CGmapTools support BS-seq data analyses with command-lines
Weilong Guo * #, Ping Zhu *, et al. (2018), CGmapTools improves the precision of heterozygous SNV calls and supports allele-specific methylation detection and visualization in bisulfite-sequencing data, Bioinformatics, 34:381–387., (* joint first authors, # corresponding)
1. Congrats to Dr. Ping Zhu! His Bioinformatics paper is online now. Using Local Alignment to Enhance Single Cell Bisulfite Sequencing Data Efficiency
, 2019. The new software package scBS-map is provided for single-cell DNA methylation analsysis.
2. CGmapTools get new citation by “Vitor Onuchic, et al., Science, 2018“, by generating the Tanghulu plot. Do you know what does “Tanghulu” stand for? You can read this page.
3. Congrats to Dr. Ping Zhu! His Nature Genetics paper is online now. Ping Zhu, et al., Single-cell DNA methylome sequencing of human preimplantation embryos, Nature Genetics, 2017. You can find some of the analysis and visualization methods in the NG paper are implemented in CGmapTools.
1. Unified data formats: CGmap and ATCGmap
Use standard CGmap/ATCGmap formats as central format, for ease of sharing methylomes. ATCGmap/CGmap could be derived from BAM files, which are produced by BS-Seeker2 or Bismark
2. Command-lines: convenient for parallel processing and to be extended
Support both Linux and MAC; Support STDIN and STDOUT, so as to support pipe
3. Binary formats: support instantly retrieving
binary compressed formats: CGbz for CGmap, ATCGbz for ATCGmap
4. Novel SNV calling strategies with high precision
introduced ambigous genotype prediction, and significantly improve the precision especially in heterozygous SNVs
5. Support ASM analyses and visualizaiton
use precise heterozygous SNVs as input, designed Tanghulu plots for showing allele-specific DNA methylation on reads
6. DMR analysis use dynamic fragmentation strategy for
useful for low-coverage (WGBS) and fragmented (RRBS) DNA methylomes
7. Multiple-levels analysis and visualization
provide user-friendly functions for visualizing methylomes at multiple levels, such as design a Tanghulu plot for visualizing methylation status on original reads, and design a Lollipop plot to reveal both lowly-covered cytosines and un-methylated cytosines in a local region
$ cgmaptools -h Program : cgmaptools (Tools for analysis in CGmap/ATCGmap format) Version: 0.0.6 Usage: cgmaptools <command> [options] Commands: -- File manipulation convert + data format conversion tools fetch + fetch a region by random accessing refill refill the missing columns intersect intersect two files merge2 + merge two files into one mergelist + merge a list of files sort sort lines by chromosome and position split + split file by chromosomes select + select lines by region/site -- SNV analysis snv snv analysis -- Methylation analysis dms differentially methylated site analysis dmr differentially methylated region analysis asm allele-specific methylation analysis mbed average methylation level in regions mbin * single sample, mC levels in bins mmbin multiple samples, mC levels in bins mfg methlation levels across fragmented region mstat * methyaltion statistic mtr methylation level to each region -- Coverage analysis oac +* overall coverage (for ATCGmap) mec +* methylation effective coverage (for CGmap) -- Graph related functions lollipop * show local mC levels as lollipop bars heatmap * global mC distribution for multiple samples fragreg * show mC profile across fragmented regions tanghulu * show local mapped reads in Tanghulu shape -- Other Utils findCCGG get MspI cutting sites for RRBS bed2fragreg get fragmented region based on region Note: Commands support figures generation are marked with "*" Commands contain sub-commands are marked with "+" Authors: GUO, Weilong; firstname.lastname@example.org; http://guoweilong.github.io ZHU, Ping; email@example.com; http://perry-zhu.github.io
- GUO, Weilong; firstname.lastname@example.org; http://guoweilong.github.io
- ZHU, Ping; email@example.com; http://perry-zhu.github.io
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