High-scoring Pairs Research Articles

High-throughput primer design by scoring in piecewise logistic model for multiple polymerase chain reaction variants

Polymerase chain reaction (PCR) variants requiring specific primer types are widely used in various PCR experiments, including generic PCR, inverse PCR, anchored PCR, and ARMS PCR. Few tools can be adapted for multiple PCR variants, and many tools select primers by filtration based on the given parameters, which result in frequent design failures. Here we introduce PrimerScore2, a robust high-throughput primer design tool that can design primers in one click for multiple PCR variants. It scores primers using a piecewise logistic model and the highest-scored primers are selected avoiding the issue of design failure and the necessity to loosen parameters to redesign, and it creatively evaluates specificity by predicting the efficiencies of all target/non-target products. To assess the prediction accuracy of the scores and efficiencies, two next generation sequencing (NGS) libraries were constructed—a 12-plex and a 57-plex—and the results showed that 17 out of 19 (89.5%) low-scoring pairs had a poor depth, 18 out of 19 (94.7%) high-scoring pairs had a high depth, and the depth ratios of the products were linearly correlated with the predicted efficiencies with a slope of 1.025 and a coefficient of determination (R2) 0.935. 116-plex and 114-plex anchored PCR panels designed by PrimerScore2 were applied to 26 maternal plasma samples with male fetuses, the results showed that the predicted fetal DNA fractions were concordant with fractions measured in gold standard method (Y fractions). PrimerScore2 was also used to design 77 monoplex Sanger sequencing primers, the sequencing results indicated that all the primers were effective.

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling.

Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions (PPIs) and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top-ranked pairs with high contact probability were supported by known PPIs and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2 and ECSIT-NDUFAF1. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in. Data of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondria. Supplementary data are available at Bioinformatics online.

RboAnalyzer webserver: web service for non-coding RNA characterization from NCBI BLAST output.

We present a web service for improving characterization of non-coding RNAs (ncRNAs) from NCBI BLAST outputs, based on a command-line application rboAnalyzer. Briefly, the application extends subject sequences of selected high scoring pairs (HSPs) in BLAST output to their plausible full length, and predicts their homology and secondary structures. The aim of the application is to aid to characterize subject RNAs in HSPs that come uncharacterized in BLAST output. The main advantages of the web-server are easy use and interactive analysis with search, filtering and data export options. The web server is freely available at rboanalyzer.elixir-czech.cz. The website frontend is implemented in Elm, while backend is implemented in Python and served by Apache.

Comparative analysis of sequence covariation methods to mine evolutionary hubs: Examples from selected GPCR families

Covariation between positions in a multiple sequence alignment may reflect structural, functional, and/or phylogenetic constraints and can be analyzed by a wide variety of methods. We explored several of these methods for their ability to identify covarying positions related to the divergence of a protein family at different hierarchical levels. Specifically, we compared seven methods on a model system composed of three nested sets of G-protein-coupled receptors (GPCRs) in which a divergence event occurred. The covariation methods analyzed were based on: χ2 test, mutual information, substitution matrices, and perturbation methods. We first analyzed the dependence of the covariation scores on residue conservation (measured by sequence entropy), and then we analyzed the networking structure of the top pairs. Two methods out of seven--OMES (Observed minus Expected Squared) and ELSC (Explicit Likelihood of Subset Covariation)--favored pairs with intermediate entropy and a networking structure with a central residue involved in several high-scoring pairs. This networking structure was observed for the three sequence sets. In each case, the central residue corresponded to a residue known to be crucial for the evolution of the GPCR family and the subfamily specificity. These central residues can be viewed as evolutionary hubs, in relation with an epistasis-based mechanism of functional divergence within a protein family.

Identification of specificity determining residues in peptide recognition domains using an information theoretic approach applied to large-scale binding maps

BackgroundPeptide Recognition Domains (PRDs) are commonly found in signaling proteins. They mediate protein-protein interactions by recognizing and binding short motifs in their ligands. Although a great deal is known about PRDs and their interactions, prediction of PRD specificities remains largely an unsolved problem.ResultsWe present a novel approach to identifying these Specificity Determining Residues (SDRs). Our algorithm generalizes earlier information theoretic approaches to coevolution analysis, to become applicable to this problem. It leverages the growing wealth of binding data between PRDs and large numbers of random peptides, and searches for PRD residues that exhibit strong evolutionary covariation with some positions of the statistical profiles of bound peptides. The calculations involve only information from sequences, and thus can be applied to PRDs without crystal structures. We applied the approach to PDZ, SH3 and kinase domains, and evaluated the results using both residue proximity in co-crystal structures and verified binding specificity maps from mutagenesis studies.DiscussionOur predictions were found to be strongly correlated with the physical proximity of residues, demonstrating the ability of our approach to detect physical interactions of the binding partners. Some high-scoring pairs were further confirmed to affect binding specificity using previous experimental results. Combining the covariation results also allowed us to predict binding profiles with higher reliability than two other methods that do not explicitly take residue covariation into account.ConclusionsThe general applicability of our approach to the three different domain families demonstrated in this paper suggests its potential in predicting binding targets and assisting the exploration of binding mechanisms.

Microbial community genomics in eastern Mediterranean Sea surface waters

Offshore waters of the eastern Mediterranean Sea are one of the most oligotrophic regions on Earth in which the primary productivity is phosphorus limited. To study the unexplored function and physiology of microbes inhabiting this system, we have analyzed a genomic library from the eastern Mediterranean Sea surface waters by sequencing both termini of nearly 5000 clones. Genome recruitment strategies showed that the majority of high-scoring pairs corresponded to genomes from the Alphaproteobacteria (SAR11-like and Rhodobacterales), Cyanobacteria (Synechococcus and high-light adapted Prochlorococcus) and diverse uncultured Gammaproteobacteria. The community structure observed, as evaluated by both protein similarity scores or metabolic potential, was similar to that found in the euphotic zone of the ALOHA station off Hawaii but very different from that of deep aphotic zones in both the Mediterranean Sea and the Pacific Ocean. In addition, a strong enrichment toward phosphate and phosphonate uptake and utilization metabolism was also observed.

GenBlastA: Enabling BLAST to identify homologous gene sequences

BLAST is an extensively used local similarity search tool for identifying homologous sequences. When a gene sequence (either protein sequence or nucleotide sequence) is used as a query to search for homologous sequences in a genome, the search results, represented as a list of high-scoring pairs (HSPs), are fragments of candidate genes rather than full-length candidate genes. Relevant HSPs ("signals"), which represent candidate genes in the target genome sequences, are buried within a report that contains also hundreds to thousands of random HSPs ("noises"). Consequently, BLAST results are often overwhelming and confusing even to experienced users. For effective use of BLAST, a program is needed for extracting relevant HSPs that represent candidate homologous genes from the entire HSP report. To achieve this goal, we have designed a graph-based algorithm, genBlastA, which automatically filters HSPs into well-defined groups, each representing a candidate gene in the target genome. The novelty of genBlastA is an edge length metric that reflects a set of biologically motivated requirements so that each shortest path corresponds to an HSP group representing a homologous gene. We have demonstrated that this novel algorithm is both efficient and accurate for identifying homologous sequences, and that it outperforms existing approaches with similar functionalities.

Homology search for genes

Life science researchers often require an exhaustive list of protein coding genes similar to a given query gene. To find such genes, homology search tools, such as BLAST or PatternHunter, return a set of high-scoring pairs (HSPs). These HSPs then need to be correlated with existing sequence annotations, or assembled manually into putative gene structures. This process is error-prone and labor-intensive, especially in genomes without reliable gene annotation. We have developed a homology search solution that automates this process, and instead of HSPs returns complete gene structures. We achieve better sensitivity and specificity by adapting a hidden Markov model for gene finding to reflect features of the query gene. Compared to traditional homology search, our novel approach identifies splice sites much more reliably and can even locate exons that were lost in the query gene. On a testing set of 400 mouse query genes, we report 79% exon sensitivity and 80% exon specificity in the human genome based on orthologous genes annotated in NCBI HomoloGene. In the same set, we also found 50 (12%) gene structures with better protein alignment scores than the ones identified in HomoloGene. The Java implementation is available for download from http://www.bioinformatics.uwaterloo.ca/software.

YAKUSA: A fast structural database scanning method

YAKUSA is a program designed for rapid scanning of a structural database with a query protein structure. It searches for the longest common substructures called SHSPs (structural high-scoring pairs) existing between a query structure and every structure in the structural database. It makes use of protein backbone internal coordinates (alpha angles) in order to describe protein structures as sequences of symbols. The structural similarities are established in 5 steps, the first 3 being analogous to those used in BLAST: (1) building up a deterministic finite automaton describing all patterns identical or similar to those in the query structure; (2) searching for all these patterns in every structure in the database; (3) extending the patterns to longer matching substructures (i.e., SHSPs); (4) selecting compatible SHSPs for each query-database structure pair; and (5) ranking the query-database structure pairs using 3 scores based on SHSP similarity, on SHSP probabilities, and on spatial compatibility of SHSPs. Structural fragment probabilities are estimated according to a mixture transition distribution model, which is an approximation of a high-order Markov chain model. With regard to sensitivity and selectivity of the structural matches, YAKUSA compares well to the best related programs, although it is by far faster: A typical database scan takes about 40 s CPU time on a desktop personal computer. It has also been implemented on a Web server for real-time searches.

TruMatch--a BLAST post-processor that identifies bona fide sequence matches to genome assemblies

BLAST is a widely used alignment tool for detecting matches between a query sequence and entries in nucleotide sequence databases. Matches (high-scoring pairs, HSPs) are assigned a score based on alignment length and quality and, by default, are reported with the top-scoring matches listed first. For certain types of searches, however, this method of reporting is not optimal. This is particularly true when searching a genome sequence with a query that was derived from the same genome, or a closely related one. If the genome is complex and the assembly is far from complete, correct matches are often relegated to low positions in the results, where they may be easily overlooked. To rectify this problem, we developed TruMatch--a program that parses standard BLAST outputs and identifies HSPs that involve query segments with unique matches to the assembly. Candidates for bona fide matches between a query sequence and a genome assembly are listed at the top of the TruMatch output. TruMatch is written in Perl and is freely available to non-commercial users via web download at the URL: http://genome.kbrin.uky.edu/fungi_tel/TruMatch/

Picking alignments from (Steiner) trees.

The application of Needleman-Wunsch alignment techniques to biological sequences is complicated by two serious problems when the sequences are long: the running time, which scales as the product of the lengths of sequences, and the difficulty in obtaining suitable parameters that produce meaningful alignments. The running time problem is often corrected by reducing the search space, using techniques such as banding, or chaining of high-scoring pairs. The parameter problem is more difficult to fix, partly because the probabilistic model, which Needleman-Wunsch is equivalent to, does not capture a key feature of biological sequence alignments, namely the alternation of conserved blocks and seemingly unrelated nonconserved segments. We present a solution to the problem of designing efficient search spaces for pair hidden Markov models that align biological sequences by taking advantage of their associated features. Our approach leads to an optimization problem, for which we obtain a 2-approximation algorithm, and that is based on the construction of Manhattan networks, which are close relatives of Steiner trees. We describe the underlying theory and show how our methods can be applied to alignment of DNA sequences in practice, successfully reducing the Viterbi algorithm search space of alignment PHMMs by three orders of magnitude.