Identification And Correction Of Systematic Error In High-throughput
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Access Identification and correction of systematic error in high-throughput sequence dataFrazerMeacham1, DarioBoffelli2, JosephDhahbi2, DavidIKMartin2, MeromitSinger3Email author and
How To Overcome Systematic Error
LiorPachter1, 3, 4Email authorBMC Bioinformatics201112:451DOI: 10.1186/1471-2105-12-451© Meacham et al; licensee how to reduce systematic error BioMed Central Ltd.2011Received: 25May2011Accepted: 21November2011Published: 21November2011 Abstract Background A feature common to all DNA sequencing technologies
Examples Of Systematic Errors
is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major functional communication training problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have random error examples been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations. Results We characterize and describe systematic errors using overlapping paired reads from high-coverage data. We show that such errors occur in approximately 1 in 1000 base pairs, and that they are highly replicable across experiments. We identify motifs that are frequent at systematic error sites, and describe a classifier that distinguishes heterozygous sites from systematic error. Our classifier is designed to accommodate data from experiments in which the allele frequencies at heterozygous sites are not necessarily 0.5 (such as in the case of RNA-Seq), and can be used with single-end datasets. Conclusions Systematic errors can easily be mistaken for heterozygous sites in individuals, or for SNPs in population analyses. Systematic errors are particularly problematic in low coverage experiments, or
Score In the top 25% of all research outputs scored by Altmetric High Attention Score compared to outputs of the same age (85th percentile) Good Attention Score compared to outputs of the same age and source (79th percentile) Mentioned by twitter11 tweeters Readers on mendeley349 Mendeley citeulike26 CiteULike What is this page? Summary Twitter You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-451 here to find out more. Title Identification and correction of systematic error in high-throughput sequence data Published in BMC Bioinformatics, November 2011 DOI 10.1186/1471-2105-12-451 Pubmed ID 22099972 Authors Frazer Meacham, Dario Boffelli, Joseph Dhahbi, David IK Martin, Meromit Singer, Lior Pachter, Meacham F, Boffelli D, Dhahbi J, Martin DI, Singer M, Pachter L https://www.altmetric.com/details/465530 Abstract A feature common to all DNA sequencing technologies is the presence of base-call errors in the sequenced reads. The implications of such errors are application specific, ranging from minor informatics nuisances to major problems affecting biological inferences. Recently developed "next-gen" sequencing technologies have greatly reduced the cost of sequencing, but have been shown to be more error prone than previous technologies. Both position specific (depending on the location in the read) and sequence specific (depending on the sequence in the read) errors have been identified in Illumina and Life Technology sequencing platforms. We describe a new type of systematic error that manifests as statistically unlikely accumulations of errors at specific genome (or transcriptome) locations. View on publisher site Alert me about new mentions Twitter Demographics The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled. Geo
van GoogleInloggenVerborgen veldenBoekenbooks.google.nl - In the last quarter century, advances in mass spectrometry (MS) have been at the forefront https://books.google.com/books?id=ZvFeCwAAQBAJ&pg=PA143&lpg=PA143&dq=identification+and+correction+of+systematic+error+in+high-throughput&source=bl&ots=NLDoBVz-Qn&sig=hBV-T4w-m_OHfMrvvN2FHxqDKrE&hl=en&sa=X&ved=0ahUKEwjK of efforts to map complex biological systems including the http://jbx.sagepub.com/content/10/6/557.abstract human metabolome, proteome, and microbiome. All of these developments have allowed MS to become a well-established molecular level technology for microorganism...https://books.google.nl/books/about/Applications_of_Mass_Spectrometry_in_Mic.html?hl=nl&id=ZvFeCwAAQBAJ&utm_source=gb-gplus-shareApplications of Mass Spectrometry in MicrobiologyMijn bibliotheekHelpGeavanceerd zoeken naar boekeneBoek kopen - € 93,16Dit boek in gedrukte systematic error vorm bestellenSpringer ShopBol.comProxis.nlselexyz.nlVan StockumZoeken in een bibliotheekAlle verkopers»Applications of Mass Spectrometry in Microbiology: From Strain Characterization to Rapid Screening for Antibiotic ResistancePlamen Demirev, Todd R. SandrinSpringer, 12 jan. 2016 - 336 pagina's 0 Recensieshttps://books.google.nl/books/about/Applications_of_Mass_Spectrometry_in_Mic.html?hl=nl&id=ZvFeCwAAQBAJIn the last quarter century, advances in mass spectrometry (MS) have been of systematic error at the forefront of efforts to map complex biological systems including the human metabolome, proteome, and microbiome. All of these developments have allowed MS to become a well-established molecular level technology for microorganism characterization. MS has demonstrated its considerable advantage as a rapid, accurate, and cost-effective method for microorganism identification, compared to conventional phenotypic techniques. In the last several years, applications of MS for microorganism characterization in research, clinical microbiology, counter-bioterrorism, food safety, and environmental monitoring have been documented in thousands of publications. Regulatory bodies in Europe, the US, and elsewhere have approved MS-based assays for infectious disease diagnostics. As of mid-2015, more than 3300 commercial MS systems for microorganism identification have been deployed worldwide in hospitals and clinical labs. While previous work has covered broader approaches in using MS to characterize microorganisms
Alerts Search this journal Advanced Journal Search » Impact Factor:2.218 | Ranking:Chemistry, Analytical 34 out of 75 | Biochemical Research Methods 44 out of 77 | Biotechnology & Applied Microbiology 73 out of 161 Source:2016 Release of Journal Citation Reports, Source: 2015 Web of Science Data Statistical Analysis of Systematic Errors in High-Throughput Screening Dmytro Kevorkov Laboratoire LACIM, Université du Québec à Montréal, Canada Vladimir Makarenkov Département d’informatique, Université du Québec à Montréal, Canada Abstract High-throughput screening (HTS) is an efficient technology for drug discovery. It allows for screening of more than 100,000 compounds a day per screen and requires effective procedures for quality control. The authors have developed a method for evaluating a background surface of an HTS assay; it can be used to correct raw HTS data. This correction is necessary to take into account systematic errors that may affect the procedure of hit selection. The described method allows one to analyze experimental HTS data and determine trends and local fluctuations of the corresponding background surfaces. For an assay with a large number of plates, the deviations of the background surface from a plane are caused by systematic errors. Their influence can be minimized by the subtraction of the systematic background from the raw data. Two experimental HTS assays from the ChemBank database are examined in this article. The systematic error present in these data was estimated and removed from them. It enabled the authors to correct the hit selection procedure for both assays. high-throughput screening systematic error background evaluation trend-surface analysis CiteULike Connotea Delicious Digg Facebook Google+ LinkedIn Mendeley Reddit StumbleUpon Twitter What's this? « Previous | Next Article » Table of Contents This Article Published online before print August 15, 2005, doi: 10.1177/1087057105276989 J Biomol Screen September 2005 vol. 10 no. 6 557-567 » Abstract Full Text (PDF) References Services Email this article to a colleague Alert me when this article is cited Alert me if a correction is posted Similar articles in this journal Similar articles in PubMed Download to citation manager Request Permissions Request Reprints Load patientINFORMation Citing Articles Load citing article information Citing articles via Scopus Citing articles via Web of Science Citing articles via Google Scholar Google Scholar Articles by Kevorkov, D. Articles by Makarenkov, V. Search for related content PubMed PubMed citation Articles by Kevorkov, D. Articles by Makarenkov, V. Related Content Load related web page information