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{"title"=>"SNPflow: A Lightweight Application for the Processing, Storing and Automatic Quality Checking of Genotyping Assays", "type"=>"journal", "authors"=>[{"first_name"=>"Hansi", "last_name"=>"Weissensteiner", "scopus_author_id"=>"26665767000"}, {"first_name"=>"Margot", "last_name"=>"Haun", "scopus_author_id"=>"7006282088"}, {"first_name"=>"Sebastian", "last_name"=>"Schönherr", "scopus_author_id"=>"26665677000"}, {"first_name"=>"Mathias", "last_name"=>"Neuner", "scopus_author_id"=>"55625538200"}, {"first_name"=>"Lukas", "last_name"=>"Forer", "scopus_author_id"=>"23469328800"}, {"first_name"=>"Günther", "last_name"=>"Specht", "scopus_author_id"=>"26665771200"}, {"first_name"=>"Anita", "last_name"=>"Kloss-Brandstätter", "scopus_author_id"=>"36159612700"}, {"first_name"=>"Florian", "last_name"=>"Kronenberg", "scopus_author_id"=>"55800251500"}, {"first_name"=>"Stefan", "last_name"=>"Coassin", "scopus_author_id"=>"23988794500"}], "year"=>2013, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"368544567", "sgr"=>"84875096541", "issn"=>"19326203", "pmid"=>"23527209", "scopus"=>"2-s2.0-84875096541", "doi"=>"10.1371/journal.pone.0059508", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)"}, "id"=>"cfd22d0b-0215-31ad-bc59-befa3f49ca2c", "abstract"=>"Single nucleotide polymorphisms (SNPs) play a prominent role in modern genetics. Current genotyping technologies such as Sequenom iPLEX, ABI TaqMan and KBioscience KASPar made the genotyping of huge SNP sets in large populations straightforward and allow the generation of hundreds of thousands of genotypes even in medium sized labs. While data generation is straightforward, the subsequent data conversion, storage and quality control steps are time-consuming, error-prone and require extensive bioinformatic support. In order to ease this tedious process, we developed SNPflow. SNPflow is a lightweight, intuitive and easily deployable application, which processes genotype data from Sequenom MassARRAY (iPLEX) and ABI 7900HT (TaqMan, KASPar) systems and is extendible to other genotyping methods as well. SNPflow automatically converts the raw output files to ready-to-use genotype lists, calculates all standard quality control values such as call rate, expected and real amount of replicates, minor allele frequency, absolute number of discordant replicates, discordance rate and the p-value of the HWE test, checks the plausibility of the observed genotype frequencies by comparing them to HapMap/1000-Genomes, provides a module for the processing of SNPs, which allow sex determination for DNA quality control purposes and, finally, stores all data in a relational database. SNPflow runs on all common operating systems and comes as both stand-alone version and multi-user version for laboratory-wide use. The software, a user manual, screenshots and a screencast illustrating the main features are available at http://genepi-snpflow.i-med.ac.at.", "link"=>"http://www.mendeley.com/research/snpflow-lightweight-application-processing-storing-automatic-quality-checking-genotyping-assays", "reader_count"=>23, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>3, "Student > Doctoral Student"=>4, "Researcher"=>4, "Student > Ph. D. Student"=>5, "Student > Master"=>2, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>3, "Student > Doctoral Student"=>4, "Researcher"=>4, "Student > Ph. D. Student"=>5, "Student > Master"=>2, "Other"=>2, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>1, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>4, "Mathematics"=>2, "Agricultural and Biological Sciences"=>4, "Medicine and Dentistry"=>3, "Psychology"=>1, "Social Sciences"=>2, "Computer Science"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>3}, "Social Sciences"=>{"Social Sciences"=>2}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>5}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>4}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>1}}, "reader_count_by_country"=>{"China"=>1}, "group_count"=>0}

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• http://doi.org/10.1016/j.biopsych.2014.04.016
• http://doi.org/10.1016/j.jpsychires.2014.12.001
• http://doi.org/10.1016/j.bbr.2017.04.047
• http://doi.org/10.1016/j.psyneuen.2018.08.030

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/990801"], "description"=>"<p>This figure shows the main interface of SNPflow. The left panel illustrates the different studies with the respective SNP reports. iPLEX Assays are grouped into subfolders, (e.g. “Plex1”) while TaqMan results are reported as single documents (e.g. “rs2071204” in “Study (4)”). Specific SNP results can be retrieved with the search field in the study overview. The right panel contains three tabs showing the QC data, detailed replicates analysis and the comparison with HapMap release 27 and the 1000 Genomes Project phase 1 (see also <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059508#pone-0059508-g004\" target=\"_blank\">figure 4</a>). The tool bar with the different options is located on the top of the window.</p>", "links"=>[], "tags"=>["interface"], "article_id"=>654905, "categories"=>["Information And Computing Sciences", "Genetics", "Biotechnology"], "users"=>["Hansi Weissensteiner", "Margot Haun", "Sebastian Schönherr", "Mathias Neuner", "Lukas Forer", "Günther Specht", "Anita Kloss-Brandstätter", "Florian Kronenberg", "Stefan Coassin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059508.g001", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Main_interface_of_SNPflow_/654905", "title"=>"Main interface of SNPflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-20 08:06:27"}
• {"files"=>["https://ndownloader.figshare.com/files/990802"], "description"=>"<p>User actions are depicted as diamonds. The two shaded diamonds represent the two major data checkpoints as described in the subsection “Processing of genotyping data” in the “Workflow” section of the main text. The numbers of the four boxed steps correspond to the steps of the four SNPflow data analysis wizard (see also <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0059508#pone-0059508-g003\" target=\"_blank\">figure 3</a>). For a detailed description of this flow chart, please see the section “Workflow” in the manuscript.</p>", "links"=>[], "tags"=>["genetics and genomics", "public health and epidemiology", "computer science"], "article_id"=>654906, "categories"=>["Information And Computing Sciences", "Genetics", "Biotechnology"], "users"=>["Hansi Weissensteiner", "Margot Haun", "Sebastian Schönherr", "Mathias Neuner", "Lukas Forer", "Günther Specht", "Anita Kloss-Brandstätter", "Florian Kronenberg", "Stefan Coassin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059508.g002", "stats"=>{"downloads"=>3, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flow_chart_of_the_main_data_processing_workflow_/654906", "title"=>"Flow chart of the main data processing workflow.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-20 08:06:50"}
• {"files"=>["https://ndownloader.figshare.com/files/1012944"], "description"=>"*<p>Evaluation based on publication.</p>#<p>Requires license.</p>†<p>User management means the definition of user roles in the sense of rights for e.g. administrators, laboratory analysts or data analysts.</p>§<p>Should be available in the ‘pre-packaged’ version of SNPator which is, however, not publically available.</p>", "links"=>[], "tags"=>["genetics and genomics", "public health and epidemiology", "computer science"], "article_id"=>673351, "categories"=>["Information And Computing Sciences", "Genetics", "Biotechnology"], "users"=>["Hansi Weissensteiner", "Margot Haun", "Sebastian Schönherr", "Mathias Neuner", "Lukas Forer", "Günther Specht", "Anita Kloss-Brandstätter", "Florian Kronenberg", "Stefan Coassin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059508.t001", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_to_related_software_/673351", "title"=>"Comparison to related software.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-03-19 00:55:51"}
• {"files"=>["https://ndownloader.figshare.com/files/990806"], "description"=>"<p>This figure shows the 4 steps of the SNPflow data analysis wizard. In brief, the first step allows the uploading of either SDS files (ABI 7900HT) or TyperAnalyzer 4.0 (MassARRAY) files. For TyperAnalyzer files, a second input field asks for the name of the result group under which results shall be grouped. The second step checks the validity of the IDs found in the uploaded and reports IDs, which are not found in the study definition and shall thus be deleted. Step 3 converts the found genotype designations to standard genotypes and allows entering the name of the result, the operator name, an optional comment and the rs-number of the SNP in HapMap and the 1000 Genomes Project. This is useful in case that a SNP ID changed between the current dbSNP release and the dbSNP release used in HapMap, as happened for some SNPs. For convenience, the result name is automatically pre-set as HapMap lookup. In multiplex assays, pagination allows navigating through the single SNPs. For quality reasons a manual confirmation of each conversion is required (Checkbox “All settings are correct”). Finally, step 4 confirms the successful analysis and brings the user to the report. Exemplary abbreviation used in this figure: NTC, Non-template control; ER, annulled samples (“error”), i.e. DNA samples present on the plates, but known to be flawed. The IDs of these samples were thus replaced with “ER” in order to avoid data collection for these samples.</p>", "links"=>[], "tags"=>["snpflow"], "article_id"=>654910, "categories"=>["Information And Computing Sciences", "Genetics", "Biotechnology"], "users"=>["Hansi Weissensteiner", "Margot Haun", "Sebastian Schönherr", "Mathias Neuner", "Lukas Forer", "Günther Specht", "Anita Kloss-Brandstätter", "Florian Kronenberg", "Stefan Coassin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059508.g003", "stats"=>{"downloads"=>2, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_SNPflow_data_analysis_wizard_/654910", "title"=>"The SNPflow data analysis wizard.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-20 08:07:34"}
• {"files"=>["https://ndownloader.figshare.com/files/990809"], "description"=>"<p>This figure exemplifies the HapMap/1000Genomes comparison. Both, frequencies and absolute genotype numbers are reported for each population. A Pearson chi square test checks the concordance with the user’s data. The user can look up the most appropriate HapMap population as well as the super populations from the 1000 Genomes Project and perform an additional plausibility check of the generated genotypes.</p>", "links"=>[], "tags"=>["genetics and genomics", "public health and epidemiology", "computer science"], "article_id"=>654913, "categories"=>["Information And Computing Sciences", "Genetics", "Biotechnology"], "users"=>["Hansi Weissensteiner", "Margot Haun", "Sebastian Schönherr", "Mathias Neuner", "Lukas Forer", "Günther Specht", "Anita Kloss-Brandstätter", "Florian Kronenberg", "Stefan Coassin"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0059508.g004", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_SNPflow_8217_s_HapMap_1000Genomes_comparison_panel_/654913", "title"=>"SNPflow’s HapMap/1000Genomes comparison panel.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-03-20 08:08:08"}

PMC Usage Stats | Further Information

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• {"unique-ip"=>"4", "full-text"=>"1", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
• {"unique-ip"=>"4", "full-text"=>"4", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
• {"unique-ip"=>"6", "full-text"=>"1", "pdf"=>"7", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
• {"unique-ip"=>"6", "full-text"=>"5", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
• {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"3"}
• {"unique-ip"=>"3", "full-text"=>"5", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}

Relative Metric

{"start_date"=>"2013-01-01T00:00:00Z", "end_date"=>"2013-12-31T00:00:00Z", "subject_areas"=>[{"subject_area"=>"/Biology and life sciences", "average_usage"=>[269, 466, 588, 697, 800, 896, 988, 1076, 1165, 1254, 1340, 1417]}, {"subject_area"=>"/Biology and life sciences/Genetics", "average_usage"=>[284, 491, 620, 738, 843, 945, 1043, 1137, 1225, 1315, 1400, 1479, 1555]}, {"subject_area"=>"/Computer and information sciences/Data management", "average_usage"=>[301, 521, 654, 776, 898, 1028, 1133, 1258, 1373, 1457, 1542, 1648, 1738]}, {"subject_area"=>"/Computer and information sciences/Information technology", "average_usage"=>[265, 446, 547, 656, 751, 809, 898, 978, 1077, 1216, 1295, 1357, 1403, 1469]}, {"subject_area"=>"/Engineering and technology/Industrial engineering", "average_usage"=>[244, 415, 530, 647, 750, 829, 915, 999, 1097, 1201, 1293, 1361, 1439]}]}

F1000Prime