MOTIVATION: Long-read sequencing technologies can be employed to detect and map DNA modifications at the nucleotide resolution on a genome-wide scale. However, published software packages neglect the integration of genomic annotation and comprehensive filtering when analyzing patterns of modified bases detected using Pacific Biosciences (PacBio) or Oxford Nanopore Technologies (ONT) data. Here, we present DNA Modification Annotation (DNAModAnnot), a R package designed for the global analysis of DNA modification patterns using adapted filtering and visualization tools. RESULTS: We tested our package using PacBio sequencing data to analyze patterns of the 6-methyladenine (6mA) in the ciliate Paramecium tetraurelia, in which high 6mA amounts were previously reported. We found P. tetraurelia 6mA genome-wide distribution to be similar to other ciliates. We also performed 5-methylcytosine (5mC) analysis in human lymphoblastoid cells using ONT data and confirmed previously known patterns of 5mC. DNAModAnnot provides a toolbox for the genome-wide analysis of different DNA modifications using PacBio and ONT long-read sequencing data. AVAILABILITY AND IMPLEMENTATION: DNAModAnnot is distributed as a R package available via GitHub (https://github.com/AlexisHardy/DNAModAnnot). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SUMMARY: Biologists produce large data sets and are in demand of rich and simple web portals in which they can upload and analyze their files. Providing such tools requires to mask the complexity induced by the needed High Performance Computing (HPC) environment. The connection between interface and computing infrastructure is usually specific to each portal. With Jflow, we introduce a Workflow Management System (WMS), composed of jQuery plug-ins which can easily be embedded in any web application and a Python library providing all requested features to setup, run and monitor workflows. AVAILABILITY AND IMPLEMENTATION: Jflow is available under the GNU General Public License (GPL) at http://bioinfo.genotoul.fr/jflow. The package is coming with full documentation, quick start and a running test portal. CONTACT: [email protected].

BACKGROUND: The Pacific oyster, Crassostrea gigas, is one of the most important aquaculture shellfish resources worldwide. Important efforts have been undertaken towards a better knowledge of its genome and transcriptome, which makes now C. gigas becoming a model organism among lophotrochozoans, the under-described sister clade of ecdysozoans within protostomes. These massive sequencing efforts offer the opportunity to assemble gene expression data and make such resource accessible and exploitable for the scientific community. Therefore, we undertook this assembly into an up-to-date publicly available transcriptome database: the GigaTON (Gigas TranscriptOme pipeliNe) database. DESCRIPTION: We assembled 2204 million sequences obtained from 114 publicly available RNA-seq libraries that were realized using all embryo-larval development stages, adult organs, different environmental stressors including heavy metals, temperature, salinity and exposure to air, which were mostly performed as part of the Crassostrea gigas genome project. This data was analyzed in silico and resulted into 56621 newly assembled contigs that were deposited into a publicly available database, the GigaTON database. This database also provides powerful and user-friendly request tools to browse and retrieve information about annotation, expression level, UTRs, splice and polymorphism, and gene ontology associated to all the contigs into each, and between all libraries. CONCLUSIONS: The GigaTON database provides a convenient, potent and versatile interface to browse, retrieve, confront and compare massive transcriptomic information in an extensive range of conditions, tissues and developmental stages in Crassostrea gigas. To our knowledge, the GigaTON database constitutes the most extensive transcriptomic database to date in marine invertebrates, thereby a new reference transcriptome in the oyster, a highly valuable resource to physiologists and evolutionary biologists.

BACKGROUND: Venn diagrams are commonly used to display list comparison. In biology, they are widely used to show the differences between gene lists originating from different differential analyses, for instance. They thus allow the comparison between different experimental conditions or between different methods. However, when the number of input lists exceeds four, the diagram becomes difficult to read. Alternative layouts and dynamic display features can improve its use and its readability. RESULTS: jvenn is a new JavaScript library. It processes lists and produces Venn diagrams. It handles up to six input lists and presents results using classical or Edwards-Venn layouts. User interactions can be controlled and customized. Finally, jvenn can easily be embeded in a web page, allowing to have dynamic Venn diagrams. CONCLUSIONS: jvenn is an open source component for web environments helping scientists to analyze their data. The library package, which comes with full documentation and an example, is freely available at http://bioinfo.genotoul.fr/jvenn.

BACKGROUND: Transcriptome sequencing is a powerful tool for measuring gene expression, but as well as some other technologies, various artifacts and biases affect the quantification. In order to correct some of them, several normalization approaches have emerged, differing both in the statistical strategy employed and in the type of corrected biases. However, there is no clear standard normalization method. RESULTS: We present a novel methodology to normalize RNA-Seq data, taking into account transcript size, GC content, and sequencing depth, which are the major quantification-related biases. In this study, we found that transcripts shorter than 600 bp have an underestimated expression level, while longer transcripts are even more overestimated that they are long. Second, it was well known that the higher the GC content (>50%), the more the transcripts are underestimated. Third, we demonstrated that the sequencing depth impacts the size bias and proposed a correction allowing the comparison of expression levels among many samples. The efficiency of our approach was then tested by comparing the correlation between normalized RNA-Seq data and qRT-PCR expression measurements. All the steps are automated in a program written in Perl and available on request. CONCLUSIONS: The methodology presented in this article identifies and corrects different biases that influence RNA-Seq quantification, and provides more accurate estimations of gene expression levels. This method can be applied to compare expression quantifications from many samples, but preferentially from the same tissue. In order to compare samples from different tissue, a calibration using several reference genes will be required.

UNLABELLED: Many different programs are available to analyze microarray images. Most programs are commercial packages, some are free. In the latter group only few propose automatic grid alignment and batch mode. More often than not a program implements only one quantification algorithm. AGScan is an open source program that works on all major platforms. It is based on the ImageJ library [Rasband (1997-2006)] and offers a plug-in extension system to add new functions to manipulate images, align grid and quantify spots. It is appropriate for daily laboratory use and also as a framework for new algorithms. AVAILABILITY: The program is freely distributed under X11 Licence. The install instructions can be found in the user manual. The software can be downloaded from http://mulcyber.toulouse.inra.fr/projects/agscan/. The questions and plug-ins can be sent to the contact listed below.