The Experts below are selected from a list of 842019 Experts worldwide ranked by ideXlab platform
Thomas L Madden - One of the best experts on this subject based on the ideXlab platform.
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magic blast an accurate rna seq Aligner for long and short reads
BMC Bioinformatics, 2019Co-Authors: Grzegorz M Boratyn, Jean Thierrymieg, Danielle Thierrymieg, Ben Busby, Thomas L MaddenAbstract:Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new Aligner based on ideas from the Magic pipeline. Magic-BLAST uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular Aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.
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magic blast an accurate dna and rna seq Aligner for long and short reads
bioRxiv, 2018Co-Authors: Grzegorz M Boratyn, Jean Thierrymieg, Danielle Thierrymieg, Ben Busby, Thomas L MaddenAbstract:Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. To address these issues, we introduce Magic-BLAST, a new Aligner based on ideas from the Magic pipeline. It uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular Aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions. It is versatile and robust to high levels of mismatches or extreme base composition and works well with very long reads. It is reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.
Shuichi Matsuda - One of the best experts on this subject based on the ideXlab platform.
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kinematic alignment produces near normal knee motion but increases contact stress after total knee arthroplasty a case study on a single implant design
Knee, 2015Co-Authors: Masahiro Ishikawa, Shinichi Kuriyama, Moritoshi Furu, Shinichiro Nakamura, Shuichi MatsudaAbstract:Abstract Background Kinematically aligned total knee arthroplasty (TKA) is of increasing interest because this method might improve postoperative patient satisfaction. In kinematic alignment the femoral component is implanted in a slightly more valgus and internally rotated position, and the tibial component is implanted in a slightly more varus and internally rotated position, than in mechanical alignment. However, the biomechanics of kinematically aligned TKA remain largely unknown. The aim of this study was to compare the kinematics and contact stresses of mechanically and kinematically aligned TKAs. Methods A musculoskeletal computer simulation was used to determine the effects of mechanically or kinematically aligned TKA. Knee kinematics were examined for mechanically aligned, kinematically aligned, and kinematically aligned outlier models. Patellofemoral and tibiofemoral contact forces were measured using finite element analysis. Results Greater femoral rollback and more external rotation of the femoral component were observed with kinematically aligned TKA than mechanically aligned TKA. However, patellofemoral and tibiofemoral contact stresses were increased in kinematically aligned TKA. Conclusions These findings suggest that kinematically aligned TKA produces near-normal knee kinematics, but that concerns for long-term outcome might arise because of high contact stresses.
Grzegorz M Boratyn - One of the best experts on this subject based on the ideXlab platform.
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magic blast an accurate rna seq Aligner for long and short reads
BMC Bioinformatics, 2019Co-Authors: Grzegorz M Boratyn, Jean Thierrymieg, Danielle Thierrymieg, Ben Busby, Thomas L MaddenAbstract:Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. We introduce Magic-BLAST, a new Aligner based on ideas from the Magic pipeline. Magic-BLAST uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular Aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions and the best at mapping reads longer than 250 bases, from any platform. It is versatile and robust to high levels of mismatches or extreme base composition, and reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.
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magic blast an accurate dna and rna seq Aligner for long and short reads
bioRxiv, 2018Co-Authors: Grzegorz M Boratyn, Jean Thierrymieg, Danielle Thierrymieg, Ben Busby, Thomas L MaddenAbstract:Next-generation sequencing technologies can produce tens of millions of reads, often paired-end, from transcripts or genomes. But few programs can align RNA on the genome and accurately discover introns, especially with long reads. To address these issues, we introduce Magic-BLAST, a new Aligner based on ideas from the Magic pipeline. It uses innovative techniques that include the optimization of a spliced alignment score and selective masking during seed selection. We evaluate the performance of Magic-BLAST to accurately map short or long sequences and its ability to discover introns on real RNA-seq data sets from PacBio, Roche and Illumina runs, and on six benchmarks, and compare it to other popular Aligners. Additionally, we look at alignments of human idealized RefSeq mRNA sequences perfectly matching the genome. We show that Magic-BLAST is the best at intron discovery over a wide range of conditions. It is versatile and robust to high levels of mismatches or extreme base composition and works well with very long reads. It is reasonably fast. It can align reads to a BLAST database or a FASTA file. It can accept a FASTQ file as input or automatically retrieve an accession from the SRA repository at the NCBI.
Francesco Vezzi - One of the best experts on this subject based on the ideXlab platform.
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a randomized numerical Aligner rna
Journal of Computer and System Sciences, 2012Co-Authors: Alberto Policriti, Alexandru I Tomescu, Francesco VezziAbstract:With the advent of new sequencing technologies able to produce an enormous quantity of short genomic sequences, new tools able to search for them inside a genomic reference sequence have emerged. Because of chemical reading errors or of the variability between organisms, one is interested in finding not only exact occurrences, but also occurrences with up to k mismatches. The contribution of this paper is twofold. On the one hand, we present a generalization of the classical Rabin-Karp string matching algorithm to solve the k-mismatch problem, with average complexity O(n+m) (n text and m pattern lengths, respectively). On the other hand, we show how to employ this idea in conjunction with an index over the text, allowing to search a pattern, with up to k mismatches, in time proportional to its length. This novel tool-rNA (randomized Numerical Aligner)-is in general faster and more accurate than other available tools like SOAP2, BWA, and BOWTIE. rNA executables and source code are freely available at http://iga-rna.sourceforge.net/.
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mrNA: The MPI Randomized Numerical Aligner
2011 IEEE International Conference on Bioinformatics and Biomedicine, 2011Co-Authors: Cristan Del Fabbro, Francesco Vezzi, Alberto PolicritiAbstract:The advent of Next Generation Sequencers (NGS) has driven the necessity to design new and more sophisticated tools in order to cope with the huge amount of data produced by these novel technologies. String alignment against a genome reference is the first and most important phase in every (re)-sequencing project. Recently, distributed tools able to align large amounts of sequences using clusters or clouds of computers, have been put forward. The aim of this work is to propose a new tool named mrNA (the MPI version of the original rNA program) able to align NGS data using a cluster of computers. mrNA was designed to tackle the main computational bottleneck of all classical parallel implementation of Aligners: references longer than 4 Gbp. mrNA, together with rNA, are open source programs downloadable at http://iga-rna.sourceforge.net/.
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a randomized numerical Aligner rna
Language and Automata Theory and Applications, 2010Co-Authors: Alberto Policriti, Alexandru I Tomescu, Francesco VezziAbstract:With the advent of new sequencing technologies able to produce an enormous quantity of short genomic sequences, new tools able to search for them inside a references sequence genome have emerged. Because of chemical reading errors or of the variability between organisms, one is interested in finding not only exact occurrences, but also occurrences with up to k mismatches. The contribution of this paper is twofold. On one hand, we present a generalization of the classical Rabin-Karp string matching algorithm to solve the k-mismatch problem, with average complexity $\mathcal{O}(n+m)$. On the other hand, we show how to employ this idea in conjunction with an index over the text, allowing to search a pattern, with up to k mismatches, in time proportional to its length. This novel tool—rNA (randomized Numerical Aligner)—outperforms available tools like SOAP2, BWA, and BOWTIE, processing up to 10 times more patterns per second on texts of (practically) significant lengths.
Serafim Batzoglou - One of the best experts on this subject based on the ideXlab platform.
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a hybrid cloud read Aligner based on minhash and kmer voting that preserves privacy
Nature Communications, 2017Co-Authors: Victoria Popic, Serafim BatzoglouAbstract:Low-cost clouds can alleviate the compute and storage burden of the genome sequencing data explosion. However, moving personal genome data analysis to the cloud can raise serious privacy concerns. Here, we devise a method named Balaur, a privacy preserving read mapper for hybrid clouds based on locality sensitive hashing and kmer voting. Balaur can securely outsource a substantial fraction of the computation to the public cloud, while being highly competitive in accuracy and speed with non-private state-of-the-art read Aligners on short read data. We also show that the method is significantly faster than the state of the art in long read mapping. Therefore, Balaur can enable institutions handling massive genomic data sets to shift part of their analysis to the cloud without sacrificing accuracy or exposing sensitive information to an untrusted third party. Outsourcing computation for genomic data processing offers the ability to allocate massive computing power and storage on demand. Here, Popic and Batzoglou develop a hybrid cloud Aligner for sequence read mapping that preserves privacy with competitive accuracy and speed.
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Automatic Parameter Learning for Multiple Local Network Alignment
Journal of Computational Biology, 2009Co-Authors: Jason Flannick, Balaji S. Srinivasan, Antal Novak, Chuong B Do, Serafim BatzoglouAbstract:We developed Graemlin 2.0, a new multiple network Aligner with (1) a new multi-stage approach to local network alignment; (2) a novel scoring function that can use arbitrary features of a multiple network alignment, such as protein deletions, protein duplications, protein mutations, and interaction losses; (3) a parameter learning algorithm that uses a training set of known network alignments to learn parameters for our scoring function and thereby adapt it to any set of networks; and (4) an algorithm that uses our scoring function to find approximate multiple network alignments in linear time. We tested Graemlin 2.0's accuracy on protein interaction networks from IntAct, DIP, and the Stanford Network Database. We show that, on each of these datasets, Graemlin 2.0 has higher sensitivity and specificity than existing network Aligners. Graemlin 2.0 is available under the GNU public license at http://graemlin.stanford.edu .
