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Since the importance of DNA-binding proteins in multiple biomolecular functions has been recognized, an increasing number of researchers are attempting to identify DNA-binding proteins. In recent years, the machine learning methods have become more and more compelling in the case of protein sequence data soaring, because of their favorable speed and accuracy. In this paper, we extract three features from the protein sequence, namely NMBAC (Normalized Moreau-Broto Autocorrelation), PSSM-DWT (Position-specific scoring matrix-Discrete Wavelet Transform), and PSSM-DCT (Position-specific scoring matrix-Discrete Cosine Transform). We also employ feature selection algorithm on these feature vectors. Then, these features are fed into the training SVM (support vector machine) model as classifier to predict DNA-binding proteins. Our method applys three datasets, namely PDB1075, PDB594 and PDB186, to evaluate the performance of our approach. The PDB1075 and PDB594 datasets are employed for Jackknife test and the PDB186 dataset is used for the independent test. Our method achieves the best accuracy in the Jacknife test, from 79.20% to 86.23% and 80.5% to 86.20% on PDB1075 and PDB594 datasets, respectively. In the independent test, the accuracy of our method comes to 76.3%. The performance of independent test also shows that our method has a certain ability to be effectively used for DNA-binding protein prediction. The data and source code are at https://doi.org/10.6084/m9.figshare.5104084.

The 14-3-3 proteins are a highly conserved family of homodimeric and heterodimeric molecules, expressed in all eukaryotic cells. In human cells, this family consists of seven distinct but highly homologous 14-3-3 isoforms. 14-3-3σ is the only isoform directly linked to cancer in epithelial cells, which is regulated by major tumor suppressor genes. For each 14-3-3 isoform, we have 1,000 peptide motifs with experimental binding affinity values. In this paper, we present a novel method for identifying peptide motifs binding to 14-3-3σ isoform. First, we propose a sampling criteria to build a predictor for each new peptide sequence. Then, we select nine physicochemical properties of amino acids to describe each peptide motif. We also use auto-cross covariance to extract correlative properties of amino acids in any two positions. Finally, we consider elastic net to predict affinity values of peptide motifs, based on ridge regression and least absolute shrinkage and selection operator (LASSO). Our method tests on the 1,000 known peptide motifs binding to seven 14-3-3 isoforms. On the 14-3-3σ isoform, our method has overall pearson-product-moment correlation coefficient (PCC) and root mean squared error (RMSE) values of 0.84 and 252.31 for N-terminal sublibrary, and 0.77 and 269.13 for C-terminal sublibrary. We predict affinity values of 16,000 peptide sequences and relative binding ability across six permutated positions similar with experimental values. We identify phosphopeptides that preferentially bind to 14-3-3σ over other isoforms. Several positions on peptide motifs are in the same amino acid category with experimental substrate specificity of phosphopeptides binding to 14-3-3σ. Our method is fast and reliable and is a general computational method that can be used in peptide-protein binding identification in proteomics research.

Current tools used in the reconstruction of ancestral gene orders often fall into event-based and adjacency-based methods according to the principles they follow. Event-based methods such as GRAPPA are very accurate but with extremely high complexity, while more recent methods based on gene adjacencies such as InferCARsPro is relatively faster, but often produces an excessive number of chromosomes. This issue is mitigated by newer methods such as GapAdj, however it sacrifices a considerable portion of accuracy. We recently developed an adjacency-based method in the probabilistic framework called PMAG to infer ancestral gene orders. PMAG relies on calculating the conditional probabilities of gene adjacencies that are found in the leaf genomes using the Bayes' theorem. It uses a novel transition model which accounts for adjacency changes along the tree branches as well as a re-rooting procedure to prevent any information loss. In this paper, we improved PMAG with a new method to assemble gene adjacencies into valid gene orders, using an exact solver for traveling salesman problem (TSP) to maximize the overall conditional probabilities. We conducted a series of simulation experiments using a wide range of configurations. The first set of experiments was to verify the effectiveness of our strategy of using the better transition model and re-rooting the tree under the targeted ancestral genome. PMAG was then thoroughly compared in terms of three measurements with its four major competitors including InferCARsPro, GapAdj, GASTS and SCJ in order to assess their performances. According to the results, PMAG demonstrates superior performance in terms of adjacency, distance and assembly accuracies, and yet achieves comparable running time, even all TSP instances were solved exactly. PMAG is available for free at http://phylo.cse.sc.edu.

BackgroundMicrosatellite markers or Simple Sequence Repeats (SSRs) are the most popular markers in population/conservation genetics. However, the development of novel microsatellite markers has been impeded by high costs, a lack of available sequence data and technical difficulties. New species-specific microsatellite markers were required to investigate the evolutionary history of the Euphratica tree, Populus euphratica, the only tree species found in the desert regions of Western China and adjacent Central Asian countries.Methodology/principal findingsA total of 94,090 non-redundant Expressed Sequence Tags (ESTs) from P. euphratica comprising around 63 Mb of sequence data were searched for SSRs. 4,202 SSRs were found in 3,839 ESTs, with 311 ESTs containing multiple SSRs. The most common motif types were trinucleotides (37%) and hexanucleotides (33%) repeats. We developed primer pairs for all of the identified EST-SSRs (eSSRs) and selected 673 of these pairs at random for further validation. 575 pairs (85%) gave successful amplification, of which, 464 (80.7%) were polymorphic in six to 24 individuals from natural populations across Northern China. We also tested the transferability of the polymorphic eSSRs to nine other Populus species. In addition, to facilitate the use of these new eSSR markers by other researchers, we mapped them onto Populus trichocarpa scaffolds in silico and compiled our data into a web-based database (http://202.205.131.253:8080/poplar/resources/static_page/index.html).ConclusionsThe large set of validated eSSRs identified in this work will have many potential applications in studies on P. euphratica and other poplar species, in fields such as population genetics, comparative genomics, linkage mapping, QTL, and marker-assisted breeding. Their use will be facilitated by their incorporation into a user-friendly web-based database.

Recent advancement of technologies has now made it routine to obtain and compare gene orders within genomes. Rearrangements of gene orders by operations such as reversal and transposition are rare events that enable researchers to reconstruct deep evolutionary histories. An important application of genome rearrangement analysis is to infer gene orders of ancestral genomes, which is valuable for identifying patterns of evolution and for modeling the evolutionary processes. Among various available methods, parsimony-based methods (including GRAPPA and MGR) are the most widely used. Since the core algorithms of these methods are solvers for the so called median problem, providing efficient and accurate median solver has attracted lots of attention in this field. The "double-cut-and-join" (DCJ) model uses the single DCJ operation to account for all genome rearrangement events. Because mathematically it is much simpler than handling events directly, parsimony methods using DCJ median solvers has better speed and accuracy. However, the DCJ median problem is NP-hard and although several exact algorithms are available, they all have great difficulties when given genomes are distant. In this paper, we present a new algorithm that combines genetic algorithm (GA) with genomic sorting to produce a new method which can solve the DCJ median problem in limited time and space, especially in large and distant datasets. Our experimental results show that this new GA-based method can find optimal or near optimal results for problems ranging from easy to very difficult. Compared to existing parsimony methods which may severely underestimate the true number of evolutionary events, the sorting-based approach can infer ancestral genomes which are much closer to their true ancestors. The code is available at http://phylo.cse.sc.edu.

Maximum parsimony (MP) methods aim to reconstruct the phylogeny of extant species by finding the most parsimonious evolutionary scenario using the species' genome data. MP methods are considered to be accurate, but they are also computationally expensive especially for a large number of species. Several disk-covering methods (DCMs), which decompose the input species to multiple overlapping subgroups (or disks), have been proposed to solve the problem in a divide-and-conquer way. We design a new DCM based on the spectral method and also develop the COGNAC (Comparing Orders of Genes using Novel Algorithms and high-performance Computers) software package. COGNAC uses the new DCM to reduce the phylogenetic tree search space and selects an output tree from the reduced search space based on the MP principle. We test the new DCM using gene order data and inversion distance. The new DCM not only reduces the number of candidate tree topologies but also excludes erroneous tree topologies which can be selected by original MP methods. Initial labeling of internal genomes affects the accuracy of MP methods using gene order data, and the new DCM enables more accurate initial labeling as well. COGNAC demonstrates superior accuracy as a consequence. We compare COGNAC with FastME and the combination of the state of the art DCM (Rec-I-DCM3) and GRAPPA. COGNAC clearly outperforms FastME in accuracy. COGNAC--using the new DCM--also reconstructs a much more accurate tree in significantly shorter time than GRAPPA with Rec-I-DCM3.

Research in bioinformatics primarily involves collection and analysis of a large volume of genomic data. Naturally, it demands efficient storage and transfer of this huge amount of data. In recent years, some research has been done to find efficient compression algorithms to reduce the size of various sequencing data. One way to improve the transmission time of large files is to apply a maximum lossless compression on them. In this paper, we present SAMZIP, a specialized encoding scheme, for sequence alignment data in SAM (Sequence Alignment/Map) format, which improves the compression ratio of existing compression tools available. In order to achieve this, we exploit the prior knowledge of the file format and specifications. Our experimental results show that our encoding scheme improves compression ratio, thereby reducing overall transmission time significantly.

Methanogens are a phylogenetically diverse group belonging to Euryarchaeota. Previously, phylogenetic approaches using large datasets revealed that methanogens can be grouped into two classes, "Class I" and "Class II". However, some deep relationships were not resolved. For instance, the monophyly of "Class I" methanogens, which consist of Methanopyrales, Methanobacteriales and Methanococcales, is disputable due to weak statistical support. In this study, we use MSOAR to identify common orthologous genes from eight methanogen species and a Thermococcale species (outgroup), and apply GRAPPA and FastME to compute distance-based gene order phylogeny. The gene order phylogeny supports two classes of methanogens, but it differs from the original classification of methanogens by placing Methanopyrales and Methanobacteriales together with Methanosarcinales in Class II rather than with Methanococcales. This study suggests a new classification scheme for methanogens. In addition, it indicates that gene order phylogeny can complement traditional sequence-based methods in addressing taxonomic questions for deep relationships.

Using gene order as a phylogenetic character has the potential to resolve previously unresolved species relationships. This character was used to resolve the evolutionary history within the genus Prochlorococcus, a group of marine cyanobacteria.Orthologous gene sets and their genomic positions were identified from 12 species of Prochlorococcus and 1 outgroup species of Synechococcus. From this data, inversion and breakpoint distance-based phylogenetic trees were computed by GRAPPA and FastME. Statistical support of the resulting topology was obtained by application of a 50% jackknife resampling technique. The result was consistent and congruent with nucleotide sequence-based and gene-content based trees. Also, a previously unresolved clade was resolved, that of MIT9211 and SS120.This is the first study to use gene order data to resolve a bacterial phylogeny at the genus level. It suggests that the technique is useful in resolving the Tree of Life.