CRISPR Recognition Tool (CRT): a tool for automatic detection of clustered regularly interspaced palindromic repeats

<p>Abstract</p> <p>Background</p> <p>Clustered Regularly Interspaced Palindromic Repeats (CRISPRs) are a novel type of direct repeat found in a wide range of bacteria and archaea. CRISPRs are beginning to attract attention because of their proposed mechanism; that is, defending their hosts against invading extrachromosomal elements such as viruses. Existing repeat detection tools do a poor job of identifying CRISPRs due to the presence of unique spacer sequences separating the repeats. In this study, a new tool, CRT, is introduced that rapidly and accurately identifies CRISPRs in large DNA strings, such as genomes and metagenomes.</p> <p>Results</p> <p>CRT was compared to CRISPR detection tools, Patscan and Pilercr. In terms of correctness, CRT was shown to be very reliable, demonstrating significant improvements over Patscan for measures precision, recall and quality. When compared to Pilercr, CRT showed improved performance for recall and quality. In terms of speed, CRT proved to be a huge improvement over Patscan. Both CRT and Pilercr were comparable in speed, however CRT was faster for genomes containing large numbers of repeats.</p> <p>Conclusion</p> <p>In this paper a new tool was introduced for the automatic detection of CRISPR elements. This tool, CRT, showed some important improvements over current techniques for CRISPR identification. CRT's approach to detecting repetitive sequences is straightforward. It uses a simple sequential scan of a DNA sequence and detects repeats directly without any major conversion or preprocessing of the input. This leads to a program that is easy to describe and understand; yet it is very accurate, fast and memory efficient, being O(<it>n</it>) in space and O(<it>nm</it>/<it>l</it>) in time.</p

Range Shortest Unique Substring queries

Let be a string of length n and be the substring of starting at position i and ending at position j. A substring of is a repeat if it occurs more than once in; otherwise, it is a unique substring of. Repeats and unique substrings are of great interest in computational biology and in information retrieval. Given string as input, the Shortest Unique Substring problem is to find a shortest substring of that does not occur elsewhere in. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over answering the following type of online queries efficiently. Given a range, return a shortest substring of with exactly one occurrence in. We present an -word data structure with query time, where is the word size. Our construction is based on a non-trivial reduction allowing us to apply a recently introduced optimal geometric data structure [Chan et al. ICALP 2018]

Compiling a domain specific language for dynamic programming

Steffen P. Compiling a domain specific language for dynamic programming. Bielefeld (Germany): Bielefeld University; 2006

MICA: desktop software for comprehensive searching of DNA databases

BACKGROUND: Molecular biologists work with DNA databases that often include entire genomes. A common requirement is to search a DNA database to find exact matches for a nondegenerate or partially degenerate query. The software programs available for such purposes are normally designed to run on remote servers, but an appealing alternative is to work with DNA databases stored on local computers. We describe a desktop software program termed MICA (K-Mer Indexing with Compact Arrays) that allows large DNA databases to be searched efficiently using very little memory. RESULTS: MICA rapidly indexes a DNA database. On a Macintosh G5 computer, the complete human genome could be indexed in about 5 minutes. The indexing algorithm recognizes all 15 characters of the DNA alphabet and fully captures the information in any DNA sequence, yet for a typical sequence of length L, the index occupies only about 2L bytes. The index can be searched to return a complete list of exact matches for a nondegenerate or partially degenerate query of any length. A typical search of a long DNA sequence involves reading only a small fraction of the index into memory. As a result, searches are fast even when the available RAM is limited. CONCLUSION: MICA is suitable as a search engine for desktop DNA analysis software

Patterns and Signals of Biology: An Emphasis On The Role of Post Translational Modifications in Proteomes for Function and Evolutionary Progression

After synthesis, a protein is still immature until it has been customized for a specific task. Post-translational modifications (PTMs) are steps in biosynthesis to perform this customization of protein for unique functionalities. PTMs are also important to protein survival because they rapidly enable protein adaptation to environmental stress factors by conformation change. The overarching contribution of this thesis is the construction of a computational profiling framework for the study of biological signals stemming from PTMs associated with stressed proteins. In particular, this work has been developed to predict and detect the biological mechanisms involved in types of stress response with PTMs in mitochondrial (Mt) and non-Mt protein. Before any mechanism can be studied, there must first be some evidence of its existence. This evidence takes the form of signals such as biases of biological actors and types of protein interaction. Our framework has been developed to locate these signals, distilled from “Big Data” resources such as public databases and the the entire PubMed literature corpus. We apply this framework to study the signals to learn about protein stress responses involving PTMs, modification sites (MSs). We developed of this framework, and its approach to analysis, according to three main facets: (1) by statistical evaluation to determine patterns of signal dominance throughout large volumes of data, (2) by signal location to track down the regions where the mechanisms must be found according to the types and numbers of associated actors at relevant regions in protein, and (3) by text mining to determine how these signals have been previously investigated by researchers. The results gained from our framework enable us to uncover the PTM actors, MSs and protein domains which are the major components of particular stress response mechanisms and may play roles in protein malfunction and disease

Reliability and applications of statistical methods based on oligonucleotide frequencies in bacterial and archaeal genomes

<p>Abstract</p> <p>Background</p> <p>The increasing number of sequenced prokaryotic genomes contains a wealth of genomic data that needs to be effectively analysed. A set of statistical tools exists for such analysis, but their strengths and weaknesses have not been fully explored. The statistical methods we are concerned with here are mainly used to examine similarities between archaeal and bacterial DNA from different genomes. These methods compare observed genomic frequencies of fixed-sized oligonucleotides with expected values, which can be determined by genomic nucleotide content, smaller oligonucleotide frequencies, or be based on specific statistical distributions. Advantages with these statistical methods include measurements of phylogenetic relationship with relatively small pieces of DNA sampled from almost anywhere within genomes, detection of foreign/conserved DNA, and homology searches. Our aim was to explore the reliability and best suited applications for some popular methods, which include relative oligonucleotide frequencies (ROF), di- to hexanucleotide zero'th order Markov methods (ZOM) and 2.order Markov chain Method (MCM). Tests were performed on distant homology searches with large DNA sequences, detection of foreign/conserved DNA, and plasmid-host similarity comparisons. Additionally, the reliability of the methods was tested by comparing both real and random genomic DNA.</p> <p>Results</p> <p>Our findings show that the optimal method is context dependent. ROFs were best suited for distant homology searches, whilst the hexanucleotide ZOM and MCM measures were more reliable measures in terms of phylogeny. The dinucleotide ZOM method produced high correlation values when used to compare real genomes to an artificially constructed random genome with similar %GC, and should therefore be used with care. The tetranucleotide ZOM measure was a good measure to detect horizontally transferred regions, and when used to compare the phylogenetic relationships between plasmids and hosts, significant correlation <it>(R</it>²= <it>0.4) </it>was found with genomic GC content and intra-chromosomal homogeneity.</p> <p>Conclusion</p> <p>The statistical methods examined are fast, easy to implement, and powerful for a number of different applications involving genomic sequence comparisons. However, none of the measures examined were superior in all tests, and therefore the choice of the statistical method should depend on the task at hand.</p

Characterizing Sequencing Artifacts

Next Generation Sequencing (NGS) introduces artifactual variants from library preparation methods and errors, which affects the accuracy of variant calling. Whole Exome Sequencing (WES) data from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) database is processed. Comparison of single nucleotide polymorphism (SNP) calls to Genome In a Bottle (GIAB) provides labels that are used to build machine learning (ML) models. The left and right flanking region (LSEQ and RSEQ) of each SNP is extracted. Nucleotide frequency, kmers of size 4 and their counts, largest homopolymer size, largest palindrome size, and largest hairpin loop size were computed and used as features in model building. The Random Forest model had a precision of 98.8%, recall of 87.3%, and accuracy of 90.2%. High scores show the model\u27s ability to correctly identify artifacts from non-artifacts and that the results are exceptionally accurate

More Time-Space Tradeoffs for Finding a Shortest Unique Substring

We extend recent results regarding finding shortest unique substrings (SUSs) to obtain new time-space tradeoffs for this problem and the generalization of finding k-mismatch SUSs. Our new results include the first algorithm for finding a k-mismatch SUS in sublinear space, which we obtain by extending an algorithm by Senanayaka (2019) and combining it with a result on sketching by Gawrychowski and Starikovskaya (2019). We first describe how, given a text T of length n and m words of workspace, with high probability we can find an SUS of length L in O(n(L/m)logL) time using random access to T, or in O(n(L/m)log2(L)loglogσ) time using O((L/m)log2L) sequential passes over T. We then describe how, for constant k, with high probability, we can find a k-mismatch SUS in O(n1+ϵL/m) time using O(nϵL/m) sequential passes over T, again using only m words of workspace. Finally, we also describe a deterministic algorithm that takes O(nτlogσlogn) time to find an SUS using O(n/τ) words of workspace, where τ is a parameter