Journal articles: 'Sequence selection'

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Zhou, Haobo, G. Jonah Rainey, Swee-Kee Wong, and John M. Coffin. "Substrate Sequence Selection by Retroviral Integrase." Journal of Virology 75, no. 3 (February 1, 2001): 1359–70. http://dx.doi.org/10.1128/jvi.75.3.1359-1370.2001.

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ABSTRACT Integration of retrovirus DNA is a specific process catalyzed by the integrase protein acting to join the viral substrate DNA (att) sequences of about 10 bases at the ends of the long terminal repeat (LTR) to various sites in the host target cell DNA. Although the interaction is sequence specific, the att sequences of different retroviruses are largely unrelated to one another and usually differ between the two ends of the viral DNA. To define substrate sequence specificity, we designed an “in vitro evolution” scheme to select an optimal substrate sequence by competitive integration in vitro from a large pool of partially randomized substrates. Integrated substrates are enriched by PCR amplification and then regenerated and subjected to subsequent cycles of selection and enrichment. Using this approach, we obtained the optimal substrate sequence of 5′-ACGACAACA-3′ for avian sarcoma-leukosis virus (ASLV) and 5′-AACA(A/C)AGCA-3′ for human immunodeficiency virus type 1, which differed from those found at both ends of the viral DNA. Clonal analysis of the integration products showed that ASLV integrase can use a wide variety of substrate sequences in vitro, although the consensus sequence was identical to the selected sequence. By a competition assay, the selected nucleotide at position 4 improved the in vitro integration efficiency over that of the wild-type sequence. Viral mutants bearing the optimal sequence replicated at wild-type levels, with the exception of some mutations disrupting the U5 RNA secondary structure important for reverse transcription, which were significantly impaired. Thus, maximizing the efficiency of integration may not be of major importance for efficient retrovirus replication.

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Supina, Jaroslav. "On sequence selection properties." Filomat 27, no. 8 (2013): 1523–44. http://dx.doi.org/10.2298/fil1308523s.

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Wattis, Jonathan A. D., and Peter V. Coveney. "Sequence Selection during Copolymerization." Journal of Physical Chemistry B 111, no. 32 (August 2007): 9546–62. http://dx.doi.org/10.1021/jp071767h.

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ARLOTTO, ALESSANDRO, and J. MICHAEL STEELE. "Optimal Sequential Selection of a Unimodal Subsequence of a Random Sequence." Combinatorics, Probability and Computing 20, no. 6 (October 5, 2011): 799–814. http://dx.doi.org/10.1017/s0963548311000411.

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We consider the problem of selecting sequentially a unimodal subsequence from a sequence of independent identically distributed random variables, and we find that a person doing optimal sequential selection does so within a factor of the square root of two as well as a prophet who knows all of the random observations in advance of any selections. Our analysis applies in fact to selections of subsequences that have d+1 monotone blocks, and, by including the case d=0, our analysis also covers monotone subsequences.

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Litviņenko, Anna, and Artūrs Āboltiņš. "Computationally Efficient Chaotic Spreading Sequence Selection for Asynchronous DS-CDMA." Electrical, Control and Communication Engineering 13, no. 1 (December 1, 2017): 75–80. http://dx.doi.org/10.1515/ecce-2017-0011.

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Abstract The choice of the spreading sequence for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems plays a crucial role for the mitigation of multiple-access interference. Considering the rich dynamics of chaotic sequences, their use for spreading allows overcoming the limitations of the classical spreading sequences. However, to ensure low cross-correlation between the sequences, careful selection must be performed. This paper presents a novel exhaustive search algorithm, which allows finding sets of chaotic spreading sequences of required length with a particularly low mutual cross-correlation. The efficiency of the search is verified by simulations, which show a significant advantage compared to non-selected chaotic sequences. Moreover, the impact of sequence length on the efficiency of the selection is studied.

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Rowland, Lee A., and David R. Shanks. "Sequence learning and selection difficulty." Journal of Experimental Psychology: Human Perception and Performance 32, no. 2 (2006): 287–99. http://dx.doi.org/10.1037/0096-1523.32.2.287.

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Bukovský, Lev, and Jaroslav Šupina. "Modifications of sequence selection principles." Topology and its Applications 160, no. 18 (December 2013): 2356–70. http://dx.doi.org/10.1016/j.topol.2013.07.030.

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SHAN, YING, HARPREET S. SAWHNEY, and ART POPE. "CLUSTERING MULTIPLE IMAGE SEQUENCES WITH A SEQUENCE-TO-SEQUENCE SIMILARITY MEASURE." International Journal of Pattern Recognition and Artificial Intelligence 19, no. 04 (June 2005): 551–64. http://dx.doi.org/10.1142/s0218001405004149.

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We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-to-sequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter, and 2D pose variations. We define sequence similarity measure as the similarity of the most similar pair of images from both sequences. This definition maximizes the chance of matching between two sequences of the same object, because it requires only part of the sequences being similar. We also introduce a weighting scheme to conduct an implicit feature selection process during the matching of two shapeme histograms. Experiments on clustering image sequences of tracked objects demonstrate the efficacy of the proposed method.

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Feng, Chunyu, Yuting Liu, Guangqi Lyu, Songyang Shang, Hongyue Xia, Junpeng Zhang, David M. Irwin, Zhe Wang, and Shuyi Zhang. "Adaptive Evolution of the Fox Coronavirus Based on Genome-Wide Sequence Analysis." BioMed Research International 2022 (April 13, 2022): 1–8. http://dx.doi.org/10.1155/2022/9627961.

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Purpose. To report the first complete fox coronavirus (CoV) genome sequence obtained through genome-wide amplifications and to understand the adaptive evolution of fox CoV. Methods. Anal swab samples were collected from 35 foxes to detect the presence of CoV and obtain the virus sequence. Phylogenetic analysis was conducted using MrBayes. The possibility of recombination within these sequences was assessed using GARD. Analysis of the levels of selection pressure experienced by these sequences was assessed using methods on both the PAML and Data Monkey platforms. Results. Of the 35 samples, two were positive, and complete genome sequences for the viruses were obtained. Phylogenetic analysis, using Bayesian methods, of these sequences, together with other CoV sequences, revealed that the fox CoV sequences clustered with canine coronavirus (CCoV) sequences, with sequences from other carnivores more distantly related. In contrast to the feline, ferret and mink CoV sequences that clustered into species-specific clades, the fox CoV fell within the CCoV clade. Minimal evidence for recombination was found among the sequences. A total of 7, 3, 14, and 2 positively selected sites were identified in the M, N, S, and 7B genes, respectively, with 99, 111, and 581 negatively selected sites identified in M, N, and S genes, respectively. Conclusion. The complete genome sequence of fox CoV has been obtained for the first time. The results suggest that the genome sequence of fox CoV may have experienced adaptive evolution in the genes replication, entry, and virulence. The number of sites in each gene that experienced negative selection is far greater than the number that underwent positive selection, suggesting that most of the sequence is highly conserved and important for viral survive. However, positive selection at a few sites likely aided these viruses to adapt to new environments.

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Chuzhanova, N. A., A. J. Jones, and S. Margetts. "Feature selection for genetic sequence classification." Bioinformatics 14, no. 2 (March 1, 1998): 139–43. http://dx.doi.org/10.1093/bioinformatics/14.2.139.

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Meyerguz, Leonid, Catherine Grasso, Jon Kleinberg, and Ron Elber. "Computational Analysis of Sequence Selection Mechanisms." Structure 12, no. 4 (April 2004): 547–57. http://dx.doi.org/10.1016/j.str.2004.02.018.

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Ostmeyer, Jared L., Lindsay Cowell, Benjamin Greenberg, and Scott Christley. "Reconstituting T Cell Receptor Selection In-Silico." Journal of Immunology 206, no. 1_Supplement (May 1, 2021): 98.02. http://dx.doi.org/10.4049/jimmunol.206.supp.98.02.

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Abstract Each T cell receptor (TCR) gene is created without regard for which substances (antigens) the receptor can recognize. T cell selection culls developing T cells when their TCRs (i) fail to recognize major histocompatibility complexes (MHCs) that act as antigen presenting platforms or (ii) recognize with high affinity self-antigens derived from healthy cells and tissue. Here, we show that we can accurately predict from the TCR if any T cell is pre- or post-selection (and by extension if that T cell would be culled). First, we analyze millions of TCR genes sequenced from mouse spleen using high-throughput receptor sequencing. To get examples of TCRs pre-selection, we develop a way to translate non-productive TCR genes that otherwise cannot express protein, obtaining candidate TCR protein sequences never subjected to T cell selection. To get examples of TCRs post-selection, we translate productive TCR genes sequenced from a peripheral location such as spleen, obtaining TCR protein sequences representing T cells that survived T cell selection. Next, we train a machine learning model to find sequence level patterns that discriminate between these two categories of TCR protein sequences, achieving a balanced classification accuracy of 73% (AUC of 0.8) on de-novo TCR β-chain protein sequences. This classification accuracy may be near the achievable limit using only the β-chain representing half of the TCR heterodimer. To verify our approach, we show our machine learning model accurately predicts that (i) productive TCRs from thymus (where T selection occurs) are predominantly pre-selection and (ii) productive TCRs from peripheral tissues like skin are post-selection.

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van Lambalgen, Michiel. "Von Mises' definition of random sequences reconsidered." Journal of Symbolic Logic 52, no. 3 (September 1987): 725–55. http://dx.doi.org/10.1017/s0022481200029728.

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AbstractWe review briefly the attempts to define random sequences (§0). These attempts suggest two theorems: one concerning the number of subsequence selection procedures that transform a random sequence into a random sequence (§§1–3 and 5); the other concerning the relationship between definitions of randomness based on subsequence selection and those based on statistical tests (§4).

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Huyen, Do Thi, Nguyen Minh Giang, Nguyen Thu Nguyet, and Truong Nam Hai. "Probe design for mining and selection of genes coding endo 1- 4 xylanase from dna metagenome data." TAP CHI SINH HOC 40, no. 1 (January 25, 2018): 39–50. http://dx.doi.org/10.15625/0866-7160/v40n1.9200.

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According to the CAZY classification, endo 1- 4 xylanase belongs to GH 5, 8, 10, 11, 30, 51, 98. However only 03 sequences of GH8, 27 sequences of GH10, 18 sequence of GH11, only one sequence of each GH30 and GH51 from CAZy and NCBI database were thouroughly experimentally studied for biological activity and characteristics of the enzyme. Through the collected sequences, two probes for endo 1- 4 xylanase of GH10 and GH11 were designed, based on the sequence homology. The GH10 probe was 338 amino acids lenghth contained all the conserved amino acid residues (16 conserved residues in all sequences, 13 residues similar in almost sequences, 14 residues conserved in many sequences) with the lowest maxscore of 189, coverage of 88% and identity of 39%. The GH11 probe was 204 amino acids contained all the conserved amino acid residues (54 conserved residues were identity in all sequences, 25 residues similar in almost sequences, 24 residues conserved in many sequences) with the lowest maxscore of 165, coverage of 84% and identity of 50%. Using the two probes, we mined only one sequence (GL0018509) for endo 1- 4 xylanase from metagenomic DNA data of free-living bacteria in Coptotermes termite gut. Prediction of three-dimention structure of GL0018509 sequence by Phyre2 and Swiss Prot showed that this sequence was high similarity (95% by Phyre2 and 93,4% by Swiss Prot) with endo 1- 4 xylanase with the 100% confidence.

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Bahubalendruni, M. V. A. Raju, B. B. V. L. Deepak, and Bibhuti Bhusan Biswal. "An advanced immune based strategy to obtain an optimal feasible assembly sequence." Assembly Automation 36, no. 2 (April 4, 2016): 127–37. http://dx.doi.org/10.1108/aa-10-2015-086.

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Purpose The purpose of this study is to develop an intelligent methodology to find out an optimal feasible assembly sequence while considering the assembly predicates. Design/methodology/approach This proposed study is carried out by using two artificial immune system-based models, namely, Bone Marrow Model and Negative Selection Algorithms, to achieve the following objectives: to obtain the possible number of assembly sequences; to obtain the feasible assembly sequences while considering different assembly predicates; and to obtain an optimal feasible assembly sequence. Findings Proposed bone-marrow model determines the possible assembly sequences to ease the intricacy of the problem formulation. Further evaluation has been carried out through negative-selection censoring and monitoring models. These developed models reduce the overall computational time to determine the optimal feasible assembly sequence. Originality/value In this paper, the novel and efficient strategies based on artificial immune system have been developed and proposed to obtain all valid assembly sequences and optimized assembly sequence for a given assembled product using assembly attributes. The introduced methodology has proven its effectiveness in achieving optimal assembly sequence with less computational time.

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Inhoff, Albrecht W., and Kelly Shindler. "Selection for fixation and selection for orthographic processing need not coincide." Behavioral and Brain Sciences 26, no. 4 (August 2003): 489–90. http://dx.doi.org/10.1017/s0140525x03340105.

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The E-Z Reader model assumes that the parafoveal selection for fixation and the subsequent selection for attention allocation encompass the same spatially distinct letter cluster. Recent data suggest, however, that an individual letter sequence is selected for fixation and that more than one letter sequence can be selected for attention allocation (processing).

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Day, Christopher M., and A. M. Tahsin Emtenan. "Impact of Phase Sequence on Cycle Length Resonance." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 11 (June 14, 2019): 398–408. http://dx.doi.org/10.1177/0361198119852069.

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The concept of resonant cycle length, that there are certain cycle lengths that may provide excellent progression owing to corridor geometry and other factors, has some currency as a potential strategy for cycle length selection. Past studies have identified resonant cycles under certain conditions and demonstrated benefits from use of the strategy as a means of selecting cycle length. The present study revisited the concept in application to flow-based models of traffic signal performance, highlighting the impact of phase sequence optimization. The phenomenon of cycle length resonance was explored for corridors with equal and randomly generated spacing between intersections, and finally for a field-calibrated corridor. Under each scenario, the performance of different cycle lengths was explored under two optimization strategies: optimization of only offsets, and optimization of both offsets and phase sequence. It was found that phase sequence has a substantial impact on the performance of coordination. Optimized phase sequences were found to yield 8% to 14% improvement in performance compared with use of the default sequence. For corridors where a resonant cycle length was evident, when phase sequences could also be adjusted, the poorer performance of non-resonant cycle lengths could be mitigated by optimizing phase sequence. Although use of a resonant cycle length is likely to yield good performance for some corridors under appropriate conditions, the use of a phase sequence optimization strategy is likely to have a strong impact on most corridors, and could be more impactful than selection of a resonant cycle length.

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Schaal, Thomas D., and Tom Maniatis. "Selection and Characterization of Pre-mRNA Splicing Enhancers: Identification of Novel SR Protein-Specific Enhancer Sequences." Molecular and Cellular Biology 19, no. 3 (March 1, 1999): 1705–19. http://dx.doi.org/10.1128/mcb.19.3.1705.

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ABSTRACT Splicing enhancers are RNA sequences required for accurate splice site recognition and the control of alternative splicing. In this study, we used an in vitro selection procedure to identify and characterize novel RNA sequences capable of functioning as pre-mRNA splicing enhancers. Randomized 18-nucleotide RNA sequences were inserted downstream from a Drosophila doublesex pre-mRNA enhancer-dependent splicing substrate. Functional splicing enhancers were then selected by multiple rounds of in vitro splicing in nuclear extracts, reverse transcription, and selective PCR amplification of the spliced products. Characterization of the selected splicing enhancers revealed a highly heterogeneous population of sequences, but we identified six classes of recurring degenerate sequence motifs five to seven nucleotides in length including novel splicing enhancer sequence motifs. Analysis of selected splicing enhancer elements and other enhancers in S100 complementation assays led to the identification of individual enhancers capable of being activated by specific serine/arginine (SR)-rich splicing factors (SC35, 9G8, and SF2/ASF). In addition, a potent splicing enhancer sequence isolated in the selection specifically binds a 20-kDa SR protein. This enhancer sequence has a high level of sequence homology with a recently identified RNA-protein adduct that can be immunoprecipitated with an SRp20-specific antibody. We conclude that distinct classes of selected enhancers are activated by specific SR proteins, but there is considerable sequence degeneracy within each class. The results presented here, in conjunction with previous studies, reveal a remarkably broad spectrum of RNA sequences capable of binding specific SR proteins and/or functioning as SR-specific splicing enhancers.

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Arlotto, Alessandro, Robert W. Chen, Lawrence A. Shepp, and J. Michael Steele. "Online Selection of Alternating Subsequences from a Random Sample." Journal of Applied Probability 48, no. 4 (December 2011): 1114–32. http://dx.doi.org/10.1239/jap/1324046022.

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We consider sequential selection of an alternating subsequence from a sequence of independent, identically distributed, continuous random variables, and we determine the exact asymptotic behavior of an optimal sequentially selected subsequence. Moreover, we find (in a sense we make precise) that a person who is constrained to make sequential selections does only about 12 percent worse than a person who can make selections with full knowledge of the random sequence.

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Arlotto, Alessandro, Robert W. Chen, Lawrence A. Shepp, and J. Michael Steele. "Online Selection of Alternating Subsequences from a Random Sample." Journal of Applied Probability 48, no. 04 (December 2011): 1114–32. http://dx.doi.org/10.1017/s0021900200008652.

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We consider sequential selection of an alternating subsequence from a sequence of independent, identically distributed, continuous random variables, and we determine the exact asymptotic behavior of an optimal sequentially selected subsequence. Moreover, we find (in a sense we make precise) that a person who is constrained to make sequential selections does only about 12 percent worse than a person who can make selections with full knowledge of the random sequence.

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Djurcic, Dragan, Malisa Zizovic, and Aleksandar Petojevic. "Note on selection principles of Kocinac." Filomat 26, no. 6 (2012): 1291–95. http://dx.doi.org/10.2298/fil1206291d.

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This paper investigates ?di, i? {2,3,4}, selection principles (which are modification of known selection principles of Kocinac) on a double sequence of double sequences of real numbers which converge to a point a?R in Pringsheim?s sense. A stronger result than one given in [6] will be proved for the ?d2 selection principle. Also, two more propositions will be proved for the Sd1 and S?1 selection principles, which 11 are also improvements of results given in [6].

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Xu, Jian, Barbara C. McCabe, and Gerald B. Koudelka. "Function-Based Selection and Characterization of Base-Pair Polymorphisms in a Promoter of Escherichia coli RNA Polymerase-ς70." Journal of Bacteriology 183, no. 9 (May 1, 2001): 2866–73. http://dx.doi.org/10.1128/jb.183.9.2866-2873.2001.

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ABSTRACT We performed two sets of in vitro selections to dissect the role of the −10 base sequence in determining the rate and efficiency with which Escherichia coli RNA polymerase-ς70forms stable complexes with a promoter. We identified sequences that (i) rapidly form heparin-resistant complexes with RNA polymerase or (ii) form heparin-resistant complexes at very low RNA polymerase concentrations. The sequences selected under the two conditions differ from each other and from the consensus −10 sequence. The selected promoters have the expected enhanced binding and kinetic properties and are functionally better than the consensus promoter sequence in directing RNA synthesis in vitro. Detailed analysis of the selected promoter functions shows that each step in this multistep pathway may have different sequence requirements, meaning that the sequence of a strong promoter does not contain the optimal sequence for each step but instead is a compromise sequence that allows all steps to proceed with minimal constraint.

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D’Souza, Roshan M., Paul K. Wright, and Carlo Se´quin. "Handling Tool Holder Collision in Optimal Tool Sequence Selection for 2.5-D Pocket Machining." Journal of Computing and Information Science in Engineering 2, no. 4 (December 1, 2002): 345–49. http://dx.doi.org/10.1115/1.1559154.

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Significant cycle time saving can be achieved in 2.5-D milling by intelligently selecting tool sequences. The problem of finding the optimal tool sequence was formulated as finding the shortest path in a single-source single-sink directed acyclic graph. The nodes in the graph represented the state of the stock after the tool named in the node was done machining and the edges represented the cost of machining. In this paper a novel method for handling tool holder collision in the graph-based algorithm for optimal tool sequence selection has been developed. The method consists of iteratively solving the graph for the shortest path, validating the solution by checking for tool holder collisions and eliminating problematic edges in the graph. Also described is a method to reduce the complexity of building the tool sequence graph in case there are tool holder collisions.

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Dragoi, George, and Susumu Tonegawa. "Selection of preconfigured cell assemblies for representation of novel spatial experiences." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1635 (February 5, 2014): 20120522. http://dx.doi.org/10.1098/rstb.2012.0522.

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Internal representations about the external world can be driven by the external stimuli or can be internally generated in their absence. It has been a matter of debate whether novel stimuli from the external world are instructive over the brain network to create de novo representations or, alternatively, are selecting from existing pre-representations hosted in preconfigured brain networks. The hippocampus is a brain area necessary for normal internally generated spatial–temporal representations and its dysfunctions have resulted in anterograde amnesia, impaired imagining of new experiences, and hallucinations. The compressed temporal sequence of place cell activity in the rodent hippocampus serves as an animal model of internal representation of the external space. Based on our recent results on the phenomenon of novel place cell sequence preplay, we submit that the place cell sequence of a novel spatial experience is determined, in part, by a selection of a set of cellular firing sequences from a repertoire of existing temporal firing sequences in the hippocampal network. Conceptually, this indicates that novel stimuli from the external world select from their pre-representations rather than create de novo our internal representations of the world.

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Oliphant, A. R., C. J. Brandl, and K. Struhl. "Defining the sequence specificity of DNA-binding proteins by selecting binding sites from random-sequence oligonucleotides: analysis of yeast GCN4 protein." Molecular and Cellular Biology 9, no. 7 (July 1989): 2944–49. http://dx.doi.org/10.1128/mcb.9.7.2944-2949.1989.

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We describe a new method for accurately defining the sequence recognition properties of DNA-binding proteins by selecting high-affinity binding sites from random-sequence DNA. The yeast transcriptional activator protein GCN4 was coupled to a Sepharose column, and binding sites were isolated by passing short, random-sequence oligonucleotides over the column and eluting them with increasing salt concentrations. Of 43 specifically bound oligonucleotides, 40 contained the symmetric sequence TGA(C/G)TCA, whereas the other 3 contained sequences matching six of these seven bases. The extreme preference for this 7-base-pair sequence suggests that each position directly contacts GCN4. The three nucleotide positions on each side of this core heptanucleotide also showed sequence preferences, indicating their effect on GCN4 binding. Interestingly, deviations in the core and a stronger sequence preference in the flanking region were found on one side of the central C . G base pair. Although GCN4 binds as a dimer, this asymmetry supports a model in which interactions on each side of the binding site are not equivalent. The random selection method should prove generally useful for defining the specificities of other DNA-binding proteins and for identifying putative target sequences from genomic DNA.

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Oliphant, A. R., C. J. Brandl, and K. Struhl. "Defining the sequence specificity of DNA-binding proteins by selecting binding sites from random-sequence oligonucleotides: analysis of yeast GCN4 protein." Molecular and Cellular Biology 9, no. 7 (July 1989): 2944–49. http://dx.doi.org/10.1128/mcb.9.7.2944.

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We describe a new method for accurately defining the sequence recognition properties of DNA-binding proteins by selecting high-affinity binding sites from random-sequence DNA. The yeast transcriptional activator protein GCN4 was coupled to a Sepharose column, and binding sites were isolated by passing short, random-sequence oligonucleotides over the column and eluting them with increasing salt concentrations. Of 43 specifically bound oligonucleotides, 40 contained the symmetric sequence TGA(C/G)TCA, whereas the other 3 contained sequences matching six of these seven bases. The extreme preference for this 7-base-pair sequence suggests that each position directly contacts GCN4. The three nucleotide positions on each side of this core heptanucleotide also showed sequence preferences, indicating their effect on GCN4 binding. Interestingly, deviations in the core and a stronger sequence preference in the flanking region were found on one side of the central C . G base pair. Although GCN4 binds as a dimer, this asymmetry supports a model in which interactions on each side of the binding site are not equivalent. The random selection method should prove generally useful for defining the specificities of other DNA-binding proteins and for identifying putative target sequences from genomic DNA.

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Pinet, Svetlana, Gary S. Dell, and F. Xavier Alario. "Tracking Keystroke Sequences at the Cortical Level Reveals the Dynamics of Serial Order Production." Journal of Cognitive Neuroscience 31, no. 7 (July 2019): 1030–43. http://dx.doi.org/10.1162/jocn_a_01401.

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Response selection is often studied by examining single responses, although most actions are performed within an overarching sequence. Understanding processes that order and execute items in a sequence is thus essential to give a complete picture of response selection. In this study, we investigate response selection by comparing single responses and response sequences as well as unimanual and bimanual sequences. We recorded EEG while participants were typing one- or two-keystroke sequences. Irrespective of stimulus modality (visual or auditory), response-locked analysis revealed distinct contralateral and ipsilateral components previously associated with activation and inhibition of alternative responses. Unimanual sequences exhibited a similar activation/inhibition pattern as single responses, but with the activation component of the pattern expressed more strongly, reflecting the fact that the hand will be used for two strokes. In contrast, bimanual sequences were associated with successive activation of each of the corresponding motor cortices controlling each keystroke and no traceable inhibitory component. In short, the activation component of the two-keystroke sequence EEG pattern can be understood from the addition of activation components of single-stroke sequences; the inhibition of the hand not being used is only evidenced when that hand is not planned for the next stroke.

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Ray, Partha, and Rebekah R. White. "Cell-SELEX Identifies a “Sticky” RNA Aptamer Sequence." Journal of Nucleic Acids 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4943072.

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Cell-SELEX is performed to select for cell binding aptamers. We employed an additional selection pressure by using RNAse to remove surface-binding aptamers and select for cell-internalizing aptamers. A common RNA sequence was identified from independent cell-SELEX procedures against two different pancreatic cancer cell lines, indicating a strong selection pressure towards this sequence from the large pool of other available sequences present in the aptamer library. The aptamer is not specific for the pancreatic cancer cell lines, and a similar sequence motif is present in previously published internalizing aptamers. The identified sequence forms a structural motif that binds to a surface protein, which either is highly abundant or has strong affinity for the selected aptamer sequence. Deselecting (removing) this sequence during cell-SELEX may increase the probability of identifying aptamers against cell type-specific targets on the cell surface.

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Doria-Rose, Nicole A., and Volker M. Vogt. "In Vivo Selection of Rous Sarcoma Virus Mutants with Randomized Sequences in the Packaging Signal." Journal of Virology 72, no. 10 (October 1, 1998): 8073–82. http://dx.doi.org/10.1128/jvi.72.10.8073-8082.1998.

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ABSTRACT Retrovirus genomes contain a sequence at the 5′ end which directs their packaging into virions. In Rous sarcoma virus, previous studies have identified important segments of the packaging signal, Ψ, and support elements of a secondary-structure prediction. To further characterize this sequence, we used an in vivo selection strategy to test large collections of mutants. We generated pools of full-length viral DNA molecules with short stretches of random sequence in Ψ and transfected each pool into avian cells. Resulting infectious virus was allowed to spread by multiple passages, so that sequences could compete and the best could be selected. This method provides information on the kinds of sequences allowed, as well as those that are most fit. Several predicted stem-loop structures in Ψ were tested. A stem at the base of element O3 was highly favored; only sequences which maintained base pairing were selected. Two other stems, at the base and in the middle of element L3, were not conserved: neither base pairing nor sequence was maintained. A single mutation, G213U, was seen upstream of the randomized region in all selected L3 stem mutants; we interpret this to mean that it compensates for the defects in L3. Randomized mutations adjacent to G213 maintained the wild-type base composition but not its sequence. The kissing-loop sequence at end of L3, postulated to function in genome dimerization, was not required for infectivity but was selected for over time. Finally, a deletion of L3 was constructed and found to be poorly infectious.

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Straub, Kristina, Mona Linde, Cosimo Kropp, Samuel Blanquart, Patrick Babinger, and Rainer Merkl. "Sequence selection by FitSS4ASR alleviates ancestral sequence reconstruction as exemplified for geranylgeranylglyceryl phosphate synthase." Biological Chemistry 400, no. 3 (February 25, 2019): 367–81. http://dx.doi.org/10.1515/hsz-2018-0344.

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Abstract For evolutionary studies, but also for protein engineering, ancestral sequence reconstruction (ASR) has become an indispensable tool. The first step of every ASR protocol is the preparation of a representative sequence set containing at most a few hundred recent homologs whose composition determines decisively the outcome of a reconstruction. A common approach for sequence selection consists of several rounds of manual recompilation that is driven by embedded phylogenetic analyses of the varied sequence sets. For ASR of a geranylgeranylglyceryl phosphate synthase, we additionally utilized FitSS4ASR, which replaces this time-consuming protocol with an efficient and more rational approach. FitSS4ASR applies orthogonal filters to a set of homologs to eliminate outlier sequences and those bearing only a weak phylogenetic signal. To demonstrate the usefulness of FitSS4ASR, we determined experimentally the oligomerization state of eight predecessors, which is a delicate and taxon-specific property. Corresponding ancestors deduced in a manual approach and by means of FitSS4ASR had the same dimeric or hexameric conformation; this concordance testifies to the efficiency of FitSS4ASR for sequence selection. FitSS4ASR-based results of two other ASR experiments were added to the Supporting Information. Program and documentation are available at https://gitlab.bioinf.ur.de/hek61586/FitSS4ASR.

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XIAO, Yue, Qihui LIANG, Peng CHENG, Lilin DAN, and Shaoqian LI. "Sequence Selection for Selected Mapping in OFDM." IEICE Transactions on Communications E94-B, no. 5 (2011): 1495–97. http://dx.doi.org/10.1587/transcom.e94.b.1495.

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Lee, Dong Wook, and Kwee-Bo Sim. "Negative Selection Algorithm for DNA Sequence Classification." International Journal of Fuzzy Logic and Intelligent Systems 4, no. 2 (September 1, 2004): 231–35. http://dx.doi.org/10.5391/ijfis.2004.4.2.231.

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Sakai, Masami. "The sequence selection properties of Cp(X)." Topology and its Applications 154, no. 3 (February 2007): 552–60. http://dx.doi.org/10.1016/j.topol.2006.07.008.

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Bukovský, Lev, and Jaroslav Šupina. "Sequence selection principles for quasi-normal convergence." Topology and its Applications 159, no. 1 (January 2012): 283–89. http://dx.doi.org/10.1016/j.topol.2011.09.034.

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LEE, J., J. COX, J. COLLETT, and A. ELLINGTON. "Exploring Sequence Space Through Automated Aptamer Selection." Journal of the Association for Laboratory Automation 10, no. 4 (August 2005): 213–18. http://dx.doi.org/10.1016/j.jala.2005.05.004.

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Kondrashov, Alexey S., Inna S. Povolotskaya, Dmitry N. Ivankov, and Fyodor A. Kondrashov. "Rate of sequence divergence under constant selection." Biology Direct 5, no. 1 (2010): 5. http://dx.doi.org/10.1186/1745-6150-5-5.

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Georges, Michel. "Towards sequence-based genomic selection of cattle." Nature Genetics 46, no. 8 (July 29, 2014): 807–9. http://dx.doi.org/10.1038/ng.3048.

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Fernández, Ariel, and Kristina Rogale. "Sequence-space selection of cooperative model proteins." Journal of Physics A: Mathematical and General 37, no. 18 (April 21, 2004): L197—L202. http://dx.doi.org/10.1088/0305-4470/37/18/l02.

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Baker, Scott E. "Selection to sequence: opportunities in fungal genomics." Environmental Microbiology 11, no. 12 (December 2009): 2955–58. http://dx.doi.org/10.1111/j.1462-2920.2009.02112.x.

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Hong, Hyoung Seok, Young Gon Kim, Sung Deok Cha, Doo Hwan Bae, and Hasan Ural. "A test sequence selection method for statecharts." Software Testing, Verification and Reliability 10, no. 4 (December 2000): 203–27. http://dx.doi.org/10.1002/1099-1689(200012)10:4<203::aid-stvr212>3.0.co;2-2.

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Fox, Sidney W. "Molecular selection in a unified evolutionary sequence." International Journal of Quantum Chemistry 30, S13 (June 19, 2009): 223–35. http://dx.doi.org/10.1002/qua.560300822.

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Chi, Peter B., and David A. Liberles. "Selection on protein structure, interaction, and sequence." Protein Science 25, no. 7 (February 11, 2016): 1168–78. http://dx.doi.org/10.1002/pro.2886.

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Gaina, Cynthia Dewi, Filphin Adolfin Amalo, and Yustinus O. P. Wuhan. "Analisis Genetik Gen Leptin Berdasarkan Sekuen DNA GenBank dan Asosiasinya dengan Reproduksi Ternak Babi." JURNAL KAJIAN VETERINER 11, no. 1 (June 20, 2023): 93–102. http://dx.doi.org/10.35508/jkv.v11i1.10162.

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A candidate gene suspected of affecting pigs' reproductive traits is known as the leptin (LEP) gene. Based on the information found in Leptin GenBank's DNA sequence database, this research aimed to determine single nucleotide polymorphisms (SNPs) and changes in nucleotides and phylogenetic trees in pigs. The data from NCBI GenBank was used to obtain three different DNA sequences of the Leptin gene in pigs. The DNA sequence data for leptin were aligned using BioEdit to establish the sites of SNPs and alterations in nucleotides. A phylogenetic tree was constructed using Mega X. DNA sequences were, and the pig LEP gene was sequenced resulted in 11 SNPs. These findings are based on the sequence regions. The phylogenetic analysis revealed that the sequence of the Leptin gene in pigs (Sus scrofa) clustered more closely with that of goats (Capra hircus) than it did with the sequence of the LEP gene among the cattle (Bos taurus and Bos indicus). The outcomes of this study can serve as a basis for more investigation into the connection between the LEP gene SNP and reproductive traits in pigs as one of potential genes for molecular selection.

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Ariani, Giacomo, and Jörn Diedrichsen. "Sequence learning is driven by improvements in motor planning." Journal of Neurophysiology 121, no. 6 (June 1, 2019): 2088–100. http://dx.doi.org/10.1152/jn.00041.2019.

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The ability to perform complex sequences of movements quickly and accurately is critical for many motor skills. Although training improves performance in a large variety of motor sequence tasks, the precise mechanisms behind such improvements are poorly understood. Here we investigated the contribution of single-action selection, sequence preplanning, online planning, and motor execution to performance in a discrete sequence production task. Five visually presented numbers cued a sequence of five finger presses, which had to be executed as quickly and accurately as possible. To study how sequence planning influenced sequence production, we manipulated the amount of time that participants were given to prepare each sequence by using a forced-response paradigm. Over 4 days, participants were trained on 10 sequences and tested on 80 novel sequences. Our results revealed that participants became faster in selecting individual finger presses. They also preplanned three or four sequence items into the future, and the speed of preplanning improved for trained, but not for untrained, sequences. Because preplanning capacity remained limited, the remaining sequence elements had to be planned online during sequence execution, a process that also improved with sequence-specific training. Overall, our results support the view that motor sequence learning effects are best characterized by improvements in planning processes that occur both before and concurrently with motor execution. NEW & NOTEWORTHY Complex skills often require the production of sequential movements. Although practice improves performance, it remains unclear how these improvements are achieved. Our findings show that learning effects in a sequence production task can be attributed to an enhanced ability to plan upcoming movements. These results shed new light on planning processes in the context of movement sequences and have important implications for our understanding of the neural mechanisms that underlie skill acquisition.

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Wittenbrink, Pia, Mira Janzen, Antonia Jennert, and Benjamin Strenge. "Expertise-dependent differences in mental representation metrics of pas de bourrée." PLOS ONE 18, no. 10 (October 5, 2023): e0292133. http://dx.doi.org/10.1371/journal.pone.0292133.

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Precise movement control is of prime importance in almost every kind of sport and greatly influences an athlete’s performance. In dancing not only motor but also cognitive skills, e.g. in the form of memorized representational structures, are essential components of the performance. This study investigated different metrics related to the long-term memory of ballet dancers with different skill levels regarding the pas de bourrée using the structural-dimensional analysis of mental representations (SDA-M) method. To this end, the Correct Action Selection Probability Analysis (CASPA) algorithm, a recent SDA-M extension that predicts the individual probabilities of correct action selections within a movement sequence, has been applied in the context of dancing for the first time. Significant positive correlations were found between participants’ degree of expertise and the proximity of their mental representation structure to the ideal reference structure, as well as between the degree of expertise and the probability of correct action selection within the movement sequence estimated by CASPA. The results indicate that increased training experience in ballet dancing is not only associated with functionally better structured mental representations of the movement sequence but also with a higher probability of correct action selection. These findings provide further evidence for SDA-M with CASPA as an auspicious tool for individualized task-related memory assessments and diagnostics in different action sequences, e.g. as the basis for mental training.

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Moore, David J., Damien C. T. Halliday, David M. Rowell, Anthony J. Robinson, and J. Scott Keogh. "Positive Darwinian selection results in resistance to cardioactive toxins in true toads (Anura: Bufonidae)." Biology Letters 5, no. 4 (May 22, 2009): 513–16. http://dx.doi.org/10.1098/rsbl.2009.0281.

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Members of the Family Bufonidae, true toads, are famous for their endogenously synthesized cardioactive steroids that serve as defensive toxins. Evolution of resistance to these toxins is not understood. We sequenced a key region of the toxin's binding site in the Na + /K + ATPase for relevant taxa representing Hyloidea (including bufonids), Ranoidea and Archaeobatrachia and tested for positive selection in a phylogenetic context. Bufonidae were distinct from other Hyloidea at 4–6 of 12 sites and, with one exception, had a homologous amino acid sequence. Melanophryniscus stelzneri had a distinct sequence, consistent with other independent evidence for a differentiated toxin. Tests within Bufonidae detected positive selection within the binding region, providing, to our knowledge, the first evidence of this type for positive selection within Amphibia. There was no evidence for positive selection on Bufonidae or M. stelzneri lineages. Sequence change in Leptodactylus ocellatus , a leptodactylid predator of Bufonidae, provides a molecular basis for predator resistance possibly associated with gene duplication.

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Luan, Mengkai, and Arash Mirifar. "The Effect of Attentional Direction on Sub-Stages of Preparing for Motor Skill Execution Across Practice." Perceptual and Motor Skills 128, no. 3 (April 30, 2021): 1292–309. http://dx.doi.org/10.1177/00315125211009026.

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While several empirical studies using dual-task methodology have examined the effect of attentional direction on motor skill execution; few have studied the effect of attentional direction on just the preparation phase of motor practice. In this study, via a keying sequence paradigm, we explored processing stages of preparation for a motor skill and disentangled the effect of attentional direction on various stages across practice. First, participants learned two keying sequences (three versus six keys). Then, they practiced the keying sequences in response to corresponding sequence labels under two block-wise alternating dual-task conditions. To dissect the preparation phase into sequence selection and sequence initiation stages, participants received varying amounts of preparation time (0, 300, 900 ms) before a starting signal instructed them to begin sequence execution. In each trial, a tone was paired with one of the three or six keypresses, and participants indicated either the keypress with which the tone was presented (skill-focused dual task) or the tone’s pitch (extraneous dual task) after the sequence execution. We found that attentional direction affected only the sequence selection stage, not the sequence initiation stage. During early practice, compared to drawing attention away from execution, directing attention toward execution led to faster sequence selection. This advantage decreased with practice and vanished during late blocks of trials. Moreover, for the execution phase, relative to directing attention toward execution, drawing attention away from execution led to better performance of keying sequence execution across practice. Thus, attentional direction alone does not fully explain the difference between performance patterns at different skill levels in the dual-task literature; rather, types of motor skills and dual task difficulty levels may also drive performance differences.

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Midgley, R. S., A. I. Bell, D. J. McGeoch, and A. B. Rickinson. "Latent Gene Sequencing Reveals Familial Relationships among Chinese Epstein-Barr Virus Strains and Evidence for Positive Selection of A11 Epitope Changes." Journal of Virology 77, no. 21 (November 1, 2003): 11517–30. http://dx.doi.org/10.1128/jvi.77.21.11517-11530.2003.

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ABSTRACT Epstein-Barr virus (EBV) strains from the highly HLA-A11-positive Chinese population are predominantly type 1 and show a variety of sequence changes (relative to the contemporary Caucasian prototype strain B95.8) in the nuclear antigen EBNA3B sequences encoding two immunodominant HLA-A11 epitopes, here called IVT and AVF. This has been interpreted by some as evidence of immune selection and by others as random genetic drift. To study epitope variation in a broader genomic context, we sequenced the whole of EBNA3B and parts of the EBNA2, 3A, and 3C genes from each of 31 Chinese EBV isolates. At each locus, type 1 viruses showed <2% nucleotide divergence from the B95.8 prototype while type 2 sequences remained even closer to the contemporary African prototype Ag876. However, type 1 isolates could clearly be divided into families based on linked patterns of sequence divergence from B95.8 across all four EBNA loci. Different patterns of IVT and AVF variation were associated with the different type 1 families, and there was additional epitope diversity within families. When the EBNA3 gene sequences of type 1 Chinese strains were subject to computer-based analysis, particular codons within the A11-epitope-coding region were among the few identified as being under positive or diversifying selection pressure. From these results, and the observation that mutant epitopes are consistently nonimmunogenic in vivo, we conclude that the immune selection hypothesis remains viable and worthy of further investigation.

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PUDIMAT, RAINER, ROLF BACKOFEN, and ERNST G. SCHUKAT-TALAMAZZINI. "FAST FEATURE SUBSET SELECTION IN BIOLOGICAL SEQUENCE ANALYSIS." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 02 (March 2009): 191–207. http://dx.doi.org/10.1142/s0218001409007107.

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Biological research produces a wealth of measured data. Neither it is easy for biologists to postulate hypotheses about the behavior or structure of the observed entity because the relevant properties measured are not seen in the ocean of measurements. Nor is it easy to design machine learning algorithms to classify or cluster the data items for the same reason. Algorithms for automatically selecting a highly predictive subset of the measured features can help to overcome these difficulties. We present an efficient feature selection strategy which can be applied to arbitrary feature selection problems. The core technique is a new method for estimating the quality of subsets from previously calculated qualities for smaller subsets by minimizing the mean standard error of estimated values with an approach common to support vector machines. This method can be integrated in many feature subset search algorithms. We have applied it with sequential search algorithms and have been able to reduce the number of quality calculations for finding accurate feature subsets by about 70%. We show these improvements by applying our approach to the problem of finding highly predictive feature subsets for transcription factor binding sites.

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Di Gioacchino, Andrea, Jonah Procyk, Marco Molari, John S. Schreck, Yu Zhou, Yan Liu, Rémi Monasson, Simona Cocco, and Petr Šulc. "Generative and interpretable machine learning for aptamer design and analysis of in vitro sequence selection." PLOS Computational Biology 18, no. 9 (September 29, 2022): e1010561. http://dx.doi.org/10.1371/journal.pcbi.1010561.

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Selection protocols such as SELEX, where molecules are selected over multiple rounds for their ability to bind to a target of interest, are popular methods for obtaining binders for diagnostic and therapeutic purposes. We show that Restricted Boltzmann Machines (RBMs), an unsupervised two-layer neural network architecture, can successfully be trained on sequence ensembles from single rounds of SELEX experiments for thrombin aptamers. RBMs assign scores to sequences that can be directly related to their fitnesses estimated through experimental enrichment ratios. Hence, RBMs trained from sequence data at a given round can be used to predict the effects of selection at later rounds. Moreover, the parameters of the trained RBMs are interpretable and identify functional features contributing most to sequence fitness. To exploit the generative capabilities of RBMs, we introduce two different training protocols: one taking into account sequence counts, capable of identifying the few best binders, and another based on unique sequences only, generating more diverse binders. We then use RBMs model to generate novel aptamers with putative disruptive mutations or good binding properties, and validate the generated sequences with gel shift assay experiments. Finally, we compare the RBM’s performance with different supervised learning approaches that include random forests and several deep neural network architectures.

Journal articles on the topic 'Sequence selection'

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