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.

Piwi-interacting RNAs (piRNAs) are a class of small non-coding RNA primarily expressed in germ cells that can silence transposons at the post-transcriptional level. Accurate prediction of piRNAs remains a significant challenge. We developed a program for piRNA annotation (Piano) using piRNA-transposon interaction information. We downloaded 13,848 Drosophila piRNAs and 261,500 Drosophila transposons. The piRNAs were aligned to transposons with a maximum of three mismatches. Then, piRNA-transposon interactions were predicted by RNAplex. Triplet elements combining structure and sequence information were extracted from piRNA-transposon matching/pairing duplexes. A support vector machine (SVM) was used on these triplet elements to classify real and pseudo piRNAs, achieving 95.3 ± 0.33% accuracy and 96.0 ± 0.5% sensitivity. The SVM classifier can be used to correctly predict human, mouse and rat piRNAs, with overall accuracy of 90.6%. We used Piano to predict piRNAs for the rice stem borer, Chilo suppressalis, an important rice insect pest that causes huge yield loss. As a result, 82,639 piRNAs were predicted in C. suppressalis. Piano demonstrates excellent piRNA prediction performance by using both structure and sequence features of transposon-piRNAs interactions. Piano is freely available to the academic community at http://ento.njau.edu.cn/Piano.html .

Previous loblolly pine (Pinus taeda L.) genetic linkage maps have been based on a variety of DNA polymorphisms, such as AFLPs, RAPDs, RFLPs, and ESTPs, but only a few SSRs (simple sequence repeats), also known as simple tandem repeats or microsatellites, have been mapped in P. taeda. The objective of this study was to integrate a large set of SSR markers from a variety of sources and published cDNA markers into a composite P. taeda genetic map constructed from two reference mapping pedigrees. A dense genetic map that incorporates SSR loci will benefit complete pine genome sequencing, pine population genetics studies, and pine breeding programs. Careful marker annotation using a variety of references further enhances the utility of the integrated SSR map.

Tandem repeats (TRs) extensively exist in the genomes of prokaryotes and eukaryotes. Based on the sequenced genomes and gene annotations of 31 plant and algal species in Phytozome version 8.0 (http://www.phytozome.net/), we examined TRs in a genome-wide scale, characterized their distributions and motif features, and explored their putative biological functions. Among the 31 species, no significant correlation was detected between the TR density and genome size. Interestingly, green alga Chlamydomonas reinhardtii (42,059 bp/Mbp) and castor bean Ricinus communis (55,454 bp/Mbp) showed much higher TR densities than all other species (13,209 bp/Mbp on average). In the 29 land plants, including 22 dicots, 5 monocots, and 2 bryophytes, 5′-UTR and upstream intergenic 200-nt (UI200) regions had the first and second highest TR densities, whereas in the two green algae (C. reinhardtii and Volvox carteri) the first and second highest densities were found in intron and coding sequence (CDS) regions, respectively. In CDS regions, trinucleotide and hexanucleotide motifs were those most frequently represented in all species. In intron regions, especially in the two green algae, significantly more TRs were detected near the intron–exon junctions. Within intergenic regions in dicots and monocots, more TRs were found near both the 5′ and 3′ ends of genes. GO annotation in two green algae revealed that the genes with TRs in introns are significantly involved in transcriptional and translational processing. As the first systematic examination of TRs in plant and green algal genomes, our study showed that TRs displayed nonrandom distribution for both intragenic and intergenic regions, suggesting that they have potential roles in transcriptional or translational regulation in plants and green algae.

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).

Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite lengths due to deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for patient prognosis. Conventional clinical diagnosis of MSI examines PCR products of a panel of microsatellite markers using electrophoresis (MSI-PCR), which is laborious, costly, and time consuming. We developed MSIpred, a python package for automatic MSI classification using a machine learning technology – support vector machine (SVM). MSIpred computes 22 features characterizing tumor somatic mutational load from mutation data in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these features to predict tumor MSI status with a SVM classifier trained by MAF data of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent testing set, MAF data of another 358 tumors, achieved overall accuracy of ≥98% and area under receiver operating characteristic (ROC) curve of 0.967. Further analysis on discrepant cases revealed that discrepancies were partially due to misclassification of MSI-PCR. Additional testing of MSIpred on non-TCGA data also validated its good classification performance. These results indicated that MSIpred is a robust pan-tumor MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis.