smoove | structural variant | Genomics library

 by   brentp Go Version: v0.2.7 License: Apache-2.0

kandi X-RAY | smoove Summary

smoove is a Go library typically used in Artificial Intelligence, Genomics applications. smoove has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.
smoove simplifies and speeds calling and genotyping SVs for short reads. It also improves specificity by removing many spurious alignment signals that are indicative of low-level noise and often contribute to spurious calls.
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                        smoove has a low active ecosystem.
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                        It has 152 star(s) with 15 fork(s). There are 10 watchers for this library.
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                        It had no major release in the last 12 months.
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                        There are 48 open issues and 97 have been closed. On average issues are closed in 1 days. There are no pull requests.
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                        It has a neutral sentiment in the developer community.
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                        The latest version of smoove is v0.2.7
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                                  kandi-Quality Quality

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                                    smoove has 0 bugs and 0 code smells.
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                                                smoove has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
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                                                smoove code analysis shows 0 unresolved vulnerabilities.
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                                                There are 0 security hotspots that need review.
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                                                          kandi-License License

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                                                            smoove is licensed under the Apache-2.0 License. This license is Permissive.
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                                                            Permissive licenses have the least restrictions, and you can use them in most projects.
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                                                                        smoove releases are available to install and integrate.
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                                                                        Installation instructions, examples and code snippets are available.
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                                                                        It has 2482 lines of code, 99 functions and 15 files.
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                                                                        It has high code complexity. Code complexity directly impacts maintainability of the code.
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                                                                                  smoove Key Features

                                                                                  lumpy and lumpy_filter
                                                                                  samtools: for CRAM support
                                                                                  gsort: to sort final VCF
                                                                                  bgzip+tabix: to compress and index final VCF
                                                                                  svtyper: to genotypes SVs
                                                                                  svtools: required for large cohorts
                                                                                  mosdepth: remove high coverage regions.
                                                                                  bcftools: version 1.5 or higher for VCF indexing and filtering.
                                                                                  duphold: to annotate depth changes within events and at the break-points.
                                                                                  parallelize calls to lumpy_filter to extract split and discordant reads required by lumpy
                                                                                  further filter lumpy_filter calls to remove high-coverage, spurious regions and user-specified chroms like 'hs37d5'; it will also remove reads that we've found are likely spurious signals. after this, it will remove singleton reads (where the mate was removed by one of the previous filters) from the discordant bams. This makes lumpy much faster and less memory-hungry.
                                                                                  calculate per-sample metrics for mean, standard deviation, and distribution of insert size as required by lumpy.
                                                                                  stream output of lumpy directly into multiple svtyper processes for parallel-by-region genotyping while lumpy is still running.
                                                                                  sort, compress, and index final VCF.

                                                                                  smoove Examples and Code Snippets

                                                                                  No Code Snippets are available at this moment for smoove.
                                                                                  Community Discussions

                                                                                  Trending Discussions on Genomics

                                                                                  search for regex match between two files using python
                                                                                  chevron right
                                                                                  Is there a way to permute inside using to variables in bash?
                                                                                  chevron right
                                                                                  BigQuery Regex to extract string between two substrings
                                                                                  chevron right
                                                                                  how to stop letter repeating itself python
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                                                                                  How to get the words within the first single quote in r using regex?
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                                                                                  Aggregating and summing columns across 1500 files by matching IDs in R (or bash)
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                                                                                  QUESTION

                                                                                  search for regex match between two files using python
                                                                                  Asked 2022-Apr-09 at 00:49

                                                                                  I´m working with two text files that look like this: File 1

                                                                                  #   See ftp://ftp.ncbi.nlm.nih.gov/genomes/README_assembly_summary.txt for a description of the columns in this file.
                                                                                  # assembly_accession    bioproject  biosample   wgs_master  refseq_category taxid   species_taxid   organism_name   infraspecific_name  isolate version_status  assembly_level  release_type    genome_rep  seq_rel_date    asm_name    submitter   gbrs_paired_asm paired_asm_comp ftp_path    excluded_from_refseq    relation_to_type_material   asm_not_live_date
                                                                                  GCF_000739415.1 PRJNA224116 SAMN02732406        na  837 837 Porphyromonas gingivalis    strain=HG66     latest  Chromosome  Major   Full    2014/08/14  ASM73941v1  University of Louisville    GCA_000739415.1 identical   https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/739/415/GCF_000739415.1_ASM73941v1         na
                                                                                  GCF_001263815.1 PRJNA224116 SAMN03366764        na  837 837 Porphyromonas gingivalis    strain=A7436        latest  Complete Genome Major   Full    2015/08/11  ASM126381v1 University of Florida   GCA_001263815.1 identical   https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/001/263/815/GCF_001263815.1_ASM126381v1            na
                                                                                  GCF_001297745.1 PRJNA224116 SAMD00040429    BCBV00000000.1  na  837 837 Porphyromonas gingivalis    strain=Ando     latest  Scaffold    Major   Full    2015/09/17  ASM129774v1 Lab. of Plant Genomics and Genetics, Department of Plant Genome Research, Kazusa DNA Research Institute GCA_001297745.1 identical   https://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/001/297/745/GCF_001297745.1_ASM129774v1            an
                                                                                  ...
                                                                                  

                                                                                  File 2:

                                                                                  #   See ftp://ftp.ncbi.nlm.nih.gov/genomes/README_assembly_summary.txt for a description of the columns in this file.
                                                                                  # assembly_accession    bioproject  biosample   wgs_master  refseq_category taxid   species_taxid   organism_name   infraspecific_name  isolate version_status  assembly_level  release_type    genome_rep  seq_rel_date    asm_name    submitter   gbrs_paired_asm paired_asm_comp ftp_path    excluded_from_refseq    relation_to_type_material   asm_not_live_date
                                                                                  GCA_000739415.1 PRJNA245225 SAMN02732406        na  837 837 Porphyromonas gingivalis    strain=HG66     latest  Chromosome  Major   Full    2014/08/14  ASM73941v1  University of Louisville    GCF_000739415.1 identical   https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/739/415/GCA_000739415.1_ASM73941v1         na
                                                                                  GCA_001263815.1 PRJNA276132 SAMN03366764        na  837 837 Porphyromonas gingivalis    strain=A7436        latest  Complete Genome Major   Full    2015/08/11  ASM126381v1 University of Florida   GCF_001263815.1 identical   https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/001/263/815/GCA_001263815.1_ASM126381v1            na
                                                                                  

                                                                                  So, I want to search for a specific pattern using regex. For example, file 1 has this pattern:

                                                                                  GCF_000739415.1
                                                                                  

                                                                                  and file 2 this one:

                                                                                  GCA_000739415.1
                                                                                  

                                                                                  The difference is the third character: F versus A. However, sometimes numbers differ. Difference between files is the third row of data. These two files have a lot of patterns like the previous one, however, there are some differences. My goal is to search for the pattern that only exists in one file and not in the other file. For example, "GCF_001297745.1 in the third row in the file 1 but not in the file 2. This should be a GCA_001297745.1"

                                                                                  I´m working on a python code:

                                                                                  # PART 1: Open and read text file
                                                                                  with open("assembly_summary_genbank.txt", 'r') as f_1:
                                                                                      contents_1 = f_1.readlines()
                                                                                  with open("assembly_summary_refseq.txt", 'r') as f_2:
                                                                                      contents_2 = f_2.readlines()
                                                                                  
                                                                                  # PART 2: Search for IDs
                                                                                  matches_1 = re.findall("GCF_[0-9]*\.[0-9]", str(contents_1))
                                                                                  matches_2 = re.findall("GCA_[0-9]*\.[0-9]", str(contents_2))
                                                                                  
                                                                                  # PART 3: Match between files
                                                                                  # Seudocode
                                                                                  for line in matches_1:
                                                                                      if matches_1 == matches_2:
                                                                                          print("PATTERN THAT ONLY EXIST IN ONE FILE")
                                                                                  

                                                                                  Part 3 refers to doing a for loop that searches for each line in both files and prints the patterns that only exist in one file and not in the other one. Any idea for doing this for loop?

                                                                                  ANSWER

                                                                                  Answered 2022-Apr-09 at 00:49

                                                                                  Perhaps you are after this?

                                                                                  import re
                                                                                  
                                                                                  given_example = "GCA_000739415.1 PRJNA245225 SAMN02732406        na  837 837 Porphyromonas gingivalis    strain=HG66     latest  Chromosome  Major   Full    2014/08/14  ASM73941v1  University of Louisville    GCF_000739415.1 identical   https://ftp.ncbi.nlm.nih.gov/genomes/all/GCA/000/739/415/GCA_000739415.1_ASM73941v1         an"
                                                                                  altered_example = "GCA_000739415.1 GCTEST_000739415.1"
                                                                                  
                                                                                  # GX[A or F]_[number; digit >= 1].[number; digit >= 1]
                                                                                  regex = r"GC[AF]_\d+.\d+"
                                                                                  
                                                                                  matches_1 = re.findall(regex, given_example)
                                                                                  matches_2 = re.findall(regex, altered_example)
                                                                                  
                                                                                  # Iteration for intersection
                                                                                  for match in matches_1:
                                                                                      if match in matches_2:
                                                                                          print(f"{match} is in both files")
                                                                                  

                                                                                  Prints

                                                                                  GCA_000739415.1 is in both files
                                                                                  GCA_000739415.1 is in both files
                                                                                  

                                                                                  But I would recommend:

                                                                                  # The preferred method for intersection, where order is not important
                                                                                  matches = list(set(matches_1) & set(matches_2))
                                                                                  

                                                                                  Which saves as:

                                                                                  ['GCA_000739415.1']
                                                                                  

                                                                                  Note the regex matches in a form of GX[A or F]_[number; digit >= 1].[number; digit >= 1]. Let me know if this is not what you are after

                                                                                  Regex demo here

                                                                                  Edit

                                                                                  I believe you are after the symmetric difference of sets for files 1 and 2. Which is a fancy way of saying "things in A & B, that are not in both"

                                                                                  Which can be done with literation:

                                                                                  # Iteration
                                                                                  # A set has no duplicates, and is unordered
                                                                                  sym_dif = set()
                                                                                  for match in matches_1:
                                                                                      if match not in matches_2:
                                                                                          sym_dif.add(match)
                                                                                  
                                                                                  >>> list(sym_dif)
                                                                                  ['GCF_001297745.1', 'GCA_001297745.1']
                                                                                  

                                                                                  I think your mistake was not using a set, you should't have any duplicates, and using matches_1 == matches_2. The lists won't be the same. You should check if it is not in the other set.

                                                                                  Or using this set notation which is the preferred method:

                                                                                  >>> list(set(matches_1).symmetric_difference(set(matches_2)))
                                                                                  ['GCF_001297745.1', 'GCA_001297745.1']
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/71789818

                                                                                  QUESTION

                                                                                  Is there a way to permute inside using to variables in bash?
                                                                                  Asked 2021-Dec-09 at 23:50

                                                                                  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.

                                                                                  1. Input files: Highland.ped - Midland.ped - Lowland.ped
                                                                                  2. Exclude-map files: HighlandMidland.txt - HighlandLowland.txt - MidlandLowland.txt
                                                                                  3. Output files: HighlandMidland - HighlandLowland - MidlandHighland - MidlandLowland - LowlandHighland - LowlandMidland

                                                                                  The general code is:

                                                                                  plink2 --file Highland --exclude HighlandMidland.txt --out HighlandMidland
                                                                                  plink2 --file Highland --exclude HighlandLowland.txt --out HighlandLowland
                                                                                  plink2 --file Midland --exclude HighlandMidland.txt --out MidlandHighland
                                                                                  plink2 --file Midland --exclude MidlandLowland.txt --out MidlandLowland
                                                                                  plink2 --file Lowland --exclude HighlandLowland.txt --out LowlandHighland
                                                                                  plink2 --file Lowland --exclude MidlandLowland.txt --out LowlandMidland
                                                                                  

                                                                                  To avoid repeating this code 6 different times I would like to use the variables listed above (1, 2 and 3) to create single output files. Outputfiles are a permutation with replacements of the inputfile names.

                                                                                  ANSWER

                                                                                  Answered 2021-Dec-09 at 23:50

                                                                                  Honestly, I think your current code is quite clear; but if you really want to write this as a loop, here's one possibility:

                                                                                  lands=(Highland Midland Lowland)
                                                                                  for (( i = 0 ; i < ${#lands[@]} ; ++i )) ; do
                                                                                    for (( j = i + 1 ; j < ${#lands[@]} ; ++j )) ; do
                                                                                      plink2 --file "${lands[i]}" --exclude "${lands[i]}${lands[j]}.txt" --out "${lands[i]}${lands[j]}"
                                                                                      plink2 --file "${lands[j]}" --exclude "${lands[i]}${lands[j]}.txt" --out "${lands[j]}${lands[i]}"
                                                                                    done
                                                                                  done
                                                                                  

                                                                                  and here's another:

                                                                                  lands=(Highland Midland Lowland)
                                                                                  for (( i = 0 ; i < ${#lands[@]} ; ++i )) ; do
                                                                                    for (( j = 0 ; j < ${#lands[@]} ; ++j )) ; do
                                                                                      if [[ "$i" != "$j" ]] ; then
                                                                                        plink2 \
                                                                                          --file "${lands[i]}" \
                                                                                          --exclude "$lands[i < j ? i : j]}$lands[i < j ? j : i]}.txt" \
                                                                                          --out "${lands[i]}${lands[j]}"
                                                                                      fi
                                                                                    done
                                                                                  done
                                                                                  

                                                                                  . . . but one common factor between both of the above is that they're much less clear than your current code!

                                                                                  Source https://stackoverflow.com/questions/70298074

                                                                                  QUESTION

                                                                                  BigQuery Regex to extract string between two substrings
                                                                                  Asked 2021-Dec-09 at 01:11

                                                                                  From this example string:

                                                                                  {&q;somerandomtext&q;:{&q;Product&q;:{&q;TileID&q;:0,&q;Stockcode&q;:1234,&q;variant&q;:&q;genomics&q;,&q;available&q;:0"}
                                                                                  

                                                                                  I'm trying to extract the Stockcode only.

                                                                                  REGEXP_REPLACE(col, r".*,&q;Stockcode&q;:/([^/$]*)\,&q;.*", r"\1")
                                                                                  

                                                                                  So the result should be

                                                                                  1234

                                                                                  however my Regex still returns the entire contents.

                                                                                  ANSWER

                                                                                  Answered 2021-Dec-09 at 01:11

                                                                                  use regexp_extract(col, r"&q;Stockcode&q;:([^/$]*?),&q;.*")

                                                                                  if applied to sample data in your question - output is

                                                                                  Source https://stackoverflow.com/questions/70283253

                                                                                  QUESTION

                                                                                  how to stop letter repeating itself python
                                                                                  Asked 2021-Nov-25 at 18:33

                                                                                  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

                                                                                  import json
                                                                                  
                                                                                  val = open("data.json")
                                                                                  val1 = json.load(val)#loads the list
                                                                                  
                                                                                  
                                                                                  a = input("Enter a Jumbled word ")#takes a word from user
                                                                                  a = list(a)#changes into list to iterate
                                                                                  
                                                                                  
                                                                                  for x in val1:#iterates words from list
                                                                                      for somethin in a:#iterates letters from list
                                                                                          if somethin in list(x):#checks if the letter is in the iterated word
                                                                                              continue
                                                                                          else:
                                                                                              break
                                                                                      else:#checks if the loop ended correctly (that means word has same letters)
                                                                                          if len(a) != len(list(x)):#checks if it has same number of letters
                                                                                              continue#returns
                                                                                          else:
                                                                                              print(x)#continues the loop to see if there are more like that
                                                                                  

                                                                                  EDIT: many people wanted the json file so here it is

                                                                                  ['Torres Strait Creole', 'good bye', 'agon', "queen's guard", 'animosity', 'price list', 'subjective', 'means', 'severe', 'knockout', 'life-threatening', 'entry into the war', 'dominion', 'damnify', 'packsaddle', 'hallucinate', 'lumpy', 'inception', 'Blankenese', 'cacophonous', 'zeptomole', 'floccinaucinihilipilificate', 'abashed', 'abacterial', 'ableism', 'invade', 'cohabitant', 'handicapped', 'obelus', 'triathlon', 'habitue', 'instigate', 'Gladstone Gander', 'Linked Data', 'seeded player', 'mozzarella', 'gymnast', 'gravitational force', 'Friedelehe', 'open up', 'bundt cake', 'riffraff', 'resourceful', 'wheedle', 'city center', 'gorgonzola', 'oaf', 'auf', 'oafs', 'galoot', 'imbecile', 'lout', 'moron', 'news leak', 'crate', 'aggregator', 'cheating', 'negative growth', 'zero growth', 'defer', 'ride back', 'drive back', 'start back', 'shy back', 'spring back', 'shrink back', 'shy away', 'abderian', 'unable', 'font manager', 'font management software', 'consortium', 'gown', 'inject', 'ISO 639', 'look up', 'cross-eyed', 'squinting', 'health club', 'fitness facility', 'steer', 'sunbathe', 'combatives', 'HTH', 'hope that helps', 'How The Hell', 'distributed', 'plum cake', 'liberalization', 'macchiato', 'caffè macchiato', 'beach volley', 'exult', 'jubilate', 'beach volleyball', 'be beached', 'affogato', 'gigabyte', 'terabyte', 'petabyte', 'undressed', 'decameter', 'sensual', 'boundary marker', 'poor man', 'cohabitee', 'night sleep', 'protruding ears', 'three quarters of an hour', 'spermophilus', 'spermophilus stricto sensu', "devil's advocate", 'sacred king', 'sacral king', 'myr', 'million years', 'obtuse-angled', 'inconsolable', 'neurotic', 'humiliating', 'mortifying', 'theological', 'rematch', 'varıety', 'be short', 'ontological', 'taxonomic', 'taxonomical', 'toxicology testing', 'on the job training', 'boulder', 'unattackable', 'inviolable', 'resinous', 'resiny', 'ionizing radiation', 'citrus grove', 'comic book shop', 'preparatory measure', 'written account', 'brittle', 'locker', 'baozi', 'bao', 'bau', 'humbow', 'nunu', 'bausak', 'pow', 'pau', 'yesteryear', 'fire drill', 'rotted', 'putto', 'overthrow', 'ankle monitor', 'somewhat stupid', 'a little stupid', 'semordnilap', 'pangram', 'emordnilap', 'person with a sunlamp tan', 'tittle', 'incompatible', 'autumn wind', 'dairyman', 'chesty', 'lacustrine', 'chronophotograph', 'chronophoto', 'leg lace', 'ankle lace', 'ankle lock', 'Babelfy', 'ventricular', 'recurrent', 'long-lasting', 'long-standing', 'long standing', 'sea bass', 'reap', 'break wind', 'chase away', 'spark', 'speckle', 'take back', 'Westphalian', 'Aeolic Greek', 'startup', 'abseiling', 'impure', 'bottle cork', 'paralympic', 'work out', 'might', 'ice-cream man', 'ice cream man', 'ice cream maker', 'ice-cream maker', 'traveling', 'special delivery', 'prizefighter', 'abs', 'ab', 'churro', 'pilfer', 'dehumanize', 'fertilize', 'inseminate', 'digitalize', 'fluke', 'stroke of luck', 'decontaminate', 'abandonware', 'manzanita', 'tule', 'jackrabbit', 'system administrator', 'system admin', 'springtime lethargy', 'Palatinean', 'organized religion', 'bearing puller', 'wheel puller', 'gear puller', 'shot', 'normalize', 'palindromic', 'lancet window', 'terminological', 'back of head', 'dragon food', 'barbel', 'Central American Spanish', 'basis', 'birthmark', 'blood vessel', 'ribes', 'dog-rose', 'dreadful', 'freckle', 'free of charge', 'weather verb', 'weather sentence', 'gipsy', 'gypsy', 'glutton', 'hump', 'low voice', 'meek', 'moist', 'river mouth', 'turbid', 'multitude', 'palate', 'peak of mountain', 'poetry', 'pure', 'scanty', 'spicy', 'spicey', 'spruce', 'surface', 'infected', 'copulate', 'dilute', 'dislocate', 'grow up', 'hew', 'hinder', 'infringe', 'inhabit', 'marry off', 'offend', 'pass by', 'brother of a man', 'brother of a woman', 'sister of a man', 'sister of a woman', 'agricultural farm', 'result in', 'rebel', 'strew', 'scatter', 'sway', 'tread', 'tremble', 'hog', 'circuit breaker', 'Southern Quechua', 'safety pin', 'baby pin', 'college student', 'university student', 'pinus sibirica', 'Siberian pine', 'have lunch', 'floppy', 'slack', 'sloppy', 'wishi-washi', 'turn around', 'bogeyman', 'selfish', 'Talossan', 'biomembrane', 'biological membrane', 'self-sufficiency', 'underevaluation', 'underestimation', 'opisthenar', 'prosody', 'Kumhar Bhag Paharia', 'psychoneurotic', 'psychoneurosis', 'levant', "couldn't-care-less attitude", 'noctambule', 'acid-free paper', 'decontaminant', 'woven', 'wheaten', 'waste-ridden', 'war-ridden', 'violence-ridden', 'unwritten', 'typewritten', 'spoken', 'abiogenetically', 'rasp', 'abstractly', 'cyclically', 'acyclically', 'acyclic', 'ad hoc', 'spare tire', 'spare wheel', 'spare tyre', 'prefabricated', 'ISO 9000', 'Barquisimeto', 'Maracay', 'Ciudad Guayana', 'San Cristobal', 'Barranquilla', 'Arequipa', 'Trujillo', 'Cusco', 'Callao', 'Cochabamba', 'Goiânia', 'Campinas', 'Fortaleza', 'Florianópolis', 'Rosario', 'Mendoza', 'Bariloche', 'temporality', 'papyrus sedge', 'paper reed', 'Indian matting plant', 'Nile grass', 'softly softly', 'abductive reasoning', 'abductive inference', 'retroduction', 'Salzburgian', 'cymotrichous', 'access point', 'wireless access point', 'dynamic DNS', 'IP address', 'electrolyte', 'helical', 'hydrometer', 'intranet', 'jumper', 'MAC address', 'Media Access Control address', 'nickel–cadmium battery', 'Ni-Cd battery', 'oscillograph', 'overload', 'photovoltaic', 'photovoltaic cell', 'refractor telescope', 'autosome', 'bacterial artificial chromosome', 'plasmid', 'nucleobase', 'base pair', 'base sequence', 'chromosomal deletion', 'deletion', 'deletion mutation', 'gene deletion', 'chromosomal inversion', 'comparative genomics', 'genomics', 'cytogenetics', 'DNA replication', 'DNA repair', 'DNA sequence', 'electrophoresis', 'functional genomics', 'retroviral', 'retroviral infection', 'acceptance criteria', 'batch processing', 'business rule', 'code review', 'configuration management', 'entity–relationship model', 'lifecycle', 'object code', 'prototyping', 'pseudocode', 'referential', 'reusability', 'self-join', 'timestamp', 'accredited', 'accredited translator', 'certify', 'certified translation', 'computer-aided design', 'computer-aided', 'computer-assisted', 'management system', 'computer-aided translation', 'computer-assisted translation', 'machine-aided translation', 'conference interpreter', 'freelance translator', 'literal translation', 'mother-tongue', 'whispered interpreting', 'simultaneous interpreting', 'simultaneous interpretation', 'base anhydride', 'binary compound', 'absorber', 'absorption coefficient', 'attenuation coefficient', 'active solar heater', 'ampacity', 'amorphous semiconductor', 'amorphous silicon', 'flowerpot', 'antireflection coating', 'antireflection', 'armored cable', 'electric arc', 'breakdown voltage','casing', 'facing', 'lining', 'assumption of Mary', 'auscultation']
                                                                                  

                                                                                  Just a example and the dictionary is full of items

                                                                                  ANSWER

                                                                                  Answered 2021-Nov-25 at 18:33

                                                                                  As 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.

                                                                                  sorted_jumbled_word = sorted(a)
                                                                                  for word in val1:
                                                                                      if len(sorted_jumbled_word) == len(word) and sorted(word) == sorted_jumbled_word:
                                                                                          print(word)
                                                                                  

                                                                                  Checking by length first reduces unnecessary sorting. If doing this repeatedly, you might want to create a dictionary of the words in the data file with their sorted versions, to avoid having to repeatedly sort them.

                                                                                  There are spaces and punctuation in some of the terms in your word list. If you want to make the comparison ignoring spaces then remove them from both the jumbled word and the list of unjumbled words, using e.g. word = word.replace(" ", "")

                                                                                  Source https://stackoverflow.com/questions/70112201

                                                                                  QUESTION

                                                                                  Split multiallelic to biallelic in vcf by plink 1.9 and its variant name
                                                                                  Asked 2021-Nov-17 at 13:56

                                                                                  I am trying to use plink1.9 to split multiallelic into biallelic. The input is that

                                                                                  1       chr1:930939:G:A 0       930939  G       A
                                                                                  1       chr1:930947:G:A 0       930947  A       G
                                                                                  1       chr1:930952:G:A;chr1:930952:G:C 0       930952  A       G
                                                                                  

                                                                                  What it done is:

                                                                                  1       chr1:930939:G:A 0       930939  G       A
                                                                                  1       chr1:930947:G:A 0       930947  A       G
                                                                                  1       chr1:930952:G:A;chr1:930952:G:C 0       930952  A       G
                                                                                  1       chr1:930952:G:A;chr1:930952:G:C 0       930952  A       G
                                                                                  

                                                                                  What I expect is:

                                                                                  1       chr1:930939:G:A 0       930939  G       A
                                                                                  1       chr1:930947:G:A 0       930947  A       G
                                                                                  1       chr1:930952:G:A 0       930952  A       G
                                                                                  1       chr1:930952:G:C 0       930952  A       G
                                                                                  

                                                                                  Please help me to make a vcf or ped or map file like what I expect. Thank you.

                                                                                  ANSWER

                                                                                  Answered 2021-Nov-17 at 09:45

                                                                                  QUESTION

                                                                                  Delete specific letter in a FASTA sequence
                                                                                  Asked 2021-Oct-12 at 21:00

                                                                                  I have a FASTA file that has about 300000 sequences but some of the sequences are like these

                                                                                  >Spike|hCoV-19/Wuhan/WH02/2019|2019-12-31|EPI_ISL_406799|Original|hCoV-19^^Wuhan|Human|General Hospital of Central Theater Command of People's Liberation Army of China|BGI & Institute of Microbiology|Hunter|China
                                                                                  MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQDVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVITEXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT*
                                                                                  
                                                                                  >Spike|hCoV-19/England/PORT-2DE4EF/2020|2020-00-00|EPI_ISL_1310367|Original|hCoV-19^^England|Human|Centre for Enzyme Innovation|COVID-19 Genomics UK (COG-UK) Consortium|Robson|United Kingdom
                                                                                  MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSVLEPLVDLPIGINITRFQTLLALHRSYLTPGDSXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLDILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXRDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYDPLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT*
                                                                                  
                                                                                  >Spike|hCoV-19/England/PORT-2DE616/2020|2020-00-00|EPI_ISL_1310384|Original|hCoV-19^^England|Human|Centre for Enzyme Innovation|COVID-19 Genomics UK (COG-UK) Consortium|Robson|United Kingdom
                                                                                  MFVFLVLLPLVSSQCVNLTTRTQLPPAYTNSFTRGVYYPDKVFRSSVLHSTQDLFLPFFSNVTWFHAIHVSGTNGTKRFDNPVLPFNDGVYFASTEKSNIIRGWIFGTTLDSKTQSLLIVNNATNVVIKVCEFQFCNDPFLGVYYHKNNKSWMESEFRVYSSANNCTFEYVSQPFLMDLEGKQGNFKNLREFVFKNIDGYFKIYSKHTPINLVRDLPQGFSVLEPLVDLPIGINITRFQTLLALHRSYLTPGDSSSGWTAGSAAYYVGYLQLRTFLLKYNENGTITDAVDCALDPLSETKCTLKSFTVEKGIYQTSNFRVQPTESIVRFPNITNLCPFGEVFNATRFASVYAWNRKRISNCVADYSVLYNSASFSTFKCYGVSPTKLNDLCFTNVYADSFVIRGDEVRQIAPGQTGKIADYNYKLPDDFTGCVIAWNSNNLDSKVGGNYNYLYRLFRKSNLKPFERDISTEIYQAGSTPCNGVEGFNCYFPLQSYGFQPTNGVGYQPYRVVVLSFELLHAPATVCGPKKSTNLVKNKCVNFNFNGLTGTGVLTESNKKFLPFQQFGRDIADTTDAVRDPQTLEILDITPCSFGGVSVITPGTNTSNQVAVLYQGVNCTEVPVAIHADQLTPTWRVYSTGSNVFQTRAGCLIGAEHVNNSYECDIPIGAGICASYQTQTNSPRRARSVASQSIIAYTMSLGAENSVAYSNNSIAIPTNFTISVTTEILPVSMTKTSVDCTMYICGDSTECSNLLLQYGSFCTQLNRALTGIAVEQDKNTQEVFAQVKQIYKTPPIKDFGGFNFSQILPDPSKPSKRSFIEDLLFNKVTLADAGFIKQYGDCLGDIAARDLICAQKFNGLTVLPPLLTDEMIAQYTSALLAGTITSGWTFGAGAALQIPFAMQMAYRFNGIGVTQNVLYENQKLIANQFNSAIGKIQDSLSSTASALGKLQDVVNQNAQALNTLVKQLSSNFGAISSVLNDILSRLDKVEAEVQIDRLITGRLQSLQTYVTQQLIRAAEIRASANLAATKMSECVLGQSKRVDFCGKGYHLMSFPQSAPHGVVFLHVTYVPAQEKNFTTAPAICHDGKAHFPREGVFVSNGTHWFVTQRNFYEPQIITTDNTFVSGNCDVVIGIVNNTVYYLLQPELDSFKEELDKYFKNHTSPDVDLGDISGINASVVNIQKEIDRLNEVAKNLNESLIDLQELGKYEQYIKWPWYIWLGFIAGLIAIVMVTIMLCCMTSCCSCLKGCCSCGSCCKFDEDDSEPVLKGVKLHYT*
                                                                                  
                                                                                  

                                                                                  I want to delete all the sequences that contain the letter x in them, how can I do that?

                                                                                  ANSWER

                                                                                  Answered 2021-Oct-12 at 20:28

                                                                                  You 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:

                                                                                  no_X_FASTA = "".join(re.findall(r">.+\n[^X]+\n",text))
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/69545912

                                                                                  QUESTION

                                                                                  How to get the words within the first single quote in r using regex?
                                                                                  Asked 2021-Oct-04 at 22:27

                                                                                  For example, I have two strings:

                                                                                  stringA = "'contentX' is not one of ['Illumina NovaSeq 6000', 'Other', 'Ion Torrent PGM', 'Illumina HiSeq X Ten', 'Illumina HiSeq 4000', 'Illumina NextSeq', 'Complete Genomics', 'Illumina Genome Analyzer II']"
                                                                                  

                                                                                  I am not familiar how to do regex and stuck to extract words within the first single quotes.

                                                                                  Expected

                                                                                  ## do regex here
                                                                                  gsub("'(.*)'", "\\1", stringA) # not working
                                                                                  
                                                                                  > "contentX"
                                                                                  

                                                                                  ANSWER

                                                                                  Answered 2021-Oct-04 at 22:27

                                                                                  For your example your pattern would be:

                                                                                  gsub("^'(.*?)'.*", "\\1", stringA)
                                                                                  

                                                                                  https://regex101.com/r/bs3lwJ/1

                                                                                  First we assert we're at the beginning of the string and that the following character is a single quote with ^'. Then we capture everything up until the next single quote in group 1, using (.*?)'.

                                                                                  Note that we need the ? in .*? otherwise .* will be "greedy" and match all the way through to the last occurrence of a single quote, rather then the next single quote.

                                                                                  Source https://stackoverflow.com/questions/69442717

                                                                                  QUESTION

                                                                                  Does Apache Spark 3 support GPU usage for Spark RDDs?
                                                                                  Asked 2021-Sep-23 at 05:53

                                                                                  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 05:53

                                                                                  As 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++.

                                                                                  Source https://stackoverflow.com/questions/69273205

                                                                                  QUESTION

                                                                                  Aggregating and summing columns across 1500 files by matching IDs in R (or bash)
                                                                                  Asked 2021-Sep-07 at 13:09

                                                                                  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:

                                                                                  #IID    HOM_REF_CT  HOM_ALT_SNP_CT  HET_SNP_CT  DIPLOID_TRANSITION_CT   DIPLOID_TRANSVERSION_CT DIPLOID_NONSNP_NONSYMBOLIC_CT   DIPLOID_SINGLETON_CT    HAP_REF_INCL_FEMALE_Y_CT    HAP_ALT_INCL_FEMALE_Y_CT    MISSING_INCL_FEMALE_Y_CT
                                                                                  LP5987245   10  0   6   53  0   52  0   67  70  32
                                                                                  LP098324    34  51  10  37  100 12  59  11  49  0
                                                                                  LP908325    0   45  39  54  68  48  51  58  31  2
                                                                                  LP0932325   7   72  0   2   92  64  13  52  0   100
                                                                                  LP08324 92  93  95  39  23  0   27  75  49  14
                                                                                  LP034252    85  46  10  69  20  8   80  81  94  23
                                                                                  

                                                                                  In reality each file has 80000 IIDs and is roughly 1-10MB in size. Each IID is unique and found once per file.

                                                                                  I would like to create a single file matched by IID with each column value summed. The column names are the same across files.

                                                                                  I have tried:

                                                                                  fnames <- list.files(pattern = "\\.scount")
                                                                                  df_list <- lapply(fnames, read.table, header = TRUE)
                                                                                  df_all <- do.call(rbind, df_list)
                                                                                  x <- aggregate(IID ~ , data = df_all, sum)
                                                                                  

                                                                                  But this is really slow for the number of files and the # at the start of the #IID column is a real pain to work around.

                                                                                  Any help would be greatly appreciated

                                                                                  ANSWER

                                                                                  Answered 2021-Sep-07 at 11:10

                                                                                  a tidyverse solution

                                                                                  df2 <- df
                                                                                  df3 <- df
                                                                                  
                                                                                  df_list <- list(df,df2,df3)
                                                                                  
                                                                                  df_all <- do.call(rbind, df_list)
                                                                                  
                                                                                  library(dplyr)
                                                                                  
                                                                                  df_all %>%
                                                                                  group_by(IID) %>%
                                                                                  summarise_all(sum)
                                                                                  

                                                                                  solution with data.table

                                                                                  df_list <- list(df,df2,df3)
                                                                                  
                                                                                  df_all <- do.call(rbind, df_list)
                                                                                  
                                                                                  library(data.table)
                                                                                  
                                                                                  setDT(df_all)
                                                                                  df_all[, lapply(.SD, sum), by=IID]
                                                                                  

                                                                                  Source https://stackoverflow.com/questions/69086946

                                                                                  QUESTION

                                                                                  Usage of compression IO functions in apache arrow
                                                                                  Asked 2021-Jun-02 at 18:58

                                                                                  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 18:58

                                                                                  Here is an example program that inflates a compressed zlib file and reads it as CSV.

                                                                                  #include 
                                                                                  
                                                                                  #include 
                                                                                  #include 
                                                                                  #include 
                                                                                  #include 
                                                                                  #include 
                                                                                  
                                                                                  arrow::Status RunMain(int argc, char **argv) {
                                                                                  
                                                                                    if (argc < 2) {
                                                                                      return arrow::Status::Invalid(
                                                                                          "You must specify a gzipped CSV file to read");
                                                                                    }
                                                                                  
                                                                                    std::string file_to_read = argv[1];
                                                                                    ARROW_ASSIGN_OR_RAISE(auto in_file,
                                                                                                          arrow::io::ReadableFile::Open(file_to_read));
                                                                                    ARROW_ASSIGN_OR_RAISE(auto codec,
                                                                                                          arrow::util::Codec::Create(arrow::Compression::GZIP));
                                                                                    ARROW_ASSIGN_OR_RAISE(
                                                                                        auto compressed_in,
                                                                                        arrow::io::CompressedInputStream::Make(codec.get(), in_file));
                                                                                  
                                                                                    auto read_options = arrow::csv::ReadOptions::Defaults();
                                                                                    auto parse_options = arrow::csv::ParseOptions::Defaults();
                                                                                    auto convert_options = arrow::csv::ConvertOptions::Defaults();
                                                                                    ARROW_ASSIGN_OR_RAISE(
                                                                                        auto table_reader,
                                                                                        arrow::csv::TableReader::Make(arrow::io::default_io_context(),
                                                                                                                      std::move(compressed_in), read_options,
                                                                                                                      parse_options, convert_options));
                                                                                  
                                                                                    ARROW_ASSIGN_OR_RAISE(auto table, table_reader->Read());
                                                                                    std::cout << "The table had " << table->num_rows() << " rows and "
                                                                                              << table->num_columns() << " columns." << std::endl;
                                                                                  
                                                                                    return arrow::Status::OK();
                                                                                  }
                                                                                  
                                                                                  int main(int argc, char **argv) {
                                                                                    arrow::Status st = RunMain(argc, argv);
                                                                                    if (!st.ok()) {
                                                                                      std::cerr << st << std::endl;
                                                                                      return 1;
                                                                                    }
                                                                                    return 0;
                                                                                  }
                                                                                  

                                                                                  Compression is handled in different ways in different parts of Arrow. The file readers typically accept an arrow::io::InputStream. You should be able to use arrow::io::CompressedInputStream to wrap an arrow::io::InputStream with decompression. This gives you whole-file compression. This is fine for something like CSV.

                                                                                  For Parquet, this approach does not work (ParquetFileReader::Open expects arrow::io::RandomAccessFile). For IPC, this approach is inefficient (unless you are reading the entire file). Effective reading of these formats involves seekable reads which is not possible with whole-file compression. Both formats support their own format-specific compression options. You only need to specify these options on write. On read the compression will be detected from the metadata (the metadata is stored uncompressed) of the file itself. If you are writing data you can find the information in parquet::ArrowWriterProperties and arrow::ipc::WriteOptions.

                                                                                  Since whole-file compression is still a thing for CSV the datasets API has recently (as of 4.0.0) added support for detecting compression from file extensions for CSV datasets. More details can be found here.

                                                                                  As for documentation and an object diagram, those are excellent topics for the user mailing list, or you are welcome to provide a pull request.

                                                                                  Source https://stackoverflow.com/questions/67799265

                                                                                  Community Discussions, Code Snippets contain sources that include Stack Exchange Network

                                                                                  Vulnerabilities

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                                                                                  Install smoove

                                                                                  you can get smoove and all dependencies via (a large) docker image:. Or, you can download a smoove binary from here: https://github.com/brentp/smoove/releases When run without any arguments, smoove will show you which of it's dependencies it can find so you can adjust your $PATH and install accordingly.

                                                                                  Support

                                                                                  A panic with a message like Segmentation fault (core dumped) | bcftools view -O z -c 1 -o is likely to mean you have an old version of bcftools. see #10. smoove will write to the system TMPDIR. For large cohorts, make sure to set this to something with a lot of space. e.g. export TMPDIR=/path/to/big. smoove requires recent version of lumpy and lumpy_filter so build those from source or get the most recent bioconda version.
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