Wet-lab Protocols Research Articles

Multicenter benchmarking of short and long read wet lab protocols for clinical viral metagenomics

Metagenomics is gradually being implemented for diagnosing infectious diseases. However, in-depth protocol comparisons for viral detection have been limited to individual sets of experimental workflows and laboratories. In this study, we present a benchmark of metagenomics protocols used in clinical diagnostic laboratories initiated by the European Society for Clinical Virology (ESCV) Network on NGS (ENNGS).A mock viral reference panel was designed to mimic low biomass clinical specimens. The panel was used to assess the performance of twelve metagenomic wet lab protocols currently in use in the diagnostic laboratories of participating ENNGS member institutions. Both Illumina and Nanopore, shotgun and targeted capture probe protocols were included. Performance metrics sensitivity, specificity, and quantitative potential were assessed using a central bioinformatics pipeline.Overall, viral pathogens with loads down to 104 copies/ml (corresponding to CT values of 31 in our PCR assays) were detected by all the evaluated metagenomic wet lab protocols. In contrast, lower abundant mixed viruses of CT values of 35 and higher were detected only by a minority of the protocols. Considering the reference panel as the gold standard, optimal thresholds to define a positive result were determined per protocol, based on the horizontal genome coverage. Implementing these thresholds, sensitivity and specificity of the protocols ranged from 67 to 100 % and 87 to 100 %, respectively.A variety of metagenomic protocols are currently in use in clinical diagnostic laboratories. Detection of low abundant viral pathogens and mixed infections remains a challenge, implying the need for standardization of metagenomic analysis for use in clinical settings.

Impact of wet-lab protocols on quality of whole-genome short-read sequences from foodborne microbial pathogens

For successful elucidation of a food-borne infection chain, the availability of high-quality sequencing data from suspected microbial contaminants is a prerequisite. Commonly, those investigations are a joint effort undertaken by different laboratories and institutes. To analyze the extent of variability introduced by differing wet-lab procedures on the quality of the sequence data we conducted an interlaboratory study, involving four bacterial pathogens, which account for the majority of food-related bacterial infections: Campylobacter spp., Shiga toxin-producing Escherichia coli, Listeria monocytogenes, and Salmonella enterica. The participants, ranging from German federal research institutes, federal state laboratories to universities and companies, were asked to follow their routine in-house protocols for short-read sequencing of 10 cultures and one isolated bacterial DNA per species. Sequence and assembly quality were then analyzed centrally. Variations within isolate samples were detected with SNP and cgMLST calling. Overall, we found that the quality of Illumina raw sequence data was high with little overall variability, with one exception, attributed to a specific library preparation kit. The variability of Ion Torrent data was higher, independent of the investigated species. For cgMLST and SNP analysis results, we found that technological sequencing artefacts could be reduced by the use of filters, and that SNP analysis was more suited than cgMLST to compare data of different contributors. Regarding the four species, a minority of Campylobacter isolate data showed the in comparison highest divergence with regard to sequence type and cgMLST analysis. We additionally compared the assembler SPAdes and SKESA for their performance on the Illumina data sets of the different species and library preparation methods and found overall similar assembly quality metrics and cgMLST statistics.

The cytotoxic potential of sinapic acid on luminal A breast cancer; a computational and experimental pharmacology approach

Breast cancer is a highly concerning and prevalent disease that impacts a significant proportion of women worldwide, whose repeated exposure to therapies leads to resistance for drugs; making it alarming to identify novel chemotherapeutic agents. Sinapic acid is a phenolic acid that occurs naturally and is known to exhibit cytotoxic action in a variety of cancer cell types. In the present study, we utilized cell cytotoxicity assays to assess the cytotoxic potential of sinapic acid on various breast cancer subtypes. In addition, we assessed the cell migration rate, cell apoptosis, and cell cycle phases. Moreover, we utilized multiple system biology tools to predict the potential targets, and molecular docking was performed on the hub targets followed by molecular dynamic (MD) simulations. Cytotoxicity assay was performed on cell lines MCF7, T47D, MDA-MB-468, and SKBR3 at different time exposures of 24, 48, and 96 h. Our results revealed sinapic acid to be potent on MCF7 and T47D cell lines. The cell cycle analysis and cell apoptotic assays revealed sinapic acid to cause cell death by apoptosis majorly in the G0/G1 phase. Computational biology revealed KIF18B and VKORC1 to possess the highest binding affinity of −6.5 and −7.5 kcal/mol; displayed stable trajectories on MD run. The cytotoxicity of sinapic acid on luminal A cell lines may be due to the modulation of VKORC1 and KIF18B with major cell death in the G0/G1 phase. However, the mechanism has been proposed via in silico tools, which need further validation using wet lab protocols. Communicated by Ramaswamy H. Sarma

Cananga odorata (Ylang-Ylang) modulate pathways involved in cancer: Gene set enrichment and network pharmacology approach

Traditionally, Cananga odorata Hook. F. & Thomson (Ylang-Ylang) is commonly used to treat various diseases and has been assessed for anti-cancer potency in experimental animal models. However, the affinity of its phytocompounds with multiple proteins involved in the pathogenesis of cancer has not been illuminated yet. The present study was framed to elucidate the molecular mechanisms of Cananga odorata for its anticancer activity via compound-gene set pathway enrichment analysis, network pharmacology, and docking studies. Initially, phytocompounds were retrieved from herbs databases and literature. Structural information of each compound was obtained from the PubChem database. Druggable characteristics and side effects were predicted using MolSoft and ADVERpred. ADMET profile was predicted using PreADMET online server. Possible target proteins of each compound were predicted by BindingDB (p≥0.7). Compounds modulating the target proteins associated with the cancer were separated based on the successful and approved targets available in the Therapeutic Target Database. STRING and KEGG pathway database was used to analyze the molecular pathways modulated by the protein targets. The interaction between compounds, proteins, and pathways was constructed by Cytoscape 3.6.1, and docking of compounds with protein target was performed using AutoDock 4.2. Among 26 compounds, 12 phytocompounds were identified to modulate 34 pathways associated with cancer. 4-hydroxy-5,6,7-trimethoxyflavanone and Reticuline showed the maximum interactions with proteins involved in cancer. All 12 compounds obeyed the rule of five and p-methoxybenzaldehyde scored the highest drug-likeness score. Micheline A and Anonaine showed the highest binding affinity with Ubiquitin-protein ligase E3 (MDM2). The current study provides the molecular documentation of phytocompounds from Cananga odorata in the regulation of multi-proteins and pathways associated with progression of cancer (mainly Gastric, Melanoma, Prostate, and Breast cancer), which can be further investigated via wet-lab protocols.

Multiplexed long-read plasmid validation and analysis using OnRamp.

Recombinant plasmid vectors are versatile tools that have facilitated discoveries in molecular biology, genetics, proteomics, and many other fields. As the enzymatic and bacterial processes used to create recombinant DNA can introduce errors, sequence validation is an essential step in plasmid assembly. Sanger sequencing is the current standard for plasmid validation; however, this method is limited by an inability to sequence through complex secondary structure and lacks scalability when applied to full-plasmid sequencing of multiple plasmids owing to read-length limits. Although high-throughput sequencing does provide full-plasmid sequencing at scale, it is impractical and costly when used outside of library-scale validation. Here, we present Oxford nanopore-based rapid analysis of multiplexed plasmids (OnRamp), an alternative method for routine plasmid validation that combines the advantages of high-throughput sequencing's full-plasmid coverage and scalability with Sanger's affordability and accessibility by leveraging nanopore's long-read sequencing technology. We include customized wet-laboratory protocols for plasmid preparation along with a pipeline designed for analysis of read data obtained using these protocols. This analysis pipeline is deployed on the OnRamp web app, which generates alignments between actual and predicted plasmid sequences, quality scores, and read-level views. OnRamp is designed to be broadly accessible regardless of programming experience to facilitate more widespread adoption of long-read sequencing for routine plasmid validation. Here we describe the OnRamp protocols and pipeline and show our ability to obtain full sequences from pooled plasmids while detecting sequence variation even in regions of high secondary structure at less than half the cost of equivalent Sanger sequencing.

Anti-tumor potential and mode of action of karanjin against breast cancer; an in-silico approach

Karanjin is a furanoflavanoid first isolated from Pongamia pinnata and has been reported to possess a cytotoxic effect on various lung, breast, leukemia, and hepatoma cancers. However, the mechanism by which karanjin persists cytotoxic effect has yet not been unleashed for breast cancer. Hence, the present study aimed to predict a possible molecular mechanism of karanjin against breast cancer and its assessment on various breast cancer cell lines. We utilized multiple system biology tools like gene set enrichment analysis, gene ontology analysis, cluster analysis, molecular docking, molecular dynamic simulations, and MMPBSA analysis to predict the lead targets. Targets for breast cancer were retrieved from DisGeNet and Therapeutic Target Prediction and matched with targets of karanjin; further, they were subjected to gene enrichment and gene ontology analysis. Molecular docking was performed on all the matched targets; the top three complexes displaying the best binding affinity and the top three hub genes predicted via gene enrichment were selected for MD simulation. Cytotoxicity assay was performed on three different cell lines T47D, MDA-MB-468, and SKBR3, at different time exposures of 24, 48, and 96 hrs. Gene enrichment analysis predicted PI3KCA, PI3KCB, and EGFR as the top 3 hub genes and the PI3K-Akt signaling pathway as the majorly modulated pathway. Molecular docking revealed CYP1A1 to possess the least binding energy (-11.7 Kcal/mol) followed by AKR1C3 (-10.2 Kcal/mol), and CYP3A4 (-10 Kcal/mol). Further, molecular dynamic simulation and MMPBSA analysis displayed the complex of karanjin with CYP1A1 and PI3KCA to be the most stable. The cytotoxicity assay revealed karanjin to possess a more significant cytotoxic effect on SKBR3 cell lines with a dose-dependent increase in efficiency and also displayed an additive/synergistic effect on the three cell lines when used in combination with a tamoxifen/gefitinib. The predictions revealed that karanjin may possess anti-breast cancer potential by the modulation of proteins CYP1A1 and PIK3CA via the PI3K-Akt signaling pathway. However, the mechanism has been proposed via in-silico tools, which need further validation using wet lab protocols; this is the future scope of the study.

Funneling modulatory peptide design with generative models: Discovery and characterization of disruptors of calcineurin protein-protein interactions.

Design of peptide binders is an attractive strategy for targeting "undruggable" protein-protein interfaces. Current design protocols rely on the extraction of an initial sequence from one known protein interactor of the target protein, followed by in-silico or in-vitro mutagenesis-based optimization of its binding affinity. Wet lab protocols can explore only a minor portion of the vast sequence space and cannot efficiently screen for other desirable properties such as high specificity and low toxicity, while in-silico design requires intensive computational resources and often relies on simplified binding models. Yet, for a multivalent protein target, dozens to hundreds of natural protein partners already exist in the cellular environment. Here, we describe a peptide design protocol that harnesses this diversity via a machine learning generative model. After identifying putative natural binding fragments by literature and homology search, a compositional Restricted Boltzmann Machine is trained and sampled to yield hundreds of diverse candidate peptides. The latter are further filtered via flexible molecular docking and an in-vitro microchip-based binding assay. We validate and test our protocol on calcineurin, a calcium-dependent protein phosphatase involved in various cellular pathways in health and disease. In a single screening round, we identified multiple 16-length peptides with up to six mutations from their closest natural sequence that successfully interfere with the binding of calcineurin to its substrates. In summary, integrating protein interaction and sequence databases, generative modeling, molecular docking and interaction assays enables the discovery of novel protein-protein interaction modulators.

A survey of current methods to detect and genotype inversions.

Polymorphic inversions are ubiquitous in humans and they have been linked to both adaptation and disease. Following their discovery in Drosophila more than a century ago, inversions have proved to be more elusive than other structural variants. A wide variety of methods for the detection and genotyping of inversions have recently been developed: multiple techniques based on selective amplification by PCR, short- and long-read sequencing approaches, principal component analysis of small variant haplotypes, template strand sequencing, optical mapping, and various genome assembly methods. Many methods apply complex wet lab protocols or increasingly refined bioinformatic analyses. This review is an attempt to provide a practical summary and comparison of the methods that are in current use, with a focus on metrics such as the maximum size of segmental duplications at inversion breakpoints that each method can tolerate, the size range of inversions that they recover, their throughput, and whether the locations of putative inversions must be known beforehand.

Resequencing of Microbial Isolates: A Lab Module to Introduce Novices to Command-Line Bioinformatics.

Familiarity with genome-scale data and the bioinformatic skills to analyze it have become essential for understanding and advancing modern biology and human health, yet many undergraduate biology majors are never exposed to hands-on bioinformatics. This paper presents a module that introduces students to applied bioinformatic analysis within the context of a research-based microbiology lab course. One of the most commonly used genomic analyses in biology is resequencing: determining the sequence of DNA bases in a derived strain of some organism, and comparing it to the known ancestral genome of that organism to better understand the phenotypic differences between them. Many existing CUREs — Course Based Undergraduate Research Experiences — evolve or select new strains of bacteria and compare them phenotypically to ancestral strains. This paper covers standardized strategies and procedures, accessible to undergraduates, for preparing and analyzing microbial whole-genome resequencing data to examine the genotypic differences between such strains. Wet-lab protocols and computational tutorials are provided, along with additional guidelines for educators, providing instructors without a next-generation sequencing or bioinformatics background the necessary information to incorporate whole-genome sequencing and command-line analysis into their class. This module introduces novice students to running software at the command-line, giving them exposure and familiarity with the types of tools that make up the vast majority of open-source scientific software used in contemporary biology. Completion of the module improves student attitudes toward computing, which may make them more likely to pursue further bioinformatics study.

Transposon-Based Tagging In Silico Using FastPCR Software.

Retrotransposons are ubiquitous, generally dispersed components of eukaryotic genomes. These properties, together with their "copy and paste" lifecycle that generates insertional polymorphism without need for excision, makes them widely useful as a molecular-genetic tags. Various tagging systems have been developed that exploit the sequence conservation of retrotransposon components, such as those found in their long terminal repeats (LTRs). To detect polymorphisms for retrotransposon insertions, marker systems generally rely on PCR amplification between the termini and some component of flanking genomic DNA. As complements to various "wet lab" protocols for retrotransposon tagging, in silico bioinformatics approaches are useful for predicting likely outcomes from unsequenced accessions on thebasis of reference genomes. In this chapter, we describe protocols for in silico retrotransposon-based fingerprinting techniques using the FastPCR software as an integrated tools environment for in silico PCR primer design and analysis.

Metagenomics-Based Proficiency Test of Smoked Salmon Spiked with a Mock Community.

An inter-laboratory proficiency test was organized to assess the ability of participants to perform shotgun metagenomic sequencing of cold smoked salmon, experimentally spiked with a mock community composed of six bacteria, one parasite, one yeast, one DNA, and two RNA viruses. Each participant applied its in-house wet-lab workflow(s) to obtain the metagenomic dataset(s), which were then collected and analyzed using MG-RAST. A total of 27 datasets were analyzed. Sample pre-processing, DNA extraction protocol, library preparation kit, and sequencing platform, influenced the abundance of specific microorganisms of the mock community. Our results highlight that despite differences in wet-lab protocols, the reads corresponding to the mock community members spiked in the cold smoked salmon, were both detected and quantified in terms of relative abundance, in the metagenomic datasets, proving the suitability of shotgun metagenomic sequencing as a genomic tool to detect microorganisms belonging to different domains in the same food matrix. The implementation of standardized wet-lab protocols would highly facilitate the comparability of shotgun metagenomic sequencing dataset across laboratories and sectors. Moreover, there is a need for clearly defining a sequencing reads threshold, to consider pathogens as detected or undetected in a food sample.

Zebrafish shares common metabolic pathways with mammalian olanzapine-induced obesity

BackgroundOlanzapine is one of the most prescribed antipsychotic agents in the pharmacotherapy of psychiatric illness; however, it is associated with multiple side effects primarily obesity. Multiple investigations have been made to model the olanzapine-induced obesity in rodent models which was found to be dose-dependent, gender-dependent, and species-dependent. Danio rerio is a choice of an animal model to understand the pathogenesis of multiple diseases. The present study dealt to understand the olanzapine-associated obesity in zebrafish using in silico and wet-lab experimental protocols by performing gene set enrichment analysis, phylogeny comparison of receptors, and assessing the effect of olanzapine on metabolic rate, lipid metabolism, body weight, and food intake in zebrafish.ResultsThe metabolic pathway was predicted to be majorly modulated by olanzapine in human, rat, mouse, and zebrafish. A clade of receptors of rat, mouse, and human receptor for feeding and satiety center was found similar to zebrafish. The decrease in lipid metabolism was observed in zebrafish larvae if exposed to olanzapine solution. Similarly, there was a significant decrease in metabolic rate in 200 μM and 400 μM concentration of olanzapine.ConclusionEnrichment analysis predicted the probable modulation of metabolic pathways in zebrafish if exposed to olanzapine. Further, olanzapine was identified to play a prime role in decreasing lipid metabolism and metabolic rate and increasing food intake and weight gain in zebrafish which suggests the validation of this model for olanzapine-induced obesity.

Dry Panels Supporting External Quality Assessment Programs for Next Generation Sequencing-Based HIV Drug Resistance Testing.

External quality assessment (EQA) is a keystone element in the validation and implementation of next generation sequencing (NGS)-based HIV drug resistance testing (DRT). Software validation and evaluation is a critical element in NGS EQA programs. While the development, sharing, and adoption of wet lab protocols is coupled with the increasing access to NGS technology worldwide, rendering it easy to produce NGS data for HIV-DRT, bioinformatic data analysis remains a bottleneck for most of the diagnostic laboratories. Several computational tools have been made available, via free or commercial sources, to automate the conversion of raw NGS data into an actionable clinical report. Although different software platforms yield equivalent results when identical raw NGS datasets are analyzed for variations at higher abundance, discrepancies arise when variations at lower frequencies are considered. This implies that validation and performance assessment of the bioinformatics tools applied in NGS HIV-DRT is critical, and the origins of the observed discrepancies should be determined. Well-characterized reference NGS datasets with ground truth on the genotype composition at all examined loci and the exact frequencies of HIV variations they may harbor, so-called dry panels, would be essential in such cases. The strategic design and construction of such panels are challenging but imperative tasks in support of EQA programs for NGS-based HIV-DRT and the validation of relevant bioinformatics tools. Here, we present criteria that can guide the design of such dry panels, which were discussed in the Second International Winnipeg Symposium themed for EQA strategies for NGS HIVDR assays.

External Quality Assessment Program for Next-Generation Sequencing-Based HIV Drug Resistance Testing: Logistical Considerations.

Next-generation sequencing (NGS) is likely to become the new standard method for HIV drug resistance (HIVDR) genotyping. Despite the significant advances in the development of wet-lab protocols and bioinformatic data processing pipelines, one often-missing critical component of an NGS HIVDR assay for clinical use is external quality assessment (EQA). EQA is essential for ensuring assay consistency and laboratory competency in performing routine biomedical assays, and the rollout of NGS HIVDR tests in clinical practice will require an EQA. In September 2019, the 2nd International Symposium on NGS HIVDR was held in Winnipeg, Canada. It convened a multidisciplinary panel of experts, including research scientists, clinicians, bioinformaticians, laboratory biologists, biostatisticians, and EQA experts. A themed discussion was conducted on EQA strategies towards such assays during the symposium. This article describes the logistical challenges identified and summarizes the opinions and recommendations derived from these discussions, which may inform the development of an inaugural EQA program for NGS HIVDR in the near future.

Effects of sample age on data quality from targeted sequencing of museum specimens: what are we capturing in time?

BackgroundNext generation sequencing (NGS) can recover DNA data from valuable extant and extinct museum specimens. However, archived or preserved DNA is difficult to sequence because of its fragmented, damaged nature, such that the most successful NGS methods for preserved specimens remain sub-optimal. Improving wet-lab protocols and comprehensively determining the effects of sample age on NGS library quality are therefore of vital importance. Here, I examine the relationship between sample age and several indicators of library quality following targeted NGS sequencing of ~ 1300 loci using 271 samples of pinned moth specimens (Helicoverpa armigera) ranging in age from 5 to 117 years.ResultsI find that older samples have lower DNA concentrations following extraction and thus require a higher number of indexing PCR cycles during library preparation. When sequenced reads are aligned to a reference genome or to only the targeted region, older samples have a lower number of sequenced and mapped reads, lower mean coverage, and lower estimated library sizes, while the percentage of adapters in sequenced reads increases significantly as samples become older. Older samples also show the poorest capture success, with lower enrichment and a higher improved coverage anticipated from further sequencing.ConclusionsSample age has significant, measurable impacts on the quality of NGS data following targeted enrichment. However, incorporating a uracil-removing enzyme into the blunt end-repair step during library preparation could help to repair DNA damage, and using a method that prevents adapter-dimer formation may result in improved data yields.

Abstract 2516: A bioinformatics framework for high-sensitivity detection and monitoring of oncogenic gene fusions in plasma cfDNA

Abstract Gene fusions represent key oncogenic driver alterations in many solid tumors that can be targeted therapeutically. Beyond FDA-approved drugs targeting ALK and ROS1 fusions in lung cancer, investigational agents targeting gene fusions involving RET, NTRK1/2/3, and FGFR2/3 have elicited significant and durable responses in a wide range of cancer types. Cell-free DNA (cfDNA) profiling provides a transformative opportunity to non-invasively and longitudinally monitor responses to these investigational therapies and identify acquired mutations that confer drug resistance. We have developed a pan-cancer, high-sensitivity NGS cancer assay (MSK-ACCESS, presented previously) to detect and monitor somatic mutations in plasma cfDNA. Here, we present an improved bioinformatics pipeline that detects gene fusions and achieves greater sensitivity and accuracy for detecting point mutations and indels. MSK-ACCESS uses ultra-high depth sequencing (~20,000x) coupled with duplex unique molecular indexing (UMI) and dual sample barcodes to achieve high sensitivity through background error suppression. The panel was designed to capture key exons and domains of 129 cancer genes as well as introns harboring recurrent breakpoints in 10 commonly rearranged genes. We have now developed a bioinformatics module for sensitive detection of structural variation from cfDNA, leveraging the high-depth sequencing data and intronic coverage. We have also added a statistical polishing method that assigns confidence to detected somatic mutations by building frequency distributions for those mutations from a large set of normal samples that has been processed with the same laboratory protocol and bioinformatics pipeline as the patient samples. In parallel, we have further improved the wet lab protocols to address challenges of capturing sufficient numbers of cfDNA molecules and removing noise, both required for detection of somatic mutations with high sensitivity and specificity. Using this method, we have successfully detected druggable fusions in plasma cfDNA from patients spanning a diverse set of cancer types. Through longitudinal cfDNA collection efforts embedded within early-phase clinical trials, we demonstrate that the levels of oncogenic driver fusions in cfDNA may scale with the burden of disease. Moreover, the ability to simultaneously call high-confidence mutations at low allele fractions has revealed novel acquired mutations indicative of polyclonal resistance to investigational targeted therapies. Citation Format: Juber Patel, Maysun Hasan, Fanli Meng, Xiaohong Jing, Grittney Tam, Ian Johnson, Youyun Zheng, Chaitanya Bandlamudi, Caitlin Stewart, Helen Won, Oliver Hampton, Alison Schram, Ezra Rosen, Alexander Drilon, Anna Varghese, David Hyman, Dana Tsui, Brian Houck-Loomis, Michael Berger. A bioinformatics framework for high-sensitivity detection and monitoring of oncogenic gene fusions in plasma cfDNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2516.

Improving Phylogeny Reconstruction at the Strain Level Using Peptidome Datasets

Typical bacterial strain differentiation methods are often challenged by high genetic similarity between strains. To address this problem, we introduce a novel in silico peptide fingerprinting method based on conventional wet-lab protocols that enables the identification of potential strain-specific peptides. These can be further investigated using in vitro approaches, laying a foundation for the development of biomarker detection and application-specific methods. This novel method aims at reducing large amounts of comparative peptide data to binary matrices while maintaining a high phylogenetic resolution. The underlying case study concerns the Bacillus cereus group, namely the differentiation of Bacillus thuringiensis, Bacillus anthracis and Bacillus cereus strains. Results show that trees based on cytoplasmic and extracellular peptidomes are only marginally in conflict with those based on whole proteomes, as inferred by the established Genome-BLAST Distance Phylogeny (GBDP) method. Hence, these results indicate that the two approaches can most likely be used complementarily even in other organismal groups. The obtained results confirm previous reports about the misclassification of many strains within the B. cereus group. Moreover, our method was able to separate the B. anthracis strains with high resolution, similarly to the GBDP results as benchmarked via Bayesian inference and both Maximum Likelihood and Maximum Parsimony. In addition to the presented phylogenomic applications, whole-peptide fingerprinting might also become a valuable complementary technique to digital DNA-DNA hybridization, notably for bacterial classification at the species and subspecies level in the future.

Biomarkers for gastric cancer: prognostic, predictive or targets of therapy?

Gastric cancer is an aggressive disease often diagnosed at an advanced stage. Despite improvements in surgical and adjuvant treatment approaches, gastric cancer remains a global public health problem with a 5-year overall survival of less than 25 %. This is a heterogeneous disease, both in terms of biology and genetics, and many prognostic biomarkers have been pointed out in the literature; nevertheless, their application remains debatable. In this review, we opted to give relevance to those biomarkers that have been the subject of studies with significant statistical power, which have been replicated and have been/are in targeted therapy clinical trials and, which as a consequence, have their prognostic and/or predictive value established. Some gastric cancer biomarkers that may help in defining the course of treatment are also discussed. Accepted practical guidelines, wet-lab protocols for the detection of these biomarkers, as well as ongoing and completed clinical trials have been compiled. In summary, clinical approaches based on the combination of correct staging with targeted and conventional systemic therapies may improve gastric cancer patients' outcome, but are only in their infancy. Some major challenges in identifying reliable prognostic/predictive biomarkers are individual genetic variation and tumour heterogeneity that often influence response to therapy and drug resistance. Prognostic and predictive biomarkers may nevertheless be extremely valuable to correctly stratify gastric cancer patients for treatment and, ultimately, improve survival.

Editorial: From Data to Results

Frequently, at scientific meetings proteome researchers meet after the formal program has ended to discuss the state of the field and their own situation over some drinks. Typically these discussions center around instrumentation, primarily mass spectrometers, with a faster scan rate, higher mass accuracy or sensitivity, and the results generated by these powerful instruments. Statements like, “this is the future,” “this will revolutionize proteomics,” or “this has changed what we do in the lab” can be heard around the table. These sentiments are certainly justified. The progress in mass spectrometric instrumentation in the short history of proteomics has been nothing but astounding. Just a decade ago virtually all proteomic data were generated on ion trap instruments with low resolution, low mass accuracy, and limited scan speed and sensitivity, compared with the instruments presently in common use. Today, a significant fraction of the proteomic data is collected on high performance hybrid mass spectrometers with high scan speed, high mass accuracy and resolution, and high sensitivity of detection. The effects of these dramatic improvements are apparent in the manuscripts being published. However, its importance notwithstanding, data collection is only half of the whole proteomic story. The capacity to annotate, analyze, store, manage, and distribute the many gigabytes of data collected is the other half of the proteomic technology tandem. Long gone are the days when the expert analyst pretended to have manually verified all the data supporting the conclusions in a proteomic paper. Therefore, proteomics today, particularly mass spectrometry-based proteomics, depends as much on computers and the software tools they use as it does on high tech instruments to generate the data. Surprisingly, at the sessions in the bar, no one ever seems to boast about the data analysis part of the proteomic enterprise. Statements such as “ this program just recovered 20% more true positive peptide identification from my data set,” “ this allowed me to analyze my data ten times faster and at a credible FDR,” or “this statistical model allowed me to identify statistically significant quantitative changes in my dataset” are seldom if ever uttered. It is unlikely that things in the realm of electronics and software are inherently less appealing or attractive than high performance devices because the same individuals boasting about their mass spectrometers are likely to also share interesting new iPhone apps. It is certainly also not the case that advances on the software side of proteomics have had a lower impact than publications describing new hardware. In fact some of the most highly cited proteomics papers are about software. I therefore propose the likely controversial thesis that the software world of proteomics is less generally discussed because few can keep up and know about the field in some depth. This view is supported by several observations. First, in a recent study (1), in which a standardized protein sample was analyzed by a number of laboratories, it was clearly apparent that the analysis of the collected data was a major weakness. Second, the adoption of new software tools for proteomic data analysis takes much longer than the adoption of new instruments or wet-lab protocols. To raise the level of awareness in the crucial area of proteomic software tools, Molecular and Cellular Proteomics will publish a series of mini reviews that summarize the current state of specific aspects of this often bewildering field. These are not tutorials, i.e. the reader still will have to figure out how to use the tools and which tools to use. The intent of this series is to discuss the significance of a particular type of analysis, the question(s) addressed, the currently available solutions and their strengths and weaknesses, and the availability and utility of germane software tools or computational resources. We expect that these mini reviews will help proteome researchers to extract more and better information from the data generated by the wonderful instruments available today and to increase the excitement about the topic so that statistical models, search engines, and XML schema can also claim the stature in bar discussions that they deserve.