| 1. |
- Andersson, Alma, et al.
(författare)
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sepal : identifying transcript profiles with spatial patterns by diffusion-based modeling
- 2021
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811 .- 1460-2059. ; 37:17, s. 2644-2650
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 2. |
- Fernandez Navarro, Jose, et al.
(författare)
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ST viewer : a tool for analysis and visualization of spatial transcriptomics datasets
- 2019
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 35:6, s. 1058-1060
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Larsson, Ludvig, et al.
(författare)
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Semla : a versatile toolkit for spatially resolved transcriptomics analysis and visualization
- 2023
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 39:10
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Tidskriftsartikel (refereegranskat)abstract
- 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/.
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| 4. |
- Navarro, Jose Fernandez, et al.
(författare)
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ST Pipeline : an automated pipeline for spatial mapping of unique transcripts
- 2017
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 33:16, s. 2591-2593
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 5. |
- Sahlin, Kristoffer, et al.
(författare)
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Improved gap size estimation for scaffolding algorithms
- 2012
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Ingår i: Bioinformatics. - Oxford : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 28:17, s. 2215-2222
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Stranneheim, Henrik, et al.
(författare)
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Classification of DNA sequences using Bloom filters
- 2010
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Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1367-4811. ; 26:13, s. 1595-1600
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Wong, Kim, et al.
(författare)
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ST Spot Detector : a web-based application for automatic spot and tissue detection for spatial Transcriptomics image datasets
- 2018
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Ingår i: Bioinformatics. - : Oxford University Press. - 1367-4803 .- 1367-4811. ; 34:11, s. 1966-1968
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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