X

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
Filter
  • Oxford University Press (OUP)  (22)
  • 1
    Online Resource
    Online Resource
    AFS: identification and quantification of species composition by metagenomic sequencing
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 9 ( 2017-05-01), p. 1396-1398
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 9 ( 2017-05-01), p. 1396-1398
    Abstract: DNA-based methods to detect and quantify taxon composition in biological materials are often based on species-specific polymerase chain reaction, limited to detecting species targeted by the assay. Next-generation sequencing overcomes this drawback by untargeted shotgun sequencing of whole metagenomes at affordable cost. Here we present AFS, a software pipeline for quantification of species composition in food. AFS uses metagenomic shotgun sequencing and sequence read counting to infer species proportions. Using Illumina data from a reference sausage comprising four species, we reveal that AFS is independent of the sequencing assay and library preparation protocol. Cost-saving short (50-bp) single-end reads and Nextera® library preparation yield reliable results. Availability and Implementation Datasets, binaries and usage instructions are available under http://all-food-seq.sourceforge.net. Raw data is available at NCBI’s SRA with accession number PRJNA271645. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btw822
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 2
    Online Resource
    Online Resource
    CUSHAW: a CUDA compatible short read aligner to large genomes based on the Burrows–Wheeler transform
    Oxford University Press (OUP) ; 2012
    In:  Bioinformatics Vol. 28, No. 14 ( 2012-07-15), p. 1830-1837
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 28, No. 14 ( 2012-07-15), p. 1830-1837
    Abstract: Motivation: New high-throughput sequencing technologies have promoted the production of short reads with dramatically low unit cost. The explosive growth of short read datasets poses a challenge to the mapping of short reads to reference genomes, such as the human genome, in terms of alignment quality and execution speed. Results: We present CUSHAW, a parallelized short read aligner based on the compute unified device architecture (CUDA) parallel programming model. We exploit CUDA-compatible graphics hardware as accelerators to achieve fast speed. Our algorithm uses a quality-aware bounded search approach based on the Burrows–Wheeler transform (BWT) and the Ferragina–Manzini index to reduce the search space and achieve high alignment quality. Performance evaluation, using simulated as well as real short read datasets, reveals that our algorithm running on one or two graphics processing units achieves significant speedups in terms of execution time, while yielding comparable or even better alignment quality for paired-end alignments compared with three popular BWT-based aligners: Bowtie, BWA and SOAP2. CUSHAW also delivers competitive performance in terms of single-nucleotide polymorphism calling for an Escherichia coli test dataset. Availability:  http://cushaw.sourceforge.net. Contact:  liuy@uni-mainz.de; bertil.schmidt@uni-mainz.de Supplementary information: Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/bts276
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2012
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 3
    Online Resource
    Online Resource
    DySC: software for greedy clustering of 16S rRNA reads
    Oxford University Press (OUP) ; 2012
    In:  Bioinformatics Vol. 28, No. 16 ( 2012-08-15), p. 2182-2183
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 28, No. 16 ( 2012-08-15), p. 2182-2183
    Abstract: Summary: Pyrosequencing technologies are frequently used for sequencing the 16S ribosomal RNA marker gene for profiling microbial communities. Clustering of the produced reads is an important but time-consuming task. We present Dynamic Seed-based Clustering (DySC), a new tool based on the greedy clustering approach that uses a dynamic seeding strategy. Evaluations based on the normalized mutual information (NMI) criterion show that DySC produces higher quality clusters than UCLUST and CD-HIT at a comparable runtime. Availability and implementation: DySC, implemented in C, is available at http://code.google.com/p/dysc/ under GNU GPL license. Contact:  bertil.schmidt@uni-mainz.de Supplementary Information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/bts355
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2012
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 4
    Online Resource
    Online Resource
    Musket: a multistage k- mer spectrum-based error corrector for Illumina sequence data
    Oxford University Press (OUP) ; 2013
    In:  Bioinformatics Vol. 29, No. 3 ( 2013-02-01), p. 308-315
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 29, No. 3 ( 2013-02-01), p. 308-315
    Abstract: Motivation: The imperfect sequence data produced by next-generation sequencing technologies have motivated the development of a number of short-read error correctors in recent years. The majority of methods focus on the correction of substitution errors, which are the dominant error source in data produced by Illumina sequencing technology. Existing tools either score high in terms of recall or precision but not consistently high in terms of both measures. Results: In this article, we present Musket, an efficient multistage k-mer-based corrector for Illumina short-read data. We use the k-mer spectrum approach and introduce three correction techniques in a multistage workflow: two-sided conservative correction, one-sided aggressive correction and voting-based refinement. Our performance evaluation results, in terms of correction quality and de novo genome assembly measures, reveal that Musket is consistently one of the top performing correctors. In addition, Musket is multi-threaded using a master–slave model and demonstrates superior parallel scalability compared with all other evaluated correctors as well as a highly competitive overall execution time. Availability: Musket is available at http://musket.sourceforge.net. Contact:  liuy@uni-mainz.de or bertil.schmidt@uni-mainz.de Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/bts690
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2013
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 5
    Online Resource
    Online Resource
    CARE: context-aware sequencing read error correction
    Oxford University Press (OUP) ; 2021
    In:  Bioinformatics Vol. 37, No. 7 ( 2021-05-17), p. 889-895
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 37, No. 7 ( 2021-05-17), p. 889-895
    Abstract: Error correction is a fundamental pre-processing step in many Next-Generation Sequencing (NGS) pipelines, in particular for de novo genome assembly. However, existing error correction methods either suffer from high false-positive rates since they break reads into independent k-mers or do not scale efficiently to large amounts of sequencing reads and complex genomes. Results We present CARE—an alignment-based scalable error correction algorithm for Illumina data using the concept of minhashing. Minhashing allows for efficient similarity search within large sequencing read collections which enables fast computation of high-quality multiple alignments. Sequencing errors are corrected by detailed inspection of the corresponding alignments. Our performance evaluation shows that CARE generates significantly fewer false-positive corrections than state-of-the-art tools (Musket, SGA, BFC, Lighter, Bcool, Karect) while maintaining a competitive number of true positives. When used prior to assembly it can achieve superior de novo assembly results for a number of real datasets. CARE is also the first multiple sequence alignment-based error corrector that is able to process a human genome Illumina NGS dataset in only 4 h on a single workstation using GPU acceleration. Availabilityand implementation CARE is open-source software written in C++ (CPU version) and in CUDA/C++ (GPU version). It is licensed under GPLv3 and can be downloaded at https://github.com/fkallen/CARE. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btaa738
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
  • 6
    Online Resource
    Online Resource
    MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities
    Oxford University Press (OUP) ; 2010
    In:  Bioinformatics Vol. 26, No. 16 ( 2010-08-15), p. 1958-1964
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 26, No. 16 ( 2010-08-15), p. 1958-1964
    Abstract: Motivation: Multiple sequence alignment is of central importance to bioinformatics and computational biology. Although a large number of algorithms for computing a multiple sequence alignment have been designed, the efficient computation of highly accurate multiple alignments is still a challenge. Results: We present MSAProbs, a new and practical multiple alignment algorithm for protein sequences. The design of MSAProbs is based on a combination of pair hidden Markov models and partition functions to calculate posterior probabilities. Furthermore, two critical bioinformatics techniques, namely weighted probabilistic consistency transformation and weighted profile–profile alignment, are incorporated to improve alignment accuracy. Assessed using the popular benchmarks: BAliBASE, PREFAB, SABmark and OXBENCH, MSAProbs achieves statistically significant accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons and Probalign. Furthermore, MSAProbs is optimized for multi-core CPUs by employing a multi-threaded design, leading to a competitive execution time compared to other aligners. Availability: The source code of MSAProbs, written in C++, is freely and publicly available from http://msaprobs.sourceforge.net. Contact:  liuy0039@ntu.edu.sg
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btq338
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2010
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 7
    Online Resource
    Online Resource
    CompleteMOTIFs: DNA motif discovery platform for transcription factor binding experiments
    Oxford University Press (OUP) ; 2011
    In:  Bioinformatics Vol. 27, No. 5 ( 2011-03-01), p. 715-717
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 27, No. 5 ( 2011-03-01), p. 715-717
    Abstract: Summary:CompleteMOTIFs (cMOTIFs) is an integrated web tool developed to facilitate systematic discovery of overrepresented transcription factor binding motifs from high-throughput chromatin immunoprecipitation experiments. Comprehensive annotations and Boolean logic operations on multiple peak locations enable users to focus on genomic regions of interest for de novo motif discovery using tools such as MEME, Weeder and ChIPMunk. The pipeline incorporates a scanning tool for known motifs from TRANSFAC and JASPAR databases, and performs an enrichment test using local or precalculated background models that significantly improve the motif scanning result. Furthermore, using the cMOTIFs pipeline, we demonstrated that multiple transcription factors could cooperatively bind to the upstream of important stem cell differentiation regulators. Availability:  http://cmotifs.tchlab.org Contact:  sekwon.kong@childrens.harvard.edu Supplementary information:  Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4811 , 1367-4803
    URL: Article
    DOI: 10.1093/bioinformatics/btq707
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2011
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 8
    Online Resource
    Online Resource
    MetaCache: context-aware classification of metagenomic reads using minhashing
    Oxford University Press (OUP) ; 2017
    In:  Bioinformatics Vol. 33, No. 23 ( 2017-12-01), p. 3740-3748
    Details
    In: Bioinformatics, Oxford University Press (OUP), Vol. 33, No. 23 ( 2017-12-01), p. 3740-3748
    Abstract: Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy. Results We introduce MetaCache—a novel software for read classification using the big data technique minhashing. Our approach performs context-aware classification of reads by computing representative subsamples of k-mers within both, probed reads and locally constrained regions of the reference genomes. As a result, MetaCache consumes significantly less memory compared to the state-of-the-art read classifiers Kraken and CLARK while achieving highly competitive sensitivity and precision at comparable speed. For example, using NCBI RefSeq draft and completed genomes with a total length of around 140 billion bases as reference, MetaCache’s database consumes only 62 GB of memory while both Kraken and CLARK fail to construct their respective databases on a workstation with 512 GB RAM. Our experimental results further show that classification accuracy continuously improves when increasing the amount of utilized reference genome data. Availability and implementation MetaCache is open source software written in C ++ and can be downloaded at http://github.com/muellan/metacache. Supplementary information Supplementary data are available at Bioinformatics online.
    Type of Medium: Online Resource
    ISSN: 1367-4803 , 1367-4811
    URL: Article
    DOI: 10.1093/bioinformatics/btx520
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2017
    detail.hit.zdb_id: 1468345-3
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 9
    Online Resource
    Online Resource
    MetaTransformer: deep metagenomic sequencing read classification using self-attention models
    Oxford University Press (OUP) ; 2023
    In:  NAR Genomics and Bioinformatics Vol. 5, No. 3 ( 2023-07-05)
    Details
    In: NAR Genomics and Bioinformatics, Oxford University Press (OUP), Vol. 5, No. 3 ( 2023-07-05)
    Abstract: Deep learning has emerged as a paradigm that revolutionizes numerous domains of scientific research. Transformers have been utilized in language modeling outperforming previous approaches. Therefore, the utilization of deep learning as a tool for analyzing the genomic sequences is promising, yielding convincing results in fields such as motif identification and variant calling. DeepMicrobes, a machine learning-based classifier, has recently been introduced for taxonomic prediction at species and genus level. However, it relies on complex models based on bidirectional long short-term memory cells resulting in slow runtimes and excessive memory requirements, hampering its effective usability. We present MetaTransformer, a self-attention-based deep learning metagenomic analysis tool. Our transformer-encoder-based models enable efficient parallelization while outperforming DeepMicrobes in terms of species and genus classification abilities. Furthermore, we investigate approaches to reduce memory consumption and boost performance using different embedding schemes. As a result, we are able to achieve 2× to 5× speedup for inference compared to DeepMicrobes while keeping a significantly smaller memory footprint. MetaTransformer can be trained in 9 hours for genus and 16 hours for species prediction. Our results demonstrate performance improvements due to self-attention models and the impact of embedding schemes in deep learning on metagenomic sequencing data.
    Type of Medium: Online Resource
    ISSN: 2631-9268
    URL: Article
    DOI: 10.1093/nargab/lqad082
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2023
    detail.hit.zdb_id: 3009998-5
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
  • 10
    Online Resource
    Online Resource
    A computational method for studying the relation between alternative splicing and DNA methylation
    Oxford University Press (OUP) ; 2016
    In:  Nucleic Acids Research Vol. 44, No. 2 ( 2016-01-29), p. e19-e19
    Details
    In: Nucleic Acids Research, Oxford University Press (OUP), Vol. 44, No. 2 ( 2016-01-29), p. e19-e19
    Type of Medium: Online Resource
    ISSN: 0305-1048 , 1362-4962
    URL: Article
    DOI: 10.1093/nar/gkv906
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2016
    detail.hit.zdb_id: 1472175-2
    SSG: 12
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Online Resource
    Open Access Request
    Availability (electronic / print)
    BTU Cottbus
    Wissenschaftspark Albert Einstein
    EUV Frankfurt
    TH Wildau
    FH Potsdam
Nothing or not found what you are looking for? Please check your search query or use the Interlibrary Loan Search.
Close ⊗
This website uses cookies and the analysis tool Matomo. Further information can be found on the KOBV privacy pages