BACKGROUND: Normalization is essential to ensure accurate analysis and proper interpretation of sequencing data, and chromosome conformation capture data such as Hi-C have particular challenges. Although several methods have been proposed, the most widely used type of normalization of Hi-C data usually casts estimation of unwanted effects as a matrix balancing problem, relying on the assumption that all genomic regions interact equally with each other. RESULTS: In order to explore the effect of copy-number variations on Hi-C data normalization, we first propose a simulation model that predict the effects of large copy-number changes on a diploid Hi-C contact map. We then show that the standard approaches relying on equal visibility fail to correct for unwanted effects in the presence of copy-number variations. We thus propose a simple extension to matrix balancing methods that model these effects. Our approach can either retain the copy-number variation effects (LOIC) or remove them (CAIC). We show that this leads to better downstream analysis of the three-dimensional organization of rearranged genomes. CONCLUSIONS: Taken together, our results highlight the importance of using dedicated methods for the analysis of Hi-C cancer data. Both CAIC and LOIC methods perform well on simulated and real Hi-C data sets, each fulfilling different needs.

SUMMARY: Non-coding RNA (ncRNA) PROfiling in small RNA (sRNA)-seq (ncPRO-seq) is a stand-alone, comprehensive and flexible ncRNA analysis pipeline. It can interrogate and perform detailed profiling analysis on sRNAs derived from annotated non-coding regions in miRBase, Rfam and RepeatMasker, as well as specific regions defined by users. The ncPRO-seq pipeline performs both gene-based and family-based analyses of sRNAs. It also has a module to identify regions significantly enriched with short reads, which cannot be classified under known ncRNA families, thus enabling the discovery of previously unknown ncRNA- or small interfering RNA (siRNA)-producing regions. The ncPRO-seq pipeline supports input read sequences in fastq, fasta and color space format, as well as alignment results in BAM format, meaning that sRNA raw data from the three current major platforms (Roche-454, Illumina-Solexa and Life technologies-SOLiD) can be analyzed with this pipeline. The ncPRO-seq pipeline can be used to analyze read and alignment data, based on any sequenced genome, including mammals and plants. AVAILABILITY: Source code, annotation files, manual and online version are available at http://ncpro.curie.fr/. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract Summary: The R/Bioconductor package HiTC facilitates the exploration of high-throughput 3C-based data. It allows users to import and export ‘C’ data, to transform, normalize, annotate and visualize interaction maps. The package operates within the Bioconductor framework and thus offers new opportunities for future development in this field. Availability and implementation: The R package HiTC is available from the Bioconductor website. A detailed vignette provides additional documentation and help for using the package. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

UNLABELLED: The R/Bioconductor package girafe facilitates the functional exploration of alignments of sequence reads from next-generation sequencing data to a genome. It allows users to investigate the genomic intervals together with the aligned reads and to work with, visualise and export these intervals. Moreover, the package operates within and extends the ever-growing Bioconductor framework and thus enables users to leverage a multitude of methods for their data in order to answer specific research questions. AVAILABILITY AND IMPLEMENTATION: The R package girafe is available from the Bioconductor web site: http://www.bioconductor.org/packages/release/bioc/html/girafe.html. An extensive vignette and the Bioconductor mailing lists provide additional documentation and help for using the package.

We present an intensity-based nonrigid registration approach for the normalization of 3-D multichannel microscopy images of cell nuclei. A main problem with cell nuclei images is that the intensity structure of different nuclei differs very much; thus, an intensity-based registration scheme cannot be used directly. Instead, we first perform a segmentation of the images from the cell nucleus channel, smooth the resulting images by a Gaussian filter, and then apply an intensity-based registration algorithm. The obtained transformation is applied to the images from the nucleus channel as well as to the images from the other channels. To improve the convergence rate of the algorithm, we propose an adaptive step length optimization scheme and also employ a multiresolution scheme. Our approach has been successfully applied using 2-D cell-like synthetic images, 3-D phantom images as well as 3-D multichannel microscopy images representing different chromosome territories and gene regions. We also describe an extension of our approach, which is applied for the registration of 3D + t (4-D) image series of moving cell nuclei.