DNA Datasets Research Articles

Prider: multiplexed primer design using linearly scaling approximation of set coverage

BackgroundDesigning oligonucleotide primers and probes is one of the key steps of various laboratory experiments such as multiplexed PCR or digital multiplexed ligation assays. When designing multiplexed primers and probes to complex, heterogeneous DNA data sets, an optimization problem can arise where the smallest number of oligonucleotides covering the largest diversity of the input dataset needs to be identified. Tools that provide this optimization in an efficient manner for large input data are currently lacking.ResultsHere we present Prider, an R package for designing primers and probes with a nearly optimal coverage for complex and large sequence sets. Prider initially prepares a full primer coverage of the input sequences, the complexity of which is subsequently reduced by removing components of high redundancy or narrow coverage. The primers from the resulting near-optimal coverage are easily accessible as data frames and their coverage across the input sequences can be visualised as heatmaps using Prider’s plotting function. Prider permits efficient design of primers to large DNA datasets by scaling linearly to increasing sequence data, regardless of the diversity of the dataset.ConclusionsPrider solves a recalcitrant problem in molecular diagnostics: how to cover a maximal sequence diversity with a minimal number of oligonucleotide primers or probes. The combination of Prider with highly scalable molecular quantification techniques will permit an unprecedented molecular screening capability with immediate applicability in fields such as clinical microbiology, epidemic virus surveillance or antimicrobial resistance surveillance.

Met v1: expanding on old estimations of biodiversity from eDNA with a new database framework.

A long-standing problem in environmental DNA has been the inability to compute across large number of datasets. Here we introduce an open-source software framework that can store a large number of environmental DNA datasets, as well as provide a platform for analysis, in an easily customizable way. We show the utility of such an approach by analyzing over 1400 arthropod metabarcode datasets. This article introduces a new software framework, met, which utilizes large numbers of metabarcode datasets to draw conclusions about patterns of diversity at large spatial scales. Given more accurate estimations on the distribution of variance in metabarcode datasets, this software framework could facilitate novel analyses that are outside the scope of currently available similar platforms. Database URL https://osf.io/spb8v/

GMEmbeddings: An R Package to Apply Embedding Techniques to Microbiome Data.

Large-scale microbiome studies investigating disease-inducing microbial roles base their findings on differences between microbial count data in contrasting environments (e.g., stool samples between cases and controls). These microbiome survey studies are often impeded by small sample sizes and database bias. Combining data from multiple survey studies often results in obvious batch effects, even when DNA preparation and sequencing methods are identical. Relatedly, predictive models trained on one microbial DNA dataset often do not generalize to outside datasets. In this study, we address these limitations by applying word embedding algorithms (GloVe) and PCA transformation to ASV data from the American Gut Project and generating translation matrices that can be applied to any 16S rRNA V4 region gut microbiome sequencing study. Because these approaches contextualize microbial occurrences in a larger dataset while reducing dimensionality of the feature space, they can improve generalization of predictive models that predict host phenotype from stool associated gut microbiota. The GMEmbeddings R package contains GloVe and PCA embedding transformation matrices at 50, 100 and 250 dimensions, each learned using ∼15,000 samples from the American Gut Project. It currently supports the alignment, matching, and matrix multiplication to allow users to transform their V4 16S rRNA data into these embedding spaces. We show how to correlate the properties in the new embedding space to KEGG functional pathways for biological interpretation of results. Lastly, we provide benchmarking on six gut microbiome datasets describing three phenotypes to demonstrate the ability of embedding-based microbiome classifiers to generalize to independent datasets. Future iterations of GMEmbeddings will include embedding transformation matrices for other biological systems. Available at: https://github.com/MaudeDavidLab/GMEmbeddings.

Eskimo Problem in the Light of New Data

Purpose. The problem of the origin of the Eskimos has received considerable attention, at the same time also providing insights about human presence in far Northeast Siberia and America. I review earlier studies and discuss some of the more exciting recent results emerging from ancient DNA data sets. I also highlight important features of genetic and archeological data and discuss key questions and future research directions. Results. The Paleo-Eskimos and Neo-Eskimos ancestors along the Q-NWT01 Y-DNA line lived in the Kolyma River basin at the turn of the Late Pleistocene and Early Holocene. The migration of the East Asian ancestors of the Paleo-Eskimos was associated with the representatives of the Neolithic cultures of Northeast Asia, which brought the ancestral haplotype for mtDNA haplogroup D2a to Alaska. The emergence of the Neo-Eskimo cultures took place in the Bering strait area on the basis of the local Paleo-Eskimo tradition and under the influence of the cultural traditions of Southwestern Alaska and Chukotka. The Ust'-Belaya culture of Chukotka could act as a genetic source for the development of the Neo-Eskimo cultures.Conclusion. Analysis of ancient DNA from human remains over the past decade has had a transformative effect on the study of the origin of the Eskimos. Data sets of ancient DNA have revealed an increasingly complex picture of human demographic history in North-East of Asia and America and development of Paleo-Eskimo and Neo-Eskimo traditions, suggesting multiple waves of migration over the Bering Strait and episodes of admixture of different groups of population, including Ancient Paleosiberian, East Asian, Paleo-Indian, Paleo-Eskimos, Neo-Eskimos and others.

Evidence for widespread gene flow and migration in the Globe Skimmer dragonfly Pantala flavescens

The global population structure and dispersal patterns of Pantala flavescens (Fabricius, 1798) are evaluated using a geographically extensive mitochondrial DNA dataset, a more limited samples of nuclear markers, wing isotopic (δ²H) data and a literature review. No spatial or temporal haplotype structure was recovered between the samples. Isotope data suggest that most samples were immigrants at the collection locations. A literature review of migration events for the species confirms regular inter-and intra-continental migrations occur (the majority reported from Asia, Africa and Australasia), with individuals and swarms dispersing thousands of kilometers over land and oceans. Migrations coincide with prevailing winds and seasonal rains, which points to a mechanism we name the “pantropical Pantala conveyor belt”, suggesting widespread gene flow is possible for an aquatic insect with excellent flying ability linked to rapid larval development.

Insights into mitochondrial DNA variation of common carp Cyprinus carpio strains in the Centre of Carpathian Basin

The assessment of genetic reserves is extremely important for the conservation of rich genetic stocks. Artificial selection can be considered as the main evolutionary force in domesticated species, which can influence the development of the genetic diversity and spatial patterns of the genetic structure of a species. Understanding the extent of genetic differentiation between breeds provides insight into breeding work and domestication processes. In this study, we examined the maternal genetic diversity and spatial pattern of genetic structures of 13 Hungarian common carp strains of six hatchery populations based on two datasets of the mitochondrial DNA: 1) a concatenated fragment (2551 bp) including 12S rRNA, tRNA-Val, 16S rRNA, cytochrome b, and control region (the first dataset), and 2) a 687 bp fragment of cytochrome b (the second dataset). Altogether 250 sequences were used in both datasets comprising 138 own samples and 112 Genbank sequences from previous studies. A total of 43 haplotypes were detected among the 138 new sequences for the second dataset, including 40 novel haplotypes and three previously recorded haplotypes. Haplotype diversity levels were relatively high (average = 0.566 ± 0.052) in most common carp strains, whereas nucleotide diversity values were moderate to low (average = 0.00474 ± 0.00083). Based on Bayesian clustering analysis using STRUCTURE, K = 3 was the most likely grouping of strains. Seven of the studied strains were grouped with high membership coefficients in the same cluster, while individuals from the other strains showed a mix. Differences in the genetic diversity of stocks between hatcheries reflect historical facts and anthropogenic events as well. Our results reveal the genetic structure of strains that reflect founding practices as well as the movements of individuals and strains between hatcheries. Our findings may have an impact on fisheries professionals as well as fish farmers who are involved in introductions, restorations, strains design, and development.

Mold, a novel software to compile accurate and reliable DNA diagnoses for taxonomic descriptions.

DNA data are increasingly being used for phylogenetic inference, and taxon delimitation and identification, but scarcely for the formal description of taxa, despite their undisputable merits in taxonomy. The uncertainty regarding the robustness of DNA diagnoses, however, remains a major impediment to their use. We have developed a new program, mold, that identifies diagnostic nucleotide combinations (DNCs) in DNA sequence alignments for selected taxa, which can be used to provide formal diagnoses of these taxa. To test the robustness of DNA diagnoses, we carry out iterated haplotype subsampling for selected query species in published DNA data sets of varying complexity. We quantify the reliability of diagnosis by diagnosing each query subsample and then checking if this diagnosis remains valid against the entire data set. We demonstrate that widely used types of diagnostic DNA characters are often absent for a query taxon or are not sufficiently reliable. We thus propose a new type of DNA diagnosis, termed "redundant DNC" (or rDNC), which takes into account unsampled genetic diversity, and constitutes a much more reliable descriptor of a taxon. mold successfully retrieves rDNCs for all but two species in the analysed data sets, even in those comprising hundreds of species. mold shows unparalleled efficiency in large DNA data sets and is the only available software capable of compiling DNA diagnoses that suit predefined criteria of reliability.

Detection of gene mutations and gene-gene fusions in circulating cell-free DNA of glioblastoma patients: an avenue for clinically relevant diagnostic analysis.

Glioblastoma (GBM) is the most common type of glioma and is uniformly fatal. Currently, tumour heterogeneity and mutation acquisition are major impedances for tailoring personalized therapy. We collected blood and tumour tissue samples from 25 GBM patients and 25 blood samples from healthy controls. Cell‐free DNA (cfDNA) was extracted from the plasma of GBM patients and from healthy controls. Tumour DNA was extracted from fresh tumour samples. Extracted DNA was sequenced using a whole‐genome sequencing procedure. We also collected 180 tumour DNA datasets from GBM patients publicly available at the TCGA/PANCANCER project. These data were analysed for mutations and gene–gene fusions that could be potential druggable targets. We found that plasma cfDNA concentrations in GBM patients were significantly elevated (22.6 ± 5 ng·mL−1), as compared to healthy controls (1.4 ± 0.4 ng·mL−1) of the same average age. We identified unique mutations in the cfDNA and tumour DNA of each GBM patient, including some of the most frequently mutated genes in GBM according to the COSMIC database (TP53, 18.75%; EGFR, 37.5%; NF1, 12.5%; LRP1B, 25%; IRS4, 25%). Using our gene–gene fusion database, ChiTaRS 5.0, we identified gene–gene fusions in cfDNA and tumour DNA, such as KDR–PDGFRA and NCDN–PDGFRA, which correspond to previously reported alterations of PDGFRA in GBM (44% of all samples). Interestingly, the PDGFRA protein fusions can be targeted by tyrosine kinase inhibitors such as imatinib, sunitinib, and sorafenib. Moreover, we identified BCR–ABL1 (in 8% of patients), COL1A1–PDGFB (8%), NIN–PDGFRB (8%), and FGFR1–BCR (4%) in cfDNA of patients, which can be targeted by analogues of imatinib. ROS1 fusions (CEP85L–ROS1 and GOPC–ROS1), identified in 8% of patient cfDNA, might be targeted by crizotinib, entrectinib, or larotrectinib. Thus, our study suggests that integrated analysis of cfDNA plasma concentration, gene mutations, and gene–gene fusions can serve as a diagnostic modality for distinguishing GBM patients who may benefit from targeted therapy. These results open new avenues for precision medicine in GBM, using noninvasive liquid biopsy diagnostics to assess personalized patient profiles. Moreover, repeated detection of druggable targets over the course of the disease may provide real‐time information on the evolving molecular landscape of the tumour.

Systematic review and meta-analysis of Mycobacterium avium subsp. paratuberculosis as environmental trigger of multiple sclerosis

Mycobacterium avium subsp. paratuberculosis (MAP) has been identified as one of the environmental agents that causes multiple sclerosis (MS). The global prevalence of MS has been upsurging over the years; however, efforts to divulge the role of MAP in MS have been limited. As a result, the present study aimed at assessing the odd ratios (ORs) associated MAP with the risk of MS. MAP-related MS data were obtained from 6 databases using the terms 'multiple sclerosis' or 'MS' and 'paratuberculosis' without regard for time or language restrictions following PRISMA standards. A total of 2,538 participants' data from 12 studies presenting anti-MAP antibodies and MAP DNA from 4 studies were fitted in random-effects (RE) and fixed-effects (FE) meta-analytic models. Furthermore, the between-study heterogeneity was measured using I2-values with a significant limit set at an I² > 75%. Analytical rigor and publication bias was determined using leave-one-out-analytics, Egger's tests, and p-curve analysis. In the FE and RE models, anti-MAP antibodies data significantly associated MS risk with MAP as 10.71 OR (95%-CI [7.78; 14.74], p-value < 0.0001) and 12.76 OR (95%-CI [8.13; 20.02], p-value < 0.0001) respectively, with an I2 value of 34.9% (95%-CI [0.0%; 67.2%]; p-value = 0.11). Similarly, the MAP DNA dataset in FE significantly present MS risk due to MAP as 5.53 OR (95%-CI [3.54; 8.66], p-value< 0.0001) while, RE showed 5.27 OR (95%-CI [3.22; 8.60], p = 0.0017), with an I2-value = 0.0% (95%-CI [0.0%; 84.7%]; p-value = 0.71). Eggers' test, on the other hand, found publication bias in anti-MAP antibodies data (intercept = 1.61, 95% CI: 0.45 – 2.77, t = 2.72, p = 0.021), but not in MAP DNA dataset (intercept = -5.57, 95% CI: -20.44 – 9.29, t = -0.74, p = 0.54). The robustness of the meta-analyses was demonstrated by all sensitivity analyses. In addition, there is no evidence of p-hacking observed (right-skewness test (PFull < 0.001, PHalf <0.001; statistical power ≥ 94% (95%-CI: 72.5%-99%)). In conclusion, the synthesis revealed a strong association between MAP and MS, indicating that MAP is a significant environmental agent that may trigger MS. Thus, early screening of MAP in MS cases may assist in the therapeutic approach to its management/treatment. Therefore, future studies should be tailored towards the role of MAP in the severity of MS phenotypes, as well as address global data gaps and low disease surveillance.

Enabling Diagnostic Resulting as a New Category of Secondary Genomic Findings.

Over the past decade, the secondary analysis of existing DNA datasets for clinical resulting has become an established practice. However, this established practice is typically limited to only one category of secondary genomic findings, the identification of “disease risk”. Diagnostic resulting has been left out of secondary genomic findings. In medical practice, diagnostic resulting is triggered when a test is ordered for a patient based on a recognizable clinical indication for evaluation; most genetic and genomic testing is carried out in support of diagnostic evaluations. The secondary analysis of existing DNA data has the potential to cost less and have more rapid turnaround times for diagnostic results compared to current DNA diagnostic approaches that typically generate a new dataset with every test ordered. Worldwide, innovative health systems could position themselves to deliver valid secondary genomic finding results in both the established category of disease risk results, as well as a new category of diagnostic results. To support the ongoing delivery of both categories of secondary findings, health systems will need comprehensive genomic datasets for patients and secure workflows that allow for repeated access to that data for on-demand secondary analysis.

Phylogenomics for Chagas Disease Vectors of the Rhodnius Genus (Hemiptera, Triatominae): What We Learn From Mito-Nuclear Conflicts and Recommendations

We provide in this study a very large DNA dataset on Rhodnius species including 36 samples representing 16 valid species of the three Rhodnius groups, pictipes, prolixus and pallescens. Samples were sequenced at low-depth with whole-genome shotgun sequencing (Illumina technology). Using phylogenomics including 15 mitochondrial genes (13.3 kb), partial nuclear rDNA (5.2 kb) and 51 nuclear protein-coding genes (36.3 kb), we resolve sticking points in the Rhodnius phylogeny. At the species level, we confirmed the species-specific status of R. montenegrensis and R. marabaensis and we agree with the synonymy of R. taquarussuensis with R. neglectus. We also invite to revisit the species-specific status of R. milesi that is more likely R. nasutus. We proposed to define a robustus species complex that comprises the four close relative species: R. marabaensis, R. montenegrensis, R. prolixus and R. robustus. As Psammolestes tertius was included in the Rhodnius clade, we strongly recommend reclassifying this species as R. tertius. At the Rhodnius group level, molecular data consistently supports the clustering of the pictipes and pallescens groups, more related to each other than they are to the prolixus group. Moreover, comparing mitochondrial and nuclear tree topologies, our results demonstrated that various introgression events occurred in all the three Rhodnius groups, in laboratory strains but also in wild specimens. We demonstrated that introgressions occurred frequently in the prolixus group, involving the related species of the robustus complex but also the pairwise R. nasutus and R. neglectus. A genome wide analysis highlighted an introgression event in the pictipes group between R. stali and R. brethesi and suggested a complex gene flow between the three species of the pallescens group, R. colombiensis, R. pallescens and R. ecuadoriensis. The molecular data supports also a sylvatic distribution of R. prolixus in Brazil (Pará state) and the monophyly of R. robustus. As we detected extensive introgression events and selective pressure on mitochondrial genes, we strongly recommend performing separate mitochondrial and nuclear phylogenies and to take advantages of mito-nuclear conflicts in order to have a comprehensive evolutionary vision of this genus.

SaAlign: Multiple DNA/RNA sequence alignment and phylogenetic tree construction tool for ultra-large datasets and ultra-long sequences based on suffix array

Multiple DNA/RNA sequence alignment is an important fundamental tool in bioinformatics, especially for phylogenetic tree construction. With DNA-sequencing improvements, the amount of bioinformatics data is constantly increasing, and various tools need to be iterated constantly. Mitochondrial genome analyses of multiple individuals and species require bioinformatics software; therefore, their performances need to be optimized. To improve the alignment of ultra-large datasets and ultra-long sequences, we optimized a dynamic programming algorithm using longest common substring methods. Ultra-large test DNA datasets, containing sequences of different lengths, some over 300 kb (kilobase), revealed that the Multiple DNA/RNA Sequence Alignment Tool Based on Suffix Tree (SaAlign) saved time and computational space. It outperformed the existing technical tools, including MAFFT and HAlign-II. For mitochondrial genome datasets having limited numbers of sequences, MAFFT performed the required tasks, but it could not handle ultra-large mitochondrial genome datasets for core dump error. We implement a multiple DNA/RNA sequence alignment tool based on Center Star strategy and use suffix array algorithm to optimize the spatial and time efficiency. Nowadays, whole-genome research and NGS technology are becoming more popular, and it is necessary to save computational resources for laboratories. That software is of great significance in these aspects, especially in the study of the whole-mitochondrial genome of plants.

THREE MSA TOOLS ANALYSIS in DNA and PROTEIN DATASETS

Multiple sequence alignment (MSA) is used to align three or more sequences of DNA, RNA and protein. It is prominent for constructing phylogenetic trees and evolutionary relationships between sequences with regard to similarities and dissimilarities. Variety of multiple sequence alignment tools are available online, each having different methods and parameters to align sequences. In this article three MSA tools; CLUSTALW, SAGA and MAFFT are used for five datasets BALiBASE_R9, DIRMBASE, SABmark and additionally constructed DNABali and ProteinBali for alignment. Result show that for both protein and DNA dataset, MAFFT may be more useful among three of MSA tool used.

NoAS-DS: Neural optimal architecture search for detection of diverse DNA signals

Neural network architectures are high-performing variable models that can solve many learning tasks. Designing architectures manually require substantial time and also prior knowledge and expertise to develop a high-accuracy model. Most of the architecture search methods are developed over the task of image classification resulting in the building of complex architectures intended for large data inputs such as images. Motivated by the applications of DNA computing in Neural Architecture Search (NAS), we propose NoAS-DS which is specifically built for the architecture search of sequence-based classification tasks. Furthermore, NoAS-DS is applied to the task of predicting binding sites. Unlike other methods that implement only Convolution layers, NoAS-DS, specifically combines Convolution and LSTM layers that helps in the process of automatic architecture building. This hybrid approach helped in achieving high accuracy results on TFBS and RBP datasets which outperformed other models in TF-DNA binding prediction tasks. The best architectures generated by the proposed model can be applied to other DNA datasets of similar nature using transfer learning technique that demonstrates its generalization capability. This greatly reduces the effort required to build new architectures for other prediction tasks.

Understanding and managing the interactive impacts of growth in urban land use and climate change on freshwater biota: Acase study using the platypus (Ornithorhynchus anatinus).

Globally, urban expansion and climate change interact to threaten stream ecosystems and are accelerating the loss of aquatic biodiversity. Waterway managers urgently need tools to understand the potential combined impacts of urbanization and climate change and to identify effective mitigating management interventions for protecting freshwater biota. We address this challenge using the semi-aquatic mammal the platypus (Ornithorhynchus anatinus) as a focal species. We developed high-resolution environmental spatial data for stream networks and spatially explicit habitat suitability models (HSMs) to explore the impact of threats and to identify the combination of management actions most likely to maintain or improve habitat suitability over the next 50years in greater Melbourne, Australia. We developed and evaluated platypus HSMs (males-and-females and females-only) including validation using an independent environmental DNA data set. Platypus occurred more commonly in larger, cooler streams with greater catchment-weighted discharge, following periods of greater stream flow. They were positively associated with near-stream forest cover and negatively associated with annual air temperature and urban stormwater runoff. Extensive reductions in suitable platypus habitat are predicted to occur under urbanization and climate change scenarios, with the greatest threat expected from reduced streamflows. This emphasizes the importance of maintaining flow regimes as part of conserving platypus in the region; however, substantial additional benefit is predicted by concurrent riparian revegetation and urban stormwater management efforts (that also have the potential to contribute to the streamflow objectives). Provision of adequate streamflows in a future with increasing water demands and water security requirements will likely require creative integrated water management solutions. Our high-resolution stream network and HSMs have allowed predictions of potential range-shifts due to urban expansion and climate change impacts at management-relevant scales and at the whole-of-landscape scale. This has enabled systematic strategic planning, priority action planning and target setting in strategic policy development.

Euphorbiambuinzauensis, a new succulent species in Kenya from the Synadenium group in Euphorbiasect.Monadenium (Euphorbiaceae).

Euphorbiambuinzauensis, a succulent new species of the Synadenium group in Euphorbiaceae from Makueni County, Kenya, is described and illustrated. Morphologically, it is most similar to E.pseudomollis, but differs mainly by its shrubby habit (up to 4 m), abaxial leaves surfaces with densely stellate hairs, 2–4-forked cymes, smaller bracts (ca. 2.5 × 3.0 mm), smaller cyathia (6 mm wide), crimson glands without narrow smooth margin, smaller fruits (ca. 8 × 7 mm) and ovoid seeds (ca. 1.8 × 2.2 mm). Furthermore, we performed a molecular phylogenetic analysis of the Synadenium group in Euphorbiasect.Monadenium, based on complete nuclear ribosomal DNA (nrDNA) datasets. This phylogenetic inference also supports it to be a distinct species. The new species is assessed as Endangered using the IUCN criteria.

Integrative taxonomy of the Selaginella helvetica group based on morphological, molecular and ecological data

AbstractThe Eurasian and Mediterranean Selaginella helvetica group is one of the taxonomically challenging groups in the cosmopolitan lycophyte genus Selaginella. Species of the S. helvetica group are all small plants with lax strobili composed of more or less isomorphic sporophylls (isosporophylls) that are basically non‐resupinate. Owing to the similar and minute morphological characteristics, the inapplicability of chloroplast DNA markers for species delimitation and the lack of range‐wide sampling, no comprehensive study has been conducted on these species so far. In this study, we performed phylogenetic analyses based on population‐scale sampling of 126 individuals, covering the entire geographical distribution and morphological variation of this group. Five chloroplast (rbcL, psbA, atpI, atpF, atpF‐H) and two nuclear DNA regions (26S rDNA, pgiC) were used to explore species diversity of this group. The phylogeny inferred from nuclear DNA datasets revealed nine well‐supported monophyletic groups, which provided important evidence for species delimitation and called for taxonomic revision of some taxa. However, in the chloroplast phylogeny, individuals of only three species (S. denticulata, S. helvetica, S. tamamontana) formed monophyletic groups, respectively. We further investigated genome size for each species by flow cytometry and evaluated 21 morphological characteristics of 79 herbarium specimens. MaxEnt analysis was performed to predict the potential suitable distribution of each species. As a result of total evidence, 10 taxa were recognized in the S. helvetica group. The delimitation of S. denticulata, S. laxistrobila, S. nipponica, S. pallidissima, S. prostrata, and S. tamamontana was congruent with previous taxonomical treatments. We reinstated S. shensiensis as an independent species and treated S. longistrobilina as its synonym. Selaginella helvetica (s.str.) is confined to Europe and North Asia, former records of it from the Himalaya‐Hengduan mountains represent an independent species (S. jiulongensis comb. nov.). The nuclear pgiC phylogeny and flow cytometry evidence indicated that S. pseudonipponica is an allotetraploid with S. shensiensis and S. nipponica as its parents. Our results highlight that nuclear DNA markers rather than chloroplast DNA markers are more informative in delimitation of closely related species in Selaginella.

Phylogenetic Signal and Bias in Paleontology.

An unprecedented amount of evidence now illuminates the phylogeny of living mammals and birds on the Tree of Life. We use this tree to measure the phylogenetic value of data typically used in paleontology (bones and teeth) from six data sets derived from five published studies. We ask three interrelated questions: 1) Can these data adequately reconstruct known parts of the Tree of Life? 2) Is accuracy generally similar for studies using morphology, or do some morphological data sets perform better than others? 3) Does the loss of non-fossilizable data cause taxa to occur in misleadingly basal positions? Adding morphology to DNA data sets usually increases congruence of resulting topologies to the well-corroborated tree, but this varies among morphological data sets. Extant taxa with a high proportion of missing morphological characters can greatly reduce phylogenetic resolution when analyzed together with fossils. Attempts to ameliorate this by deleting extant taxa missing morphology are prone to decreased accuracy due to long-branch artifacts. We find no evidence that fossilization causes extinct taxa to incorrectly appear at or near topologically basal branches. Morphology comprises the evidence held in common by living taxa and fossils, and phylogenetic analysis of fossils greatly benefits from inclusion of molecular and morphological data sampled for living taxa, whatever methods are used for phylogeny estimation. [Concatenation; fossilization; morphology; parsimony; systematics; taphonomy; total-evidence.]

Establishing community reference samples, data and call sets for benchmarking cancer mutation detection using whole-genome sequencing.

The lack of samples for generating standardized DNA datasets for setting up a sequencing pipeline or benchmarking the performance of different algorithms limits the implementation and uptake of cancer genomics. Here, we describe reference call sets obtained from paired tumor-normal genomic DNA (gDNA) samples derived from a breast cancer cell line-which is highly heterogeneous, with an aneuploid genome, and enriched in somatic alterations-and a matched lymphoblastoid cell line. We partially validated both somatic mutations and germline variants in these call sets via whole-exome sequencing (WES) with different sequencing platforms and targeted sequencing with >2,000-fold coverage, spanning 82% of genomic regions with high confidence. Although the gDNA reference samples are not representative of primary cancer cells from a clinical sample, when setting up a sequencing pipeline, they not only minimize potential biases from technologies, assays and informatics but also provide a unique resource for benchmarking 'tumor-only' or 'matched tumor-normal' analyses.

Influence of an efficient Hierarchical Clustering Algorithm in analyzing Cancer affected DNA Dataset

This research work presents an influence of hierarchical clustering approach in anlyzing cancer affected DNA data set. The primary objective of this research work is to identify the best clustering algorithms to group cancer-affected DNA datasets. Data analysis shows an important role in bioinformatics. Data analysis technique used for grouping the data objects is based on unsupervised learning. Clustering is an unsupervised learning technique in data mining. It groups a set of clusters from the entire dataset. In this research work, 700 cancer-affected DNA datasets are considered for analysis. This research work compares three types of Clustering techniques, K-Means (KM), K-Medoids (KMS), and Hierarchical Clustering (HC), to group cancer-affected DNA. Each algorithm has some strengths and weaknesses. These clustering algorithms are compared in detail based on various parameters. Results prove that the hierarchical clustering algorithms show lesser execution time and increased accuracy than other KM and KMS algorithms.