PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.369983v1?rss=1

Authors: Agostinetto, G., Sandionigi, A., Chahed, A., Brusati, A., Parladori, E., Balech, B., Bruno, A., Pescini, D., Casiraghi, M.

Abstract:

Background: The increasing availability of multi omics data is leading to continually revise estimates of existing biodiversity data. In particular, the molecular data enable to characterize novel species yet unknown and to increase the information linked to those already observed with new genomic data. For this reason, the management and visualization of existing molecular data, and their related metadata, through the implementation of easy to use IT tools have become a key point for the development of future research. The more users are able to access biodiversity related information, the greater the ability of the scientific community to expand the knowledge in this area. Results: In our research we have focused on the development of ExTaxsI (Exploring Taxonomies Information), an IT tool able to retrieve biodiversity data stored in NCBI databases and provide a simple and explorable visualization. Through the three case studies presented here, we have shown how an efficient organization of the data already present can lead to obtaining new information that is fundamental as a starting point for new research. Our approach was also able to highlight the limits in the distribution data availability, a key factor to consider in the experimental design phase of broad spectrum studies, such as metagenomics. Conclusions: ExTaxI can easily produce explorable visualization of molecular data and its metadata, with the aim to help researchers to improve experimental designs and highlight the main gaps in the coverage of available data. Keywords: Biodiversity; Data visualization; Molecular data; Database; Data integration; Taxonomy gaps

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.370437v1?rss=1

Authors: Khaki, S., Pham, H., Han, Y., Kent, W., Wang, L.

Abstract:

The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society. The explosion of collectable data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisions are made at key points of the breeding cycle. However, due to the sheer size of a breeding program and current resource limitations, the ability to collect precise data on individual plants is not possible. In particular, efficient phenotyping of crops to record its color, shape, chemical properties, disease susceptibility, etc. is severely limited due to labor requirements and, oftentimes, expert domain knowledge. In this paper, we propose a deep learning based approach, named DeepStand, for image-based corn stand counting at early phenological stages. The proposed method adopts a truncated VGG-16 network as a backbone feature extractor and merges multiple feature maps with different scales to make the network robust against scale variation. Our extensive computational experiments suggest that our proposed method can successfully count corn stands and out-perform other state-of-the-art methods. It is the goal of our work to be used by the larger agricultural community as a way to enable high-throughput phenotyping without the use of extensive time and labor requirements.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.04.368043v1?rss=1

Authors: Wang, Z., Zhong, Y., Ye, Z., Zeng, L., Chen, Y., Shi, M., Qian, M., Zhang, M. Q.

Abstract:

Distinguishing cell types and cell states is one of the fundamental questions in single-cell studies. Meanwhile, exploring the lineage relations among cells and finding the path and critical points in the cell fate transition are also of great importance. Existing unsupervised clustering methods and lineage trajectory reconstruction methods often face several challenges such as clustering data of arbitrary shapes, tracking precise trajectories and identifying critical points. Certain adaptive landscape approach, which constructs a pseudo-energy landscape of the dynamical system, may be used to explore such problems. However, algorithms based on rigorous metastability theory for constructing the landscape of individual cells are still lacking. Thus, we propose Markov hierarchical clustering algorithm (MarkovHC), which reconstructs multi-scale pseudo-energy landscape by exploiting underlying metastability structure in an exponentially perturbed Markov chain. A Markov process describes the random walk of a hypothetically traveling cell in the corresponding pseudo-energy landscape over possible gene expression states. Technically, MarkovHC integrates tasks of cell classification, trajectory reconstruction, and critical point identification in a single theoretical framework consistent with topological data analysis (TDA). In addition to the algorithm development and simulation tests, we also applied MarkovHC to diverse types of real biological data: single-cell RNA-Seq data, cytometry data, and single-cell ATAC-Seq data. Remarkably, when applying to single-cell RNA-Seq data of human ESC derived progenitor cells, MarkovHC could not only successfully identify known cell types, but also discover new cell types and stages. In addition, when using MarkovHC to analyze single-cell RNA-Seq data of human preimplantation embryos in early development, the hierarchical structure of the lineage trajectories were faithfully reconstituted. Furthermore, the critical points representing important stage transitions had also been identified by MarkovHC from early gastric cancer data. In summary, these results demonstrate that MarkovHC is a powerful tool based on rigorous metastability theory to explore hierarchical structures of biological data, identify cell population (basin) and critical point (stage transition), and track lineage trajectory (differentiation path).

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.370197v1?rss=1

Authors: Reed, E. R., Monti, S.

Abstract:

As high-throughput genomics assays become more efficient and cost effective, their utilization has become standard in large-scale biomedical projects. These studies are often explorative, in that relationships between samples are not explicitly defined a priori, but rather emerge from data-driven discovery and annotation of molecular subtypes, thereby informing hypotheses and independent evaluation. Here, we present K2Taxonomer, a novel unsupervised recursive partitioning algorithm and associated R package that utilize ensemble learning to identify robust subgroups in a "taxonomy-like" structure (https://github.com/montilab/K2Taxonomer). K2Taxonomer was devised to accommodate different data paradigms, and is suitable for the analysis of both bulk and single-cell transcriptomics data. For each of these data types, we demonstrate the power of K2Taxonomer to discover known relationships in both simulated and human tissue data. We conclude with a practical application on breast cancer tumor infiltrating lymphocyte (TIL) single-cell profiles, in which we identified co-expression of translational machinery genes as a dominant transcriptional program shared by T cells subtypes, associated with better prognosis in breast cancer tissue bulk expression data.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.04.368019v1?rss=1

Authors: Huang, Y., Sanguinetti, G.

Abstract:

RNA splicing is an important driver of heterogeneity in single cells, and a major determinant of the dynamical state of developing cells. However, the intrinsic coverage limitations of scRNA-seq technologies make it challenging to associate specific splicing events to cell-level phenotypes. Here, we present BRIE2, a scalable computational method that resolves these issues by regressing single-cell transcriptomic data against cell-level features. We show on different biological systems that BRIE2 effectively identifies differential splicing events that are associated with disease or developmental lineages, and detects differential momentum genes for improving RNA velocity analyses. BRIE2 therefore extends the scope of single-cell transcriptomic experiments towards the identification of splicing phenotypes associated with biological changes at the single-cell level.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.03.366674v1?rss=1

Authors: Velten, B., Braunger, J. M., Arnol, D., Argelaguet, R., Stegle, O.

Abstract:

Factor analysis is among the most-widely used methods for dimensionality reduction in genome biology, with applications from personalized health to single-cell studies. Existing implementations of factor analysis assume independence of the observed samples, an assumption that fails in emerging spatio-temporal profiling studies. Here, we present MEFISTO, a flexible and versatile toolbox for modelling high-dimensional data when spatial or temporal dependencies between the samples are known. MEFISTO maintains the established benefits of factor analysis for multi-modal data, but enables performing spatio-temporally informed dimensionality reduction, interpolation and separation of smooth from non-smooth patterns of variation. Moreover, MEFISTO can integrate multiple related datasets by simultaneously identifying and aligning the underlying patterns of variation in a data-driven manner. We demonstrate MEFISTO through applications to an evolutionary atlas of mammalian organ development, where the model reveals conserved and evolutionary diverged developmental programs. In applications to a longitudinal microbiome study in infants, birth mode and diet were highlighted as major causes for heterogeneity in the temporally-resolved microbiome over the first years of life. Finally, we demonstrate that the proposed framework can also be applied to spatially resolved transcriptomics.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.03.367367v1?rss=1

Authors: Eslami Rasekh, M., Hernandez, Y., Drinan, S. D., Fuxman Bass, J. I., Benson, G.

Abstract:

Variable Number Tandem Repeats (VNTRs) are tandem repeat (TR) loci that vary in copy number across a population. Using our program, VNTRseek, we analyzed human whole-genome sequencing datasets from 2,770 individuals in order to detect minisatellite VNTRs, i.e., those with pattern sizes ranging from 7 bp to 126 bp, and with array lengths up to 230 bp. We detected 35,638 VNTR loci and classified 5,676 as common (occurring in >5% of the population). Common VNTR loci were found to be enriched in genomic regions with regulatory function, i.e., transcription start sites and enhancers. Investigation of the common VNTRs in the context of population ancestry revealed that 1,096 loci contained population-specific alleles and that those could be used to classify individuals into super-populations with near perfect accuracy. Comparison of genotyping results with proximal genes indicated that in 120 cases (118 genes), expression differences correlated with VNTR genotype. We validated our predictions in several ways, including experimentally, through identification of predicted alleles in long reads, and by comparisons showing consistency between sequencing platforms. This study is the most comprehensive analysis of minisatellites VNTRs in the human population to date.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.02.364935v1?rss=1

Authors: Levy, J., Bobak, C. A., Christensen, B., Vaickus, L. J., O'Malley, A. J.

Abstract:

Network analysis methods are useful to better understand and contextualize relationships between entities. While statistical and machine learning prediction models generally assume independence between actors, network-based statistical methods for social network data allow for dyadic dependence between actors. While numerous methods have been developed for the R statistical software to analyze such data, deep learning methods have not been implemented in this language. Here, we introduce GCN4R, an R library for fitting graph neural networks on independent networks to aggregate actor covariate information to yield meaningful embeddings for a variety of network-based tasks (e.g. community detection, peer effects models, social influence). We provide an extensive overview of insights and methods utilized by the deep learning community on learning on social and biological networks, followed by a tutorial that demonstrates some of the capabilities of the GCN4R framework to make these methods more accessible to the R research community.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.04.368449v1?rss=1

Authors: Miranda, P., Weber, G.

Abstract:

Background: DNA mismatches can affect the efficiency of PCR techniques if the intended target has mismatches in primers or probes regions. The accepted rule is that mismatches are detrimental as they reduce the hybridization temperatures, yet a more quantitative assessment is rarely performed. Methods: We calculate the hybridization temperatures of primer/probe sets after aligning to SARS-COV-2, SARS-COV-1 and non-SARS genomes, considering all possible combinations of single, double and triple consecutive mismatches. We consider the mismatched hybridization temperature within a range of 5 {degrees}C to the fully matched reference temperature. Results: We obtained the alignments of 19 PCR primers sets that were recently reported for the detection of SARS-CoV-2 and to 21665 SARS-CoV-2 genomes as well as 323 genomes of other viruses of the coronavirus family of which 10 are SARS-CoV-1. We find that many incompletely aligned primers become fully aligned to most of the SARS-CoV-2 when mismatches are considered. However, we also found that many cross-align to SARS-CoV-1 and non-SARS genomes. Conclusions: Some primer/probe sets only align substantially to most SARS-CoV-2 genomes if mismatches are taken into account. Unfortunately, by the same mechanism, almost 75% of these sets also align to some SARS-CoV-1 and non-SARS viruses. It is therefore recommended to consider mismatch hybridization for the design of primers whenever possible, especially to avoid undesired cross-reactivity.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.05.369512v1?rss=1

Authors: Mitrofanov, A., Alkhnbashi, O. S., Shmakov, S. A., Makarova, K. S., Koonin, E. V., Backofen, R.

Abstract:

CRISPR-Cas are adaptive immune systems that degrade foreign genetic elements in archaea and bacteria. In carrying out their immune functions, CRISPR-Cas systems heavily rely on RNA components. These CRISPR (cr) RNAs are repeat-spacer units that are produced by processing of pre-crRNA, the transcript of CRISPR arrays, and guide Cas protein(s) to the cognate invading nucleic acids, enabling their destruction. Several bioinformatics tools have been developed to detect CRISPR arrays based solely on DNA sequences, but all these tools employ the same strategy of looking for repetitive patterns, which might correspond to CRISPR array repeats. The identified patterns are evaluated using a fixed, built-in scoring function, and arrays exceeding a cut-off value are reported. Here, we instead introduce a data-driven approach that uses machine learning to detect and differentiate true CRISPR arrays from false ones based on several features. Our CRISPR detection tool, CRISPRIdentify, performs three steps: detection, feature extraction and classification based on manually curated sets of positive and negative examples of CRISPR arrays. The identified CRISPR arrays are then reported to the user accompanied by detailed annotation. We demonstrate that our approach identifies not only previously detected CRISPR arrays, but also CRISPR array candidates not detected by other tools. Compared to other methods, our tool has a drastically reduced false-positive rate. In contrast to the existing tools, our approach not only provides the user with the basic statistics on the identified CRISPR arrays but also produces a certainty score as a practical measure of the likelihood that a given genomic region is a CRISPR array.

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