scholarly journals VARIFI—Web-Based Automatic Variant Identification, Filtering and Annotation of Amplicon Sequencing Data

2019 ◽  
Vol 9 (1) ◽  
pp. 10
Author(s):  
Milica Krunic ◽  
Peter Venhuizen ◽  
Leonhard Müllauer ◽  
Bettina Kaserer ◽  
Arndt von Haeseler
Keyword(s):  
Genetic Variants ◽  
Confidence Score ◽  
Amplicon Sequencing ◽  
Personal Genome ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Web Based ◽  
Sequencing Errors ◽  
Sequencing Technologies ◽  
Variant Information

Fast and affordable benchtop sequencers are becoming more important in improving personalized medical treatment. Still, distinguishing genetic variants between healthy and diseased individuals from sequencing errors remains a challenge. Here we present VARIFI, a pipeline for finding reliable genetic variants (single nucleotide polymorphisms (SNPs) and insertions and deletions (indels)). We optimized parameters in VARIFI by analyzing more than 170 amplicon-sequenced cancer samples produced on the Personal Genome Machine (PGM). In contrast to existing pipelines, VARIFI combines different analysis methods and, based on their concordance, assigns a confidence score to each identified variant. Furthermore, VARIFI applies variant filters for biases associated with the sequencing technologies (e.g., incorrectly identified homopolymer-associated indels with Ion Torrent). VARIFI automatically extracts variant information from publicly available databases and incorporates methods for variant effect prediction. VARIFI requires little computational experience and no in-house compute power since the analyses are conducted on our server. VARIFI is a web-based tool available at varifi.cibiv.univie.ac.at.

Variant calling and quality control of large-scale human genome sequencing data

2019 ◽  
Vol 3 (4) ◽  
pp. 399-409 ◽  
Author(s):  
Brandon Jew ◽  
Jae Hoon Sul
Keyword(s):  
Quality Control ◽  
Genome Sequencing ◽  
Genetic Variants ◽  
Large Scale ◽  
Variant Calling ◽  
Sequencing Data ◽  
Computational Approaches ◽  
Sequencing Errors ◽  
Human Genome Sequencing ◽  
Number Of Individuals

Abstract Next-generation sequencing has allowed genetic studies to collect genome sequencing data from a large number of individuals. However, raw sequencing data are not usually interpretable due to fragmentation of the genome and technical biases; therefore, analysis of these data requires many computational approaches. First, for each sequenced individual, sequencing data are aligned and further processed to account for technical biases. Then, variant calling is performed to obtain information on the positions of genetic variants and their corresponding genotypes. Quality control (QC) is applied to identify individuals and genetic variants with sequencing errors. These procedures are necessary to generate accurate variant calls from sequencing data, and many computational approaches have been developed for these tasks. This review will focus on current widely used approaches for variant calling and QC.

scholarly journals Rbec: a tool for analysis of amplicon sequencing data from synthetic microbial communities

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Pengfan Zhang ◽  
Stjin Spaepen ◽  
Yang Bai ◽  
Stephane Hacquard ◽  
Ruben Garrido-Oter
Keyword(s):  
Microbial Communities ◽  
Amplicon Sequencing ◽  
Fungal Communities ◽  
Polymorphic Variation ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Extensive Evaluation ◽  
Culture Independent ◽  
Reference Sequences ◽  
Synthetic Microbial Communities

AbstractSynthetic microbial communities (SynComs) constitute an emerging and powerful tool in biological, biomedical, and biotechnological research. Despite recent advances in algorithms for the analysis of culture-independent amplicon sequencing data from microbial communities, there is a lack of tools specifically designed for analyzing SynCom data, where reference sequences for each strain are available. Here we present Rbec, a tool designed for the analysis of SynCom data that accurately corrects PCR and sequencing errors in amplicon sequences and identifies intra-strain polymorphic variation. Extensive evaluation using mock bacterial and fungal communities show that our tool outperforms current methods for samples of varying complexity, diversity, and sequencing depth. Furthermore, Rbec also allows accurate detection of contaminants in SynCom experiments.

scholarly journals Natrix: A Snakemake-based workflow for processing, clustering, and taxonomically assigning amplicon sequencing reads

2020 ◽  
Author(s):  
Marius Welzel ◽  
Anja Lange ◽  
Dominik Heider ◽  
Michael Schwarz ◽  
Bernd Freisleben ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
Workflow Management ◽  
Amplicon Sequencing ◽  
Version Control ◽  
Marker Genes ◽  
Sequencing Data ◽  
Taxonomic Assignment ◽  
Ecological Processes ◽  
Sequencing Technologies ◽  
User Friendly

AbstractSequencing of marker genes amplified from environmental samples, known as amplicon sequencing, allows us to resolve some of the hidden diversity and elucidate evolutionary relationships and ecological processes among complex microbial communities. The analysis of large numbers of samples at high sequencing depths generated by high throughput sequencing technologies requires effcient, flexible, and reproducible bioinformatics pipelines. Only a few existing workflows can be run in a user-friendly, scalable, and reproducible manner on different computing devices using an effcient workflow management system. We present Natrix, an open-source bioinformatics workflow for preprocessing raw amplicon sequencing data. The workflow contains all analysis steps from quality assessment, read assembly, dereplication, chimera detection, split-sample merging, sequence representative assignment (OTUs or ASVs) to the taxonomic assignment of sequence representatives. The workflow is written using Snakemake, a workflow management engine for developing data analysis workflows. In addition, Conda is used for version control. Thus, Snakemake ensures reproducibility and Conda offers version control of the utilized programs. The encapsulation of rules and their dependencies support hassle-free sharing of rules between workflows and easy adaptation and extension of existing workflows. Natrix is freely available on GitHub (https://github.com/MW55/Natrix).

scholarly journals Extensive sequencing of seven human genomes to characterize benchmark reference materials

2015 ◽  
Author(s):  
Justin M Zook ◽  
David Catoe ◽  
Jennifer McDaniel ◽  
Lindsay Vang ◽  
Noah Spies ◽  
...  
Keyword(s):  
Human Genome ◽  
Reference Materials ◽  
De Novo ◽  
Variant Calling ◽  
Genome Project ◽  
Genome Comparison ◽  
Personal Genome ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Human Genomes

The Genome in a Bottle Consortium, hosted by the National Institute of Standards and Technology (NIST) is creating reference materials and data for human genome sequencing, as well as methods for genome comparison and benchmarking. Here, we describe a large, diverse set of sequencing data for seven human genomes; five are current or candidate NIST Reference Materials. The pilot genome, NA12878, has been released as NIST RM 8398. We also describe data from two Personal Genome Project trios, one of Ashkenazim Jewish ancestry and one of Chinese ancestry. The data come from 12 technologies: BioNano Genomics, Complete Genomics paired-end and LFR, Ion Proton exome, Oxford Nanopore, Pacific Biosciences, SOLiD, 10X Genomics GemCodeTM WGS, and Illumina exome and WGS paired-end, mate-pair, and synthetic long reads. Cell lines, DNA, and data from these individuals are publicly available. Therefore, we expect these data to be useful for revealing novel information about the human genome and improving sequencing technologies, SNP, indel, and structural variant calling, and de novo assembly.

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 Current challenges and best-practice protocols for microbiome analysis

2019 ◽  
Author(s):  
Richa Bharti ◽  
Dominik G Grimm
Keyword(s):  
16S Rrna ◽  
Best Practice ◽  
Metagenomic Sequencing ◽  
Sequencing Data ◽  
Experimental Conditions ◽  
Sequencing Errors ◽  
Sequencing Technologies ◽  
Control Assembly ◽  
Computationally Intensive ◽  
Downstream Analysis

Abstract Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

The humankind genome: from genetic diversity to the origin of human diseases

Genome ◽  
2013 ◽  
Vol 56 (12) ◽  
pp. 705-716 ◽  
Author(s):  
Jose E. Belizário
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Model Organism ◽  
Copy Number Variations ◽  
Genomic Variation ◽  
Human Diseases ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Full Characterization ◽  
Sequencing Technologies

Genome-wide association studies have failed to establish common variant risk for the majority of common human diseases. The underlying reasons for this failure are explained by recent studies of resequencing and comparison of over 1200 human genomes and 10 000 exomes, together with the delineation of DNA methylation patterns (epigenome) and full characterization of coding and noncoding RNAs (transcriptome) being transcribed. These studies have provided the most comprehensive catalogues of functional elements and genetic variants that are now available for global integrative analysis and experimental validation in prospective cohort studies. With these datasets, researchers will have unparalleled opportunities for the alignment, mining, and testing of hypotheses for the roles of specific genetic variants, including copy number variations, single nucleotide polymorphisms, and indels as the cause of specific phenotypes and diseases. Through the use of next-generation sequencing technologies for genotyping and standardized ontological annotation to systematically analyze the effects of genomic variation on humans and model organism phenotypes, we will be able to find candidate genes and new clues for disease’s etiology and treatment. This article describes essential concepts in genetics and genomic technologies as well as the emerging computational framework to comprehensively search websites and platforms available for the analysis and interpretation of genomic data.

scholarly journals Genomic Analysis of Multidrug-Resistant Mycobacterium tuberculosis Strains From Patients in Kazakhstan

2021 ◽  
Vol 12 ◽  
Author(s):  
Asset Daniyarov ◽  
Askhat Molkenov ◽  
Saule Rakhimova ◽  
Ainur Akhmetova ◽  
Dauren Yerezhepov ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Mycobacterium Tuberculosis ◽  
Genetic Variants ◽  
Genomic Analysis ◽  
Public Health Problem ◽  
Multidrug Resistant ◽  
Whole Genome ◽  
Drug Resistant ◽  
Nucleotide Polymorphisms ◽  
Sequencing Technologies

Tuberculosis (TB) is an infectious disease that remains an essential public health problem in many countries. Despite decreasing numbers of new cases worldwide, the incidence of antibiotic-resistant forms (multidrug resistant and extensively drug-resistant) of TB is increasing. Next-generation sequencing technologies provide a high-throughput approach to identify known and novel potential genetic variants that are associated with drug resistance in Mycobacterium tuberculosis (Mtb). There are limited reports and data related to whole-genome characteristics of drug-resistant Mtb strains circulating in Kazakhstan. Here, we report whole-genome sequencing and analysis results of eight multidrug-resistant strains collected from TB patients in Kazakhstan. Genotyping and validation of all strains by MIRU-VNTR and spoligotyping methodologies revealed that these strains belong to the Beijing family. The spectrum of specific and potentially novel genomic variants (single-nucleotide polymorphisms, insertions, and deletions) related to drug resistance was identified and annotated. ResFinder, CARD, and CASTB antibiotic resistance databases were used for the characterization of genetic variants in genes associated with drug resistance. Our results provide reference data and genomic profiles of multidrug-resistant isolates for further comparative studies and investigations of genetic patterns in drug-resistant Mtb strains.

scholarly journals Finding genetic variants in plants without complete genomes

2019 ◽  
Author(s):  
Yoav Voichek ◽  
Detlef Weigel
Keyword(s):  
Genetic Variants ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Structural Variants ◽  
Sequencing Data ◽  
New Associations ◽  
Maize Populations ◽  
Genome Wide ◽  
Genomic Regions

AbstractStructural variants and presence/absence polymorphisms are common in plant genomes, yet they are routinely overlooked in genome-wide association studies (GWAS). Here, we expand the genetic variants detected in GWAS to include major deletions, insertions, and rearrangements. We first use raw sequencing data directly to derive short sequences, k-mers, that mark a broad range of polymorphisms independently of a reference genome. We then link k-mers associated with phenotypes to specific genomic regions. Using this approach, we re-analyzed 2,000 traits measured in Arabidopsis thaliana, tomato, and maize populations. Associations identified with k-mers recapitulate those found with single-nucleotide polymorphisms (SNPs), however, with stronger statistical support. Moreover, we identified new associations with structural variants and with regions missing from reference genomes. Our results demonstrate the power of performing GWAS before linking sequence reads to specific genomic regions, which allow detection of a wider range of genetic variants responsible for phenotypic variation.

scholarly journals Tandem repeats structure of gel-forming mucin domains could be revealed by SMRT sequencing data

2020 ◽  
Author(s):  
Tiange Lang
Keyword(s):  
Single Molecule ◽  
Tandem Repeats ◽  
Nucleotide Polymorphisms ◽  
Sequencing Data ◽  
Coding Region ◽  
Smrt Sequencing ◽  
Single Nucleotide ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Great Complexity

Abstract Background. Gel-forming mucin domains of mucin genes show great complexity with tandem repeats (TRs), thus make it difficult to study the sequences. Methods. With the coming of single molecule real-time (SMRT) sequencing technologies, we manage to present sequence structure of mucin domains via SMRT long reads for MUC2, MUC5AC, MUC5B and MUC6. Results. Our study shows that for different individuals, single nucleotide polymorphisms (SNPs) could be found in mucin domains of MUC2, MUC5AC, MUC5B and MUC6, while different number of tandem repeats could be found in mucin domains of MUC2 and MUC6. Conclusions. This information will provided new insights on getting the sequence for Tandem Repeat parts which locate in coding region.

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