possible candidates for roles in chemosensitivity for compound MOAs that target primarily, but not exclusively, kinases, nucleic acid synthesis, protein synthesis, apoptosis and tubulin. This analysis may contribute towards the goals of cancer drug discovery, development decision making, and explanation of mechanisms.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.20.391300v1?rss=1
Authors: Neely, B. A., Stemmer, P., Searle, B. C., Herring, L. E., Martin, L., Midha, M. K., Phinney, B. S., Shan, B., Palmblad, M., Wang, Y., Jagtap, P. D., Kirkpatrick, J. M.
Abstract:
Despite the advantages of fewer missing values by collecting fragment ion data on all analytes in the sample, as well as the potential for deeper coverage, the adoption of data-independent acquisition (DIA) in core facility settings has been slow. The Association of Biomolecular Resource Facilities conducted a large interlaboratory study to evaluate DIA performance in laboratories with various instrumentation. Participants were supplied with generic methods and a uniform set of test samples. The resulting 49 DIA datasets act as benchmarks and have utility in education and tool development. The sample set consisted of a tryptic HeLa digest spiked with high or low levels of four exogenous proteins. Data are available in MassIVE MSV000086479. Additionally, we demonstrate how the data can be analysed by focusing on two datasets using different library approaches and show the utility of select summary statistics. These data can be used by DIA newcomers, software developers, or DIA experts evaluating performance with different platforms, acquisition settings and skill levels.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.19.389981v1?rss=1
Authors: Ness-Cohn, E., Braun, R.
Abstract:
The circadian rhythm drives the oscillatory expression of thousands of genes across all tissues. The recent revolution in high-throughput transcriptomics, coupled with the significant implications of the circadian clock for human health, has sparked an interest in circadian profiling studies to discover genes under circadian control. Here we present TimeCycle: a topology-based rhythm detection method designed to identify cycling transcripts. For a given time-series, the method reconstructs the state space using time-delay embedding, a data transformation technique from dynamical systems. In the embedded space, Takens' theorem proves that the dynamics of a rhythmic signal will exhibit circular patterns. The degree of circularity of the embedding is calculated as a persistence score using persistent homology, an algebraic method for discerning the topological features of data. By comparing the persistence scores to a bootstrapped null distribution, cycling genes are identified. Results in both synthetic and biological data highlight TimeCycle's ability to identify cycling genes across a range of sampling schemes, number of replicates, and missing data. Comparison to competing methods highlights their relative strengths, providing guidance as to the optimal choice of cycling detection method.
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Link to bioRxiv paper:
http://biorxiv.org/cgi/content/short/2020.11.20.391318v1?rss=1
Authors: Karatzas, E., Baltoumas, F. A., Panayiotou, N. A., Schneider, R., Pavlopoulos, G. A.
Abstract:
Efficient integration and visualization of heterogeneous biomedical information in a single view is a key challenge. In this study, we present Arena3Dweb, the first, fully interactive and dependency-free, web application which allows the visualization of multilayered graphs in 3D space. With Arena3Dweb, users can integrate multiple networks in a single view along with their intra- and inter-layer connections. For clearer and more informative views, users can choose between a plethora of layout algorithms and apply them on a set of selected layers either individually or in combination. Users can align networks and highlight node topological features, whereas each layer as well as the whole scene can be translated, rotated and scaled in 3D space. User-selected edge colors can be used to highlight important paths, while node positioning, coloring and resizing can be adjusted on-the-fly. In its current version, Arena3Dweb supports weighted and unweighted undirected graphs and is written in R, Shiny and JavaScript. We demonstrate the functionality of Arena3Dweb using two different use-case scenarios; one regarding drug repurposing for SARS-CoV-2 and one related to GPCR signaling pathways implicated in melanoma. Arena3Dweb is available at http://bib.fleming.gr:3838/Arena3D
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