This article has been cited by other articles in PMC. Abstract Background Innumerable opportunities for new genomic research have been stimulated by advancement in high-throughput next-generation sequencing NGS. However, the pitfall of NGS data abundance is the complication of distinction between true biological variants and sequence error alterations during downstream analysis.
PBcR is a program that trims and corrects individual long-read sequences by first mapping short-read sequences to them, and computes an accurate hybrid consensus sequence. The corrected PBcR reads can then be exported for other application or can be de novo assembled alone in combination with other data.
This program merges a molecular barcoding approach with in silico removing of highly stereotypical background artifacts with the aim of increasing the efficiency of the capture of sequencing-based circulating tumor DNA ctDNA detection.
PoreSeq is an open source program and Python library.
It provides features as: Hamming allows one run of a massively parallel pyrosequencer to process up to samples simultaneously. The tagged barcoding strategy can be used to obtain sequences from hundreds of samples in a single sequencing run, and to perform phylogenetic analyses of microbial communities from pyrosequencing data.
The combination of error-correcting barcodes and massively parallel sequencing rapidly revolutionizes our understanding of microbial habitats located throughout our biosphere, as well as those associated with our human bodies.
This approach uses three steps: The software is composed of four modules: SGA-ICE is an iterative error correction pipeline that runs SGA in multiple rounds of k-mer-based correction with an increasing k-mer size, followed by a final round of overlap-based correction.
By combining the advantages of small and large k-mers, this approach corrects more errors in repeats and minimizes the total amount of erroneous reads.
The software follows a "Hierarchical Genome Assembly Process" constituted of several steps for generating a genome assembly from a set of sequencing reads. Each step is accomplished with different command line tools implementing different sets of algorithms to accomplish the work.Abstract.
Motivation: Next-generation sequencing produces vast amounts of data with errors that are difficult to distinguish from true biological variation whe.
Innovative technologies. At Illumina, our goal is to apply innovative technologies to the analysis of genetic variation and function, making studies possible that were not even imaginable just a few years ago.
Background. Recent advances in the next-generation sequencing (NGS) methods allow for analyzing the unprecedented number of viral variants from infected patients and present a novel opportunity for understanding viral evolution, drug resistance and immune escape [1,2].However, the increase in quantity of data had a detrimental effect on quality of reads. The rapid development of next-generation DNA sequencing has revolutionized biological and ecological research in the last few years. error-correction methods. Apart from providing explicit recommendations useful to practitioners, the review serves to identify the current state of the art and promising directions for future research.
With fast development and wide applications of next-generation sequencing (NGS) technologies, genomic sequence information is within reach to aid the achievement of goals to decode life mysteries, make better crops, detect pathogens, and improve life qualities.
A survey of error-correction methods for next-generation sequencing. we provide a comprehensive review of many error-correction methods, and establish a . A survey of error-correction methods for next-generation sequencing XiaoYang, Sriram tranceformingnlp.comlingam and Srinivas Aluru Submitted: 23rd January ; Received (in revised form): 7th March Introduction.
The advent of short-read sequencing machines gave rise to a new generation of assembly algorithms and software.
This survey reviews algorithms for de novo whole-genome shotgun assembly from next-generation sequencing data. It describes and compares algorithms that have been presented in the scientific literature and implemented in software.