NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes
Publication Date
January 28, 2016
Journal
PLOS Computational Biology
Authors
Gabriel A. Al Ghalith, Emmanuel Montassier, Henry N. Ward & Dan Knights
Volume
12
Issue
1
Pages
e1004658
DOI
https://dx.plos.org/10.1371/journal.pcbi.1004658
Publisher URL
http://journals.plos.org/ploscompbiol/article?id=10.1371%2Fjournal.pcbi.1004658
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/26820746
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4731464
Europe PMC
http://europepmc.org/abstract/MED/26820746
Scopus
84956738953
Mendeley
http://www.mendeley.com/research/ninjaops-fast-accurate-marker-gene-alignment-using-concatenated-ribosomes
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Mendeley | Further Information

{"title"=>"NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes", "type"=>"journal", "authors"=>[{"first_name"=>"Gabriel A.", "last_name"=>"Al-Ghalith", "scopus_author_id"=>"56596663900"}, {"first_name"=>"Emmanuel", "last_name"=>"Montassier", "scopus_author_id"=>"35603945300"}, {"first_name"=>"Henry N.", "last_name"=>"Ward", "scopus_author_id"=>"57091184500"}, {"first_name"=>"Dan", "last_name"=>"Knights", "scopus_author_id"=>"35181437000"}], "year"=>2016, "source"=>"PLoS Computational Biology", "identifiers"=>{"issn"=>"15537358", "arxiv"=>"arXiv:1011.1669v3", "scopus"=>"2-s2.0-84956738953", "pui"=>"608034585", "doi"=>"10.1371/journal.pcbi.1004658", "isbn"=>"9788578110796", "sgr"=>"84956738953", "pmid"=>"26820746"}, "id"=>"623e0ed0-cd59-3158-970f-ee781a6a14d1", "abstract"=>"Author Summary The analysis of the microbial communities in and around us is a growing field of study, partly because of its major implications for human health, and partly because high-throughput DNA sequencing technology has only recently emerged to enable us to quantitatively study them. One of the most fundamental steps in analyzing these microbial communities is matching the microbial marker genes in environmental samples with existing databases to determine which microbes are present. The current techniques for doing this analysis are either slow or closed-source. We present an alternative technique that takes advantage of a high-speed Burrows-Wheeler alignment procedure combined with rapid filtering and parsing of the data to remove bottlenecks in the pipeline. We achieve an order-of-magnitude speedup over conventional techniques without sacrificing accuracy or memory use, and in some cases improving both significantly. Thus our method allows more biologists to process their own sequencing data without specialized computing resources, and it obtains more accurate and even optimal taxonomic annotation for their marker gene sequencing data.", "link"=>"http://www.mendeley.com/research/ninjaops-fast-accurate-marker-gene-alignment-using-concatenated-ribosomes", "reader_count"=>59, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Researcher"=>13, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>4, "Student > Master"=>13, "Other"=>2, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Researcher"=>13, "Student > Doctoral Student"=>5, "Student > Ph. D. Student"=>17, "Student > Postgraduate"=>4, "Student > Master"=>13, "Other"=>2, "Student > Bachelor"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>2, "Environmental Science"=>4, "Biochemistry, Genetics and Molecular Biology"=>4, "Agricultural and Biological Sciences"=>32, "Medicine and Dentistry"=>1, "Social Sciences"=>2, "Computer Science"=>5, "Immunology and Microbiology"=>4, "Earth and Planetary Sciences"=>2, "Economics, Econometrics and Finance"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>2}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>4}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>32}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Unspecified"=>{"Unspecified"=>2}, "Environmental Science"=>{"Environmental Science"=>4}}, "reader_count_by_country"=>{"New Zealand"=>1, "Canada"=>1, "United States"=>6, "Estonia"=>1}, "group_count"=>0}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/4281200"], "description"=>"<p>NINJA core programs are represented by pentagons, data files by cylinders, processes within a program as lists, index operations as rounded rectangles, and other swappable programs by other shapes. The entire upper-left branch of the schematic (from input references to bowtie-build and TaxMap) does not need to be performed if using an existing database, such as that supplied with NINJA. The python wrapper encompasses the remaining two branches (bottom and right) for convenience. In general, Ninja_prep prepares the concatesome, Ninja_filter prepares the reads for alignment, bowtie2 (or any BWT-enabled aligner) performs the alignment, and Ninja_parse merges the various pieces into a complete OTU table.</p>", "links"=>[], "tags"=>["generation sequencing pipelines", "Other features", "novel heuristic approaches", "concatenated reference sequences", "genomics data", "reference chromosome", "Operational Taxonomic Units", "contig assembly", "BW input", "memory footprint", "10 minutes", "Concatenated Ribosomes", "BIOM", "marker gene sequencing studies", "bioinformatics technologies", "microbiome studies", "reference genomes", "NGS", "Marker Gene Alignment", "gut microbiome 16 S genes", "NINJA", "generation sequencing", "novel method", "OTU"], "article_id"=>2630176, "categories"=>["Biophysics", "Space Science", "Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Gabriel A. Al-Ghalith", "Emmanuel Montassier", "Henry N. Ward", "Dan Knights"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004658.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Schematic_of_the_NINJA_pipeline_/2630176", "title"=>"Schematic of the NINJA pipeline.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 04:13:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/4281216"], "description"=>"<p>These histograms show the distribution of matches by algorithm, where NINJA match proportion is in red, USEARCH in blue, and the overlap in purple. The right-most bars in each histogram show more red than purple, indicating NINJA aligned more reads at higher accuracy than USEARCH in the corresponding bins. The thick vertical bars correspond to the average alignment accuracy, and the thin curves represent the interpolated distribution. A Student’s t-test on the mean alignment accuracy shows NINJA’s mean ID was 98.9%, while USEARCH 8 produced 98.7% with p-value < 2.2e-16. For USEARCH 8, fast settings are used (corresponding to the defaults in the QIIME pipeline). Total number of unique sequences mapped are 168,761, 170,063, and 169,313, for NINJA [default], NINJA [max], and USEARCH 8, respectively.</p>", "links"=>[], "tags"=>["generation sequencing pipelines", "Other features", "novel heuristic approaches", "concatenated reference sequences", "genomics data", "reference chromosome", "Operational Taxonomic Units", "contig assembly", "BW input", "memory footprint", "10 minutes", "Concatenated Ribosomes", "BIOM", "marker gene sequencing studies", "bioinformatics technologies", "microbiome studies", "reference genomes", "NGS", "Marker Gene Alignment", "gut microbiome 16 S genes", "NINJA", "generation sequencing", "novel method", "OTU"], "article_id"=>2630192, "categories"=>["Biophysics", "Space Science", "Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Gabriel A. Al-Ghalith", "Emmanuel Montassier", "Henry N. Ward", "Dan Knights"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004658.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Comparison_of_NINJA_and_USEARCH_8_best_match_accuracy_using_all_unique_matches_/2630192", "title"=>"Comparison of NINJA and USEARCH 8 best match accuracy using all unique matches.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 04:13:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/4281224"], "description"=>"<p>Each point on the graph represents a sequence for which both tools found a valid alignment. A point’s position along the X axis corresponds to alignment score (in %ID) for the match chosen by USEARCH 8, and its position on the Y axis corresponds to the alignment score against the match chosen by NINJA. Points along the diagonal represent sequences for which both tools picked the same quality match. Points above the diagonal correspond to sequences for which NINJA produced more accurate hits, and points below the diagonal represent sequences for which USEARCH 8 produced more accurate hits. Note the presence of a line at the top of the graph showing a number of sequences for which NINJA selected a perfect match from the database while USEARCH 8 could not.</p>", "links"=>[], "tags"=>["generation sequencing pipelines", "Other features", "novel heuristic approaches", "concatenated reference sequences", "genomics data", "reference chromosome", "Operational Taxonomic Units", "contig assembly", "BW input", "memory footprint", "10 minutes", "Concatenated Ribosomes", "BIOM", "marker gene sequencing studies", "bioinformatics technologies", "microbiome studies", "reference genomes", "NGS", "Marker Gene Alignment", "gut microbiome 16 S genes", "NINJA", "generation sequencing", "novel method", "OTU"], "article_id"=>2630200, "categories"=>["Biophysics", "Space Science", "Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Gabriel A. Al-Ghalith", "Emmanuel Montassier", "Henry N. Ward", "Dan Knights"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004658.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Alignment_accuracy_of_NINJA_vs_USEARCH_8_where_both_reported_a_match_/2630200", "title"=>"Alignment accuracy of NINJA vs USEARCH 8 (where both reported a match).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 04:13:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/4281210"], "description"=>"<p>Multi-threaded alignments are faster. For NINJA only, this represents the entire time from parsing the initial FASTA file to the completion of the OTU table. The sortMeRNA program took substantially longer than USEARCH 8 (approx. 8000s; bar not shown to preserve scale).</p>", "links"=>[], "tags"=>["generation sequencing pipelines", "Other features", "novel heuristic approaches", "concatenated reference sequences", "genomics data", "reference chromosome", "Operational Taxonomic Units", "contig assembly", "BW input", "memory footprint", "10 minutes", "Concatenated Ribosomes", "BIOM", "marker gene sequencing studies", "bioinformatics technologies", "microbiome studies", "reference genomes", "NGS", "Marker Gene Alignment", "gut microbiome 16 S genes", "NINJA", "generation sequencing", "novel method", "OTU"], "article_id"=>2630186, "categories"=>["Biophysics", "Space Science", "Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Gabriel A. Al-Ghalith", "Emmanuel Montassier", "Henry N. Ward", "Dan Knights"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004658.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Benchmark_of_runtimes_for_NINJA_compared_to_USEARCH_8_in_a_single_threaded_environment_/2630186", "title"=>"Benchmark of runtimes for NINJA compared to USEARCH 8 in a single-threaded environment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 04:13:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/4281182", "https://ndownloader.figshare.com/files/4281184", "https://ndownloader.figshare.com/files/4281186", "https://ndownloader.figshare.com/files/4281188", "https://ndownloader.figshare.com/files/4281190", "https://ndownloader.figshare.com/files/4281192", "https://ndownloader.figshare.com/files/4281194"], "description"=>"<div><p>The explosion of bioinformatics technologies in the form of next generation sequencing (NGS) has facilitated a massive influx of genomics data in the form of short reads. Short read mapping is therefore a fundamental component of next generation sequencing pipelines which routinely match these short reads against reference genomes for contig assembly. However, such techniques have seldom been applied to microbial marker gene sequencing studies, which have mostly relied on novel heuristic approaches. We propose NINJA Is Not Just Another OTU-Picking Solution (NINJA-OPS, or NINJA for short), a fast and highly accurate novel method enabling reference-based marker gene matching (picking Operational Taxonomic Units, or OTUs). NINJA takes advantage of the Burrows-Wheeler (BW) alignment using an artificial reference chromosome composed of concatenated reference sequences, the “concatesome,” as the BW input. Other features include automatic support for paired-end reads with arbitrary insert sizes. NINJA is also free and open source and implements several pre-filtering methods that elicit substantial speedup when coupled with existing tools. We applied NINJA to several published microbiome studies, obtaining accuracy similar to or better than previous reference-based OTU-picking methods while achieving an order of magnitude or more speedup and using a fraction of the memory footprint. NINJA is a complete pipeline that takes a FASTA-formatted input file and outputs a QIIME-formatted taxonomy-annotated BIOM file for an entire MiSeq run of human gut microbiome 16S genes in under 10 minutes on a dual-core laptop.</p></div>", "links"=>[], "tags"=>["generation sequencing pipelines", "Other features", "novel heuristic approaches", "concatenated reference sequences", "genomics data", "reference chromosome", "Operational Taxonomic Units", "contig assembly", "BW input", "memory footprint", "10 minutes", "Concatenated Ribosomes", "BIOM", "marker gene sequencing studies", "bioinformatics technologies", "microbiome studies", "reference genomes", "NGS", "Marker Gene Alignment", "gut microbiome 16 S genes", "NINJA", "generation sequencing", "novel method", "OTU"], "article_id"=>2630170, "categories"=>["Biophysics", "Space Science", "Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Gabriel A. Al-Ghalith", "Emmanuel Montassier", "Henry N. Ward", "Dan Knights"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004658.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004658.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004658.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004658.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004658.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004658.s006", "https://dx.doi.org/10.1371/journal.pcbi.1004658.s007"], "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/NINJA_OPS_Fast_Accurate_Marker_Gene_Alignment_Using_Concatenated_Ribosomes/2630170", "title"=>"NINJA-OPS: Fast Accurate Marker Gene Alignment Using Concatenated Ribosomes", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-01-28 04:13:30"}
  • {"files"=>["https://ndownloader.figshare.com/files/4281234"], "description"=>"<p>Example of the Burrows-Wheeler transform on both small sequences (top) and a concatenated longer sequence (bottom). The concatenated sequence is more easily searchable using the LF walk. NINJA forms a BWT-compatible concatesome that can be used interchangeably among various BWT-based aligners as an artificial reference chromosome. This concatenated sequence serves as a reference against which environmentally-obtained marker sequences are aligned.</p>", "links"=>[], "tags"=>["generation sequencing pipelines", "Other features", "novel heuristic approaches", "concatenated reference sequences", "genomics data", "reference chromosome", "Operational Taxonomic Units", "contig assembly", "BW input", "memory footprint", "10 minutes", "Concatenated Ribosomes", "BIOM", "marker gene sequencing studies", "bioinformatics technologies", "microbiome studies", "reference genomes", "NGS", "Marker Gene Alignment", "gut microbiome 16 S genes", "NINJA", "generation sequencing", "novel method", "OTU"], "article_id"=>2630206, "categories"=>["Biophysics", "Space Science", "Microbiology", "Genetics", "Molecular Biology", "Evolutionary Biology", "Environmental Sciences not elsewhere classified", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified"], "users"=>["Gabriel A. Al-Ghalith", "Emmanuel Montassier", "Henry N. Ward", "Dan Knights"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004658.g005", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Burrows_Wheeler_transform_on_concatenated_sequences_/2630206", "title"=>"Burrows-Wheeler transform on concatenated sequences.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2016-01-28 04:13:30"}

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{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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