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Background Recent advancements in in situ gene expression technologies constitute a new and rapidly evolving field of transcriptomics. With the recent launch of the 10x Genomics Visium platform, such methods have started to become widely adopted. The experimental protocol is conducted on individual tissue sections collected from a larger tissue sample. The two-dimensional nature of this data requires multiple consecutive sections to be collected from the sample in order to construct a comprehensive three-dimensional map of the tissue. However, there is currently no software available that lets the user process the images, align stacked experiments, and finally visualize them together in 3D to create a holistic view of the tissue. Results We have developed an R package named STUtility that takes 10x Genomics Visium data as input and provides features to perform standardized data transformations, alignment of multiple tissue sections, regional annotation, and visualizations of the combined data in a 3D model framework. Conclusions STUtility lets the user process, analyze and visualize multiple samples of spatially resolved RNA sequencing and image data from the 10x Genomics Visium platform. The package builds on the Seurat framework and uses familiar APIs and well-proven analysis methods. An introduction to the software package is available at https://ludvigla.github.io/STUtility_web_site/ .

Background Single-cell transcriptomics allows researchers to investigate complex communities of heterogeneous cells. It can be applied to stem cells and their descendants in order to chart the progression from multipotent progenitors to fully differentiated cells. While a variety of statistical and computational methods have been proposed for inferring cell lineages, the problem of accurately characterizing multiple branching lineages remains difficult to solve. Results We introduce Slingshot, a novel method for inferring cell lineages and pseudotimes from single-cell gene expression data. In previously published datasets, Slingshot correctly identifies the biological signal for one to three branching trajectories. Additionally, our simulation study shows that Slingshot infers more accurate pseudotimes than other leading methods. Conclusions Slingshot is a uniquely robust and flexible tool which combines the highly stable techniques necessary for noisy single-cell data with the ability to identify multiple trajectories. Accurate lineage inference is a critical step in the identification of dynamic temporal gene expression.

Background Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results We describe spatialLIBD , an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user’s computer or through a web application. Conclusions spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD . It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform.

Background Understanding the relationship between the millions of functional DNA elements and their protein regulators, and how they work in conjunction to manifest diverse phenotypes, is key to advancing our understanding of the mammalian genome. Next-generation sequencing technology is now used widely to probe these protein-DNA interactions and to profile gene expression at a genome-wide scale. As the cost of DNA sequencing continues to fall, the interpretation of the ever increasing amount of data generated represents a considerable challenge. Results We have developed ngs.plot – a standalone program to visualize enrichment patterns of DNA-interacting proteins at functionally important regions based on next-generation sequencing data. We demonstrate that ngs.plot is not only efficient but also scalable. We use a few examples to demonstrate that ngs.plot is easy to use and yet very powerful to generate figures that are publication ready. Conclusions We conclude that ngs.plot is a useful tool to help fill the gap between massive datasets and genomic information in this era of big sequencing data.

Background Sequencing data has become a standard measure of diverse cellular activities. For example, gene expression is accurately measured by RNA sequencing (RNA-Seq) libraries, protein-DNA interactions are captured by chromatin immunoprecipitation sequencing (ChIP-Seq), protein-RNA interactions by crosslinking immunoprecipitation sequencing (CLIP-Seq) or RNA immunoprecipitation (RIP-Seq) sequencing, DNA accessibility by assay for transposase-accessible chromatin (ATAC-Seq), DNase or MNase sequencing libraries. The processing of these sequencing techniques involves library-specific approaches. However, in all cases, once the sequencing libraries are processed, the result is a count table specifying the estimated number of reads originating from each genomic locus. Differential analysis to determine which loci have different cellular activity under different conditions starts with the count table and iterates through a cycle of data assessment, preparation and analysis. Such complex analysis often relies on multiple programs and is therefore a challenge for those without programming skills. Results We developed DEBrowser as an R bioconductor project to interactively visualize every step of the differential analysis, without programming. The application provides a rich and interactive web based graphical user interface built on R’s shiny infrastructure. DEBrowser allows users to visualize data with various types of graphs that can be explored further by selecting and re-plotting any desired subset of data. Using the visualization approaches provided, users can determine and correct technical variations such as batch effects and sequencing depth that affect differential analysis. We show DEBrowser’s ease of use by reproducing the analysis of two previously published data sets. Conclusions DEBrowser is a flexible, intuitive, web-based analysis platform that enables an iterative and interactive analysis of count data without any requirement of programming knowledge.

Background High-throughput transcriptomic datasets are often examined to discover new actors and regulators of a biological response. To this end, graphical interfaces have been developed and allow a broad range of users to conduct standard analyses from RNA-seq data, even with little programming experience. Although existing solutions usually provide adequate procedures for normalization, exploration or differential expression, more advanced features, such as gene clustering or regulatory network inference, often miss or do not reflect current state of the art methodologies. Results We developed here a user interface called DIANE (Dashboard for the Inference and Analysis of Networks from Expression data) designed to harness the potential of multi-factorial expression datasets from any organisms through a precise set of methods. DIANE interactive workflow provides normalization, dimensionality reduction, differential expression and ontology enrichment. Gene clustering can be performed and explored via configurable Mixture Models, and Random Forests are used to infer gene regulatory networks. DIANE also includes a novel procedure to assess the statistical significance of regulator-target influence measures based on permutations for Random Forest importance metrics. All along the pipeline, session reports and results can be downloaded to ensure clear and reproducible analyses. Conclusions We demonstrate the value and the benefits of DIANE using a recently published data set describing the transcriptional response of Arabidopsis thaliana under the combination of temperature, drought and salinity perturbations. We show that DIANE can intuitively carry out informative exploration and statistical procedures with RNA-Seq data, perform model based gene expression profiles clustering and go further into gene network reconstruction, providing relevant candidate genes or signalling pathways to explore. DIANE is available as a web service ( https://diane.bpmp.inrae.fr ), or can be installed and locally launched as a complete R package.

Background The human transcriptome annotation is regarded as one of the most complete of any eukaryotic species. However, limitations in sequencing technologies have biased the annotation toward multi-exonic protein coding genes. Accurate high-throughput long read transcript sequencing can now provide additional evidence for rare transcripts and genes such as mono-exonic and non-coding genes that were previously either undetectable or impossible to differentiate from sequencing noise. Results We developed the Transcriptome Annotation by Modular Algorithms (TAMA) software to leverage the power of long read transcript sequencing and address the issues with current data processing pipelines. TAMA achieved high sensitivity and precision for gene and transcript model predictions in both reference guided and unguided approaches in our benchmark tests using simulated Pacific Biosciences (PacBio) and Nanopore sequencing data and real PacBio datasets. By analyzing PacBio Sequel II Iso-Seq sequencing data of the Universal Human Reference RNA (UHRR) using TAMA and other commonly used tools, we found that the convention of using alignment identity to measure error correction performance does not reflect actual gain in accuracy of predicted transcript models. In addition, inter-read error correction can cause major changes to read mapping, resulting in potentially over 6 K erroneous gene model predictions in the Iso-Seq based human genome annotation. Using TAMA’s genome assembly based error correction and gene feature evidence, we predicted 2566 putative novel non-coding genes and 1557 putative novel protein coding gene models. Conclusions Long read transcript sequencing data has the power to identify novel genes within the highly annotated human genome. The use of parameter tuning and extensive output information of the TAMA software package allows for in depth exploration of eukaryotic transcriptomes. We have found long read data based evidence for thousands of unannotated genes within the human genome. More development in sequencing library preparation and data processing are required for differentiating sequencing noise from real genes in long read RNA sequencing data.

Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

Background Next-generation sequencing (NGS) informs many biological questions with unprecedented depth and nucleotide resolution. These assays have created a need for analytical tools that enable users to manipulate data nucleotide-by-nucleotide robustly and easily. Furthermore, because many NGS assays encode information jointly within multiple properties of read alignments ― for example, in ribosome profiling, the locations of ribosomes are jointly encoded in alignment coordinates and length ― analytical tools are often required to extract the biological meaning from the alignments before analysis. Many assay-specific pipelines exist for this purpose, but there remains a need for user-friendly, generalized, nucleotide-resolution tools that are not limited to specific experimental regimes or analytical workflows. Results Plastid is a Python library designed specifically for nucleotide-resolution analysis of genomics and NGS data. As such, Plastid is designed to extract assay-specific information from read alignments while retaining generality and extensibility to novel NGS assays. Plastid represents NGS and other biological data as arrays of values associated with genomic or transcriptomic positions, and contains configurable tools to convert data from a variety of sources to such arrays. Plastid also includes numerous tools to manipulate even discontinuous genomic features, such as spliced transcripts, with nucleotide precision. Plastid automatically handles conversion between genomic and feature-centric coordinates, accounting for splicing and strand, freeing users of burdensome accounting. Finally, Plastid’s data models use consistent and familiar biological idioms, enabling even beginners to develop sophisticated analytical workflows with minimal effort. Conclusions Plastid is a versatile toolkit that has been used to analyze data from multiple NGS assays, including RNA-seq, ribosome profiling, and DMS-seq. It forms the genomic engine of our ORF annotation tool, ORF-RATER, and is readily adapted to novel NGS assays. Examples, tutorials, and extensive documentation can be found at https://plastid.readthedocs.io .

Background Next generation sequencing (NGS) technology has revolutionized genomic and genetic research. The pace of change in this area is rapid with three major new sequencing platforms having been released in 2011: Ion Torrent’s PGM, Pacific Biosciences’ RS and the Illumina MiSeq. Here we compare the results obtained with those platforms to the performance of the Illumina HiSeq, the current market leader. In order to compare these platforms, and get sufficient coverage depth to allow meaningful analysis, we have sequenced a set of 4 microbial genomes with mean GC content ranging from 19.3 to 67.7%. Together, these represent a comprehensive range of genome content. Here we report our analysis of that sequence data in terms of coverage distribution, bias, GC distribution, variant detection and accuracy. Results Sequence generated by Ion Torrent, MiSeq and Pacific Biosciences technologies displays near perfect coverage behaviour on GC-rich, neutral and moderately AT-rich genomes, but a profound bias was observed upon sequencing the extremely AT-rich genome of Plasmodium falciparum on the PGM, resulting in no coverage for approximately 30% of the genome. We analysed the ability to call variants from each platform and found that we could call slightly more variants from Ion Torrent data compared to MiSeq data, but at the expense of a higher false positive rate. Variant calling from Pacific Biosciences data was possible but higher coverage depth was required. Context specific errors were observed in both PGM and MiSeq data, but not in that from the Pacific Biosciences platform. Conclusions All three fast turnaround sequencers evaluated here were able to generate usable sequence. However there are key differences between the quality of that data and the applications it will support.