These Are Not the K-mers You Are Looking For: Efficient Online K-mer Counting Using a Probabilistic Data Structure
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{"title"=>"These are not the K-mers you are looking for: Efficient online K-mer counting using a probabilistic data structure", "type"=>"journal", "authors"=>[{"first_name"=>"Qingpeng", "last_name"=>"Zhang", "scopus_author_id"=>"55922164900"}, {"first_name"=>"Jason", "last_name"=>"Pell", "scopus_author_id"=>"55205024000"}, {"first_name"=>"Rosangela", "last_name"=>"Canino-Koning", "scopus_author_id"=>"55204861400"}, {"first_name"=>"Adina Chuang", "last_name"=>"Howe", "scopus_author_id"=>"55204975200"}, {"first_name"=>"C. Titus", "last_name"=>"Brown", "scopus_author_id"=>"22958999700"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "arxiv"=>"1309.2975", "scopus"=>"2-s2.0-84904876565", "pui"=>"373631577", "doi"=>"10.1371/journal.pone.0101271", "isbn"=>"1932-6203", "sgr"=>"84904876565", "pmid"=>"25062443"}, "id"=>"a44e4414-b32e-313e-accd-553a4d3d80d0", "abstract"=>"K-mer abundance analysis is widely used for many purposes in nucleotide sequence analysis, including data preprocessing for de novo assembly, repeat detection, and sequencing coverage estimation. We present the khmer software package for fast and memory efficient online counting of k-mers in sequencing data sets. Unlike previous methods based on data structures such as hash tables, suffix arrays, and trie structures, khmer relies entirely on a simple probabilistic data structure, a Count-Min Sketch. The Count-Min Sketch permits online updating and retrieval of k-mer counts in memory which is necessary to support online k-mer analysis algorithms. On sparse data sets this data structure is considerably more memory efficient than any exact data structure. In exchange, the use of a Count-Min Sketch introduces a systematic overcount for k-mers; moreover, only the counts, and not the k-mers, are stored. Here we analyze the speed, the memory usage, and the miscount rate of khmer for generating k-mer frequency distributions and retrieving k-mer counts for individual k-mers. We also compare the performance of khmer to several other k-mer counting packages, including Tallymer, Jellyfish, BFCounter, DSK, KMC, Turtle and KAnalyze. Finally, we examine the effectiveness of profiling sequencing error, k-mer abundance trimming, and digital normalization of reads in the context of high khmer false positive rates. khmer is implemented in C++ wrapped in a Python interface, offers a tested and robust API, and is freely available under the BSD license at github.com/ged-lab/khmer.", "link"=>"http://www.mendeley.com/research/these-not-kmers-looking-efficient-online-kmer-counting-using-probabilistic-data-structure-7", "reader_count"=>123, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>40, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>1, "Student > Master"=>13, "Other"=>4, "Student > Bachelor"=>6, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Librarian"=>1, "Researcher"=>40, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>43, "Student > Postgraduate"=>1, "Student > Master"=>13, "Other"=>4, "Student > Bachelor"=>6, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>6, "Unspecified"=>6, "Biochemistry, Genetics and Molecular Biology"=>15, "Agricultural and Biological Sciences"=>61, "Medicine and Dentistry"=>2, "Business, Management and Accounting"=>1, "Physics and Astronomy"=>1, "Chemistry"=>2, "Social Sciences"=>1, "Computer Science"=>25, "Immunology and Microbiology"=>2, "Earth and Planetary Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>6}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Chemistry"=>{"Chemistry"=>2}, "Social Sciences"=>{"Social Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>2}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>61}, "Computer Science"=>{"Computer Science"=>25}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>15}, "Unspecified"=>{"Unspecified"=>6}}, "reader_count_by_country"=>{"Netherlands"=>1, "South Korea"=>1, "Czech Republic"=>1, "United States"=>6, "Japan"=>1, "United Kingdom"=>2, "Italy"=>1, "France"=>1, "Belarus"=>1, "Portugal"=>1, "Germany"=>1, "Spain"=>1}, "group_count"=>5}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1610302"], "description"=>"<p><b>A comparison of assembling reads digitally normalized with low memory/high false positive rates. The reads were digitally normalized to C = 20 (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101271#pone.0101271-Brown1\" target=\"_blank\">[21]</a> for more information) and were assembled using Velvet. We measured total length of assembly, as well as percent of true MG1655 genome covered by the assembly using QUAST.</b></p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "genome", "low-memory"], "article_id"=>1118682, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.t005", "stats"=>{"downloads"=>8, "page_views"=>108, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_E_coli_genome_assembly_after_low_memory_digital_normalization_/1118682", "title"=>"<i>E. coli</i> genome assembly after low-memory digital normalization.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610282"], "description"=>"<p>All programs executed in time approximately linear with the number of input reads.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "takes", "k-mer", "counting", "tools", "abundance"], "article_id"=>1118663, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.g001", "stats"=>{"downloads"=>2, "page_views"=>64, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_the_time_it_takes_for_k_mer_counting_tools_to_calculate_k_mer_abundance_histograms_with_time_y_axis_in_seconds_against_data_set_size_in_number_of_reads_x_axis_/1118663", "title"=>"Comparison of the time it takes for k-mer counting tools to calculate k-mer abundance histograms, with time (y axis, in seconds) against data set size (in number of reads, x axis).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610297"], "description"=>"<p>The increasing numbers of unique k-mers are a sign of the increasing sequencing error towards the 3′ end of reads. Note that there are only 69 starting positions for 32-mers in a 100 base read.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "k-mers", "untrimmed", "100-bp", "illumina", "shotgun"], "article_id"=>1118677, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.g007", "stats"=>{"downloads"=>3, "page_views"=>71, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Number_of_unique_k_mers_y_axis_by_starting_position_within_read_x_axis_in_an_untrimmed_E_coli_100_bp_Illumina_shotgun_data_set_for_k_17_and_k_32_/1118677", "title"=>"Number of unique k-mers (y axis) by starting position within read (x axis) in an untrimmed <i>E. coli</i> 100-bp Illumina shotgun data set, for k = 17 and k = 32.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610296"], "description"=>"<p>The five data sets are the same as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101271#pone-0101271-g005\" target=\"_blank\">Figure 5</a>.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "percent", "miscount", "k-mers", "incorrect", "plotted"], "article_id"=>1118676, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.g006", "stats"=>{"downloads"=>7, "page_views"=>144, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relation_between_percent_miscount_8212_amount_by_which_the_count_for_k_mers_is_incorrect_relative_to_its_true_count_8212_on_the_y_axis_plotted_against_false_positive_rate_x_axis_for_five_data_sets_/1118676", "title"=>"Relation between percent miscount — amount by which the count for k-mers is incorrect relative to its true count — on the y axis, plotted against false positive rate (x axis), for five data sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610293"], "description"=>"<p>BFCounter, DSK, Turtle, KAnalyze, and KMC do not support this functionality.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "k-mer", "counting", "tools", "retrieve", "counts", "randomly", "chosen", "k-mers", "plotted", "queried"], "article_id"=>1118673, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.g004", "stats"=>{"downloads"=>4, "page_views"=>90, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_for_several_k_mer_counting_tools_to_retrieve_the_counts_of_9_7_m_randomly_chosen_k_mers_y_axis_plotted_against_the_number_of_distinct_k_mers_in_the_data_set_being_queried_x_axis_/1118673", "title"=>"Time for several k-mer counting tools to retrieve the counts of 9.7 m randomly chosen k-mers (y axis), plotted against the number of distinct k-mers in the data set being queried (x axis).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610290"], "description"=>"<p>Note that khmer does not use the disk during counting or retrieval, although its hash tables can be saved for reuse.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "usage", "k-mer", "counting", "tools", "abundance", "histograms", "gb", "plotted", "k-mers"], "article_id"=>1118671, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.g003", "stats"=>{"downloads"=>6, "page_views"=>95, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Disk_storage_usage_of_different_k_mer_counting_tools_to_calculate_k_mer_abundance_histograms_in_GB_y_axis_plotted_against_the_number_of_distinct_k_mers_in_the_data_set_x_axis_/1118671", "title"=>"Disk storage usage of different k-mer counting tools to calculate k-mer abundance histograms in GB (y axis), plotted against the number of distinct k-mers in the data set (x axis).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610301"], "description"=>"<p><b>The results of digitally normalizing a 5 m read </b><b><i>E. coli</i></b><b> data set (1.4 GB) to C = 20 with k = 20 under several memory usage/false positive rates. The false positive rate (column 1) is empirically determined. We measured reads remaining, number of “true” k-mers missing from the data at each step, and the number of total k-mers remaining. Note: at high false positive rates, reads are erroneously removed due to inflation of k-mer counts.</b></p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools"], "article_id"=>1118681, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.t004", "stats"=>{"downloads"=>7, "page_views"=>55, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Low_memory_digital_normalization_/1118681", "title"=>"Low-memory digital normalization.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610284"], "description"=>"<p>Memory usage of k-mer counting tools when calculating k-mer abundance histograms, with maximum resident program size (y axis, in GB) plotted against the total number of distinct k-mers in the data set (x axis, billions of k-mers).</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "usage", "k-mer", "counting", "tools", "abundance", "plotted", "k-mers", "billions"], "article_id"=>1118666, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.g002", "stats"=>{"downloads"=>26, "page_views"=>83, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Memory_usage_of_k_mer_counting_tools_when_calculating_k_mer_abundance_histograms_with_maximum_resident_program_size_y_axis_in_GB_plotted_against_the_total_number_of_distinct_k_mers_in_the_data_set_x_axis_billions_of_k_mers_/1118666", "title"=>"Memory usage of k-mer counting tools when calculating k-mer abundance histograms, with maximum resident program size (y axis, in GB) plotted against the total number of distinct k-mers in the data set (x axis, billions of k-mers).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610299"], "description"=>"<p><b>The results of trimming reads at unique (erroneous) k-mers from a 5 m read </b><b><i>E. coli</i></b><b> data set (1.4 GB) in under 30 MB of RAM. After each iteration, we measured the total number of distinct k-mers in the data set, the total number of unique (and likely erroneous) k-mers remaining, and the number of unique k-mers present at the 3' end of reads.</b></p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "low-memory", "k-mer"], "article_id"=>1118679, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.t003", "stats"=>{"downloads"=>2, "page_views"=>61, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Iterative_low_memory_k_mer_trimming_/1118679", "title"=>"Iterative low-memory k-mer trimming.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610300"], "description"=>"<p>Benchmark soil metagenome data sets for k-mer counting performance, taken from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0101271#pone.0101271-Howe1\" target=\"_blank\">[11]</a>.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "metagenome", "sets", "k-mer", "counting", "taken"], "article_id"=>1118680, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.t001", "stats"=>{"downloads"=>13, "page_views"=>113, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmark_soil_metagenome_data_sets_for_k_mer_counting_performance_taken_from_11_/1118680", "title"=>"Benchmark soil metagenome data sets for k-mer counting performance, taken from [11].", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610298"], "description"=>"<p>Data sets used for analyzing miscounts.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "sets", "analyzing"], "article_id"=>1118678, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.t002", "stats"=>{"downloads"=>3, "page_views"=>56, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Data_sets_used_for_analyzing_miscounts_/1118678", "title"=>"Data sets used for analyzing miscounts.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-07-25 03:13:45"}
  • {"files"=>["https://ndownloader.figshare.com/files/1610294"], "description"=>"<p>The five data sets were chosen to have the same total number of distinct k-mers: one metagenome data set; a set of randomly generated k-mers; a set of reads, chosen with 3x coverage and 1% error, from a randomly generated genome; a simulated set of error-free reads (3x) chosen from a randomly generated genome and a set of <i>E. coli</i> reads.</p>", "links"=>[], "tags"=>["Computational biology", "genome analysis", "Genomic databases", "genetics", "genomics", "molecular biology", "Molecular biology techniques", "Sequencing techniques", "Genome sequencing", "Sequence analysis", "Computing methods", "cloud computing", "software engineering", "Software tools", "miscount", "k-mers", "incorrect", "plotted"], "article_id"=>1118674, "categories"=>["Biological Sciences"], "users"=>["Qingpeng Zhang", "Jason Pell", "Rosangela Canino-Koning", "Adina Chuang Howe", "C. Titus Brown"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0101271.g005", "stats"=>{"downloads"=>4, "page_views"=>77, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Relation_between_average_miscount_8212_amount_by_which_the_count_for_k_mers_is_incorrect_8212_on_the_y_axis_plotted_against_false_positive_rate_x_axis_for_five_data_sets_/1118674", "title"=>"Relation between average miscount — amount by which the count for k-mers is incorrect — on the y axis, plotted against false positive rate (x axis), for five data sets.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-07-25 03:13:45"}

PMC Usage Stats | Further Information

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Relative Metric

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