scholarly journals FALCON-meta: a method to infer metagenomic composition of ancient DNA

2018 ◽  
Author(s):  
Diogo Pratas ◽  
Armando J. Pinho ◽  
Raquel M. Silva ◽  
João M. O. S. Rodrigues ◽  
Morteza Hosseini ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Ancient Dna ◽  
Computational Cost ◽  
Next Generation ◽  
Link Type ◽  
Alignment Free ◽  
Sample Composition ◽  
Metagenomic Sample ◽  
Consequent Increase ◽  
Generation Sequencing

The general approaches to detect and quantify metagenomic sample composition are based on the alignment of the reads, according to an existing database containing reference microbial sequences. However, without proper parameterization, these methods are not suitable for ancient DNA. Quantifying somewhat dissimilar sequences by alignment methods is problematic, due to the need of fine-tuned thresholds, considering relaxed edit distances and the consequent increase of computational cost. Additionally, the choice of the thresholds poses the problem of how to quantify similarity without producing overestimated measures. We propose FALCON-meta, a compression-based method to infer metagenomic composition of next-generation sequencing samples. This unsupervised alignment-free method runs efficiently on FASTQ samples. FALCON-meta quickly learns how to give importance to the models that cooperate to predict similarity, incorporating parallelism and flexibility for multiple hardware characteristics. It shows substantial identification capabilities in ancient DNA without overestimation. In one of the examples, we found and authenticated an ancient Pseudomonas bacteria in a Mammoth mitogenome.FALCON-meta can be accessed at https://github.com/pratas/falcon.

A-Z of ancient DNA protocols for shotgun Illumina Next Generation Sequencing v2 (protocols.io.bvt9n6r6)

protocols.io ◽  
2021 ◽  
Author(s):  
James A ◽  
Franziska Aron ◽  
Gunnar U ◽  
Irina Velsko ◽  
Eirini Skourtanioti ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Ancient Dna ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals A-Z of ancient DNA protocols for shotgun Illumina Next Generation Sequencing v1 (protocols.io.bj8nkrve)

protocols.io ◽  
2020 ◽  
Author(s):  
James A ◽  
Franziska Aron ◽  
Gunnar U ◽  
Irina Velsko ◽  
Eirini Skourtanioti ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Ancient Dna ◽  
Next Generation ◽  
Generation Sequencing

scholarly journals Alignment-Free Sequence Comparison Based on Next-Generation Sequencing Reads

2013 ◽  
Vol 20 (2) ◽  
pp. 64-79 ◽  
Author(s):  
Kai Song ◽  
Jie Ren ◽  
Zhiyuan Zhai ◽  
Xuemei Liu ◽  
Minghua Deng ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Sequence Comparison ◽  
Next Generation ◽  
Alignment Free ◽  
Generation Sequencing

scholarly journals CoverView: a sequence quality evaluation tool for next generation sequencing data

2018 ◽  
Vol 3 ◽  
pp. 36 ◽  
Author(s):  
Márton Münz ◽  
Shazia Mahamdallie ◽  
Shawn Yost ◽  
Andrew Rimmer ◽  
Emma Poyastro-Pearson ◽  
...  
Keyword(s):  
Quality Control ◽  
Next Generation Sequencing ◽  
Quality Evaluation ◽  
Reference Sample ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Evaluation Tool ◽  
Link Type ◽  
Generation Sequencing

Quality assurance and quality control are essential for robust next generation sequencing (NGS). Here we present CoverView, a fast, flexible, user-friendly quality evaluation tool for NGS data. CoverView processes mapped sequencing reads and user-specified regions to report depth of coverage, base and mapping quality metrics with increasing levels of detail from a chromosome-level summary to per-base profiles. CoverView can flag regions that do not fulfil user-specified quality requirements, allowing suboptimal data to be systematically and automatically presented for review. It also provides an interactive graphical user interface (GUI) that can be opened in a web browser and allows intuitive exploration of results. We have integrated CoverView into our accredited clinical cancer predisposition gene testing laboratory that uses the TruSight Cancer Panel (TSCP). CoverView has been invaluable for optimisation and quality control of our testing pipeline, providing transparent, consistent quality metric information and automatic flagging of regions that fall below quality thresholds. We demonstrate this utility with TSCP data from the Genome in a Bottle reference sample, which CoverView analysed in 13 seconds. CoverView uses data routinely generated by NGS pipelines, reads standard input formats, and rapidly creates easy-to-parse output text (.txt) files that are customised by a simple configuration file. CoverView can therefore be easily integrated into any NGS pipeline. CoverView and detailed documentation for its use are freely available at github.com/RahmanTeamDevelopment/CoverView/releases and www.icr.ac.uk/CoverView

scholarly journals Prostate cancer heterogeneity assessment with multi-regional sampling and alignment-free methods

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Ross G Murphy ◽  
Aideen C Roddy ◽  
Shambhavi Srivastava ◽  
Esther Baena ◽  
David J Waugh ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Cancer Heterogeneity ◽  
Alignment Free ◽  
Treatment Indications ◽  
Patient Heterogeneity ◽  
Genomic Locations ◽  
Generation Sequencing

Abstract Combining alignment-free methods for phylogenetic analysis with multi-regional sampling using next-generation sequencing can provide an assessment of intra-patient tumour heterogeneity. From multi-regional sampling divergent branching, we validated two different lesions within a patient’s prostate. Where multi-regional sampling has not been used, a single sample from one of these areas could misguide as to which drugs or therapies would best benefit this patient, due to the fact these tumours appear to be genetically different. This application has the power to render, in a fraction of the time used by other approaches, intra-patient heterogeneity and decipher aberrant biomarkers. Another alignment-free method for calling single-nucleotide variants from raw next-generation sequencing samples has determined possible variants and genomic locations that may be able to characterize the differences between the two main branching patterns. Alignment-free approaches have been applied to relevant clinical multi-regional samples and may be considered as a valuable option for comparing and determining heterogeneity to help deliver personalized medicine through more robust efforts in identifying targetable pathways and therapeutic strategies. Our study highlights the application these tools could have on patient-aligned treatment indications.

scholarly journals Next generation sequencing reads comparison with an alignment-free distance

2014 ◽  
Vol 7 (1) ◽  
pp. 869 ◽  
Author(s):  
Emanuel Weitschek ◽  
Daniele Santoni ◽  
Giulia Fiscon ◽  
Maria De Cola ◽  
Paola Bertolazzi ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Next Generation ◽  
Free Distance ◽  
Alignment Free ◽  
Generation Sequencing

scholarly journals treedata.table: a wrapper for data.table that enables fast manipulation of large phylogenetic trees matched to data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12450
Author(s):  
Cristian Román Palacios ◽  
April Wright ◽  
Josef Uyeda
Keyword(s):  
Next Generation Sequencing ◽  
Phylogenetic Trees ◽  
Next Generation ◽  
Data Repositories ◽  
Data Manipulation ◽  
Link Type ◽  
Recent Advances ◽  
Phylogenetic Data ◽  
Public Data ◽  
Generation Sequencing

The number of terminals in phylogenetic trees has significantly increased over the last decade. This trend reflects recent advances in next-generation sequencing, accessibility of public data repositories, and the increased use of phylogenies in many fields. Despite R being central to the analysis of phylogenetic data, manipulation of phylogenetic comparative datasets remains slow, complex, and poorly reproducible. Here, we describe the first R package extending the functionality and syntax of data.table to explicitly deal with phylogenetic comparative datasets. treedata.table significantly increases speed and reproducibility during the data manipulation steps involved in the phylogenetic comparative workflow in R. The latest release of treedata.table is currently available through CRAN (https://cran.r-project.org/web/packages/treedata.table/). Additional documentation can be accessed through rOpenSci (https://ropensci.github.io/treedata.table/).

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