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DNA Sequence alignment using programme by algorithm

DNA Sequence alignment using programme by algorithm

Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that "forces" the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem. These include slow but formally correct methods like dynamic programming. These also include efficient, heuristic algorithms or probabilistic methods designed for large-scale database search, that do not guarantee to find best matches (Nocedal and Wright, 2006).
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A Design of a Hybrid System for DNA Sequence Alignment

A Design of a Hybrid System for DNA Sequence Alignment

Many algorithms were used to solve the sequence alignment problem like Needleman-Wunsch, Smith- Waterman, BLAST and FASTA, Suffix Trees, Hirschberg’s algorithm, Four Russian Algorithm and so on [4]-[7]. For example, Needlman Wunsch algorithm is a dynamic programming based algorithm, it finds the best global alignment for two sequences [6]. The algorithm Complexity is O (n × m) for sequence lengths n and m. Also Smith-Waterman algorithm finds the optimal local alignment between two sequences using the technique of dynamic programming [7].
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Cluster Analysis Method for Multiple Sequence Alignment

Cluster Analysis Method for Multiple Sequence Alignment

Very short or very similar sequences can be aligned by hand. However, most interesting problems require the alignment of lengthy, highly variable or extremely numerous sequences that cannot be aligned solely by human effort. Instead, human knowledge is applied in constructing algorithms to produce high-quality sequence alignments, and occasionally in adjusting the final results to reflect patterns that are difficult to represent algorithmically (especially in the case of nucleotide sequences). Computational approaches to sequence alignment generally fall into two categories: global alignments and local alignments. Calculating a global alignment is a form of global optimization that “forces” the alignment to span the entire length of all query sequences. By contrast, local alignments identify regions of similarity within long sequences that are often widely divergent overall. Local alignments are often preferable, but can be more difficult to calculate because of the additional challenge of identifying the regions of similarity. A variety of computational algorithms have been applied to the sequence alignment problem, including slow but formally optimizing methods like dynamic programming, and efficient, but not as thorough heuristic algorithms or probabilistic methods designed for large-scale database search.
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Benchmarking of alignment free sequence comparison methods

Benchmarking of alignment free sequence comparison methods

unintended biases of partial comparison. To date, no com- prehensive benchmarking platform has been established for AF sequence comparison to select algorithms for dif- ferent sequence types (e.g., genes, proteins, regulatory ele- ments, or genomes) under different evolutionary scenarios (e.g., high mutability or horizontal gene transfer (HGT)). As a result, users of these methods cannot easily identify appropriate tools for the problems at hand and are instead often confused by a plethora of existing programs of un- clear applicability to their study. Finally, as for other soft- ware tools in bioinformatics, the results of most AF tools strongly depend on the specified parameter values. For many AF methods, the word length k is a crucial param- eter. Note, however, that words are used in different ways by different AF methods, so there can be no universal op- timal word length k for all AF programs. Instead, different optimal word lengths have to be identified for the different methods. In addition, best parameter values may depend on the data-analysis task at hand, for instance, whether a set of protein sequences is to be grouped into protein fam- ilies or superfamilies.
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Evaluating Sequence Alignment for Learning Inflectional Morphology

Evaluating Sequence Alignment for Learning Inflectional Morphology

high level of accuracy, creating them is difficult and requires a great deal of engineering overhead. Recently, more automatic, machine learning methods have been utilized. These systems have required far less handcrafting of rules, but also do not perform as well. Specifically, work by Durrett and DeNero (2013) exploits sequence alignment systems across strings, a technique originally de- veloped for DNA analysis. They showed that by computing minimum edit operations between two strings and having a semi-markov conditional ran- dom field (CRF) (Sarawagi and Cohen, 2004) pre- dict when wordform edits rules were to be used, a system could achieve state-of-the-art performance in completing morphological paradigms for En- glish and German.
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A Tabu Search Approach to Multiple Sequence Alignment

A Tabu Search Approach to Multiple Sequence Alignment

damaged [1]. The genetic information stored by DNA is transferred to RNA for protein synthesis. Protein synthesis is important because it accomplishes most of the functions of living cells such as catalyzing chemical reactions, providing structural support and helping protect the immune system. An organism’s genome contains its complete set of DNA. The arrangement of bases within the genome spells out specific instructions about an organism. These instructions spell out the way an organism looks, the organs it has, as well as all its biological components. In 2003, the human genome was sequenced with 3 billion base pairs. However, although the sequencing is known, there is still uncertainty with regard to the exact instructions spelled out for various subsequences within the human genome. To clear up some of the uncertainty about the genetic information within the human genome, researchers have used DNA sequencing. Researchers have discovered that DNA sequences of different organisms are often related. Also, similar genes are often conserved across divergent species and commonly perform similar or even identical functions. Thus, through sequence alignment of genes from various species, sequence patterns may be analyzed [27]. In general, DNA sequencing provides insight into the physical characteristics of an organism, the functions of proteins, the origins of diseases and possible cures for ailments.
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Phonetic Vector Representations for Sound Sequence Alignment

Phonetic Vector Representations for Sound Sequence Alignment

Most studies in computational linguistics or nat- ural language processing treat the phonetic seg- ments as categorical units, which prevents analyz- ing or exploiting the similarities or differences be- tween these units. Alignment of sound sequences, a crucial step in a number of different fields of in- quiry, is one of the tasks that suffers if the segments are treated as distinct symbols with no notion of similarity. As a result, alignment algorithms com- monly employed in practice (e.g., Needleman and Wunsch, 1970) use a scoring function based on similarity of the individual units.
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BioBCDM: A novel integrated tool in Sequence alignment

BioBCDM: A novel integrated tool in Sequence alignment

Bioinformatics is a rapid processing field. Both the experimental technologies and the computer based methods are in dynamic phase of development. While some years ago human experts would check every program output, nowadays sequence analysis routines are being applied in an automatic fashion creating annotation that is included in various databases. Many of the Bioinformatics tools exist individually and the need for integrated tool arises mainly to save time for the users and to facilitate easy pavement for the biologists to get the required output in minimal interval of time. Although the quality of many existing tools has increased dramatically, the possibility of error and in particular its perpetuation by further automatic methods exists. Certainly, the BioBCDM Tool will be an optimum tool for the biologists. As there is plenty of tool emerging in Bioinformatics, these types of integrated tools will be of great use in minimising the work. Our future work will include integrating other useful tools.
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Assessing the efficiency of multiple sequence alignment programs

Assessing the efficiency of multiple sequence alignment programs

As stated in related papers [21,22], no available MSA program outperformed all others in all test cases. For the first five reference sets, our results indicated that T-Coffee, Probcons, MAFFT and Probalign were defin- itely superior with regard to alignment accuracy in all BAliBASE datasets, consistent with similar publications [7,8,21,22]. All four programs have a consistency-based approach in their algorithms, thus being a successful im- provement in sequence alignment. Despite meeting cer- tain consistency criteria, DIALIGN-TX is based on local pairwise alignments and is known to be outperformed by global aligners [5]. Nevertheless, we observed that the consistency-based approach may not offer alone the highest quality of alignment. CLUSTAL OMEGA did well when aligning some datasets with long N/C ter- minal ends from full-length sequences (BB) and has no consistency. The presence of these non-conserved resi- dues at terminal ends, on the other hand, contributed to reduce the scores in the alignments generated by T-Coffee and Probcons, which produced the highest SP/TC scores when aligning the truncated sequences (BBS). Despite having an iterative refinement step, which could improve results, Probcons is still a global align- ment program, thus being more prone to alignment errors induced by the presence of non-conserved resi- dues at terminal ends [20]. Certainly MAFFT, Probalign and even CLUSTAL OMEGA may be preferred over T-Coffee and Probcons when aligning sequences with these long terminal extensions. The combination of it- erative refinement strategy with consistency from local alignments in MAFFT (L-INS-i method) might have contributed to prevent and correct the alignment of the full-length sequences [22]. Similarly, the suboptimal alignments (determined by variations of the Temperature parameter) generated by the partition function of Proba- lign, might as well improved the ability of this program to deal with sequences with non-conserved terminal ex- tensions [8]. Apparently, the profile HMM of long se- quences also improved the alignments produced by CLUSTAL OMEGA.
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A Sequence Alignment Model Based on the Averaged Perceptron

A Sequence Alignment Model Based on the Averaged Perceptron

We trained the edit model on both data sets us- ing both the sampling procedure outlined above and the self-generation training regime, in each case for 20 epochs, producing models of orders from 1 to 3. However, we found that the efficiency of the phrase- based SMT system described in the previous section would be limited for this task, mainly due to two reasons: the character-based phrase models due to possible unseen phrases in an evaluation corpus, and the character sequence model as all candidate tran- scriptions confidently belong to the target language. Therefore, to make the phrase-based SMT system robust against data sparseness for the ranking task, we also make use of the IBM Model 4 (Brown et al., 1993) in both directions. The experiments show that IBM Model 4 is a reliable model for the ranking task. For each evaluation pair, we then ranked all available evaluation transcriptions, recording where in this list the true transcription fell.
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Progressive Multiple Sequence Alignment with Indel Evolution

Progressive Multiple Sequence Alignment with Indel Evolution

Nowadays, the supply of multiple sequence alignment tools is impressively wide and the decision of which tool to use depends mostly on individual pref- erences, the type of data to be processed and the size of the dataset. One can identify four main strands of multiple sequence alignment purposes, that are structure prediction; database searching ; sequence comparison ; and phyloge- netic analysis requiring, underneath, different mathematical models [97, 98]. The multiple sequence alignment tools can be further classified by the im- plied inference methods as local/global alignment, using iterative refinement feature, based on sum-of-pairs (SP) optimality criterion or probabilistic (poste- rior probability or maximum likelihood). These references provide a quick link to further information: [4, 25, 32, 66, 86, 98, 107, 111, 116, 120, 144, 155, 161]. In this thesis we will focus only on evolutionary alignment that repre- sent character homology between related sequences. They, for instance, are fundamental prerequisite for phylogenetic inference [98]. Multiple sequence alignment are conventionally represented in a table where taxa are placed in succession in rows and the sequence characters are the columns of the table. In this representation a character state is obtained by intersecting row and columns [98]. Gaps are added for the correct positioning of the homologous characters – sharing a common ancestor –together in the same column. It is clear at this point that the fundamental units for an homology hypothesis are the characters which translates in the a proper gaps disposal inside the table [38]. It is worth mentioning the distinction between the information represented in the phylogeny and the one in a multiple sequence alignment. A phylogenentic tree reproduce the relationship among taxa defined by their sequences, whereas an alignment depicts in columns the relationship among characters [98]. When studying homologies we have to focus therefore on the evolutionary events on characters [98], in this sense, homology exists only if referred to phylogeny [6, 16].
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Molecular Cloning, Sequence Analysis and Expression Analysis of an NtWRKY6 from Tobacco (Nicotiana tabacum L ) in Abiotic Strsss

Molecular Cloning, Sequence Analysis and Expression Analysis of an NtWRKY6 from Tobacco (Nicotiana tabacum L ) in Abiotic Strsss

To better understand the structure of the deduced amino acid sequence encoded by tobacco WRKY6, a homolo- gous alignment was carried out with Clustal W program using the amino acid sequences of WRKY6 and 19 pro- teins (Figure 2). Sequence alignment revealed that all proteins discussed above appeared to be rather conserved in structure. These proteins all had only one WRKY structure (WRKYGQK) and the zinc-finger structure is C-X4-C-X23-H-X1-H. Additionally, the NtWRKY6 shared a very high identity of NtWRKY1 (BAA82107.1) with 97%, and lower identity of ZmWRKY53 (NP 001147949.1) and Gm WRKY2 (XP 003526383.1) with 41%.
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Identification of Structural Signatures Within Transcription Factor Binding Sites and Their Flanking Regions in Saccharomyces cerevisiae Enhanced Through ΔTRX Based Multiple Sequence Alignment

Identification of Structural Signatures Within Transcription Factor Binding Sites and Their Flanking Regions in Saccharomyces cerevisiae Enhanced Through ΔTRX Based Multiple Sequence Alignment

  This   analysis   is   supported   by   two   different   alignment   methods:   center   and   ∆TRX  based  multiple  sequence  alignment.  While  most  sequence  alignment  methods   thus  far  have  focused  solely  on  the  DNA,  RNA  or  amino  acid  sequence  level  by  only   taking   into   account   the   composition   of   the   individual   entities,   few   approaches   to   date  rely  on  other  underlying  structural  features.  Similar  to  the  approach  taken  by   Salama  and  Stekel  [25],  who  used  base  stacking  free  energies,  we  will  use  change  in   TRX   (ΔTRX)   measures   to   guide   the   alignment   of   TFBSs.   Instead   of   comparing   sequences   based   on   their   single   nucleotide   properties,   ∆TRX   based   multiple   sequence  alignment  aligns  the  given  sequences  based  on  the  characteristics  of  their   DNA   backbone   phosphate   links   as   captured   by   the   TRX   score.   By   aligning   the   sequences   based   on   their   structural   properties   we   hope   to   better   highlight   their   shared   characteristic   structural   signatures.   This   method   of   alignment   will   then   be   contrasted  to  the  more  commonly  used  center  alignment  of  TFBSs.      
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A study of plasmid biology in Bacillus thuringiensis subspecies kurstaki HD1 Dipel

A study of plasmid biology in Bacillus thuringiensis subspecies kurstaki HD1 Dipel

Most sequencing protocols require the use of single-stranded template DNA for use in the reactions. The vector of choice in the cloning of DNA fragments for sequencing is the E. coli bacteriophage M13. M13 is a single-stranded DNA phage and, following infection, the M13 ( + ) strand DNA serves as a template for the synthesis of the complementary (-) strand. The phage is easily purified from the supernatants of infected E. coli cultures and is an ideal source of single-stranded DNA which may then be u sed in the sequencing reactions. M13 DNA has been modified (Messing e t al., 1977) to generate a s eries of derivatives known as the M13mp vectors w h ich allow a number of sequencing strategies to be easily followed. Using this system, vectors carrying an insert are identifiable u s ing a blue/white colour screen as previously described for the pUC series of vectors (Section 3.4). More recent developments have enabled sequencing reactions to be carried out directly from clones in double-stranded plasmids (Chen and Seeburg, 1985) although M13 remains the vector of choice and pieces of DNA are often subcloned into this vector for sequencing. In this instance, the use of a plasmid-based sequencing strategy using the pUC series of vectors was not thought to be a viable proposition as difficulties had previously been encountered possibly resulting from the high copy number of these vectors or high levels of polypeptide expression from them. on denaturing polyacrylamide gels and visualization b y autoradiography allows the sequence downstream of the polylinker to be determined.
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Bioinformatics Analysis of the Novel Conserved Micropeptides Encoded by the Plants of Family Brassicaceae

Bioinformatics Analysis of the Novel Conserved Micropeptides Encoded by the Plants of Family Brassicaceae

Recently, we drew attention to the interesting fact that chemically synthesized miR156a encoded by plants of genus Brassica represses the epithelial–mesenchymal transition of human nasopharyngeal cancer cells and can be regarded as the main medicinal anticancer substance in broccoli assuming the ability of plant miRNAs to pass through the gastrointestinal tract of mammals [29]. Striking similarities in general phenotypic activities between human miR200a/b and plant miR156a suggested us that a comparison with the coding potential between the corresponding pri-miRNAs could identify parallels in the encoded miPEPs. We show here that more than 20 genomes of genus Brassica encode miPEPs of 33 amino acids with ORFs positioned in the 5’-proximal regions of pri-miR156a. Although these plant micropeptides demonstrate no sequence similarity to miPEPs-200a/b, their striking homology throughout plant species of whole family Brassicaceae suggests functional significance of miPEP-156a.
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c17348582d5590d3ee52c55bfd8117f09e8658e5.pdf

c17348582d5590d3ee52c55bfd8117f09e8658e5.pdf

phenotype, genotype, and predicted impact on protein function. Each variant has to meet specific quality control thresholds determined by the laboratory. In terms of functional annotation, protein-altering variants, including truncating variants (stop gain/loss, start loss, or frameshift), missense variants, canonical splice-site variants, and vari- ants within the intron-exon boundary (flanking the exonic boundaries) predicted to affect splicing are prioritized, followed by other types of variants such as silent and in- frame indels affecting protein-coding regions. A systematic analysis of the stratified European and African Americans in the Exome Variant Server and Broad Institute Exome Aggregation Consortium databases can be used for deter- mining minor allele frequency estimations to establish (1) germline de novo mutations, also absent in the available control populations (extended to include mitochondrial DNA sequence by requiring mother to have only the reference allele while the patient has only the mutant allele); (2) recessive homozygous genotypes, which are heterozy- gous in both parents, never homozygous in controls, with a control allele frequency of less than 0.5%; (3) hemizygous X chromosome variants inherited from an unaffected hetero- zygous mother, with a control allele frequency of less than 1% and never observed in male controls or homozygous in female controls; and (4) compound heterozygous genotypes in the patient (1 variant inherited from each heterozygous parent, with the 2 variants occurring at different genomic positions within the same gene), for which neither variant is ever homozygous in controls, and each has a control allele frequency of less than 1%. For the compound heterozygous genotypes, trio analysis facilitates variant phasing.
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STRUCTURE PREDICTION TO ANALYSE GENETIC ALTERATION IMPLEMENTING Q MEAN VARIATION ANALYSIS AND PHYLOGENETIC TREE CONSTRUCTION IN PREDIABETIC AND DIABETIC SUBJECT

STRUCTURE PREDICTION TO ANALYSE GENETIC ALTERATION IMPLEMENTING Q MEAN VARIATION ANALYSIS AND PHYLOGENETIC TREE CONSTRUCTION IN PREDIABETIC AND DIABETIC SUBJECT

Protein Structure Prediction is the process of prediction of the three dimensional structure of a protein from its amino acid sequence. It is the inference of the three- dimensional structure of a protein from its amino acid sequence—that is, the prediction of its folding and its secondary and tertiary structure from its primary structure. Structure prediction is fundamentally different from the inverse problem of protein design. Protein structure prediction is one of the most important goals pursued by bioinformatics and theoretical chemistry; it is highly important in medicine (for example, in drug design) and biotechnology (for example, in the design of novel enzymes). The QMEAN Z-score in the provides an estimate of the "degree of nativeness" of the structural features observed in the model on a global scale. QMEAN Z-scores around zero (0) indicate good agreement between the model structure and experimental structures of similar size. Scores of -4.0 or below are an indication of models with low quality [Graph: 1]. Phylogenetic tree
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Phylogenetic analysis of S1 gene of infectious bronchitis virus reveals emergence of new genotype

Phylogenetic analysis of S1 gene of infectious bronchitis virus reveals emergence of new genotype

antibodies in chickens. Evolution of new genotype is primarily associated with the alteration in S1 protein sequence (Kant et al. 1992, Cavanagh et al. 1988). Therefore characterization of IBV is mainly based on the analysis of the variable S1 gene or the expressed S1 protein (Fellahi et al. 2015, Lee et al. 2003). Several IBV variants are distributed globally. More than 20 IBV serotypes are differentiated worldwide that evolved from genomic insertions, deletions, substitutions and/or RNA recombinations of S1 gene (Alvarado et al. 2005, Gelb et al. 1991). This large diversity is the actual cause of vaccine failure or partial efficacy of vaccine and hence new outbreaks reported regularly (Cavanagh 2003). All above facts make the S1 gene most suitable candidate for viral characterization, serotyping, immunological studies, host-virus interaction studies etc. Therefore in this experiment the S1 gene of isolate was sequenced and is characterized to identify its relationship to reference IBV strains by nucleotide sequence analysis.
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A Deep Ensemble Model with Slot Alignment for Sequence to Sequence Natural Language Generation

A Deep Ensemble Model with Slot Alignment for Sequence to Sequence Natural Language Generation

In this paper we presented our ensemble atten- tional encoder-decoder model for generating natu- ral utterances from MRs. Moreover, we presented novel methods of representing the MRs to improve performance. Our results indicate that the pro- posed utterance splitting applied to the training set greatly improves the neural model’s accuracy and ability to generalize. The ensembling method paired with the reranking based on slot alignment also contributed to the increase in quality of the generated utterances, while minimizing the num- ber of slots that are not realized during the genera- tion. This also enables the use of a less aggressive delexicalization, which in turn stimulates diversity in the produced utterances.
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Evidence that the intra amoebal Legionella drancourtii acquired a sterol reductase gene from eukaryotes

Evidence that the intra amoebal Legionella drancourtii acquired a sterol reductase gene from eukaryotes

Among the 4,386 L. drancourtii ORFs, 262 putative pro- teins showed significant alignment with C. "P. amoe- bophila", including two for which this species was the best match. These included a hypothetical Zn-dependent hydrolase of the beta-lactamase fold (data not shown) and a 7-dehydrocholesterol reductase (also referred to as sterol delta-7 reductase [GenBank:FJ197317]) (Table 1). We focused on the sterol reductase because the ten best matches were distributed into two groups: i) a prokaryotic group composed of the two amoeba-associated bacteria L. drancourtii and Candidatus "P. amoebophila" and the obli- gate intracellular agent of Q fever, Coxiella burnetii (C. bur- netii), which is able to survive in Acanthamoeba castellanii [10], and ii) a eukaryotic group composed of viridiplan- tae, fungi and metazoa (Table 1).
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