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

Mapping Intimacies ◽

10.1101/056200 ◽

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

The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab028 ◽

2021 ◽

Vol 3 (2) ◽

Author(s):

Xueyi Dong ◽

Luyi Tian ◽

Quentin Gouil ◽

Hasaru Kariyawasam ◽

Shian Su ◽

...

Keyword(s):

Differential Expression ◽

Expression Analysis ◽

Differential Expression Analysis ◽

Transcriptomic Analysis ◽

Statistical Testing ◽

Rna Seq ◽

Sequencing Data ◽

Short Read ◽

Sequencing Platform ◽

Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

ComplexBrowser: a tool for identification and quantification of protein complexes in large scale proteomics datasets

10.1101/573774 ◽

2019 ◽

Author(s):

Wojciech Michalak ◽

Vasileios Tsiamis ◽

Veit Schwämmle ◽

Adelina Rogowska-Wrzesińska

Keyword(s):

T Cell Activation ◽

Protein Complex ◽

Quantitative Proteomics ◽

Large Scale ◽

Protein Complexes ◽

Quantitative Measure ◽

Exploratory Analysis ◽

List Type ◽

Link Type ◽

Complex Components

AbstractWe have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomics data that focuses on protein complexes. The software uses information from CORUM and Complex Portal databases to identify protein complex components. Based on the expression changes of individual complex subunits across the proteomics experiment it calculates Complex Fold Change (CFC) factor that characterises the overall protein complex expression trend and the level of subunit co-regulation. Thus up- and down-regulated complexes can be identified. It provides interactive visualisation of protein complexes composition and expression for exploratory analysis. It also incorporates a quality control step that includes normalisation and statistical analysis based on Limma test. ComplexBrowser performance was tested on two previously published proteomics studies identifying changes in protein expression in human adenocarcinoma tissue and during activation of mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found co-ordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues and identification of chromatin remodelling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and also provide novel biological details about protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.A fully functional demo version of ComplexBrowser v1.0 is available online via http://computproteomics.bmb.sdu.dk/Apps/ComplexBrowser/The source code can be downloaded from: https://bitbucket.org/michalakw/complexbrowserHighlightsAutomated analysis of protein complexes in proteomics experimentsQuantitative measure of the coordinated changes in protein complex componentsInteractive visualisations for exploratory analysis of proteomics resultsIn briefComplexBrowser is capable of identifying protein complexes in datasets obtained from large scale quantitative proteomics experiments. It provides, in the form of the CFC factor, a quantitative measure of the coordinated changes in complex components. This facilitates assessing the overall trends in the processes governed by the identified protein complexes providing a new and complementary way of interpreting proteomics experiments.

Snaptron: querying and visualizing splicing across tens of thousands of RNA-seq samples

10.1101/097881 ◽

2017 ◽

Cited By ~ 2

Author(s):

Christopher Wilks ◽

Phani Gaddipati ◽

Abhinav Nellore ◽

Ben Langmead

Keyword(s):

Tissue Specificity ◽

Rna Seq ◽

Sequencing Data ◽

Transcription Start ◽

Link Type ◽

Alternative Transcription ◽

Web App ◽

Inverted Indexing ◽

Splice Junctions ◽

Splicing Patterns

AbstractAs more and larger genomics studies appear, there is a growing need for comprehensive and queryable cross-study summaries. Snaptron is a search engine for summarized RNA sequencing data with a query planner that leverages R-tree, B-tree and inverted indexing strategies to rapidly execute queries over 146 million exon-exon splice junctions from over 70,000 human RNA-seq samples. Queries can be tailored by constraining which junctions and samples to consider. Snaptron can also rank and score junctions according to tissue specificity or other criteria. Further, Snaptron can rank and score samples according to the relative frequency of different splicing patterns. We outline biological questions that can be explored with Snaptron queries, including a study of novel exons in annotated genes, of exonization of repetitive element loci, and of a recently discovered alternative transcription start site for the ALK gene. Web app and documentation are at http://snaptron.cs.jhu.edu. Source code is at https://github.com/ChristopherWilks/snaptron under the MIT license.

gplas: a comprehensive tool for plasmid analysis using short-read graphs

Bioinformatics ◽

10.1093/bioinformatics/btaa233 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3874-3876 ◽

Cited By ~ 1

Author(s):

Sergio Arredondo-Alonso ◽

Martin Bootsma ◽

Yaïr Hein ◽

Malbert R C Rogers ◽

Jukka Corander ◽

...

Keyword(s):

Large Scale ◽

Sequence Data ◽

Bacterial Genome ◽

Workflow Management ◽

Supplementary Information ◽

Whole Genome Sequencing Data ◽

Network Partitioning ◽

Sequencing Data ◽

Genetic Traits ◽

Short Read

Abstract Summary Plasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data are often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and network partitioning based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short-read sequence data. Availability and implementation Gplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git. Supplementary information Supplementary data are available at Bioinformatics online.

SSCC: a novel computational framework for rapid and accurate clustering large single cell RNA-seq data

10.1101/344242 ◽

2018 ◽

Cited By ~ 2

Author(s):

Xianwen Ren ◽

Liangtao Zheng ◽

Zemin Zhang

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Large Scale ◽

Random Projection ◽

Rna Seq ◽

Sequencing Data ◽

Computational Framework ◽

Human Blood Cells ◽

Single Cell Rna Sequencing ◽

Data Volume

ABSTRACTClustering is a prevalent analytical means to analyze single cell RNA sequencing data but the rapidly expanding data volume can make this process computational challenging. New methods for both accurate and efficient clustering are of pressing needs. Here we proposed a new clustering framework based on random projection and feature construction for large scale single-cell RNA sequencing data, which greatly improves clustering accuracy, robustness and computational efficacy for various state-of-the-art algorithms benchmarked on multiple real datasets. On a dataset with 68,578 human blood cells, our method reached 20% improvements for clustering accuracy and 50-fold acceleration but only consumed 66% memory usage compared to the widely-used software package SC3. Compared to k-means, the accuracy improvement can reach 3-fold depending on the concrete dataset. An R implementation of the framework is available from https://github.com/Japrin/sscClust.

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

10.1101/147462 ◽

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

NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

10.1101/2020.11.06.371724 ◽

2020 ◽

Author(s):

Eliah G. Overbey ◽

Amanda M. Saravia-Butler ◽

Zhe Zhang ◽

Komal S. Rathi ◽

Homer Fogle ◽

...

Keyword(s):

Expression Profiles ◽

Gene Expression Profiles ◽

Rna Seq ◽

Sequencing Data ◽

Analysis Pipeline ◽

Short Read ◽

Gene Quantification ◽

Working Groups ◽

Using Data ◽

Data Analysis Pipeline

SummaryWith the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

Large-Scale Search of Transcriptomic Read Sets with Sequence Bloom Trees

10.1101/017087 ◽

2015 ◽

Cited By ~ 6

Author(s):

Brad Solomon ◽

Carleton Kingsford

Keyword(s):

Large Scale ◽

Rna Seq ◽

Large Collection ◽

Short Read ◽

Indexing Scheme ◽

Short Read Sequencing ◽

Sequence Read Archive ◽

Gene Isoform ◽

Expressed Sequence ◽

Novel Isoforms

Enormous databases of short-read RNA-seq sequencing experiments such as the NIH Sequence Read Archive (SRA) are now available. However, these collections remain difficult to use due to the inability to search for a particular expressed sequence. A natural question is which of these experiments contain sequences that indicate the expression of a particular sequence such as a gene isoform, lncRNA, or uORF. However, at present this is a computationally demanding question at the scale of these databases. We introduce an indexing scheme, the Sequence Bloom Tree (SBT), to support sequence-based querying of terabase-scale collections of thousands of short-read sequencing experiments. We apply SBT to the problem of finding conditions under which query transcripts are expressed. Our experiments are conducted on a set of 2652 publicly available RNA-seq experiments contained in the NIH for the breast, blood, and brain tissues, comprising 5 terabytes of sequence. SBTs of this size can be queried for a 1000 nt sequence in 19 minutes using less than 300 MB of RAM, over 100 times faster than standard usage of SRA-BLAST and 119 times faster than STAR. SBTs allow for fast identification of experiments with expressed novel isoforms, even if these isoforms were unknown at the time the SBT was built. We also provide some theoretical guidance about appropriate parameter selection in SBT and propose a sampling-based scheme for potentially scaling SBT to even larger collections of files. While SBT can handle any set of reads, we demonstrate the effectiveness of SBT by searching a large collection of blood, brain, and breast RNA-seq files for all 214,293 known human transcripts to identify tissue-specific transcripts. The implementation used in the experiments below is in C++ and is available as open source at http://www.cs.cmu.edu/~ckingsf/software/bloomtree.

RNA sequencing data: hitchhiker's guide to expression analysis

10.7287/peerj.preprints.27283 ◽

2018 ◽

Author(s):

Koen Van Den Berge ◽

Katharina Hembach ◽

Charlotte Soneson ◽

Simone Tiberi ◽

Lieven Clement ◽

...

Keyword(s):

Gene Expression ◽

Rna Sequencing ◽

Large Scale ◽

Science Studies ◽

Rna Seq ◽

Sequencing Data ◽

Data Types ◽

The Past ◽

Long Read ◽

Statistical Approaches

Gene expression is the fundamental level at which the result of various genetic and regulatory programs are observable. The measurement of transcriptome-wide gene expression has convincingly switched from microarrays to sequencing in a matter of years. RNA sequencing (RNA-seq) provides a quantitative and open system for profiling transcriptional outcomes on a large scale and therefore facilitates a large diversity of applications, including basic science studies, but also agricultural or clinical situations. In the past 10 years or so, much has been learned about the characteristics of the RNA-seq datasets as well as the performance of the myriad of methods developed. In this review, we give an overall view of the developments in RNA-seq data analysis, including experimental design, with an explicit focus on quantification of gene expression and statistical approaches for differential expression. We also highlight emerging data types, such as single-cell RNA-seq and gene expression profiling using long-read technologies.

GEO2RNAseq: An easy-to-use R pipeline for complete pre-processing of RNA-seq data

10.1101/771063 ◽

2019 ◽

Cited By ~ 2

Author(s):

Bastian Seelbinder ◽

Thomas Wolf ◽

Steffen Priebe ◽

Sylvie McNamara ◽

Silvia Gerber ◽

...

Keyword(s):

Gene Expression ◽

Single Species ◽

Gene Expression Omnibus ◽

Rna Seq ◽

Sequencing Data ◽

Interacting Species ◽

Link Type ◽

Fastq Format ◽

Standard Tool ◽

Processing Steps

ABSTRACTIn transcriptomics, the study of the total set of RNAs transcribed by the cell, RNA sequencing (RNA-seq) has become the standard tool for analysing gene expression. The primary goal is the detection of genes whose expression changes significantly between two or more conditions, either for a single species or for two or more interacting species at the same time (dual RNA-seq, triple RNA-seq and so forth). The analysis of RNA-seq can be simplified as many steps of the data pre-processing can be standardised in a pipeline.In this publication we present the “GEO2RNAseq” pipeline for complete, quick and concurrent pre-processing of single, dual, and triple RNA-seq data. It covers all pre-processing steps starting from raw sequencing data to the analysis of differentially expressed genes, including various tables and figures to report intermediate and final results. Raw data may be provided in FASTQ format or can be downloaded automatically from the Gene Expression Omnibus repository. GEO2RNAseq strongly incorporates experimental as well as computational metadata. GEO2RNAseq is implemented in R, lightweight, easy to install via Conda and easy to use, but still very flexible through using modular programming and offering many extensions and alternative workflows.GEO2RNAseq is publicly available at https://anaconda.org/xentrics/r-geo2rnaseq and https://bitbucket.org/thomas_wolf/geo2rnaseq/overview, including source code, installation instruction, and comprehensive package documentation.