Biology subjects

Ma'ayan, A.

Publications and source records attributed to Ma'ayan, A..

25 records · Page 2Linked to original sources

bioRxiv2019

Connecting omics signatures of diseases, drugs, and mechanisms of actions with iLINCS

There are only a few platforms that integrate multiple omics data types, bioinformatics tools, and interfaces for integrative analyses and visualization that do not require programming skills. Among these, iLINCS is unique in scope and versatility of the data provided and the analytics facilitated. iLINCS (http://ilincs.org) is an integrative web-based platform for analysis of omics data and signatures of cellular perturbations. The platform facilitates analysis of user-submitted omics signatures of diseases and cellular perturbations in the context of a large compendium of pre-computed signatures (>200,000), as well as mining and re-analysis of the large collection of omics datasets (>12,000), pre-computed signatures, and their connections. Analytics workflows driven by user-friendly interfaces enable users with only conceptual understanding of the analysis strategy to execute sophisticated analyses of omics signatures, such as systems biology analyses and interpretation of signatures, mechanism of action analysis, and signature-driven drug repositioning. In summary, iLINCS workflows integrate vast omics data resources and a range of analytics and interactive visualization tools into a comprehensive platform for analysis of omics signatures.

bioinformatics↗

bioRxiv2019

PKMYT1 is a computationally predicted target for kidney cancer

Protein Kinase Membrane Associated Tyrosine/Threonine 1 (PKMYT1) is an understudied member of the serine/threonine protein kinase family. PKMYT1 is listed as a dark kinase according to the Illuminating the Druggable Genome (IDG) target development level classification. Using a combination of bioinformatics tools that we developed, we predict that targeting PKMYT1 is potentially beneficial for treating kidney cancer.

bioinformatics↗

bioRxiv2019

FAIRshake: toolkit to evaluate the findability, accessibility, interoperability, and reusability of research digital resources

As more datasets, tools, workflows, APIs, and other digital resources are produced by the research community, it is becoming increasingly difficult to harmonize and organize these efforts for maximal synergistic integrated utilization. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have prompted many stakeholders to consider strategies for tackling this challenge by making these digital resources follow common standards and best practices so that they can become more integrated and organized. Faced with the question of how to make digital resources more FAIR, it has become imperative to measure what it means to be FAIR. The diversity of resources, communities, and stakeholders have different goals and use cases and this makes assessment of FAIRness particularly challenging. To begin resolving this challenge, the FAIRshake toolkit was developed to enable the establishment of community-driven FAIR metrics and rubrics paired with manual, semi- and fully-automated FAIR assessment capabilities. The FAIRshake toolkit contains a database that lists registered digital resources, with their associated metrics, rubrics, and assessments. The FAIRshake toolkit also has a browser extension and a bookmarklet that enables viewing and submitting assessments from any website. The FAIR assessment results are visualized as an insignia that can be viewed on the FAIRshake website, or embedded within hosting websites. Using FAIRshake, a variety of bioinformatics tools, datasets listed on dbGaP, APIs registered in SmartAPI, workflows in Dockstore, and other biomedical digital resources were manually and automatically assessed for FAIRness. In each case, the assessments revealed room for improvement, which prompted enhancements that significantly upgraded FAIRness scores of several digital resources.

bioinformatics↗

bioRxiv2018

Fate Before Function: Specification of the Hair Follicle Niche Occurs Prior to its Formation and Is Progenitor Dependent

Cell fate transitions are essential for specialization of stem cells and their niches, but the precise timing and sequence of molecular events during embryonic development are largely unknown. Here, we show that dermal condensates (DC), signaling niches for epithelial progenitors in hair placodes, are specified before niche formation and function. With 3D/4D microscopy we identify unclustered DC precursors. With population-based and single-cell transcriptomics we define a molecular time-lapse of dynamic niche signatures and the developmental trajectory as the DC lineage emerges from fibroblasts. Co-expression of downregulated fibroblast and upregulated DC genes in niche precursors reveals a transitory molecular state following a proliferation shutdown. Waves of transcription factor and signaling molecule expression then consolidate DC niche formation. Finally, ablation of epidermal Wnt signaling and placode-derived FGF20 demonstrates their requirement for DC-precursor specification. These findings uncover a progenitor-dependent niche precursor fate and the transitory molecular events controlling niche formation and function.\n\nGraphical Abstract\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC=\"FIGDIR/small/414839_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (78K):\norg.highwire.dtl.DTLVardef@13fa73aorg.highwire.dtl.DTLVardef@1fbd280org.highwire.dtl.DTLVardef@1b8f66borg.highwire.dtl.DTLVardef@3a1ef9_HPS_FORMAT_FIGEXP M_FIG C_FIG HIGHLIGHTSO_LIPrecursors of the hair follicle niche are specified before niche cluster formation\nC_LIO_LIBulk/single cell RNA-seq defines early niche fate at molecular transitional state\nC_LIO_LISuccessive waves of transcription factor/signaling genes mark niche fate acquisition\nC_LIO_LINiche fate acquisition is not \"pre-programmed\" and requires FGF20 from progenitors\nC_LI

developmental biology↗

bioRxiv2018

Elysium: RNA-seq Alignment in the Cloud

MotivationRNA-sequencing (RNA-seq) is currently the leading technology for genome-wide transcript quantification. Mapping the raw reads to transcript and gene level counts can be achieved by a variety of aligners and pipelines. The diversity of processing options reduces interoperability. In addition, the alignment step requires significant computational resources and basic programming knowledge. Elysium enables users of all skill levels to perform a uniform and free RNA-seq alignment in the cloud.\n\nResultsThe Elysium infrastructure is comprised of four components: A file upload API that enables storage of FASTQ files on Amazon S3 without Amazon credentials; an API to handle the cloud alignment job scheduling for uploaded files; and a graphical user interface (GUI) to provide intuitive access to users that do not have command-line access skills.\n\nAvailabilityThe Elysium source code is available under the Apache Licence 2.0 on GitHub at: https://github.com/maayanlab/elysium\n\nThe service of cloud based RNA-seq alignment is freely accessible through the Elysium GUI at: http://elysium.cloud

bioinformatics↗

bioRxiv2018

BioJupies: Automated Generation of Interactive Notebooks for RNA-seq Data Analysis in the Cloud

Interactive notebooks can make bioinformatics data analyses more transparent, accessible and reusable. However, creating notebooks requires computer programming expertise. Here we introduce BioJupies, a web server that enables automated creation, storage, and deployment of Jupyter Notebooks containing RNA-seq data analyses. Through an intuitive interface, novice users can rapidly generate tailored reports to analyze and visualize their own raw sequencing files, their gene expression tables, or fetch data from >5,500 published studies containing >250,000 preprocessed RNA-seq samples. Generated notebooks have executable code of the entire pipeline, rich narrative text, interactive data visualizations, and differential expression and enrichment analyses. The notebooks are permanently stored in the cloud and made available online through a persistent URL. The notebooks are downloadable, customizable, and can run within a Docker container. By providing an intuitive user interface for notebook generation for RNA-seq data analysis, starting from the raw reads, all the way to a complete interactive and reproducible report, BioJupies is a useful resource for experimental and computational biologists. BioJupies is freely available as a web-based application from: http://biojupies.cloud and as a Chrome extension from the Chrome Web Store.

bioinformatics↗

bioRxiv2017

Massive Mining of Publicly Available RNA-seq Data from Human and Mouse

RNA-sequencing (RNA-seq) is currently the leading technology for genome-wide transcript quantification. While the volume of RNA-seq data is rapidly increasing, the currently publicly available RNA-seq data is provided mostly in raw form, with small portions processed non- uniformly. This is mainly because the computational demand, particularly for the alignment step, is a significant barrier for global and integrative retrospective analyses. To address this challenge, we developed all RNA-seq and ChIP-seq sample and signature search (ARCHS4), a web resource that makes the majority of previously published RNA-seq data from human and mouse freely available at the gene count level. Such uniformly processed data enables easy integration for downstream analyses. For developing the ARCHS4 resource, all available FASTQ files from RNA-seq experiments were retrieved from the Gene Expression Omnibus (GEO) and aligned using a cloud-based infrastructure. In total 137,792 samples are accessible through ARCHS4 with 72,363 mouse and 65,429 human samples. Through efficient use of cloud resources and dockerized deployment of the sequencing pipeline, the alignment cost per sample is reduced to less than one cent. ARCHS4 is updated automatically by adding newly published samples to the database as they become available. Additionally, the ARCHS4 web interface provides intuitive exploration of the processed data through querying tools, interactive visualization, and gene landing pages that provide average expression across cell lines and tissues, top co-expressed genes, and predicted biological functions and protein-protein interactions for each gene based on prior knowledge combined with co-expression. Benchmarking the quality of these predictions, co-expression correlation data created from ARCHS4 outperforms co-expression data created from other major gene expression data repositories such as GTEx and CCLE.\n\nARCHS4 is freely accessible at: http://amp.pharm.mssm.edu/archs4

bioinformatics↗