Spontaneous mutations in maize pollen are frequent in some lines and arise mainly from retrotranspositions and deletions.

While studying spontaneous mutations at the maize () locus, we made the unexpected discovery that specific low-copy number retrotransposons are mobile in the pollen of some maize lines, but not of others. We conducted large-scale genetic experiments to isolate new mutations from several stocks and recovered spontaneous stable mutations only in the pollen parent in reciprocal crosses. Most of the new stable mutations resulted from either insertions of low-copy number long terminal repeat (LTR) retrotransposons or deletions, the same two classes of mutations that predominated in a collection of spontaneous mutations [Wessler S (1997) , pp 385-386].

The maize W22 genome provides a foundation for functional genomics and transposon biology.

The maize W22 inbred has served as a platform for maize genetics since the mid twentieth century. To streamline maize genome analyses, we have sequenced and de novo assembled a W22 reference genome using short-read sequencing technologies. We show that significant structural heterogeneity exists in comparison to the B73 reference genome at multiple scales, from transposon composition and copy number variation to single-nucleotide polymorphisms.

Highly interwoven communities of a gene regulatory network unveil topologically important genes for maize seed development.

The complex interactions between transcription factors (TFs) and their target genes in a spatially and temporally specific manner are crucial to all cellular processes. Reconstruction of gene regulatory networks (GRNs) from gene expression profiles can help to decipher TF-gene regulations in a variety of contexts; however, the inevitable prediction errors of GRNs hinder optimal data mining of RNA-Seq transcriptome profiles. Here we perform an integrative study of Zea mays (maize) seed development in order to identify key genes in a complex developmental process.

iTRAQ-Based Proteomics Analysis and Network Integration for Kernel Tissue Development in Maize.

Grain weight is one of the most important yield components and a developmentally complex structure comprised of two major compartments (endosperm and pericarp) in maize ( L.), however, very little is known concerning the coordinated accumulation of the numerous proteins involved. Herein, we used isobaric tags for relative and absolute quantitation (iTRAQ)-based comparative proteomic method to analyze the characteristics of dynamic proteomics for endosperm and pericarp during grain development.

Integrated regulatory network reveals the early salt tolerance mechanism of Populus euphratica.

Salinization is one of the major factors that threaten the existence of plants worldwide. Populus euphratica has been deemed to be a promising candidate for stress response research because of its high capacity to tolerate extreme salt stress. We carried out a genome-wide transcriptome analysis to identify the differentially expressed genes (DEGs) response to salt shock and elucidate the early salt tolerance mechanisms in P.

Rolling-circle amplification of centromeric Helitrons in plant genomes.

The unusual eukaryotic Helitron transposons can readily capture host sequences and are, thus, evolutionarily important. They are presumed to amplify by rolling-circle replication (RCR) because some elements encode predicted proteins homologous to RCR prokaryotic transposases. In support of this replication mechanism, it was recently shown that transposition of a bat Helitron generates covalently closed circular intermediates.

Dynamic Proteomic Characteristics and Network Integration Revealing Key Proteins for Two Kernel Tissue Developments in Popcorn.

The formation and development of maize kernel is a complex dynamic physiological and biochemical process that involves the temporal and spatial expression of many proteins and the regulation of metabolic pathways. In this study, the protein profiles of the endosperm and pericarp at three important developmental stages were analyzed by isobaric tags for relative and absolute quantification (iTRAQ) labeling coupled with LC-MS/MS in popcorn inbred N04. Comparative quantitative proteomic analyses among developmental stages and between tissues were performed, and the protein networks were integrated.

Mining hidden polymorphic sequence motifs from divergent plant .

As a major driving force of genome evolution, transposons have been deviating from their original connotation as "junk" DNA ever since their important roles were revealed. The recently discovered transposons have been investigated in diverse eukaryotic genomes because of their remarkable gene-capture ability and other features that are crucial to our current understanding of genome dynamics. are not canonical transposons in that they do not end in inverted repeats or create target site duplications, which makes them difficult to identify.

HelitronScanner uncovers a large overlooked cache of Helitron transposons in many plant genomes.

Transposons make up the bulk of eukaryotic genomes, but are difficult to annotate because they evolve rapidly. Most of the unannotated portion of sequenced genomes is probably made up of various divergent transposons that have yet to be categorized. Helitrons are unusual rolling circle eukaryotic transposons that often capture gene sequences, making them of considerable evolutionary importance.

InsertionMapper: a pipeline tool for the identification of targeted sequences from multidimensional high throughput sequencing data.

Background: The advent of next-generation high-throughput technologies has revolutionized whole genome sequencing, yet some experiments require sequencing only of targeted regions of the genome from a very large number of samples. These regions can be amplified by PCR and sequenced by next-generation methods using a multidimensional pooling strategy. However, there is at present no available generalized tool for the computational analysis of target-enriched NGS data from multidimensional pools.

PlantLoc: an accurate web server for predicting plant protein subcellular localization by substantiality motif.

Knowledge of subcellular localizations (SCLs) of plant proteins relates to their functions and aids in understanding the regulation of biological processes at the cellular level. We present PlantLoc, a highly accurate and fast webserver for predicting the multi-label SCLs of plant proteins. The PlantLoc server has two innovative characters: building localization motif libraries by a recursive method without alignment and Gene Ontology information; and establishing simple architecture for rapidly and accurately identifying plant protein SCLs without a machine learning algorithm.

Genome of the long-living sacred lotus (Nelumbo nucifera Gaertn.).

Background: Sacred lotus is a basal eudicot with agricultural, medicinal, cultural and religious importance. It was domesticated in Asia about 7,000 years ago, and cultivated for its rhizomes and seeds as a food crop. It is particularly noted for its 1,300-year seed longevity and exceptional water repellency, known as the lotus effect.

Shape string: a new feature for prediction of DNA-binding residues.

Protein-DNA interactions are involved in many biological processes essential for gene expression and regulation. To understand the molecular mechanisms of protein-DNA recognition, it is crucial to analyze and identify DNA-binding residues of protein-DNA complexes. Here, we proposed a novel descriptor shape string and another two related features shape string PSSM and shape string pair composition to characterize DNA-binding residues.

Predicting gram-positive bacterial protein subcellular localization based on localization motifs.

The subcellular localization of proteins is closely related to their functions. In this work, we propose a novel approach based on localization motifs to improve the accuracy of predicting subcellular localization of Gram-positive bacterial proteins. Our approach performed well on a five-fold cross validation with an overall success rate of 89.

DSP: a protein shape string and its profile prediction server.

Many studies have demonstrated that shape string is an extremely important structure representation, since it is more complete than the classical secondary structure. The shape string provides detailed information also in the regions denoted random coil. But few services are provided for systematic analysis of protein shape string.

Retrieving backbone string neighbors provides insights into structural modeling of membrane proteins.

Identification of protein structural neighbors to a query is fundamental in structure and function prediction. Here we present BS-align, a systematic method to retrieve backbone string neighbors from primary sequences as templates for protein modeling. The backbone conformation of a protein is represented by the backbone string, as defined in Ramachandran space.

Identification of the subcellular localization of mycobacterial proteins using localization motifs.

Mycobacterium, the most common disease-causing genus, infects billions of people and is notoriously difficult to treat. Understanding the subcellular localization of mycobacterial proteins can provide essential clues for protein function and drug discovery. In this article, we present a novel approach that focuses on local sequence information to identify localization motifs that are generated by a merging algorithm and are selected based on a binomially distributed model.

A novel structural position-specific scoring matrix for the prediction of protein secondary structures.

Motivation: The precise prediction of protein secondary structure is of key importance for the prediction of 3D structure and biological function. Although the development of many excellent methods over the last few decades has allowed the achievement of prediction accuracies of up to 80%, progress seems to have reached a bottleneck, and further improvements in accuracy have proven difficult.

Results: We propose for the first time a structural position-specific scoring matrix (SPSSM), and establish an unprecedented database of 9 million sequences and their SPSSMs.

Improving the performance of β-turn prediction using predicted shape strings and a two-layer support vector machine model.

Background: The β-turn is a secondary protein structure type that plays an important role in protein configuration and function. Development of accurate prediction methods to identify β-turns in protein sequences is valuable. Several methods for β-turn prediction have been developed; however, the prediction quality is still a challenge and there is substantial room for improvement.

Using predicted shape string to enhance the accuracy of γ-turn prediction.

Numerous methods for predicting γ-turns in proteins have been developed. However, the results they generally provided are not very good, with a Matthews correlation coefficient (MCC)≤0.18.

Direct decomposition of three-way arrays using a non-negative approximation.

Non-negative matrix approximation (NNMA) has been used in diverse scientific fields, but it still has some major limitations. In the present study a novel trilinear decomposition method, termed three-way NNMA (TWNNMA), was developed. The method decomposes three-way arrays directly without unfolding and overcomes the restriction of locking zero elements in the deduced multiplicative update rules by adding a positive symmetric matrix.

Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data.

Background: Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduction and classification are considerable challenges in statistical machine learning. We therefore propose a novel approach for dimensionality reduction and tested it using published high-resolution SELDI-TOF data for ovarian cancer.

Local combinational variables: an approach used in DNA-binding helix-turn-helix motif prediction with sequence information.

Sequence-based approach for motif prediction is of great interest and remains a challenge. In this work, we develop a local combinational variable approach for sequence-based helix-turn-helix (HTH) motif prediction. First we choose a sequence data set for 88 proteins of 22 amino acids in length to launch an optimized traversal for extracting local combinational segments (LCS) from the data set.