Huge Number Of Sequences Research Articles

Toehold-Mediated Strand Displacement in Random Sequence Pools.

Toehold-mediated strand displacement (TMSD) has been used extensively for molecular sensing and computing in DNA-based molecular circuits. As these circuits grow in complexity, sequence similarity between components can lead to cross-talk, causing leak, altered kinetics, or even circuit failure. For small non-biological circuits, such unwanted interactions can be designed against. In environments containing a huge number of sequences, taking all possible interactions into account becomes infeasible. Therefore, a general understanding of the impact of sequence backgrounds on TMSD reactions is of great interest. Here, we investigate the impact of random DNA sequences on TMSD circuits. We begin by studying individual interfering strands and use the obtained data to build machine learning models that estimate kinetics. We then investigate the influence of pools of random strands and find that the kinetics are determined by only a small subpopulation of strongly interacting strands. Consequently, their behavior can be mimicked by a small collection of such strands. The equilibration of the circuit with the background sequences strongly influences this behavior, leading to up to 1 order of magnitude difference in reaction speed. Finally, we compare two established and one novel technique that speed up TMSD reactions in random sequence pools: a three-letter alphabet, protection of toeholds by intramolecular secondary structure, or by an additional blocking strand. While all of these techniques were useful, only the latter can be used without sequence constraints. We expect that our insights will be useful for the construction of TMSD circuits that are robust to molecular noise.

Two Efficient Techniques to Find Approximate Overlaps between Sequences.

The next-generation sequencing (NGS) technology outputs a huge number of sequences (reads) that require further processing. After applying prefiltering techniques in order to eliminate redundancy and to correct erroneous reads, an overlap-based assembler typically finds the longest exact suffix-prefix match between each ordered pair of the input reads. However, another trend has been evolving for the purpose of solving an approximate version of the overlap problem. The main benefit of this direction is the ability to skip time-consuming error-detecting techniques which are applied in the prefiltering stage. In this work, we present and compare two techniques to solve the approximate overlap problem. The first adapts a compact prefix tree to efficiently solve the approximate all-pairs suffix-prefix problem, while the other utilizes a well-known principle, namely, the pigeonhole principle, to identify a potential overlap match in order to ultimately solve the same problem. Our results show that our solution using the pigeonhole principle has better space and time consumption over an FM-based solution, while our solution based on prefix tree has the best space consumption between all three solutions. The number of mismatches (hamming distance) is used to define the approximate matching between strings in our work.

Evaluating the coupling efficiency of phosphorylated amino acids for SPOT synthesis

A high demand of interest concerning binding assays to study the consequences of posttranscriptional phosphorylation may be addressed by peptide array-based methods. A crucial factor for de novo chemical approaches to generate such arrays is the possibility to rationally permutate phosphorylation events along a huge number of sequences. The simple principle behind this advantage is the stepwise synthesis of peptides, which allows the incorporation of either phosphorylated or nonphosphorylated derivates at serine, threonine, and tyrosine positions. In spite of several reported applications of phosphopeptide arrays, there is, to our best knowledge, no reported analysis of the efficiency of the involved techniques. Here, we analyze different coupling conditions to introduce phosphoamino acids in standard SPOT synthesis. Our results clearly indicate that EEDQ is the preferable activator and can also be used in fully automated SPOT synthesis.

A Fast Clustering System for a Huge Number of Nucleotide Sequences

Single pass sequences of mRNA, called ESTs, have been determined extensively. They have been accumulated in the dbEST database in GenBank. The number of ESTs in dbEST has become more than eight million in August 2002. By clustering and assembling ESTs, we can conduct the following analyses. First, we can obtain complete ORF sequences based on ESTs that are fragment sequences of mRNA and do not include complete open reading frames (ORFs). Second, we can examine expression profiles of genes by counting the number of ESTs obtained from each library in each cluster. Third, redundancy of EST sequences can be reduced by clustering. Finally, we can pick up ESTs that are representatives of clusters. Full-length sequences were determined for clones of representative ESTs chosen from clusters of unknown genes during the full-length human cDNA sequencing project supported by New Energy and Industrial Technology Developmental Organization (NEDO) in Japan [2]. To select clones for full-length sequencing, more than one million EST sequences were determined and clustered in this project. Now 21,243 human full-length cDNA sequences obtained in this project are published in DDBJ. Thus clustering and assembling of a huge number of ESTs are indispensable for EST analyses. It is computationally hard to see the whole relationship among millions of sequences for the purpose of clustering. Some currently available tools require more than a few days to complete clustering of such a huge number of sequences, while it is impossible for other tools to complete the task. In this paper, we present an algorithm for clustering and assembling that can complete the entire process for clustering and assembling in O(N log N) time where N is the number of sequences. We also implemented this algorithm and succeeded in assembling more than a million sequences within half a day.