wf-single-cell | following single-cell kits | Genomics library
kandi X-RAY | wf-single-cell Summary
kandi X-RAY | wf-single-cell Summary
The following single-cell kits from 10x Genomics are currently supported:. Oxford Nanopore has developed a protocol for sequencing single-cell libraries from 10x, which can be found on the Nanopore Community website. The inputs to Sockeye are raw nanopore reads (FASTQ) generated from the sequencing instrument and reference files that can be downloaded from 10x. The pipeline outputs gene x cell, and transcript x cell expression matrices, as well as a BAM file of aligned reads tagged with cell barcode and UMI information. The BLAZE preprint provided useful benchmarking of the original sockeye implementation. This assisted in the selection of appropriate parameters for cell cut-off thresholds and for defining the limits of the cell x gene matrix. The isoform selection procedure used in this workflow was adapted from that found in the FLAMES package.
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wf-single-cell Key Features
wf-single-cell 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 wf-single-cell
fastq: A fastq file or directory containing fastq input files or directories of input files.
ref_genome_dir The path to the 10x reference genome directory (see Downloading reference data below)
10x sample metadata using either: The following parameters, which are applied to all samples (the default): kit_name options: 3prime (default), 5prime, multiome kit_version 3prime options: v2, v3 (default) 5prime options: v1 multiome options: v1 expected_cells [500]
plot_umaps: This flag controls whether UMAP projections are generated and dispalyed in the report (default false). If UMAP output is required apply like: --plot_umaps. or single_cell_sample_sheet (not to be confused with the optional MinKNOW sample_sheet) allowing per sample configuration.
configs.stats.json: provides a summary of sequencing statistics and observed read configurations, such as n_reads: number of total reads in the input fastq(s) rl_mean: mean read length n_fl: total number of reads with the read1-->TSO or TSO'-->read1' adapter configuration (i.e. full-length reads) n_plus: number of reads with the read1-->TSO configuration n_minus: number of reads with the TSO'-->read1' configuration
bams: Folder of bam alignment files where each alignment contains the following sequence tags CB: corrected cell barcode sequence CR: uncorrected cell barcode sequence CY: Phred quality scores of the uncorrected cell barcode sequence UB: corrected UMI sequence UR: uncorrected UMI sequence UY: Phred quality scores of the uncorrected UMI sequence The bam files are output per chromosome (default) unless --merge_bam is set.
gene_expression.processed.tsv: TSV containing the gene (rows) x cell (columns) expression matrix, processed and normalized according to:
matrix_min_genes: cells with fewer than this number of expressed genes will be removed
matrix_min_cells: genes present in fewer than this number of cells will be removed
matrix_max_mito: cells with more than this percentage of counts belonging to mitochondrial genes will be removed
matrix_norm_count: normalize all cells to this number of total counts per cell
transcript_matrix_processed.tsv: TSV containing the transcript (rows) x cell (columns) expression matrix, processed and normalized in the same manner as the genes. These expression values are determined by first generating a transcriptome per sample using stringtie and then assigning reads to transcripts aligning them to this transcriptome with minimap2. Only reads that map unambiguously to a reference transcript are assigned. The assembled transcripts with the following gffcompare class codes are excluded: i, p, s or u. See the gffcompare and this image, and only cells and genes that pass the gene filtering described above are included.
read_tags.tsv: TSV file witjh the following columns: read_id gene (assigned gene) transcript (assigned transcript id) barcode (corrected barcode) umi )corrected umi
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