Gene ID Transfer with R between Symbol, Gene ID and KEGG ID

1945-01-08 00:00:00 +0000

  • Transfer between Gene ID and Gene Symbol:
library("org.Hs.eg.db")
symbol <- as.list(org.Hs.egALIAS2EG)
symbol2geneid<-data.frame(names(symbol),as.character(symbol))
  • Transfer between ENSG and ENST with Gene Symbol
    library("org.Hs.eg.db")
    symbol <- as.list(org.Hs.egALIAS2EG)
    symbol2geneid<-data.frame(names(symbol),as.character(symbol))
    
  • Transfer between ENSG and ENST with Gene ID
    library("org.Hs.eg.db")
    symbol <- as.list(org.Hs.egALIAS2EG)
    symbol2geneid<-data.frame(names(symbol),as.character(symbol))
    
  • Transfer between ENSG and ENST with KEGG ID
    library("org.Hs.eg.db")
    symbol <- as.list(org.Hs.egALIAS2EG)
    symbol2geneid<-data.frame(names(symbol),as.character(symbol))
    
  • Transfer between ENSG and ENST with KEGG ID
    library("org.Hs.eg.db")
    symbol <- as.list(org.Hs.egALIAS2EG)
    symbol2geneid<-data.frame(names(symbol),as.character(symbol))
    

Disclosure.

  • All the opinions are my own and not the views of my employer
  • All the blogs are my own and not the views of my employer
  • All the opinions are my own and not the views of my employer
  • All the contents are my own and should never be taken seriously
  • All the contents are only used for help. reminding me if misleading happens
  • All the figures are only used for non-profit education. reminding me if infrigement happens

Human Population Genetics and related data operation

1944-01-08 00:00:00 +0000

  • How to download dbSNP153 in hg38 human genome reference
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.38.gz
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.38.gz.md5
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.38.gz.tbi
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.38.gz.tbi.md5
    wget https://raw.githubusercontent.com/Shicheng-Guo/AnnotationDatabase/master/GCF_000001405.38_GRCh38.p12_assembly_report.txt
    gawk -v RS="(\r)?\n" 'BEGIN { FS="\t" } !/^#/ { if ($10 != "na") print $7,$10; else print $7,$5 }' GCF_000001405.38_GRCh38.p12_assembly_report.txt > dbSNP-to-UCSC-GRCh38.p12.map
    perl -p -i -e '{s/chr//}' dbSNP-to-UCSC-GRCh38.p12.map
    bcftools annotate --rename-chrs dbSNP-to-UCSC-GRCh38.p12.map GCF_000001405.38.gz | gawk '/^#/ && !/^##contig=/ { print } !/^#/ { if( $1!="na" ) print }' | bgzip -c > GCF_000001405.38.dbSNP153.GRCh38p12b.GATK.vcf.gz
    
  • How to download dbSNP153 in hg19 human genome reference
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.25.gz
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.25.gz.md5
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.25.gz.tbi
    wget https://ftp.ncbi.nih.gov/snp/redesign/latest_release/VCF/GCF_000001405.25.gz.tbi.md5
    
  • How to change GTEx eQTL

Disclosure.

  • All the opinions are my own and not the views of my employer
  • All the blogs are my own and not the views of my employer
  • All the opinions are my own and not the views of my employer
  • All the contents are my own and should never be taken seriously
  • All the contents are only used for help. reminding me if misleading happens
  • All the figures are only used for non-profit education. reminding me if infrigement happens

How to set up github and use it to accelerate your research

1943-01-08 00:00:00 +0000

  • if you are the first time to use github, what you need to do first are:
    • Get a github account
    • Download and install git.
    • Set up git with your user name and email
  • Now I am assume you use Linux/Ubuntu, I don’t use MAC
    • Open a terminal/shell and type to config your github (name, email and color):
       git config --global user.name "Shicheng-Guo"
       git config --global user.email "Shicheng.Guo@hotmail.com"
       git config --global color.ui true
      
    • you can make more config if you want, for example, if you want to use emacs:
      git config --global core.editor emacs
      
    • the guide to setting up password-less logins (SSH keys connection).
      ssh-keygen -t rsa -C "Shicheng.Guo@hotmail.com"
      less ~/.ssh/id_rsa.pub
      
    • Now you will see ssh key and copy it and paste to SSH Keys in your github Account Settings and test the setting with the following command. You can have multiple key for different computers so that all of them can be connected with your github account.
      ssh -T git@github.com
      
    • Hope you can receive the following response
      Hi username! You've successfully authenticated, but Github does not provide shell access.
      
  • Okay, Now we start for the second step, to create new project or upload codes to existed project

Disclosure.

  • All the opinions are my own and not the views of my employer
  • All the blogs are my own and not the views of my employer
  • All the opinions are my own and not the views of my employer
  • All the contents are my own and should never be taken seriously
  • All the contents are only used for help. reminding me if misleading happens
  • All the figures are only used for non-profit education. reminding me if infrigement happens

Environment Setting in All My Previous Working Station

1942-01-08 00:00:00 +0000

This post is to record all the environment setting for my previous work stations. the best way should be record the installation for all these tools, however, they are always being updated so here I only record them.

HPC @ MCRI between 2017-2019

module load default-environment
PATH=/gpfs/home/yez/software/vcftools:$PATH
PATH=/gpfs/home/yez/software/jdk1.8.0_131/bin:$PATH
PATH=/gpfs/home/yez/tools/PLINK2:$PATH
PATH=/gpfs/home/guosa/tools/R-3.0.0/bin:$PATH
PATH=/gpfs/home/yez/tools/TOPHAT/tophat-2.0.6.Linux_x86_64/tophat-2.0.6.Linux_x86_64:$PATH
PATH=/gpfs/home/yez/tools/STRUCTURE/structure_kernel_src:$PATH
PATH=/gpfs/home/yez/tools/BEDtools/bedtools-2.17.0/bin:$PATH
PATH=/gpfs/home/yez/tools/samtools/samtools-0.1.19:$PATH
PATH=/gpfs/home/guosa/hpc/tools:$PATH
PATH=/gpfs/home/guosa/hpc/tools/vcftools_0.1.13/bin:$PATH
PATH=/gpfs/home/guosa/hpc/tools/ucscutility:$PATH
PATH=/gpfs/home/guosa/hpc/tools:$PATH
PATH=/gpfs/home/guosa/hpc/tools/bcftools-1.9:$PATH
PATH=/gpfs/home/guosa/hpc/tools/tabix-0.2.6:$PATH
PATH=/gpfs/home/guosa/hpc/tools/phase.2.1.1.linux:$PATH
PATH=/gpfs/home/guosa/tools/Bismark_v0.19.1:$PATH
PATH=/gpfs/home/guosa/tools/TrimGalore-0.5.0:$PATH
PATH=/gpfs/home/guosa/hpc/tools/gatk-4.0.6.0:$PATH
PATH=/gpfs/home/guosa/hpc/tools:$PATH
PATH=/gpfs/home/guosa/hpc/tools/bowtie2-2.3.4.1-linux-x86_64:$PATH
PATH=/gpfs/home/guosa/hpc/bin/:$PATH
PATH=/gpfs/home/guosa/hpc/tools/gcta_1.91.6beta:$PATH
PATH=/gpfs/home/guosa/hpc/tools/annovar:$PATH
PATH=/gpfs/home/guosa/hpc/tools/GenGen-1.0.1:$PATH
PATH=/gpfs/home/guosa/hpc/tools/cassi-v2.51-linux-x86_32:$PATH
PATH=/gpfs/apps/R/3.2.0/bin:$PATH
PATH=/gpfs/home/guosa/hpc/tools/rvtests/executable:$PATH
PATH=/gpfs/home/guosa/hpc/tools/snpEff:$PATH
PATH=/gpfs/home/guosa/hpc/tools/samtools-1.9:$PATH
PATH=/gpfs/home/guosa/hpc/tools/snpEff:$PATH
PATH=/gpfs/home/guosa/hpc/bin/aloft/aloft/aloft-predict:$PATH
PATH=/gpfs/home/guosa/hpc/tools/Python-3.7.3:$PATH
PATH=/gpfs/home/guosa/hpc/tools/libbios-1.0.0:$PATH
PATH=/gpfs/home/guosa/hpc/tools/snpEff:$PATH
PATH=/gpfs/home/guosa/hpc/tools/snpEff/scripts:$PATH
PATH=/gpfs/home/guosa/hpc/tools/Python-2.7.15:$PATH
PATH=/gpfs/home/guosa/hpc/tools/aloft/aloft-annotate:$PATH
PATH=/gpfs/home/guosa/hpc/tools/hisat2-2.1.0:$PATH
PATH=/gpfs/home/guosa/hpc/tools/FLASH2:$PATH
PATH=~/hpc/tools/mixcr-3.0.9:$PATH
PATH=/gpfs/home/guosa/hpc/tools/bbmap:$PATH
PATH=/gpfs/home/guosa/hpc/tools/vcftools/src/cpp:$PATH
PATH=/gpfs/home/guosa/hpc/tools/vcftools_0.1.13/bin:$PATH
PATH=/gpfs/home/guosa/hpc/tools/Python-3.7.3:$PATH
PATH=/gpfs/home/guosa/tools/TrimGalore-0.5.0:$PATH
PATH=/cm/shared/apps/sratoolkit/bin:$PATH
PATH=/gpfs/home/guosa/hpc/tools/fastq_screen_v0.14.0:$PATH
PATH=/gpfs/home/guosa/hpc/tools/fs_4.0.1:$PATH
PATH=/gpfs/home/guosa/hpc/tools/bcftools:$PATH
PATH=/gpfs/home/guosa/hpc/tools/STAR-2.7.3a/bin/Linux_x86_64_static:$PATH
PATH=/gpfs/home/guosa/hpc/tools/bbmap:$PATH
PATH=/gpfs/home/guosa/hpc/tools/miniconda3/bin:$PATH
PATH=/gpfs/home/guosa/hpc/tools/samtools-1.9:$PATH
PATH=/gpfs/home/guosa/hpc/tools/bcftools:$PATH
PATH=/gpfs/home/guosa/hpc/tools/salmon-latest_linux_x86_64/bin:$PATH
PATH=/gpfs/home/guosa/hpc/tools/cufflinks-2.2.1.Linux_x86_64:$PATH
export CFLAGS="-I/usr/local/include"
export LDFLAGS="-L/usr/local/lib"
alias chtc1="ssh nu_guos@submit-1.chtc.wisc.edu"
alias chtc2="ssh nu_guos@submit-2.chtc.wisc.edu"
alias chtc3="ssh nu_guos@submit-3.chtc.wisc.edu"
alias ll="ls -larth"

CHG1 @ MCRI 2017-2019

PATH=/home/guosa/hpc/tools:$PATH
PATH=/home/guosa/hpc/tools/tabix-0.2.6:$PATH
alias ll="ls -lsrth"
alias chtc="ssh nu_guos@128.105.244.191"
alias snpsift="java -jar ~/hpc/tools/snpEff/SnpSift.jar"
PATH=~/hpc/tools/bin:$PATH
PATH=~/hpc/bin:$PATH
PATH=/home/guosa/hpc/tools/Bismark_v0.19.1/:$PATH
PATH=/home/guosa/hpc/tools/bcftools-1.7:$PATH
PATH=/home/guosa/hpc/tools/annovar:$PATH
PATH=/home/guosa/hpc/tools/gatk-4.0.12.0:$PATH
PATH=/home/guosa/hpc/tools/Python-2.7.15:$PATH
PATH=/home/guosa/hpc/tools/aloft/aloft-annotate:$PATH
PATH=/home/guosa/hpc/tools/bcftools-1.9:$PATH
PATH=/home/guosa/hpc/tools/mskcc-vcf2maf-5453f80:$PATH
PATH=/home/guosa/hpc/tools/hisat2-2.1.0:$PATH
PATH=/home/guosa/.linuxbrew/Homebrew/Library/Homebrew/vendor/portable-ruby/current/bin:$PATH
PATH=/home/guosa/.linuxbrew/bin:$PATH
PATH=/home/guosa/hpc/tools/jdk-12.0.2/bin:$PATH
PATH=/home/guosa/hpc/tools/apache-maven-3.6.1/bin:$PATH
PATH=/home/guosa/hpc/tools/mixcr-3.0.9:$PATH
PATH=/home/guosa/hpc/tools/seqtk:$PATH
PATH=/home/guosa/hpc/tools/vdjtools-1.2.1:$PATH
PATH=~/hpc/tools/vcftools/src/cpp:$PATH
PATH=/home/guosa/hpc/tools/bcftools-1.9:$PATH
PATH=/home/guosa/hpc/tools/GenomeAnalysisTK-3.8-1-0-gf15c1c3ef:$PATH
PATH=/home/guosa/hpc/tools/jre1.8.0_221/bin/:$PATH
PATH=/home/guosa/hpc/tools/eigensoft/bin:$PATH
PATH=/home/guosa/hpc/tools/CancerLocator:$PATH
PATH=/home/guosa/hpc/tools/CLARKSCV1.2.6.1/exe:$PATH
PATH=/gpfs/home/guosa/hpc/tools/cufflinks-2.2.1.Linux_x86_64:$PATH

Disclosure.

  • All the opinions are my own and not the views of my employer
  • All the blogs are my own and not the views of my employer
  • All the opinions are my own and not the views of my employer
  • All the contents are my own and should never be taken seriously
  • All the contents are only used for help. reminding me if misleading happens
  • All the figures are only used for non-profit education. reminding me if infrigement happens

How to Install HTseq in Linux and STAR for RNA-seq

1941-01-08 00:00:00 +0000

The current post want to use STAR and HTseq together to estimation gene expression for RNA-seq:

How to install lastest version of HTseq (Htseq-count)? HTseq is python based approach (>2.7 or 3.4).

  • python Requirement:
pip install deeptoolsintervals
pip install matplotlib
pip install numpydoc
pip install plotly
pip install py2bit
pip install pyBigWig
pip install scipy
  • and then
pip install HTSeq
  • Here I suppose you use STAR to mapping RNA-seq fastq to human genome (hg19)
  • –outSAMstrandField intronMotif option adds an XS attribute to the spliced alignments in the BAM file, which is required by Cufflinks for unstranded RNA-seq data.
  • before run htseq-count, you’d better to download human gtf files like this way:
    wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_32/GRCh37_mapping/gencode.v32lift37.annotation.gtf.gz
    gunzip gencode.v32lift37.annotation.gtf.gz
    
  • and now run htseq to the bam files created by STAR to generate FPKM matrix
    htseq-count -m intersection-nonempty -t exon -i gene_id -f bam STAR.output.bam gencode.v32lift37.annotation.gtf -o output
    
  • if you do not want to use HTseq, you can also try cufflinks

    cufflinks --library-type fr-firststrand --outFilterIntronMotifs RemoveNoncanonical
    
  • How to infer the RNA-seq library type by salmon if you don’t have such information?

  • before run salmon, you need to download fasta file for the transcripts like the following example:
wget http://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_32/GRCh37_mapping/gencode.v32lift37.transcripts.fa.gz
gunzip gencode.v32lift37.transcripts.fa.gz
  • another tools called : RSeQC is also a powerful and much easier tool to infer RNA-seq strands. you can download required knowngene.hg19.bed12 from UCSC or my annotationdb.
 pip install RSeQC
 wget https://raw.githubusercontent.com/Shicheng-Guo/AnnotationDatabase/master/knowngene.hg19.bed12
 infer_experiment.py -s 2000000 -r knowngene.hg19.bed12 -i UW040LPS_ATTCCT_L002Aligned.sortedByCoord.out.bam

  • more related gtfs such as hg38, hg19 can be found in this links:
hg38 GTFs: http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/genes/
hg19 GTFs: http://hgdownload.soe.ucsc.edu/goldenPath/hg19/bigZips/genes/

  • then now you can run selmon to infer the RNA-seq library type:
salmon quant -t gencode.v32lift37.transcripts.fa -l A -a UW040LPS_ATTCCT_L002Aligned.sortedByCoord.out.bam -o salmon_quant
  • Finally

How to apply MACS2 to identify methylation peaks with MBD-seq

1940-01-08 00:00:00 +0000

BooK

  • https://github.com/Shicheng-Guo/HowtoBook/tree/master/APP/macs2/ChIP-Seq-Slides-Shicheng-Guo-2020-MACS2.pdf
  • https://github.com/Shicheng-Guo/HowtoBook/tree/master/APP/macs2/ENCODE 3 - ChIPSeq Pipeline - Google Docs.pdf

usage: macs2 callpeak [-h] -t TFILE [TFILE ...] [-c [CFILE [CFILE ...]]]
                      [-f {AUTO,BAM,SAM,BED,ELAND,ELANDMULTI,ELANDEXPORT,BOWTIE,BAMPE,BEDPE}]
                      [-g GSIZE] [-s TSIZE] [--keep-dup KEEPDUPLICATES]
                      [--outdir OUTDIR] [-n NAME] [-B] [--verbose VERBOSE]
                      [--trackline] [--SPMR] [--nomodel] [--shift SHIFT]
                      [--extsize EXTSIZE] [--bw BW] [--d-min D_MIN]
                      [-m MFOLD MFOLD] [--fix-bimodal] [-q QVALUE | -p PVALUE]
                      [--scale-to {large,small}] [--ratio RATIO]
                      [--down-sample] [--seed SEED] [--tempdir TEMPDIR]
                      [--nolambda] [--slocal SMALLLOCAL] [--llocal LARGELOCAL]
                      [--max-gap MAXGAP] [--min-length MINLEN] [--broad]
                      [--broad-cutoff BROADCUTOFF] [--cutoff-analysis]
                      [--call-summits] [--fe-cutoff FECUTOFF]
                      [--buffer-size BUFFER_SIZE] [--to-large]

optional arguments:
  -h, --help            show this help message and exit

Input files arguments:
  -t TFILE [TFILE ...], --treatment TFILE [TFILE ...]
                        ChIP-seq treatment file. If multiple files are given
                        as '-t A B C', then they will all be read and pooled
                        together. REQUIRED.
  -c [CFILE [CFILE ...]], --control [CFILE [CFILE ...]]
                        Control file. If multiple files are given as '-c A B
                        C', they will be pooled to estimate ChIP-seq
                        background noise.
  -f {AUTO,BAM,SAM,BED,ELAND,ELANDMULTI,ELANDEXPORT,BOWTIE,BAMPE,BEDPE}, --format {AUTO,BAM,SAM,BED,ELAND,ELANDMULTI,ELANDEXPORT,BOWTIE,BAMPE,BEDPE}
                        Format of tag file, "AUTO", "BED" or "ELAND" or
                        "ELANDMULTI" or "ELANDEXPORT" or "SAM" or "BAM" or
                        "BOWTIE" or "BAMPE" or "BEDPE". The default AUTO
                        option will let MACS decide which format (except for
                        BAMPE and BEDPE which should be implicitly set) the
                        file is. Please check the definition in README. Please
                        note that if the format is set as BAMPE or BEDPE,
                        MACS2 will call its special Paired-end mode to call
                        peaks by piling up the actual ChIPed fragments defined
                        by both aligned ends, instead of predicting the
                        fragment size first and extending reads. Also please
                        note that the BEDPE only contains three columns, and
                        is NOT the same BEDPE format used by BEDTOOLS.
                        DEFAULT: "AUTO"
  -g GSIZE, --gsize GSIZE
                        Effective genome size. It can be 1.0e+9 or 1000000000,
                        or shortcuts:'hs' for human (2.7e9), 'mm' for mouse
                        (1.87e9), 'ce' for C. elegans (9e7) and 'dm' for
                        fruitfly (1.2e8), Default:hs
  -s TSIZE, --tsize TSIZE
                        Tag size/read length. This will override the auto
                        detected tag size. DEFAULT: Not set
  --keep-dup KEEPDUPLICATES
                        It controls the behavior towards duplicate tags at the
                        exact same location -- the same coordination and the
                        same strand. The 'auto' option makes MACS calculate
                        the maximum tags at the exact same location based on
                        binomal distribution using 1e-5 as pvalue cutoff; and
                        the 'all' option keeps every tags. If an integer is
                        given, at most this number of tags will be kept at the
                        same location. Note, if you've used samtools or picard
                        to flag reads as 'PCR/Optical duplicate' in bit 1024,
                        MACS2 will still read them although the reads may be
                        decided by MACS2 as duplicate later. If you plan to
                        rely on samtools/picard/any other tool to filter
                        duplicates, please remove those duplicate reads and
                        save a new alignment file then ask MACS2 to keep all
                        by '--keep-dup all'. The default is to keep one tag at
                        the same location. Default: 1

Output arguments:
  --outdir OUTDIR       If specified all output files will be written to that
                        directory. Default: the current working directory
  -n NAME, --name NAME  Experiment name, which will be used to generate output
                        file names. DEFAULT: "NA"
  -B, --bdg             Whether or not to save extended fragment pileup, and
                        local lambda tracks (two files) at every bp into a
                        bedGraph file. DEFAULT: False
  --verbose VERBOSE     Set verbose level of runtime message. 0: only show
                        critical message, 1: show additional warning message,
                        2: show process information, 3: show debug messages.
                        DEFAULT:2
  --trackline           Tells MACS to include trackline with bedGraph files.
                        To include this trackline while displaying bedGraph at
                        UCSC genome browser, can show name and description of
                        the file as well. However my suggestion is to convert
                        bedGraph to bigWig, then show the smaller and faster
                        binary bigWig file at UCSC genome browser, as well as
                        downstream analysis. Require -B to be set. Default:
                        Not include trackline.
  --SPMR                If True, MACS will SAVE signal per million reads for
                        fragment pileup profiles. It won't interfere with
                        computing pvalue/qvalue during peak calling, since
                        internally MACS2 keeps using the raw pileup and
                        scaling factors between larger and smaller dataset to
                        calculate statistics measurements. If you plan to use
                        the signal output in bedGraph to call peaks using
                        bdgcmp and bdgpeakcall, you shouldn't use this option
                        because you will end up with different results.
                        However, this option is recommended for displaying
                        normalized pileup tracks across many datasets. Require
                        -B to be set. Default: False

Shifting model arguments:
  --nomodel             Whether or not to build the shifting model. If True,
                        MACS will not build model. by default it means
                        shifting size = 100, try to set extsize to change it.
                        It's highly recommended that while you have many
                        datasets to process and you plan to compare different
                        conditions, aka differential calling, use both
                        'nomodel' and 'extsize' to make signal files from
                        different datasets comparable. DEFAULT: False
  --shift SHIFT         (NOT the legacy --shiftsize option!) The arbitrary
                        shift in bp. Use discretion while setting it other
                        than default value. When NOMODEL is set, MACS will use
                        this value to move cutting ends (5') towards 5'->3'
                        direction then apply EXTSIZE to extend them to
                        fragments. When this value is negative, ends will be
                        moved toward 3'->5' direction. Recommended to keep it
                        as default 0 for ChIP-Seq datasets, or -1 * half of
                        EXTSIZE together with EXTSIZE option for detecting
                        enriched cutting loci such as certain DNAseI-Seq
                        datasets. Note, you can't set values other than 0 if
                        format is BAMPE or BEDPE for paired-end data. DEFAULT:
                        0.
  --extsize EXTSIZE     The arbitrary extension size in bp. When nomodel is
                        true, MACS will use this value as fragment size to
                        extend each read towards 3' end, then pile them up.
                        It's exactly twice the number of obsolete SHIFTSIZE.
                        In previous language, each read is moved 5'->3'
                        direction to middle of fragment by 1/2 d, then
                        extended to both direction with 1/2 d. This is
                        equivalent to say each read is extended towards 5'->3'
                        into a d size fragment. DEFAULT: 200. EXTSIZE and
                        SHIFT can be combined when necessary. Check SHIFT
                        option.
  --bw BW               Band width for picking regions to compute fragment
                        size. This value is only used while building the
                        shifting model. Tweaking this is not recommended.
                        DEFAULT: 300
  --d-min D_MIN         Minimum fragment size in basepair. Any predicted
                        fragment size less than this will be excluded.
                        DEFAULT: 20
  -m MFOLD MFOLD, --mfold MFOLD MFOLD
                        Select the regions within MFOLD range of high-
                        confidence enrichment ratio against background to
                        build model. Fold-enrichment in regions must be lower
                        than upper limit, and higher than the lower limit. Use
                        as "-m 10 30". This setting is only used while
                        building the shifting model. Tweaking it is not
                        recommended. DEFAULT:5 50
  --fix-bimodal         Whether turn on the auto pair model process. If set,
                        when MACS failed to build paired model, it will use
                        the nomodel settings, the --exsize parameter to extend
                        each tags towards 3' direction. Not to use this
                        automate fixation is a default behavior now. DEFAULT:
                        False

Peak calling arguments:
  -q QVALUE, --qvalue QVALUE
                        Minimum FDR (q-value) cutoff for peak detection.
                        DEFAULT: 0.05. -q, and -p are mutually exclusive.
  -p PVALUE, --pvalue PVALUE
                        Pvalue cutoff for peak detection. DEFAULT: not set.
                        -q, and -p are mutually exclusive. If pvalue cutoff is
                        set, qvalue will not be calculated and reported as -1
                        in the final .xls file.
  --scale-to {large,small}
                        When set to 'small', scale the larger sample up to the
                        smaller sample. When set to 'larger', scale the
                        smaller sample up to the bigger sample. By default,
                        scale to 'small'. This option replaces the obsolete '
                        --to-large' option. The default behavior is
                        recommended since it will lead to less significant
                        p/q-values in general but more specific results. Keep
                        in mind that scaling down will influence control/input
                        sample more. DEFAULT: 'small', the choice is either
                        'small' or 'large'.
  --ratio RATIO         When set, use a custom scaling ratio of ChIP/control
                        (e.g. calculated using NCIS) for linear scaling.
                        DEFAULT: ingore
  --down-sample         When set, random sampling method will scale down the
                        bigger sample. By default, MACS uses linear scaling.
                        Warning: This option will make your result unstable
                        and irreproducible since each time, random reads would
                        be selected. Consider to use 'randsample' script
                        instead. <not implmented>If used together with --SPMR,
                        1 million unique reads will be randomly picked.</not
                        implemented> Caution: due to the implementation, the
                        final number of selected reads may not be as you
                        expected! DEFAULT: False
  --seed SEED           Set the random seed while down sampling data. Must be
                        a non-negative integer in order to be effective.
                        DEFAULT: not set
  --tempdir TEMPDIR     Optional directory to store temp files. DEFAULT: /tmp
  --nolambda            If True, MACS will use fixed background lambda as
                        local lambda for every peak region. Normally, MACS
                        calculates a dynamic local lambda to reflect the local
                        bias due to the potential chromatin accessibility.
  --slocal SMALLLOCAL   The small nearby region in basepairs to calculate
                        dynamic lambda. This is used to capture the bias near
                        the peak summit region. Invalid if there is no control
                        data. If you set this to 0, MACS will skip slocal
                        lambda calculation. *Note* that MACS will always
                        perform a d-size local lambda calculation while the
                        control data is available. The final local bias would
                        be the maximum of the lambda value from d, slocal, and
                        llocal size windows. While control is not available, d
                        and slocal lambda won't be considered. DEFAULT: 1000
  --llocal LARGELOCAL   The large nearby region in basepairs to calculate
                        dynamic lambda. This is used to capture the surround
                        bias. If you set this to 0, MACS will skip llocal
                        lambda calculation. *Note* that MACS will always
                        perform a d-size local lambda calculation while the
                        control data is available. The final local bias would
                        be the maximum of the lambda value from d, slocal, and
                        llocal size windows. While control is not available, d
                        and slocal lambda won't be considered. DEFAULT: 10000.
  --max-gap MAXGAP      Maximum gap between significant sites to cluster them
                        together. The DEFAULT value is the detected read
                        length/tag size.
  --min-length MINLEN   Minimum length of a peak. The DEFAULT value is the
                        predicted fragment size d. Note, if you set a value
                        smaller than the fragment size, it may have NO effect
                        on the result. For BROAD peak calling, try to set a
                        large value such as 500bps. You can also use '--
                        cutoff-analysis' option with default setting, and
                        check the column 'avelpeak' under different cutoff
                        values to decide a reasonable minlen value.
  --broad               If set, MACS will try to call broad peaks using the
                        --broad-cutoff setting. Please tweak '--broad-cutoff'
                        setting to control the peak calling behavior. At the
                        meantime, either -q or -p cutoff will be used to
                        define regions with 'stronger enrichment' inside of
                        broad peaks. The maximum gap is expanded to 4 * MAXGAP
                        (--max-gap parameter). As a result, MACS will output a
                        'gappedPeak' and a 'broadPeak' file instead of
                        'narrowPeak' file. Note, a broad peak will be reported
                        even if there is no 'stronger enrichment' inside.
                        DEFAULT: False
  --broad-cutoff BROADCUTOFF
                        Cutoff for broad region. This option is not available
                        unless --broad is set. If -p is set, this is a pvalue
                        cutoff, otherwise, it's a qvalue cutoff. Please note
                        that in broad peakcalling mode, MACS2 uses this
                        setting to control the overall peak calling behavior,
                        then uses -q or -p setting to define regions inside
                        broad region as 'stronger' enrichment. DEFAULT: 0.1
  --cutoff-analysis     While set, MACS2 will analyze number or total length
                        of peaks that can be called by different p-value
                        cutoff then output a summary table to help user decide
                        a better cutoff. The table will be saved in
                        NAME_cutoff_analysis.txt file. Note, minlen and maxgap
                        may affect the results. WARNING: May take ~30 folds
                        longer time to finish. The result can be useful for
                        users to decide a reasonable cutoff value. DEFAULT:
                        False

Post-processing options:
  --call-summits        If set, MACS will use a more sophisticated signal
                        processing approach to find subpeak summits in each
                        enriched peak region. DEFAULT: False
  --fe-cutoff FECUTOFF  When set, the value will be used to filter out peaks
                        with low fold-enrichment. Note, MACS2 use 1.0 as
                        pseudocount while calculating fold-enrichment.
                        DEFAULT: 1.0

Other options:
  --buffer-size BUFFER_SIZE
                        Buffer size for incrementally increasing internal
                        array size to store reads alignment information. In
                        most cases, you don't have to change this parameter.
                        However, if there are large number of
                        chromosomes/contigs/scaffolds in your alignment, it's
                        recommended to specify a smaller buffer size in order
                        to decrease memory usage (but it will take longer time
                        to read alignment files). Minimum memory requested for
                        reading an alignment file is about # of CHROMOSOME *
                        BUFFER_SIZE * 8 Bytes. DEFAULT: 100000

Obsolete options:
  --to-large            Obsolete option. Please use '--scale-to large'
                        instead.

DNA methylation and Epigenetic research in Human Population

1939-01-08 00:00:00 +0000

DNA methylation

RNA splicing

transcription factors

nucleosomes

the 3D structure of chromatin

prions

toxicity assessment

Insurance: YouSurance is the first company to use epigenetic biomarkers to assess life insurance applicants’ health and lifespan. Are you concerned about insurance companies making such uses of the findings of epigenetic research?

Forensic investigation: It has been noted that it may now be possible to produce a profile of a criminal suspect out of a drop of blood that provides details regarding age, diet, smoking status, medications, polluted environment the suspect lives in, and if they have a traumatic history of abuse. Are you concerned with epigenetic knowledge being used for forensic investigations?

Immigration: Findings from epigenetic research regarding “biological age” may be used by immigration agencies to prove the age of undocumented minor migrants seeking asylum. Are you concerned with epigenetic findings being used in immigration?

Direct-to-consumer epigenetic testing: Private companies have started offering online direct-to-consumer epigenetic testing for a variety of conditions related to health and well-being, such as biological aging, smoke exposure, and skin type. Most of these companies provide consumers with health and lifestyle advice based on their epigenetic profile. Are you concerned with epigenetics tests being offered directly to the public by private companies?

Developmental Epigenetics (i.e. the state and role of epigenetic mechanisms during development)

Functional Epigenetics (i.e. the biological function of epigenetic mechanisms)

Disease Epigenetics (i.e. identification of epigenetic ‘signatures’ of diseases)

Predictive Epigenetics (i.e. the identification of epigenetic risks or probabilities)

Environmental Epigenetics (i.e. the impact of environmental exposures on epigenomes)

Nutritional Epigenetics (i.e. the impact of nutrition on epigenomes)

Social Epigenetics (i.e. the impact of social exposures on epigenomes)

Behavioral Epigenetics (i.e. the impact of diet, lifestyle and other behaviors on epigenomes)

Diagnostic Epigenetics (i.e. development and optimization of epigenetic tests and diagnoses)

Pharmacological Epigenetics (development and optimization of epigenetic drugs/treatments)

Methods in Epigenetics (i.e., development and optimization of research methods)

Epigenetic inheritance

Multi-Omics of Characterization of the Cancer Cell Line Encyclopedia

1938-02-28 00:00:00 +0000

  • The CancerCell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity
  • 01/22/2020: Quantitative Proteomics of the Cancer Cell Line Encyclopedia: https://gygi.med.harvard.edu/publications/ccle
  • 01/22/2020: NCI60 proteome resource is a web application that facilitates comprehensive
  • 05/23/2019: Next-generation characterization of the Cancer Cell Line Encyclopedia: https://doi.org/10.1038/s41586-019-1186-3
  • 03/29/2012: The CancerCell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

Dac Demethylation

1937-02-28 00:00:00 +0000

Protocol for DNA demethylation with 5-aza-2’-deoxycytidine (DAC) treatment

Riblast_ncrna_target

1936-02-28 00:00:00 +0000

How to apply RIblast to predict ncRNA interaction target

cd ~/hpc/tools/RIblast/extdata
wget http://ftp.ensembl.org/pub/release-97/fasta/homo_sapiens/ncrna/Homo_sapiens.GRCh38.ncrna.fa.gz
wget http://ftp.ensembl.org/pub/release-97/fasta/homo_sapiens/cdna/Homo_sapiens.GRCh38.cdna.all.fa.gz 
wget http://hgdownload.soe.ucsc.edu/goldenPath/hg38/bigZips/mrna.fa.gz -O Homo_sapiens.GRCh38.mrna.fa.gz
wget ftp://ftp.ebi.ac.uk/pub/databases/gencode/Gencode_human/release_31/gencode.v31.transcripts.fa.gz

gunzip Homo_sapiens.GRCh38.ncrna.fa.gz
gunzip Homo_sapiens.GRCh38.cdna.all.fa.gz 
gunzip Homo_sapiens.GRCh38.mrna.fa.gz 
gunzip gencode.v31.transcripts.fa.gz

../RIblast db -i Homo_sapiens.GRCh38.mrna.fa  -o Homo_sapiens.GRCh38.mrna.fa.db
../RIblast db -i Homo_sapiens.GRCh38.cdna.all.fa  -o Homo_sapiens.GRCh38.cdna.all.fa.db
../RIblast db -i Homo_sapiens.GRCh38.ncrna.fa  -o Homo_sapiens.GRCh38.ncrna.fa.db
../RIblast db -i gencode.v31.transcripts.fa  -o gencode.v31.transcripts.fa.db

grep AC004585.1 Homo_sapiens.GRCh38.ncrna.fa.txt > AC004585.fa
../RIblast ris -i AC004585.fa -o AC004585.txt -d Homo_sapiens.GRCh38.cdna.all.fa.db