Pairwise alignment

Ebook "Introduction to computational biology: An evolutionary approach" Analysis of molecular sequence data is the main subject of this introduction to computational biology. There are two closely connected aspects to biological sequences: (i) their relative position in the space of all other sequences, and (ii) their movement through this sequence space in evolutionary time. Accordingly, the first part of the book deals with classical methods of sequence analysis: pairwise alignment, exact string matching, multiple alignment, and hidden Markov models.

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Ebook "Theory and mathematical methods for bioinformatics" on biomathematics published within the past ten years have consisted of collections of standard bioinformatics problems and informational methods, and focus mainly on the logistics of implementing and making use of various websites, databases, software packages and serving platforms. While these types of books do introduce some mathematical and computational methods alongside the software packages, they are lacking in a systematic and professional treatment of the mathematics behind these methods.

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Ebook "Developing bioinformatics computer skills" includes content: Audience for this book, biology in the computer age, computational approaches to biological questions, setting up your workstation, files and directories in unix, working on a unix system, biological research on the web, sequence analysis, pairwise alignment, and database searching,...and other contents.

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Pairwise sequence alignment is widely used in many biological tools and applications. Existing GPU accelerated implementations mainly focus on calculating optimal alignment score and omit identifying the optimal alignment itself. In GATK HaplotypeCaller (HC), the semi-global pairwise sequence alignment with traceback has so far been difficult to accelerate effectively on GPUs.

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Whole-genome approaches are widely preferred for species delineation in prokaryotes. However, these methods require pairwise alignments and calculations at the whole-genome level and thus are computationally intensive. To address this problem, a strategy consisting of sieving (pre-selecting closely related genomes) followed by alignment and calculation has been proposed.

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The maximal sensitivity for local alignments makes the Smith-Waterman algorithm a popular choice for protein sequence database search based on pairwise alignment. However, the algorithm is compute-intensive due to a quadratic time complexity. Corresponding runtimes are further compounded by the rapid growth of sequence databases.

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Protein sequence profile-profile alignment is an important approach to recognizing remote homologs and generating accurate pairwise alignments. It plays an important role in protein sequence database search, protein structure prediction, protein function prediction, and phylogenetic analysis.

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Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate.

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Comparing and aligning genomes is a key step in analyzing closely related genomes. Despite the development of many genome aligners in the last 15 years, the problem is not yet fully resolved, even when aligning closely related bacterial genomes of the same species.

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Sequence alignment algorithms are a key component of many bioinformatics applications. Though various fast Smith-Waterman local sequence alignment implementations have been developed for x86 CPUs, most are embedded into larger database search tools.

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Computing alignments between two or more sequences are common operations frequently performed in computational molecular biology. The continuing growth of biological sequence databases establishes the need for their efficient parallel implementation on modern accelerators.

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Many computational tools that detect recombination in viruses are not adapted for the ongoing genomic revolution. A computational tool is needed, that will rapidly scan hundreds/thousands of genomes or sequence fragments and detect candidate recombination events that may later be further analyzed with more sensitive and specialized methods.

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The incorporation of alignment-free features in supervised big data models did not significantly improve ortholog detection in yeast proteomes regarding the classification qualities achieved with just alignment-based similarity measures.

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Sequence alignment is crucial in genomics studies. However, optimal multiple sequence alignment (MSA) is NP-hard. Thus, modern MSA methods employ progressive heuristics, breaking the problem into a series of pairwise alignments guided by a phylogeny.

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Pairwise alignment of short DNA sequences with affine-gap scoring is a common processing step performed in a range of bioinformatics analyses. Dynamic programming (i.e. Smith-Waterman algorithm) is widely used for this purpose.

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An attempt has been made to determine the phylogenetic relationship to trace out the evolutionary pattern of the test sequence of Xiphinema pachydermum partial 28S rRNA gene and to find out relationship of the same with other selected sequences of NCBI by constructing phylogenetic trees. Nucleotide sequence of 28S ribosomal RNA gene of Xiphinema pachydermum from Solan, Himachal Pradesh (AM779749.1) showed maximum homology of 93% with Xiphinema inaequale isolate HP 28S large subunit ribosomal RNA gene, partial sequence (HM163210.1) from Baijnath, Himachal Pradesh.

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Large-scale comparative studies of DNA fingerprints prefer automated chip capillary electrophoresis over conventional gel planar electrophoresis due to the higher precision of the digitalization process. However, the determination of band sizes is still limited by the device resolution and sizing accuracy. Band matching, therefore, remains the key step in DNA fingerprint analysis.

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Chương này gồm có những nội dung chính sau: Giới thiệu tóm tắt về dự án Human Genome; bài toán sequence alignment: Các vấn đề cần giải quyết, scoring system, lập trình động cho vấn đề pairwise alignment; bài toán local sequence alignment. Mời tham khảo.

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This paper describes a novel method for computing a consensus translation from the outputs of multiple machine translation (MT) systems. The outputs are combined and a possibly new translation hypothesis can be generated. Similarly to the well-established ROVER approach of (Fiscus, 1997) for combining speech recognition hypotheses, the consensus translation is computed by voting on a confusion network.

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In this paper, we proposed a new supervised word sense disambiguation (WSD) method based on a pairwise alignment technique, which is used generally to measure a similarity between DNA sequences. The new method obtained 2.8%-14.2% improvements of the accuracy in our experiment for WSD.

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