An approach to delineate primers for a group of poorly conserved sequences incorporating the common motif region

Glutathione synthetase (gshB) has previously been reported to confer tolerance to acidic soil condition in Rhizobium species. Cloning the gene coding for this enzyme necessitates the designing of proper primer sets which in turn depends on the identification of high quality sequence similarity in multiple global alignments. In this experiment, a group of homologous gene sequences related to gshB gene (accession no: gi-86355669:327589-328536) of Rhizobium etli CFN 42, were extracted from NCBI nucleotide sequence databases using BLASTN and were analyzed for designing degenerate primers. However, the T-coffee multiple global alignment results did not show any block of conserved region for the above sequence set to design the primers. Therefore, we attempted to identify the location of common motif region based on multiple local alignments employing the MEME algorithm supported with MAST and Primer3. The results revealed some common motif regions that enabled us to design the primer sets for related gshB gene sequences. The result will be validated in wet lab.     

iProphet: multi-level integrative analysis of shotgun proteomic data improves peptide and protein identification rates and error estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.     

Predicting antigenicity of proteins in a bacterial proteome; a protein microarray and naïve Bayes classification approach

Discovery of novel antigens associated with infectious diseases is fundamental to the development of serodiagnostic tests and protein subunit vaccines against existing and emerging pathogens. Efforts to predict antigenicity have relied on a few computational algorithms predicting signal peptide sequences (SignalP), transmembrane domains, or subcellular localization (pSort). An empirical protein microarray approach was developed to scan the entire proteome of any infectious microorganism and empirically determine immunoglobulin reactivity against all the antigens from a microorganism in infected individuals. The current database from this activity contains quantitative antibody reactivity data against 35,000 proteins derived from 25 infectious microorganisms and more than 30 million data points derived from 15,000 patient sera. Interrogation of these data sets has revealed ten proteomic features that are associated with antigenicity, allowing an in silico protein sequence and functional annotation based approach to triage the least likely antigenic proteins from those that are more likely to be antigenic. The first iteration of this approach applied to Brucella melitensis predicted 37% of the bacterial proteome containing 91% of the antigens empirically identified by probing proteome microarrays. In this study, we describe a na?ve Bayes classification approach that can be used to assign a relative score to the likelihood that an antigen will be immunoreactive and serodiagnostic in a bacterial proteome. This algorithm predicted 20% of the B. melitensis proteome including 91% of the serodiagnostic antigens, a nearly twofold improvement in specificity of the predictor. These results give us confidence that further development of this approach will lead to further improvements in the sensitivity and specificity of this in silico predictive algorithm.     

New Molecular Screening Tools for Analysis of Free-Living Diazotrophs in Soil

Free-living nitrogen-fixing prokaryotes (diazotrophs) are ubiquitous in soil and are phylogenetically and physiologically highly diverse. Molecular methods based on universal PCR detection of the nifH marker gene have been successfully applied to describe diazotroph populations in the environment. However, the use of highly degenerate primers and low-stringency amplification conditions render these methods prone to amplification bias, while less degenerate primer sets will not amplify all nifH genes. We have developed a fixed-primer-site approach with six PCR protocols using less degenerate to nondegenerate primer sets that all amplify the same nifH fragment as a previously published PCR protocol for universal amplification. These protocols target different groups of diazotrophs and allowed for direct comparison of the PCR products by use of restriction fragment length polymorphism fingerprinting. The new protocols were optimized on DNA from 14 reference strains and were subsequently tested with bulk DNA extracts from six soils. These analyses revealed that the new PCR primer sets amplified nifH sequences that were not detected by the universal primer set. Furthermore, they were better suited to distinguish between diazotroph populations in the different soils. Because the novel primer sets were not specific for monophyletic groups of diazotrophs, they do not serve as an identification tool; however, they proved powerful as fingerprinting tools for subsets of soil diazotroph communities.     

Primers containing universal bases reduce multiple amoA gene specific DGGE band patterns when analysing the diversity of beta-ammonia oxidizers in the environment

The gene encoding the active site of the ammonia monooxygenase (amoA) has been exploited as molecular marker for studying ammonia-oxidizing bacteria (AOB) diversity in the environment. Primers amplifying functional genes are often degenerated and therefore produce multiple band patterns, when analysed with the Denaturing gradient gel electrophoresis (DGGE) approach. To improve the DGGE band patterns we have designed new primer sets which contain inosine residues and are specific for the amoA gene. Primers were evaluated analysing pure AOB cultures and two habitats (wastewater treatment plant, soda pools). We found that the application of inosine primers helped to reduce the apparent complexity of the DGGE band pattern. Comparison of sequences from environmental samples using either degenerated or inosine containing amoA primers retrieved both identical and additional sequences. Both primer sets seem to be limited in their ability to detect the presence of all AOB by DGGE analyses.     

Deconstructing the Polymerase Chain Reaction: Understanding and Correcting Bias Associated with Primer Degeneracies and Primer-Template Mismatches

The polymerase chain reaction (PCR) is sensitive to mismatches between primer and template, and mismatches can lead to inefficient amplification of targeted regions of DNA template. In PCRs in which a degenerate primer pool is employed, each primer can behave differently. Therefore, inefficiencies due to different primer melting temperatures within a degenerate primer pool, in addition to mismatches between primer binding sites and primers, can lead to a distortion of the true relative abundance of targets in the original DNA pool. A theoretical analysis indicated that a combination of primer-template and primer-amplicon interactions during PCR cycles 3–12 is potentially responsible for this distortion. To test this hypothesis, we developed a novel amplification strategy, entitled “Polymerase-exonuclease (PEX) PCR”, in which primer-template interactions and primer-amplicon interactions are separated. The PEX PCR method substantially and significantly improved the evenness of recovery of sequences from a mock community of known composition, and allowed for amplification of templates with introduced mismatches near the 3’ end of the primer annealing sites. When the PEX PCR method was applied to genomic DNA extracted from complex environmental samples, a significant shift in the observed microbial community was detected. Furthermore, the PEX PCR method provides a mechanism to identify which primers in a primer pool are annealing to target gDNA. Primer utilization patterns revealed that at high annealing temperatures in the PEX PCR method, perfect match annealing predominates, while at lower annealing temperatures, primers with up to four mismatches with templates can contribute substantially to amplification. The PEX PCR method is simple to perform, is limited to PCR mixes and a single exonuclease step which can be performed without reaction cleanup, and is recommended for reactions in which degenerate primer pools are used or when mismatches between primers and template are possible.     

BiomarkerDigger: A versatile disease proteome database and analysis platform for the identification of plasma cancer biomarkers

We have developed a proteome database (DB), BiomarkerDigger ( http://biomarkerdigger.org ) that automates data analysis, searching, and metadata‐gathering function. The metadata‐gathering function searches proteome DBs for protein–protein interaction, Gene Ontology, protein domain, Online Mendelian Inheritance in Man, and tissue expression profile information and integrates it into protein data sets that are accessed through a search function in BiomarkerDigger. This DB also facilitates cross‐proteome comparisons by classifying proteins based on their annotation. BiomarkerDigger highlights relationships between a given protein in a proteomic data set and any known biomarkers or biomarker candidates. The newly developed BiomarkerDigger system is useful for multi‐level synthesis, comparison, and analyses of data sets obtained from currently available web sources. We demonstrate the application of this resource to the identification of a serological biomarker for hepatocellular carcinoma by comparison of plasma and tissue proteomic data sets from healthy volunteers and cancer patients.     

Integrated platform for manual and high‐throughput statistical validation of tandem mass spectra

As proteomic data sets increase in size and complexity, the necessity for database‐centric software systems able to organize, compare, and visualize all the proteomic experiments in a lab grows. We recently developed an integrated platform called high‐throughput autonomous proteomic pipeline (HTAPP) for the automated acquisition and processing of quantitative proteomic data, and integration of proteomic results with existing external protein information resources within a lab‐based relational database called PeptideDepot. Here, we introduce the peptide validation software component of this system, which combines relational database‐integrated electronic manual spectral annotation in Java with a new software tool in the R programming language for the generation of logistic regression spectral models from user‐supplied validated data sets and flexible application of these user‐generated models in automated proteomic workflows. This logistic regression spectral model uses both variables computed directly from SEQUEST output in addition to deterministic variables based on expert manual validation criteria of spectral quality. In the case of linear quadrupole ion trap (LTQ) or LTQ‐FTICR LC/MS data, our logistic spectral model outperformed both XCorr (242% more peptides identified on average) and the X!Tandem E‐value (87% more peptides identified on average) at a 1% false discovery rate estimated by decoy database approach.     

PCR-based approach for detection of novel Bacillus thuringiensis cry genes.

A two-step strategy, named exclusive PCR or E-PCR, has been developed to overcome the main limitation of PCR, which is the detection of already-known sequences only. This strategy allows the ability to detect and further clone and sequence genes for which no specific primers are available and in which a variable region exists between two conserved regions. This approach has been applied to Bacillus thuringiensis cryI genes by the use of mixtures of degenerate and specific primers recognizing well-known sequences. The first step allows the accurate identification of already-characterized cryI genes by the use of three primers. During the second step, the same sets of primers are used to exclude known sequences and to positively detect cryI genes unrecognized by any specific primer. The method, as well as its application to detect, clone, and sequence a novel cryIB gene, is described in this article.     

High throughput proteome screening for biomarker detection

Mass spectrometry-based quantitative proteomics has become an important component of biological and clinical research. Current methods, while highly developed and powerful, are falling short of their goal of routinely analyzing whole proteomes mainly because the wealth of proteomic information accumulated from prior studies is not used for the planning or interpretation of present experiments. The consequence of this situation is that in every proteomic experiment the proteome is rediscovered. In this report we describe an approach for quantitative proteomics that builds on the extensive prior knowledge of proteomes and a platform for the implementation of the method. The method is based on the selection and chemical synthesis of isotopically labeled reference peptides that uniquely identify a particular protein and the addition of a panel of such peptides to the sample mixture consisting of tryptic peptides from the proteome in question. The platform consists of a peptide separation module for the generation of ordered peptide arrays from the combined peptide sample on the sample plate of a MALDI mass spectrometer, a high throughput MALDI-TOF/TOF mass spectrometer, and a suite of software tools for the selective analysis of the targeted peptides and the interpretation of the results. Applying the method to the analysis of the human blood serum proteome we demonstrate the feasibility of using mass spectrometry-based proteomics as a high throughput screening technology for the detection and quantification of targeted proteins in a complex system.     

荔枝SSR标记的研究

以无核荔枝A4号为实验材料,应用选择性扩增微卫星（SAM）法分离、克隆了100个简单序列重复（SSR）序列,其中88个非重复,可用。加上搜索数据库所获得的1个SSR序列,一共89个序列用于特异引物的设计。仅从71个序列的82个基因座设计出特异引物。合成41条特异引物(与5′锚定简并引物配对,个别相互配对),对其中的39个基因座进行检测。其中15对引物扩增出相应大小的片段,另外11对引物扩增出非预期片段。最后,以37个荔枝种质的基因组DNA为模板,从26对出带的引物中,筛选出多态性引物21对,获得了22个荔枝基因座特异性SSR标记。     

AutoDimer: a screening tool for primer-dimer and hairpin structures

The ability to select short DNA oligonucleotide sequences capable of binding solely to their intended target is of great importance in developing nucleic acid based detection technologies. Applications such as multiplex PCR rely on primers binding to unique regions in a genome. Competing side reactions with other primer pairs or template DNA decrease PCR efficiency: Freely available primer design software such as Primer3 screens for potential hairpin and primer-dimer interactions while selecting a single primer pair. The development of multiplex PCR assays (in the range of 5 to 20 loci) requires the screening of all primer pairs for potential cross-reactivity. However, a logistical problem results due to the number of total number of comparisons required. Comparing the primer set for a 10-plex assay (20 total primer sequences) results in 210 primer-primer combinations that must be screened. The ability to screen sets of candidate oligomers rapidly for potential cross-reactivity reduces overall assay devlelopment time. Here we report the application of a familiar sliding algorithm for comparing two strands of DNA in an overlapping fashion. The algorithm has been employed in a software package wherein the user can compare multiple sequences in a single computational run. After the screening is completed, a score is assigned to potential duplex interactions exceeding a user-defined threshold. Additional criteria of predicted melting temperature (Tm) and free energy of melting (deltaG) are included for further ranking. Sodium counterion and total stand concentrations can be adjusted for the Tm and deltaG calculations. The predicted interactions are saved in a text file for further evaluation.     

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.     

K-tuple frequency in the human genome and polymerase chain reaction.

The frequency occurrences of K-tuple (overlapping sequences of defined length, K) were computed from the known human genome sequences. The significance of these frequencies for the whole human genome was tested by polymerase chain reaction (PCR). A computer programs based on these results was written to choose primers to amplify DNA target sequences, either of human genes or of human infectious agents. The software also gave nested primer sequences which were used to synthesize non radioactive probes by PCR. We applied these two methods, primer selection and non radioactive probes, to easily and quickly set up very efficient PCR sets to work in the human genome context.     

Diversity of Phytases in the Rumen

Examples of a new class of phytase related to protein tyrosine phosphatases (PTP) were recently isolated from several anaerobic bacteria from the rumen of cattle. In this study, the diversity of PTP-like phytase gene sequences in the rumen was surveyed by using the polymerase chain reaction (PCR). Two sets of degenerate primers were used to amplify sequences from rumen fluid total community DNA and genomic DNA from nine bacterial isolates. Four novel PTP-like phytase sequences were retrieved from rumen fluid, whereas all nine of the anaerobic bacterial isolates investigated in this work contained PTP-like phytase sequences. One isolate, Selenomonas lacticifex, contained two distinct PTP-like phytase sequences, suggesting that multiple phytate hydrolyzing enzymes are present in this bacterium. The degenerate primer and PCR conditions described here, as well as novel sequences obtained in this study, will provide a valuable resource for future studies on this new class of phytase. The observed diversity of microbial phytases in the rumen may account for the ability of ruminants to derive a significant proportion of their phosphorus requirements from phytate.     

Optimization of primer design for the detection of variable genomic lesions in cancer

Primer approximation multiplex PCR (PAMP) is a new experimental protocol for efficiently assaying structural variation in genomes. PAMP is particularly suited to cancer genomes where the precise breakpoints of alterations such as deletions or translocations vary between patients. The design of PCR primer sets for PAMP is challenging because a large number of primer pairs are required to detect alterations in the hundreds of kilobases range that can occur in cancer. These sets of primers must achieve high coverage of the region of interest, while avoiding primer dimers and satisfying the physico-chemical constraints of good PCR primers. We describe a natural formulation of these constraints as a combinatorial optimization problem. We show that the PAMP primer design problem is NP-hard, and design algorithms based on simulated annealing and integer programming, that provide good solutions to this problem in practice. The algorithms are applied to a test region around the known CDKN2A deletion, which show excellent results even in a 1:49 mixture of mutated:wild-type cells. We use these test results to help set design parameters for larger problems. We can achieve near-optimal designs for regions close to 1 Mb.