DNA Datasets Research Articles

Integrated Taxonomy for Halistemma Species from the Northwest Pacific Ocean

During a survey of the siphonophore community in the Kuroshio Extension, Northwest Pacific Ocean, a new Halistemma Huxley, 1859 was described using integrated molecular and morphological approaches. The Halistemma isabu sp. nov. nectophore is most closely related morphologically to H. striata Totton, 1965 and H. maculatum Pugh and Baxter, 2014. These species can be differentiated by their nectosac shape, thrust block size, ectodermal cell patches and ridge patterns. The new species’ bracts are divided into two distinct types according to the number of teeth. Type A bracts are more closely related to ventral bracts in H. foliacea (Quoy and Gaimard, 1833) while Type B bracts are more similar to H. rubrum (Vogt, 1852). Each type differs, however, from the proximal end shape, distal process and bracteal canal. Both of the new species’ morphological type and phylogenetic position within the genus Halistemma are supported by phylogenetic analysis of concatenated DNA dataset (mtCOI, 16S rRNA and 18S rRNA). Integrated morphological and molecular approaches to the taxonomy of siphonophores showed a clear delimitation of the new species from the congeners. Halistemma isabu sp. nov. is distributed with the congeners H. rubrum, H. cupulifera, H. foliacea and H. striata in the northwestern Pacific Ocean.

Do I have something in my teeth? The trouble with genetic analyses of diet from archaeological dental calculus

Dental calculus and other preserved microbiome substrates are an attractive target for dietary reconstruction in past populations through a variety of physical, chemical, and molecular means. Recently, studies have attempted to reconstruct diet from archaeological dental calculus using archaeogenetic techniques. While dental calculus may provide a relatively stable environment for DNA preservation, the detection of plants and animals possibly consumed by an individual through DNA analysis is primarily hindered by microbial richness and incomplete reference databases. Moreover, high genomic similarity within eukaryotic groups - such as mammals - can obfuscate precise taxonomic identification. In the current study we demonstrate the challenges associated with accurate taxonomic identification and authentication of dietary taxa in ancient DNA data using both synthetic and ancient dental calculus datasets. We highlight common errors and sources of contamination across ancient DNA datasets, provide recommendations for dietary DNA validation, and call for caution in the interpretation of diet from dental calculus and other archaeological microbiome substrates.

Genetic Divergence Across Glacial Refugia Despite Interglacial Gene Flow in a Crested Newt

MtDNA-based phylogeography has illuminated the impact of the Pleistocene Ice Age on species distribution dynamics and the build-up of genetic divergence. The well-known shortcomings of mtDNA in biogeographical inference can be compensated by integrating multilocus data and species distribution modelling into phylogeography. We re-visit the phylogeography of the Italian crested newt (Triturus carnifex), a species distributed in two of Europe’s main glacial refugia, the Balkan and Italian Peninsulas. While a new 51 nuclear DNA marker dataset supports the existence of three lineages previously suggested by mtDNA (Balkan, northern Italy and southern Italy), the nuclear DNA dataset also provides improved resolution where these lineages have obtained secondary contact. We observe geographically restricted admixture at the contact between the Balkan and northern Italy gene pools and identify a potential mtDNA ghost lineage here. At the contact between the northern and southern Italy gene pools we find admixture over a broader area, as well as asymmetric mtDNA introgression. Our species distribution model is in agreement with a distribution restricted to distinct refugia during Pleistocene glacial cycles and postglacial expansion with secondary contact. Our study supports: (1) the relevance of the north-western Balkan Peninsula as a discrete glacial refugium; (2) the importance of north-eastern Italy and the northern Apennine as suture zones; and (3) the applicability of a refugia-within-refugia scenario within the Italian Peninsula.

Phylogeny and biogeography of Sagittaria (Alismataceae) revisited: evidence for cryptic diversity and colonization out of South America.

Sagittaria is a genus of ca. 40 species in the aquatic plant family Alismataceae with a nearly global distribution, and a center of diversity in the New World. Two thirds of the known species are native to the Americas, while only a few species are distributed in Africa, Asia and Europe. A previous biogeographic analysis of the genus suggested an African origin for the genus with subsequent dispersal to North America and then to East Asia. Here we expanded the taxon sampling with a focus on the New World taxa and applied species delimitation and biogeographic analyses to revise the knowledge of the phylogeny and evolution of the genus. We obtained largely similar topologies from the chloroplast DNA and nuclear DNA (ITS) data sets. The 74 accessions sampled for our analyses were delimited into 29 species and several cryptic taxa were revealed in widely distributed species. Biogeographic analysis supported basal diversification in South America and subsequent colonization to North America and Asia.

Big Data in metagenomics: Apache Spark vs MPI.

The progress of next-generation sequencing has lead to the availability of massive data sets used by a wide range of applications in biology and medicine. This has sparked significant interest in using modern Big Data technologies to process this large amount of information in distributed memory clusters of commodity hardware. Several approaches based on solutions such as Apache Hadoop or Apache Spark, have been proposed. These solutions allow developers to focus on the problem while the need to deal with low level details, such as data distribution schemes or communication patterns among processing nodes, can be ignored. However, performance and scalability are also of high importance when dealing with increasing problems sizes, making in this way the usage of High Performance Computing (HPC) technologies such as the message passing interface (MPI) a promising alternative. Recently, MetaCacheSpark, an Apache Spark based software for detection and quantification of species composition in food samples has been proposed. This tool can be used to analyze high throughput sequencing data sets of metagenomic DNA and allows for dealing with large-scale collections of complex eukaryotic and bacterial reference genome. In this work, we propose MetaCache-MPI, a fast and memory efficient solution for computing clusters which is based on MPI instead of Apache Spark. In order to evaluate its performance a comparison is performed between the original single CPU version of MetaCache, the Spark version and the MPI version we are introducing. Results show that for 32 processes, MetaCache-MPI is 1.65× faster while consuming 48.12% of the RAM memory used by Spark for building a metagenomics database. For querying this database, also with 32 processes, the MPI version is 3.11× faster, while using 55.56% of the memory used by Spark. We conclude that the new MetaCache-MPI version is faster in both building and querying the database and uses less RAM memory, when compared with MetaCacheSpark, while keeping the accuracy of the original implementation.

GeTallele: A Method for Analysis of DNA and RNA Allele Frequency Distributions.

Variant allele frequencies (VAF) are an important measure of genetic variation that can be estimated at single-nucleotide variant (SNV) sites. RNA and DNA VAFs are used as indicators of a wide-range of biological traits, including tumor purity and ploidy changes, allele-specific expression and gene-dosage transcriptional response. Here we present a novel methodology to assess gene and chromosomal allele asymmetries and to aid in identifying genomic alterations in RNA and DNA datasets. Our approach is based on analysis of the VAF distributions in chromosomal segments (continuous multi-SNV genomic regions). In each segment we estimate variant probability, a parameter of a random process that can generate synthetic VAF samples that closely resemble the observed data. We show that variant probability is a biologically interpretable quantitative descriptor of the VAF distribution in chromosomal segments which is consistent with other approaches. To this end, we apply the proposed methodology on data from 72 samples obtained from patients with breast invasive carcinoma (BRCA) from The Cancer Genome Atlas (TCGA). We compare DNA and RNA VAF distributions from matched RNA and whole exome sequencing (WES) datasets and find that both genomic signals give very similar segmentation and estimated variant probability profiles. We also find a correlation between variant probability with copy number alterations (CNA). Finally, to demonstrate a practical application of variant probabilities, we use them to estimate tumor purity. Tumor purity estimates based on variant probabilities demonstrate good concordance with other approaches (Pearson's correlation between 0.44 and 0.76). Our evaluation suggests that variant probabilities can serve as a dependable descriptor of VAF distribution, further enabling the statistical comparison of matched DNA and RNA datasets. Finally, they provide conceptual and mechanistic insights into relations between structure of VAF distributions and genetic events. The methodology is implemented in a Matlab toolbox that provides a suite of functions for analysis, statistical assessment and visualization of Genome and Transcriptome allele frequencies distributions. GeTallele is available at: https://github.com/SlowinskiPiotr/GeTallele.

AuthentiCT: a model of ancient DNA damage to estimate the proportion of present-day DNA contamination

Contamination from present-day DNA is a fundamental issue when studying ancient DNA from historical or archaeological material, and quantifying the amount of contamination is essential for downstream analyses. We present AuthentiCT, a command-line tool to estimate the proportion of present-day DNA contamination in ancient DNA datasets generated from single-stranded DNA libraries. The prediction is based solely on the patterns of post-mortem damage observed on ancient DNA sequences. The method has the power to quantify contamination from as few as 10,000 mapped sequences, making it particularly useful for analysing specimens that are poorly preserved or for which little data is available.

Towards real-time DNA biometrics using GPU-accelerated processing

Abstract Decoding the human genome in the past decades has brought into focus a computationally intensive operation through DNA profiling. The typical search space for these kinds of problems is extremely large and requires specialized hardware and algorithms to perform the necessary sequence analysis. In this paper, we propose an innovative and scalable approach to exact multi-pattern matching of nucleotide sequences by harnessing the massively parallel computing power found in commodity graphical processing units. Our approach places careful consideration on preprocessing of DNA datasets and runtime performance, while exploiting the full capabilities of the heterogeneous platform it runs on. Finally, we evaluate our models against real-world DNA sequences.

Phylogenetic relationships and the maternal donor of Roegneria (Triticeae: Poaceae) based on three nuclear DNA sequences (ITS, Acc1, and Pgk1) and one chloroplast region (trnL‐F)

AbstractRoegneria is a polyploid perennial genus in the tribe Triticeae. Some species of Roegneria are morphologically similar to genus Elymus and have been classified in Elymus. To investigate the delimitation and phylogenetic relationships of Roegneria, nuclear (ITS, Acc1, and Pgk1) and chloroplast (trnL–trnF) DNA regions were sequenced for 38 allopolyploid species and 32 diploid species of Triticeae. Phylogenetic analyses of nuclear DNA revealed that all Roegneria species were included in the St and Y genome clades, and that the Y genome was closely related to the V and Xp genomes. The chloroplast DNA dataset showed that Roegneria species were grouped with Pseudoroegneria species. The Pseudoroegneria species from the Middle East (P. libanotica and P. tauri) and Central Asia (P. strigosa) were more closely related to Roegneria species. The results suggested that: (i) the species containing the St and Y genomes should be segregated from Elymus and treated as a distinct genus, Roegneria, based on the genomic constitution; (ii) P. libanotica, P. tauri, and/or P. strigosa potentially served as the maternal donor of the St genome in Roegneria; (iii) The Y genome of Roegneria originated from a diploid Y genome species, and the V and Xp genomes may have contributed to Y genome formation; (iv) among Roegneria species of previously uncertain genomic constitution, R. seriotina was tetraploid and possessed the StY genomes, E. calcicolus was hexaploid with the StYH genomic constitution and should be classified in Campeiostachys, R. glaucifolia possessed the StStY genomes, and R. tschimganica had the genomic constitution St1St2Y.

Going back to the roots: Evaluating Bayesian phylogeographic models with discrete trait uncertainty.

Phylogeography is a popular way to analyze virus sequences annotated with discrete, epidemiologically-relevant, trait data. For applied public health surveillance, a key quantity of interest is often the state at the root of the inferred phylogeny. In epidemiological terms, this represents the geographic origin of the observed outbreak. Since determining the origin of an outbreak is often critical for public health intervention, it is prudent to understand how well phylogeographic models perform this root state classification task under various analytical scenarios. Specifically, we investigate how discrete state space and sequence data set influence the root state classification accuracy. We performed phylogeographic inference on several simulated DNA data sets while i) increasing the number of sequences and ii) increasing the total number of possible discrete trait values. We show that phylogeographic models tend to perform best at intermediate sequence data set sizes. Further, we demonstrate that a popular metric used for evaluation of phylogeographic models, the Kullback-Leibler (KL) divergence, both increases with discrete state space and data set sizes. Further, by modeling phylogeographic root state classification accuracy using logistic regression, we show that KL is not supported as a predictor of model accuracy, indicating its limited utility for assessing phylogeographic model performance on empirical data. These results suggest that relying solely on the KL metric may lead to artificially inflated support for models with finer discretization schemes and larger data set sizes. These results will be important for public health practitioners seeking to use phylogeographic models for applied infectious disease surveillance.

A novel alignment-free DNA sequence similarity analysis approach based on top-k n-gram match-up

DNA sequence similarity analysis is an essential task in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. As an alternative to alignment-based sequence comparison methods, which result in high computational cost, alignment-free methods have emerged that calculate similarity by digitizing the sequence in a different space. In this paper, we proposed an alignment-free DNA sequence similarity analysis method based on top-k n-gram matches, with the prediction that common repeating DNA subsections indicate high similarity between DNA sequences. In our method, we determined DNA sequence similarities by measuring similarity among feature vectors created according to top-k n-gram match-up scores without the use of similarity functions. We applied the similarity calculation for three different DNA data sets of different lengths. The phylogenetic relationships revealed by our method show that our trees coincide almost completely with the results of the MEGA software, which is based on sequence alignment. Our findings show that a certain number of frequently recurring common sequence patterns have the power to characterize DNA sequences.

Comparative analysis of mitochondrial DNA datasets indicates that Cylicostephanus minutus represents a species complex

Cyathostomins are one kind of the most important parasites in equids. Cylicostephanus minutus is a member of the subfamily Cyathostominae. In the present study, we determined the complete mitochondrial (mt) genomes from four Cs. minutus isolates and reconstructed the phylogenetic relationship of Strongylidae to test the hypothesis that Cs. minutus represents a species complex. The complete mt genome sequences of Cs. minutus were 13,772–13,822 bp in length, and contained 36 genes (12 protein coding genes, 22 tRNA genes, two rRNA genes), and two non-coding regions (NCRs). The intraspecific identity of nucleotide sequences and amino acid sequences in Cs. minutus (1–4) were 89.3–97.9% and 97.0–98.8%, respectively. Two operational taxonomic units (OTUs) were determined based on the mt genome sequences, OTU 2 (Csm 1 and Csm 2) and OTU 3 (Csm 3 and Csm 4). Sequence analysis showed the divergence between OTU 2 and OTU 3 was 8.9–10.7%. Pairwise comparisons of 12 protein coding genes between OTU 2 and OTU 3 showed a difference of 3.0–13.3% at the nucleotide level and 0–6.7% at the amino acid level. Phylogenetic analysis showed the separation of Cs. minutus isolates from the same host into different distinct clades based on mt genomes. Comparisons of partial mt cox1, nad5, and cytb and ITS2 sequences from 20 Cs. minutus isolates from the same host and the same geographical location with other Cs. minutus sequences available in GenBank revealed significant nucleotide differences. Phylogenetic analysis showed a separation of Cs. minutus into three distinct clades. Thus, the comparative and phylogenetic analyses of mtDNA datasets indicated that Cs. minutus represents a complex of at least three species. Our results have further confirmed the existence of a cryptic Cs. minutus species, and provides a reference for the taxonomical, population genetics, and systematics studies of other cyathostomin species.

16S rRNA metagenomic data of microbial diversity of Pheidole decarinata Santschi (Hymenoptera: Formicidae) workers.

Metagenomic datasets of the microbial DNA of workers of a Pheidole decarinata Santschi (Hymenoptera: Formicidae) around houses with three replicates were presented. Next-generation sequencing of the microbial DNA was performed on an Illumina Miseq platform. QIIME (version 1.9.1) was used to analyze the raw fastq files. Metagenome of the three (3) samples consist of 333,708 sequences representing 137,359,149 bps with an average length of 413.67 bps. The sequence data is available at the NCBI SRA with the bioproject number PRJNA632430. Community analysis revealed Proteobacteria was the predominant (84.77%) microbial community present in the microbial DNA of workers of the P. decarinata.

Present-Day DNA Contamination in Ancient DNA Datasets.

Present-day contamination can lead to false conclusions in ancient DNA studies. A number of methods are available to estimate contamination, which use a variety of signals and are appropriate for different types of data. Here an overview of currently available methods highlighting their strengths and weaknesses is provided, and a classification based on the signals used to estimate contamination is proposed. This overview aims at enabling researchers to choose the most appropriate methods for their dataset. Based on this classification, potential avenues for the further development of methods are discussed.

Sequence Compression Benchmark (SCB) database-A comprehensive evaluation of reference-free compressors for FASTA-formatted sequences.

BackgroundNearly all molecular sequence databases currently use gzip for data compression. Ongoing rapid accumulation of stored data calls for a more efficient compression tool. Although numerous compressors exist, both specialized and general-purpose, choosing one of them was difficult because no comprehensive analysis of their comparative advantages for sequence compression was available.FindingsWe systematically benchmarked 430 settings of 48 compressors (including 29 specialized sequence compressors and 19 general-purpose compressors) on representative FASTA-formatted datasets of DNA, RNA, and protein sequences. Each compressor was evaluated on 17 performance measures, including compression strength, as well as time and memory required for compression and decompression. We used 27 test datasets including individual genomes of various sizes, DNA and RNA datasets, and standard protein datasets. We summarized the results as the Sequence Compression Benchmark database (SCB database, http://kirr.dyndns.org/sequence-compression-benchmark/), which allows custom visualizations to be built for selected subsets of benchmark results.ConclusionWe found that modern compressors offer a large improvement in compactness and speed compared to gzip. Our benchmark allows compressors and their settings to be compared using a variety of performance measures, offering the opportunity to select the optimal compressor on the basis of the data type and usage scenario specific to a particular application.

Novel insights on demographic history of tribal and caste groups from West Maharashtra (India) using genome-wide data

The South Asian subcontinent is characterized by a complex history of human migrations and population interactions. In this study, we used genome-wide data to provide novel insights on the demographic history and population relationships of six Indo-European populations from the Indian State of West Maharashtra. The samples correspond to two castes (Deshastha Brahmins and Kunbi Marathas) and four tribal groups (Kokana, Warli, Bhil and Pawara). We show that tribal groups have had much smaller effective population sizes than castes, and that genetic drift has had a higher impact in tribal populations. We also show clear affinities between the Bhil and Pawara tribes, and to a lesser extent, between the Warli and Kokana tribes. Our comparisons with available modern and ancient DNA datasets from South Asia indicate that the Brahmin caste has higher Ancient Iranian and Steppe pastoralist contributions than the Kunbi Marathas caste. Additionally, in contrast to the two castes, tribal groups have very high Ancient Ancestral South Indian (AASI) contributions. Indo-European tribal groups tend to have higher Steppe contributions than Dravidian tribal groups, providing further support for the hypothesis that Steppe pastoralists were the source of Indo-European languages in South Asia, as well as Europe.

Speeding up multi-SVMs through modified working set selection

Support vector machines (SVMs) possess good accuracy in big data classification. However, the computational cost in both training and testing stages is a critical issue. In this study, we speed up the training speed through an integrated working set selection which is based on a modification of the selection pool of the working set of SVMs. In the testing stage, a lost-min strategy is proposed to accelerate the voting algorithm used in multi-SVMs. The number of the used binary classifiers is reduced from an order of to (nearly to or ). The proposed methods were tested with DNA dataset (bioinformatics), Usps datasets (handwritten digits), Letter dataset (English alphabet) and Satimage dataset (satellite imagery of Earth). We further theoretically derive the time complexity of the proposed method approaches to algorithm in the case of high accuracy. This result is demonstrated through the experimental results for these datasets.

Mitochondrial DNA dataset suggest that the genus Sphaerirostris Golvan, 1956 is a synonym of the genus Centrorhynchus Lühe, 1911.

Our present genetic data of Acanthocephala, especially the mitochondrial (mt) genomes, remains very limited. In the present study, the nearly complete mt genome sequences of Sphaerirostris lanceoides (Petrochenko, 1949) was sequenced and determined for the first time based on specimens collected from the Indian pond heron Ardeola grayii (Sykes) (Ciconiiformes: Ardeidae) in Pakistan. The mt genome of S. lanceoides is 13 478 bp in size and contains 36 genes, including 12 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs) and two ribosomal RNA genes (rRNAs). Moreover, in order to clarify the phylogenetic relationship of the genera Centrorhynchus and Sphaerirostris, and to test the systematic position of S. lanceoides in the Centrorhynchidae, the phylogenetic analyses were performed using Bayesian inference and maximum likelihood methods, based on concatenated nucleotide sequences of 12 PCGs, rRNAs and tRNAs. The phylogenetic results further confirmed the monophyly of the order Polymorphida and the paraphyly of the order Echinorhynchida in the class Palaeacanthocephala. Our results also challenged the validity of the genus Sphaerirostris (Polymorphida: Centrorhynchidae) and showed a sister relationship between S. lanceoides and S. picae (Rudolphi, 1819).

Trans-NanoSim characterizes and simulates nanopore RNA-sequencing data.

BackgroundCompared with second-generation sequencing technologies, third-generation single-molecule RNA sequencing has unprecedented advantages; the long reads it generates facilitate isoform-level transcript characterization. In particular, the Oxford Nanopore Technology sequencing platforms have become more popular in recent years owing to their relatively high affordability and portability compared with other third-generation sequencing technologies. To aid the development of analytical tools that leverage the power of this technology, simulated data provide a cost-effective solution with ground truth. However, a nanopore sequence simulator targeting transcriptomic data is not available yet.FindingsWe introduce Trans-NanoSim, a tool that simulates reads with technical and transcriptome-specific features learnt from nanopore RNA-sequncing data. We comprehensively benchmarked Trans-NanoSim on direct RNA and complementary DNA datasets describing human and mouse transcriptomes. Through comparison against other nanopore read simulators, we show the unique advantage and robustness of Trans-NanoSim in capturing the characteristics of nanopore complementary DNA and direct RNA reads.ConclusionsAs a cost-effective alternative to sequencing real transcriptomes, Trans-NanoSim will facilitate the rapid development of analytical tools for nanopore RNA-sequencing data. Trans-NanoSim and its pre-trained models are freely accessible at https://github.com/bcgsc/NanoSim.

DNA sequence similarity analysis using image texture analysis based on first-order statistics

Similarity is one of the key processes of DNA sequence analysis in computational biology and bioinformatics. In nearly all research that explores evolutionary relationships, gene function analysis, protein structure prediction and sequence retrieving, it is necessary to perform similarity calculations. One major task in alignment-free DNA sequence similarity calculations is to develop novel mathematical descriptors for DNA sequences. In this paper, we present a novel approach to DNA sequence similarity analysis studies using similarity calculations of texture images. Texture analysis methods, which are a subset of digital image processing methods, are used here with the assumption that these calculations can be adapted to alignment-free DNA sequence similarity analysis methods. Gray-level textures were created by the values assigned to the nucleotides in the DNA sequences. Similarity calculations were made between these textures using histogram-based texture analyses based on first-order statistics. We obtained texture features for 3 different DNA data sets of different lengths, and calculated the similarity matrices. The phylogenetic relationships revealed by our method shows our trees to be similar to the results of the MEGA software, which is based on sequence alignment. Our findings show that texture analysis metrics can be used to characterize DNA sequences.