atac_dnase_pipelines | ATAC-seq and DNase-seq processing pipeline | Genomics library
kandi X-RAY | atac_dnase_pipelines Summary
kandi X-RAY | atac_dnase_pipelines Summary
this pipeline has been deprecated as of june 2018. please update your pipelines to the wdl-based pipeline at [this pipeline is designed for automated end-to-end quality control and processing of atac-seq or dnase-seq data. the pipeline can be run on compute clusters with job submission engines or stand alone machines. it inherently makes uses of parallelized/distributed computing. pipeline installation is also easy as most dependencies are automatically installed. the pipeline can be run end-to-end i.e. starting from raw fastq files all the way to peak calling and signal track generation; or can be started from intermediate stages as well (e.g. alignment files). the pipeline supports single-end or paired-end atac-seq or dnase-seq data (with or without replicates). the pipeline produces pretty html reports that include quality
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Top functions reviewed by kandi - BETA
- Try to detect the most likely adapter type
- Return a list of adapter counts and counts for each line
- Get the read length of a fastq file
- Returns a file handle
- Parse command line arguments
- Find file name under given file name
atac_dnase_pipelines Key Features
atac_dnase_pipelines Examples and Code Snippets
Community Discussions
Trending Discussions on Genomics
QUESTION
I´m working with two text files that look like this: File 1
...ANSWER
Answered 2022-Apr-09 at 00:49Perhaps you are after this?
QUESTION
I'm using the software plink2 (https://www.cog-genomics.org/plink/2.0/) and I'm trying to iterate over 3 variables.
This software admits an input file with .ped extention file and an exclude file with .txt extention which contains a list of names to be excluded from the input file.
The idea is to iterate over the input files and then over exclude files to generate single outputfiles.
- Input files: Highland.ped - Midland.ped - Lowland.ped
- Exclude-map files: HighlandMidland.txt - HighlandLowland.txt - MidlandLowland.txt
- Output files: HighlandMidland - HighlandLowland - MidlandHighland - MidlandLowland - LowlandHighland - LowlandMidland
The general code is:
...ANSWER
Answered 2021-Dec-09 at 23:50Honestly, I think your current code is quite clear; but if you really want to write this as a loop, here's one possibility:
QUESTION
From this example string:
...ANSWER
Answered 2021-Dec-09 at 01:11use regexp_extract(col, r"&q;Stockcode&q;:([^/$]*?),&q;.*")
if applied to sample data in your question - output is
QUESTION
I am making a code which takes in jumble word and returns a unjumbled word , the data.json contains a list and here take a word one-by-one and check if it contains all the characters of the word and later checking if the length is same , but the problem is when i enter a word as helol then the l is checked twice and giving me some other outputs including the main one(hello). i know why does it happen but i cant get a fix to it
...ANSWER
Answered 2021-Nov-25 at 18:33As I understand it you are trying to identify all possible matches for the jumbled string in your list. You could sort the letters in the jumbled word and match the resulting list against sorted lists of the words in your data file.
QUESTION
I am trying to use plink1.9 to split multiallelic into biallelic. The input is that
...ANSWER
Answered 2021-Nov-17 at 09:45I used bcftools to complete the task.
QUESTION
I have a FASTA file that has about 300000 sequences but some of the sequences are like these
...ANSWER
Answered 2021-Oct-12 at 20:28You can match your non-X containing FASTA entries with the regex >.+\n[^X]+\n. This checks for a substring starting with > having a first line of anything (the FASTA header), which is followed by characters not containing an X until you reach a line break.
For example:
QUESTION
For example, I have two strings:
...ANSWER
Answered 2021-Oct-04 at 22:27For your example your pattern would be:
QUESTION
I am currently trying to run genomic analyses pipelines using Hail(library for genomics analyses written in python and Scala). Recently, Apache Spark 3 was released and it supported GPU usage.
I tried spark-rapids library start an on-premise slurm cluster with gpu nodes. I was able to initialise the cluster. However, when I tried running hail tasks, the executors keep getting killed.
On querying in Hail forum, I got the response that
That’s a GPU code generator for Spark-SQL, and Hail doesn’t use any Spark-SQL interfaces, only the RDD interfaces.
So, does Spark3 not support GPU usage for RDD interfaces?
...ANSWER
Answered 2021-Sep-23 at 05:53As of now, spark-rapids doesn't support GPU usage for RDD interfaces.
Source: Link
Apache Spark 3.0+ lets users provide a plugin that can replace the backend for SQL and DataFrame operations. This requires no API changes from the user. The plugin will replace SQL operations it supports with GPU accelerated versions. If an operation is not supported it will fall back to using the Spark CPU version. Note that the plugin cannot accelerate operations that manipulate RDDs directly.
Here, an answer from spark-rapids team
Source: Link
We do not support running the RDD API on GPUs at this time. We only support the SQL/Dataframe API, and even then only a subset of the operators. This is because we are translating individual Catalyst operators into GPU enabled equivalent operators. I would love to be able to support the RDD API, but that would require us to be able to take arbitrary java, scala, and python code and run it on the GPU. We are investigating ways to try to accomplish some of this, but right now it is very difficult to do. That is especially true for libraries like Hail, which use python as an API, but the data analysis is done in C/C++.
QUESTION
I have 1500 files with the same format (the .scount file format from PLINK2 https://www.cog-genomics.org/plink/2.0/formats#scount), an example is below:
...ANSWER
Answered 2021-Sep-07 at 11:10a tidyverse solution
QUESTION
I have been implementing a suite of RecordBatchReaders for a genomics toolset. The standard unit of work is a RecordBatch. I ended up implementing a lot of my own compression and IO tools instead of using the existing utilities in the arrow cpp platform because I was confused about them. Are there any clear examples of using the existing compression and file IO utilities to simply get a file stream that inflates standard zlib data? Also, an object diagram for the cpp platform would be helpful in ramping up.
...ANSWER
Answered 2021-Jun-02 at 18:58Here is an example program that inflates a compressed zlib file and reads it as CSV.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install atac_dnase_pipelines
General computer
[Java](#java)
[Conda](#conda)
[BigDataScript](#bigdatascript)
[Pipeline](#pipeline)
[Dependencies](#dependencies)
[Genome data](#genome-data)
Kundaje lab’s clusters
[Pipeline](#pipeline)
Stanford NEW SCG cluster
[Conda](#conda)
[BigDataScript](#bigdatascript)
[Pipeline](#pipeline)
[Dependencies](#dependencies)
Stanford OLD SCG cluster
[Conda](#conda)
[BigDataScript](#bigdatascript)
[Pipeline](#pipeline)
[Dependencies](#dependencies)
Stanford Sherlock cluster
[Conda](#conda)
[BigDataScript](#bigdatascript)
[Pipeline](#pipeline)
[Dependencies](#dependencies)
The pipeline does not need internet connection but installers (install_dependencies.sh and install_genome_data.sh) do need it. So the workaround should be that first install dependencies and genome data on a computer that is connected to the internet and then move Conda and genome database directories to your internet-free one. Both computers should have THE SAME LINUX VERSION.
On your computer that has an internet access,
Follow [the installation instruction for general computers](#installation)
Move your Miniconda3 directory to $HOME/miniconda3 on your internet-free computer.
Move your genome database directory, which has bds_atac_species.conf and directories per species, to $HOME/genome_data on your internet-free computer. $HOME/genome_data on your internet-free computer should have bds_atac_species.conf.
Move your BDS directory $HOME/.bds to $HOME/.bds on your internet-free computer.
Move your pipeline directory atac_dnase_pipelines/ to $HOME/atac_dnase_pipelines/ on your internet-free computer.
On your internet-free computer,
Add your miniconda3/bin and BDS binary to $PATH in your bash initialization script ($HOME/.bashrc or $HOME/.bash_profile). ``` export PATH="$PATH:$HOME/miniconda3/bin" export PATH="$PATH:$HOME/.bds" ```
Modify [default] section in $HOME/atac_dnase_pipelines/default.env. ``` [default] conda_bin_dir=$HOME/miniconda3/bin species_file=$HOME/genome_data/bds_atac_species.conf ```
Modify all paths in $HOME/genome_data/bds_atac_species.conf so that they correctly point to the right files.
Check BDS version. ` $ bds -version Bds 0.99999e (build 2016-08-26 06:34), by Pablo Cingolani `
Make sure that your java rumtime version is >= 1.8. ` $ java -version java version "1.8.0_111" Java™ SE Runtime Environment (build 1.8.0_111-b14) Java HotSpot™ 64-Bit Server VM (build 25.111-b14, mixed mode) `
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