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Peng X, Dorman KS. AmpliCI: a high-resolution model-based approach for denoising Illumina amplicon data. Bioinformatics 2021; 36:5151-5158. [PMID: 32697845 PMCID: PMC7850112 DOI: 10.1093/bioinformatics/btaa648] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2020] [Revised: 05/14/2020] [Accepted: 07/16/2020] [Indexed: 01/04/2023] Open
Abstract
Motivation Next-generation amplicon sequencing is a powerful tool for investigating microbial communities. A main challenge is to distinguish true biological variants from errors caused by amplification and sequencing. In traditional analyses, such errors are eliminated by clustering reads within a sequence similarity threshold, usually 97%, and constructing operational taxonomic units, but the arbitrary threshold leads to low resolution and high false-positive rates. Recently developed ‘denoising’ methods have proven able to resolve single-nucleotide amplicon variants, but they still miss low-frequency sequences, especially those near more frequent sequences, because they ignore the sequencing quality information. Results We introduce AmpliCI, a reference-free, model-based method for rapidly resolving the number, abundance and identity of error-free sequences in massive Illumina amplicon datasets. AmpliCI considers the quality information and allows the data, not an arbitrary threshold or an external database, to drive conclusions. AmpliCI estimates a finite mixture model, using a greedy strategy to gradually select error-free sequences and approximately maximize the likelihood. AmpliCI has better performance than three popular denoising methods, with acceptable computation time and memory usage. Availability and implementation Source code is available at https://github.com/DormanLab/AmpliCI. Supplementary information Supplementary material are available at Bioinformatics online.
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Affiliation(s)
- Xiyu Peng
- Department of Statistics, Ames, IA 50011, USA.,Interdepartmental Program in Bioinformatics and Computational Biology, Ames, IA 50011, USA
| | - Karin S Dorman
- Department of Statistics, Ames, IA 50011, USA.,Interdepartmental Program in Bioinformatics and Computational Biology, Ames, IA 50011, USA.,Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50011, USA
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2
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Tahir M, Sardaraz M, Mehmood Z, Khan MS. ESREEM: Efficient Short Reads Error Estimation Computational Model for Next-generation Genome Sequencing. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200614171832] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Aims:
To assess the error profile in NGS data, generated from high throughput
sequencing machines.
Background:
Short-read sequencing data from Next Generation Sequencing (NGS) are currently
being generated by a number of research projects. Depicting the errors produced by NGS
platforms and expressing accurate genetic variation from reads are two inter-dependent phases. It
has high significance in various analyses, such as genome sequence assembly, SNPs calling,
evolutionary studies, and haplotype inference. The systematic and random errors show incidence
profile for each of the sequencing platforms i.e. Illumina sequencing, Pacific Biosciences, 454
pyrosequencing, Complete Genomics DNA nanoball sequencing, Ion Torrent sequencing, and
Oxford Nanopore sequencing. Advances in NGS deliver galactic data with the addition of errors.
Some ratio of these errors may emulate genuine true biological signals i.e., mutation, and may
subsequently negate the results. Various independent applications have been proposed to correct
the sequencing errors. Systematic analysis of these algorithms shows that state-of-the-art models
are missing.
Objective:
In this paper, an effcient error estimation computational model called ESREEM is
proposed to assess the error rates in NGS data.
Methods:
The proposed model prospects the analysis that there exists a true linear regression
association between the number of reads containing errors and the number of reads sequenced. The
model is based on a probabilistic error model integrated with the Hidden Markov Model (HMM).
Result:
The proposed model is evaluated on several benchmark datasets and the results obtained are
compared with state-of-the-art algorithms.
Conclusions:
Experimental results analyses show that the proposed model efficiently estimates errors
and runs in less time as compared to others.
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Affiliation(s)
- Muhammad Tahir
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock,Pakistan
| | - Muhammad Sardaraz
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock,Pakistan
| | - Zahid Mehmood
- Department of Software Engineering, University of Engineering and Technology, Taxila,Pakistan
| | - Muhammad Saud Khan
- Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock,Pakistan
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3
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Fu S, Wang A, Au KF. A comparative evaluation of hybrid error correction methods for error-prone long reads. Genome Biol 2019; 20:26. [PMID: 30717772 PMCID: PMC6362602 DOI: 10.1186/s13059-018-1605-z] [Citation(s) in RCA: 67] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2018] [Accepted: 12/05/2018] [Indexed: 12/20/2022] Open
Abstract
BACKGROUND Third-generation sequencing technologies have advanced the progress of the biological research by generating reads that are substantially longer than second-generation sequencing technologies. However, their notorious high error rate impedes straightforward data analysis and limits their application. A handful of error correction methods for these error-prone long reads have been developed to date. The output data quality is very important for downstream analysis, whereas computing resources could limit the utility of some computing-intense tools. There is a lack of standardized assessments for these long-read error-correction methods. RESULTS Here, we present a comparative performance assessment of ten state-of-the-art error-correction methods for long reads. We established a common set of benchmarks for performance assessment, including sensitivity, accuracy, output rate, alignment rate, output read length, run time, and memory usage, as well as the effects of error correction on two downstream applications of long reads: de novo assembly and resolving haplotype sequences. CONCLUSIONS Taking into account all of these metrics, we provide a suggestive guideline for method choice based on available data size, computing resources, and individual research goals.
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Affiliation(s)
- Shuhua Fu
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Anqi Wang
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Kin Fai Au
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Biostatistics, University of Iowa, Iowa City, IA, 52242, USA.
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, 43210, USA.
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4
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Lee B, Moon T, Yoon S, Weissman T. DUDE-Seq: Fast, flexible, and robust denoising for targeted amplicon sequencing. PLoS One 2017; 12:e0181463. [PMID: 28749987 PMCID: PMC5531809 DOI: 10.1371/journal.pone.0181463] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2017] [Accepted: 06/30/2017] [Indexed: 11/29/2022] Open
Abstract
We consider the correction of errors from nucleotide sequences produced by next-generation targeted amplicon sequencing. The next-generation sequencing (NGS) platforms can provide a great deal of sequencing data thanks to their high throughput, but the associated error rates often tend to be high. Denoising in high-throughput sequencing has thus become a crucial process for boosting the reliability of downstream analyses. Our methodology, named DUDE-Seq, is derived from a general setting of reconstructing finite-valued source data corrupted by a discrete memoryless channel and effectively corrects substitution and homopolymer indel errors, the two major types of sequencing errors in most high-throughput targeted amplicon sequencing platforms. Our experimental studies with real and simulated datasets suggest that the proposed DUDE-Seq not only outperforms existing alternatives in terms of error-correction capability and time efficiency, but also boosts the reliability of downstream analyses. Further, the flexibility of DUDE-Seq enables its robust application to different sequencing platforms and analysis pipelines by simple updates of the noise model. DUDE-Seq is available at http://data.snu.ac.kr/pub/dude-seq.
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Affiliation(s)
- Byunghan Lee
- Electrical and Computer Engineering, Seoul National University, Seoul, Korea
| | - Taesup Moon
- College of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea
- * E-mail: (TM); (SY)
| | - Sungroh Yoon
- Electrical and Computer Engineering, Seoul National University, Seoul, Korea
- Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea
- Neurology and Neurological Sciences, Stanford University, Stanford, California, United States of America
- * E-mail: (TM); (SY)
| | - Tsachy Weissman
- Electrical Engineering, Stanford University, Stanford, California, United States of America
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Niland CN, Jankowsky E, Harris ME. Optimization of high-throughput sequencing kinetics for determining enzymatic rate constants of thousands of RNA substrates. Anal Biochem 2016; 510:1-10. [PMID: 27296633 DOI: 10.1016/j.ab.2016.06.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2016] [Accepted: 06/03/2016] [Indexed: 12/12/2022]
Abstract
Quantification of the specificity of RNA binding proteins and RNA processing enzymes is essential to understanding their fundamental roles in biological processes. High-throughput sequencing kinetics (HTS-Kin) uses high-throughput sequencing and internal competition kinetics to simultaneously monitor the processing rate constants of thousands of substrates by RNA processing enzymes. This technique has provided unprecedented insight into the substrate specificity of the tRNA processing endonuclease ribonuclease P. Here, we investigated the accuracy and robustness of measurements associated with each step of the HTS-Kin procedure. We examine the effect of substrate concentration on the observed rate constant, determine the optimal kinetic parameters, and provide guidelines for reducing error in amplification of the substrate population. Importantly, we found that high-throughput sequencing and experimental reproducibility contribute to error, and these are the main sources of imprecision in the quantified results when otherwise optimized guidelines are followed.
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Affiliation(s)
- Courtney N Niland
- Department of Biochemistry, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Eckhard Jankowsky
- Center for RNA Molecular Biology, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
| | - Michael E Harris
- Department of Biochemistry, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
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Alic AS, Ruzafa D, Dopazo J, Blanquer I. Objective review of de novostand-alone error correction methods for NGS data. WILEY INTERDISCIPLINARY REVIEWS: COMPUTATIONAL MOLECULAR SCIENCE 2016. [DOI: 10.1002/wcms.1239] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Andy S. Alic
- Institute of Instrumentation for Molecular Imaging (I3M); Universitat Politècnica de València; València Spain
| | - David Ruzafa
- Departamento de Quìmica Fìsica e Instituto de Biotecnologìa, Facultad de Ciencias; Universidad de Granada; Granada Spain
| | - Joaquin Dopazo
- Department of Computational Genomics; Príncipe Felipe Research Centre (CIPF); Valencia Spain
- CIBER de Enfermedades Raras (CIBERER); Valencia Spain
- Functional Genomics Node (INB) at CIPF; Valencia Spain
| | - Ignacio Blanquer
- Institute of Instrumentation for Molecular Imaging (I3M); Universitat Politècnica de València; València Spain
- Biomedical Imaging Research Group GIBI 2; Polytechnic University Hospital La Fe; Valencia Spain
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Laehnemann D, Borkhardt A, McHardy AC. Denoising DNA deep sequencing data-high-throughput sequencing errors and their correction. Brief Bioinform 2016; 17:154-79. [PMID: 26026159 PMCID: PMC4719071 DOI: 10.1093/bib/bbv029] [Citation(s) in RCA: 177] [Impact Index Per Article: 22.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2015] [Revised: 04/09/2015] [Indexed: 12/23/2022] Open
Abstract
Characterizing the errors generated by common high-throughput sequencing platforms and telling true genetic variation from technical artefacts are two interdependent steps, essential to many analyses such as single nucleotide variant calling, haplotype inference, sequence assembly and evolutionary studies. Both random and systematic errors can show a specific occurrence profile for each of the six prominent sequencing platforms surveyed here: 454 pyrosequencing, Complete Genomics DNA nanoball sequencing, Illumina sequencing by synthesis, Ion Torrent semiconductor sequencing, Pacific Biosciences single-molecule real-time sequencing and Oxford Nanopore sequencing. There is a large variety of programs available for error removal in sequencing read data, which differ in the error models and statistical techniques they use, the features of the data they analyse, the parameters they determine from them and the data structures and algorithms they use. We highlight the assumptions they make and for which data types these hold, providing guidance which tools to consider for benchmarking with regard to the data properties. While no benchmarking results are included here, such specific benchmarks would greatly inform tool choices and future software development. The development of stand-alone error correctors, as well as single nucleotide variant and haplotype callers, could also benefit from using more of the knowledge about error profiles and from (re)combining ideas from the existing approaches presented here.
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8
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Abstract
BACKGROUND Continued advances in next generation short-read sequencing technologies are increasing throughput and read lengths, while driving down error rates. Taking advantage of the high coverage sampling used in many applications, several error correction algorithms have been developed to improve data quality further. However, correcting errors in high coverage sequence data requires significant computing resources. METHODS We propose a different approach to handle erroneous sequence data. Presently, error rates of high-throughput platforms such as the Illumina HiSeq are within 1%. Moreover, the errors are not uniformly distributed in all reads, and a large percentage of reads are indeed error-free. Ability to predict such perfect reads can significantly impact the run-time complexity of applications. We present a simple and fast k-spectrum analysis based method to identify error-free reads. The filtration process to identify and weed out erroneous reads can be customized at several levels of stringency depending upon the downstream application need. RESULTS Our experiments show that if around 80% of the reads in a dataset are perfect, then our method retains almost 99.9% of them with more than 90% precision rate. Though filtering out reads identified as erroneous by our method reduces the average coverage by about 7%, we found the remaining reads provide as uniform a coverage as the original dataset. We demonstrate the effectiveness of our approach on an example downstream application: we show that an error correction algorithm, Reptile, which rely on collectively analyzing the reads in a dataset to identify and correct erroneous bases, instead use reads predicted to be perfect by our method to correct the other reads, the overall accuracy improves further by up to 10%. CONCLUSIONS Thanks to the continuous technological improvements, the coverage and accuracy of reads from dominant sequencing platforms have now reached an extent where we can envision just filtering out reads with errors, thus making error correction less important. Our algorithm is a first attempt to propose and demonstrate this new paradigm. Moreover, our demonstration is applicable to any error correction algorithm as a downstream application, this in turn gives a new class of error correcting algorithms as a by product.
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9
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Allam A, Kalnis P, Solovyev V. Karect: accurate correction of substitution, insertion and deletion errors for next-generation sequencing data. Bioinformatics 2015; 31:3421-8. [DOI: 10.1093/bioinformatics/btv415] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2014] [Accepted: 07/08/2015] [Indexed: 11/12/2022] Open
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10
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Heo Y, Wu XL, Chen D, Ma J, Hwu WM. BLESS: bloom filter-based error correction solution for high-throughput sequencing reads. ACTA ACUST UNITED AC 2014; 30:1354-62. [PMID: 24451628 DOI: 10.1093/bioinformatics/btu030] [Citation(s) in RCA: 66] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
MOTIVATION Rapid advances in next-generation sequencing (NGS) technology have led to exponential increase in the amount of genomic information. However, NGS reads contain far more errors than data from traditional sequencing methods, and downstream genomic analysis results can be improved by correcting the errors. Unfortunately, all the previous error correction methods required a large amount of memory, making it unsuitable to process reads from large genomes with commodity computers. RESULTS We present a novel algorithm that produces accurate correction results with much less memory compared with previous solutions. The algorithm, named BLoom-filter-based Error correction Solution for high-throughput Sequencing reads (BLESS), uses a single minimum-sized Bloom filter, and is also able to tolerate a higher false-positive rate, thus allowing us to correct errors with a 40× memory usage reduction on average compared with previous methods. Meanwhile, BLESS can extend reads like DNA assemblers to correct errors at the end of reads. Evaluations using real and simulated reads showed that BLESS could generate more accurate results than existing solutions. After errors were corrected using BLESS, 69% of initially unaligned reads could be aligned correctly. Additionally, de novo assembly results became 50% longer with 66% fewer assembly errors. AVAILABILITY AND IMPLEMENTATION Freely available at http://sourceforge.net/p/bless-ec CONTACT dchen@illinois.edu SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yun Heo
- Department of Electrical and Computer Engineering, Department of Bioengineering and Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
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11
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Sleep JA, Schreiber AW, Baumann U. Sequencing error correction without a reference genome. BMC Bioinformatics 2013; 14:367. [PMID: 24350580 PMCID: PMC3879328 DOI: 10.1186/1471-2105-14-367] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2013] [Accepted: 12/10/2013] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND Next (second) generation sequencing is an increasingly important tool for many areas of molecular biology, however, care must be taken when interpreting its output. Even a low error rate can cause a large number of errors due to the high number of nucleotides being sequenced. Identifying sequencing errors from true biological variants is a challenging task. For organisms without a reference genome this difficulty is even more challenging. RESULTS We have developed a method for the correction of sequencing errors in data from the Illumina Solexa sequencing platforms. It does not require a reference genome and is of relevance for microRNA studies, unsequenced genomes, variant detection in ultra-deep sequencing and even for RNA-Seq studies of organisms with sequenced genomes where RNA editing is being considered. CONCLUSIONS The derived error model is novel in that it allows different error probabilities for each position along the read, in conjunction with different error rates depending on the particular nucleotides involved in the substitution, and does not force these effects to behave in a multiplicative manner. The model provides error rates which capture the complex effects and interactions of the three main known causes of sequencing error associated with the Illumina platforms.
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Affiliation(s)
- Julie A Sleep
- Australian Centre for Plant Functional Genomics, The University of Adelaide, Urrbrae, SA 5064, Australia
- Phenomics and Bioinformatics Research Centre, University of South Australia, Mawson Lakes, SA 5095, Australia
| | - Andreas W Schreiber
- ACRF South Australian Cancer Genome Facility, Centre for Cancer Biology, SA Pathology, Adelaide, SA 5000, Australia
- School of Molecular and Biomedical Science, University of Adelaide, Adelaide, SA 5000, Australia
| | - Ute Baumann
- Australian Centre for Plant Functional Genomics, The University of Adelaide, Urrbrae, SA 5064, Australia
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Aita T, Ichihashi N, Yomo T. Probabilistic model based error correction in a set of various mutant sequences analyzed by next-generation sequencing. Comput Biol Chem 2013; 47:221-30. [PMID: 24184706 DOI: 10.1016/j.compbiolchem.2013.09.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2013] [Revised: 09/13/2013] [Accepted: 09/27/2013] [Indexed: 01/14/2023]
Abstract
To analyze the evolutionary dynamics of a mutant population in an evolutionary experiment, it is necessary to sequence a vast number of mutants by high-throughput (next-generation) sequencing technologies, which enable rapid and parallel analysis of multikilobase sequences. However, the observed sequences include many errors of base call. Therefore, if next-generation sequencing is applied to analysis of a heterogeneous population of various mutant sequences, it is necessary to discriminate between true bases as point mutations and errors of base call in the observed sequences, and to subject the sequences to error-correction processes. To address this issue, we have developed a novel method of error correction based on the Potts model and a maximum a posteriori probability (MAP) estimate of its parameters corresponding to the "true sequences". Our method of error correction utilizes (1) the "quality scores" which are assigned to individual bases in the observed sequences and (2) the neighborhood relationship among the observed sequences mapped in sequence space. The computer experiments of error correction of artificially generated sequences supported the effectiveness of our method, showing that 50-90% of errors were removed. Interestingly, this method is analogous to a probabilistic model based method of image restoration developed in the field of information engineering.
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Affiliation(s)
- Takuyo Aita
- Exploratory Research for Advanced Technology, Japan Science and Technology Agency, Yamadaoka 1-5, Suita, Osaka, Japan
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13
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Abstract
The extremely high error rates reported by Keegan et al. in ‘A platform-independent method for detecting errors in metagenomic sequencing data: DRISEE’ (PLoS Comput Biol 2012;8:e1002541) for many next-generation sequencing datasets prompted us to re-examine their results. Our analysis reveals that the presence of conserved artificial sequences, e.g. Illumina adapters, and other naturally occurring sequence motifs accounts for most of the reported errors. We conclude that DRISEE reports inflated levels of sequencing error, particularly for Illumina data. Tools offered for evaluating large datasets need scrupulous review before they are implemented.
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Rosen MJ, Callahan BJ, Fisher DS, Holmes SP. Denoising PCR-amplified metagenome data. BMC Bioinformatics 2012; 13:283. [PMID: 23113967 PMCID: PMC3563472 DOI: 10.1186/1471-2105-13-283] [Citation(s) in RCA: 65] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2012] [Accepted: 10/19/2012] [Indexed: 11/25/2022] Open
Abstract
Background PCR amplification and high-throughput sequencing theoretically enable the characterization of the finest-scale diversity in natural microbial and viral populations, but each of these methods introduces random errors that are difficult to distinguish from genuine biological diversity. Several approaches have been proposed to denoise these data but lack either speed or accuracy. Results We introduce a new denoising algorithm that we call DADA (Divisive Amplicon Denoising Algorithm). Without training data, DADA infers both the sample genotypes and error parameters that produced a metagenome data set. We demonstrate performance on control data sequenced on Roche’s 454 platform, and compare the results to the most accurate denoising software currently available, AmpliconNoise. Conclusions DADA is more accurate and over an order of magnitude faster than AmpliconNoise. It eliminates the need for training data to establish error parameters, fully utilizes sequence-abundance information, and enables inclusion of context-dependent PCR error rates. It should be readily extensible to other sequencing platforms such as Illumina.
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Affiliation(s)
- Michael J Rosen
- Department of Applied Physics, Stanford University, CA, USA.
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Yang X, Chockalingam SP, Aluru S. A survey of error-correction methods for next-generation sequencing. Brief Bioinform 2012; 14:56-66. [DOI: 10.1093/bib/bbs015] [Citation(s) in RCA: 177] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Wijaya E, Frith MC, Asai K, Horton P. RecountDB: a database of mapped and count corrected transcribed sequences. Nucleic Acids Res 2011; 40:D1089-92. [PMID: 22139942 PMCID: PMC3245132 DOI: 10.1093/nar/gkr1172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
The field of gene expression analysis continues to benefit from next-generation sequencing generated data, which enables transcripts to be measured with unmatched accuracy and resolution. But the high-throughput reads from these technologies also contain many errors, which can compromise the ability to accurately detect and quantify rare transcripts. Fortunately, techniques exist to ameliorate the affects of sequencer error. We present RecountDB, a secondary database derived from primary data in NCBI's short read archive. RecountDB holds sequence counts from RNA-seq and 5′ capped transcription start site experiments, corrected and mapped to the relevant genome. Via a searchable and browseable interface users can obtain corrected data in formats useful for transcriptomic analysis. The database is currently populated with 2265 entries from 45 organisms and continuously growing. RecountDB is publicly available at: http://recountdb.cbrc.jp.
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Affiliation(s)
- Edward Wijaya
- Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8562, Japan
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