| 1. |
- Han, Yanan, et al.
(author)
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Comparison of EM-seq and PBAT methylome library methods for low-input DNA
- 2022
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In: Epigenetics. - : Informa UK Limited. - 1559-2294 .- 1559-2308. ; 17:10, s. 1195-1204
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Journal article (peer-reviewed)abstract
- DNA methylation is the most studied epigenetic mark involved in regulation of gene expression. For low input samples, a limited number of methods for quantifying DNA methylation genome-wide has been evaluated. Here, we compared a series of input DNA amounts (1-10ng) from two methylome library preparation protocols, enzymatic methyl-seq (EM-seq) and post-bisulfite adaptor tagging (PBAT) adapted from single-cell PBAT. EM-seq takes advantage of enzymatic activity while PBAT relies on conventional bisulfite conversion for detection of DNA methylation. We found that both methods accurately quantified DNA methylation genome-wide. They produced expected distribution patterns around genomic features, high C-T transition efficiency at non-CpG sites and high correlation between input amounts. However, EM-seq performed better in regard to library and sequencing quality, i.e. EM-seq produced larger insert sizes, higher alignment rates and higher library complexity with lower duplication rate compared to PBAT. Moreover, EM-seq demonstrated higher CpG coverage, better CpG site overlap and higher consistency between input series. In summary, our data suggests that EM-seq overall performed better than PBAT in whole-genome methylation quantification of low input samples.
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| 2. |
- Kristanl, Matej, et al.
(author)
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The Seventh Visual Object Tracking VOT2019 Challenge Results
- 2019
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In: 2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW). - : IEEE COMPUTER SOC. - 9781728150239 ; , s. 2206-2241
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Conference paper (peer-reviewed)abstract
- The Visual Object Tracking challenge VOT2019 is the seventh annual tracker benchmarking activity organized by the VOT initiative. Results of 81 trackers are presented; many are state-of-the-art trackers published at major computer vision conferences or in journals in the recent years. The evaluation included the standard VOT and other popular methodologies for short-term tracking analysis as well as the standard VOT methodology for long-term tracking analysis. The VOT2019 challenge was composed of five challenges focusing on different tracking domains: (i) VOT-ST2019 challenge focused on short-term tracking in RGB, (ii) VOT-RT2019 challenge focused on "real-time" short-term tracking in RGB, (iii) VOT-LT2019 focused on long-term tracking namely coping with target disappearance and reappearance. Two new challenges have been introduced: (iv) VOT-RGBT2019 challenge focused on short-term tracking in RGB and thermal imagery and (v) VOT-RGBD2019 challenge focused on long-term tracking in RGB and depth imagery. The VOT-ST2019, VOT-RT2019 and VOT-LT2019 datasets were refreshed while new datasets were introduced for VOT-RGBT2019 and VOT-RGBD2019. The VOT toolkit has been updated to support both standard short-term, long-term tracking and tracking with multi-channel imagery. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The dataset, the evaluation kit and the results are publicly available at the challenge website(1).
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| 3. |
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| 4. |
- Needhamsen, Maria, et al.
(author)
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Integration of small RNAs from plasma and cerebrospinal fluid for classification of multiple sclerosis
- 2022
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In: Frontiers in Genetics. - : Frontiers Media SA. - 1664-8021. ; 13
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Journal article (peer-reviewed)abstract
- Multiple Sclerosis (MS) is an autoimmune, neurological disease, commonly presenting with a relapsing-remitting form, that later converts to a secondary progressive stage, referred to as RRMS and SPMS, respectively. Early treatment slows disease progression, hence, accurate and early diagnosis is crucial. Recent advances in large-scale data processing and analysis have progressed molecular biomarker development. Here, we focus on small RNA data derived from cell-free cerebrospinal fluid (CSF), cerebrospinal fluid cells, plasma and peripheral blood mononuclear cells as well as CSF cell methylome data, from people with RRMS (n = 20), clinically/radiologically isolated syndrome (CIS/RIS, n = 2) and neurological disease controls (n = 14). We applied multiple co-inertia analysis (MCIA), an unsupervised and thereby unbiased, multivariate method for simultaneous data integration and found that the top latent variable classifies RRMS status with an Area Under the Receiver Operating Characteristics (AUROC) score of 0.82. Variable selection based on Lasso regression reduced features to 44, derived from the small RNAs from plasma (20), CSF cells (8) and cell-free CSF (16), with a marginal reduction in AUROC to 0.79. Samples from SPMS patients (n = 6) were subsequently projected on the latent space and differed significantly from RRMS and controls. On contrary, we found no differences between relapse and remission or between inflammatory and non-inflammatory disease controls, suggesting that the latent variable is not prone to inflammatory signals alone, but could be MS-specific. Hence, we here showcase that integration of small RNAs from plasma and CSF can be utilized to distinguish RRMS from SPMS and neurological disease controls.
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| 5. |
- Zheleznyakova, Galina Yurevna, et al.
(author)
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Small noncoding RNA profiling across cellular and biofluid compartments and their implications for multiple sclerosis immunopathology
- 2021
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences. - 0027-8424 .- 1091-6490. ; 118:17
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Journal article (peer-reviewed)abstract
- Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease affecting the central nervous system (CNS). Small non-coding RNAs (sncRNAs) and, in particular, microRNAs (miRNAs) have frequently been associated with MS. Here, we performed a comprehensive analysis of all classes of sncRNAs in matching samples of peripheral blood mononuclear cells (PBMCs), plasma, cerebrospinal fluid (CSF) cells, and cell-free CSF from relapsing-remitting (RRMS, n = 12 in relapse and n = 11 in remission) patients, secondary progressive (SPMS, n = 6) MS patients, and noninflammatory and inflammatory neurological disease controls (NINDC, n = 11; INDC, n = 5). We show widespread changes in miRNAs and sncRNA-derived fragments of small nuclear, nucleolar, and transfer RNAs. In CSF cells, 133 out of 133 and 115 out of 117 differentially expressed sncRNAs were increased in RRMS relapse compared to remission and RRMS compared to NINDC, respectively. In contrast, 65 out of 67 differentially expressed PBMC sncRNAs were decreased in RRMS compared to NINDC. The striking contrast between the periphery and CNS suggests that sncRNA-mediated mechanisms, including alternative splicing, RNA degradation, and mRNA translation, regulate the transcriptome of pathogenic cells primarily in the CNS target organ.
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