scholarly journals MetaSRA: normalized sample-specific metadata for the Sequence Read Archive

2016 ◽  
Author(s):  
Matthew N. Bernstein ◽  
AnHai Doan ◽  
Colin N. Dewey
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Sample Type ◽  
Computational Pipeline ◽  
Sources Of Information ◽  
Sequencing Data ◽  
Encode Project ◽  
Link Type ◽  
Sequence Read Archive ◽  
Biological Insight

AbstractMotivationThe NCBI’s Sequence Read Archive (SRA) promises great biological insight if one could analyze the data in the aggregate; however, the data remain largely underutilized, in part, due to the poor structure of the metadata associated with each sample. The rules governing submissions to the SRA do not dictate a standardized set of terms that should be used to describe the biological samples from which the sequencing data are derived. As a result, the metadata include many synonyms, spelling variants, and references to outside sources of information. Furthermore, manual annotation of the data remains intractable due to the large number of samples in the archive. For these reasons, it has been difficult to perform large-scale analyses that study the relationships between biomolecular processes and phenotype across diverse diseases, tissues, and cell types present in the SRA.ResultsWe present MetaSRA, a database of normalized SRA sample-specific metadata following a schema inspired by the metadata organization of the ENCODE project. This schema involves mapping samples to terms in biomedical ontologies, labeling each sample with a sample-type category, and extracting real-valued properties. We automated these tasks via a novel computational pipeline.AvailabilityThe MetaSRA database is available at http://deweylab.biostat.wisc.edu/metasra. Software implementing our computational pipeline is available at https://github.com/deweylab/[email protected]

scholarly journals Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Deborah O. Dele-Oni ◽  
Karen E. Christianson ◽  
Shawn B. Egri ◽  
Alvaro Sebastian Vaca Jacome ◽  
Katherine C. DeRuff ◽  
...  
Keyword(s):  
Large Scale ◽  
Cell Model ◽  
Cellular Responses ◽  
Proteomic Profiling ◽  
Reduced Representation ◽  
Link Type ◽  
Original Dataset ◽  
Quality Control Metrics ◽  
Biological Insight ◽  
Chromatin Profiling

AbstractWhile gene expression profiling has traditionally been the method of choice for large-scale perturbational profiling studies, proteomics has emerged as an effective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profiles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profiling, GCP). Here, we expand our original dataset to include profiles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe filtering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of “connectivity” to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifiers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics.

scholarly journals Identifying common and novel cell types in single-cell RNA-sequencing data using FR-Match

2021 ◽  
Author(s):  
Yun Zhang ◽  
Brian Aevermann ◽  
Rohan Gala ◽  
Richard H. Scheuermann
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Sample Type ◽  
Cell Type ◽  
Sequencing Data ◽  
Excellent Performance ◽  
Single Cell Rna Sequencing ◽  
Accurate Performance ◽  
Cross Platform ◽  
Tissue Region

Reference cell type atlases powered by single cell transcriptomic profiling technologies have become available to study cellular diversity at a granular level. We present FR-Match for matching query datasets to reference atlases with robust and accurate performance for identifying novel cell types and non-optimally clustered cell types in the query data. This approach shows excellent performance for cross-platform, cross-sample type, cross-tissue region, and cross-data modality cell type matching.

scholarly journals PinAPL-Py: A comprehensive web-application for the analysis of CRISPR/Cas9 screens

2017 ◽  
Author(s):  
Philipp N. Spahn ◽  
Tyler Bath ◽  
Ryan J. Weiss ◽  
Jihoon Kim ◽  
Jeffrey D. Esko ◽  
...  
Keyword(s):  
Web Application ◽  
Large Scale ◽  
Sequencing Data ◽  
Bioinformatic Tools ◽  
Link Type ◽  
Screening Experiments ◽  
Independent Analysis ◽  
Wide Range ◽  
Set Up ◽  
Sequence Quality

AbstractBackgroundLarge-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise.ResultsTo make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots.ConclusionsPinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions, documentation and test datasets. The source code is available at https://github.com/LewisLabUCSD/PinAPL-Py

scholarly journals The single-cell eQTLGen consortium

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
MGP van der Wijst ◽  
DH de Vries ◽  
HE Groot ◽  
G Trynka ◽  
CC Hon ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Sequencing Data ◽  
Scale Population ◽  
Trait Locus ◽  
Different Cell Types ◽  
Affect Gene Expression

In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated genetic risk factors through so-called expression quantitative trait locus (eQTL) analysis. Single-cell RNA-sequencing creates enormous opportunities for mapping eQTLs across different cell types and in dynamic processes, many of which are obscured when using bulk methods. Rapid increase in throughput and reduction in cost per cell now allow this technology to be applied to large-scale population genetics studies. To fully leverage these emerging data resources, we have founded the single-cell eQTLGen consortium (sc-eQTLGen), aimed at pinpointing the cellular contexts in which disease-causing genetic variants affect gene expression. Here, we outline the goals, approach and potential utility of the sc-eQTLGen consortium. We also provide a set of study design considerations for future single-cell eQTL studies.

Machine learning and statistical methods for clustering single-cell RNA-sequencing data

2019 ◽  
Vol 21 (4) ◽  
pp. 1209-1223 ◽  
Author(s):  
Raphael Petegrosso ◽  
Zhuliu Li ◽  
Rui Kuang
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Statistical Methods ◽  
Large Scale ◽  
Time Series Data ◽  
Single Cells ◽  
Transcriptome Profiling ◽  
Cell Types ◽  
Series Data ◽  
Sequencing Data

Abstract   Single-cell RNAsequencing (scRNA-seq) technologies have enabled the large-scale whole-transcriptome profiling of each individual single cell in a cell population. A core analysis of the scRNA-seq transcriptome profiles is to cluster the single cells to reveal cell subtypes and infer cell lineages based on the relations among the cells. This article reviews the machine learning and statistical methods for clustering scRNA-seq transcriptomes developed in the past few years. The review focuses on how conventional clustering techniques such as hierarchical clustering, graph-based clustering, mixture models, $k$-means, ensemble learning, neural networks and density-based clustering are modified or customized to tackle the unique challenges in scRNA-seq data analysis, such as the dropout of low-expression genes, low and uneven read coverage of transcripts, highly variable total mRNAs from single cells and ambiguous cell markers in the presence of technical biases and irrelevant confounding biological variations. We review how cell-specific normalization, the imputation of dropouts and dimension reduction methods can be applied with new statistical or optimization strategies to improve the clustering of single cells. We will also introduce those more advanced approaches to cluster scRNA-seq transcriptomes in time series data and multiple cell populations and to detect rare cell types. Several software packages developed to support the cluster analysis of scRNA-seq data are also reviewed and experimentally compared to evaluate their performance and efficiency. Finally, we conclude with useful observations and possible future directions in scRNA-seq data analytics. Availability All the source code and data are available at https://github.com/kuanglab/single-cell-review.

scholarly journals Verification of Arabidopsis stock collections using SNPmatch - an algorithm for genotyping high-plexed samples

2017 ◽  
Author(s):  
Rahul Pisupati ◽  
Ilka Reichardt ◽  
Ümit Seren ◽  
Pamela Korte ◽  
Viktoria Nizhynska ◽  
...  
Keyword(s):  
Arabidopsis Thaliana ◽  
Genetic Variation ◽  
Phenotypic Variation ◽  
Large Scale ◽  
Command Line ◽  
Web Interface ◽  
Sequencing Data ◽  
Link Type ◽  
Gregor Mendel ◽  
Low Coverage

AbstractLarge-scale studies such as the ​Arabidopsis thaliana 1001 Genomes Project aim to understand genetic variation in populations and link it to phenotypic variation. Such studies require routine genotyping of stocks to avoid sample contamination and mix-ups. To genotype samples efficiently and economically, sequencing must be inexpensive a​nd data processing simple. Here we present SNPmatch, a tool which identifies the most likely strain (inbred line, or “accession”) from a SNP database. We tested the tool by performing low-coverage sequencing of over 2000 strains. SNPmatch could readily genotype samples correctly from 1-fold coverage sequencing data, and could also identify the parents of F1 or F2 individuals. SNPmatch can be run either on the command line or through AraGeno (https://arageno.gmi.oeaw.ac.at), a web interface that permits sample genotyping from a user-uploaded VCF or BED file.Availability and implementation: https://github.com/Gregor-Mendel-Institute/SNPmatch.git

scholarly journals Comparison of computational methods for imputing single-cell RNA-sequencing data

2017 ◽  
Author(s):  
Lihua Zhang ◽  
Shihua Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Large Scale ◽  
Real Data ◽  
Cell Types ◽  
Biological Functions ◽  
Sequencing Data ◽  
Imputation Methods ◽  
Future Studies ◽  
Single Cell Rna Sequencing

AbstractSingle-cell RNA-sequencing (scRNA-seq) is a recent breakthrough technology, which paves the way for measuring RNA levels at single cell resolution to study precise biological functions. One of the main challenges when analyzing scRNA-seq data is the presence of zeros or dropout events, which may mislead downstream analyses. To compensate the dropout effect, several methods have been developed to impute gene expression since the first Bayesian-based method being proposed in 2016. However, these methods have shown very diverse characteristics in terms of model hypothesis and imputation performance. Thus, large-scale comparison and evaluation of these methods is urgently needed now. To this end, we compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations. Some defects of these methods such as scalability, robustness and unavailability in some situations need to be addressed in future studies.

scholarly journals FILER: large-scale, harmonized FunctIonaL gEnomics Repository

2021 ◽  
Author(s):  
Pavel P. Kuksa ◽  
Prabhakaran Gangadharan ◽  
Zivadin Katanic ◽  
Lauren Kleidermacher ◽  
Alexandre Amlie-Wolf ◽  
...  
Keyword(s):  
Functional Genomics ◽  
Large Scale ◽  
Cell Types ◽  
Data Sources ◽  
Reproducible Research ◽  
Functional Genomic ◽  
Data Types ◽  
Link Type ◽  
Annotation Data ◽  
Large Scale Analysis

AbstractMotivationQuerying massive collections of functional genomic and annotation data, linking and summarizing the query results across data sources and data types are important steps in high-throughput genomic and genetic analytical workflows. However, accomplishing these steps is difficult because of the heterogeneity and breadth of data sources, experimental assays, biological conditions (e.g., tissues, cell types), data types, and file formats.ResultsFunctIonaL gEnomics Repository (FILER) is a large-scale, harmonized functional genomics data catalog uniquely providing: 1) streamlined access to >50,000 harmonized, annotated functional genomic and annotation datasets across >20 integrated data sources, >1,100 biological conditions/tissues/cell types, and >20 experimental assays; 2) a scalable, indexing-based genomic querying interface; 3) ability for users to analyze and annotate their own experimental data against reference datasets. This rich resource spans >17 Billion genomic records for both GRCh37/hg19 and GRCh38/hg38 genome builds. FILER scales well with the experimental (query) data size and the number of reference datasets and data sources. When evaluated on large-scale analysis tasks, FILER demonstrated great efficiency as the observed running time for querying 1000x more genomic intervals (106 vs. 103) against all 7×109 hg19 FILER records increased sub-linearly by only a factor of 15x. Together, these features facilitate reproducible research and streamline querying, integrating, and utilizing large-scale functional genomics and annotation data.Availability and implementationFILER can be 1) freely accessed at https://lisanwanglab.org/FILER,2) deployed on cloud or local servers (https://bitbucket.org/wanglab-upenn/FILER), and 3) integrated with other pipelines using provided scripts.

scholarly journals The Lair: A resource for exploratory analysis of published RNA-Seq data

2016 ◽  
Author(s):  
Harold Pimentel ◽  
Pascal Sturmfels ◽  
Nicolas Bray ◽  
Páll Melsted ◽  
Lior Pachter
Keyword(s):  
Large Scale ◽  
Exploratory Analysis ◽  
Technical Expertise ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Link Type ◽  
Short Read Archive ◽  
Published Research

AbstractIncreased emphasis on reproducibility of published research in the last few years has led to the large-scale archiving of sequencing data. While this data can, in theory, be used to reproduce results in papers, it is typically not easily usable in practice. We introduce a series of tools for processing and analyzing RNA-Seq data in the Short Read Archive, that together have allowed us to build an easily extendable resource for analysis of data underlying published papers. Our system makes the exploration of data easily accessible and usable without technical expertise. Our database and associated tools can be accessed at The Lair: http://pachterlab.github.io/lair

UNCURL-App: Interactive Database-Driven Analysis of Single Cell RNA Sequencing Data

2020 ◽  
Author(s):  
Yue Zhang ◽  
Shunfu Mao ◽  
Sumit Mukherjee ◽  
Sreeram Kannan ◽  
Georg Seelig
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Biological Knowledge ◽  
Analysis Tool ◽  
Sequencing Data ◽  
Link Type ◽  
Analysis Process ◽  
Single Cell Rna Sequencing ◽  
Key Innovations

AbstractAnalysis of single cell RNA sequencing (scRNA-Seq) datasets is a complex and time-consuming process, requiring both biological knowledge and technical skill. In order to simplify and systematize this process, we introduce UNCURL-App, an online GUI-based interactive scRNA-Seq analysis tool. UNCURL-App introduces two key innovations: First, prior knowledge in the form of cell type, anatomy, and Gene Ontology databases is integrated directly with the rest of the analysis process, allowing users to automatically map cell clusters to known cell types based on gene expression. Second, tools for interactive re-analysis allow the user to iteratively create, merge, or delete clusters in order to arrive at an optimal mapping between clusters and cell types.AvailabilityThe website is at https://uncurl.cs.washington.edu/. Source code is available at https://github.com/yjzhang/uncurl_app

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