scholarly journals Improving Species Identification of Ancient Mammals Based on Next-Generation Sequencing Data

Genes ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 509
Author(s):  
Tian Lan ◽  
Yu Lin ◽  
Jacob Njaramba-Ngatia ◽  
Xiao Guo ◽  
Ren Li ◽  
...  
Keyword(s):  
Species Identification ◽  
Ancient Dna ◽  
Dna Sequences ◽  
Next Generation Sequencing Data ◽  
Whole Genome Sequencing Data ◽  
Local Alignment ◽  
Sequencing Data ◽  
Mammal Species ◽  
Query Coverage ◽  
Ancient Remains

The taxonomical identification merely based on morphology is often difficult for ancient remains. Therefore, universal or specific PCR amplification followed by sequencing and BLAST (basic local alignment search tool) search has become the most frequently used genetic-based method for the species identification of biological samples, including ancient remains. However, it is challenging for these methods to process extremely ancient samples with severe DNA fragmentation and contamination. Here, we applied whole-genome sequencing data from 12 ancient samples with ages ranging from 2.7 to 700 kya to compare different mapping algorithms, and tested different reference databases, mapping similarities and query coverage to explore the best method and mapping parameters that can improve the accuracy of ancient mammal species identification. The selected method and parameters were tested using 152 ancient samples, and 150 of the samples were successfully identified. We further screened the BLAST-based mapping results according to the deamination characteristics of ancient DNA to improve the ability of ancient species identification. Our findings demonstrate a marked improvement to the normal procedures used for ancient species identification, which was achieved through defining the mapping and filtering guidelines to identify true ancient DNA sequences. The guidelines summarized in this study could be valuable in archaeology, paleontology, evolution, and forensic science. For the convenience of the scientific community, we wrote a software script with Perl, called AncSid, which is made available on GitHub.

scholarly journals Meta-Align: A Novel HMM-based Algorithm for Pairwise Alignment of Error-Prone Sequencing Reads

2020 ◽  
Author(s):  
Kentaro Tomii ◽  
Shravan Kumar ◽  
Degui Zhi ◽  
Steven E. Brenner
Keyword(s):  
Dna Sequences ◽  
Viterbi Algorithm ◽  
Query Sequence ◽  
Pairwise Alignment ◽  
Next Generation Sequencing Data ◽  
Alignment Algorithm ◽  
Sequencing Data ◽  
Sequencing Errors ◽  
Insertion And Deletion ◽  
Protein Space

AbstractBackgroundInsertion and deletion sequencing errors are relatively common in next-generation sequencing data and produce long stretches of mistranslated sequence. These frameshifting errors can cause very serious damages to downstream data analysis of reads. However, it is possible to obtain more precise alignment of DNA sequences by taking into account both coding frame and sequencing errors estimated by quality scores.ResultsHere we designed and proposed a novel hidden Markov model (HMM)-based pairwise alignment algorithm, Meta-Align, that aligns DNA sequences in the protein space, incorporating quality scores from the DNA sequences and allowing frameshifts caused by insertions and deletions. Our model is based on both an HMM transducer of a pair HMM and profile HMMs for all possible amino acid pairs. A Viterbi algorithm over our model produces the optimal alignment of a pair of metagenomic reads taking into account all possible translating frames and gap penalties in both the protein space and the DNA space. To reduce the sheer number of states of this model, we also derived and implemented a computationally feasible model, leveraging the degeneracy of the genetic code. In a benchmark test on a diverse set of simulated reads based on BAliBASE we show that Meta-Align outperforms TBLASTX which compares the six-frame translations of a nucleotide query sequence against the six-frame translations of a nucleotide sequence database using the BLAST algorithm. We also demonstrate the effects of incorporating quality scores on Meta-Align.ConclusionsMeta-Align will be particularly effective when applied to error-prone DNA sequences. The package of our software can be downloaded at https://github.com/shravan-repos/Metaalign.

scholarly journals Detection of structural mosaicism from targeted and whole-genome sequencing data

2016 ◽  
Author(s):  
Daniel A. King ◽  
Alejandro Sifrim ◽  
Tomas W. Fitzgerald ◽  
Raheleh Rahbari ◽  
Emma Hobson ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Exome Sequencing ◽  
Genome Sequencing ◽  
Developmental Disorders ◽  
Large Fraction ◽  
Clinical Diagnostics ◽  
Next Generation Sequencing Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data

ABSTRACTStructural mosaic abnormalities are large post-zygotic mutations present in a subset of cells and have been implicated in developmental disorders and cancer. Such mutations have been conventionally assessed in clinical diagnostics using cytogenetic or microarray testing. Modern disease studies rely heavily on exome sequencing, yet an adequate method for the detection of structural mosaicism using targeted sequencing data is lacking. Here, we present a method, called MrMosaic, to detect structural mosaic abnormalities using deviations in allele fraction and read coverage from next generation sequencing data. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) simulations were used to calculate detection performance across a range of mosaic event sizes, types, clonalities, and sequencing depths. The tool was applied to 4,911 patients with undiagnosed developmental disorders, and 11 events in 9 patients were detected. In 8 of 11 cases, mosaicism was observed in saliva but not blood, suggesting that assaying blood alone would miss a large fraction, possibly more than 50%, of mosaic diagnostic chromosomal rearrangements.

scholarly journals read_haps: using read haplotypes to detect same species contamination in DNA sequences

2020 ◽  
Author(s):  
Hannes P. Eggertsson ◽  
Bjarni V. Halldorsson
Keyword(s):  
Data Analysis ◽  
Genome Sequencing ◽  
Dna Sequences ◽  
Diploid Species ◽  
Reliable Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Short Read ◽  
Polymorphic Snps

AbstractMotivationData analysis is requisite on reliable data. In genetics this includes verifying that the sample is not contaminated with another, a problem ubiquitous in biology.ResultsIn human, and other diploid species, DNA contamination from the same species can be found by the presence of three haplotypes between polymorphic SNPs. read_haps is a tool that detects sample contamination from short read whole genome sequencing data.Availabilitygithub.com/DecodeGenetics/[email protected]

Comparison of microbiome samples: methods and computational challenges

2020 ◽  
Author(s):  
Matteo Comin ◽  
Barbara Di Camillo ◽  
Cinzia Pizzi ◽  
Fabio Vandin
Keyword(s):  
Next Generation Sequencing ◽  
Microbial Communities ◽  
Species Identification ◽  
Disease Status ◽  
Next Generation Sequencing Data ◽  
Data Sets ◽  
Efficient Computation ◽  
Sequencing Data ◽  
Reference Genomes ◽  
Generation Sequencing

Abstract The study of microbial communities crucially relies on the comparison of metagenomic next-generation sequencing data sets, for which several methods have been designed in recent years. Here, we review three key challenges in the comparison of such data sets: species identification and quantification, the efficient computation of distances between metagenomic samples and the identification of metagenomic features associated with a phenotype such as disease status. We present current solutions for such challenges, considering both reference-based methods relying on a database of reference genomes and reference-free methods working directly on all sequencing reads from the samples.

scholarly journals Blood group typing from whole-genome sequencing data

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242168
Author(s):  
Julien Paganini ◽  
Peter L. Nagy ◽  
Nicholas Rouse ◽  
Philippe Gouret ◽  
Jacques Chiaroni ◽  
...  
Keyword(s):  
Whole Genome Sequencing ◽  
Blood Group ◽  
Genome Sequencing ◽  
Hla Typing ◽  
Next Generation Sequencing Data ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Sequencing Data ◽  
Personalized Care ◽  
Genome Data

Many questions can be explored thanks to whole-genome data. The aim of this study was to overcome their main limits, software availability and database accuracy, and estimate the feasibility of red blood cell (RBC) antigen typing from whole-genome sequencing (WGS) data. We analyzed whole-genome data from 79 individuals for HLA-DRB1 and 9 RBC antigens. Whole-genome sequencing data was analyzed with software allowing phasing of variable positions to define alleles or haplotypes and validated for HLA typing from next-generation sequencing data. A dedicated database was set up with 1648 variable positions analyzed in KEL (KEL), ACKR1 (FY), SLC14A1 (JK), ACHE (YT), ART4 (DO), AQP1 (CO), CD44 (IN), SLC4A1 (DI) and ICAM4 (LW). Whole-genome sequencing typing was compared to that previously obtained by amplicon-based monoallelic sequencing and by SNaPshot analysis. Whole-genome sequencing data were also explored for other alleles. Our results showed 93% of concordance for blood group polymorphisms and 91% for HLA-DRB1. Incorrect typing and unresolved results confirm that WGS should be considered reliable with read depths strictly above 15x. Our results supported that RBC antigen typing from WGS is feasible but requires improvements in read depth for SNV polymorphisms typing accuracy. We also showed the potential for WGS in screening donors with rare blood antigens, such as weak JK alleles. The development of WGS analysis in immunogenetics laboratories would offer personalized care in the management of RBC disorders.

scholarly journals SeqTailor: a user-friendly webserver for the extraction of DNA or protein sequences from next-generation sequencing data

2019 ◽  
Vol 47 (W1) ◽  
pp. W623-W631 ◽  
Author(s):  
Peng Zhang ◽  
Bertrand Boisson ◽  
Peter D Stenson ◽  
David N Cooper ◽  
Jean-Laurent Casanova ◽  
...  
Keyword(s):  
Dna Sequences ◽  
Structure Prediction ◽  
Protein Sequences ◽  
Extraction Process ◽  
Genomic Variation ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Efficient Manner ◽  
Genomic Variants ◽  
User Friendly

Abstract Human whole-genome-sequencing reveals about 4 000 000 genomic variants per individual. These data are mostly stored as VCF-format files. Although many variant analysis methods accept VCF as input, many other tools require DNA or protein sequences, particularly for splicing prediction, sequence alignment, phylogenetic analysis, and structure prediction. However, there is no existing webserver capable of extracting DNA/protein sequences for genomic variants from VCF files in a user-friendly and efficient manner. We developed the SeqTailor webserver to bridge this gap, by enabling rapid extraction of (i) DNA sequences around genomic variants, with customizable window sizes and options to annotate the splice sites closest to the variants and to consider the neighboring variants within the window; and (ii) protein sequences encoded by the DNA sequences around genomic variants, with built-in SnpEff annotator and customizable window sizes. SeqTailor supports 11 species, including: human (GRCh37/GRCh38), chimpanzee, mouse, rat, cow, chicken, lizard, zebrafish, fruitfly, Arabidopsis and rice. Standalone programs are provided for command-line-based needs. SeqTailor streamlines the sequence extraction process, and accelerates the analysis of genomic variants with software requiring DNA/protein sequences. It will facilitate the study of genomic variation, by increasing the feasibility of sequence-based analysis and prediction. The SeqTailor webserver is freely available at http://shiva.rockefeller.edu/SeqTailor/.

scholarly journals Compression of Next-Generation Sequencing Data and of DNA Digital Files

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 151
Author(s):  
Bruno Carpentieri
Keyword(s):  
Next Generation Sequencing ◽  
Dna Sequences ◽  
Network Traffic ◽  
Large Scale ◽  
Genomic Data ◽  
Biological Data ◽  
Next Generation Sequencing Data ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing

The increase in memory and in network traffic used and caused by new sequenced biological data has recently deeply grown. Genomic projects such as HapMap and 1000 Genomes have contributed to the very large rise of databases and network traffic related to genomic data and to the development of new efficient technologies. The large-scale sequencing of samples of DNA has brought new attention and produced new research, and thus the interest in the scientific community for genomic data has greatly increased. In a very short time, researchers have developed hardware tools, analysis software, algorithms, private databases, and infrastructures to support the research in genomics. In this paper, we analyze different approaches for compressing digital files generated by Next-Generation Sequencing tools containing nucleotide sequences, and we discuss and evaluate the compression performance of generic compression algorithms by confronting them with a specific system designed by Jones et al. specifically for genomic file compression: Quip. Moreover, we present a simple but effective technique for the compression of DNA sequences in which we only consider the relevant DNA data and experimentally evaluate its performances.

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