PaperPlayer biorxiv bioinformatics

Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.11.01.364083v1?rss=1

Authors: Qureshi, H. K., Magony, A., Hadzhiev, Y., Wozniak, K., Jasiulewicz, A., Mueller, F., Sik, A.

Abstract:

4D fluorescence microscopy allows the study of spatiotemporal cell dynamics in embryonic development in unprecedented detail, yet the uneven scattering of light within developing embryos presents challenges in discerning fine details. We present a tool which pre-processes large in vivo 4D microscopy datasets and can then track the movements and lineage histories of rapidly dividing embryonic stem cells. This solution offers a robust, simple segmentation technique to segment high intensity fluorescent features in highly scattered datasets, such as from deep within a developing zebrafish embryo. This tool then offers lineage tracing functionality by tracking rapidly dividing nuclei and their progeny, while also accounting for the jumps rapidly moving features make between frames in low frame rate data. Based on the user having prior knowledge of their imaging subject, this tool has applications in datasets with limitations with signal to noise, and frame rate. It can provide information regarding the position and movement of rapidly dividing nuclei. We demonstrate this pipeline in developing early zebrafish embryos.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.361162v1?rss=1

Authors: Speir, M. L., Bhaduri, A., Markov, N. S., Moreno, P., Nowakowski, T. J., Papatheodorou, I., Pollen, A. A., Seninge, L., Kent, W. J., Haeussler, M.

Abstract:

Summary: As the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets. Availability and implementation: We provide the UCSC Cell Browser as a free website where users can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu. Contact: [email protected]

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.360263v1?rss=1

Authors: Mission:Cure,, Golden, M.

Abstract:

Despite decades of clinical investigations, there is currently no effective treatment for patients diagnosed with Chronic Pancreatitis (CP). Computational drug repurposing holds promise to rapidly identify therapeutics which may prove efficacious against the disease. Using a literature-derived knowledge graph, we train multiple machine learning models using embeddings based on i) the network topology of regulation bipartite networks, ii) protein primary structures and iii) molecule substructures. Using these models, we predict approved drugs that down-regulate the disease, and assess their proposed respective drug targets and mechanism of actions. We analyse the highest predicted drugs and find a diverse range of regulatory mechanisms including inhibition of fibrosis, inflammation, immmune response, oxidative stress and calcium homeostasis. Notably, we identify resiniferatoxin, a potent analogue of capsaicin, as a promising repurposable candidate due to its antiinflammatory properties, nociceptive pain suppression, and regulation of calcium homeostatis (through potentiation of mutant cystic fibrosis transmembrane conductance regulator (CFTR)). Resiniferatoxin may also regulate intracellular acinar Ca2+ via agonism of transient receptor potential vanilloid subfamily member 6 (TRPV6). We believe the potential of this repurposable drug warrants further in silico and in vitro testing, particularly the affect of the TRPV6 agonism on disease pathogenesis.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.30.362384v1?rss=1

Authors: Wang, Y., Chun, R. F., Adhikari, S., Lopez, C. M., Henrich, M., Yacoubian, V., Lin, L., Adams, J. S., Xing, Y.

Abstract:

Nearly all human multi-exonic genes undergo alternative splicing (AS) via regulation by RNA-binding proteins (RBPs), but few studies have examined the temporal dynamics of AS and its regulation during cell differentiation in the bone niche. We sought to evaluate how AS, under the control of RBPs, affects cell fate commitment during induced osteogenic differentiation of human bone marrow-derived multipotent stem/stromal progenitor cells (MSPCs). We generated a time-course RNA sequencing (RNA-seq) dataset representative of induced MSPC differentiation to osteoblasts. Our analysis revealed widespread AS changes, coordinated with differential RBP expression, at multiple time points, including many AS changes in non-differentially expressed genes. We also developed a computational approach to profile the dynamics and regulation of AS by RBPs using time-course RNA-seq data, by combining temporal patterns of exon skipping and RBP expression with RBP binding sites in the vicinity of regulated exons. In total we identified nine RBPs as potential key splicing regulators during MSPC osteogenic differentiation. Perturbation of one candidate, KHDRBS3, inhibited osteogenesis and bone formation in vitro, validating our computational prediction of "driver" RBPs. Overall, our work highlights a high degree of complexity in the splicing regulation of MSPC osteogenic differentiation. Our computational approach may be applied to other time-course data to explore dynamic AS changes and associated regulatory mechanisms in other biological processes or disease trajectories.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.361402v1?rss=1

Authors: Shajii, A. R., Numanagic, I., Leighton, A. T., Greenyer, H., Amarasinghe, S., Berger, B.

Abstract:

Exponentially-growing next-generation sequencing data requires high-performance tools and algorithms. Nevertheless, the implementation of high-performance computational genomics software is inaccessible to many scientists because it requires extensive knowledge of low-level software optimization techniques, forcing scientists to resort to high-level software alternatives that are less efficient. Here, we introduce Seq--- a Python-based optimization framework that combines the power and usability of high-level languages like Python with the performance of low-level languages like C or C++. Seq allows for shorter, simpler code, is readily usable by a novice programmer, and obtains significant performance improvements over existing languages and frameworks. We showcase and evaluate Seq by implementing seven standard, widely-used applications from all stages of the genomics analysis pipeline, including genome index construction, finding maximal exact matches, long-read alignment and haplotype phasing, and demonstrate its implementations are up to an order of magnitude faster than existing hand-optimized implementations, with just a fraction of the code. By enabling researchers of all backgrounds to easily implement high-performance analysis tools, Seq further opens the door to the democratization and scalability of computational genomics.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.360701v1?rss=1

Authors: Oda, T.

Abstract:

SurfStamp is an application that is used to generate textures for surface models of proteins. The textures contain information about surface residues and the information is drawn directly on the 3D object of the models. This approach is more intuitive than the labeling functions that most three-dimensional (3D) structure viewers use to show residue information. Therefore, the use of this application enables researchers, readers, or audiences to easily determine which residues are contributing the surface they are focusing on.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.361352v1?rss=1

Authors: Punturi, S., Punturi, N., Bainbridge, M., Haricharan, S.

Abstract:

Cell cycle checkpoint kinases activated by DNA repair pathways, ATM/Chk2 and ATR/Chk1 are important tumor suppressors. Germline mutations in these genes associate with increased cancer incidence. Specifically, ATM and CHEK2 germline variants associate with the incidence of estrogen receptor (ER)+ breast cancer with poor patient outcome. More recent investigations identified somatic inactivation of ATM/Chk2 as causal to endocrine therapy resistance in ER+ breast cancer patients. However, the relative contributions of germline and somatic inactivation of these cell cycle checkpoint kinases to ER+/HER2- and ER-/PR-/HER2- or triple negative breast cancer (TNBC) incidence and tumor characteristics has not been systematically assessed. Here, we comprehensively compare the association of ATM/CHEK2 and ATR/CHEK1 germline and somatic mutations with age, tumor stage and PR status at diagnosis, and metastatic potential using a meta-dataset compiled from six independent primary and metastatic patient datasets. We observe significantly higher frequency of ATM (*~29.6%) and CHEK2 (9.5%) mutations in metastatic ER+/HER2- breast cancers relative to TNBC (*~11% and 5% respectively)(p=0.03). CHEK2 mutations associate with higher PR positivity, higher tumor stage and younger age at diagnosis for metastatic ER+/HER2- breast cancer patients. These associations are primarily driven by germline, rather than somatic CHEK2 mutations. However, somatic CHEK2 mutations associate with more rapid disease progression on early rounds of endocrine therapy. These results provide the first systematic analysis of the contribution of germline and somatic cell cycle checkpoint kinase mutations to tumor characteristics affecting patient prognosis and treatment outcome. Results of this study suggest more streamlined use of the status of these checkpoint kinases as prognostic and/or predictive biomarkers for ER+/HER2- breast cancer patients.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.361451v1?rss=1

Authors: Koch, F. C., Sutton, G. J., Voineagu, I., Vafaee, F. C.

Abstract:

A typical single-cell RNA sequencing (scRNA-seq) experiment will measure on the order of 20,000 transcripts and thousands, if not millions, of cells. The high dimensionality of such data presents serious complications for traditional data analysis methods and, as such, methods to reduce dimensionality play an integral role in many analysis pipelines. However, few studies benchmark the performance of these methods on scRNA-seq data, with existing comparisons assessing performance via downstream analysis accuracy measures which may confound the interpretation of their results. Here, we present the most comprehensive benchmark of dimensionality reduction methods in scRNA-seq data to date, utilizing over 300,000 compute hours to assess the performance of over 25,000 low dimension embeddings across 33 dimensionality reduction methods and 55 scRNA-seq datasets (ranging from 66-27,500 cells). We employ a simple-yet-novel approach which does not rely on the results of downstream analyses. Internal validation measures (IVMs), traditionally used as an unsupervised method to assess clustering performance, are repurposed to measure how well-formed biological clusters are after dimensionality reduction. Performance was further evaluated using nearly 200,000,000 iterations of DBSCAN, a density-based clustering algorithm, showing that hyperparameter optimization using IVMs as the objective function leads to near-optimal clustering. Methods were also assessed on the extent to which they preserve the global structure of the data, and on their computational memory and time requirements across a large range of sample sizes. Our comprehensive benchmarking analysis provides a valuable resource for researchers and aims to guide best practice for dimensionality reduction in scRNA-seq analyses, and we highlight LDA (Latent Dirichlet Allocation) and PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding) as high-performing algorithms.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.361360v1?rss=1

Authors: Dang, T., Kishino, H.

Abstract:

A central focus of microbiome studies is the characterization of differences in the microbiome composition across groups of samples. A major challenge is the high dimensionality of microbiome datasets, which significantly reduces the power of current approaches for identifying true differences and increases the chance of false discoveries. We have developed a new framework to address these issues by combining (i) identifying a few significant features by a massively parallel forward variable selection procedure, (ii) mapping the selected species on a phylogenetic tree, and (iii) predicting functional profiles by functional gene enrichment analysis from metagenomic 16S rRNA data. We demonstrated the performance of the proposed approach by analyzing two published datasets from large-scale case-control studies: (i) 16S rRNA gene amplicon data for Clostridioides difficile infection (CDI) and (ii) shotgun metagenomics data for human colorectal cancer (CRC). The proposed approach improved the accuracy from 81% to 99.01% for CDI and from 75.14% to 90.17% for CRC. We identified a core set of 96 species that were significantly enriched in CDI and a core set of 75 species that were enriched in CRC. Moreover, although the quality of the data differed for the functional profiles predicted from the 16S rRNA dataset and functional metagenome profiling, our approach performed well for both databases and detected main functions that can be used to diagnose and study further the growth stage of diseases.

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Link to bioRxiv paper:

http://biorxiv.org/cgi/content/short/2020.10.29.360669v1?rss=1

Authors: Deutsch, A., Nava-Sedeno, J. M., Syga, S., Hatzikirou, H.

Abstract:

Collective dynamics in multicellular systems such as biological organs and tissues plays a key role in biological development, regeneration, and pathological conditions. Collective dynamics - understood as population behaviour arising from the interplay of the constituting discrete cells - can be studied with mathematical models. Off- and on-lattice agent-based models allow to analyse the link between individual cell and collective behaviour. Notably, in on-lattice agent-based models known as cellular automata, collective behaviour can not only be analysed through computer simulations, but predicted with mathematical methods. However, classical cellular automaton models fail to replicate key aspects of collective migration, which is a central instance of collective behaviour in multicellular systems. To overcome drawbacks of classical on-lattice models, we introduce a novel on-lattice, agent-based modelling class for collective cell migration, which we call biological lattice-gas cellular automaton (BIO-LGCA). The BIO-LGCA is characterised by synchronous time updates, and the explicit consideration of individual cell velocities. While rules in classical cellular automata are typically chosen ad hoc, we demonstrate that rules for cell-cell and cell-environment interactions in the BIO-LGCA can also be derived from experimental single cell migration data or biophysical laws for individual cell migration. Furthermore, we present elementary BIO-LGCA models of fundamental cell interactions, which may be combined in a modular fashion to model complex multicellular phenomena. Finally, we present a mathematical mean-field analysis of a BIO-LGCA model that allows to predict collective patterns for a particular cell-cell interaction.

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