Database-based Method Research Articles

NASTRA: accurate analysis of short tandem repeat markers by nanopore sequencing with repeat-structure-aware algorithm.

Short-tandem repeats (STRs) are the type of genetic markers extensively utilized in biomedical and forensic applications. Due to sequencing noise in nanopore sequencing, accurate analysis methods are lacking. We developed NASTRA, an innovative tool for Nanopore Autosomal Short Tandem Repeat Analysis, which overcomes traditional database-based methods' limitations and provides a precise germline analysis of STR genetic markers without the need for allele sequence reference. Demonstrating high accuracy in cell line authentication testing and paternity testing, NASTRA significantly surpasses existing methods in both speed and accuracy. This advancement makes it a promising solution for rapid cell line authentication and kinship testing, highlighting the potential of nanopore sequencing for in-field applications.

Application and Comparison of Machine Learning and Database-Based Methods in Taxonomic Classification of High-Throughput Sequencing Data.

The advent of high-throughput sequencing technologies has not only revolutionized the field of bioinformatics but has also heightened the demand for efficient taxonomic classification. Despite technological advancements, efficiently processing and analyzing the deluge of sequencing data for precise taxonomic classification remains a formidable challenge. Existing classification approaches primarily fall into two categories, database-based methods and machine learning methods, each presenting its own set of challenges and advantages. On this basis, the aim of our study was to conduct a comparative analysis between these two methods while also investigating the merits of integrating multiple database-based methods. Through an in-depth comparative study, we evaluated the performance of both methodological categories in taxonomic classification by utilizing simulated data sets. Our analysis revealed that database-based methods excel in classification accuracy when backed by a rich and comprehensive reference database. Conversely, while machine learning methods show superior performance in scenarios where reference sequences are sparse or lacking, they generally show inferior performance compared with database methods under most conditions. Moreover, our study confirms that integrating multiple database-based methods does, in fact, enhance classification accuracy. These findings shed new light on the taxonomic classification of high-throughput sequencing data and bear substantial implications for the future development of computational biology. For those interested in further exploring our methods, the source code of this study is publicly available on https://github.com/LoadStar822/Genome-Classifier-Performance-Evaluator. Additionally, a dedicated webpage showcasing our collected database, data sets, and various classification software can be found at http://lab.malab.cn/~tqz/project/taxonomic/.

Identification of bacteriophage genome sequences with representation learning.

MotivationBacteriophages/phages are the viruses that infect and replicate within bacteria and archaea, and rich in human body. To investigate the relationship between phages and microbial communities, the identification of phages from metagenome sequences is the first step. Currently, there are two main methods for identifying phages: database-based (alignment-based) methods and alignment-free methods. Database-based methods typically use a large number of sequences as references; alignment-free methods usually learn the features of the sequences with machine learning and deep learning models.ResultsWe propose INHERIT which uses a deep representation learning model to integrate both database-based and alignment-free methods, combining the strengths of both. Pre-training is used as an alternative way of acquiring knowledge representations from existing databases, while the BERT-style deep learning framework retains the advantage of alignment-free methods. We compare INHERIT with four existing methods on a third-party benchmark dataset. Our experiments show that INHERIT achieves a better performance with the F1-score of 0.9932. In addition, we find that pre-training two species separately helps the non-alignment deep learning model make more accurate predictions.Availability and implementationThe codes of INHERIT are now available in: https://github.com/Celestial-Bai/INHERIT.Supplementary information Supplementary data are available at Bioinformatics online.

A hybrid pre-joined service network in graph database and memory for service composition

To make full use of the large space and persistency provided by a database and the fast processing speed of main memory, we propose a hybrid pre-joined service network method combining in-graph-database and in-memory calculations. This method firstly stores services' information and combinations in a graph database. Then, for a user's request, it retrieves candidate services from the database and constructs a service network in memory. After that, services are picked and composed to fulfil an optimal solution in memory. We assess the performance of our method by conducting experiments and comparing the approach with other database-based methods. Experiment results indicate the efficiency of this method in comparison, and it can always find solutions and lead to higher users' satisfaction.

False Discovery Rate Estimation for Hybrid Mass Spectral Library Search Identifications in Bottom-up Proteomics.

We present a method for FDR estimation of mass spectral library search identifications made by a recently developed method for peptide identification, the hybrid search, based on an extension of the target-decoy approach. In addition to estimating confidence for a given identification, this allows users to compare and integrate identifications from the hybrid mass spectral library search method with other peptide identification methods, such as a sequence database-based method. In addition to a score, each hybrid score is associated with a "DeltaMass" value, which is the difference in mass of the search and library peptide, which can correspond to the mass of a modification. We explored the relation between FDR and DeltaMass using 100 concatenated random decoy libraries and discovered that a small number of DeltaMass values were especially likely to result from decoy searches. Using these values, FDR values could be adjusted for these specific values and a reliable FDR generated for any DeltaMass value. Finally, using this method, we find and examine common, reliable identifications made by the hybrid search for a range of proteomic studies.

Grass habitat analysis and phytolith-based quantitative reconstruction of Asian monsoon climate change in the sand-loess transitional zone, northern China

AbstractWe investigated climate niches of grasses at regional scales and quantitatively reconstruct Asian monsoon precipitation at the sand-loess transitional zone in northern China. Our results provide direct evidence that certain grass lineages have been specialized in specific habitats: Pooideae grasses stand out and occupy a much cooler environment than all other subfamilies; Pooideae, Aristidoideae, and Chloridoieae occupy dry environments. Pooideae grasses occupy the coldest and driest environments compared to all other subfamilies, with a mean annual temperature (MAT) and precipitation (MAP) of ~13.6 to ~15.3°C and 224 to ~1674 mm, respectively, at a regional scale. We built a database for grasses and their corresponding climate parameters. Based on this database, past climate parameters at the margin of the Asian summer monsoon since ~70 ka were quantitatively reconstructed by phytolith assemblages. They show that this area was dominated by cold- and dry-adapted grasses since ~70 ka with a MAT and MAP of ~3.3 to ~11.0 °C and ~442 to ~900 mm, respectively, generally consistent with the results of phytolith-based transfer function reconstructions and with the results of previous nearby pollen-based quantitative reconstructions. With the improvement of the species-climate and ecosystem dataset, our database-based method is a promising quantitative reconstruction approach to past climatic change in the monsoon region.

TranSurVeyor: an improved database-free algorithm for finding non-reference transpositions in high-throughput sequencing data.

Transpositions transfer DNA segments between different loci within a genome; in particular, when a transposition is found in a sample but not in a reference genome, it is called a non-reference transposition. They are important structural variations that have clinical impact. Transpositions can be called by analyzing second generation high-throughput sequencing datasets. Current methods follow either a database-based or a database-free approach. Database-based methods require a database of transposable elements. Some of them have good specificity; however this approach cannot detect novel transpositions, and it requires a good database of transposable elements, which is not yet available for many species. Database-free methods perform de novo calling of transpositions, but their accuracy is low. We observe that this is due to the misalignment of the reads; since reads are short and the human genome has many repeats, false alignments create false positive predictions while missing alignments reduce the true positive rate. This paper proposes new techniques to improve database-free non-reference transposition calling: first, we propose a realignment strategy called one-end remapping that corrects the alignments of reads in interspersed repeats; second, we propose a SNV-aware filter that removes some incorrectly aligned reads. By combining these two techniques and other techniques like clustering and positive-to-negative ratio filter, our proposed transposition caller TranSurVeyor shows at least 3.1-fold improvement in terms of F1-score over existing database-free methods. More importantly, even though TranSurVeyor does not use databases of prior information, its performance is at least as good as existing database-based methods such as MELT, Mobster and Retroseq. We also illustrate that TranSurVeyor can discover transpositions that are not known in the current database.

Aircraft Vertical Route Optimization by Beam Search and Initial Search Space Reduction

This paper describes an optimization algorithm that provides an economical vertical navigation profile by finding the combinations of climb, cruise, and descent speeds, as well as altitudes, for an aircraft to minimize flight costs. The computational algorithm takes advantage of a space search reduction methodology to reduce the initial number of available speed and altitude combinations. The optimal solution was found by implementing the beam search algorithm. A bounding function that correctly estimates the flight cost by considering step climbs was developed to reduce the number of calculations required by the beam search algorithm. The full-flight fuel burn cost was obtained using a performance database-based method. The algorithm uses a numerical performance model instead of equations of motion to compute fuel burn. The database was developed by using flight experimental data. To validate the algorithm, its results were compared to those of three other algorithms: an exhaustive search, beam search, and search space reduction. The solution provided by the algorithm was also compared to the solution provided by a flight management system. Following this comparison, the algorithm systematically found the optimal solutions, which were better in terms of flight cost than those provided by the flight management system.

Estimating Sparse Gene Regulatory Networks Using a Bayesian Linear Regression

In this paper, we propose a gene regulatory network (GRN) estimation method, which assumes that such networks are typically sparse, using time-series microarray datasets. We represent the regulatory relationships between the genes using weights, with the "net" regulation influence on a gene's expression being the summation of the independent regulatory inputs. We estimate the weights using a Bayesian linear regression method for sparse parameter vectors. We apply our proposed method to the extraction of differential gene expression software selected genes of a human buffy-coat microarray expression profile dataset of ventilator-associated pneumonia (VAP), and compare the estimation result with the GRNs estimated using both a correlation coefficient method and a database-based method ingenuity pathway analysis. A biological analysis of the resulting consensus network that is derived using the GRNs, estimated with both our and the correlation-coefficient methods results in four biologically meaningful subnetworks. Also, our method performs either better than or competitively with the existing well-established GRN estimation methods. Moreover, it performs comparatively with respect to: 1) the ground-truth GRNs for the in silico 50- and 100-gene datasets reported recently in the DREAM3 challenge and 2) the GRN estimated using a mutual information-based method for the top-ranked Bayesian analysis of time series (a Bayesian user-friendly software for analyzing time-series microarray experiments) selected genes of the VAP dataset.

How representative are the known structures of the proteins in a complete genome? A comprehensive structural census

Determining how representative the known structures are of the proteins encoded by a complete genome is important for assessing to what extent our current picture of protein stability and folding is overly influenced by biases in the structure databank (PDB). It is also important for improving database-based methods of structure prediction and genome annotation. The known structures are compared to the proteins encoded by eight complete microbial genomes in terms of simple statistics such as sequence length, composition and secondary structure. The known structures are represented by a collection of nonhomologous domains from the PDB and a smaller list of 'biophysical proteins' on which folding experiments have concentrated. The proteins encoded by the genomes are considered as a whole and divided into various regions, such as known-structure homologue, low complexity (nonglobular), transmembrane or linker. Various tests are performed to assess the significance of the reported differences, in both a practical and a statistical sense. The proteins encoded by the genomes are significantly different from those in the PDB. Their sequence lengths, which follow an extreme value distribution, are longer than the PDB proteins and much longer than the biophysical proteins. Their composition differs from the PDB proteins in having more Lys, Ile, Asn and Gln and less Cys and Trp. This is true overall and especially for the regions corresponding to soluble proteins of as yet unknown fold. Secondary-structure prediction on these uncharacterized regions indicates that they contain on average more helical structure than the PDB; differences about this mean are small, with yeast having slightly more sheet structure and Haemophilus influenzae and Helicobacter pylori more helical structure. Further information is available through the GeneCensus system at http://bioinfo.mbb.yale.edu/genome.