scholarly journals sppIDer: a species identification tool to investigate hybrid genomes with high-throughput sequencing

2018 ◽  
Author(s):  
Quinn K. Langdon ◽  
David Peris ◽  
Brian Kyle ◽  
Chris Todd Hittinger
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Rapid Identification ◽  
Sequencing Data ◽  
Pure Species ◽  
High Throughput Sequencing Data ◽  
Interspecies Hybrids ◽  
Evolutionary Trajectories ◽  
Low Coverage ◽  
Reference Genomes

AbstractThe genomics era has expanded our knowledge about the diversity of the living world, yet harnessing high-throughput sequencing data to investigate alternative evolutionary trajectories, such as hybridization, is still challenging. Here we present sppIDer, a pipeline for the characterization of interspecies hybrids and pure species,that illuminates the complete composition of genomes. sppIDer maps short-read sequencing data to a combination genome built from reference genomes of several species of interest and assesses the genomic contribution and relative ploidy of each parental species, producing a series of colorful graphical outputs ready for publication. As a proof-of-concept, we use the genus Saccharomyces to detect and visualize both interspecies hybrids and pure strains, even with missing parental reference genomes. Through simulation, we show that sppIDer is robust to variable reference genome qualities and performs well with low-coverage data. We further demonstrate the power of this approach in plants, animals, and other fungi. sppIDer is robust to many different inputs and provides visually intuitive insight into genome composition that enables the rapid identification of species and their interspecies hybrids. sppIDer exists as a Docker image, which is a reusable, reproducible, transparent, and simple-to-run package that automates the pipeline and installation of the required dependencies (https://github.com/GLBRC/sppIDer).

scholarly journals Linkage Disequilibrium Estimation in Low Coverage High-Throughput Sequencing Data

Genetics ◽  
2018 ◽  
Vol 209 (2) ◽  
pp. 389-400 ◽  
Author(s):  
Timothy P. Bilton ◽  
John C. McEwan ◽  
Shannon M. Clarke ◽  
Rudiger Brauning ◽  
Tracey C. van Stijn ◽  
...  
Keyword(s):  
Linkage Disequilibrium ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Low Coverage

scholarly journals Heap: a highly sensitive and accurate SNP detection tool for low-coverage high-throughput sequencing data

DNA Research ◽  
2017 ◽  
Vol 24 (4) ◽  
pp. 397-405 ◽  
Author(s):  
Masaaki Kobayashi ◽  
Hajime Ohyanagi ◽  
Hideki Takanashi ◽  
Satomi Asano ◽  
Toru Kudo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
Snp Detection ◽  
Detection Tool ◽  
High Throughput Sequencing Data ◽  
Highly Sensitive ◽  
Low Coverage

scholarly journals Accounting for Errors in Low Coverage High-Throughput Sequencing Data When Constructing Genetic Maps Using Biparental Outcrossed Populations

Genetics ◽  
2018 ◽  
Vol 209 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Timothy P. Bilton ◽  
Matthew R. Schofield ◽  
Michael A. Black ◽  
David Chagné ◽  
Phillip L. Wilcox ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Genetic Maps ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Low Coverage

scholarly journals Bazam: A rapid method for read extraction and realignment of high throughput sequencing data

2018 ◽  
Author(s):  
Simon P Sadedin ◽  
Alicia Oshlack
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Variant Calling ◽  
Genomic Data ◽  
Selective Extraction ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Time Required ◽  
Genomic Regions ◽  
Reference Genomes

AbstractBackgroundAs costs of high throughput sequencing have fallen, we are seeing vast quantities of short read genomic data being generated. Often, the data is exchanged and stored as aligned reads, which provides high compression and convenient access for many analyses. However, aligned data becomes outdated as new reference genomes and alignment methods become available. Moreover, some applications cannot utilise pre-aligned reads at all, necessitating conversion back to raw format (FASTQ) before they can be used. In both cases, the process of extraction and realignment is expensive and time consuming.FindingsWe describe Bazam, a tool that efficiently extracts the original paired FASTQ from reads stored in aligned form (BAM or CRAM format). Bazam extracts reads in a format that directly allows realignment with popular aligners with high concurrency. Through eliminating steps and increasing the accessible concurrency, Bazam facilitates up to a 90% reduction in the time required for realignment compared to standard methods. Bazam can support selective extraction of read pairs from focused genomic regions, further increasing efficiency for targeted analyses. Bazam is additionally suitable as a base for other applications that require efficient paired read information, such as quality control, structural variant calling and alignment comparison.ConclusionsBazam offers significant improvements for users needing to realign genomic data.

scholarly journals Detecting Selection in Low-Coverage High-Throughput Sequencing Data using Principal Component Analysis

2021 ◽  
Author(s):  
Jonas Meisner ◽  
Anders Albrechtsen ◽  
Kristian Hanghøj
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
False Positive Rate ◽  
Principal Component ◽  
Human Populations ◽  
Population Genetic Study ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Positive Rate ◽  
Low Coverage

1AbstractIdentification of selection signatures between populations is often an important part of a population genetic study. Leveraging high-throughput DNA sequencing larger sample sizes of populations with similar ancestries has become increasingly common. This has led to the need of methods capable of identifying signals of selection in populations with a continuous cline of genetic differentiation. Individuals from continuous populations are inherently challenging to group into meaningful units which is why existing methods rely on principal components analysis for inference of the selection signals. These existing methods require called genotypes as input which is problematic for studies based on low-coverage sequencing data. Here, we present two selections statistics which we have implemented in the PCAngsd framework. These methods account for genotype uncertainty, opening for the opportunity to conduct selection scans in continuous populations from low and/or variable coverage sequencing data. To illustrate their use, we applied the methods to low-coverage sequencing data from human populations of East Asian and European ancestries and show that the implemented selection statistics can control the false positive rate and that they identify the same signatures of selection from low-coverage sequencing data as state-of-the-art software using high quality called genotypes. Moreover, we show that PCAngsd outperform selection statistics obtained from called genotypes from low-coverage sequencing data.

scholarly journals Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽  
2018 ◽  
Vol 39 ◽  
pp. 29-40 ◽  
Author(s):  
Sten Anslan ◽  
R. Henrik Nilsson ◽  
Christian Wurzbacher ◽  
Petr Baldrian ◽  
Leho Tedersoo ◽  
...  
Keyword(s):  
High Throughput ◽  
High Throughput Sequencing ◽  
Computation Time ◽  
Potential Effect ◽  
Data Sets ◽  
Sequencing Data ◽  
Operational Taxonomic Units ◽  
High Throughput Sequencing Data ◽  
Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

scholarly journals Improvements and impacts of GRCh38 human reference on high throughput sequencing data analysis

Genomics ◽  
2017 ◽  
Vol 109 (2) ◽  
pp. 83-90 ◽  
Author(s):  
Yan Guo ◽  
Yulin Dai ◽  
Hui Yu ◽  
Shilin Zhao ◽  
David C. Samuels ◽  
...  
Keyword(s):  
Data Analysis ◽  
High Throughput ◽  
High Throughput Sequencing ◽  
Sequencing Data ◽  
High Throughput Sequencing Data ◽  
Sequencing Data Analysis
Sign in / Sign up

Export Citation Format

Share Document