scholarly journals DR2S: An Integrated Algorithm Providing Reference-Grade Haplotype Sequences from Heterozygous Samples

2020 ◽  
Author(s):  
Steffen Klasberg ◽  
Alexander H. Schmidt ◽  
Vinzenz Lange ◽  
Gerhard Schöfl
Keyword(s):  
Allelic Variation ◽  
R Package ◽  
Full Length ◽  
Reference Sequence ◽  
Read Length ◽  
Sequencing Data ◽  
High Quality ◽  
Reference Allele ◽  
Sequencing Technologies ◽  
Generation Sequencing

AbstractBackgroundHigh resolution HLA genotyping of donors and recipients is a crucially important prerequisite for haematopoetic stem-cell transplantation and relies heavily on the quality and completeness of immuno-genetic reference sequence databases of allelic variation.ResultsHere, we report on DR2S, an R package that leverages the strengths of two sequencing technologies – the accuracy of next-generation sequencing with the read length of third-generation sequencing technologies like PacBio’s SMRT sequencing or ONT’s nanopore sequencing – to reconstruct fully-phased high-quality full-length haplotype sequences. Although optimised for HLA and KIR genes, DR2S is applicable to all loci with known reference sequences provided that full-length sequencing data is available for analysis. In addition, DR2S integrates supporting tools for easy visualisation and quality control of the reconstructed haplotype to ensure suitability for submission to public allele databases.ConclusionsDR2S is a largely automated workflow designed to create high-quality fully-phased reference allele sequences for highly polymorphic gene regions such as HLA or KIR. It has been used by biologists to successfully characterise and submit more than 500 HLA alleles and more than 500 KIR alleles to the IPD-IMGT/HLA and IPD-KIR databases.

scholarly journals DR2S: an integrated algorithm providing reference-grade haplotype sequences from heterozygous samples

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Steffen Klasberg ◽  
Alexander H. Schmidt ◽  
Vinzenz Lange ◽  
Gerhard Schöfl
Keyword(s):  
Allelic Variation ◽  
R Package ◽  
Full Length ◽  
Reference Sequence ◽  
Read Length ◽  
Sequencing Data ◽  
High Quality ◽  
Reference Allele ◽  
Sequencing Technologies ◽  
Generation Sequencing

Abstract Background High resolution HLA genotyping of donors and recipients is a crucially important prerequisite for haematopoetic stem-cell transplantation and relies heavily on the quality and completeness of immunogenetic reference sequence databases of allelic variation. Results Here, we report on , an R package that leverages the strengths of two sequencing technologies—the accuracy of next-generation sequencing with the read length of third-generation sequencing technologies like PacBio’s SMRT sequencing or ONT’s nanopore sequencing—to reconstruct fully-phased high-quality full-length haplotype sequences. Although optimised for HLA and KIR genes, is applicable to all loci with known reference sequences provided that full-length sequencing data is available for analysis. In addition, integrates supporting tools for easy visualisation and quality control of the reconstructed haplotype to ensure suitability for submission to public allele databases. Conclusions is a largely automated workflow designed to create high-quality fully-phased reference allele sequences for highly polymorphic gene regions such as HLA or KIR. It has been used by biologists to successfully characterise and submit more than 500 HLA alleles and more than 500 KIR alleles to the IPD-IMGT/HLA and IPD-KIR databases.

scholarly journals A comprehensive toolkit to enable MinION sequencing in any laboratory

2018 ◽  
Author(s):  
Miriam Schalamun ◽  
David Kainer ◽  
Eleanor Beavan ◽  
Ramawatar Nagar ◽  
David Eccles ◽  
...  
Keyword(s):  
Read Length ◽  
Full Potential ◽  
Sequencing Data ◽  
Special Equipment ◽  
High Quality ◽  
Sequencing Technologies ◽  
Entry Cost ◽  
Long Read ◽  
Hmw Dna

AbstractLong-read sequencing technologies are transforming our ability to assemble highly complex genomes. Realising their full potential relies crucially on extracting high quality, high molecular weight (HMW) DNA from the organisms of interest. This is especially the case for the portable MinION sequencer which potentiates all laboratories to undertake their own genome sequencing projects, due to its low entry cost and minimal spatial footprint. One challenge of the MinION is that each group has to independently establish effective protocols for using the instrument, which can be time consuming and costly. Here we present a workflow and protocols that enabled us to establish MinION sequencing in our own laboratories, based on optimising DNA extractions from a challenging plant tissue as a case study. Following the workflow illustrated we were able to reliably and repeatedly obtain > 8.5 Gb of long read sequencing data with a mean read length of 13 kb and an N50 of 26 kb. Our protocols are open-source and can be performed in any laboratory without special equipment. We also illustrate some more elaborate workflows which can increase mean and average read lengths if this is desired. We envision that our workflow for establishing MinION sequencing, including the illustration of potential pitfalls, will be useful to others who plan to establish long-read sequencing in their own laboratories.

Measuring evolutionary cancer dynamics from genome sequencing, one patient at a time

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Giulio Caravagna
Keyword(s):  
Genome Sequencing ◽  
Cancer Evolution ◽  
Sequencing Data ◽  
Evolutionary Forces ◽  
Sequencing Technologies ◽  
Cancer Genome Sequencing ◽  
Multiple Resolutions ◽  
Multiple Patients ◽  
Single Tumour ◽  
Generation Sequencing

AbstractCancers progress through the accumulation of somatic mutations which accrue during tumour evolution, allowing some cells to proliferate in an uncontrolled fashion. This growth process is intimately related to latent evolutionary forces moulding the genetic and epigenetic composition of tumour subpopulations. Understanding cancer requires therefore the understanding of these selective pressures. The adoption of widespread next-generation sequencing technologies opens up for the possibility of measuring molecular profiles of cancers at multiple resolutions, across one or multiple patients. In this review we discuss how cancer genome sequencing data from a single tumour can be used to understand these evolutionary forces, overviewing mathematical models and inferential methods adopted in field of Cancer Evolution.

Computational Approaches for Transcriptome Assembly Based on Sequencing Technologies

2020 ◽  
Vol 15 (1) ◽  
pp. 2-16
Author(s):  
Yuwen Luo ◽  
Xingyu Liao ◽  
Fang-Xiang Wu ◽  
Jianxin Wang
Keyword(s):  
De Novo ◽  
Transcriptome Assembly ◽  
Critical Role ◽  
High Sensitivity ◽  
Biological Properties ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Long Reads ◽  
Massive Sequencing ◽  
Generation Sequencing

Transcriptome assembly plays a critical role in studying biological properties and examining the expression levels of genomes in specific cells. It is also the basis of many downstream analyses. With the increase of speed and the decrease in cost, massive sequencing data continues to accumulate. A large number of assembly strategies based on different computational methods and experiments have been developed. How to efficiently perform transcriptome assembly with high sensitivity and accuracy becomes a key issue. In this work, the issues with transcriptome assembly are explored based on different sequencing technologies. Specifically, transcriptome assemblies with next-generation sequencing reads are divided into reference-based assemblies and de novo assemblies. The examples of different species are used to illustrate that long reads produced by the third-generation sequencing technologies can cover fulllength transcripts without assemblies. In addition, different transcriptome assemblies using the Hybrid-seq methods and other tools are also summarized. Finally, we discuss the future directions of transcriptome assemblies.

scholarly journals Detecting somatic mutations in genomic sequences by means of Kolmogorov–Arnold analysis

2015 ◽  
Vol 2 (8) ◽  
pp. 150143 ◽  
Author(s):  
V. G. Gurzadyan ◽  
H. Yan ◽  
G. Vlahovic ◽  
A. Kashin ◽  
P. Killela ◽  
...  
Keyword(s):  
Clinical Diagnostics ◽  
Genomic Research ◽  
Genomic Sequences ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Cancer Genome Sequencing ◽  
Frequent Mutations ◽  
Using Data ◽  
First Time ◽  
Generation Sequencing

The Kolmogorov–Arnold stochasticity parameter technique is applied for the first time to the study of cancer genome sequencing, to reveal mutations. Using data generated by next-generation sequencing technologies, we have analysed the exome sequences of brain tumour patients with matched tumour and normal blood. We show that mutations contained in sequencing data can be revealed using this technique, thus providing a new methodology for determining subsequences of given length containing mutations, i.e. its value differs from those of subsequences without mutations. A potential application for this technique involves simplifying the procedure of finding segments with mutations, speeding up genomic research and accelerating its implementation in clinical diagnostics. Moreover, the prediction of a mutation associated with a family of frequent mutations in numerous types of cancers based purely on the value of the Kolmogorov function indicates that this applied marker may recognize genomic sequences that are in extremely low abundance and can be used in revealing new types of mutations.

scholarly journals NGSphy: phylogenomic simulation of next-generation sequencing data

2017 ◽  
Author(s):  
Merly Escalona ◽  
Sara Rocha ◽  
David Posada
Keyword(s):  
Next Generation Sequencing ◽  
Variant Calling ◽  
Gene Families ◽  
Common Species ◽  
Next Generation Sequencing Data ◽  
Phylogenomic Analysis ◽  
Next Generation ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Generation Sequencing

AbstractMotivationAdvances in sequencing technologies have made it feasible to obtain massive datasets for phylogenomic inference, often consisting of large numbers of loci from multiple species and individuals. The phylogenomic analysis of next-generation sequencing (NGS) data implies a complex computational pipeline where multiple technical and methodological decisions are necessary that can influence the final tree obtained, like those related to coverage, assembly, mapping, variant calling and/or phasing.ResultsTo assess the influence of these variables we introduce NGSphy, an open-source tool for the simulation of Illumina reads/read counts obtained from haploid/diploid individual genomes with thousands of independent gene families evolving under a common species tree. In order to resemble real NGS experiments, NGSphy includes multiple options to model sequencing coverage (depth) heterogeneity across species, individuals and loci, including off-target or uncaptured loci. For comprehensive simulations covering multiple evolutionary scenarios, parameter values for the different replicates can be sampled from user-defined statistical distributions.AvailabilitySource code, full documentation and tutorials including a quick start guide are available at http://github.com/merlyescalona/[email protected]. [email protected]

scholarly journals RNA Transcriptome Mapping with GraphMap

2017 ◽  
Author(s):  
Krešimir Križanović ◽  
Ivan Sović ◽  
Ivan Krpelnik ◽  
Mile Šikić
Keyword(s):  
Third Generation ◽  
Sequencing Data ◽  
Mapping Algorithm ◽  
Gene Annotations ◽  
Sequencing Technologies ◽  
Third Generation Sequencing ◽  
Oxford Nanopore ◽  
Rna Mapping ◽  
Synthetic Datasets ◽  
Generation Sequencing

AbstractNext generation sequencing technologies have made RNA sequencing widely accessible and applicable in many areas of research. In recent years, 3rd generation sequencing technologies have matured and are slowly replacing NGS for DNA sequencing. This paper presents a novel tool for RNA mapping guided by gene annotations. The tool is an adapted version of a previously developed DNA mapper – GraphMap, tailored for third generation sequencing data, such as those produced by Pacific Biosciences or Oxford Nanopore Technologies devices. It uses gene annotations to generate a transcriptome, uses a DNA mapping algorithm to map reads to the transcriptome, and finally transforms the mappings back to genome coordinates. Modified version of GraphMap is compared on several synthetic datasets to the state-of-the-art RNAseq mappers enabled to work with third generation sequencing data. The results show that our tool outperforms other tools in general mapping quality.

scholarly journals MerCat: a versatile k-mer counter and diversity estimator for database-independent property analysis obtained from metagenomic and/or metatranscriptomic sequencing data

2017 ◽  
Author(s):  
Richard Allen White III ◽  
Ajay Panyala ◽  
Kevin Glass ◽  
Sean Colby ◽  
Kurt R Glaesemann ◽  
...  
Keyword(s):  
Software Package ◽  
Compositional Analysis ◽  
Direct Analysis ◽  
Reference Sequence ◽  
Next Generation Sequencing Data ◽  
Sequence Database ◽  
Sequencing Data ◽  
Independent Property ◽  
Generation Sequencing ◽  
Modular Property

MerCat (“ Mer - Cat enate”) is a parallel, highly scalable and modular property software package for robust analysis of features in next-generation sequencing data. Using assembled contigs and raw sequence reads from any platform as input, MerCat performs k-mer counting of any length k, resulting in feature abundance counts tables. MerCat allows for direct analysis of data properties without reference sequence database dependency commonly used by search tools such as BLAST for compositional analysis of whole community shotgun sequencing (e.g., metagenomes and metatranscriptomes).

scholarly journals An exploration of assembly strategies and quality metrics on the accuracy of the Knightia excelsa (rewarewa) genome.

2021 ◽  
Author(s):  
Ann McCartney ◽  
Elena Hilario ◽  
Seung-Sub Choi ◽  
Joseph Guhlin ◽  
Jessie Prebble ◽  
...  
Keyword(s):  
New Zealand ◽  
De Novo ◽  
Quality Metrics ◽  
Read Length ◽  
Model Organisms ◽  
Sequencing Data ◽  
Contig Assembly ◽  
High Quality ◽  
Aotearoa New Zealand ◽  
Long Read

We used long read sequencing data generated from Knightia excelsaI R.Br, a nectar producing Proteaceae tree endemic to Aotearoa New Zealand, to explore how sequencing data type, volume and workflows can impact final assembly accuracy and chromosome construction. Establishing a high-quality genome for this species has specific cultural importance to Māori, the indigenous people, as well as commercial importance to honey producers in Aotearoa New Zealand. Assemblies were produced by five long read assemblers using data subsampled based on read lengths, two polishing strategies, and two Hi-C mapping methods. Our results from subsampling the data by read length showed that each assembler tested performed differently depending on the coverage and the read length of the data. Assemblies that used longer read lengths (>30 kb) and lower coverage were the most contiguous, kmer and gene complete. The final genome assembly was constructed into pseudo-chromosomes using all available data assembled with FLYE, polished using Racon/Medaka/Pilon combined, scaffolded using SALSA2 and AllHiC, curated using Juicebox, and validated by synteny with Macadamia. We highlighted the importance of developing assembly workflows based on the volume and type of sequencing data and establishing a set of robust quality metrics for generating high quality assemblies. Scaffolding analyses highlighted that problems found in the initial assemblies could not be resolved accurately by utilizing Hi-C data and that scaffolded assemblies were more accurate when the underlying contig assembly was of higher accuracy. These findings provide insight into what is required for future high-quality de-novo assemblies of non-model organisms.

Sign in / Sign up

Export Citation Format

Share Document