A MATLAB-based tool for accurate detection of perfect overlapping and nested inverted repeats in DNA sequences

An updated overview of experimental and computational approaches to identify non-canonical DNA/RNA structures with emphasis on G-quadruplexes and R-loops

Multiple types of non-canonical nucleic acid structures play essential roles in DNA recombination and replication, transcription, and genomic instability and have been associated with several human diseases. Thus, an increasing number of experimental and bioinformatics methods have been developed to identify these structures. To date, most reviews have focused on the features of non-canonical DNA/RNA structure formation, experimental approaches to mapping these structures, and the association of these structures with diseases. In addition, two reviews of computational algorithms for the prediction of non-canonical nucleic acid structures have been published. One of these reviews focused only on computational approaches for G4 detection until 2020. The other mainly summarized the computational tools for predicting cruciform, H-DNA and Z-DNA, in which the algorithms discussed were published before 2012. Since then, several experimental and computational methods have been developed. However, a systematic review including the conformation, sequencing mapping methods and computational prediction strategies for these structures has not yet been published. The purpose of this review is to provide an updated overview of conformation, current sequencing technologies and computational identification methods for non-canonical nucleic acid structures, as well as their strengths and weaknesses. We expect that this review will aid in understanding how these structures are characterised and how they contribute to related biological processes and diseases.

Design of Synthetic Mammalian Promoters Using Highly Palindromic Subsequences

To express transgenes in specific cell types and states, promoters for endogenous genes are commonly created by truncating the sequence upstream of the transcriptional start site until the promoter is no longer functional. In this paper, we developed a method to design shorter synthetic mammalian promoters for endogenous genes by concatenating only its highly palindromic subsequences with a minimal core promoter. After developing metrics for palindromic density, analysis across all the human and mouse promoters showed higher palindromic density than expected by random. As experimental demonstrations, we applied the method to the CMV promoter (reduced to 432 nucleotides) and the mouse synapsin-1 promoter (383 nucleotides) to express fluorescent protein as reporters. Remarkably, the highly palindromic subsequences of these synthetic promoters contained sites important for strong constitutive expression and neuron-specific expression. As a resource to the community, we created enhancer sequences for all the human and mouse promoters.

Finding and Characterizing Repeats in Plant Genomes

Plant genomes contain a particularly high proportion of repeated structures of various types. This chapter proposes a guided tour of the available software that can help biologists to scan automatically for these repeats in sequence data or check hypothetical models intended to characterize their structures. Since transposable elements (TEs) are a major source of repeats in plants, many methods have been used or developed for this broad class of sequences. They are representative of the range of tools available for other classes of repeats and we have provided two sections on this topic (for the analysis of genomes or directly of sequenced reads), as well as a selection of the main existing software. It may be hard to keep up with the profusion of proposals in this dynamic field and the rest of the chapter is devoted to the foundations of an efficient search for repeats and more complex patterns. We first introduce the key concepts of the art of indexing and mapping or querying sequences. We end the chapter with the more prospective issue of building models of repeat families. We present the Machine Learning approach first, seeking to build predictors automatically for some families of ET, from a set of sequences known to belong to this family. A second approach, the linguistic (or syntactic) approach, allows biologists to describe themselves and check the validity of models of their favorite repeat family.

LIRBase: a comprehensive database of long inverted repeats in eukaryotic genomes

Small RNAs (sRNAs) constitute a large portion of functional elements in eukaryotic genomes. Long inverted repeats (LIRs) can be transcribed into long hairpin RNAs (hpRNAs), which can further be processed into small interfering RNAs (siRNAs) with vital biological roles. In this study, we systematically identified a total of 6 619 473 LIRs in 424 eukaryotic genomes and developed LIRBase (https://venyao.xyz/lirbase/), a specialized database of LIRs across different eukaryotic genomes aiming to facilitate the annotation and identification of LIRs encoding long hpRNAs and siRNAs. LIRBase houses a comprehensive collection of LIRs identified in a wide range of eukaryotic genomes. In addition, LIRBase not only allows users to browse and search the identified LIRs in any eukaryotic genome(s) of interest available in GenBank, but also provides friendly web functionalities to facilitate users to identify LIRs in user-uploaded sequences, align sRNA sequencing data to LIRs, perform differential expression analysis of LIRs, predict mRNA targets for LIR-derived siRNAs, and visualize the secondary structure of candidate long hpRNAs encoded by LIRs. As demonstrated by two case studies, collectively, LIRBase bears the great utility for systematic investigation and characterization of LIRs and functional exploration of potential roles of LIRs and their derived siRNAs in diverse species.

IUPACpal: efficient identification of inverted repeats in IUPAC-encoded DNA sequences

BACKGROUND

An inverted repeat is a DNA sequence followed downstream by its reverse complement, potentially with a gap in the centre. Inverted repeats are found in both prokaryotic and eukaryotic genomes and they have been linked with countless possible functions. Many international consortia provide a comprehensive description of common genetic variation making alternative sequence representations, such as IUPAC encoding, necessary for leveraging the full potential of such broad variation datasets.

RESULTS

We present IUPACPAL, an exact tool for efficient identification of inverted repeats in IUPAC-encoded DNA sequences allowing also for potential mismatches and gaps in the inverted repeats.

CONCLUSION

Within the parameters that were tested, our experimental results show that IUPACPAL compares favourably to a similar application packaged with EMBOSS. We show that IUPACPAL identifies many previously unidentified inverted repeats when compared with EMBOSS, and that this is also performed with orders of magnitude improved speed.

PCIR: a database of Plant Chloroplast Inverted Repeats

Inverted repeats (IRs) serve as potential biomarkers for genomic instability, DNA replication and other genetic processes. However, little information can be found in databases to help researchers recognize potential IR nucleotides, explore junction sites and annotate related functional genes. Plant Chloroplast Inverted Repeats (PCIR) is an interactive, web-based platform containing various sequenced chloroplast genomes that enables detection, searching and visualization of large-scale detailed information on IRs. PCIR contains many datasets, including 21 433 IRs, 113 plants chloroplast genomes, 16 948 functional genes and 21 659 visual maps. This database offers an online prediction tool for detecting IRs based on DNA sequences. PCIR can also analyze phylogenetic relationships using IR information among different species and provide users with high-quality marker maps. This database will be a valuable resource for IR distribution patterns, related genes and architectural features.

Generic Repeat Finder: A High-Sensitivity Tool for Genome-Wide De Novo Repeat Detection

Comprehensive and accurate annotation of the repeatome, including transposons, is critical for deepening our understanding of repeat origins, biogenesis, regulatory mechanisms, and roles. Here, we developed Generic Repeat Finder (GRF), a tool for genome-wide repeat detection based on fast, exhaustive numerical calculation algorithms integrated with optimized dynamic programming strategies. GRF sensitively identifies terminal inverted repeats (TIRs), terminal direct repeats (TDRs), and interspersed repeats that bear both inverted and direct repeats. GRF also detects DNA or RNA transposable elements characterized by these repeats in plant and animal genomes. For TIRs and TDRs, GRF identifies spacers in the middle and mismatches/insertions or deletions in terminal repeats, showing their alignment or base-pairing information. GRF helps improve the annotation for various DNA transposons and retrotransposons, such as miniature inverted-repeat transposable elements (MITEs), long terminal repeat (LTR) retrotransposons, and non-LTR retrotransposons, including long interspersed nuclear elements and short interspersed nuclear elements in plants. We used GRF to perform TIR/TDR, interspersed-repeat, and MITE detection in several species, including Arabidopsis (Arabidopsis thaliana), rice (Oryza sativa), and mouse (Mus musculus). As a generic bioinformatics tool in repeat finding implemented as a parallelized C++ program, GRF was faster and more sensitive than the existing inverted repeat/MITE detection tools based on numerical approaches (i.e. detectIR and detectMITE) in Arabidopsis and mouse. GRF is more sensitive than Inverted Repeat Finder in TIR detection, LTR_FINDER in short TDR detection (≤1,000 nt), and phRAIDER in interspersed repeat detection in Arabidopsis and rice. GRF is an open source available from Github.

detectMITE: A novel approach to detect miniature inverted repeat transposable elements in genomes

Miniature inverted repeat transposable elements (MITEs) are prevalent in eukaryotic genomes, including plants and animals. Classified as a type of non-autonomous DNA transposable elements, they play important roles in genome organization and evolution. Comprehensive and accurate genome-wide detection of MITEs in various eukaryotic genomes can improve our understanding of their origins, transposition processes, regulatory mechanisms, and biological relevance with regard to gene structures, expression, and regulation. In this paper, we present a new MATLAB-based program called detectMITE that employs a novel numeric calculation algorithm to replace conventional string matching algorithms in MITE detection, adopts the Lempel-Ziv complexity algorithm to filter out MITE candidates with low complexity, and utilizes the powerful clustering program CD-HIT to cluster similar MITEs into MITE families. Using the rice genome as test data, we found that detectMITE can more accurately, comprehensively, and efficiently detect MITEs on a genome-wide scale than other popular MITE detection tools. Through comparison with the potential MITEs annotated in Repbase, the widely used eukaryotic repeat database, detectMITE has been shown to find known and novel MITEs with a complete structure and full-length copies in the genome. detectMITE is an open source tool (https://sourceforge.net/projects/detectmite).

detectIR: a novel program for detecting perfect and imperfect inverted repeats using complex numbers and vector calculation

Inverted repeats are present in abundance in both prokaryotic and eukaryotic genomes and can form DNA secondary structures--hairpins and cruciforms that are involved in many important biological processes. Bioinformatics tools for efficient and accurate detection of inverted repeats are desirable, because existing tools are often less accurate and time consuming, sometimes incapable of dealing with genome-scale input data. Here, we present a MATLAB-based program called detectIR for the perfect and imperfect inverted repeat detection that utilizes complex numbers and vector calculation and allows genome-scale data inputs. A novel algorithm is adopted in detectIR to convert the conventional sequence string comparison in inverted repeat detection into vector calculation of complex numbers, allowing non-complementary pairs (mismatches) in the pairing stem and a non-palindromic spacer (loop or gaps) in the middle of inverted repeats. Compared with existing popular tools, our program performs with significantly higher accuracy and efficiency. Using genome sequence data from HIV-1, Arabidopsis thaliana, Homo sapiens and Zea mays for comparison, detectIR can find lots of inverted repeats missed by existing tools whose outputs often contain many invalid cases. detectIR is open source and its source code is freely available at: https://sourceforge.net/projects/detectir.

The combined use of antimullerian hormone and age to predict the ovarian response to controlled ovarian hyperstimulation in poor responders: A novel approach

CONTEXT:

Reduced ovarian response to stimulation represents one of the most intractable problems in infertility treatment. As failed cycle can cause considerable amount of emotional and economical loss, there are various attempts made to predict ovarian response.

AIMS:

To evaluate different factors influencing outcome of assisted reproduction in women with predicted reduced response (antimullerian hormone between 1 and 5 pmol/L) and to develop a model using of AMH and age to predict the number of oocytes in poor responders.

SETTINGS AND DESIGN:

Retrospective study in a teaching hospital.

MATERIALS AND METHODS:

We analyzed 85 cycles (57 women) with predicted reduced response with serum AMH value between 1 and 5 pmol/L. Standard ovarian stimulation protocol was used. Primary outcome measures were clinical pregnancy rates and oocytes retrieved.

STATISTICAL ANALYSIS USED:

Data were analyzed using Microsoft excel and MetlabR software.

RESULTS:

Clinical pregnancy rate/ET was 20.33%, in this group. AMH and age was analyzed using linear regression model which produced an equation to give predicted oocyte count if AMH and age are known. (Oocytes = age × (-ß) + Serum AMH × α) (Constant ß=0.0102 and α = 1.0407).

CONCLUSIONS:

Combined use of serum AMH and age to predict ovarian response within reduced responder group should be further evaluated. For first time, we suggested combining both factors to predict ovarian response using a simple equation which allow developing tailored strategy.