Abstract Due to their intrinsic heterogeneity and plasticity, pediatric brain tumors present highly complex clinical challenges. To provide insight into the molecular scene of these tumors, we generated spatial transcriptomics data from 19 distinct patients, 7 of which suffered a relapse of the disease. In this cohort, spanning 59 tissue sections across 8 diagnoses and 4 tumor grades, we recovered diagnosis-specific gene expression patterns that could be linked to processes characteristic of developmental stages. We also identified between 2 to 4 spatial archetypal niches per section, which could then be related to 11 main biological themes, and to distinct celltype-like transcriptomic signatures. For example, we noted strong spatial correlations between developmental archetypes and oligodendrocyte lineage signal in pilocytic astrocytoma. Lastly, we utilised spatially inferred copy-number variants for the profiling of relapse-associated tentative tumor clones. These clones were then related via spatial geographical analysis to regions with strong blood vessel signatures. Overall, these results provide vital details into the progression and maintenance of pediatric brain tumors, offering novel edges to be exploited in personalized medicine.

SUMMARY: Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation. AVAILABILITY AND IMPLEMENTATION: The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.

MOTIVATION: Collection of spatial signals in large numbers has become a routine task in multiple omics-fields, but parsing of these rich datasets still pose certain challenges. In whole or near-full transcriptome spatial techniques, spurious expression profiles are intermixed with those exhibiting an organized structure. To distinguish profiles with spatial patterns from the background noise, a metric that enables quantification of spatial structure is desirable. Current methods designed for similar purposes tend to be built around a framework of statistical hypothesis testing, hence we were compelled to explore a fundamentally different strategy. RESULTS: We propose an unexplored approach to analyze spatial transcriptomics data, simulating diffusion of individual transcripts to extract genes with spatial patterns. The method performed as expected when presented with synthetic data. When applied to real data, it identified genes with distinct spatial profiles, involved in key biological processes or characteristic for certain cell types. Compared to existing methods, ours seemed to be less informed by the genes' expression levels and showed better time performance when run with multiple cores. AVAILABILITYAND IMPLEMENTATION: Open-source Python package with a command line interface (CLI), freely available at https://github.com/almaan/sepal under an MIT licence. A mirror of the GitHub repository can be found at Zenodo, doi: 10.5281/zenodo.4573237. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Technological developments in the emerging field of spatial transcriptomics have opened up an unexplored landscape where transcript information is put in a spatial context. Clustering commonly constitutes a central component in analyzing this type of data. However, deciding on the number of clusters to use and interpreting their relationships can be difficult. RESULTS: We introduce SpatialCPie, an R package designed to facilitate cluster evaluation for spatial transcriptomics data. SpatialCPie clusters the data at multiple resolutions. The results are visualized with pie charts that indicate the similarity between spatial regions and clusters and a cluster graph that shows the relationships between clusters at different resolutions. We demonstrate SpatialCPie on several publicly available datasets. CONCLUSIONS: SpatialCPie provides intuitive visualizations of cluster relationships when dealing with Spatial Transcriptomics data.

MOTIVATION: Spatial Transcriptomics (ST) is a technique that combines high-resolution imaging with spatially resolved transcriptome-wide sequencing. This novel type of data opens up many possibilities for analysis and visualization, most of which are either not available with standard tools or too complex for normal users. RESULTS: Here, we present a tool, ST Viewer, which allows real-time interaction, analysis and visualization of Spatial Transcriptomics datasets through a seamless and smooth user interface. AVAILABILITY AND IMPLEMENTATION: The ST Viewer is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_viewer. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Motiviation: Spatial Transcriptomics (ST) is a method which combines high resolution tissue imaging with high troughput transcriptome sequencing data. This data must be aligned with the images for correct visualization, a process that involves several manual steps. Results: Here we present ST Spot Detector, a web tool that automates and facilitates this alignment through a user friendly interface. Contact: [email protected]. Supplementary information: Supplementary data are available at Bioinformatics online.

MOTIVATION: In recent years we have witnessed an increase in novel RNA-seq based techniques for transcriptomics analysis. Spatial transcriptomics is a novel RNA-seq based technique that allows spatial mapping of transcripts in tissue sections. The spatial resolution adds an extra level of complexity, which requires the development of new tools and algorithms for efficient and accurate data processing. RESULTS: Here we present a pipeline to automatically and efficiently process RNA-seq data obtained from spatial transcriptomics experiments to generate datasets for downstream analysis. AVAILABILITY AND IMPLEMENTATION: The ST Pipeline is open source under a MIT license and it is available at https://github.com/SpatialTranscriptomicsResearch/st_pipeline. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: The use of short reads from High Throughput Sequencing (HTS) techniques is now commonplace in de novo assembly. Yet, obtaining contiguous assemblies from short reads is challenging, thus making scaffolding an important step in the assembly pipeline. Different algorithms have been proposed but many of them use the number of read pairs supporting a linking of two contigs as an indicator of reliability. This reasoning is intuitive, but fails to account for variation in link count due to contig features.We have also noted that published scaffolders are only evaluated on small datasets using output from only one assembler. Two issues arise from this. Firstly, some of the available tools are not well suited for complex genomes. Secondly, these evaluations provide little support for inferring a software's general performance. RESULTS: We propose a new algorithm, implemented in a tool called BESST, which can scaffold genomes of all sizes and complexities and was used to scaffold the genome of P. abies (20 Gbp). We performed a comprehensive comparison of BESST against the most popular stand-alone scaffolders on a large variety of datasets. Our results confirm that some of the popular scaffolders are not practical to run on complex datasets. Furthermore, no single stand-alone scaffolder outperforms the others on all datasets. However, BESST fares favorably to the other tested scaffolders on GAGE datasets and, moreover, outperforms the other methods when library insert size distribution is wide. CONCLUSION: We conclude from our results that information sources other than the quantity of links, as is commonly used, can provide useful information about genome structure when scaffolding.

MOTIVATION: One of the important steps of genome assembly is scaffolding, in which contigs are linked using information from read-pairs. Scaffolding provides estimates about the order, relative orientation and distance between contigs. We have found that contig distance estimates are generally strongly biased and based on false assumptions. Since erroneous distance estimates can mislead in subsequent analysis, it is important to provide unbiased estimation of contig distance. RESULTS: In this article, we show that state-of-the-art programs for scaffolding are using an incorrect model of gap size estimation. We discuss why current maximum likelihood estimators are biased and describe what different cases of bias we are facing. Furthermore, we provide a model for the distribution of reads that span a gap and derive the maximum likelihood equation for the gap length. We motivate why this estimate is sound and show empirically that it outperforms gap estimators in popular scaffolding programs. Our results have consequences both for scaffolding software, structural variation detection and for library insert-size estimation as is commonly performed by read aligners. AVAILABILITY: A reference implementation is provided at https://github.com/SciLifeLab/gapest. SUPPLEMENTARY INFORMATION: Supplementary data are availible at Bioinformatics online.

MOTIVATION: New generation sequencing technologies producing increasingly complex datasets demand new efficient and specialized sequence analysis algorithms. Often, it is only the 'novel' sequences in a complex dataset that are of interest and the superfluous sequences need to be removed. RESULTS: A novel algorithm, fast and accurate classification of sequences (FACSs), is introduced that can accurately and rapidly classify sequences as belonging or not belonging to a reference sequence. FACS was first optimized and validated using a synthetic metagenome dataset. An experimental metagenome dataset was then used to show that FACS achieves comparable accuracy as BLAT and SSAHA2 but is at least 21 times faster in classifying sequences. AVAILABILITY: Source code for FACS, Bloom filters and MetaSim dataset used is available at http://facs.biotech.kth.se. The Bloom::Faster 1.6 Perl module can be downloaded from CPAN at http://search.cpan.org/ approximately palvaro/Bloom-Faster-1.6/ CONTACTS: [email protected]; [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.