scholarly journals Powerful Inference with the D-statistic on Low-Coverage Whole-Genome Data

2017 ◽  
Author(s):  
Samuele Soraggi ◽  
Carsten Wiuf ◽  
Anders Albrechtsen
Keyword(s):  
Error Correction ◽  
Genetic Relationship ◽  
High Throughput Sequencing ◽  
Sequencing Depth ◽  
Human Populations ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
External Population

ABSTRACTThe detection of ancient gene flow between human populations is an important issue in population genetics. A common tool for detecting ancient admixture events is the D-statistic. The D-statistic is based on the hypothesis of a genetic relationship that involves four populations, whose correctness is assessed by evaluating specific coincidences of alleles between the groups.When working with high throughput sequencing data calling genotypes accurately is not always possible, therefore the D-statistic currently samples a single base from the reads of one individual per population. This implies ignoring much of the information in the data, an issue especially striking in the case of ancient genomes.We provide a significant improvement to overcome the problems of the D-statistic by considering all reads from multiple individuals in each population. We also apply type-specific error correction to combat the problems of sequencing errors and show a way to correct for introgression from an external population that is not part of the supposed genetic relationship, and how this leads to an estimate of the admixture rate.We prove that the D-statistic is approximated by a standard normal. Furthermore we show that our method outperforms the traditional D-statistic in detecting admixtures. The power gain is most pronounced for low/medium sequencing depth (1-10X) and performances are as good as with perfectly called genotypes at a sequencing depth of 2X. We show the reliability of error correction on scenarios with simulated errors and ancient data, and correct for introgression in known scenarios to estimate the admixture rates.

Inference of viral quasispecies with a paired de Bruijn graph

2020 ◽  
Author(s):  
Borja Freire ◽  
Susana Ladra ◽  
Jose R Paramá ◽  
Leena Salmela
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Viral Quasispecies ◽  
Sequencing Data ◽  
De Bruijn Graphs ◽  
Sequencing Errors ◽  
High Throughput Sequencing Data ◽  
De Bruijn

Abstract Motivation RNA viruses exhibit a high mutation rate and thus they exist in infected cells as a population of closely related strains called viral quasispecies. The viral quasispecies assembly problem asks to characterize the quasispecies present in a sample from high-throughput sequencing data. We study the de novo version of the problem, where reference sequences of the quasispecies are not available. Current methods for assembling viral quasispecies are either based on overlap graphs or on de Bruijn graphs. Overlap graph-based methods tend to be accurate but slow, whereas de Bruijn graph-based methods are fast but less accurate. Results We present viaDBG, which is a fast and accurate de Bruijn graph-based tool for de novo assembly of viral quasispecies. We first iteratively correct sequencing errors in the reads, which allows us to use large k-mers in the de Bruijn graph. To incorporate the paired-end information in the graph, we also adapt the paired de Bruijn graph for viral quasispecies assembly. These features enable the use of long-range information in contig construction without compromising the speed of de Bruijn graph-based approaches. Our experimental results show that viaDBG is both accurate and fast, whereas previous methods are either fast or accurate but not both. In particular, viaDBG has comparable or better accuracy than SAVAGE, while being at least nine times faster. Furthermore, the speed of viaDBG is comparable to PEHaplo but viaDBG is able to retrieve also low abundance quasispecies, which are often missed by PEHaplo. Availability and implementation viaDBG is implemented in C++ and it is publicly available at https://bitbucket.org/bfreirec1/viadbg. All datasets used in this article are publicly available at https://bitbucket.org/bfreirec1/data-viadbg/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals ANGSD-wrapper: utilities for analyzing next generation sequencing data

2016 ◽  
Author(s):  
Arun Durvasula ◽  
Paul J Hoffman ◽  
Tyler V Kent ◽  
Chaochih Liu ◽  
Thomas J Y Kono ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Molecular Ecology ◽  
Principal Component ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
User Friendly

High throughput sequencing has changed many aspects of population genetics, molecular ecology, and related fields, affecting both experimental design and data analysis. The software package ANGSD allows users to perform a number of population genetic analyses on high-throughput sequencing data. ANGSD uses probabilistic approaches to calculate genome-wide descriptive statistics. The package makes use of genotype likelihood estimates rather than SNP calls and is specifically designed to produce more accurate results for samples with low sequencing depth. ANGSD makes use of full genome data while handling a wide array of sampling and experimental designs. Here we present ANGSD-wrapper, a set of wrapper scripts that provide a user-friendly interface for running ANGSD and visualizing results. ANGSD-wrapper supports multiple types of analyses including esti- mates of nucleotide sequence diversity and performing neutrality tests, principal component analysis, estimation of admixture proportions for individuals samples, and calculation of statistics that quantify recent introgression. ANGSD-wrapper also provides interactive graphing of ANGSD results to enhance data exploration. We demonstrate the usefulness of ANGSD-wrapper by analyzing resequencing data from populations of wild and domesticated Zea. ANGSD-wrapper is freely available from https://github.com/mojaveazure/angsd-wrapper.

scholarly journals Handling of targeted amplicon sequencing data focusing on index hopping and demultiplexing using a nested metabarcoding approach in ecology

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasemin Guenay-Greunke ◽  
David A. Bohan ◽  
Michael Traugott ◽  
Corinna Wallinger
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Cost Effective ◽  
Amplicon Sequencing ◽  
Sequencing Depth ◽  
Sequencing Error ◽  
Sequencing Data ◽  
Large Sample ◽  
Sequencing Errors ◽  
Plant Feeding

AbstractHigh-throughput sequencing platforms are increasingly being used for targeted amplicon sequencing because they enable cost-effective sequencing of large sample sets. For meaningful interpretation of targeted amplicon sequencing data and comparison between studies, it is critical that bioinformatic analyses do not introduce artefacts and rely on detailed protocols to ensure that all methods are properly performed and documented. The analysis of large sample sets and the use of predefined indexes create challenges, such as adjusting the sequencing depth across samples and taking sequencing errors or index hopping into account. However, the potential biases these factors introduce to high-throughput amplicon sequencing data sets and how they may be overcome have rarely been addressed. On the example of a nested metabarcoding analysis of 1920 carabid beetle regurgitates to assess plant feeding, we investigated: (i) the variation in sequencing depth of individually tagged samples and the effect of library preparation on the data output; (ii) the influence of sequencing errors within index regions and its consequences for demultiplexing; and (iii) the effect of index hopping. Our results demonstrate that despite library quantification, large variation in read counts and sequencing depth occurred among samples and that the sequencing error rate in bioinformatic software is essential for accurate adapter/primer trimming and demultiplexing. Moreover, setting an index hopping threshold to avoid incorrect assignment of samples is highly recommended.

scholarly journals ANGSD-wrapper: utilities for analyzing next generation sequencing data

2016 ◽  
Author(s):  
Arun Durvasula ◽  
Paul J Hoffman ◽  
Tyler V Kent ◽  
Chaochih Liu ◽  
Thomas J Y Kono ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Molecular Ecology ◽  
Principal Component ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
Genome Wide ◽  
User Friendly

High throughput sequencing has changed many aspects of population genetics, molecular ecology, and related fields, affecting both experimental design and data analysis. The software package ANGSD allows users to perform a number of population genetic analyses on high-throughput sequencing data. ANGSD uses probabilistic approaches to calculate genome-wide descriptive statistics. The package makes use of genotype likelihood estimates rather than SNP calls and is specifically designed to produce more accurate results for samples with low sequencing depth. ANGSD makes use of full genome data while handling a wide array of sampling and experimental designs. Here we present ANGSD-wrapper, a set of wrapper scripts that provide a user-friendly interface for running ANGSD and visualizing results. ANGSD-wrapper supports multiple types of analyses including esti- mates of nucleotide sequence diversity and performing neutrality tests, principal component analysis, estimation of admixture proportions for individuals samples, and calculation of statistics that quantify recent introgression. ANGSD-wrapper also provides interactive graphing of ANGSD results to enhance data exploration. We demonstrate the usefulness of ANGSD-wrapper by analyzing resequencing data from populations of wild and domesticated Zea. ANGSD-wrapper is freely available from https://github.com/mojaveazure/angsd-wrapper.

scholarly journals QUARTIC: QUick pArallel algoRithms for high-Throughput sequencIng data proCessing

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 240
Author(s):  
Frédéric Jarlier ◽  
Nicolas Joly ◽  
Nicolas Fedy ◽  
Thomas Magalhaes ◽  
Leonor Sirotti ◽  
...  
Keyword(s):  
High Throughput ◽  
Message Passing ◽  
High Performance ◽  
Message Passing Interface ◽  
High Throughput Sequencing ◽  
Genome Structure ◽  
Sequencing Data ◽  
Genome Data ◽  
High Throughput Sequencing Data ◽  
Time To Delivery

Life science has entered the so-called ’big data era’ where biologists, clinicians and bioinformaticians are overwhelmed with unprecedented amount of data. High-throughput sequencing has revolutionized genomics and offers new insights to decipher the genome structure. However, using these data for daily clinical practice care and diagnosis purposes is challenging as the data are bigger and bigger. Therefore, we implemented software using Message Passing Interface such that the alignment and sorting of sequencing reads can easily scale on high-performance computing architecture. Our implementation makes it possible to reduce the time to delivery to few minutes, even on large whole-genome data using several hundreds of cores.

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