Sequence-based Algorithm Research Articles

A moving block sequence-based evolutionary algorithm for resource-constrained project scheduling problems

A moving block sequence-based evolutionary algorithm for resource-constrained project scheduling problems

A moving block sequence-based evolutionary algorithm for resource-constrained project scheduling problems

In this paper, a new representation for resource-constrained project scheduling problems (RCPSPs), namely moving block sequence (MBS), is proposed. In RCPSPs, every activity has fixed duration and resource demands, therefore, it can be modelled as a rectangle block whose height represents the resource demand and width the duration. Naturally, a project that consists of N activities can be represented as the permutation of N blocks that satisfy the precedence constraints among activities. To decode an MBS to a valid schedule, four move modes are designed according to the situations that how every block can be moved from its initial position to an appropriate location that can minimise the makespan of the project. Based on MBS, the multiagent evolutionary algorithm (MAEA) is used to solve RCPSPs. The proposed algorithm is labelled as MBSMAEA-RCPSP, and by comparing with several state-of-the-art algorithms on benchmark J30, J60, J90 and J120, the effectiveness of MBSMAEA-RCPSP is clearly illustrated.

An Anti-Interference Synchronization for OFDM Systems Based on Scrambling Sequence

To improve the performance of the synchronization in OFDM systems in the presence of interference, a new synchronization algorithm based on scrambling sequence is proposed in this paper, which redesigns the training sequence with the constant amplitude zero auto-correlation (CAZAC) sequence and an extra scrambling operation. Compared with the traditional scrambling sequence-based algorithm, the simulations carried out both in AWGN and COST 207 4-path rural area (RA) channel show that the proposed synchronization algorithm can still maintain the sharper correlation peak, and achieve accurate symbol timing estimation and carrier frequency offset (CFO) estimation in the interference environment, with a lower out-of-band energy of the spectrum due to a new scrambling sequence. Furthermore, owing to the particularity of the component elements from the scrambling sequence, a low complexity implementation structure is presented here to realize the correlation function of the proposed algorithm with fewer hardware resources.

Detection of cooperatively bound transcription factor pairs using ChIP-seq peak intensities and expectation maximization.

Transcription factors (TFs) often work cooperatively, where the binding of one TF to DNA enhances the binding affinity of a second TF to a nearby location. Such cooperative binding is important for activating gene expression from promoters and enhancers in both prokaryotic and eukaryotic cells. Existing methods to detect cooperative binding of a TF pair rely on analyzing the sequence that is bound. We propose a method that uses, instead, only ChIP-seq peak intensities and an expectation maximization (CPI-EM) algorithm. We validate our method using ChIP-seq data from cells where one of a pair of TFs under consideration has been genetically knocked out. Our algorithm relies on our observation that cooperative TF-TF binding is correlated with weak binding of one of the TFs, which we demonstrate in a variety of cell types, including E. coli, S. cerevisiae and M. musculus cells. We show that this method performs significantly better than a predictor based only on the ChIP-seq peak distance of the TFs under consideration. This suggests that peak intensities contain information that can help detect the cooperative binding of a TF pair. CPI-EM also outperforms an existing sequence-based algorithm in detecting cooperative binding. The CPI-EM algorithm is available at https://github.com/vishakad/cpi-em.

An in-silico method for identifying aggregation rate enhancer and mitigator mutations in proteins

Newly synthesized polypeptides must pass stringent quality controls in cells to ensure appropriate folding and function. However, mutations, environmental stresses and aging can reduce efficiencies of these controls, leading to accumulation of protein aggregates, amyloid fibrils and plaques. In-vitro experiments have shown that even single amino acid substitutions can drastically enhance or mitigate protein aggregation kinetics. In this work, we have collected a dataset of 220 unique mutations in 25 proteins and classified them as enhancers or mitigators on the basis of their effect on protein aggregation rate. The data were analyzed via machine learning to identify features capable of distinguishing between aggregation rate enhancers and mitigators. Our initial Support Vector Machine (SVM) model separated such mutations with an overall accuracy of 69%. When local secondary structures at the mutation sites were considered, the accuracies further improved by 13–15%. The machine-learnt features are distinct for each secondary structure class at mutation sites. Protein stability and flexibility changes are important features for mutations in α-helices. β-strand propensity, polarity and charge become important when mutations occur in β-strands and ability to form secondary structure, helical tendency and aggregation propensity are important for mutations lying in coils. These results have been incorporated into a sequence-based algorithm (available at http://www.iitm.ac.in/bioinfo/aggrerate-disc/) capable of predicting whether a mutation will enhance or mitigate a protein's aggregation rate.This algorithm will find several applications towards understanding protein aggregation in human diseases, enable in-silico optimization of biopharmaceuticals and enzymes for improved biophysical attributes and de novo design of bio-nanomaterials.

Hidden long evolutionary memory in a model biochemical network.

We introduce a minimal model for the evolution of functional protein-interaction networks using a sequence-based mutational algorithm, and apply the model to study neutral drift in networks that yield oscillatory dynamics. Starting with a functional core module, random evolutionary drift increases network complexity even in the absence of specific selective pressures. Surprisingly, we uncover a hidden order in sequence space that gives rise to long-term evolutionary memory, implying strong constraints on network evolution due to the topology of accessible sequence space.

SPRINT: ultrafast protein-protein interaction prediction of the entire human interactome

BackgroundProteins perform their functions usually by interacting with other proteins. Predicting which proteins interact is a fundamental problem. Experimental methods are slow, expensive, and have a high rate of error. Many computational methods have been proposed among which sequence-based ones are very promising. However, so far no such method is able to predict effectively the entire human interactome: they require too much time or memory.ResultsWe present SPRINT (Scoring PRotein INTeractions), a new sequence-based algorithm and tool for predicting protein-protein interactions. We comprehensively compare SPRINT with state-of-the-art programs on seven most reliable human PPI datasets and show that it is more accurate while running orders of magnitude faster and using very little memory.ConclusionSPRINT is the only sequence-based program that can effectively predict the entire human interactome: it requires between 15 and 100 min, depending on the dataset. Our goal is to transform the very challenging problem of predicting the entire human interactome into a routine task.AvailabilityThe source code of SPRINT is freely available from https://github.com/lucian-ilie/SPRINT/and the datasets and predicted PPIs from www.csd.uwo.ca/faculty/ilie/SPRINT/.

Bacterial Expression of Human Butyrylcholinesterase as a Tool for Nerve Agent Bioscavengers Development.

Human butyrylcholinesterase is a performant stoichiometric bioscavenger of organophosphorous nerve agents. It is either isolated from outdated plasma or functionally expressed in eukaryotic systems. Here, we report the production of active human butyrylcholinesterase in a prokaryotic system after optimization of the primary sequence through the Protein Repair One Stop Shop process, a structure- and sequence-based algorithm for soluble bacterial expression of difficult eukaryotic proteins. The mutant enzyme was purified to homogeneity. Its kinetic parameters with substrate are similar to the endogenous human butyrylcholinesterase or recombinants produced in eukaryotic systems. The isolated protein was prone to crystallize and its 2.5-Å X-ray structure revealed an active site gorge region identical to that of previously solved structures. The advantages of this alternate expression system, particularly for the generation of butyrylcholinesterase variants with nerve agent hydrolysis activity, are discussed.

SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides

BackgroundCell-penetrating peptides (CPPs) are short peptides (5–30 amino acids) that can enter almost any cell without significant damage. On account of their high delivery efficiency, CPPs are promising candidates for gene therapy and cancer treatment. Accordingly, techniques that correctly predict CPPs are anticipated to accelerate CPP applications in future therapeutics. Recently, computational methods have been reportedly successful in predicting CPPs. Unfortunately, the predictive performance of existing methods is not satisfactory and reliable so as to accurately identify CPPs.ResultsIn this study, we propose a novel computational predictor called SkipCPP-Pred to further improve the predictive performance. The novelty of the proposed predictor is that we present a sequence-based feature representation algorithm called adaptive k-skip-n-gram that sufficiently captures the intrinsic correlation information of residues. By fusing the proposed adaptive skip features with a random forest (RF) classifier, we successfully construct the prediction model of SkipCPP-Pred. The various jackknife results demonstrate that the proposed SkipCPP-Pred is 3.6% higher than state-of-the-art CPP predictors in terms of accuracy. Moreover, we construct a high-quality benchmark dataset by reducing the data redundancy and enhancing the similarity between the positive and negative classes. Using this dataset to build prediction models, we can successfully avoid the performance bias lying in existing methods and yield a promising predictive model.ConclusionsThe proposed SkipCPP-Pred is a simple and fast sequence-based predictor featured with the adaptive k-skip-n-gram model for the improved prediction of CPPs. Currently, SkipCPP-Pred is publicly available from an online webserver (http://server.malab.cn/SkipCPP-Pred/Index.html).

ToaSTR: A web-based forensic tool for the analysis of short tandem repeats in massively parallel sequencing data

We developed the web application toaSTR (www.toastr.de), a user-friendly tool for STR allele calling in MPS data independent of the instrument platform and the forensic kit used. toaSTR comes up with a clean, intuitive graphical user interface and well-documented parameter settings. Users can select from a wide range of STR markers to configure custom marker panels. This software supports both commercial and in-house multiplex PCR kits as well as various library preparation chemistries. Its sequence-based stutter-modelling algorithm automatically distinguishes biological (iso-)alleles, stutter, and artefacts to assist the interpretation of mixed samples. toaSTR features comprehensive data visualization with interactive diagrams and an adjustable tabular overview of sequence observations. Results are concordant with CE-based fragment analysis and can be exported for further analysis in biostatistical software or as PDF document with sequence description according to the ISFG-recommended nomenclature. An initial compatibility and concordance study confirmed the software’s functionality and concordant allele calling across different MPS platforms, STR amplification kits, and library preparation chemistries.

A novel hybrid algorithm for generalized traveling salesman problems in different environments

A swap sequence-based particle swarm optimization (SSPSO) technique and genetic algorithm (GA) are used in tandem to develop a hybrid algorithm to solve generalized traveling salesman problem. Local search algorithm K-Opt is occasionally used to move any stagnant solution. Here, SSPSO is used to find the sequence of groups of a solution in which a tour to be made and cities from different groups of the sequence are selected using GA. The K-Opt algorithm (for K=3) is used periodically for a predefined number of iterations to improve the quality of the solutions. The algorithm is capable of solving the problem in crisp as well as in imprecise environment. For this purpose, a general fitness evaluation rule for the solutions is proposed. The efficiency of the algorithm is tested in crisp environment using different size benchmark problems available in TSPLIB. In crisp environment, the algorithm gives 100% success rate for problems up to considerably large sizes. Imprecise problems are generated from crisp problems randomly using a rule and are solved using the proposed approach. The obtained results are discussed. Moreover it is observed that the proposed algorithm finds multiple optimal paths, when they exist, both for the crisp problems and their fuzzy variations.

BPP: a sequence-based algorithm for branch point prediction.

Although high-throughput sequencing methods have been proposed to identify splicing branch points in the human genome, these methods can only detect a small fraction of the branch points subject to the sequencing depth, experimental cost and the expression level of the mRNA. An accurate computational model for branch point prediction is therefore an ongoing objective in human genome research. We here propose a novel branch point prediction algorithm that utilizes information on the branch point sequence and the polypyrimidine tract. Using experimentally validated data, we demonstrate that our proposed method outperforms existing methods. Availability and implementation: https://github.com/zhqingit/BPP. djguo@cuhk.edu.hk. Supplementary data are available at Bioinformatics online.

A moving block sequence-based evolutionary algorithm for resource investment project scheduling problems

Inspired by the representation designed for floorplanning problems, in this paper, we proposed a new representation, namely the moving block sequence (MBS), for resource investment project scheduling problems (RIPSPs). Since each activity of a project in RIPSPs has fixed duration and resource demand, we consider an activity as a rectangle block whose width is equal to the duration of the activity and height the resource needed by the activity. Four move modes are designed for activities, by using which the activity can move to the appropriate position. Therefore, the new representation of the project of RIPSPs consists of two parts: an activity list and a move mode list. By initializing the move modes randomly for each activity and moving it appropriately, the activity list can be decoded into valid solutions of RIPSPs. Since the decoding method of MBS guarantees that after moved, each activity is scheduled in the left-most and bottom-most position within a coordinate, which means that each activity in the corresponding project is arranged as early as possible when the precedence constraints and resource demands are satisfied. In addition, the multiagent evolutionary algorithm (MAEA) is employed to incorporate with the newly designed MBS representation in solving RIPSPs. With the intrinsic properties of MBS in mind, four behaviors, namely the crossover, mutation, competition, and self-learning operators are designed for agents in MAEA. To test the performance of our algorithm, 450 problem instances are used and the experimental results demonstrate the good performance of the proposed representation.

Improved sequence-based localization applied in coal mine

Complex geographical environment brings tremendous challenges to get information of localization in underground coal mines. Sequence-based localization is a simple method; without calculating distance during the positioning stage in real time, this method uses the received signal strength indication matched degree between unknown node and regions to locate. However, sequence-based localization has a great issue on poor marginal nodes localization. Sequence–centroid localization contributes to improving this issue, but the location error on the boundary of whole area is unsatisfactory as well. This article proposes an improved sequence-based localization method which is integrated with quantum-behaved particle swarm optimization, as quantum-behaved particle swarm optimization makes good use of the search performance of global optimal solution. In our simulation, we consider that ZigBee devices can be used to construct wireless sensor networks and locate personnel location. The results prove that the improved sequence-based localization algorithm provides comparable accuracy than sequence-based localization.

Routed roads: Probabilistic vision-based place recognition for changing conditions, split streets and varied viewpoints

Vision-based place recognition is becoming an increasingly viable component of navigation systems for autonomous robots and personal aids. However, attaining robustness to variations in environmental conditions—such as time of day, weather and season—and camera viewpoint remains a major challenge. Featureless, sequence-based place recognition techniques have demonstrated promise, but often rely on long image sequences, manually-tuned parameters and exhaustive sequence match searching through multiple locations and image scales. In this paper, we address these deficiencies by implementing a condition-invariant, sequence-based place recognition algorithm suitable for networked environments, such as city streets, and routes with lateral platform shift, such as multiple-lane roads. We achieve this capability by augmenting the traditional 1D image database with a directed graph to describe the branching of contiguous sections of imagery at intersections. A particle filter is then used to efficiently explore these paths, as well as various lateral positions synthesized by rescaling imagery. Our proposed approach eliminates manual tuning of sequence length parameters, improves localization on branched routes, improves overall place recognition accuracy and coverage, and reduces computational requirements. We evaluated the new method against the original SeqSLAM and SMART algorithms on two day–night, road-based datasets and a summer–winter train dataset, where it attained superior precision-recall performance and coverage in all environments. Together, these contributions represent a significant step towards the provision of a robust, near parameter-free condition- and viewpoint-invariant visual place recognition capability for vehicles and robots.

Revealing aperiodic aspects of solenoid proteins from sequence information.

Repeat proteins, which contain multiple repeats of short sequence motifs, form a large but seldom-studied group of proteins. Methods focusing on the analysis of 3D structures of such proteins identified many subtle effects in length distribution of individual motifs that are important for their functions. However, similar analysis was yet not applied to the vast majority of repeat proteins with unknown 3D structures, mostly because of the extreme diversity of the underlying motifs and the resulting difficulty to detect those. We developed FAIT, a sequence-based algorithm for the precise assignment of individual repeats in repeat proteins and introduced a framework to classify and compare aperiodicity patterns for large protein families. FAIT extracts repeat positions by post-processing FFAS alignment matrices with image processing methods. On examples of proteins with Leucine Rich Repeat (LRR) domains and other solenoids like proteins, we show that the automated analysis with FAIT correctly identifies exact lengths of individual repeats based entirely on sequence information. https://github.com/GodzikLab/FAIT CONTACT: adam@godziklab.org Supplementary data are available at Bioinformatics online.

Toner Savings Based on Quasi-Random Sequences and a Perceptual Study for Green Printing.

Toner savings in monochromatic printing are an important target for improving green computing performance and more specifically green printing. In order to extend the lifetime of the printer cartridge, some options are available for laser printers, usually reducing the number of dots with respect to the normal print quality. However available algorithms and patents do not provide a method for dynamically adapting the percentage of toner savings to the required printing quality. In this paper, we introduce a new quasi-random sequence-based algorithm for reducing the number of dots in the printing process, able to achieve optimal discrepancy and low computational complexity, for all print quality levels. In order to reduce patterns in the removed dots, blue noise dithering is applied when the desired percentage of toner savings is moderate. The proposed solution can be easily implemented in the printer firmware, given its low computational complexity. In order to verify the results from a perceptual point of view, an extended test with 135 volunteers and more than 5000 comparisons has been performed, besides checking that toner is effectively saved. Results show that the proposed approach can produce a reduction of the perceived quality almost directly proportional to the number of monochromatic dots skipped, with only a reduced influence from the font used. The perceptual results are better in the proposal than in the previous approaches. The proposed algorithm appears to be a promising technique for improving green printing in monochromatic laser printers without using custom fonts.

Force Spectroscopy of Nucleosomes at the Promoters of the Lh Genes Reveals two Distinct Strategies for their Regulation

Luteinizing hormone (LH) is a heterodimeric protein that regulates function of the gonads, and is composed of two subunits, which are encoded by two different genes: chorionic gonadotropin-a (Cga) and Lhs. A sequence-based algorithm designed to predict nucleosome positions on DNA suggests that the promoters of both genes, and in particular the binding regions for important transcription factors, are packed into nucleosomes. Since gene expression requires that the promoter be accessible to the cellular transcription machinery, we hypothesized that nucleosome positioning regulates basal expression of these genes. We are performing single-molecule force spectroscopy with optical tweezers, complemented by MNase mapping, to probe the architecture of chromatin on both gene promoters. In gonadotrope cells, the promoter of Lhs is occupied by a nucleosome containing the variant H2A.Z, while the Cga promoter appears nucleosome-depleted. Optical tweezers experiments show that a nucleosome reconstituted on the Cga promoter is weaker, and poorly localized, as compared to the one on the promoter of LHs. This may suggest that it is evicted, in vivo, by chromatin remodelers. In contrast, the first nucleosome downstream of the transcription start site contains H2A.Z in Cga but H2A in Lhs, and is well localized on both genes, although more weakly bound on the Lhs than the Cga gene. Hence, these two genes are apparently regulated by distinct strategies, tailored to obtain higher expression for Cga, which encodes a subunit shared also by other hormones, and lower for Lhs, which is unique to this hormone. Our results highlight how DNA sequence, the usage of alternative histone variants and, possibly, remodeling machinery act synergistically to modulate gene expression.

Regulation of CD19 Exon 2 Inclusion in B-Lymphoid Cells By Splicing Factors and Epigenetic Marks

CD19 is expressed broadly on the surface of B-cells during normal development and malignant growth, making it a good target for immunotherapy. While immunotherapies targeting CD19 have had great success against pediatric B-cell acute lymphoblastic leukemia (B-ALL), relapses lacking the CD19 epitope still occur (Maude et al., 2014). We have discovered that alternative splicing of CD19, in particular the skipping of exon 2, is responsible for the loss of CD19 extracellular domains, causing resistance to therapy (Sotillo et al., 2015). Here we investigate the molecular mechanism of CD19 exon 2 skipping.The sequence-based algorithm AVISPA (Barash et al., 2013) predicts several splicing factors (SF) to bind near exon 2. We used RNA crosslink immunoprecipitation (CLIP) in nuclear lysates from Nalm-6 B-ALL cells to test the direct binding to exon 2 of 9 AVISPA-predicted SFs and 6 SFs commonly involved in exon skipping. This allowed us to identify SRSF3, hnRNP-A, and hnRNP-C as CD19 exon 2-bound proteins. Subsequent siRNA knockdown experiments reveled that downregulation of SRSF3, but not hnRNP-C, increases the frequency of exon 2 skipping in a dose dependent manner, suggesting that SRSF3 promotes the inclusion of exon 2. To further validate the role of SRSF3 in CD19 splicing we mined the publicly available GSE52834 dataset where 22 RNA binding proteins were knocked down in the GM19238 lymphoblastoid cell line. Of all siRNAs tested, only the anti-SRSF3 siRNA caused an increase in exon 2 skipping, suggesting that SRSF3 is indeed the key regulator of CD19 splicing.Interestingly, SRSF3 has been shown to interact with PSIP1, a cofactor known to "read" modified histone H3K36me3 (Pradeepa et al., 2012), suggesting a convergence of splicing-based and epigenetics mechanisms. Indeed, exonic regions in genomic DNA are enriched for H3K36me3, and knockdown of Setd2, the H3K36 methyltransferase, results in changes in exon inclusion (Luco et al., 2010; Brown et al., 2012; Hnilicova and Stanek, 2011). Thus, we are currently investigating the connection between the H3K36me3 marks in the CD19 locus and alternative splicing of CD19. Our data could suggest a method of restoring full-length CD19 expression in immunotherapy-resistant cancers using epigenetic drugs.Maude, S L, Noelle, F, Shaw, PA, Aplenc, R, Barrett, DM, Bunin, NJ, Chew, A, Gonzalez, VE, Zheng, Z, Lacey, SF, Mahnke, YD, Melenhorst, JJ, Rheingold, SR, Shen, A, Teachey, DT, Levine, BL, June CH, Porter, DL, and Grupp, SA. Chimeric antigen receptor T cells for sustained remissions in leukemia. N Engl J Med 2014; 371: 1507-1517.Sotillo, E, Barrett, D, Bagashev, A, Black, K, Lanauze, C, Oldridge, D, Sussman, R, Harrington, C, Chung, EY, Hofmann, TJ, Maude, SL, Martinez, NM, Raman, P, Ruella, M, Allman, D, Jacoby, E, Fry, T, Barash, Y, Lynch, KW, Mackall, C, Maris, J, Grupp, SA, and Thomas-Tikhonenko, A. Alternative splicing of CD19 mRNA in leukemias escaping CART-19 immunotherapy eliminates the cognate epitope andcontributes to treatment failure. 2015AACR Annual Meeting, Philadelphia.Barash Y, Vaquero-Garcia J, González-Vallinas J, Xiong HY, Gao W, Lee LJ, and Frey BJ. AVISPA: a web tool for the prediction and analysis of alternative splicing. Genome Biol 2013; 14(10):R114.Pradeepa, MM, Sutherland, HG, Ule, J, Grimes, GR, and Bickmore, WA. Psip1/Ledgf p52 binds methylated histone H3K36 and splicing factors and contributes to the regulation of alternative splicing. PLOS Genets 2012; 8:e1002717.Luco, RF, Pan, Q, Tominaga, K, Blencowe, BJ, Pereira-Smith, OM, Misteli, T. Regulation of alternative splicing by histone modifications. Science 2010; 327: 996-1000.Brown, SJ, Stoilov, P, and Xing, Y. Chromatin and epigenetic regulation of pre-mRNA processing. Human Mol Genets 2012; 21:R90-R96.Hnilicova, J, and Stanek, D. Where splicing joins chromatin. Nucleus 2011; 2:182-188. DisclosuresNo relevant conflicts of interest to declare.

Identification of universal selectivity‐determining positions in cytochrome P450 monooxygenases by systematic sequence‐based literature mining

Cytochrome P450 monooxygenases (CYPs) are a large, highly diverse protein family with a common fold. The sequences, structures, and functions of CYPs have been extensively studied resulting in more than 53,000 scientific articles. A sequence-based literature mining algorithm was designed to systematically analyze this wealth of information on SNPs, designed mutations, structural interactions, or functional roles of individual residues. Structurally corresponding positions in different CYPs were compared and universal selectivity-determining positions were identified. Based on the Cytochrome P450 Engineering Database (www.CYPED.BioCatNet.de) and a standard numbering scheme for all CYPs, 4000 residues in 168 CYPs mentioned in 2400 articles could be assigned to 440 structurally corresponding standard positions of the CYP fold, covering 96% of all standard positions. Seventeen individual standard positions were mentioned in the context of more than 32 different CYPs. The majority of these most frequently mentioned positions are located on the six substrate recognition sites and are involved in control of selectivity, such as the well-studied position 87 in CYP102A1 (P450(BM-3)) which was mentioned in the articles on 63 different CYPs. The recurrent citation of the 17 frequently mentioned positions for different CYPs suggests their universal functional relevance.