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المؤلفون: Roberto R. Expósito, Marco Martínez-Sánchez, Juan Touriño
المصدر: BMC Bioinformatics. 23
مصطلحات موضوعية: Apache Spark, Applied Mathematics, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA, Genomics, DNA, Biochemistry, Distributed processing, Computer Science Applications, Big data, Structural Biology, Error correction, Molecular Biology, Software, Algorithms
الوصف: Background In recent years, huge improvements have been made in the context of sequencing genomic data under what is called Next Generation Sequencing (NGS). However, the DNA reads generated by current NGS platforms are not free of errors, which can affect the quality of downstream analysis. Although error correction can be performed as a preprocessing step to overcome this issue, it usually requires long computational times to analyze those large datasets generated nowadays through NGS. Therefore, new software capable of scaling out on a cluster of nodes with high performance is of great importance. Results In this paper, we present SparkEC, a parallel tool capable of fixing those errors produced during the sequencing process. For this purpose, the algorithms proposed by the CloudEC tool, which is already proved to perform accurate corrections, have been analyzed and optimized to improve their performance by relying on the Apache Spark framework together with the introduction of other enhancements such as the usage of memory-efficient data structures and the avoidance of any input preprocessing. The experimental results have shown significant improvements in the computational times of SparkEC when compared to CloudEC for all the representative datasets and scenarios under evaluation, providing an average and maximum speedups of 4.9$$\times$$ × and 11.9$$\times$$ × , respectively, over its counterpart. Conclusion As error correction can take excessive computational time, SparkEC provides a scalable solution for correcting large datasets. Due to its distributed implementation, SparkEC speed can increase with respect to the number of nodes in a cluster. Furthermore, the software is freely available under GPLv3 license and is compatible with different operating systems (Linux, Windows and macOS).
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المؤلفون: Dongju Chen, Taiping Shi, Qi Li, Chengcheng Song, Minghui Shao, Yuhang Cai, Xi Wang, Chunli Wang, Pei Meng
المصدر: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-19 (2021)
BMC Bioinformaticsمصطلحات موضوعية: Genome instability, Tumor purity and ploidy correction, DNA Copy Number Variations, DNA Repair, QH301-705.5, Computer applications to medicine. Medical informatics, R858-859.7, Biology, Biochemistry, Segmentation, Structural Biology, Neoplasms, Genotype, Humans, Copy-number variation, Copy number variations, Biology (General), Homologous Recombination, Molecular Biology, Gene, Ploidies, Applied Mathematics, Research, Recursion (computer science), High-Throughput Nucleotide Sequencing, Genomics, Computer Science Applications, Tree (data structure), Ploidy, DNA microarray, Homologous Recombination Deficiency, Genomic scar analysis, Homologous recombination deficiency, Algorithm, Algorithms
الوصف: Background The gain or loss of large chromosomal regions or even whole chromosomes is termed as genomic scarring and can be observed as copy number variations resulting from the failure of DNA damage repair. Results In this study, a new algorithm called genomic scar analysis (GSA) has developed and validated to calculate homologous recombination deficiency (HRD) score. The two critical submodules were tree recursion (TR) segmentation and filtering, and the estimation and correction of the tumor purity and ploidy. Then, this study evaluated the rationality of segmentation and genotype identification by the GSA algorithm and compared with other two algorithms, PureCN and ASCAT, found that the segmentation result of GSA algorithm was more logical. In addition, the results indicated that the GSA algorithm had an excellent predictive effect on tumor purity and ploidy, if the tumor purity was more than 20%. Furtherly, this study evaluated the HRD scores and BRCA1/2 deficiency status of 195 clinical samples, and the results indicated that the accuracy was 0.98 (comparing with Affymetrix OncoScan™ assay) and the sensitivity was 95.2% (comparing with BRCA1/2 deficiency status), both were well-behaved. Finally, HRD scores and 16 genes mutations (TP53 and 15 HRR pathway genes) were analyzed in 17 cell lines, the results showed that there was higher frequency in HRR pathway genes in high HRD score samples. Conclusions This new algorithm, named as GSA, could effectively and accurately calculate the purity and ploidy of tumor samples through NGS data, and then reflect the degree of genomic instability and large-scale copy number variations of tumor samples.
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المؤلفون: Vladimir Nikolić, René L. Warren, Johnathan Wong, Theodora Lo, Lauren Coombe, Inanc Birol, Janet X. Li
المصدر: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-13 (2021)
BMC Bioinformaticsمصطلحات موضوعية: Scaffold, Computer science, QH301-705.5, Genome scaffolding, Computer applications to medicine. Medical informatics, R858-859.7, Sequence assembly, Genomics, Computational biology, Long reads, Biochemistry, Genome, Structural Biology, Scaffolder, Humans, Biology (General), Minimizers, Molecular Biology, Repetitive Sequences, Nucleic Acid, Contig, Applied Mathematics, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA, Pipeline (software), Computer Science Applications, Misassembly correction, Scalability, DNA microarray, Software, De novo genome assembly
الوصف: Background Generating high-quality de novo genome assemblies is foundational to the genomics study of model and non-model organisms. In recent years, long-read sequencing has greatly benefited genome assembly and scaffolding, a process by which assembled sequences are ordered and oriented through the use of long-range information. Long reads are better able to span repetitive genomic regions compared to short reads, and thus have tremendous utility for resolving problematic regions and helping generate more complete draft assemblies. Here, we present LongStitch, a scalable pipeline that corrects and scaffolds draft genome assemblies exclusively using long reads. Results LongStitch incorporates multiple tools developed by our group and runs in up to three stages, which includes initial assembly correction (Tigmint-long), followed by two incremental scaffolding stages (ntLink and ARKS-long). Tigmint-long and ARKS-long are misassembly correction and scaffolding utilities, respectively, previously developed for linked reads, that we adapted for long reads. Here, we describe the LongStitch pipeline and introduce our new long-read scaffolder, ntLink, which utilizes lightweight minimizer mappings to join contigs. LongStitch was tested on short and long-read assemblies of Caenorhabditis elegans, Oryza sativa, and three different human individuals using corresponding nanopore long-read data, and improves the contiguity of each assembly from 1.2-fold up to 304.6-fold (as measured by NGA50 length). Furthermore, LongStitch generates more contiguous and correct assemblies compared to state-of-the-art long-read scaffolder LRScaf in most tests, and consistently improves upon human assemblies in under five hours using less than 23 GB of RAM. Conclusions Due to its effectiveness and efficiency in improving draft assemblies using long reads, we expect LongStitch to benefit a wide variety of de novo genome assembly projects. The LongStitch pipeline is freely available at https://github.com/bcgsc/longstitch.
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المؤلفون: Yingyu Lin, Yibin Xu, Zhe Liu, Huiwei Zhou, Chengkun Lang, Junjie Hou
المصدر: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-16 (2021)
BMC Bioinformaticsمصطلحات موضوعية: Relation (database), Computer science, QH301-705.5, Knowledge Bases, Computer applications to medicine. Medical informatics, R858-859.7, Information Storage and Retrieval, Knowledge distillation, computer.software_genre, Biochemistry, law.invention, Named-entity recognition, Structural Biology, law, Biology (General), Molecular Biology, Distillation, Label re-correction, Recall, business.industry, Research, Applied Mathematics, Perspective (graphical), Biomedical information, Construct (python library), Computer Science Applications, ComputingMethodologies_PATTERNRECOGNITION, Filter (video), Artificial intelligence, business, Biomedical named entity recognition, computer, Natural language processing
الوصف: Background Biomedical named entity recognition is one of the most essential tasks in biomedical information extraction. Previous studies suffer from inadequate annotated datasets, especially the limited knowledge contained in them. Methods To remedy the above issue, we propose a novel Biomedical Named Entity Recognition (BioNER) framework with label re-correction and knowledge distillation strategies, which could not only create large and high-quality datasets but also obtain a high-performance recognition model. Our framework is inspired by two points: (1) named entity recognition should be considered from the perspective of both coverage and accuracy; (2) trustable annotations should be yielded by iterative correction. Firstly, for coverage, we annotate chemical and disease entities in a large-scale unlabeled dataset by PubTator to generate a weakly labeled dataset. For accuracy, we then filter it by utilizing multiple knowledge bases to generate another weakly labeled dataset. Next, the two datasets are revised by a label re-correction strategy to construct two high-quality datasets, which are used to train two recognition models, respectively. Finally, we compress the knowledge in the two models into a single recognition model with knowledge distillation. Results Experiments on the BioCreative V chemical-disease relation corpus and NCBI Disease corpus show that knowledge from large-scale datasets significantly improves the performance of BioNER, especially the recall of it, leading to new state-of-the-art results. Conclusions We propose a framework with label re-correction and knowledge distillation strategies. Comparison results show that the two perspectives of knowledge in the two re-corrected datasets respectively are complementary and both effective for BioNER.
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المؤلفون: Robert C. Glen, Timothy M. D. Ebbels, Alina Peluso
المساهمون: Peluso, Alina [0000-0003-2895-0406], Apollo - University of Cambridge Repository, European Molecular Biology Laboratory, National Institutes of Health
المصدر: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-18 (2021)
BMC Bioinformaticsمصطلحات موضوعية: Genetic Markers, Biochemistry & Molecular Biology, Multivariate statistics, Bioinformatics, Permutation, lcsh:Computer applications to medicine. Medical informatics, Models, Biological, Biochemistry, Biochemical Research Methods, Normal distribution, Correlated tests, 03 medical and health sciences, symbols.namesake, MWAS, Structural Biology, Šidák correction, Statistics, Metabolomics, Multiple testing, lcsh:QH301-705.5, Molecular Biology, 01 Mathematical Sciences, 030304 developmental biology, Mathematics, 0303 health sciences, Science & Technology, Applied Mathematics, 030302 biochemistry & molecular biology, Univariate, 06 Biological Sciences, Computer Science Applications, Bonferroni correction, Biotechnology & Applied Microbiology, lcsh:Biology (General), FWER, Multiple comparisons problem, Metabolome, symbols, lcsh:R858-859.7, Mathematical & Computational Biology, 08 Information and Computing Sciences, MWSL, Life Sciences & Biomedicine, Research Article, Statistical Distributions, Type I and type II errors
الوصف: Background The search for statistically significant relationships between molecular markers and outcomes is challenging when dealing with high-dimensional, noisy and collinear multivariate omics data, such as metabolomic profiles. Permutation procedures allow for the estimation of adjusted significance levels without assuming independence among metabolomic variables. Nevertheless, the complex non-normal structure of metabolic profiles and outcomes may bias the permutation results leading to overly conservative threshold estimates i.e. lower than those from a Bonferroni or Sidak correction. Methods Within a univariate permutation procedure we employ parametric simulation methods based on the multivariate (log-)Normal distribution to obtain adjusted significance levels which are consistent across different outcomes while effectively controlling the type I error rate. Next, we derive an alternative closed-form expression for the estimation of the number of non-redundant metabolic variates based on the spectral decomposition of their correlation matrix. The performance of the method is tested for different model parametrizations and across a wide range of correlation levels of the variates using synthetic and real data sets. Results Both the permutation-based formulation and the more practical closed form expression are found to give an effective indication of the number of independent metabolic effects exhibited by the system, while guaranteeing that the derived adjusted threshold is stable across outcome measures with diverse properties.
وصف الملف: application/pdf; text/xml
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المؤلفون: Wang, Linyu, Zhong, Xiaodan, Wang, Shuo, Liu, Yuanning
المصدر: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-1 (2021)
BMC Bioinformaticsمصطلحات موضوعية: RNA, Untranslated, QH301-705.5, Applied Mathematics, Computer applications to medicine. Medical informatics, R858-859.7, Correction, Computational Biology, Biochemistry, Computer Science Applications, Structural Biology, Nucleic Acid Conformation, Neural Networks, Computer, Biology (General), Sequence Alignment, Molecular Biology
الوصف: Studies have proven that the same family of non-coding RNAs (ncRNAs) have similar functions, so predicting the ncRNAs family is helpful to the research of ncRNAs functions. The existing calculation methods mainly fall into two categories: the first type is to predict ncRNAs family by learning the features of sequence or secondary structure, and the other type is to predict ncRNAs family by the alignment among homologs sequences. In the first type, some methods predict ncRNAs family by learning predicted secondary structure features. The inaccuracy of predicted secondary structure may cause the low accuracy of those methods. Different from that, ncRFP directly learning the features of ncRNA sequences to predict ncRNAs family. Although ncRFP simplifies the prediction process and improves the performance, there is room for improvement in ncRFP performance due to the incomplete features of its input data. In the secondary type, the homologous sequence alignment method can achieve the highest performance at present. However, due to the need for consensus secondary structure annotation of ncRNA sequences, and the helplessness for modeling pseudoknots, the use of the method is limited.In this paper, a novel method "ncDLRES", which according to learning the sequence features, is proposed to predict the family of ncRNAs based on Dynamic LSTM (Long Short-term Memory) and ResNet (Residual Neural Network).ncDLRES extracts the features of ncRNA sequences based on Dynamic LSTM and then classifies them by ResNet. Compared with the homologous sequence alignment method, ncDLRES reduces the data requirement and expands the application scope. By comparing with the first type of methods, the performance of ncDLRES is greatly improved.
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المؤلفون: Hamid Mushtaq, Jonathan Lévy, Nauman Ahmed, Shanshan Ren, Zaid Al-Ars, Koen Bertels
المصدر: BMC Bioinformatics, Vol 20, Iss 1, Pp 1-4 (2019)
BMC Bioinformaticsمصطلحات موضوعية: Computer science, Sequence alignment, computer.software_genre, lcsh:Computer applications to medicine. Medical informatics, Biochemistry, 03 medical and health sciences, 0302 clinical medicine, Structural Biology, Molecular Biology, Throughput (business), lcsh:QH301-705.5, Gene Library, 030304 developmental biology, 0303 health sciences, Sequence Analysis, RNA, Applied Mathematics, Published Erratum, Correction, Computational Biology, High-Throughput Nucleotide Sequencing, DNA, Sequence Analysis, DNA, Computer Science Applications, lcsh:Biology (General), 030220 oncology & carcinogenesis, RNA, lcsh:R858-859.7, Data mining, DNA microarray, Sequence Alignment, computer, Algorithms, Software
الوصف: Due the computational complexity of sequence alignment algorithms, various accelerated solutions have been proposed to speedup this analysis. NVBIO is the only available GPU library that accelerates sequence alignment of high-throughput NGS data, but has limited performance. In this article we present GASAL2, a GPU library for aligning DNA and RNA sequences that outperforms existing CPU and GPU libraries.The GASAL2 library provides specialized, accelerated kernels for local, global and all types of semi-global alignment. Pairwise sequence alignment can be performed with and without traceback. GASAL2 outperforms the fastest CPU-optimized SIMD implementations such as SeqAn and Parasail, as well as NVIDIA's own GPU-based library known as NVBIO. GASAL2 is unique in performing sequence packing on GPU, which is up to 750x faster than NVBIO. Overall on Geforce GTX 1080 Ti GPU, GASAL2 is up to 21x faster than Parasail on a dual socket hyper-threaded Intel Xeon system with 28 cores and up to 13x faster than NVBIO with a query length of up to 300 bases and 100 bases, respectively. GASAL2 alignment functions are asynchronous/non-blocking and allow full overlap of CPU and GPU execution. The paper shows how to use GASAL2 to accelerate BWA-MEM, speeding up the local alignment by 20x, which gives an overall application speedup of 1.3x vs. CPU with up to 12 threads.The library provides high performance APIs for local, global and semi-global alignment that can be easily integrated into various bioinformatics tools.
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المؤلفون: Jianhan Lin, Yuanjie Liu
المصدر: BMC Bioinformatics, Vol 20, Iss 1, Pp 1-17 (2019)
BMC Bioinformaticsمصطلحات موضوعية: Baseline correction, Computer science, 02 engineering and technology, Derivative, lcsh:Computer applications to medicine. Medical informatics, 01 natural sciences, Biochemistry, Signal, Passing accumulation, Structural Biology, Position (vector), 0202 electrical engineering, electronic engineering, information engineering, Humans, First derivative, Molecular Biology, lcsh:QH301-705.5, business.industry, Noise (signal processing), Methodology Article, Applied Mathematics, 010401 analytical chemistry, Detector, Computational Biology, Signal Processing, Computer-Assisted, Pattern recognition, Thresholding, 0104 chemical sciences, Computer Science Applications, Signal extraction, lcsh:Biology (General), Feature (computer vision), lcsh:R858-859.7, 020201 artificial intelligence & image processing, Artificial intelligence, business, Algorithms
الوصف: Background Automatic signal-feature extraction algorithms are crucial for profile processing in bioinformatics. Both baseline drift and noise seriously affect the position and peak area of signals. An efficient algorithm named the derivative passing accumulation (DPA) method for simultaneous baseline correction and signal extraction is presented in this article. It is an efficient method using only the first-order derivatives which are obtained through taking the simple differences. Results We developed a new signal feature extracting procedure. The vector representing the discrete first-order derivative was divided into negative and positive parts and then accumulated to build a signal descriptor. The signals and background fluctuations are easily separated according to this descriptor via thresholding. In addition, the signal peaks are simultaneously located by checking the corresponding intervals in the descriptor. Therefore, the eternal issues of parsing the 1-dimensional output of detectors in biological instruments are solved together. Thereby, the baseline is corrected, and the signal peaks are extracted. Conclusions We have introduced a new method for signal peak picking, where baseline computation and peak identification are performed jointly. The testing results of both authentic and artificially synthesized data illustrate that the new method is powerful, and it could be a better choice for practical processing.
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المؤلفون: Robert H. S. Kraus, Richard Kuo, Ralf C. Mueller, Jacqueline Smith, Lel Eory, Nicolai Mallig
المصدر: BMC Bioinformatics
Mueller, R C, Mallig, N, Smith, J, Eöry, L, Kuo, R I & Kraus, R H S 2021, ' Correction to : Avian Immunome DB: an example of a user‑friendly interface for extracting genetic information ', BMC Bioinformatics, vol. 22, no. 1, pp. 472 . https://doi.org/10.1186/s12859-021-04388-x
BMC Bioinformatics, Vol 22, Iss 1, Pp 1-1 (2021)مصطلحات موضوعية: User Friendly, Information retrieval, Base Sequence, QH301-705.5, Computer science, Interface (Java), Applied Mathematics, Computer applications to medicine. Medical informatics, R858-859.7, Correction, Proteins, Molecular Sequence Annotation, Genomics, Biochemistry, Computer Science Applications, User-Computer Interface, Immunome, Structural Biology, Databases, Genetic, Animals, Amino Acid Sequence, Biology (General), Chickens, Molecular Biology
الوصف: Genomic and genetic studies often require a target list of genes before conducting any hypothesis testing or experimental verification. With the ever-growing number of sequenced genomes and a variety of different annotation strategies, comes the potential for ambiguous gene symbols, making it cumbersome to capture the "correct" set of genes. In this article, we present and describe the Avian Immunome DB (AVIMM) for easy gene property extraction as exemplified by avian immune genes. The avian immune system is characterised by a cascade of complex biological processes underlaid by more than 1000 different genes. It is a vital trait to study particularly in birds considering that they are a significant driver in spreading zoonotic diseases. With the completion of phase II of the B10K ("Bird 10,000 Genomes") consortium's whole-genome sequencing effort, we have included 363 annotated bird genomes in addition to other publicly available bird genome data which serve as a valuable foundation for AVIMM.A relational database with avian immune gene evidence from Gene Ontology, Ensembl, UniProt and the B10K consortium has been designed and set up. The foundation stone or the "seed" for the initial set of avian immune genes is based on the well-studied model organism chicken (Gallus gallus). Gene annotations, different transcript isoforms, nucleotide sequences and protein information, including amino acid sequences, are included. Ambiguous gene names (symbols) are resolved within the database and linked to their canonical gene symbol. AVIMM is supplemented by a command-line interface and a web front-end to query the database.The internal mapping of unique gene symbol identifiers to canonical gene symbols allows for an ambiguous gene property search. The database is organised within core and feature tables, which makes it straightforward to extend for future purposes. The database design is ready to be applied to other taxa or biological processes. Currently, the database contains 1170 distinct avian immune genes with canonical gene symbols and 612 synonyms across 363 bird species. While the command-line interface readily integrates into bioinformatics pipelines, the intuitive web front-end with download functionality offers sophisticated search functionalities and tracks the origin for each record. AVIMM is publicly accessible at https://avimm.ab.mpg.de .
وصف الملف: application/pdf
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المؤلفون: Everardo Gutiérrez-Millán, Emily E. Burke, Nicholas J. Eagles, Jacob Leonard, Leonardo Collado-Torres, Brianna K. Barry, Joshua M. Stolz, Israel Aguilar-Ordoñez, Louise A Huuki, Violeta Larios Serrato, Andrew E. Jaffe, BaDoi N. Phan
المصدر: BMC Bioinformatics, Vol 22, Iss 1, Pp 1-2 (2021)
BMC Bioinformaticsمصطلحات موضوعية: Sequence Analysis, RNA, QH301-705.5, Computer science, Applied Mathematics, Pipeline (computing), Computer applications to medicine. Medical informatics, R858-859.7, Correction, High-Throughput Nucleotide Sequencing, RNA-Seq, Computational biology, Biochemistry, Workflow, Computer Science Applications, Bioconductor, Structural Biology, Expression analysis, Scalability, Biology (General), DNA microarray, Molecular Biology, Software
الوصف: RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step-such as alignment of reads to a reference genome-of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses.In response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided ( http://research.libd.org/SPEAQeasy/ ).SPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. The goal is to provide a flexible pipeline that is immediately usable by researchers, regardless of their technical background or computing environment.
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