beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types.

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set

Desktop cutting of paper using a single emitter laser diode and inkjet printing

International audienceLaser cutting of paper is widely used in the paper conversion industry. CO 2 lasers are well suited for this type of applications. Desktop printing is a large market both for digital photography, document management and graphics applications, but it still lacks advanced cutting and scoring ability, and CO 2 lasers seem costly to be integrated in mass-market printers. For that reason, mass-scalable and low-cost semiconductor laser diodes would be very advantageous to add paper cutting and scoring features in desktop printers. However, common paper can not be cut properly using visible or Near Infrared (NIR) laser diode since it has a very poor absorption at these wavelengths. We report here an innovative solution to achieve paper cutting or scoring using a 1 W single emitter NIR laser diode, within an inkjet printer. A special ink that absorbs the NIR light, and that penetrates all through the paper, is first disposed on the lines to be cut. Then, the laser diode goes along the lines to be cut. We show that a cutting speed of 2m/min can be achieved on 80g/m 2 conventional paper. The influence of the optical properties of the ink on the cutting speed are discussed, as well as focussing issues. In particular, we show that invisible inks are suitable, and very clear-cut edges can be obtained. The perspective of this technique are discussed

Software for Computing and Annotating Genomic Ranges

We describe Bioconductor infrastructure for representing and computing on annotated genomic ranges and integrating genomic data with the statistical computing features of R and its extensions. At the core of the infrastructure are three packages: IRanges, GenomicRanges, and GenomicFeatures. These packages provide scalable data structures for representing annotated ranges on the genome, with special support for transcript structures, read alignments and coverage vectors. Computational facilities include efficient algorithms for overlap and nearest neighbor detection, coverage calculation and other range operations. This infrastructure directly supports more than 80 other Bioconductor packages, including those for sequence analysis, differential expression analysis and visualization

Two Contrasting Classes of Nucleolus-Associated Domains in Mouse Fibroblast Heterochromatin [preprint]

In interphase eukaryotic cells, almost all heterochromatin is located adjacent to the nucleolus or to the nuclear lamina, thus defining Nucleolus Associated Domains (NADs) and Lamina Associated Domains (LADs), respectively. Here, we determined the first genome-scale map of murine NADs in mouse embryonic fibroblasts (MEFs) via deep sequencing of chromatin associated with purified nucleoli. We developed a Bioconductor package called NADfinder and demonstrated that it identifies NADs more accurately than other peak-calling tools, due to its critical feature of chromosome-level local baseline correction. We detected two distinct classes of NADs. Type I NADs associate frequently with both the nucleolar periphery and with the nuclear lamina, and generally display characteristics of constitutive heterochromatin, including late DNA replication, enrichment of H3K9me3 and little gene expression. In contrast, Type II NADs associate with nucleoli but do not overlap with LADs. Type II NADs tend to replicate earlier, display greater gene expression, and are more often enriched in H3K27me3 than Type I NADs. The nucleolar associations of both classes of NADs were confirmed via DNA-FISH, which also detected Type I but not Type II probes enriched at the nuclear lamina. Interestingly, Type II NADs are enriched in distinct gene classes, notably factors important for differentiation and development. In keeping with this, we observed that a Type II NAD is developmentally regulated, present in MEFs but not in undifferentiated embryonic stem (ES) cells

ChIPpeakAnno: a Bioconductor package to annotate ChIP-seq and ChIP-chip data

<p>Abstract</p> <p>Background</p> <p>Chromatin immunoprecipitation (ChIP) followed by high-throughput sequencing (ChIP-seq) or ChIP followed by genome tiling array analysis (ChIP-chip) have become standard technologies for genome-wide identification of DNA-binding protein target sites. A number of algorithms have been developed in parallel that allow identification of binding sites from ChIP-seq or ChIP-chip datasets and subsequent visualization in the University of California Santa Cruz (UCSC) Genome Browser as custom annotation tracks. However, summarizing these tracks can be a daunting task, particularly if there are a large number of binding sites or the binding sites are distributed widely across the genome.</p> <p>Results</p> <p>We have developed <it>ChIPpeakAnno </it>as a Bioconductor package within the statistical programming environment R to facilitate batch annotation of enriched peaks identified from ChIP-seq, ChIP-chip, cap analysis of gene expression (CAGE) or any experiments resulting in a large number of enriched genomic regions. The binding sites annotated with <it>ChIPpeakAnno </it>can be viewed easily as a table, a pie chart or plotted in histogram form, i.e., the distribution of distances to the nearest genes for each set of peaks. In addition, we have implemented functionalities for determining the significance of overlap between replicates or binding sites among transcription factors within a complex, and for drawing Venn diagrams to visualize the extent of the overlap between replicates. Furthermore, the package includes functionalities to retrieve sequences flanking putative binding sites for PCR amplification, cloning, or motif discovery, and to identify Gene Ontology (GO) terms associated with adjacent genes.</p> <p>Conclusions</p> <p><it>ChIPpeakAnno </it>enables batch annotation of the binding sites identified from ChIP-seq, ChIP-chip, CAGE or any technology that results in a large number of enriched genomic regions within the statistical programming environment R. Allowing users to pass their own annotation data such as a different Chromatin immunoprecipitation (ChIP) preparation and a dataset from literature, or existing annotation packages, such as <it>GenomicFeatures </it>and <it>BSgenom</it>e, provides flexibility. Tight integration to the <it>biomaRt </it>package enables up-to-date annotation retrieval from the BioMart database.</p

A Defined Terminal Region of the E. coli Chromosome Shows Late Segregation and High FtsK Activity

Background: The FtsK DNA-translocase controls the last steps of chromosome segregation in E. coli. It translocates sister chromosomes using the KOPS DNA motifs to orient its activity, and controls the resolution of dimeric forms of sister chromosomes by XerCD-mediated recombination at the dif site and their decatenation by TopoIV. Methodology: We have used XerCD/dif recombination as a genetic trap to probe the interaction of FtsK with loci located in different regions of the chromosome. This assay revealed that the activity of FtsK is restricted to a,400 kb terminal region of the chromosome around the natural position of the dif site. Preferential interaction with this region required the tethering of FtsK to the division septum via its N-terminal domain as well as its translocation activity. However, the KOPSrecognition activity of FtsK was not required. Displacement of replication termination outside the FtsK high activity region had no effect on FtsK activity and deletion of a part of this region was not compensated by its extension to neighbouring regions. By observing the fate of fluorescent-tagged loci of the ter region, we found that segregation of the FtsK high activity region is delayed compared to that of its adjacent regions. Significance: Our results show that a restricted terminal region of the chromosome is specifically dedicated to the last step

beachmat: A Bioconductor C++ API for accessing high-throughput biological data from a variety of R matrix types.

Biological experiments involving genomics or other high-throughput assays typically yield a data matrix that can be explored and analyzed using the R programming language with packages from the Bioconductor project. Improvements in the throughput of these assays have resulted in an explosion of data even from routine experiments, which poses a challenge to the existing computational infrastructure for statistical data analysis. For example, single-cell RNA sequencing (scRNA-seq) experiments frequently generate large matrices containing expression values for each gene in each cell, requiring sparse or file-backed representations for memory-efficient manipulation in R. These alternative representations are not easily compatible with high-performance C++ code used for computationally intensive tasks in existing R/Bioconductor packages. Here, we describe a C++ interface named beachmat, which enables agnostic data access from various matrix representations. This allows package developers to write efficient C++ code that is interoperable with dense, sparse and file-backed matrices, amongst others. We evaluated the performance of beachmat for accessing data from each matrix representation using both simulated and real scRNA-seq data, and defined a clear memory/speed trade-off to motivate the choice of an appropriate representation. We also demonstrate how beachmat can be incorporated into the code of other packages to drive analyses of a very large scRNA-seq data set

Demonstration of paper cutting using single emitter laser diode and infrared-absorbing ink

International audienceWe show that conventional paper can be cut using a 1W laser diode, provided the cutting lines are first traced using a proper ink. The ink should absorb the laser light, and penetrate deep into the paper. An " invisible ink " that is transparent in the visible range and absorbing in the infrared has been successfully tested. The paper is tidily cut. The laser power required to cut the paper is proportional to the displacement speed of the paper. Cutting speeds exceeding 3 cm.s-1 .W-1 have been demonstrated. At higher speeds, the paper is not cut through, but easy-tearable lines and easy-folding lines are obtained. The whole inking and laser cutting process may be integrated into next generations of personal inkjet printers and expand document creation abilities

A portable and unilateral nmr device to measure tree water and locate conductive tissues

International audienc