Large DNA Sequence Data Sets Research Articles

An Efficient Exact Algorithm for Planted Motif Search on Large DNA Sequence Datasets.

DNA motif is the pattern shared by similar fragments in DNA sequences, which plays a key role in regulating gene expression, and DNA motif discovery has become a key research topic. Exact planted ( l, d )-motif search (PMS) is one of the motif discovery approaches, which aims to find from t sequences all the ( l, d )-motifs that are motifs of l length appearing in at least qt sequences with at most d mismatches. The existing exact PMS algorithms are only suitable for small datasets of DNA sequences. The development of high-throughput sequencing technology generates vast amount of DNA sequence data, which brings challenges to solving exact PMS problems efficiently. Therefore, we propose an efficient exact PMS algorithm called PMmotif for large datasets of DNA sequences, after analyzing the time complexity of the existing exact PMS algorithms. PMmotif finds ( l, d )-motifs with strategy by searching the branches on the pattern tree that may contain ( l, d )-motifs. It is verified by experiments that the running time ratio of some existing excellent PMS algorithms to PMmotif is between 14.83 and 58.94. In addition, for the first time, PMmotif can solve the ( 15,5 )and ( 17,6 ) challenge problem instances on large DNA sequence datasets (3000 sequences of length 200) within 24 hours.

Status Analysis and Future Development Planning of Fitness APP Based on Intelligent Word Frequency Analysis

In order to analyze the current situation analysis and future development plan of fitness APP, this paper analyzes the current situation of fitness APP combined with the intelligent word frequency analysis algorithm. In order to deal with the problem that the existing qPMS algorithm is very time-consuming to discover motifs in the large DNA sequence dataset D, a structural motif discovery algorithm for large DNA sequence datasets, SMS, is proposed. Moreover, this paper performs motif discovery by mining substrings with high frequency in the input sequence. Through data research, it can be seen that the fitness APP based on intelligent word frequency analysis proposed in this paper has good results. On this basis, this paper analyzes the problems existing in the existing fitness APP and provides suggestions for its future development.

Translation of DNA Sequence to Chemical Structure in DNA-Encoded Libraries.

Analysis of DEL selection results requires the generation of large DNA sequence data sets and the conversion of DNA sequence to chemical structure. SAM and BAM formats are the generic formats for storing sequence alignment results. However, such formats are not directly applicable for analysis of DEL selection results due to the lack of reference sequences. In this protocol, reference sequences are constructed from DEL structure files using the central dogma of molecular biology analogy. Enabling the use of SAM and BAM formats drastically facilitates data access while also reducing data storage requirements and permits the efficient translation of DNA sequence to chemical structure of DEL compounds.

Changes in Fungal Community Structure in Freshwater Canals across a Gradient of Urbanization

Fungi are an important, yet often, neglected component of the aquatic microflora, and is responsible for primary decomposition and further processing of organic matter. By comparison, the ecological roles of terrestrial fungi have been well-studied, but the diversity and function of fungi that populate aquatic environments remain poorly understood. Here, the impact of urbanization on fungal diversity and community composition in the canal system of Suzhou was assessed by sequencing the internal transcribed spacer 1 (ITS1) region of the rRNA operon. It was amplified from environmental DNA that has been extracted from water samples and pre-deployed decomposing leaves collected from nine sampling locations (high, medium and low urbanization) over two seasons. The fungal diversity and community composition were determined by bioinformatic analysis of the large DNA sequence datasets generated to identify operational taxonomic units (OTUs) for phylogenetic assignment; over 1 million amplicons were sequenced from 36 samples. The alpha-diversity estimates showed high differences in fungal diversity between water and leaf samples, and winter versus summer. Higher numbers of fungal OTUs were identified in both water and leaf samples collected in the summer, and fungal diversity was also generally higher in water than on colonized leaves in both seasons. The fungal community on leaves was usually dominated by Ascomycetes, especially in winter, while water samples contained more diversity at phylum level with Chytridiomycetes often prominent, particularly in summer. At a genus level, a very high relative abundance of Alternaria on leaves was observed in winter at all locations, in contrast to very low abundance of this genus across all water samples. Fungal community composition also varied between sampling locations (i.e., urbanization); in cluster analysis, samples from high urbanization locations formed a distinct cluster, with medium and low urbanization samples clustering together or in some instances, separately. Redundancy analysis shed further light on the relationships between variation in fungal community composition and water physico-chemical properties. Fungal community diversity variation and correlation with different parameters is discussed in detail, but overall, the influence of season outweighed that of urbanization. This study is significant in cataloguing the impact of urbanization on fungal diversity to inform future restoration of urban canal systems on the importance of protecting the natural aquatic fungal flora.

A Guide to Carrying Out a Phylogenomic Target Sequence Capture Project.

High-throughput DNA sequencing techniques enable time- and cost-effective sequencing of large portions of the genome. Instead of sequencing and annotating whole genomes, many phylogenetic studies focus sequencing effort on large sets of pre-selected loci, which further reduces costs and bioinformatic challenges while increasing coverage. One common approach that enriches loci before sequencing is often referred to as target sequence capture. This technique has been shown to be applicable to phylogenetic studies of greatly varying evolutionary depth. Moreover, it has proven to produce powerful, large multi-locus DNA sequence datasets suitable for phylogenetic analyses. However, target capture requires careful considerations, which may greatly affect the success of experiments. Here we provide a simple flowchart for designing phylogenomic target capture experiments. We discuss necessary decisions from the identification of target loci to the final bioinformatic processing of sequence data. We outline challenges and solutions related to the taxonomic scope, sample quality, and available genomic resources of target capture projects. We hope this review will serve as a useful roadmap for designing and carrying out successful phylogenetic target capture studies.

Minor allele frequency thresholds strongly affect population structure inference with genomic data sets.

A common method of minimizing errors in large DNA sequence data sets is to drop variable sites with a minor allele frequency (MAF) below some specified threshold. Although widespread, this procedure has the potential to alter downstream population genetic inferences and has received relatively little rigorous analysis. Here we use simulations and an empirical single nucleotide polymorphism data set to demonstrate the impacts of MAF thresholds on inference of population structure-often the first step in analysis of population genomic data. We find that model-based inference of population structure is confounded when singletons are included in the alignment, and that both model-based and multivariate analyses infer less distinct clusters when more stringent MAF cutoffs are applied. We propose that this behaviour is caused by the combination of a drop in the total size of the data matrix and by correlations between allele frequencies and mutational age. We recommend a set of best practices for applying MAF filters in studies seeking to describe population structure with genomic data.

Pathogenicity and Penetrance of Germline SDHA Variants in Pheochromocytoma and Paraganglioma (PPGL)

Germline SDHA mutations are reported in a minority of pheochromocytoma/paraganglioma (PPGL) cases but are associated with an increased risk of malignancy, leading some to advocate cascade genetic testing and surveillance screening of “at-risk” first-degree relatives. However, such approaches rely on accurate estimates of variant pathogenicity and disease penetrance, which may have been subject to ascertainment and reporting biases, although the recent provision of large population-based DNA sequence data sets may provide a potentially unbiased resource to aid variant interpretation. Thus, the aim of the current study was to evaluate the pathogenicity and penetrance of SDHA variants reported in literature-based PPGL cases by comparing their frequency to those occurring in the Genome Aggregation Database (GnomAD) data set, which provides high-quality DNA sequence data on 138,632 individuals. In total, 39 different missense or loss-of-function (LOF) SDHA variants were identified in 95 PPGL index cases. Notably, many of the PPGL-associated SDHA alleles were observed at an unexpectedly high frequency in the GnomAD cohort, with ~1% and ~0.1% of the background population harboring a rare missense or LOF variant, respectively. Although the pathogenicity of several SDHA alleles was supported by significant enrichment in PPGL cases relative to GnomAD controls, calculations of disease penetrance based on allele frequencies in the respective cohorts resulted in much lower estimates than previously reported, ranging from 0.1% to 4.9%. Thus, although this study provides support for the etiological role of SDHA in PPGL formation, it suggests that most variant carriers will not manifest PPGLs and are unlikely to benefit from periodic surveillance screening.

FuzzyID2: A software package for large data set species identification via barcoding and metabarcoding using hidden Markov models and fuzzy set methods.

Species identification through DNA barcoding or metabarcoding has become a key approach for biodiversity evaluation and ecological studies. However, the rapid accumulation of barcoding data has created some difficulties: for instance, global enquiries to a large reference library can take a very long time. We here devise a two-step searching strategy to speed identification procedures of such queries. This firstly uses a Hidden Markov Model (HMM) algorithm to narrow the searching scope to genus level and then determines the corresponding species using minimum genetic distance. Moreover, using a fuzzy membership function, our approach also estimates the credibility of assignment results for each query. To perform this task, we developed a new software pipeline, FuzzyID2, using Python and C++. Performance of the new method was assessed using eight empirical data sets ranging from 70 to 234,535 barcodes. Five data sets (four animal, one plant) deployed the conventional barcode approach, one used metabarcodes, and two were eDNA-based. The results showed mean accuracies of generic and species identification of 98.60% (with a minimum of 95.00% and a maximum of 100.00%) and 94.17% (with a range of 84.40%-100.00%), respectively. Tests with simulated NGS sequences based on realistic eDNA and metabarcode data demonstrated that FuzzyID2 achieved a significantly higher identification success rate than the commonly used Blast method, and the TIPP method tends to find many fewer species than either FuzztID2 or Blast. Furthermore, data sets with tens of thousands of barcodes need only a few seconds for each query assignment using FuzzyID2. Our approach provides an efficient and accurate species identification protocol for biodiversity-related projects with large DNA sequence data sets.

Molecular Resources from Transcriptomes in the Brassicaceae Family.

The rapidly falling costs and the increasing availability of large DNA sequence data sets facilitate the fast and affordable mining of large molecular markers data sets for comprehensive evolutionary studies. The Brassicaceae (mustards) are an important species-rich family in the plant kingdom with taxa distributed worldwide and a complex evolutionary history. We performed Simple Sequence Repeats (SSRs) mining using de novo assembled transcriptomes from 19 species across the Brassicaceae in order to study SSR evolution and provide comprehensive sets of molecular markers for genetic studies within the family. Moreover, we selected the genus Cochlearia to test the transferability and polymorphism of these markers among species. Additionally, we annotated Cochlearia pyrenaica transcriptome in order to identify the position of each of the mined SSRs. While we introduce a new set of tools that will further enable evolutionary studies across the Brassicaceae, we also discuss some broader aspects of SSR evolution. Overall, we developed 2012 ready-to-use SSR markers with their respective primers in 19 Brassicaceae species and a high quality annotated transcriptome for C. pyrenaica. As indicated by our transferability test with the genus Cochlearia these SSRs are transferable to species within the genus increasing exponentially the number of targeted species. Also, our polymorphism results showed substantial levels of variability for these markers. Finally, despite its complex evolutionary history, SSR evolution across the Brassicaceae family is highly conserved and we found no deviation from patterns reported in other Angiosperms.

Next-generation sequencing technologies: breaking the sound barrier of human genetics.

Demand for new technologies that deliver fast, inexpensive and accurate genome information has never been greater. This challenge has catalysed the rapid development of advances in next-generation sequencing (NGS). The generation of large volumes of sequence data and the speed of data acquisition are the primary advantages over previous, more standard methods. In 2013, the Food and Drug Administration granted marketing authorisation for the first high-throughput NG sequencer, Illumina’s MiSeqDx, which allowed the development and use of a large number of new genome-based tests. Here, we present a review of template preparation, nucleic acid sequencing and imaging, genome assembly and alignment approaches as well as recent advances in current and near-term commercially available NGS instruments. We also outline the broad range of applications for NGS technologies and provide guidelines for platform selection to best address biological questions of interest. DNA sequencing has revolutionised biological and medical research, and is poised to have a similar impact on the practice of medicine. This tool is but one of an increasing arsenal of developing tools that enhance our capabilities to identify, quantify and functionally characterise the components of biological networks that keep us healthy or make us sick. Despite advances in other ‘omic’ technologies, DNA sequencing and analysis, in many respects, have played the leading role to date. The new technologies provide a bridge between genotype and phenotype, both in man and model organisms, and have revolutionised how risk of developing a complex human disease may be assessed. The generation of large DNA sequence data sets is producing a wealth of medically relevant information on a large number of individuals and populations that will potentially form the basis of truly individualised medical care in the future.

Suffix tree searcher: exploration of common substrings in large DNA sequence sets.

BackgroundLarge DNA sequence data sets require special bioinformatics tools to search and compare them. Such tools should be easy to use so that the data can be easily accessed by a wide array of researchers. In the past, the use of suffix trees for searching DNA sequences has been limited by a practical need to keep the trees in RAM. Newer algorithms solve this problem by using disk-based approaches. However, none of the fastest suffix tree algorithms have been implemented with a graphical user interface, preventing their incorporation into a feasible laboratory workflow.ResultsSuffix Tree Searcher (STS) is designed as an easy-to-use tool to index, search, and analyze very large DNA sequence datasets. The program accommodates very large numbers of very large sequences, with aggregate size reaching tens of billions of nucleotides. The program makes use of pre-sorted persistent "building blocks" to reduce the time required to construct new trees. STS is comprised of a graphical user interface written in Java, and four C modules. All components are automatically downloaded when a web link is clicked. The underlying suffix tree data structure permits extremely fast searching for specific nucleotide strings, with wild cards or mismatches allowed. Complete tree traversals for detecting common substrings are also very fast. The graphical user interface allows the user to transition seamlessly between building, traversing, and searching the dataset.ConclusionsThus, STS provides a new resource for the detection of substrings common to multiple DNA sequences or within a single sequence, for truly huge data sets. The re-searching of sequence hits, allowing wild card positions or mismatched nucleotides, together with the ability to rapidly retrieve large numbers of sequence hits from the DNA sequence files, provides the user with an efficient method of evaluating the similarity between nucleotide sequences by multiple alignment or use of Logos. The ability to re-use existing suffix tree pieces considerably shortens index generation time. The graphical user interface enables quick mastery of the analysis functions, easy access to the generated data, and seamless workflow integration.

An Efficient Approach to Mining Maximal Contiguous Frequent Patterns from Large DNA Sequence Databases

Mining interesting patterns from DNA sequences is one of the most challenging tasks in bioinformatics and computational biology. Maximal contiguous frequent patterns are preferable for expressing the function and structure of DNA sequences and hence can capture the common data characteristics among related sequences. Biologists are interested in finding frequent orderly arrangements of motifs that are responsible for similar expression of a group of genes. In order to reduce mining time and complexity, however, most existing sequence mining algorithms either focus on finding short DNA sequences or require explicit specification of sequence lengths in advance. The challenge is to find longer sequences without specifying sequence lengths in advance. In this paper, we propose an efficient approach to mining maximal contiguous frequent patterns from large DNA sequence datasets. The experimental results show that our proposed approach is memory-efficient and mines maximal contiguous frequent patterns within a reasonable time.

Molecular Evolutionary Analysis of Cancer Cell Lines

With genome-wide cancer studies producing large DNA sequence data sets, novel computational approaches toward better understanding the role of mutations in tumor survival and proliferation are greatly needed. Tumors are widely viewed to be influenced by Darwinian processes, yet molecular evolutionary analysis, invaluable in other DNA sequence studies, has seen little application in cancer biology. Here, we describe the phylogenetic analysis of 353 cancer cell lines based on multiple sequence alignments of 3,252 nucleotides and 1,170 amino acids built from the concatenation of variant codons and residues across 494 and 523 genes, respectively. Reconstructed phylogenetic trees cluster cell lines by shared DNA variant patterns rather than cancer tissue type, suggesting that tumors originating from diverse histologies have similar oncogenic pathways. A well-supported clade of 91 cancer cell lines representing multiple tumor types also had significantly different gene expression profiles from the remaining cell lines according to statistical analyses of mRNA microarray data. This suggests that phylogenetic clustering of tumor cell lines based on DNA variants might reflect functional similarities in cellular pathways. Positive selection analysis revealed specific DNA variants that might be potential driver mutations. Our study shows the potential role of molecular evolutionary analyses in tumor classification and the development of novel anticancer strategies.

Genetic Algorithm Based Probabilistic Motif Discovery in Unaligned Biological Sequences

Finding motif in biosequences is the most important primitive operation in computational biology. There are many computational requirements for a motif discovery algorithm such as computer memory space requirement and computational complexity. To overcome the complexity of motif discovery, we propose an alternative solution integrating genetic algorithm and Fuzzy Art machine learning approaches for eliminating multiple sequence alignment process. Problem statement: More than a hundred methods had been proposed for motif discovery in recent years, representing a large variation with respect to both algorithmic approaches as well as the underlying models of regulatory regions. The aim of this study was to develop an alternative solution for motif discovery, which benefits from both data mining and genetic algorithm, and which at the same time eliminates the cost caused by use of multiple sequence alignment. Approach: Genetic algorithm based probabilistic Motif discovery model was designed to solve the problem. The proposed algorithm was implemented using Matlab and also tested with large DNA sequence data sets and synthetic data sets. Results: Results obtained by the proposed model to find the motif in terms of speed and length are compared with the existing method. Our proposed method finds Length of 11 in 18 sec and length of 15 in 24 sec but the existing methods finds length of 11 in 34 sec. Compare to other techniques the proposed one was outperforms the popular existing method. Conclusion: In this study, we proposed a model to discover motif in large set of unaligned sequences in considerably minimum time. Length of motif was also long. The proposed algorithm will be implemented using Matlab and was tested with large DNA sequence data sets and synthetic data sets.

Divergence population genetics of chimpanzees.

The divergence of two subspecies of common chimpanzees (Pan troglodytes troglodytes and P. t. verus) and the bonobo (P. paniscus) was studied using a recently developed method for analyzing population divergence. Under the isolation with migration model, the posterior probability distributions of divergence time, migration rates, and effective population sizes were estimated for large multilocus DNA sequence data sets drawn from the literature. The bonobo and the common chimpanzee are estimated to have diverged approximately 0.86 to 0.89 MYA, and the divergence of the two common chimpanzee subspecies is estimated to have occurred 0.42 MYA. P. t. troglodytes appears to have had a larger effective population size (22,400 to 27,900) compared with P. paniscus, P. t. verus, and the ancestral populations of these species. No evidence of gene flow was found in the comparisons involving P. paniscus; however a clear signal of unidirectional gene flow was found from P. t. verus to P. t. troglodytes (2Nm = 0.51).