DIFFERENCES OF POSTURE ON PUSH-OFF PHASE BETWEEN ACTUAL SPEED SKATING AND SLIDE-BOARD TRAINING

The slide-board training is a feasible technology to exercise skating during the off-season. While slide-board is much different from ice surface of the actual skating situation, it may distort actual skating posture. The purpose of this study was to analyze the differences in posture during push-off phase between an actual speed skating condition and on slideboard. The result showed that on the slide-board distance between two feet were shorter, so were the rotation angles of both feet, the hip angle was lower during the whole phase, while knee and ankle angles were higher. In conclusion, the restriction of the space on slide-board affected the position and rotation of both stable and push-off feet as well as the joint extension of the stable leg. Hence, the structural design of slide-board needs to be improved to facilitate the extension of knee and ankle in the medial-lateral direction

DNA methylation heterogeneity profiles of 928 CCLE cell lines

Motivation Bisulfite sequencing data carry invaluable information about epigenetic states of a cell population beyond DNA methylation levels. Phased DNA methylation states (DNA methylation pattern; i.e., an array of DNA methylation states of CpGs simultaneously covered by a single read) can serve as a local barcode representing the epigenetic state of a single cell. Therefore we can compute approximate epigenetic diversity through measuring the diversity of DNA methylation patterns (inter-molecule / inter-cellular heterogeneity). On the other hand, DNA methylation patterns also inform us of the local disorder of DNA methylation states, which already have been shown to have prognostic potential (Landau et al., 2014). To facilitate studies on such concept of DNA methylation heterogeneity, we developed an efficient software named Metheor and here provide a comprehensive DNA methylation profiles of 928 cancer cell lines from cancer cell line encyclopedia (CCLE) computed by Metheor. Data processing Raw reduced representation bisulfite sequencing  (RRBS) reads for 928 CCLE cell lines were downloaded under SRA study accession SRP186687, and preprocessed using Trim Galore! v0.6.7 with --rrbs option. Reads were then aligned to hg38 reference genome using Bismark v0.23.1. The resulting alignments are used to compute DNA methylation heterogeneity levels (see below) through Metheor v0.1.0. Seven measures for DNA methylation heterogeneity Profiles of seven DNA methylation heterogeneity measures are provided in this dataset. Proportion of discordant reads (PDR) Local pairwise methylation disorder (LPMD) Methylation haplotype load (MHL) Epipolymorphism (PM) Methylation entropy (ME) Fraction of discordant read pairs (FDRP) Quantitative fraction of discordant pairs (qFDRP) For a more detailed description of those measures, please refer to this GitHub repository. Data tables We provide 7 tables for DNA methylation heterogeneity profiles and an additional table that contains the average methylation level information. ccle.pdr.csv: Table for average proportion of discordant reads (PDR) for various genomic contexts ccle.lpmd.csv:Table for average local pairwise methylation disorder (LPMD) for various genomic contexts ccle.mhl.csv: Table for average methylation haplotype load (MHL) for various genomic contexts ccle.pm.csv: Table for average epipolymorphism (PM) for various genomic contexts ccle.me.csv: Table for average methylation entropy (ME) for various genomic contexts. ccle.fdrp.csv: Table for average FDRP levels for various genomic contexts. ccle.qfdrp.csv: Table for average qFDRP levels for various genomic contexts. ccle.beta.csv: Table for average DNA methylation levels for various genomic contexts. Schema for data tables All data tables are in comma-separated values (csv) format sharing the following columns: cell_line_name: Identifier for the cell line. run_accession: SRA run accession of the corresponding RRBS data. tissue: Tissue collection site. disease: Full disease type (e.g., carcinoma (ductal carcinoma), carcinoma (squamous_cell_carcinoma), or lymphoid_noeplasm (Hodgkin_lymphoma)) disease_primary: General disease type (e.g., carcinoma or lymphoid_neoplasm). disease_secondary: Specific disease type (e.g., ductal carcinoma, squamous_cell_carcinoma or Hodgkin_lymphoma). disease_stage: Indicates whether tissue sample is from primary or metastatic site. age_at_sampling: Age of tissue donor at sampling if known. Otherwise, values are left empty. sex: Sex of tissue donor if known. Otherwise, values are left empty. ethnicity: Ethnicity of tissue donor if known. Otherwise, values are left empty. genomewide: Genomewide average DNA methylation heterogeneity levels. promoter: Average DNA methylation heterogeneity levels at promoters of protein-coding genes. cgi: Average DNA methylation heterogeneity levels at CpG islands. Annotations were downloaded from UCSC table browser. cpg_shore: Average DNA methylation heterogeneity levels at CpG shores. CpG shores are defined as 2kb regions flanking upstream or downstream of CpG islands. Regions overlapping CpG islands were excluded. cpg_shelf: Average DNA methylation heterogeneity levels at CpG shelves. CpG shelves are defined as 2kb regions flanking upstream or downstream of (CpG island + CpG shore) regions. Regions overlapping CpG islands or shores were excluded. methylation_canyon: Average DNA methylation heterogeneity levels at methylation canyons. DNA methylation canyons are defined as broad (> 3.5kb) under-methylated regions (Jeong et al., 2014), and their hg38 annotations were downloaded from (Su et al., 2018). exon: Average DNA methylation heterogeneity levels at exons of protein coding genes. intron: Average DNA methylation heterogeneity levels at introns of protein coding genes. gene_body: Average DNA methylation heterogeneity levels at gene bodies of protein coding genes. LINE: Average DNA methylation heterogeneity levels at LINEs. Annotations were downloaded from UCSC table browser (hg38, Repeats-RepeatMasker). SINE: Average DNA methylation heterogeneity levels at SINEs LTR: Average DNA methylation heterogeneity levels at LTR retrotransposons Availability of Metheor The source code for Metheor can be found at https://github.com/dohlee/metheor You can install Metheor using conda at commandline: $ conda install -c dohlee metheor</p

Metheor: Ultrafast DNA methylation heterogeneity calculation from bisulfite read alignments.

Phased DNA methylation states within bisulfite sequencing reads are valuable source of information that can be used to estimate epigenetic diversity across cells as well as epigenomic instability in individual cells. Various measures capturing the heterogeneity of DNA methylation states have been proposed for a decade. However, in routine analyses on DNA methylation, this heterogeneity is often ignored by computing average methylation levels at CpG sites, even though such information exists in bisulfite sequencing data in the form of phased methylation states, or methylation patterns. In this study, to facilitate the application of the DNA methylation heterogeneity measures in downstream epigenomic analyses, we present a Rust-based, extremely fast and lightweight bioinformatics toolkit called Metheor. As the analysis of DNA methylation heterogeneity requires the examination of pairs or groups of CpGs throughout the genome, existing softwares suffer from high computational burden, which almost make a large-scale DNA methylation heterogeneity studies intractable for researchers with limited resources. In this study, we benchmark the performance of Metheor against existing code implementations for DNA methylation heterogeneity measures in three different scenarios of simulated bisulfite sequencing datasets. Metheor was shown to dramatically reduce the execution time up to 300-fold and memory footprint up to 60-fold, while producing identical results with the original implementation, thereby facilitating a large-scale study of DNA methylation heterogeneity profiles. To demonstrate the utility of the low computational burden of Metheor, we show that the methylation heterogeneity profiles of 928 cancer cell lines can be computed with standard computing resources. With those profiles, we reveal the association between DNA methylation heterogeneity and various omics features. Source code for Metheor is at https://github.com/dohlee/metheor and is freely available under the GPL-3.0 license

Schematic illustration of proportion of discorant reads (PDR).

Schematic illustration of proportion of discorant reads (PDR).</p

Association between methylation entropy and cancer stemness.

(A) Genes were ranked by the Pearson’s correlation between their expression and average methylation entropy levels across promoters. Red dots represent 3,680 genes having statistically significant correlations (Benjamini-Hochberg adjusted p-value WNT7A and CTNND2). (E) The association between promoter methylation entropy levels and the activity of Wnt signaling pathway. *two-tailed independent t-test p < 0.05; In D-E, Pearson’s correlation coefficients and associated p-values are shown. In D, p-values were adjusted using Benjamini-Hochberg procedure.</p

Robustness of LPMD against the choice of genomic distance window.

Robustness of LPMD against the choice of genomic distance window.</p

Benchmarking the running time and memory usage of Metheor using simulated pseudo-WGBS dataset.

Benchmarking the running time and memory usage of Metheor using simulated pseudo-WGBS dataset.</p

Details of the algorithms used in Metheor implementation and simulated data preparation used for benchmark.

Details of the algorithms used in Metheor implementation and simulated data preparation used for benchmark.</p

Performance benchmark and validity of the results.

Benchmarking the running time of Metheor using (A) simulated RRBS dataset and (B) Ewing sarcoma RRBS dataset. Values below the name of each of the measures denote the amount of speedup (in fold) in Metheor compared to its benchmark counterpart. Benchmarking the memory usage of Metheor using (C) simulated RRBS dataset and (D) Ewing sarcoma RRBS dataset. Values below the name of each of the measures denote the amount of memory usage reduction (in fold) in Metheor compared to its benchmark counterpart. All the benchmark experiments were repeated for three times, except for MHL. Lines denote the average wall time and shades represent the 95% confidence interval. The wall time for MHL computation was measured for only once. (E) Validity of the results. CpG-wise (PDR, MHL, FDRP and qFDRP) and CpG quartet-wise (PM and ME) methylation heterogeneity levels were compared between Metheor and the corresponding reference implementations. Pearson’s correlation coefficient and corresponding p-values are shown for FDRP and qFDRP.</p

Overview of Metheor.

(A) The input for Metheor is bisulfite read alignment tagged with Bismark methylation call strings. Using each of the seven subcommands shown, Metheor computes the corresponding DNA methylation heterogeneity measure. If reads were aligned with a tool other than Bismark, Metheor can still add tag for methylation call string with metheor tag subcommand to make alignment file compatible for Metheor run. (B) Schematic diagram for DNA methylation heterogeneity measures and benchmark settings in this study. [5] denote the Perl script provided by the authors along with the article proposing the utility of MHL. (C, D) Schematic diagram illustrating (C) read-centric algorithm and (D) CpG-centric algorithm for the computation of DNA methylation heterogeneity. The advantages (plus symbol) and disadvantages (minus symbol) are shown below the diagrams. (E) Distribution of the average number of CpGs per sequencing read for the RRBS data from 928 CCLE cell lines. (F) Genomewide average levels of proportion of discordant reads (PDR) and local pairwise methylation discordance (LPMD) against varying read lengths. (G) Schematic illustration for the definition of local pairwise methylation discordance (LPMD) and examples. The proportion of reads having different DNA methylation states for a pair of CpGs (red arrows) are computed.</p