IMP ver. 1.4 docker image

<p>This upload contains the the IMP docker image ver. 1.4</p> <p>For more information visit the IMP website: http://r3lab.uni.lu/web/imp/</p> <p>Documentation available at: http://r3lab.uni.lu/web/imp/</p

IMP small scale test dataset

<p>This file contains the test data set used within the article:</p> <p><strong>IMP: a reproducible pipeline for reference-independent integrated metagenomic and metatranscriptomic analyses</strong></p> <p>Shaman Narayanasamy^†, Yohan Jarosz^†, Emilie E.L. Muller, Cédric C. Laczny, Malte Herold, Anne Kaysen, Anna Heintz-Buschart, Nicolás Pinel, Patrick May, and Paul Wilmes^*</p> <p>Preprint: http://biorxiv.org/content/early/2016/02/10/039263</p> <p> </p> <p>This test data set was used for benchmarking the run times of IMP. They are derived by selecting the first 5% of reads from a wastewater sludge microbial community dataset (see manuscript and original publication of data: 10.1038/ncomms6603). Also included are the respective preprocessed FASTQ files such that IMP can be tested without running the preprocessing step. A README file inside the folder briefly describes the different FASTQ files contained in the folder.</p

IMP test data set

<p>This file contains the test data set used within the article:</p> <p><strong>IMP: a reproducible pipeline for reference-independent integrated metagenomic and metatranscriptomic analyses</strong></p> <p>Shaman Narayanasamy^†, Yohan Jarosz^†, Emilie E.L. Muller, Cédric C. Laczny, Malte Herold, Anne Kaysen, Anna Heintz-Buschart, Nicolás Pinel, Patrick May, and Paul Wilmes^*</p> <p>Preprint: http://biorxiv.org/content/early/2016/02/10/039263</p> <p>This test data set was used for benchmarking the run times of IMP. They are derived by selecting the first 5% of reads from a wastewater sludge microbial community dataset (see manuscript). Also included are the respective preprocessed FASTQ files such that IMP can be tested without running the preprocessing step. A README file inside the folder briefly describes the different FASTQ files contained in the folder.</p

Additional file 1: of IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses

Supplementary figures and notes. Figures S1–S3 and Notes S1–S2. Detailed figure legends available within file. (PDF 1047 kb

HTSstation modules.

<p>Different workflows are obtained as combinations of the two pre-processing modules (dealing with raw sequences) and the four application-specific modules (manipulating sequence alignments and genomic profiles). Results can be visualized using our genomic browser GDV or sent to external post-processing tools for downstream analysis.</p

HTSstation: A Web Application and Open-Access Libraries for High-Throughput Sequencing Data Analysis

<div><p>The HTSstation analysis portal is a suite of simple web forms coupled to modular analysis pipelines for various applications of High-Throughput Sequencing including ChIP-seq, RNA-seq, 4C-seq and re-sequencing. HTSstation offers biologists the possibility to rapidly investigate their HTS data using an intuitive web application with heuristically pre-defined parameters. A number of open-source software components have been implemented and can be used to build, configure and run HTS analysis pipelines reactively. Besides, our programming framework empowers developers with the possibility to design their own workflows and integrate additional third-party software. The HTSstation web application is accessible at <a href="http://htsstation.epfl.ch" target="_blank">http://htsstation.epfl.ch</a>.</p></div

Summary of the application-specific analyses.

<p>Each module is dedicated to the analysis of a particular type of HTS experiment. The ChIP-seq module seeks significant interactions of protein with DNA, the RNA-seq module quantifies the expression levels of transcripts and compares them between conditions, the 4C-seq module identifies physical interactions along the DNA sequence and the re-sequencing module discovers polymorphisms.</p

Architecture of HTSstation.

<p>This platform offers different access modalities. The web interface targets biologists and occasional users. Command-line execution of underlying executables (bbcfutils) will fit the needs of more advanced users. Users with programming skills can import the libraries (bbcflib) and implement new workflows. GenRep (genomic repository) provides consistency and versioning of reference genome data and Bein (workflow manager) handles dispatching, tracking and documenting programs executions and outputs in a computing environment-independent manner: analyses can be launched locally, or dispatched to a cloud or a cluster.</p

Design of the web interface.

<p>Input form of the mapping module: results from a previous job are imported using its unique key (1), e.g. from a demultiplexing task. Users provide inputs as URLs and organize them hierarchically in groups and runs (2) where groups represent distinct experimental conditions (3) and runs are distinct replicates (4). When relevant, some groups can be selected as representing the control condition. Selected runs can be moved between groups (5). All modules have a section with general parameters (e.g. email and analysis name) (6) and module-specific options (7).</p

Genomic and differential analysis views of RNA-seq data.

<p><b>A</b>) Differential expression analysis of coding genes (MA-plot) shows a few significantly differential genes which is consistent with the signal displayed along the genome: <b>1</b>) <i>Alox15</i>, on chromosome 11 is strongly over-expressed in the KO condition, unlike the next gene downstream (<i>Pelp1</i>) <b>2</b>) <i>Col4a1</i> and <i>Col4a2</i> (located next to each-other on chromosome 8) are both over-expressed in the KO condition, <b>3</b>) <i>Trim28</i>/<i>Kap1</i> is the knocked-out gene and is consistently strongly suppressed. Remark that the exon structure of the genes appears clearly in the genomic view. <b>B</b>) Reads were also mapped and quantified on the Repbase collection of repetitive elements. Plots of read coverage along the sequence of three representative examples show that <i>RLT1IAP</i> is highly over-expressed in KO mice, <i>MERVL</i> is slightly over-expressed and <i>SINEB1</i> is under-expressed.</p