Recent Patents on DNA & Gene Sequences (Discontinued) - Volume 7, Issue 2, 2013

Motivation: Pairwise sequence alignment has received a new motivation due to the advent of recent patents in next-generation sequencing technologies, particularly so for the application of re-sequencing---the assembly of a genome directed by a reference sequence. After the fast alignment between a factor of the reference sequence and a high-quality fragment of a short read by a short-read alignment programme, an important problem is to find the alignment between a relatively short succeeding factor of the reference sequence and the remaining low-quality part of the read allowing a number of mismatches and the insertion of a single gap in the alignment. Results: We present GapMis, a tool for pairwise sequence alignment with a single gap. It is based on a simple algorithm, which computes a different version of the traditional dynamic programming matrix. The presented experimental results demonstrate that GapMis is more suitable and efficient than most popular tools for this task.

In this paper, we present efficient algorithms for finding indeterminate Arc-Annotated patterns in indeterminate Arc-Annotated references. Our algorithms run in O(m + nm/w) time where n and m are respectively the length of our reference and pattern strings and w is the target machine word size. Here we have assumed the alphabet size to be constant, because, indeterminate Arc-Annotated sequences are used to model biological sequences. Clearly, for short patterns, our algorithms run in linear time and efficient algorithms for matching short patterns to reference genomes have huge applications in practical settings. We have also applied our algorithms to scan the ncRNAs without pseudoknots. We scanned three whole human chromosomes and it took only 2.5 - 4 minutes to scan one whole chromosome for an ncRNA family. Some relevant patents are discussed in [1, 2].

Multiple sequence alignment (MSA) is one of the topics of bio informatics that has seriously been researched. It is known as NP-complete problem. It is also considered as one of the most important and daunting tasks in computational biology. Concerning this a wide number of heuristic algorithms have been proposed to find optimal alignment. Among these heuristic algorithms are genetic algorithms (GA). The GA has mainly two major weaknesses: it is time consuming and can cause local minima. One of the significant aspects in the GA process in MSA is to maximize the similarities between sequences by adding and shuffling the gaps of Solution Coding (SC). Several ways for SC have been introduced. One of them is the Permutation Coding (PC). We propose a hybrid algorithm based on genetic algorithms (GAs) with a PC and 2-opt algorithm. The PC helps to code the MSA solution which maximizes the gain of resources, reliability and diversity of GA. The use of the PC opens the area by applying all functions over permutations for MSA. Thus, we suggest an algorithm to calculate the scoring function for multiple alignments based on PC, which is used as fitness function. The time complexity of the GA is reduced by using this algorithm. Our GA is implemented with different selections strategies and different crossovers. The probability of crossover and mutation is set as one strategy. Relevant patents have been probed in the topic.

Motif finding in DNA, RNA and proteins plays an important role in life science research. Recent patents concerning motif finding in biomolecular data are recorded in the DNA Patent Database which serves as a resource for policy makers and members of the general public interested in fields like genomics, genetics and biotechnology. In this paper, we present a computational approach to mining for RNA tertiary motifs in genomic sequences. Specifically, we describe a method, named CSminer, and show, as a case study, the application of CSminer to genome-wide search for coaxial helical stackings in RNA 3-way junctions. A coaxial helical stacking occurs in an RNA 3-way junction where two separate helical elements form a pseudocontiguous helix and provide thermodynamic stability to the RNA molecule as a whole. Experimental results demonstrate the effectiveness of our approach.

In this paper we present a method for finding infrequent simple motifs in a finite set of sequences. The method uses a lattice structure and minimal forbidden patterns. It is based on a method for solving the Simple Motif Problem and has the potential to discover new patents in biological macromolecules. Indeed, the extracted motifs can help biologists to learn about the biological functions of these macromolecules and, consequently, can help them to understand the mechanisms of the biological processes in which these sequences are involved.

An absent word (also called a forbidden word or an unword in other contexts) in a sequence is a segment that does not appear in the given sequence. It is a minimal absent word if all its proper factors occur in the given sequence. In this article, we review the concept of minimal absent words, which includes the notion of shortest absent words but is much stronger. We present an efficient method for computing the minimal absent words of bounded length for DNA sequence using a Trie of bounded depth, representing bounded length factors. This method outputs the whole set of minimal absent words and furthermore our technique provides a linear-time algorithm with less memory usage than previous solutions. We also present an approach to distinguish sequences of different organisms using their minimal absent words. Our solution applies a length-weighted index to discriminate sequences and the results show that we can build phylogenetic tree based on the patent collected information.

The Serine Protease Inhibitors (Serpins) have been a focus of research by biomedical industries due to their critical role in human health. The use of serpin in the treatment of many diseases was widely investigated through the identification of new genes encoding these proteins in all kingdoms of life. The characterization of these genes revealed that they encoded proteins having low sequence homologies. Future developments are focusing not only on the protease inhibition activity, but also on the other effects due to the interactions of serpins with other components such as hormone transport. Here we give a concise overview of the most recent patents that have been reported in this field of research.

Directed evolution shortcuts million-year-scale natural evolution in a matter of weeks and generates tens of millions of sequence variants in a single test tube. A team of researchers used random DNA flanked by homologous sequences for in vivo homologous recombination, known as multiplex automated genome engineering (MAGE) to select the most active gene variants. They also adopted this approach to replace hundreds of stop codons in the E. coli genome, showing potential for genome-wide engineering. The blank codon created was harnessed to enlarge the amino acid alphabet, and unnatural amino acid has been incorporated to polypeptides. In phage-assisted continuous evolution (PACE), the target activity was linked to the expression of a protein required for the production of infectious phage, and researchers obtained activities with novel affinities to T3 promoter, ATP, etc. In vitro recombination enables the generation of massive number of artificial lives of potential values. Random combinatorial DNA approach has also been harnessed to construct G-H loop sequences of type O FMDV VP1 gene, and 100 novel radical sequence variants were obtained in a single experiment, which paves the way for the future investigations on the potential development of a polyvalent vaccine to cope with rapid viral variations. The enormous combinatorial diversity of these methods conferred high mutation rates at either full length genes or targeted regions unmatched by natural evolution or previous directed evolution methods. Interactions of mutations or epistasis may have generated beneficial phenotypes from neutral and deleterious mutations. Selection for desired phenotypes may create sequence variants that might never occur in evolution. Accelerated molecular evolution methods, capitalized on random DNA strings, continuous evolution, unnatural amino acids or in vitro recombination, provide infinite opportunities for research, industrial and medical applications.

A technique has emerged over the past decade combining chromatin immunoprecipitation with DNA microarray analysis. This is a powerful and sensitive strategy that has been used extensively to characterise protein interactions with chromatin and epigenetic changes such as acetylation and methylation throughout the genome of different organisms. This technique has revolutionised our understanding of molecular genomics, continues to be widely used and is currently being applied in novel areas of cancer research. In this publication we review the historical context of this technology and offer current and future perspectives on how this technique is currently being developed and modified to allow its use in novel areas of research. We discuss the potential for this technique and its ongoing important role in biological research particularly in relation to cancer research. We also offer insight into the potential clinical application of this technology in stratified medicine, particularly in the field of cancer therapy.