Browse
Submit Data
Databases
API
Help

Dataset Information

20 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Universal count correction for high-throughput sequencing.

ABSTRACT: We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called FIXSEQ. We demonstrate that FIXSEQ substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.

SUBMITTER: Hashimoto TB

PROVIDER: S-EPMC3945112 | biostudies-literature | 2014 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Universal count correction for high-throughput sequencing.

Hashimoto Tatsunori B TB Edwards Matthew D MD Gifford David K DK

PLoS computational biology 20140306 3

We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called FIXSEQ. We demonstrate that FIXSEQ substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternati ...[more]

PMID: 24603409

Similar Datasets

Denoising DNA deep sequencing data-high-throughput sequencing errors and their correction.

Project description: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.

| S-EPMC4719071 | biostudies-literature

Error correction of high-throughput sequencing datasets with non-uniform coverage.

Project description:MotivationThe continuing improvements to high-throughput sequencing (HTS) platforms have begun to unfold a myriad of new applications. As a result, error correction of sequencing reads remains an important problem. Though several tools do an excellent job of correcting datasets where the reads are sampled close to uniformly, the problem of correcting reads coming from drastically non-uniform datasets, such as those from single-cell sequencing, remains open.ResultsIn this article, we develop the method Hammer for error correction without any uniformity assumptions. Hammer is based on a combination of a Hamming graph and a simple probabilistic model for sequencing errors. It is a simple and adaptable algorithm that improves on other tools on non-uniform single-cell data, while achieving comparable results on normal multi-cell data.Availabilityhttp://www.cs.toronto.edu/~pashadag.Contactpmedvedev@cs.ucsd.edu.

| S-EPMC3117386 | biostudies-literature

Correction: Targeted high throughput sequencing in hereditary ataxia and spastic paraplegia.

Project description:[This corrects the article DOI: 10.1371/journal.pone.0174667.].

| S-EPMC5638532 | biostudies-literature

Systematic bias in high-throughput sequencing data and its correction by BEADS.

Project description:Genomic sequences obtained through high-throughput sequencing are not uniformly distributed across the genome. For example, sequencing data of total genomic DNA show significant, yet unexpected enrichments on promoters and exons. This systematic bias is a particular problem for techniques such as chromatin immunoprecipitation, where the signal for a target factor is plotted across genomic features. We have focused on data obtained from Illumina's Genome Analyser platform, where at least three factors contribute to sequence bias: GC content, mappability of sequencing reads, and regional biases that might be generated by local structure. We show that relying on input control as a normalizer is not generally appropriate due to sample to sample variation in bias. To correct sequence bias, we present BEADS (bias elimination algorithm for deep sequencing), a simple three-step normalization scheme that successfully unmasks real binding patterns in ChIP-seq data. We suggest that this procedure be done routinely prior to data interpretation and downstream analyses.

| S-EPMC3159482 | biostudies-literature

BLESS: bloom filter-based error correction solution for high-throughput sequencing reads.

Project description:MotivationRapid 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.ResultsWe 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 implementationFreely available at http://sourceforge.net/p/bless-ecContactdchen@illinois.eduSupplementary informationSupplementary data are available at Bioinformatics online.

| S-EPMC6365934 | biostudies-literature

Designing small universal k-mer hitting sets for improved analysis of high-throughput sequencing.

Project description:With the rapidly increasing volume of deep sequencing data, more efficient algorithms and data structures are needed. Minimizers are a central recent paradigm that has improved various sequence analysis tasks, including hashing for faster read overlap detection, sparse suffix arrays for creating smaller indexes, and Bloom filters for speeding up sequence search. Here, we propose an alternative paradigm that can lead to substantial further improvement in these and other tasks. For integers k and L > k, we say that a set of k-mers is a universal hitting set (UHS) if every possible L-long sequence must contain a k-mer from the set. We develop a heuristic called DOCKS to find a compact UHS, which works in two phases: The first phase is solved optimally, and for the second we propose several efficient heuristics, trading set size for speed and memory. The use of heuristics is motivated by showing the NP-hardness of a closely related problem. We show that DOCKS works well in practice and produces UHSs that are very close to a theoretical lower bound. We present results for various values of k and L and by applying them to real genomes show that UHSs indeed improve over minimizers. In particular, DOCKS uses less than 30% of the 10-mers needed to span the human genome compared to minimizers. The software and computed UHSs are freely available at github.com/Shamir-Lab/DOCKS/ and acgt.cs.tau.ac.il/docks/, respectively.

| S-EPMC5645146 | biostudies-literature

Systematic bias in high-throughput sequencing data and its correction by BEADS

Project description:ChIP-seq and input sequence data used in the development and evaluation of the BEADS normalization method. Examination of ChIP and input sequence reads across the worm genome

2011-05-25 | E-GEOD-29427 | biostudies-arrayexpress

Systematic bias in high-throughput sequencing data and its correction by BEADS

Project description:ChIP-seq and input sequence data used in the development and evaluation of the BEADS normalization method.

2011-05-25 | GSE29427 | GEO

Analysis and correction of compositional bias in sparse sequencing count data.

Project description:BackgroundCount data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size.ResultsWe demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.ConclusionsCompositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.

| S-EPMC6219007 | biostudies-literature

Universal correction of enzymatic sequence bias

Project description:Coupling molecular biology to high throughput sequencing has revolutionized the study of biology. Molecular genomics techniques are continually refined to provide higher resolution mapping of nucleic acid interactions and nucleic acid structure. These assays are converging on single-nucleotide resolution measurements, but the sequence preferences of molecular biology enzymes can interfere with the accurate interpretation of the data. Enzymatic sequence preferences manifest more prominently as the resolution of these assays increase. We developed seqOutBias to seek out enzymatic sequence bias from experimental data and scale individual sequence reads to correct the bias. We show that this software efficiently and successfully corrects the sequence bias resulting from DNase-seq, TACh-seq, ATAC-seq, MNase-seq, and PRO-seq data.

2016-12-21 | GSE92674 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data